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Missing Value Imputation for Multi-View Urban Statistical Data via Spatial Correlation Learning

As a developing trend of urbanization, massive amounts of urban statistical data with multiple views (e.g., views of Population and Economy) are increasingly collected and benefited to diverse domains, including transportation service, regional analysis, etc. Unfortunately, these statistical data that are divided into fine-grained regions usually suffer from missing value problem during the acquisition and storage processes. It is mianly caused by some inevitable circumstances, e.g., the document defacement, statistical difficulty in remote districts, and inaccurate information cleaning, etc. Those missing entries which make valuable information invisible may distort the further urban analysis. To improve the quality of missing data imputation, we propose an improved spatial multi-kernel learning method to guide the imputation process incorporating with the adaptive-weight non-negative matrix factorization strategy. Our model takes into account the regional latent similarities and the real geographical positions as well as the correlations among various views that are able to complete missing values precisely. We conduct intensive experiments to evaluate our method and compare with other state-of-the-art approaches on real-world datasets. All the empirical results show that the proposed model outperforms all the other state-of-the-art methods. Additionally, our model represents a strong generalization ability across multiple cities.

Evolutionary and functional analysis reveals the crucial roles of receptor-like proteins in resistance to Valsa canker in Rosaceae

Rosaceae is an economically important plant family that can be affected by a multitude of pathogenic microbes, some of which can cause dramatic losses in production. As a type of pattern-recognition receptor, receptor-like proteins (RLPs) are considered vital regulators of plant immunity. Based on genome-wide identification, bioinformatic analysis, and functional determination, we investigated the evolutionary characteristics of RLPs, and specifically those that regulate Valsa canker, a devastating fungal disease affecting apple and pear production. A total of 3028 RLPs from the genomes of 19 species, including nine Rosaceae, were divided into 24 subfamilies. Five subfamilies and seven co-expression modules were found to be involved in the responses to Valsa canker signals of the resistant pear rootstock Pyrus betulifolia 'Duli-G03'. Fourteen RLPs were subsequently screened as candidate genes for regulation of resistance. Among these, PbeRP23 (Chr13.g24394) and PbeRP27 (Chr16.g31400) were identified as key resistance genes that rapidly enhance the resistance of 'Duli-G03' and strongly initiate immune responses, and hence they have potential for further functional exploration and breeding applications for resistance to Valsa canker. In addition, as a consequence of this work we have established optimal methods for the classification and screening of disease-resistant RLPs.

Orchid classification using homogeneous ensemble of small deep convolutional neural network

Orchids are flowering plants in the large and diverse family Orchidaceae. Orchid flowers may share similar visual characteristics even they are from different species. Thus, classifying orchid species from images is a hugely challenging task. Motivated by the inadequacy of the current state-of-the-art general-purpose image classification methods in differentiating subtle differences between orchid flower images, we propose a hybrid model architecture to better classify the orchid species from images. The model architecture is composed of three parts: the global prediction network (GPN), the local prediction network (LPN), and the ensemble neural network (ENN). The GPN predicts the orchid species by global features of orchid flowers. The LPN looks into local features such as the organs of orchid plant via a spatial transformer network. Finally, the ENN fuses the intermediate predictions from the GPN and the LPN modules and produces the final prediction. All modules are implemented based on a robust convolutional neural network with transfer learning methodology from notable existing models. Due to the interplay between the modules, we also guidelined the training steps necessary for achieving higher predictive performance. The classification results based on an extensive in-house Orchids-52 dataset demonstrated the superiority of the proposed method compared to the state of the art.

Hidden images in Atxurra Cave (Northern Spain): A new proposal for visibility analyses of Palaeolithic rock art in subterranean environments

Visibility has been the subject of study in Palaeolithic rock art research ever since the discovery of Altamira Cave in 1879. Nevertheless, until now, the different approaches have been based on subjective assessments, due to computational limitations for a more objective methodology. Nowadays, cutting-edge technologies such as GIS allow us to address spatial studies in caves and overcome their geomorphologically complex and closed characteristics. Here we describe an innovative methodology that uses computing tools available to any researcher to study the viewsheds of the graphic units in decorated caves. We have tested its validity on the recently discovered rock art ensemble of Atxurra Cave, in Northern Spain. We demonstrate that this technology (GIS), widely used in other fields of archaeology, especially in outdoor studies, is also useable in caverns, taking into account the complex morphologies -ceilings and diverse floor-levels, for example. These programmes have also allowed us to consider the lighting systems used by the prehistoric groups inside the cave, as well as various data previously estimated by other authors, such as the height of individuals during the European LUP. The dynamism of these tools -2.5D-, as well as the advancement of new 3D GIS technologies, will allow in the future remarkable progress in these types of structural studies for a better understanding of Palaeolithic cave art phenomena.

Plasticity in over-compensatory growth along an alpine meadow degradation gradient on the Qinghai-Tibetan Plateau

Over-compensatory growth of plants after disturbance is generally preferred by grassland users and managers because of more forage. How the grassland productivity and the plant growth condition before disturbance affect the compensatory growth are important for grazing management and the understanding of grassland degradation, yet they are not well understood. A clipping experiment was conducted on the Qinghai-Tibetan Plateau to understand the compensatory growth and conditions for the occurrence of over-compensatory at alpine meadows with different degradation status. Results showed the competition for light constrains the plant growth post-clipping at non-degraded and slightly degraded alpine meadows, while the reduction of soil nitrogen limits it at heavily degraded alpine meadow. The biomass accumulated post-clipping was positively correlated with the growing season biomass in unclipped plots and the biomass at clipping in clipped plots. When the aboveground biomass at clipping was less than 40.10 g m-2 and the growing season biomass was between 38 and 97 g m-2, the over-compensatory growth of alpine meadow could occur. Higher clipping rate is required for the alpine meadow with high productivity but the maximum clipping rate should be less than 0.71 to induce the over-compensatory growth. Equal-compensatory occurred at non-degraded and slightly degraded, while over-compensatory growth was observed at moderately degraded and a marginally significant over-compensatory growth at heavily degraded alpine meadow. The over-compensatory growth occurred at moderately degraded alpine meadow is mainly due to the performance of forbs. Our results suggest that grazing at moderately degraded alpine meadow may induce the over-compensatory growth at the community level, but the over-compensatory growth of forbs at moderately degraded alpine meadow may aggravate the alpine meadow degradation.

A group arousal analysis based on the movement synchronization of audiences

Recent years have witnessed the rapid growth of performing arts in Korea as well as worldwide. Advances in performing arts technologies have allowed for a shift from one-way performances to interactive ones. The audience's arousal is one of the most important features in interactive performances. An audience consists of a group of people responding collectively to a stimulus. Here arousal is a critical factor influencing audience satisfaction and can be measured based on the audience's behavior. The total group arousal of an audience is formed by exchanging emotional effects with surroundings. In this regard, empirical approaches are not sufficient in comparison to various theoretical approaches to group arousal. Previous studies have generally evaluated group arousal by the sum of group members' emotions recognized from their faces, gestures, or voice. However, it is not easy to apply real-time data from individuals to performing arts. In addition, it is difficult to set sensors for audiences to retrieve human data. In this regard, this paper proposes a method for empirically measuring group arousal based on the rapid movement synchronization of a given group. In the proposed method, the extent to which each member's movement response is synchronized with differential images and histograms is measured first, and then group arousal is calculated by the degree of this synchronization. The performance of the proposed method is evaluated through an experiment by setting a threshold for deciding whether there is a response to a stimulus. The experimental results for 15 groups indicate the accuracy of the proposed method to be 82 %.

Efficient Sentinel Mining Using Bitmaps on Modern Processors

This paper proposes a highly efficient bitmap-based approach for discovery of so-called sentinels. Sentinels represent schema level relationships between changes over time in certain measures in a multidimensional data cube. Sentinels are actionable and notify users based on previous observations, for example, that revenue might drop within two months if an increase in customer problems combined with a decrease in website traffic is observed. We significantly extend prior art by representing the sentinel mining problem by bitmap operations, using bitmapped encoding of so-called indication streams. We present a very efficient algorithm, SentBit, that is 2-3 orders of magnitude faster than the state of the art, and utilizes CPU specific instructions and the multicore architectures available on modern processors. The SentBit algorithm scales efficiently to very large data sets, which is verified by extensive experiments on both real and synthetic data.

Hierarchical Quatsome-RGD Nanoarchitectonic Surfaces for Enhanced Integrin-Mediated Cell Adhesion

The synthesis and study of the tripeptide Arg-Gly-Asp (RGD), the binding site of different extracellular matrix proteins, e.g., fibronectin and vitronectin, has allowed the production of a wide range of cell adhesive surfaces. Although the surface density and spacing of the RGD peptide at the nanoscale have already shown a significant influence on cell adhesion, the impact of its hierarchical nanostructure is still rather unexplored. Accordingly, a versatile colloidal system named quatsomes, based on fluid nanovesicles formed by the self-assembling of cholesterol and surfactant molecules, has been devised as a novel template to achieve hierarchical nanostructures of the RGD peptide. To this end, RGD was anchored on the vesicle's fluid membrane of quatsomes, and the RGD-functionalized nanovesicles were covalently anchored to planar gold surfaces, forming a state of quasi-suspension, through a long poly(ethylene glycol) (PEG) chain with a thiol termination. An underlying self-assembled monolayer (SAM) of a shorter PEG was introduced for vesicle stabilization and to avoid unspecific cell adhesion. In comparison with substrates featuring a homogeneous distribution of RGD peptides, the resulting hierarchical nanoarchitectonic dramatically enhanced cell adhesion, despite lower overall RGD molecules on the surface. The new versatile platform was thoroughly characterized using a multitechnique approach, proving its enhanced performance. These findings open new methods for the hierarchical immobilization of biomolecules on surfaces using quatsomes as a robust and novel tissue engineering strategy.

MODS: Fast and robust method for two-view matching

A novel algorithm for wide-baseline matching called MODS matching on demand with view synthesis is presented. The MODS algorithm is experimentally shown to solve a broader range of wide-baseline problems than the state of the art while being nearly as fast as standard matchers on simple problems. The apparent robustness vs. speed trade-off is finessed by the use of progressively more time-consuming feature detectors and by on-demand generation of synthesized images that is performed until a reliable estimate of geometry is obtained. We introduce an improved method for tentative correspondence selection, applicable both with and without view synthesis. A modification of the standard first to second nearest distance rule increases the number of correct matches by 5-20% at no additional computational cost. Performance of the MODS algorithm is evaluated on several standard publicly available datasets, and on a new set of geometrically challenging wide baseline problems that is made public together with the ground truth. Experiments show that the MODS outperforms the state-of-the-art in robustness and speed. Moreover, MODS performs well on other classes of difficult two-view problems like matching of images from different modalities, with wide temporal baseline or with significant lighting changes. (c) 2015 Elsevier Inc. All rights reserved.

(La1-x Mx )2 (Nb0.45 Yb0.55 )2 O7-δ (M=Ca, Sr, Ba) Ionic Conductors Promoted by Foreign/Domestic Dual Acceptor-Doping Strategies

In this work, a class of ionic conductor (La1-x Mx )2 (Nb0.45 Yb0.55 )2 O7-δ (M=Ca, Sr, and Ba) with a cubic pyrochlore structure was reported. Two strategies were adopted to increase the concentration of oxygen vacancies favoring the hydration reaction to introduce protons. One was increasing the cation ratio between Yb and Nb over unity, the other was doping divalent alkaline earth elements to replace trivalent La. Proton conduction was evidenced by confirming the proton incorporation and H/D isotope effect in electrical conductivity. Doping Ca, Sr, and Ba further promoted the proton conduction. The results of crystal structure refinement indicated that the extrinsically introduced oxygen vacancies by the two strategies were accommodated in the tetrahedra (48 f) containing two La and two Yb/Nb cations, while the tetrahedra containing four La cations (8a) were fully occupied by oxide ions. A discussion was thereby performed, leading to the suggestion that not all the tetrahedra in the cubic pyrochlore structure of (La1-x Mx )2 (Nb0.45 Yb0.55 )2 O7-δ helped in incorporating and conducting protons, and only the oxygen vacancies surrounded by four Y cations (48 f site) or two La and two Y cations (8b site) were hydratable. It is thereby suggested that to enhance the proton conduction in pyrochlore oxides, an effective strategy might be tuning the ability of hydration or protonation of the tetrahedra to increase the proton concentration and expand the route for proton conduction.

Bleomycin-Induced Flagellate Dermatitis: Revisited

Flagellate dermatitis caused by bleomycin is a rare side effect with a distinctive pattern of whip-like, linear streaks. The clinical presentation has become uncommon nowadays as bleomycin use in conventional chemotherapy regimens has decreased. We present a case of a 30-year-old female diagnosed with ovarian germ cell tumour, managed with bleomycin, etoposide, and cisplatin (BEP) and later developed a widespread rash indicative of classic flagellate dermatitis. This brief report emphasizes the significance of detection and management of this transient dermatological complication in patients receiving bleomycin.

NTT Architecture for a Linux-Ready RISC-V Fully-Homomorphic Encryption Accelerator

This paper proposes two architectures for the acceleration of Number Theoretic Transforms (NTTs) using a novel Montgomery-based butterfly. We first design a custom NTT hardware accelerator for Field-Programmable Gate Arrays (FPGAs). The butterfly architecture is expanded to a Modular Arithmetic Logic Unit (MALU) and for greater reuse and easier programmability a six-stage pipeline Linux-ready RISC-V core is extended with custom instructions. The performance of the proposed architectures is assessed on a Xilinx Ultrascale+ FPGA and with an Application-Specific Integrated Circuit (ASIC) on 28nm CMOS technology. In FPGA, the results for custom acceleration show reductions of 30%, 90% and 42% in the number of Lookup tables (LUTs) and registers, Block RAMs (BRAMs) and Digital Signal Processors (DSPs), while providing a speedup of 1.9 times, in comparison with the state of the art. The ASIC results show that at 1 GHz the proposed architecture is in average 45% and 52% less area and power hungry, respectively, compared to the state of the art. Furthermore, the proposed MALU, operating as an additional execution unit, increases the overall area of the extended RISC-V core by only 10%, without significant changes in the frequency of operation.

Rhizobium radiobacter-Induced Peritonitis: A Case Report and Literature Analysis

Rhizobium radiobacter (R. radiobacter) is a gram-negative bacterium, primarily a soil contaminant and rarely pathogenic to humans. Only a few cases of peritonitis secondary to R. radiobacter have been reported worldwide. A 66-year-old male with end-stage renal disease who was on peritoneal dialysis (PD) developed R. radiobacter-induced peritonitis. We have treated the infection successfully with intraperitoneal antibiotics and managed to keep his PD catheter intact without interruption in PD treatment. More prolonged antibiotic therapy and frequent clinical follow-up is required to treat this infection. Better clinician awareness is needed to prevent this rare infection.

SimCVD: Simple Contrastive Voxel-Wise Representation Distillation for Semi-Supervised Medical Image Segmentation

Automated segmentation in medical image analysis is a challenging task that requires a large amount of manually labeled data. However, most existing learning-based approaches usually suffer from limited manually annotated medical data, which poses a major practical problem for accurate and robust medical image segmentation. In addition, most existing semi-supervised approaches are usually not robust compared with the supervised counterparts, and also lack explicit modeling of geometric structure and semantic information, both of which limit the segmentation accuracy. In this work, we present SimCVD, a simple contrastive distillation framework that significantly advances state-of-the-art voxel-wise representation learning. We first describe an unsupervised training strategy, which takes two views of an input volume and predicts their signed distance maps of object boundaries in a contrastive objective, with only two independent dropout as mask. This simple approach works surprisingly well, performing on the same level as previous fully supervised methods with much less labeled data. We hypothesize that dropout can be viewed as a minimal form of data augmentation and makes the network robust to representation collapse. Then, we propose to perform structural distillation by distilling pair-wise similarities. We evaluate SimCVD on two popular datasets: the Left Atrial Segmentation Challenge (LA) and the NIH pancreas CT dataset. The results on the LA dataset demonstrate that, in two types of labeled ratios (i.e., 20% and 10%), SimCVD achieves an average Dice score of 90.85% and 89.03% respectively, a 0.91% and 2.22% improvement compared to previous best results. Our method can be trained in an end-to-end fashion, showing the promise of utilizing SimCVD as a general framework for downstream tasks, such as medical image synthesis, enhancement, and registration.

Venglustat combined with imiglucerase for neurological disease in adults with Gaucher disease type 3: the LEAP trial

Gaucher disease type 3 is a chronic neuronopathic disorder with wide-ranging effects, including hepatosplenomegaly, anaemia, thrombocytopenia, skeletal disease and diverse neurological manifestations. Biallelic mutations in GBA1 reduce lysosomal acid β-glucosidase activity, and its substrates, glucosylceramide and glucosylsphingosine, accumulate. Enzyme replacement therapy and substrate reduction therapy ameliorate systemic features of Gaucher disease, but no therapies are approved for neurological manifestations. Venglustat is an investigational, brain-penetrant, glucosylceramide synthase inhibitor with potential to improve the disease by rebalancing influx of glucosylceramide with impaired lysosomal recycling. The Phase 2, open-label LEAP trial (NCT02843035) evaluated orally administered venglustat 15 mg once-daily in combination with maintenance dose of imiglucerase enzyme replacement therapy during 1 year of treatment in 11 adults with Gaucher disease type 3. Primary endpoints were venglustat safety and tolerability and change in concentration of glucosylceramide and glucosylsphingosine in CSF from baseline to Weeks 26 and 52. Secondary endpoints included change in plasma concentrations of glucosylceramide and glucosylsphingosine, venglustat pharmacokinetics in plasma and CSF, neurologic function, infiltrative lung disease and systemic disease parameters. Exploratory endpoints included changes in brain volume assessed with volumetric MRI using tensor-based morphometry, and resting functional MRI analysis of regional brain activity and connectivity between resting state networks. Mean (SD) plasma venglustat AUC0-24 on Day 1 was 851 (282) ng•h/ml; Cmax of 58.1 (26.4) ng/ml was achieved at a median tmax 2.00 h. After once-daily venglustat, plasma concentrations (4 h post-dose) were higher compared with Day 1, indicating ∼2-fold accumulation. One participant (Patient 9) had low-to-undetectable venglustat exposure at Weeks 26 and 52. Based on mean plasma and CSF venglustat concentrations (excluding Patient 9), steady state appeared to be reached on or before Week 4. Mean (SD) venglustat concentration at Week 52 was 114 (65.8) ng/ml in plasma and 6.14 (3.44) ng/ml in CSF. After 1 year of treatment, median (inter-quartile range) glucosylceramide decreased 78% (72, 84) in plasma and 81% (77, 83) in CSF; median (inter-quartile range) glucosylsphingosine decreased 56% (41, 60) in plasma and 70% (46, 76) in CSF. Ataxia improved slightly in nine patients: mean (SD, range) total modified Scale for Assessment and Rating of Ataxia score decreased from 2.68 [1.54 (0.0 to 5.5)] at baseline to 1.55 [1.88 (0.0 to 5.0)] at Week 52 [mean change: -1.14 (95% CI: -2.06 to -0.21)]. Whole brain volume increased slightly in patients with venglustat exposure and biomarker reduction in CSF (306.7 ± 4253.3 mm3) and declined markedly in Patient 9 (-13894.8 mm3). Functional MRI indicated stronger connectivity at Weeks 26 and 52 relative to baseline between a broadly distributed set of brain regions in patients with venglustat exposure and biomarker reduction but not Patient 9, although neurocognition, assessed by Vineland II, deteriorated in all domains over time, which illustrates disease progression despite the intervention. There were no deaths, serious adverse events or discontinuations. In adults with Gaucher disease type 3 receiving imiglucerase, addition of once-daily venglustat showed acceptable safety and tolerability and preliminary evidence of clinical stability with intriguing but intrinsically inconsistent signals in selected biomarkers, which need to be validated and confirmed in future research.

PolyVerif: An Open-Source Environment for Autonomous Vehicle Validation and Verification Research Acceleration

Validation and Verification (V&V) of Artificial Intelligence (AI) based cyber physical systems such as Autonomous Vehicles (AVs) is currently a vexing and unsolved problem. AVs integrate subsystems in areas such as detection, sensor fusion, localization, perception, and path planning. Each of these subsystems contains significant AI content integrated with traditional hardware and software components. The complexity for validating even a subsystem is daunting and the task of validating the whole system is nearly impossible. Fundamental research in advancing the state-of-the-art for AV V&V is required. However, for V&V researchers, it is exceedingly difficult to make progress because of the massive infrastructure requirements to demonstrate the viability of any solution. This paper presents PolyVerif, the world's first open-source solution focused on V&V researchers with the objective of accelerating the state-of-the-art for AV V&V research. PolyVerif provides an AI design and verification framework consisting of a digital twin creation process, an open-source AV engine, access to several open-source physics based simulators, and open-source symbolic test generation engines. PolyVerif's objective is to arm V&V researchers with a framework which extends the state-of-the-art on any one of the many major axes of interest and use the remainder of the infrastructure to quickly demonstrate the viability of their solution. Given its open-source nature, researchers can also contribute their innovations to the project. Using this critical property of open-source environments, the innovation rate of the whole research community to solve these vexing issues can be greatly accelerated. Finally, the paper also presents results from several projects which have used PolyVerif.

Recent Advances in Smart Contracts: A Technical Overview and State of the Art

Smart contracts, as an added functionality to blockchain, have received increased attention recently. They are executable programs whose instance and state are stored in blockchain. Hence, smart contracts and blockchain enable a trustable, trackable, and irreversible protocol without the need for trusted third parties which generally constitute a single point of failure. If a user creates and distributes a smart contract, others will be able to interact with it while the underlying blockchain ensures a trustable execution. In this paper, we aim to introduce state-of-the-art technologies of the smart contract protocol. We firstly introduce the history of blockchain and smart contracts followed by their step-by-step operations. Then, we introduce the survey results which are classified into four categories based on their purposes: cryptography, access management, social application, and smart contract structure. By presenting the most recent knowledge, this paper will contribute to the advances and proliferation of smart contracts.

Industrial side streams as sustainable substrates for microbial production of poly(3-hydroxybutyrate) (PHB)

Poly(3-hydroxybutyrate) (PHB) is a microbially produced biopolymer that is emerging as a propitious alternative to petroleum-based plastics owing to its biodegradable and biocompatible properties. However, to date, the relatively high costs related to the PHB production process are hampering its widespread commercialization. Since feedstock costs add up to half of the total production costs, ample research has been focusing on the use of inexpensive industrial side streams as carbon sources. While various industrial side streams such as second-generation carbohydrates, lignocellulose, lipids, and glycerol have been extensively investigated in liquid fermentation processes, also gaseous sources, including carbon dioxide, carbon monoxide, and methane, are gaining attention as substrates for gas fermentation. In addition, recent studies have investigated two-stage processes to convert waste gases into PHB via organic acids or alcohols. In this review, a variety of different industrial side streams are discussed as more sustainable and economical carbon sources for microbial PHB production. In particular, a comprehensive overview of recent developments and remaining challenges in fermentation strategies using these feedstocks is provided, considering technical, environmental, and economic aspects to shed light on their industrial feasibility. As such, this review aims to contribute to the global shift towards a zero-waste bio-economy and more sustainable materials.

IL-Y, a synthetic member of the IL-12 cytokine family, suppresses the development of type 1 diabetes in NOD mice

The IL-12 family of heterodimeric cytokines, consisting of IL-12, IL-23, IL-27, and IL-35, has important roles in regulating the immune response. IL-12 family members are comprised of a heterodimer consisting of α and β chains: IL-12 (p40 and p35), IL-23 (p40 and p19), IL-27 (Ebi3 and p28), and IL-35 (Ebi3 and p35). Given the combinatorial nature of the IL-12 family, we generated adenoviral vectors expressing two putative IL-12 family members not yet found naturally, termed IL-X (Ebi3 and p19) and IL-Y (p40 and p28), as single-chain molecules. Single chain IL-Y (scIL-Y), but not scIL-X, was able to stimulate significantly a unique cytokine/chemokine expression profile as well as activate STAT3 in mice, in part, through a pathway involving IL-27Rα in splenocytes. Adenoviral-mediated, intratumoral delivery of scIL-Y increased tumor growth in contrast to the anti-tumor effects of scIL-12 and scIL-23. Similarly, treatment of prediabetic NOD mice by intravenous injection of Ad.scIL-Y prevented the onset of hyperglycemia. Analysis of cells from Ad.scIL-Y-treated NOD mice demonstrated that scIL-Y reduced expression of inflammatory mediators such as IFN-γ. Our data demonstrate that a novel, synthetic member of the IL-12 family, termed IL-Y, confers unique immunosuppressive effects in two different disease models and thus could have therapeutic applications.

Development and function of the fetal adrenal

The adrenal cortex undergoes multiple structural and functional rearrangements to satisfy the systemic needs for steroids during fetal life, postnatal development, and adulthood. A fully functional adrenal cortex relies on the proper subdivision in regions or 'zones' with distinct but interconnected functions, which evolve from the early embryonic stages to adulthood, and rely on a fine-tuned gene network. In particular, the steroidogenic activity of the fetal adrenal is instrumental in maintaining normal fetal development and growth. Here, we review and discuss the most recent advances in our understanding of embryonic and fetal adrenal development, including the known causes for adrenal dys-/agenesis, and the steroidogenic pathways that link the fetal adrenal with the hormone system of the mother through the fetal-placental unit. Finally, we discuss what we think are the major open questions in the field, including, among others, the impact of osteocalcin, thyroid hormone, and other hormone systems on adrenal development and function, and the reliability of rodents as models of adrenal pathophysiology.

A texture-based method for modeling the background and detecting moving objects

This paper presents a novel and efficient texture-based method for modeling the background and detecting moving objects from a video sequence. Each pixel is modeled as a group of adaptive local binary pattern histograms that are calculated over a circular region around the pixel. The approach provides us with many advantages compared to the state-of-the-art. Experimental results clearly justify our model.

A novel method for accurate ground parameter estimation in MV networks

This paper presents a novel method for ground parameter estimation that enables the accurate estimation of the total ground capacitance and conductance in MV networks. The novelty of the method is that the effect of the leakage impedance of the inductive voltage transformer can be significantly reduced compared to the prior-art method. The key idea of the novel method is based on dual inductive voltage transformers for resonant earthed neutral networks. Until now, it has been necessary to simplify the equivalent circuit model by neglecting the effect of the inductive voltage transformer leakage impedance to enable ground parameter estimation with the prior-art signal injection-based method. The experimental test results clearly demonstrate the significant effect of the inductive voltage transformer leakage impedance on ground parameter estimation with the prior-art method and show that the proposed method is effective at improving the accuracy of ground parameter estimation, which is beneficial for the security risk assessment of underground cables, the configuration and expansion capacity of the Petersen coil and the detection of high-impedance ground faults.

The Effect of Semantic Transparency in a Flanker Task

This study tried to replicate and extend the semantic transparency morphological effect using the flanker lexical decision paradigm (Grainger et al., 2020). In the first experiment, stems were used as flankers of target words that could be truly morphological (hunt hunter hunt), pseudomorphological (corn corner corn), or form-related with the flanker (broth brothel broth). In half of the trials, a related flanker was employed, and in the other half, an unrelated word was presented as flanker (e.g., table player table). The results showed a facilitative effect for the related condition as a main effect with no difference between experimental conditions. These results were interpreted in terms of an orthographic facilitation taking place when whole stems are presented as flankers. In the second experiment, short derivational suffixes were used as flankers of the same targets employed in the first experiment. The results showed an inhibitory effect of the same magnitude for the transparent and pseudomorphological conditions with no effect for the form condition. This finding suggests an inhibitory effect by which morphemes activate several lexical candidates that compete for recognition. Overall, the results are interpreted in terms of the cognitive requirements of the experimental task, the items selected, and the current models of morphological processing.

The Treachery of Images: Redefining the Structural System of Havana's National Art Schools

This paper illustrates the contribution that on-site survey and graphical documentation offer to the structural comprehension of 20th century architectural and civil engineering heritage and, therefore, to its sustainable conservation. The research herein presented has identified the true structural system of Havana's National Art Schools, an internationally well-known architectural masterpiece that was recently investigated within the drafting of a comprehensive conservation management plan. This iconic complex was built right after the Castro's revolution and was meant to embody Cuba's newfound freedom. To this end, the complex was supposed to be built using Catalan vaulting, a technique loaded with significance due to its provenance, affordability, and flexibility. While most of the literature, the architectural features, and the very designers assert that no concrete nor steel were employed during construction, recent studies suggested that a reinforced concrete core might be hidden behind the masonry-like appearance of the five buildings. The structural analysis performed in order to draft a conservation and management plan for the school site thus became a hermeneutic opportunity to address this topic. Combining direct observation, documentary research, and nondestructive analyses (infrared thermography and magnetometer testing), it was possible to finally redefine the structural nature of these notorious architectures, which are indeed mostly made of reinforced concrete.

Localization of Non-Linearly Modeled Autonomous Mobile Robots Using Out-of-Sequence Measurements

This paper presents a state of the art of the estimation algorithms dealing with Out-of-Sequence (OOS) measurements for non-linearly modeled systems. The state of the art includes a critical analysis of the algorithm properties that takes into account the applicability of these techniques to autonomous mobile robot navigation based on the fusion of the measurements provided, delayed and OOS, by multiple sensors. Besides, it shows a representative example of the use of one of the most computationally efficient approaches in the localization module of the control software of a real robot (which has non-linear dynamics, and linear and non-linear sensors) and compares its performance against other approaches. The simulated results obtained with the selected OOS algorithm shows the computational requirements that each sensor of the robot imposes to it. The real experiments show how the inclusion of the selected OOS algorithm in the control software lets the robot successfully navigate in spite of receiving many OOS measurements. Finally, the comparison highlights that not only is the selected OOS algorithm among the best performing ones of the comparison, but it also has the lowest computational and memory cost.

Fast Fixed-Outline 3-D IC Floorplanning With TSV Co-Placement

Through-silicon vias (TSVs) are used to connect inter-die signals in a 3-D IC. Unlike conventional vias, TSVs occupy device area and are very large compared to logic gates. However, most previous 3-D floorplanners only view TSVs as points. As a result, whitespace redistribution is necessary for TSV insertion after the initial floorplan is computed, which leads to suboptimal layouts. In this paper, we propose a very efficient 3-D floorplanner to simultaneously floorplan the functional modules and place the TSVs and to optimize the total wirelength under fixed-outline constraint. Compared to the state-of-the-art 3-D floorplanner with TSV planning, our design consistently produces better floorplans with 15% shorter wirelength and 31% fewer TSVs on average. Our algorithm is extremely fast and only takes a few seconds to floorplan benchmarks with hundreds of modules compared to hours as required by the previous state-of-the-art floorplanner.

Pain E-motion Faces Database (PEMF): Pain-related micro-clips for emotion research

A large number of publications have focused on the study of pain expressions. Despite the growing knowledge, the availability of pain-related face databases is still very scarce compared with other emotional facial expressions. The Pain E-Motion Faces Database (PEMF) is a new open-access database currently consisting of 272 micro-clips of 68 different identities. Each model displays one neutral expression and three pain-related facial expressions: posed, spontaneous-algometer and spontaneous-CO2 laser. Normative ratings of pain intensity, valence and arousal were provided by students of three different European universities. Six independent coders carried out a coding process on the facial stimuli based on the Facial Action Coding System (FACS), in which ratings of intensity of pain, valence and arousal were computed for each type of facial expression. Gender and age effects of models across each type of micro-clip were also analysed. Additionally, participants' ability to discriminate the veracity of pain-related facial expressions (i.e., spontaneous vs posed) was explored. Finally, a series of ANOVAs were carried out to test the presence of other basic emotions and common facial action unit (AU) patterns. The main results revealed that posed facial expressions received higher ratings of pain intensity, more negative valence and higher arousal compared with spontaneous pain-related and neutral faces. No differential effects of model gender were found. Participants were unable to accurately discriminate whether a given pain-related face represented spontaneous or posed pain. PEMF thus constitutes a large open-source and reliable set of dynamic pain expressions useful for designing experimental studies focused on pain processes.

A summary of the REVERB challenge: state-of-the-art and remaining challenges in reverberant speech processing research

In recent years, substantial progress has been made in the field of reverberant speech signal processing, including both single- and multichannel dereverberation techniques and automatic speech recognition (ASR) techniques that are robust to reverberation. In this paper, we describe the REVERB challenge, which is an evaluation campaign that was designed to evaluate such speech enhancement (SE) and ASR techniques to reveal the state-of-the-art techniques and obtain new insights regarding potential future research directions. Even though most existing benchmark tasks and challenges for distant speech processing focus on the noise robustness issue and sometimes only on a single- channel scenario, a particular novelty of the REVERB challenge is that it is carefully designed to test robustness against reverberation, based on both real, single- channel, and multichannel recordings. This challenge attracted 27 papers, which represent 25 systems specifically designed for SE purposes and 49 systems specifically designed for ASR purposes. This paper describes the problems dealt within the challenge, provides an overview of the submitted systems, and scrutinizes them to clarify what current processing strategies appear effective in reverberant speech processing.

Survey of State-of-the-Art Mixed Data Clustering Algorithms

Mixed data comprises both numeric and categorical features, and mixed datasets occur frequently in many domains, such as health, finance, and marketing. Clustering is often applied to mixed datasets to find structures and to group similar objects for further analysis. However, clustering mixed data are challenging because it is difficult to directly apply mathematical operations, such as summation or averaging, to the feature values of these datasets. In this paper, we present a taxonomy for the study of mixed data clustering algorithms by identifying five major research themes. We then present the state-of-the-art review of the research works within each research theme. We analyze the strengths and weaknesses of these methods with pointers for future research directions. At last, we present an in-depth analysis of the overall challenges in this field, highlight open research questions, and discuss guidelines to make progress in the field.

The Changing Role of ENGOs in Water Governance: Institutional Entrepreneurs?

The changing role of the state in the last quarter century has been an important contemporary concern for policy makers, scholars, and the public. Equally, there is increasing recognition among governance scholars that nongovernment actors are exerting new kinds of influence over governance systems and contributing in novel ways to governance processes. The role of environmental nongovernmental organizations (ENGOs) is particularly pertinent given the continued involvement of ENGOs within collaborative, adaptive, and co-management governance, across several contexts and regions. This paper uses an analytical framework derived from recent studies on institutional entrepreneurs, to examine the skills ENGOs are applying in order to orchestrate change. An empirical case of governance for water in Canada's Lake Simcoe region provides the foundation for the research. Drawing on a mixed methods approach, the research finds that ENGOs in Lake Simcoe have taken on a role as an institutional entrepreneur, and thereby have altered the relationship between governance actors in this setting. A key outcome of their actions is a more dominant, engaged, and influential role for ENGOs in a critical, regional governance system.

Inhibition of LDL receptor-related protein 3 suppresses chondrogenesis of stem cells, inhibits proliferation, and promotes apoptosis

Articular cartilage defects remain the most common and challenging joint disease. Cartilage lacks the self-healing capacity after injury due to its avascularity. Recently, stem cell-based therapy has been applied for cartilage regeneration. However, the critical target for stem cells during chondrogenesis remains unclear. We first reported that LDL receptor-related protein 3 (LRP3) expression was markedly increased during chondrogenesis in stem cells. Furthermore, LRP3 was an effective chondrogenic stimulator, as confirmed by knockdown and overexpression experiments and RNA sequencing. In addition, inhibition of LRP3 suppressed proliferation and induced apoptosis. Therefore, our study first defined a new chondrogenic stimulator, LRP3, with detailed clarification, which provided a novel target for stem cell-based cartilage regeneration.

Stem cell-based models of early mammalian development

The complex process by which a single-celled zygote develops into a viable embryo is nothing short of a miraculous wonder of the natural world. Elucidating how this process is orchestrated in humans has long eluded the grasp of scientists due to ethical and practical limitations. Thankfully, pluripotent stem cells that resemble early developmental cell types possess the ability to mimic specific embryonic events. As such, murine and human stem cells have been leveraged by scientists to create in vitro models that aim to recapitulate different stages of early mammalian development. Here, we examine the wide variety of stem cell-based embryo models that have been developed to recapitulate and study embryonic events, from pre-implantation development through to early organogenesis. We discuss the applications of these models, key considerations regarding their importance within the field, and how such models are expected to grow and evolve to achieve exciting new milestones in the future.

Subcutaneous Administration of Medications and Fluids by Nonprofessional Caregivers at Home

Background: Patients requiring home-based palliative care have advanced complex illnesses with functional limitations and decline. This retrospective study reviewed caregiver administration of subcutaneous (SQ) medications and fluids when symptom control could not be achieved using the oral route. Methods: Medical records from September 1, 2017 to February 28, 2018 were reviewed for 272 consecutive patients who received SQ administration of medications or fluids at a home-based palliative care program. We analyzed the clinical characteristics of patients and caregivers, medications administered, and catheter outcomes. Results: Patients' median age was 74 years, and 163 (60%) were women. The most common cancer diagnoses were stomach 26 (12%), lung 22 (10%), and colorectal 20 (9%). Dementia 24 (44%), cerebrovascular disease 9 (16%), and congestive heart failure 7 (13%) were the most frequent nonmalignant diseases. Poor symptom control 162 (60%) and impaired oral intake 107 (39%) were the most common indications for an SQ route of administration. Nonprofessional caregivers trained by a nurse administered medications to 218 patients (80%). During interventions, the patients received a mean of 4 medications (±2 standard deviation). A total of 903 catheters were inserted, 15/732 (2%) catheters handled by nonprofessional caregivers caused a local infection, compared with 3/171 (1.8%) of catheters handled by nurses. Hydromorphone was the most common opioid used (57%), followed by morphine (35%). The median length of stay in the program was 24 days (interquartile range: 11-60). Conclusions: SQ administration of medications and fluids by nonprofessional caregivers trained by health care professionals is feasible and promising, but additional testing is needed.

Embedded Intelligence: State-of-the-Art and Research Challenges

Recent years have seen deployments of increasingly complex artificial intelligent (AI) and machine learning techniques being implemented on cloud server architectures and embedded into edge computing devices for supporting Internet of Things (IoT) and mobile applications. It is important to note that these embedded intelligence (EI) deployments on edge devices and cloud servers have significant differences in terms of objectives, models, platforms and research challenges. This paper presents a comprehensive survey on EI from four aspects: (1) First, the state-of-the-art for EI using a set of evaluation criteria is proposed and reviewed; (2) Second, EI for both cloud server accelerators and low-complexity edge devices are discussed; (3) Third, the various techniques for EI are categorized and discussed from the system, algorithm, architecture and technology levels; and (4) The paper concludes with the lessons learned and the future prospects are discussed in terms of the key role EI is likely to play in emerging technologies and applications such as Industry 4.0. This paper aims to give useful insights and future prospects for the developments in this area of study and highlight the challenges for practical deployments.

psi-Net: Stacking Densely Convolutional LSTMs for Sub-Cortical Brain Structure Segmentation

Sub-cortical brain structure segmentation is of great importance for diagnosing neuropsychiatric disorders. However, developing an automatic approach to segmenting sub-cortical brain structures remains very challenging due to the ambiguous boundaries, complex anatomical structures, and large variance of shapes. This paper presents a novel deep network architecture, namely Psi-Net, for sub-cortical brain structure segmentation, aiming at selectively aggregating features and boosting the information propagation in a deep convolutional neural network (CNN). To achieve this, we first formulate a densely convolutional LSTM module (DC-LSTM) to selectively aggregate the convolutional features with the same spatial resolution at the same stage of a CNN. This helps to promote the discriminativeness of features at each CNN stage. Second, we stack multiple DC-LSTMs from the deepest stage to the shallowest stage to progressively enrich low-level feature maps with high-level context. We employ two benchmark datasets on sub-cortical brain structure segmentation, and perform various experiments to evaluate the proposed Psi-Net. The experimental results show that our network performs favorably against the state-of-the-art methods on both benchmark datasets.

Reliability of Commercial UVC LEDs: 2022 State-of-the-Art

With this study, we report on the reliability of the most recent commercial UVC LED devices. The current COVID-19 pandemic urged the development of antiviral technologies, and one of the most effective is based on UVC irradiation, which can be effectively achieved by means of Deep UV LEDs. The development of antiviral systems based on UVC LEDs strongly depends on their efficacy and reliability. We propose an in-depth analysis of four different state-of-the-art commercial LEDs suitable for disinfection applications. LEDs have been subjected to a controlled stress test near their application limits, and their reliability and characteristics have been analyzed and studied. Results indicate a still limited reliability, with a degradation possibly related to an increase in Shockley-Read-Hall (SRH) recombination. Finally, some relevant product design suggestions will be proposed based on the results of this work.

Radiotherapy of non-small-cell lung cancer in the era of EGFR gene mutations and EGF receptor tyrosine kinase inhibitors

Non-small-cell lung cancer (NSCLC) occurs, approximately, in 80-85% of all cases of lung cancer. The majority of patients present locally advanced or metastatic disease when diagnosed, with poor prognosis. The discovery of activating mutations in the EGFR gene has started a new era of personalized treatment for NSCLC patients. To improve the treatment outcome in patients with unresectable NSCLC and, in particular, EGFR mutated, a combined strategy of radiotherapy and medical treatment can be undertaken. In this review we will discuss preclinical data regarding EGF receptor (EGFR) tyrosine kinase inhibitors (TKIs) and radiotherapy, available clinical trials investigating efficacy and toxicity of combined treatment (thoracic or whole brain radiotherapy and EGFR-TKIs) and, also, the role of local radiation in mutated EGFR patients who developed EGFR-TKI resistance.

Prediction of Harvested Energy for Wireless Sensor Node

Energy harvesting wireless sensor nodes are interesting for the Internet of Things, since they can provide continuous operation by adapting workload not only to the current energy reserves, but to the amount of energy that can be harvested in the future also. We present a multistage day ahead hourly solar energy prediction algorithm. The predictor uses cloud cover and precipitation probability predictions from weather forecast obtained once per day for 24 hours in advance. To compensate for short-term weather changes until the next weather forecast data is obtained, forecast errors of humidity and atmospheric pressure are fed to the fuzzy logic filter. The filter adjusts predictions of cloud cover and precipitation probability, which are applied to the clear-sky radiation model in order to obtain prediction of solar energy. The prediction of solar energy is additionally corrected based on the energy prediction error in the preceding time slot. The results show that the proposed predictor outperforms the state-of-the-art predictors in terms of prediction error. Proposed predictor and state-of-the-art predictors were also evaluated using a simulated wireless sensor node with the simple energy management algorithm, where the proposed predictor was the most efficient at maintaining energy neutrality.

Complexity measures and features for times series classification

Time series classification is a growing problem in different disciplines due to the progressive digitalization of the world. The best state-of-the-art algorithms focus on performance, seeking the best possible results, leaving interpretability at a second level, if any. Furthermore, interpretable proposals are far from providing competitive results. In this work, focused on time series classification, we propose a new representation of time series based on a robust and complete set of features. This new representation allows extracting more meaningful information on the underlying time series structure to develop effective classifiers whose results are much easier to interpret than current state-of-the-art models. The proposed feature set allows using the traditional vector-based classification algorithms in time series problems, significantly increasing the number of techniques available for this type of problem. To evaluate the performance of our proposal, we have used the state-of-the-art repository of time series classification, UCR, composed of 112 datasets. The experimental results show that through this representation, more interpretable classifiers can be obtained which are competitive. More specifically, they obtain no statistically significant differences from the second and third-best models of the state-of-the-art. Apart from competitive results in accuracy, our proposal is able to improve the model interpretability based on the set of features proposed.

LSB based non blind predictive edge adaptive image steganography

Image steganography is the art of hiding secret message in grayscale or color images. Easy detection of secret message for any state-of-art image steganography can break the stego system. To prevent the breakdown of the stego system data is embedded in the selected area of an image which reduces the probability of detection. Most of the existing adaptive image steganography techniques achieve low embedding capacity. In this paper a high capacity Predictive Edge Adaptive image steganography technique is proposed where selective area of cover image is predicted using Modified Median Edge Detector (MMED) predictor to embed the binary payload (data). The cover image used to embed the payload is a grayscale image. Experimental results show that the proposed scheme achieves better embedding capacity with minimum level of distortion and higher level of security. The proposed scheme is compared with the existing image steganography schemes. Results show that the proposed scheme achieves better embedding rate with lower level of distortion.

Targeting inhibition of microtubule affinity regulating kinase 4 by Harmaline: Strategy to combat Alzheimer's disease

Microtubule-affinity regulating kinase 4 (MARK4) is linked with the development of cancer, diabetes and neurodegenerative diseases. Due to its direct role in the hyperphosphorylation of tau protein, MARK4 is considered as an attractive target to fight Alzheimer's disease (AD) and neuroinflammation. In the present study, we have selected Harmaline (HAR), an alkaloid of Paganum harmala, to investigate its MARK4 inhibitory potential and its binding mechanism. Molecular docking and fluorescence binding studies were carried out to estimate the binding affinity of the HAR with the MARK4. We observed an excellent binding affinity of HAR to the MARK4 (K = 107 M-1), further complemented by isothermal titration calorimetric measurements. In addition, HAR significantly inhibits the kinase activity of MARK4 (IC50 value of 4.46 μM). Structural investigations suggested that HAR binds to the active site pocket and forms several non-covalent interactions with biologically important residues of MARK4. All-atom molecular dynamics simulation studies further advocated that the MARK4-HAR complex is stabilized throughout the trajectory of 200 ns and causes a little conformational change. All these findings suggest that HAR is a potential MARK4 inhibitor that can be implicated in managing MARK4-associated diseases, including AD.

Speeding up the product release: a second-sphere contribution from Tyr191 to the reactivity of L-lactate oxidase revealed in crystallographic and kinetic studies of site-directed variants

Among α-hydroxy acid-oxidizing flavoenzymes l-lactate oxidase (LOX) is unique in featuring a second-sphere tyrosine (Tyr191 in Aerococcus viridans LOX; avLOX) at the binding site for the substrate's carboxylate group. Y191F, Y191L and Y191A variants of avLOX were constructed to affect a hydrogen-bond network connecting Tyr191 to the carboxylate of the bound ligand via the conserved Tyr40 and to examine consequent effects on enzymatic reactivity. Kinetic studies at 20 °C and pH 6.5 revealed that release of pyruvate product was decreased 4.7-fold (Y191F), 19-fold (Y191L) and 28-fold (Y191A) compared with wild-type enzyme (~ 141 s(-1)) and thus became mainly rate limiting for l-lactate oxidation by the variants at a steady-state under air-saturated conditions. In the Y191L and the Y191A variants, but not in the Y191F variant, l-lactate binding was also affected strongly by the site-directed substitution. Reduction of the flavin cofactor by l-lactate and its reoxidation by molecular oxygen were, however, comparatively weakly affected by the replacements of Tyr191. Unlike the related lactate monooxygenase, which prevents the fast dissociation of pyruvate to promote its oxidative decarboxylation by H2 O2 into acetate, CO2 and water as final reaction products, all avLOX variants retained their native oxidase activity where catalytic turnover results in the equivalent formation of H2O2. The 1.9 Å crystal structure of the Y191F variant bound with FMN and pyruvate revealed a strictly locally disruptive effect of the site-directed substitution. Product off-rates appear to be dictated by partitioning of residues including Tyr191 from an active-site lid loop into bulk solvent and modulation of the hydrogen bond strength that links Tyr40 with the pyruvate's carboxylate group. Overall, this study emphasizes the possibly high importance of contributions from second-sphere substrate-binding residues to the fine-tuning of reactivity in α-hydroxy acid-oxidizing flavoenzymes, requiring that the catalytic steps of flavin reduction and oxidation are properly timed with the physical step of α-keto acid product release.

Towards Low-Power High-Efficiency RF and Microwave Energy Harvesting

Since the very beginning of RF and microwave integrated techniques and energy harvesting, Schottky diodes are most often used in mixing and rectifying circuits. However, in specific W power-harvesting applications, the Schottky diode technique fails to provide a satisfactory RF-dc conversion efficiency mainly because of its high zero-bias junction resistance. This paper examines the state-of-the-art low-power microwave-to-dc energy conversion techniques. A comprehensive picture of the state-of-the-art on this aspect is given graphically, which compares different technologies such as transistor, diode, and CMOS schemes. Subsequent to the highlighted limitations of current devices, this work introduces, for the first time, a nonlinear component for low-power rectification based on a recent discovery in spintronics, namely, the spindiode. Along with an analysis of the role of nonlinearity and zero bias resistance in the rectification process of the spindiode, it is shown how the spindiode could enhance the rectification efficiency even at a very low-power level and how this technique would shift the design paradigms of diode-based devices and circuits.

Towards a new horizon of optoelectronic devices with liquid crystals

The liquid crystal having high viscosity is attracting increasing attention as a new type of quality organic semiconductor, i.e., self-organizing molecular semiconductor In this article, its state-and-the-art in materials, properties related to charge carrier transport, and device applications, is reviewed briefly. In addition, the further researches needed towards its practical applications are discussed.

Charge-Redistribution Based Quadratic Operators for Neural Feature Extraction

This paper presents a SAR converter based mixed-signal multiplier for the feature extraction of neural signals using quadratic operators. After a thorough analysis of design principles and circuit-level aspects, the proposed architecture is explored for the implementation of two quadratic operators often used for the characterization of neural activity, the moving average energy (MAE) operator and the nonlinear energy operator (NEO). Programmable chips for both operators have been implemented in a HV-180 nm CMOS process. Experimental results confirm their suitability for energy computation and action potential detection and the accomplished areaxpower performance is compared to prior art. The MAE and NEO prototypes, at a sampling rate of 30kS/s, consume 116 nW and 178 nW, respectively, and digitize both the input neural signal and the operator outcome, with no need for digital multipliers.

New microstrip elliptic function bandpass filters using the edge-coupled folded split ring resonators

In this paper, new microstrip cross-coupled elliptic,function (CCEF) bandpass filters (BPFs) using folded split-ring resonators (FSRR) art proposed. The CCEF BPFs using these resonators are designed with maintaining a good filter performance and reducing the size significantly. The four-pole and three-pole CCEF BPFs show a 3-dB bandwidth of 3.0% at the center-frequency of 2.0 GHz, and insertion losses of 4.28 and 2.77 dB, respectively. (c) 2006 Wiley Periodicals, Inc.

Silicon Nitride Nanopores Formed by Simple Chemical Etching: DNA Translocations and TEM Imaging

We demonstrate DNA translocations through silicon nitride pores formed by simple chemical etching on glass substrates using microscopic amounts of hydrofluoric acid. DNA translocations and transmission electron microscopy (TEM) prove the fabrication of nanopores and allow their characterization. From ionic measurements on 318 chips, we report the effective pore diameters ranging from zero (pristine membranes) and sub-nm to over 100 nm, within 50 μm diameter membranes. The combination of ionic conductance, DNA current blockades, TEM imaging, and electron energy loss spectroscopy (EELS) provides comprehensive information about the pore area and number, from single to few pores, and pore structure. We also show the formation of thinned membrane regions as precursors of pores. The average pore density, about 5 × 10-4 pores/μm2, allows pore number adjustment statistically (0, 1, or more). This simple and affordable chemical method for making solid-state nanopores accelerates their adoption for DNA sensing and characterization applications.

Gaviss : Boosting the Performance of GPU-Accelerated NFV Systems via Data Sharing

GPUs have demonstrated the capability of significantly improving the performance of network functions (NF). In an Network Function Virtualization (NFV) system, multiple NFs form a service chain to provide services. However, NFs in state-of-the-art GPU-accelerated NFV systems still utilize a GPU independently where each NF needs to transfer data to the GPU memory for acceleration. As a result, a packet might be transferred into the GPU memory by each NF when it passes through the service chain. We find these expensive and repetitive transfers are the main factor that limits the overall performance of an NFV system. We propose Gaviss, a GPU-accelerated NFV system with effective data sharing. By sharing packets in the GPU memory among network functions, a packet needs to be transferred to the GPU only once, eliminating the performance overhead caused by repetitive transfers. Extensive experimental results show that Gaviss can improve the overall throughput by 2.6-13.2x and reduce the latency by up to 37.9%, when compared with state-of-the-art approaches. Moreover, Gaviss also demonstrates up to 2.5x higher price-performance ratio than CPU-based implementations, making GPUs competitive for building NFV systems.

GLISTR: Glioma Image Segmentation and Registration

We present a generative approach for simultaneously registering a probabilistic atlas of a healthy population to brain magnetic resonance (MR) scans showing glioma and segmenting the scans into tumor as well as healthy tissue labels. The proposed method is based on the expectation maximization (EM) algorithm that incorporates a glioma growth model for atlas seeding, a process which modifies the original atlas into one with tumor and edema adapted to best match a given set of patient's images. The modified atlas is registered into the patient space and utilized for estimating the posterior probabilities of various tissue labels. EM iteratively refines the estimates of the posterior probabilities of tissue labels, the deformation field and the tumor growth model parameters. Hence, in addition to segmentation, the proposed method results in atlas registration and a low-dimensional description of the patient scans through estimation of tumor model parameters. We validate the method by automatically segmenting 10 MR scans and comparing the results to those produced by clinical experts and two state-of-the-art methods. The resulting segmentations of tumor and edema outperform the results of the reference methods, and achieve a similar accuracy from a second human rater. We additionally apply the method to 122 patients scans and report the estimated tumor model parameters and their relations with segmentation and registration results. Based on the results from this patient population, we construct a statistical atlas of the glioma by inverting the estimated deformation fields to warp the tumor segmentations of patients scans into a common space.

Epidemiological profile, spatial patterns and priority areas for surveillance and control of leishmaniasis in Brazilian border strip, 2009-2017

Leishmaniasis represents a major neglected public health problem and the control measures have not been successful in Brazil. Recent studies have shown leishmaniasis importance to Brazilian border zones which reinforces the need to investigate its spread in those regions. This study aimed to analyze epidemiologic profile and its spatial distribution or aggregation process in both tegumentary and visceral leishmaniasis in the Brazilian border strip from 2009 to 2017. This is an ecological study of the epidemiological profile and spatial patterns encompassing municipalities in the Brazilian border strip. The presence of spatial autocorrelation and determination of priority areas for disease control were performed using global (Moran I) and local (LISA) Moran spatial techniques. The annual mean coefficients of tegumentary and visceral leishmaniasis were 29.8 and 0.6 by 100,000 inhabitants in the border strip, respectively. The indigenous population rates of leishmaniasis in the border zone appears to be higher than in the rest of the country (cutaneous changed from 33.2% to 6.6% and visceral rising from 1.0% to 17.5%) in the period. The most affected municipalities were located in the North and Central arches of the border zone. The results can subsidize the development of more targeted and effective strategies that can contribute to the surveillance and control of leishmaniasis in border zones, as the provision of epidemiological and spatial data on the disease. For better control of the disease, we recommend and emphasize the need to integrate public health policies of neighboring countries.

Segmented MS/MS acquisition of a1 ion-based strategy for in-depth proteome quantitation

In-depth proteome quantitation is of great significance for understanding protein functions, advancing biological, medical, environmental and metabolic engineering research. Herein, benefiting from the high formation efficiencies and intensities of dimethyl-labeled a1 ions for accurate quantitation, we developed an in-depth a1 ion-based proteome quantitation method, named deep-APQ, by a sequential MS/MS acquisition of the high mass range for identification and the low mass range for a1 ion intensity extraction to increase quantitative protein number and sequence coverage. By the analysis of HeLa protein digests, our developed method showed deeper quantitative coverage than our previously reported a1 ion-based quantitation method without mass range segmentation and lower missing values than widely-used label-free quantitation method. It also exhibited excellent accuracy and precision within a 20-fold dynamic range. We further integrated a workflow combining the deep-APQ method with highly efficient sample preparation, high-pH and low-pH reversed-phase separation and high-field asymmetric waveform ion mobility spectrometry (FAIMS) to study E. coli proteome responses under the nutritional conditions of glucose and acetate. A total of 3447 proteins were quantified, representing 82% of protein-coding genes, with the average sequence coverage up to 40%, demonstrating the high coverage of quantitation results. We found that most of the quantified proteins related to chemotaxis were differentially expressed, including the low-abundance proteins such as tap, trg, aer, cheA and cheB, indicating that chemotaxis would play an important role for E. coli cell to survive from acetate toxicity. The above results demonstrated that the deep-APQ method is of great promising to achieve the deep-coverage proteome quantitation with high confidence.

Coerced Abortion - The Neglected Face of Reproductive Coercion

Reproductive coercion encompasses a collection of pregnancy promoting and pregnancy avoiding behaviours. Coercion may vary in severity and be perpetrated by intimate partners or others. Research is complicated by the inclusion of behaviours that do not necessarily involve an intention to influence reproduction, such as contraceptive sabotage. These behaviours are the most common, but are not always included in survey instruments. This may explain why the prevalence of reproductive coercion varies widely. Prevalence also varies when coerced abortion is included in survey instruments. When it is, it seems roughly comparable in prevalence to coercion intended to impregnate. The extent and nature of coerced abortion can also be derived from studies that explore the reasons why women access abortion, the relationship between abortion and intimate partner violence, and online blogs and forums. This narrative review of reproductive coercion examines the evidence and attempts to comprehend why coerced abortion has been neglected.

Metabolite profiling analysis of hepatitis B virus-induced liver cirrhosis patients with minimal hepatic encephalopathy using gas chromatography-time-of-flight mass spectrometry and ultra-performance liquid chromatography-quadrupole-time-of-flight mass spectrometry

This study used gas chromatography-time-of-flight mass spectrometry (GC-TOFMS) and ultra-performance liquid chromatography-quadrupole TOFMS (UPLC-QTOFMS) metabonomic analytical techniques in combination with bioinformatics and pattern recognition analysis methods to analyze the serum metabolite profiling of hepatitis B virus (HBV)-induced liver cirrhosis patients with minimal hepatic encephalopathy (MHE), to find the specific biomarkers of MHE, to reveal the pathogenesis of MHE, and to determine a promising approach for early diagnosis of MHE. Serum samples of 100 normal controls (NC group), 29 HBV-induced liver cirrhosis patients with MHE (MHE group), and 24 HBV-induced liver cirrhosis patients without MHE [comprising 12 cases of compensated cirrhosis (CS group) and 12 cases of decompensated cirrhosis (DS group)] were collected and employed into GC-TOFMS and UPLC-QTOFMS platforms for serum metabolite detection; the outcome data were then analyzed using principal component analysis and orthogonal partial least squares-discriminant analysis (OPLS-DA). There were no significant differential metabolites between the NC group and the CS group. A series of key differential metabolites were detected. According to the variable influence in projection values and P-values, 60 small-molecule metabolites were considered to be dysregulated in the MHE group (compared to the NC group); 27 of these 60 dysregulated differential metabolites were considered to be the potential biomarkers (see Table 4, marked in bold); 66 small-molecule metabolites were considered to be dysregulated in the DS group (compared to the NC group); 34 of these 66 dysregulated differential metabolites were considered to be the potential biomarkers (see Table 5, marked in bold). According to the fold-change values, 9 of these 27 metabolites, namely valine, oxalic acid, erythro-sphingosine, 4,7,10,13,16,19-docosahexaenoic acid, isoleucine, allo-isoleucine, thyroxine, rac-octanoyl carnitine, and tocopherol (vitamin E), were downregulated in the MHE group (compared to the NC group); the other 18, namely adenine, glycochenodeoxycholic acid, fucose, allothreonine, glycohyocholic acid, glycoursodeoxycholic acid, tyrosine, taurocheno-deoxycholate, phenylalanine, 2-hydroxy-3-methyl-butanoic acid, hydroxyacetic acid, taurocholate, sorbitol, rhamnose, tauroursodeoxycholate, tolbutamide, pyroglutamic acid, and malic acid, were upregulated; 6 of these 34 metabolites were downregulated in the DS group (compared to the NC group), and the other 28 were upregulated, as shown in Table 5. (a) GC-TOFMS and UPLC-QTOFMS metabonomic analytical platforms can detect a range of metabolites in the serum; this might be of great help to study the pathogenesis of MHE and may provide a new approach for the early diagnosis of MHE. (b) Metabonomics analysis in combination with pattern recognition analysis might have great potential to distinguish the HBV-induced liver cirrhosis patients who have MHE from the normal healthy population and HBV-induced liver cirrhosis patients without MHE.

Multi-Scale Segmentation and Surface Fitting for Measuring 3-D Macular Holes

Macular holes are blinding conditions, where a hole develops in the central part of retina, resulting in reduced central vision. The prognosis and treatment options are related to a number of variables, including the macular hole size and shape. High-resolution spectral domain optical coherence tomography allows precise imaging of the macular hole geometry in three dimensions, but the measurement of these by human observers is time-consuming and prone to high inter-and intra-observer variability, being characteristically measured in 2-D rather than 3-D. We introduce several novel techniques to automatically retrieve accurate 3-D measurements of the macular hole, including: surface area, base area, base diameter, top area, top diameter, height, and minimum diameter. Specifically, we introduce a multi-scale 3-D level set segmentation approach based on a state-of-the-art level set method, and we introduce novel curvature-based cutting and 3-D measurement procedures. The algorithm is fully automatic, and we validate our extracted measurements both qualitatively and quantitatively, where our results show the method to be robust across a variety of scenarios. Our automated processes are considered a significant contribution for clinical applications.

The Complex Process of Using the Interconnected Knee Arthroplasty Device Clearance Pathway

Background: The clearance of medical devices by the US Food and Drug Administration (FDA) has remained largely unchanged since 1976, when the Medical Device Amendments Act established a system classifying devices into 3 categories based on safety risk to the consumer. The system allows for the clearance of many orthopedics devices through the 510(k) premarket pathway, which is based on "predicate ancestors," previously cleared devices that are "substantially equivalent." Purpose: We sought to trace the predicate ancestors of modern total knee arthroplasty (TKA) devices, specifically those recently cleared for marketing by the 510(k) pathway that claim substantial equivalence to prior devices, despite potential differences in material science and design. In addition, we aimed to document which TKA devices cleared by the 510(k) pathway have substantial equivalence to devices that have since been recalled by the FDA. Methods: To create a comprehensive list of TKA devices, we used FDA Classification Process Codes corresponding to knee arthroplasty to search the FDA's databases from May 28, 1976, the start of the 510(k) process, to May 1, 2021. Of 1309 resulting devices, 89 were excluded as not related to arthroplasty. For each of the remaining devices, we analyzed the descendant devices that claimed substantial equivalence, either directly or indirectly. We used data of recalled designs to determine both the absolute number of recalled devices and the number of currently cleared devices that presented substantial equivalence claims upon predicates that have since been recalled. Results: Of 1220 knee devices cleared or approved, 6 (0.5%) were approved through the premarket approval application (PMA) process, and 1214 (99.5%) were cleared through the 510(k) pathway. Of the 1214 cleared devices, 217 (17.9%) have been recalled and 204 (16.8%) have ties to at least 1 recalled predicate device linked through generational claims of substantial equivalence. We found 90 devices (7.4%) linked directly to a recalled predicate device. Conclusions: Most knee arthroplasty devices are cleared for marketing through reliance on a complex web of equivalency to previously cleared predicates. We found that many TKA devices thus connected were cleared decades apart, with multiple iterations of design and material modifications. Many currently marketed TKA devices have claimed equivalency to predicates that have been recalled. Our findings suggest the need for novel regulatory strategies that might further patient safety while balancing the unwanted effects of regulatory burden.

Edge-Preserving PET Image Reconstruction Using Trust Optimization Transfer

Iterative image reconstruction for positron emission tomography can improve image quality by using spatial regularization. The most commonly used quadratic penalty often oversmoothes sharp edges and fine features in reconstructed images, while nonquadratic penalties can preserve edges and achieve higher contrast recovery. Existing optimization algorithms such as the expectation maximization (EM) and preconditioned conjugate gradient (PCG) algorithms work well for the quadratic penalty, but are less efficient for high-curvature or nonsmooth edge-preserving regularizations. This paper proposes a new algorithm to accelerate edge-preserving image reconstruction by using two strategies: trust surrogate and optimization transfer descent. Trust surrogate approximates the original penalty by a smoother function at each iteration, but guarantees the algorithm to descend monotonically; Optimization transfer descent accelerates a conventional optimization transfer algorithm by using conjugate gradient and line search. Results of computer simulations and real 3-D data show that the proposed algorithm converges much faster than the conventional EM and PCG for smooth edge-preserving regularization and can also be more efficient than the current state-of-art algorithms for the nonsmooth l(1) regularization.

Coping with a lack of evidence: living-donor kidney transplantation in the initial phase of the SARS-CoV-2 pandemic

Due to immunosuppressive therapy, transplant patients are more susceptible to viral and bacterial infections. A potentially deadly new virus haunted us in 2020: SARS-CoV‑2, causing coronavirus disease 19 (COVID-19). We analyzed the consequences of this previously unknown risk for our living-donor transplant program in the first year of the pandemic. After the complete lockdown in spring 2020, our transplant center in Linz resumed the living-donor kidney transplantation program from June to September 2020, between the first and second waves of COVID-19 in Austria. We compared the outcomes of these living-donor kidney transplantations with the transplant outcomes of the corresponding periods of the three previous years. From June 4 to September 9, 2020, five living-donor kidney transplantations were performed. All donors and recipients were screened for COVID 19 infection by PCR testing the day before surgery. Kidney transplant recipients remained isolated in single rooms until discharge from hospital. All recipients and donors remained SARS-CoV‑2 negative during the follow-up of 10 months and have been fully vaccinated to date. The number of living transplants in the studied period of 2020 was constant compared to the same months of 2017, 2018, and 2019. Living-donor kidney transplantation can be continued using testing for SARS-CoV‑2 and meticulous hygienic precautions in epidemiologically favorable phases of the SARS-CoV‑2 pandemic. Donors and recipients should be carefully selected and informed about risks and benefits.

Complete mitochondrial genome of the American flamingo, Phoenicopterus ruber (Phoenicopteriformes, Phoenicopteridae)

The American flamingo, Phoenicopterus ruber (P. ruber), is a large species of flamingo closely related to the greater flamingo and Chilean flamingo. In this paper, the complete mitochondrial genome sequence of P. ruber has been assembled for the first time. It was 17 476 bp in length and consisted of 13 typical vertebrate protein-coding genes, 22 tRNA genes, 2 rRNA genes and 2 control regions. COI and ND3 genes used GTG and ATC as start codons respectively, but the remaining protein-coding genes were encoded beginning with orthodox ATG codon. Two triplet codons (TAA, AGG) and one single T base were employed as stop codons. The arrangement of the overall genes and noncoding regions was identical to the same genus flamingo Phoenicopterus roseus. The AT content (54.27%) was higher than the GC content. Phylogenetic analysis was performed using 12 protein-coding genes, combined with other 11 species from the same Neognathae, which validated the responsibility and utility of this new mitochondrial genome.

Cell Envelope Stress Response in Pseudomonas aeruginosa

Bacteria sense their environment via the cell envelope, which in Gram-negative bacteria comprises the outer membrane, the periplasmic space, and the inner membrane. Pseudomonas aeruginosa is an opportunistic pathogen which is exposed to different cell wall stresses imposed by exposure to antibiotics, osmotic pressure, and long-time colonization of host tissues such as the lung in cystic fibrosis patients. In response to these stresses, P. aeruginosa is able to respond by establishing a cell envelope stress response involving different regulatory pathways including the extra-cytoplasmic sigma factors AlgU, SigX, and SbrI and other two-component sensor/response regulators and effectors. This chapter aims to review the different factors leading to the activation of the cell envelope stress response in P. aeruginosa and the genetic determinants involved in this response, which is crucial for the survival of the bacterium upon exposure to different stressful conditions.

Adaptive Developmental Resonance Network

Adaptive resonance theory (ART) networks, including developmental resonance network (DRN), basically use a vigilance parameter as a hyperparameter to determine whether a current input can belong to any existing categories or not. The problem here is that the clustering quality of those networks is sensitive to the vigilance parameter so that the users are required to fine-tune the parameter delicately beforehand. Another problem is that those networks only deal with a hyperrectangular decision boundary, which means they cannot learn categories of arbitrary shape. In addition, the order of data processing is a critical factor to categorize clusters correctly because each category can expand its boundary into the areas of other categories erroneously. To deal with these problems, we propose an advanced version of DRN, Adaptive DRN (A-DRN), which learns the vigilance parameters assigned for individual category nodes as well as category weights. The proposed A-DRN combines close categories to construct a cluster that contains the categories identifying a cluster boundary of arbitrary shape. Our A-DRN also employs a sliding window. The sliding window buffers sequential data points to presume the data distribution roughly, which helps our network to have a robust and consistent performance to a random order of input data. Through the experiments, we empirically demonstrate the effectiveness of A-DRN in both synthetic and real-world benchmark data sets.

IgE type multiple myeloma exhibits hypermutated phenotype and tumor reactive T cells

Multiple myeloma (MM) is a hematological malignancy originating from malignant and clonally expanding plasma cells. MM can be molecularly stratified, and its clonal evolution deciphered based on the Ig heavy and light chains of the respective malignant plasma cell clone. Of all MM subtypes, IgE type MM accounts for only <0.1% of cases and is associated with an aggressive clinical course and consequentially dismal prognosis. In such malignancies, adoptive transfer of autologous lymphocytes specifically targeting presented (neo)epitopes encoded by either somatically mutated or specifically overexpressed genes has resulted in substantial objective clinical regressions even in relapsed/refractory disease. However, there are no data on the genetic and immunological characteristics of this rare and aggressive entity. Here, we comprehensively profiled IgE type kappa MM on a genomic and immune repertoire level by integrating DNA- and single-cell RNA sequencing and comparative profiling against non-IgE type MM samples. We demonstrate distinct pathophysiological mechanisms as well as novel opportunities for targeting IgE type MM. Our data further provides the rationale for patient-individualized neoepitope-targeting cell therapy in high tumor mutation burden MM.

Deep mutational scanning and massively parallel kinetics of plasminogen activator inhibitor-1 functional stability to probe its latency transition

Plasminogen activator inhibitor-1 (PAI-1), a member of the serine protease inhibitor superfamily of proteins, is unique among serine protease inhibitors for exhibiting a spontaneous conformational change to a latent or inactive state. The functional half-life for this transition at physiologic temperature and pH is ∼1 to 2 h. To better understand the molecular mechanisms underlying this transition, we now report on the analysis of a comprehensive PAI-1 variant library expressed on filamentous phage and selected for functional stability after 48 h at 37 °C. Of the 7201 possible single amino acid substitutions in PAI-1, we identified 439 that increased the functional stability of PAI-1 beyond that of the WT protein. We also found 1549 single amino acid substitutions that retained inhibitory activity toward the canonical target protease of PAI-1 (urokinase-like plasminogen activator), whereas exhibiting functional stability less than or equal to that of WT PAI-1. Missense mutations that increase PAI-1 functional stability are concentrated in highly flexible regions within the PAI-1 structure. Finally, we developed a method for simultaneously measuring the functional half-lives of hundreds of PAI-1 variants in a multiplexed, massively parallel manner, quantifying the functional half-lives for 697 single missense variants of PAI-1 by this approach. Overall, these findings provide novel insight into the mechanisms underlying the latency transition of PAI-1 and provide a database for interpreting human PAI-1 genetic variants.

Barrier height tuning in Ti/4H-SiC Schottky diodes

In this work, we investigated the effects of annealing temperature and metal thickness on the Schottky barrier height in state-of-the-art Ti/4H-SiC rectifiers. By varying these two parameters, a controlled lowering of the Schottky barrier height has been obtained, thus giving the possibility to improve the efficiency of device in terms of power consumption.

Cloud tomography applied to sky images: A virtual testbed

Two tomographic techniques are applied to two simulated sets of sky images with different cloud fraction. The Algebraic Reconstruction Technique (ART) is applied to optical depth maps from sky images to reconstruct 3-D cloud extinction coefficients without considering multiple scattering effects. Reconstruction accuracy is explored for different products, including surface irradiance and extinction coefficients, and as a function of the number of available sky imagers and setup distance. Increasing the number of imagers improves the accuracy of the 3-D reconstruction: for surface irradiance, the error decreases significantly up to four imagers at which point the improvements become marginal. But using nine imagers gives more robust results in practical situations in which the circumsolar region of images has to be excluded due to poor cloud detection. The ideal distance between imagers was also explored: for a cloud height of 1 km, increasing distance up to 3 km (the domain length) improved the 3-D reconstruction. An iterative reconstruction technique that iteratively updated the source function improved the results of the ART by minimizing the error between input red radiance images and reconstructed red radiance simulations. For the best case of a nine-imager deployment, the ART and iterative method resulted in 53.4% and 33.6% relative mean absolute error for the extinction coefficients, respectively.

Deep Neural Networks With Region-Based Pooling Structures for Mammographic Image Classification

Breast cancer is one of the most frequently diagnosed solid cancers. Mammography is the most commonly used screening technology for detecting breast cancer. Traditional machine learning methods of mammographic image classification or segmentation using manual features require a great quantity of manual segmentation annotation data to train the model and test the results. But manual labeling is expensive, time-consuming, and laborious, and greatly increases the cost of system construction. To reduce this cost and the workload of radiologists, an end-to-end full-image mammogram classification method based on deep neural networks was proposed for classifier building, which can be constructed without bounding boxes or mask ground truth label of training data. The only label required in this method is the classification of mammographic images, which can be relatively easy to collect from diagnostic reports. Because breast lesions usually take up a fraction of the total area visualized in the mammographic image, we propose different pooling structures for convolutional neural networks(CNNs) instead of the common pooling methods, which divide the image into regions and select the few with high probability of malignancy as the representation of the whole mammographic image. The proposed pooling structures can be applied on most CNN-based models, which may greatly improve the models' performance on mammographic image data with the same input. Experimental results on the publicly available INbreast dataset and CBIS dataset indicate that the proposed pooling structures perform satisfactorily on mammographic image data compared with previous state-of-the-art mammographic image classifiers and detection algorithm using segmentation annotations.

Covalent Organic Frameworks as Efficient Photoinitiators and Cross-Linkers To Fabricate Highly Stretchable Hydrogels

In this work, two kinds of imine-type covalent organic framework (COF) nanoparticles are demonstrated as efficient photocatalytic initiators to trigger the free-radical polymerization of acrylamide (AM) to prepare polyacrylamide (PAM) hydrogels under visible light irradiation, without any assistance from the co-initiator. Simultaneously, the COF nanoparticles bearing vinyl side groups (COF-V) promote covalent cross-linking of the polymer chains, which significantly reinforces the mechanical properties of the nanocomposite hydrogel. The obtained PAM/COF-V hydrogel is highly stretchable with an extraordinary elongation up to 3300% strain. On the other hand, the COF nanoparticles modified with methoxy moieties (COF-OMe) endow the resulting PAM/COF-OMe hydrogel with a promising fluorescence feature. In addition, this strategy provides a visible-light-regulated photocatalytic polymerization approach with a simplified recipe to fabricate COF-based nanocomposite hydrogels or resins with diverse functions.

Loud Auditory Distractors Are More Difficult to Ignore After All

Working memory performance is markedly disrupted when task-irrelevant sound is played during item presentation or retention. In a preregistered replication study, we systematically examined the role of intensity in two types of auditory distraction. The first type of distraction is the changing-state effect (i.e., increased disruption by changing-state relative to steady-state sequences). The second type is the auditory deviant effect (i.e., increased disruption by auditory deviant relative to steady-state sequences). In previous experiments, the changing-state effect was independent of intensity. Whether a deviation in intensity leads to an increase in disruption has not yet been examined. We replicated the classic finding that the increased disruption by changing-state relative to steady-state sequences is independent of intensity. Contrary to previous studies, we found an unexpected main effect of intensity. Steady-state and changing-state sequences presented at 75 dB(A) were more disruptive than presented at 45 dB(A), suggesting that intensity plays a more important role than previously assumed in the disruption of working memory performance. Furthermore, we tested the prediction of the violation of expectancy account, according to which deviant distractors at a lower and higher intensity than the rest of the sequence should be equally disruptive. Our results were consistent with this prediction.

Profiling of key heat shock proteins and their relationship with male sexual behavior and seminal characteristics in Kankrej (Bos indicus) breeding bulls during different seasons

The goal of this study is to use indirect ELISA to determine the concentration of major heat shock proteins (Hsps) in Kankrej (Bos indicus) breeding bulls and their relationship with certain male phenotypic traits including sexual behavior, sperm quality, and bull fertility in different seasons. The seasonal fluctuation in the concentration of three major Hsps (60, 70, and 90) was determined using an indirect enzyme-linked immunosorbent assay (ELISA). According to the findings, Hsps levels are significantly higher during the summer season and are associated with both fresh and post-thawed semen quality traits in Kankrej breeding bulls. The better sexual behavior of bulls and seminal parameters of fresh or thawed semen was observed in the winter season together with the lower concentrations of HSPs. These could suggest negative association between HSPs with bull sexual behavior and seminal parameters. As a result, the concentration of Hsps in breeding bulls may be a useful indicator for determining fertility traits.

Dynamic Binary Countdown for Massive IoT Random Access in Dense 5G Networks

Massive connectivity for Internet of Things applications is expected to challenge the way access reservation protocols are designed in 5G networks. Since the number of devices and their density are envisioned to be orders of magnitude larger, state-of-the-art access reservation, random access (RA) procedure, might be a bottleneck for end-to-end delay. This would be especially challenging for burst arrival scenarios: semisynchronous triggering of a large number of devices due to a common event (blackout, emergency alarm, etc.). In this paper, to improve RA procedure scalability, we propose to combine binary countdown contention resolution (BCCR) with the state-of-theart access class barring (ACB). We present a joint analysis of ACB and BCCR and apply a framework for treating RA as a biobjective optimization, minimizing the resource consumption and maximizing the throughput of the procedure in every contention round. We use this framework to devise dynamic load-adaptive algorithm and simulatively illustrate that the proposed algorithm reduces the burst resolution delay while consuming less resources compared to the state-of-the-art techniques.

Slice Sorting for Unequal Loss Protection of Video Streams

In this letter, we propose a novel unequal loss protection scheme, which allocates FEC codes to video slices according to their impact on the GOP distortion. This is evaluated taking the concealment procedure and the drift effect into account. Simulation results show that the proposed algorithm outperforms state-of-the-art approaches, reducing the gap with the error-free performance curve. Moreover, the complexity of the additional stage required to pilot the protection allocation stage is negligible with respect to traditional ULP schemes.

Towards less mutilating treatments in patients with advanced non-melanoma skin cancers by earlier use of immune checkpoint inhibitors

Merkel cell carcinoma (MCC), advanced cutaneous squamous cell carcinoma (cSCC), and advanced basal cell carcinoma (BCC) are rare, and the often frail patients may require potentially mutilating local treatments. Immune checkpoint inhibitors (ICIs) are effective in melanoma and are moving towards the neoadjuvant setting. This systematic review explores data supporting the transition of ICIs from the metastatic to the (neo)adjuvant setting non-melanoma skin cancer (NMSC) and describes how knowledge from melanoma can be utilized. ICI response rates in advanced NMSC and melanoma are comparable. Five early phase studies show effectivity of neoadjuvant ICIs in melanoma and adjuvant treatment is standard-of-care. Eight adjuvant and 12 neoadjuvant ICI studies are ongoing for NMSC. Encouragingly, data from two small neoadjuvant ICI studies in NMSC, demonstrated complete responses in approximately half of patients. In conclusion, neoadjuvant ICI treatment has potential to avert mutilating treatments in NMSC. Progress can be accelerated by learning from melanoma.

Learning New Parts for Landmark Localization in Whole-Body CT Scans

The goal of this work is to reliably and accurately localize anatomical landmarks in 3-D computed tomography scans, particularly for the deformable registration of whole-body scans, which show huge variation in posture, and the spatial distribution of anatomical features. Parts-based graphical models (GM) have shown attractive properties for this task because they capture naturally anatomical relationships between landmarks. Unfortunately, standard GMs are learned from manually annotated training images and the quantity of landmarks is limited by the high cost of expert annotation. We propose a novel method that automatically learns new corresponding landmarks from a database of 3-D whole-body CT scans, using a limited initial set of expert-labeled ground-truth landmarks. The newly learned landmarks, called B-landmarks, are used to build enriched GMs. We compare our method of deformable registration based on such GM landmarks to a conventional deformable registration method and to a "baseline" state-of-the-art GM. The results show our method finds new relevant anatomical correspondences and improves by up to 35% the matching accuracy of highly variable skeletal and soft-tissue landmarks of clinical interest.

Gender Affirmation Surgery on the Rise: Analysis of Trends and Outcomes

Purpose: Gender-affirming surgery (GAS) has become an important component of the treatment of gender dysphoria. Although the frequency of these procedures is on the rise, a complete safety profile has yet to be established. The goal of our study is to analyze the trends and outcomes of these surgical procedures. Methods: All patients with a primary diagnosis of gender dysphoria undergoing GAS were identified from the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) database between the years 2009 and 2018. Patient demographics and 30-day postoperative outcomes were recorded. We performed a multivariate logistic regression for postoperative complications, controlling for several confounding variables. Results: We identified 2956 patients, of which 1767 (59.78%) were transgender men and 1189 (40.22%) were transgender women. The number of patients undergoing GAS per year increased from 7 in 2010 to 1069 in 2018, a 152-fold increase. For patients undergoing top surgery, Black race (odds ratio [OR] = 2.255, 95% confidence interval [CI] 1.189-4.277, p = 0.013) and diabetes (OR = 4.156, 95% CI 1.571-10.999, p = 0.004) were independent predictors of 30-day postoperative complications. For patients undergoing bottom surgery, total operative time in minutes (OR = 1.005, 95% CI 1.003-1.007, p = 0.001) was an independent predictor of 30-day postoperative complications. Conclusion: The demand for GAS has increased exponentially since 2014. While postoperative complication rates are acceptable, Black race was shown to be an independent predictor of postoperative morbidity in patients undergoing top surgery, a finding that calls for further investigation of racial disparities among transgender patients.

Adaptive Hierarchical Dual Consistency for Semi-Supervised Left Atrium Segmentation on Cross-Domain Data

Semi-supervised learning provides great significance in left atrium (LA) segmentation model learning with insufficient labelled data. Generalising semi-supervised learning to cross-domain data is of high importance to further improve model robustness. However, the widely existing distribution difference and sample mismatch between different data domains hinder the generalisation of semi-supervised learning. In this study, we alleviate these problems by proposing an Adaptive Hierarchical Dual Consistency (AHDC) for the semi-supervised LA segmentation on cross-domain data. The AHDC mainly consists of a Bidirectional Adversarial Inference module (BAI) and a Hierarchical Dual Consistency learning module (HDC). The BAI overcomes the difference of distributions and the sample mismatch between two different domains. It mainly learns two mapping networks adversarially to obtain two matched domains through mutual adaptation. The HDC investigates a hierarchical dual learning paradigm for cross-domain semi-supervised segmentation based on the obtained matched domains. It mainly builds two dual-modelling networks for mining the complementary information in both intra-domain and inter-domain. For the intra-domain learning, a consistency constraint is applied to the dual-modelling targets to exploit the complementary modelling information. For the inter-domain learning, a consistency constraint is applied to the LAs modelled by two dual-modelling networks to exploit the complementary knowledge among different data domains. We demonstrated the performance of our proposed AHDC on four 3D late gadolinium enhancement cardiac MR (LGE-CMR) datasets from different centres and a 3D CT dataset. Compared to other state-of-the-art methods, our proposed AHDC achieved higher segmentation accuracy, which indicated its capability in the cross-domain semi-supervised LA segmentation.

Analyzing wet weather flow management using state of the art tools

Optimal secondary clarifier performance is crucial to meet treatment requirements, especially when treating peak wet weather flows (PWWFs), to prevent high effluent suspended solids (ESS) concentrations and elevated sludge blankets. A state-of-the-art computational fluid dynamic (CFD) model was successfully used as a design and diagnostic tool to optimize performance for municipal wastewater treatment plants subject to significant PWWFs. Two case studies are presented. For Case Study 1, the model was used to determine the number of secondary clarifiers that will be necessary to treat future PWWF conditions for a plant under design. For Case Study 2, the model was used to identify modifications that are currently being made to increase the clarifier capacity for handling PWWF.

Bioactive Secondary Plant Metabolites from Euphorbia umbellata (PAX) BRUYNS (Euphorbiaceae)

The species Euphorbia umbellata has been used to treat inflammatory diseases, cancer, and ulcers. Biological activities reported in the literature, including antiproliferative, cytotoxic and anti-inflammatory, are attributed to the chemical constituents present in its composition as terpenes and polyphenolic compounds. The most recurrently verified metabolites in the Euphorbiaceae family plant species are terpenes, of which euphol is a major constituent with broadly reported cytotoxic, antinociceptive and anti-inflammatory effects; it frequently appears in various extracts obtained from the plant. Euphol has a documented inhibitory effect on neutrophil chemotaxis and can modulate the complement system. Since complement system activation is intimately intertwined with autoimmune and inflammatory diseases, tumor growth promotion and metastasis, plant metabolites from Euphorbia umbellata might influence the outcomes of inflammatory processes. We believe that this is the first review presenting the current knowledge on Euphorbia umbellata secondary metabolites and their biological activities.

Mechanochemical Preparation of Edge-Selectively justify Hydroxylated Graphene Nanosheets Using Persulfate via a Sulfate Radical-Mediated Process

The production of water-dispersed graphene with low defects remains a challenge. The dry ball milling of graphite with additives produces edge-selectively functionalized graphene. However, the "inert" additives require a long milling time and cause inevitable in-plane defects. Here, the mechanochemical reaction of graphite with persulfate solved the above drawback and produced edge-selectively hydroxylated graphene (EHG) nanosheets through a 2 h ball-milling and a subsequent 0.5 h sonication. The mechanochemical cleavage of persulfate yielded SO4 ⋅- to spontaneously oxidize graphite to form the carbon radical cations selectively at edges, followed by hydroxylation with water of moisture. Because the O-O bond dissociation energy of persulfate is 20 % of the graphitic C-C bond, the rather low milling energy allowed the hydroxylation of graphite at edges with nearly no in-plane defects. The obtained EHG showed high water-dispersibility and excellent photothermal and electrochemical properties, thereby opening up a new door to fabricate graphene-based composites.

The advances of calcium oxalate calculi associated drugs and targets

Kidney stones constitute a disease of the urinary system of high incidence that has only a few available stone dissolving drugs as treatment options. The mechanism of calcium oxalate stone formation is still largely unclear. Various aspects and theories for initiation and formation of the renal stones have been suggested, and the complex multistep formation process of the kidney stones includes supersaturation, nucleation, growth and aggregation of a crystal, and crystal retention in cells after adhesion. During the initial stage of crystal formation, high concentrations of oxalate exposure may damage the renal tubular cells and cause oxidative stress after which the cells may be attached to the crystal thus supporting the oxalate-induced injury as the driving factor of crystal precipitation and cellular adhesion. However, at present, although various drugs targeting kidney stones have been proposed and evaluated both in vitro and in vivo, clinical drugs for stone dissolution have rarely been explored. Moreover, numerous advances in renal calcium oxalate stone associated target and drugs warrant their summarization until now, which could be further discussed and may provide potential ideas or options for exploration of renal calcium oxalate stone treatment targets and drugs.

FLDNet: Light Dense CNN for Fingerprint Liveness Detection

Fingerprint liveness detection has gained increased attention recently due to the growing threat of spoof presentation attacks. Among the numerous attempts to deal with this problem, the Convolutional Neural Networks (CNN) based methods have shown impressive performance and great potential. However, there is a need for improving the generalization ability and reducing the complexity. Therefore, we propose a lightweight (0.48M parameters) and efficient network architecture, named FLDNet, with an attention pooling layer which overcomes the weakness of Global Average Pooling (GAP) in fingerprint anti-spoofing tasks. FLDNet consists of modified dense blocks which incorporate the residual path. The designed block architecture is compact and effectively boosts the detection accuracy. Experimental results on two datasets, LivDet 2013 and 2015, show the proposed approach achieves state-of-the-art performance in intra-sensor, cross-material and cross-sensor testing scenarios. For example, on LivDet 2015 dataset, FLDNet achieves 1.76 & x0025; Average Classification Error (ACE) over all sensors and 3.31 & x0025; against unkown spoof materials compared to 2.82 & x0025; and 5.45 & x0025; achieved by state-of-the-art methods.

CAxCNN: Towards the Use of Canonic Sign Digit Based Approximation for Hardware-Friendly Convolutional Neural Networks

The design of hardware-friendly architectures with low computational overhead is desirable for low latency realization of CNN on resource-constrained embedded platforms. In this work, we propose CAxCNN, a Canonic Sign Digit (CSD) based approximation methodology for representing the filter weights of pre-trained CNNs.The proposed CSD representation allows the use of multipliers with reduced computational complexity. The technique can be applied on top of state-of-the-art CNN quantization schemes in a complementary manner. Our experimental results on a variety of CNNs, trained on MNIST, CIFAR-10 and ImageNet datasets, demonstrate that our methodology provides CNN designs with multiple levels of classification accuracy, without requiring any retraining, and while having a low area and computational overhead. Furthermore, when applied in conjunction with a state-of-art quantization scheme, CAxCNN allows the use of multipliers, which offer 77% logic area reduction, as compared to their accurate counterpart, while incurring a drop in Top-1 accuracy of just 5.63% for a VGG-16 network trained on ImageNet.

Angular selective window systems: Assessment of technical potential for energy savings

Static angular selective shading systems block direct sunlight and admit daylight within a specific range of incident solar angles. The objective of this study is to quantify their potential to reduce energy use and peak demand in commercial buildings using state-of-the art whole-building computer simulation software that allows accurate modeling of the behavior of optically-complex fenestration systems such as angular selective systems. Three commercial systems were evaluated: a micro-perforated screen, a tubular shading structure, and an expanded metal mesh. This evaluation was performed through computer simulation for multiple climates (Chicago, Illinois and Houston, Texas), window-to-wall ratios (0.15-0.60), building codes (ASHRAE 90.1-2004 and 2010) and lighting control configurations (with and without). The modeling of the optical complexity of the systems took advantage of the development of state-of-the-art versions of the EnergyPlus, Radiance and Window simulation tools. Results show significant reductions in perimeter zone energy use; the best system reached 28% and 47% savings, respectively, without and with daylighting controls (ASHRAE 90.1-2004, south facade, Chicago, WWR= 0.45). Angular selectivity and thermal conductance of the angle-selective layer, as well as spectral selectivity of low-emissivity coatings, were identified as factors with significant impact on performance. (C) 2014 Elsevier B.V. All rights reserved.

Poisson image denoising by piecewise principal component analysis and its application in single-particle X-ray diffraction imaging

This study describes an improved method for Poisson image denoising that is based on a state-of-the-art Poisson denoising approach known as non-local principal component analysis (NLPCA). The new method is referred to as PieceWise Principal Component Analysis (PWPCA). In PWPCA, the given image is first split into pieces, then NLPCA is run on each image piece, and finally the entire image is reconstituted by a weighted combination of the NLPCA-processed image pieces. Using standard test images with Poisson noise, the authors show that PWPCA restores images more effectively than state-of-the-art Poisson denoising approaches. In addition, and to the best of their knowledge, they show the first application of such approaches to single-particle X-ray free-electron laser (XFEL) data. They show that the resolution of three-dimensional reconstruction from XFEL diffraction images is improved when the data are preprocessed with PWPCA. XFELs are currently under rapid development to allow high-resolution biomolecular structure determination at near-physiological conditions. Data analysis methods developments follow these technological advances and are expected to have high impact in structural biology and drug design. This study contributes to these developments. As little experimental single-particle XFEL data is available still, the XFEL experiments shown here were performed with simulated data.

Two decades of rice research in Indonesia and the Philippines: A systematic review and research agenda for the social sciences

While rice studies are abundant, they usually focus on macro-level rice production and yield data, genetic diversity, cultivar varieties, and agrotechnological innovations. Moreover, many of these studies are either region-wide or concentrated on countries in the Global North. Collecting, synthesizing, and analyzing the different themes and topic areas in rice research since the beginning of the 21st century, especially in the Global South, remain unaddressed areas. This study contributes to filling these research lacunae by systematically reviewing 2243 rice-related articles cumulatively written by more than 6000 authors and published in over 900 scientific journals. Using the PRISMA 2020 guidelines, this study screened and retrieved articles published from 2001 to 2021 on the various topics and questions surrounding rice research in Indonesia and the Philippines-two rice-producing and -consuming, as well as emerging economies in Southeast Asia. Using a combination of bibliometrics and quantitative content analysis, this paper discusses the productive, relevant, and influential rice scholars; key institutions, including affiliations, countries, and funders; important articles and journals; and knowledge hotspots in these two countries. It also discusses the contributions of the social sciences, highlights key gaps, and provides a research agenda across six interdisciplinary areas for future studies. This paper mainly argues that an interdisciplinary and comparative inquiry of potentially novel topic areas and research questions could deepen and widen scholarly interests beyond conventional natural science-informed rice research in Indonesia and the Philippines. Finally, this paper serves other researchers in their review of other crops in broader global agriculture.

Spread Estimation With Non-Duplicate Sampling in High-Speed Networks

Per-flow spread measurement in high-speed networks has many practical applications. It is a more difficult problem than the traditional per-flow size measurement. Most prior work is based on sketches, focusing on reducing their space requirements in order to fit in on-chip cache memory. This design allows the measurement to be performed at the line rate, but it suffers from expensive computation for spread queries (unsuitable for online operations) and large errors in spread estimation for small flows. This paper complements the prior art with a new spread estimator design based on an on-chip/off-chip model. By storing traffic statistics in off-chip memory, our new design faces a key technical challenge to design an efficient online module of non-duplicate sampling that cuts down the off-chip memory access. We first propose a two-stage solution for non-duplicate sampling, which is efficient but cannot handle well a sampling probability that is either too small or too big. We then address this limitation through a three-stage solution that is more space-efficient. Our analysis shows that the proposed spread estimator is highly configurable for a variety of probabilistic performance guarantees. We implement our spread estimator in hardware using FPGA. The experiment results based on real Internet traffic traces show that our estimator produces spread estimation with much better accuracy than the prior art, reducing the mean relative (absolute) error by about one order of magnitude. Moreover, it increases the query throughput by around three orders of magnitude, making it suitable for supporting online queries in real time.

Spatial and seasonal distribution of microplastic in surface water of Bueng Boraphet Wetland-a Ramsar wetland in Thailand

This study sought to assess microplastic contamination in the surface water of the inland freshwater wetland, Bueng Boraphet Wetland, Thailand, which is a lentic system with various land-use patterns, including community areas, agricultural areas, and natural resource conservation areas. In 2019, water samples were collected during the wet and dry periods from the three land-use zones at depths of 0-30 cm using a plankton net with 333 µm in mesh size. The water samples were digested via a wet peroxide oxidation process prior to the identification of microplastic morphology using a stereomicroscope. The polymer types of microplastic were analyzed using Fourier transform infrared spectrophotometry. Microplastic was found to range from 0.00 to 4.61 particles/m2 (0.34 ± 0.81 particles/m2) or 0.0 to 19.57 particles/m3 (1.44 ± 3.4 particles/m3). Furthermore, significantly high amounts of microplastics were found in samples from the community area. No microplastic was detected in the sample from the natural resource conservation area. The quantity of microplastic did not significantly differ between the sampling periods. Polymer types, including polyester, polypropylene, and polyethylene terephthalate, were identified in this study. The microplastics were predominantly small and were colored black or red. Microplastic with a fiber shape (93.8%) was observed. Besides the specific gravity of the microplastic and hydrological characteristics, the high concentration of microplastics found in samples from the community area (0.62 ± 0.79 particles/m2 or 2.63 ± 3.36 particles/m3) is likely related to the high human pressure. As microplastic contamination can impact aquatic animals and wetland ecosystems, appropriate control measures for human activities and plastic waste management are required.

Contained Morcellation for Laparoscopic Myomectomy Within a Specially Designed Bag

A technique of contained morcellation of uterine myomas within a bag specially designed for 2-port morcellation during laparoscopic myomectomy is described. Ten patients underwent in-bag morcellation of myomas with a tissue isolation bag (MorSafe) between November 2014 and January 2015. The MorSafe tissue isolation bag is a retort-shaped bag made of medical-grade flexible plastic material with the wider opening of 134 mm in diameter and the tail end measuring 4 mm in diameter, allowing easy accomodation of specimens up to 12 cm in diameter. This technique involves placing the myomas into the isolation bag within the abdomen, exteriorizing the tail end of the bag, insufflating the bag within the peritoneal cavity, and morcellating the myomas under vision. Demographic and perioperative characteristics were studied. The mean operative time was 117 minutes (range, 75-195 minutes), the mean time for specimen introduction into the bag was 12.5 minutes (range, 7-22 minutes), and the mean time for morcellation and bag removal was 24.8 minutes (range, 10-50 minutes). There were no complications related to the in-bag morcellation technique, and there was no visual evidence of damage to the isolation bag. In-bag morcellation using this new bag is a feasible technique for morcellating uterine myomas in a contained manner and may provide an option to minimize the risks of open power morcellation while preserving the benefits of minimally invasive surgery.

Methylglyoxal induces multiple serine phosphorylation in insulin receptor substrate 1 via the TAK1-p38-mTORC1 signaling axis in adipocytes

Certain metabolic intermediates produced during metabolism are known to regulate a wide range of cellular processes. Methylglyoxal (MG), a natural metabolite derived from glycolysis, has been shown to negatively influence systemic metabolism by inducing glucose intolerance, insulin resistance, and diabetic complications. MG plays a functional role as a signaling molecule that initiates signal transduction. However, the specific relationship between MG-induced activation of signal transduction and its negative effects on metabolism remains unclear. Here, we found that MG activated mammalian target of rapamycin complex 1 (mTORC1) signaling via p38 mitogen-activated protein kinase in adipocytes, and that the transforming growth factor-β-activated kinase 1 (TAK1) is needed to activate p38-mTORC1 signaling following treatment with MG. We also found that MG increased the phosphorylation levels of serine residues in insulin receptor substrate (IRS)-1, which is involved in its negative regulation, thereby attenuating insulin-stimulated tyrosine phosphorylation in IRS-1. The negative effect of MG on insulin-stimulated IRS-1 tyrosine phosphorylation was exerted due to the MG-induced activation of the TAK1-p38-mTORC1 signaling axis. The involvement of the TAK1-p38-mTORC1 signaling axis in the induction of IRS-1 multiple serine phosphorylation was not unique to MG, as the proinflammatory cytokine, tumor necrosis factor-α, also activated the same signaling axis. Therefore, our findings suggest that MG-induced activation of the TAK1-p38-mTORC1 signaling axis caused multiple serine phosphorylation on IRS-1, potentially contributing to insulin resistance.

Native Top-Down Mass Spectrometry Reveals a Role for Interfacial Glycans on Therapeutic Cytokine and Hormone Assemblies

Oligomerization and glycosylation modulate therapeutic glycoprotein stability and efficacy. The interplay between these two critical attributes on therapeutic glycoproteins, is however often hard to define. Here, we present a native top-down mass spectrometry (MS) approach to assess the glycosylation status of therapeutic cytokine and hormone assemblies and relate interfacial glycan occupancy to complex stability. We found that interfacial O-glycan stabilizes tumor necrosis factor-α trimer. On the contrary, interferon-β1a dimerization is independent of glycosylation. Moreover, we discovered a unique distribution of N-glycans on the follicle-stimulating hormone α subunit. We found that the interfacial N-glycan, at Asn52 of the α subunit, interacts extensively with the β subunit to regulate the dimer assembly. Overall, we have exemplified a method to link glycosylation with assembly status, for cytokines and hormones, critical for informing optimal stability and bioavailability.

A Redox-Controlled Substrate Engineering Strategy for Site-Specific Enzymatic Fucosylation

Fucosylation is one of the most common modifications of oligo-N-acetyllactosamine (oligo-LacNAc) glycans. However, none of known fucosyltransferases (FucTs) could install the α1,3-linked fucose to the oligo-LacNAc substrates in a site-specific manner. Here, we report a facile and general redox-controlled substrate engineering strategy for the site-specific α1,3-fucosylation of complex glycans containing multiple LacNAc units. This strategy takes advantage of an operationally simple oxidation enzyme module by using galactose oxidase (GOase) to convert the LacNAc unit into oxidized C6'-aldehyde LacNAc sequence, which is not a good substrate for recombinant α1,3-FucT from Helicobacter pylori strain 26695 (Hpα1,3FucT), enabling the site-specific α1,3-fucosylation at intact LacNAc sites. The general applicability and robustness of this strategy were demonstrated by the synthesis of a variety of structurally well-defined fucosides of linear and branched O- and N-linked glycans.

Endometriosis and Infertility: A Long-Life Approach to Preserve Reproductive Integrity

Laparoscopic surgery was originally considered the gold standard in the treatment of endometriosis-related infertility. Assisted reproductive technology (ART) was indicated as second-line treatment or in the case of male factor. The combined approach of surgery followed by ART proved to offer higher chances of pregnancy in infertile women with endometriosis. However, it was highlighted how pelvic surgery for endometriosis, especially in cases of ovarian endometriomas, could cause iatrogenic damage due to ovarian reserve loss, adhesion formation (scarring), and ischemic damage. Furthermore, in the last few years, the trend to delay the first childbirth, recent technological advances in ultrasound diagnosis, and technological progress in clinical and laboratory aspects of ART have certainly influenced the approach to infertility and endometriosis with, ART assuming a more relevant role. Management of endometriosis should take into account that the disease is chronic and involves the reproductive system. Consequently, treatment and counselling should aim to preserve the chances of pregnancy for the patient, even if it is not associated with infertility. This review will analyse the evolution of the management of infertility associated with endometriosis and propose an algorithm for treatment decision-making based on the most recent acquisitions.

Image Steganography With Symmetric Embedding Using Gaussian Markov Random Field Model

Recent advances on adaptive steganography show that the performance of image steganographic communication can be improved by incorporating the non-additive models that capture the dependencies among adjacent pixels. In this paper, a Gaussian Markov Random Field model (GMRF) with four-element cross neighborhood is proposed to characterize the interactions among local elements of cover images, and the problem of secure image steganography is formulated as the one of minimization of KL-divergence in terms of a series of low-dimensional clique structures associated with GMRF by taking advantages of the conditional independence of GMRF. The adoption of the proposed GMRF tessellates the cover image into two disjoint subimages, and an alternating iterative optimization scheme is developed to effectively embed the given payload while minimizing the total KL-divergence between cover and stego, i.e., the statistical detectability. Experimental results demonstrate that the proposed GMRF outperforms the prior arts of model based schemes, e.g., MiPOD, and rivals the state-of-the-art HiLL for practical steganography, where the selection channel knowledges are unavailable to steganalyzers.

The Rich Nonprofits Get Richer: Centering Psychiatric Grant Funding at the Margins

Community psychiatrists serve multiple institutional roles, and at times these roles may include the review of grant proposals from nonprofit organizations. In this column, the authors argue that privilege and social capital can easily become concentrated among a small group of centralized model organizations and influence the grant review process. Established and wealthy nonprofits can co-opt the growing interest in health equity by leveraging their existing resources, thereby excluding emerging organizations within communities in need. By applying a structural lens to this problem, funding entities can identify approaches that more effectively promote equity throughout the grant life cycle.

Interconnect-Aware Area and Energy Optimization for In-Memory Acceleration of DNNs

State-of-the-art in-memory computing (IMC) architectures employ an array of homogeneous tiles and severely underutilize processing elements (PEs). In this article, the authors propose an area and energy optimization methodology to generate a heterogeneous IMC architecture coupled with an optimized Network-on-Chip (NoC) for deep neural network (DNN) acceleration. -Yiran Chen, Duke University

Fast and Efficient Convolutional Accelerator for Edge Computing

Convolutional neural networks (CNNs) are a vital approach in machine learning. However, their high complexity and energy consumption make them challenging to embed in mobile applications at the edge requiring real-time processes such as smart phones. In order to meet the real-time constraint of edge devices, recently proposed custom hardware CNN accelerators have exploited parallel processing elements (PEs) to increase throughput. However, this straightforward parallelization of PEs and high memory bandwidth require high data movement, leading to large energy consumption. As a result, only a certain number of PEs can be instantiated when designing bandwidth-limited custom accelerators targeting edge devices. While most bandwidth-limited designs claim a peak performance of a few hundred giga operations per second, their average runtime performance is substantially lower than their roofline when applied to state-of-the-art CNNs such as AlexNet, VGGNet and ResNet, as a result of low resource utilization and arithmetic intensity. In this work, we propose a zero-activation-skipping convolutional accelerator (ZASCA) that avoids noncontributory multiplications with zero-valued activations. ZASCA employs a dataflow that minimizes the gap between its average and peak performances while maximizing its arithmetic intensity for both sparse and dense representations of activations, targeting the bandwidth-limited edge computing scenario. More precisely, ZASCA achieves a performance efficiency of up to 94 percent over a set of state-of-the-art CNNs for image classification with dense representation where the performance efficiency is the ratio between the average runtime performance and the peak performance. Using its zero-skipping feature, ZASCA can further improve the performance efficiency of the state-of-the-art CNNs by up to 1.9 x depending on the sparsity degree of activations. The implementation results in 65-nm TSMC CMOS technology show that, compared to the most energy-efficient accelerator, ZASCA can process convolutions from 5.5 x to 17.5 x faster, and is between 2.1 x and 4.5 x more energy efficient while occupying 2.1 x less silicon area.

Eye pupil localization with an ensemble of randomized trees

We describe a method for eye pupil localization based on an ensemble of randomized regression trees and use several publicly available datasets for its quantitative and qualitative evaluation. The method compares well with reported state-of-the-art and runs in real-time on hardware with limited processing power, such as mobile devices. (C) 2013 Elsevier Ltd. All rights reserved.

(Dis)entangled bodies or the (be)holder vs. the spectator: Detached views of Early Cycladic figures and figurines

Third millennium B.C. anthropomorphic marble sculpture from the Aegean Cyclades, the so-called Early Cycladic figures and figurines, have fascinated art aficionados and scholars alike for over a century. This has led to a tremendous amount of aesthetic appreciation and monetary value for the aforementioned artifacts. However, a distorted and more important dissociated and decontextualized view of the figurines as objets d' art has traditionally impeded interpretative approaches to a great extent. With recent advances in the neurosciences and especially the rapprochement between the neuro- and the social sciences a new range of possibilities is offered for the study of the brain as manifested in technical acts, in this case the making, using, and sometimes breaking, of sculpture, and the reciprocal shaping of the world by the mind and the mind by the world that surrounds it. A multisensory view of EC sculpture is suggested here corroborated by ethnographic evidence as a research avenue of great potential. (C) 2015 Elsevier Ltd and INQUA. All rights reserved.

Pazopanib-laden lipid based nanovesicular delivery with augmented oral bioavailability and therapeutic efficacy against non-small cell lung cancer

The present investigation deals with the pazopanib-loaded solid lipid nanoparticles (Pazo-SLNs) and their in-vitro and in-vivo assessments. Quality by design approach employing the Plackett-Burman and central composite design was used to identify the formulation variables, including drug/lipid ratio, organic/aqueous phase ratio, and surfactant concentration with a significant impact on the process and to fabricate a safe and efficacious novel oral dosage form of pazopanib. Particle size, drug loading, entrapment efficiency, and zeta potential of optimal Pazo-SLNs formulation were 210.03 ± 7.68 nm, 13.35 ± 0.95 %, 79.05 ± 2.55 % and -18.29 ± 1.89 mV (n = 3) respectively. FTIR study affirmed the absence of incompatibilities between the drug and the excipients. DSC and XRD measurements substantiated the amorphous form of pazopanib entrapped within the SLNs. Pazo-SLNs demonstrated high cellular uptake, showed substantial cytotoxicity to A-549 lung cancer cells due to apoptotic mode and inhibited tyrosine kinase in-vitro. Pazo-SLNs were found to be stable for three months. SLNs greatly ameliorated the pharmacokinetic behavior and bioavailability (9.5 folds) of pazopanib with a sustained-release pattern (92.67 ± 4.68 % within 24 h). A biodistribution study corroborated the lung targeting potential of Pazo-SLNs. Thus, SLNs could potentially boost the oral route efficacy of pazopanib against cancer cells.

The APISSER Methodology for Systematic Literature Reviews in Engineering

[Background] Every research topic is first to be addressed by understanding its current state of the art. A systematic literature review is a trustworthy method for establishing the published state of the art of any given topic. In engineering sciences, we have failed to consistently, methodologically, and thoroughly execute systematic literature reviews at the beginning of every research path, and to standardize the method to do so. Currently available methodologies fail to link a method to a customized and much-needed tool support. If the high-effort demanding task of executing a systematic literature review is not well tool-supported, it will soon become manually unmanageable to handle the large amount of data involved. [Objective] Therefore, we want to take a step forward towards standardizing the methodology for executing systematic literature reviews in engineering by proposing a tool-supported and task-oriented engineering flow methodology to execute systematic literature reviews in engineering. [Method] Based on the well-known and proven Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology from medical sciences, we adapted and enhanced the method to follow a task-oriented engineering flow and to be supported by customized tools. [Results] In this paper, we first present the APISSER methodology for systematic literature reviews in engineering, and then show its practical application in a case study. [Conclusion] We have shown how the method successfully results in the collection of valid literature to report a trustworthy published state of the art in engineering sciences.

The mechanism of renewable energy consumption, technological innovation and carbon productivity-an empirical study of Chinese data

Renewable energy consumption has a strong impetus in promoting energy conservation and emission reduction, which is a new path leading to clean and low-carbon development. Based on that, this paper uses the data of carbon productivity, renewable energy power consumption level, technological progress, national economic development level, population, energy efficiency, industrial structure rationality, and other data in 30 provinces in China from 2011 to 2020, based on the STIRPAT model, conducts an empirical analysis on the impact of renewable energy power consumption on carbon productivity in Chinese provinces while considering both the spatial horizontal dimension and the temporal vertical dimension. The empirical results show that (1) Chinese carbon productivity presents an obvious spatial spillover effect and presents the spatial positive correlation distribution characteristics of "high-high" type agglomeration and "low-low" type agglomeration. (2) The utilization of renewable energy plays a positive role in promoting the development of low-carbon economy. The perspective of the horizontal spatial dimension shows a positive spatial spillover effect. The perspective of the longitudinal time dimension shows a marginal increase in the overall improvement of the environment. (3) Among the seven regions in China, the consumption of renewable energy in North China, East China, and Central China brings a dominant effect on carbon productivity. (4) About 29% of the positive effect of renewable energy consumption on carbon productivity is indirectly realized by technological progress. Finally, the article puts forward targeted policy suggestions.

Quantitative magnetic resonance imaging biomarkers for cortical pathology in multiple sclerosis at 7 T

Substantial cortical gray matter tissue damage, which correlates with clinical disease severity, has been revealed in multiple sclerosis (MS) using advanced magnetic resonance imaging (MRI) methods at 3 T and the use of ultra-high field, as well as in histopathology studies. While clinical assessment mainly focuses on lesions using T 1 - and T 2 -weighted MRI, quantitative MRI (qMRI) methods are capable of uncovering subtle microstructural changes. The aim of this ultra-high field study is to extract possible future MR biomarkers for the quantitative evaluation of regional cortical pathology. Because of their sensitivity to iron, myelin, and in part specifically to cortical demyelination, T 1 , T 2 , R 2 * , and susceptibility mapping were performed including two novel susceptibility markers; in addition, cortical thickness as well as the volumes of 34 cortical regions were computed. Data were acquired in 20 patients and 16 age- and sex-matched healthy controls. In 18 cortical regions, large to very large effect sizes (Cohen's d ≥ 1) and statistically significant differences in qMRI values between patients and controls were revealed compared with only four regions when using more standard MR measures, namely, volume and cortical thickness. Moreover, a decrease in all susceptibility contrasts ( χ , χ + , χ - ) and R 2 * values indicates that the role of cortical demyelination might outweigh inflammatory processes in the form of iron accumulation in cortical MS pathology, and might also indicate iron loss. A significant association between susceptibility contrasts as well as R 2 * of the caudal middle frontal gyrus and disease duration was found (adjusted R2 : 0.602, p = 0.0011). Quantitative MRI parameters might be more sensitive towards regional cortical pathology compared with the use of conventional markers only and therefore may play a role in early detection of tissue damage in MS in the future.