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Few-Shot Learning by a Cascaded Framework With Shape-Constrained Pseudo Label Assessment for Whole Heart Segmentation

Automatic and accurate 3D cardiac image segmentation plays a crucial role in cardiac disease diagnosis and treatment. Even though CNN based techniques have achieved great success in medical image segmentation, the expensive annotation, large memory consumption, and insufficient generalization ability still pose challenges to their application in clinical practice, especially in the case of 3D segmentation from high-resolution and large-dimension volumetric imaging. In this paper, we propose a few-shot learning framework by combining ideas of semi-supervised learning and self-training for whole heart segmentation and achieve promising accuracy with a Dice score of 0.890 and a Hausdorff distance of 18.539 mm with only four labeled data for training. When more labeled data provided, the model can generalize better across institutions. The key to success lies in the selection and evolution of high-quality pseudo labels in cascaded learning. A shape-constrained network is built to assess the quality of pseudo labels, and the self-training stages with alternative global-local perspectives are employed to improve the pseudo labels. We evaluate our method on the CTA dataset of the MM-WHS 2017 Challenge and a larger multi-center dataset. In the experiments, our method outperforms the state-of-the-art methods significantly and has great generalization ability on the unseen data. We also demonstrate, by a study of two 4D (3D+T) CTA data, the potential of our method to be applied in clinical practice.

Infrastructure, ecology and art

In keeping with the 'industrial ecology' metaphor. roads and railways should be regarded as forming an ecosystem with their surroundings, just like industrial systems (factory premises. manufacturing industries) that have had to start functioning as ecosystems in which the flow of energy, water. raw materials and waste products has been made to recycle as far as possible. In this type of system approach, the flow of material. transport, emissions and energy and the habitats of plants and animals are geographically, systematically and functionally integrated, especially when they are based on a greater degree of interweaving between man and nature than is currently the case. In short, a new way of looking at the meaning of ecology in relation to the physical infrastructure. In the article, examples are given about the practical implication of linking ecological patterns and processes within the design process of civil engineering object, as well as the possible art's contribution in forming infrastructural landscapes. (C) 2002 Elsevier Science B.V. All rights reserved.

Pleistocene climate variability in eastern Africa influenced hominin evolution

Despite more than half a century of hominin fossil discoveries in eastern Africa, the regional environmental context of hominin evolution and dispersal is not well established due to the lack of continuous palaeoenvironmental records from one of the proven habitats of early human populations, particularly for the Pleistocene epoch. Here we present a 620,000-year environmental record from Chew Bahir, southern Ethiopia, which is proximal to key fossil sites. Our record documents the potential influence of different episodes of climatic variability on hominin biological and cultural transformation. The appearance of high anatomical diversity in hominin groups coincides with long-lasting and relatively stable humid conditions from ~620,000 to 275,000 years bp (episodes 1-6), interrupted by several abrupt and extreme hydroclimate perturbations. A pattern of pronounced climatic cyclicity transformed habitats during episodes 7-9 (~275,000-60,000 years bp), a crucial phase encompassing the gradual transition from Acheulean to Middle Stone Age technologies, the emergence of Homo sapiens in eastern Africa and key human social and cultural innovations. Those accumulative innovations plus the alignment of humid pulses between northeastern Africa and the eastern Mediterranean during high-frequency climate oscillations of episodes 10-12 (~60,000-10,000 years bp) could have facilitated the global dispersal of H. sapiens.

Assessment of human-induced effects in the Sultan marshes (Ramsar Protection), Kayseri (Turkey)

This study examines the drying in the Sultan Marshes and the spatio-temporal change of different land cover classes. Corine land cover change outputs were examined for four periods (1990-2000; 2000-2006; 2006-2012; and 2012-2018). During these analyses, the period when the water area changes in the lakes occur the most was determined. Moreover, other land cover changes occurring in the region were defined. The LCC results were compared and discussed in terms of some human factors (i.e., human development index and terrestrial human footprint). According to the results of this study, it was observed that there was a severe decline in the lake surface water located in the Sultan Marshes National Park Area. The water's surface in the lakes decreased by 50% in the 2000s compared to previous years and decreased until 2006. This withdrawal was prominent especially in Lake Yay and Lake Çöl. Considering the human factors (Human Development Index) and variables (terrestrial Human Footprint) in terms of the spatio-temporal land cover change, it is seen that the human development in the region increased from 0.54 to 0.81 from 1990 to 2018, and the human footprint increased the most in 1993. Water area changes occurred at a high rate between 1990-2000 and 2000-2006. It results from the growing demand for basic needs (such as water consumption and food diversity) with increasing human development and expanded agricultural practices in the region and overuse of the ground and aboveground waters that are the source of the lakes. Especially between 1990 and 2000, the high number of human interventions in the region caused the human footprint to be higher in 1993 than in 2009. Unless the Sultan Marshes have the proper planning and policies, it faces the danger of complete drying up with the effects of climate change in the future.

Health Outcomes, Income and Income Inequality: Revisiting the Empirical Relationship

In this paper we revisit the relationship between health outcomes, income, and income inequality by applying alternative panel methodologies to a dataset of high-income countries spanning the time period 1980-2017. In this direction, we adopt alternative methodological frameworks in order to provide a) meaningful results by taking into account standard errors that alleviate problems of cross-sectional (spatial) and temporal dependence, and b) insights into the underlying relationships at several points of the conditional distribution of the health outcomes dependent variables. The evidence strongly supports the significant role that income plays in determining health outcomes. The findings relating to income inequality and nonlinear terms are more fragmented in that their significance and sign-direction depend on the functional form and the respective quantiles of the distribution the relationships are evaluated.

Nature-Based Solutions and Sustainable Urban Planning in the European Environmental Policy Framework: Analysis of the State of the Art and Recommendations for Future Development

Sustainable urban planning (SUP) is crucial in the development of sustainable cities, as also underlined by the New Urban Agenda. Nature-based solutions (NBS) are increasingly being recognized for their potential to offer multiple benefits that are necessary in order to cope with present and future urban challenges. The European policy framework, including the recently released European Green Deal, could strongly boost the role and recognition of NBS and SUP as drivers of sustainable and inclusive urban transition. Through a content analysis of current environmental European policies, strategies and agreements, this paper provides (i) an overview of the state of the art of the environmental European policy framework and the recognized role of NBS and SUP in reaching defined objectives, and (ii) insights on where NBS and SUP could play a larger role within this framework. On this basis, the paper identifies gaps and develops recommendations for a better integration of such concepts into the current framework.

Robust Automatic Knee MR Slice Positioning Through Redundant and Hierarchical Anatomy Detection

Diagnostic magnetic resonance (MR) image quality is highly dependent on the position and orientation of the slice groups, due to the intrinsic high in-slice and low through-slice resolutions of MR imaging. Hence, the higher speed, accuracy, and reproducibility of automatic slice positioning [1], [2] make it highly desirable over manual slice positioning. However, imaging artifacts, diseases, joint articulation, variations across ages and demographics as well as the extremely high performance requirements prevent state-of-the-art methods, such as volumetric registration, to be an off-the-shelf solution. In this paper, we address all these issues through an automatic slice positioning framework based on redundant and hierarchical learning. Our method has two hallmarks that are specifically designed to achieve high robustness and accuracy. 1) A redundant set of anatomy detectors are learned to provide local appearance cues. These detections are pruned and assembled according to a distributed anatomy model, which captures group-wise spatial configurations among anatomy primitives. This strategy brings about a high level of robustness and works even if a large portion of the target is distorted, missing, or occluded. 2) The detectors are learned and invoked in a hierarchical fashion, with each local detection scheduled and iterated according to its intrinsic invariance property. This iterative alignment process is shown to dramatically improve alignment accuracy. The proposed system is extensively validated on a large dataset including 744 clinical MR scans. Compared to state-of-the-art methods, our method exhibits superior performance in terms of robustness, accuracy, and reproducibility. The methodology is general and can be applied to other anatomies and other imaging modalities.

Surgical Management of Gastro-oesophageal Reflux Disease After One Anastomosis Gastric Bypass - a Systematic Review

Gastro-oesophageal reflux disease (GORD) after one anastomosis gastric bypass (OAGB) remains a concern. We reviewed the current literature on revisional surgery after OAGB for GORD. MEDLINE, EMBASE, and PubMed databases were searched. We identified 21 studies, appraising 13,658 OAGB patients. A total of 230 (1.6%) patients underwent revisional surgery for GORD. Revision to Roux-en-Y configuration was performed in 211 (91.7%) patients. Six (2.6%) patients had a Braun entero-enterostomy added to the OAGB. Thirteen (5.6%) patients underwent excluded stomach fundoplication (ESF). Reflux symptoms resolved in 112 (48.6%) patients, persisted in 13 (5.6%) patients, and were not reported in 105 (45.6%) patients. Revisional surgery after OAGB for GORD appears to be rare, and when required, conversion to Roux-en-Y configuration is the commonest choice.

First proteome study of sporadic flowering in bamboo species (Bambusa vulgaris and Dendrocalamus manipureanus) reveal the boom is associated with stress and mobile genetic elements

Bamboo species are the fastest-growing plants having a long vegetative cycle. Abrupt switching from the vegetative phase to the reproductive phase via sporadic flowering boom, occasionally leads to death of bamboo clumps, and threatens the existence of many bamboo species. To apprehend the molecular mechanism driving sporadic flowering, proteome changes in the initial and advanced floral buds of two edible bamboo species (Bambusa vulgaris and Dendrocalamus manipureanus) was dissected by two-dimensional gel electrophoresis (2-DE). A total of 39 differentially expressed peptide spots were identified by matrix-assisted laser desorption ionization time-of-flight/time-of-flight mass spectrometry (MALDI-TOF-TOF/MS). In both B. vulgaris and D. manipureanus, identified proteins were categorized as transposon-related, defence and stress-related, cell cycle related, metabolism related, signal transduction related, and some lacked known putative domains. Proteins such as SEPALLATA3, ubiquitin, histone 3, thaumatin-like protein, putative tethering factor, SF-assemblin, polyubiquitin, mitochondrial carrier-like protein and RPT2-like protein were significantly expressed. Differences in D. manipureanus and B. vulgaris suggested that bamboo species have diverse 'drivers' or 'passengers' genes that govern natural sporadic flowering boom. This first floral proteomics analysis of bamboos revealed that sporadic boom is a highly energetic process, associated with stress elements, mobile genetic elements and signal transduction cross-talk elements.

A New Likelihood Function for Consistent Phase Series Estimation in Distributed Scatterer Interferometry

The proper use of distributed scatterer (DS) can improve both the density and quality of synthetic aperture radar (SAR) interferometry (InSAR) measurements. A critical step in DS interferometry (DSI) is the restoration of a consistent phase series from SAR interferogram stacks. Most state-of-the-art algorithms adopt an approximate likelihood function to calculate the likelihood by replacing the true coherence matrix with its estimation, more specifically, the sample coherence matrix (SCM). However, this approximation has a drawback in that the coherence estimates are greatly biased when the coherence is low. In this study, we derive a new likelihood function without such an approximation. Accordingly, a DSI framework using this function for phase estimation and point selection is provided. In this framework, the new likelihood function serves as a cost function for phase estimation and a quality measure for DS selection. Its performance is investigated by experiments in a simulation study and a real-world case study using Sentinel-1 data over Shenzhen airport in China. The results reveal that the proposed DSI framework outperforms the existing state-of-the-art approaches in different scenarios, in terms of providing a more accurate estimation and improving DS density and coverage.

Hidden Markov models combining discrete symbols and continuous attributes in handwriting recognition

Prior arts in handwritten word recognition model either discrete features or continuous features, but not both. This paper combines discrete symbols and continuous attributes into structural handwriting features and model, them by transition-emitting and state-emitting hidden Markov models. The models are rigorously defined and experiments have proven their effectiveness.

Design and synthesis of multi-targeted nanoparticles for gene delivery to breast cancer tissues

Biocompatibility of nanoparticles is the most essential factor in their use in clinical applications. In this study, hyperbranched spermine (HS), hyperbranched spermine-polyethylene glycol-folic acid (HSPF), and hyperbranched spermine-polyethylene glycol-glucose (HSPG) were synthesized for DNA protection and gene delivery to breast cancer cells. The synthesis of HSPG and HSPF was confirmed using proton nuclear magnetic resonance (H-NMR), Fourier-transform infrared spectroscopy (FTIR), and thermogravimetric analysis (TGA) spectroscopy. The HS/DNA, HSPF/DNA, HSPG/DNA, and hyperbranched spermine-polyethylene glycol-folic acid/glucose/DNA (HSPFG/DNA) nanoparticles were prepared by combining different concentrations of HS, HSPF, and HSPG with the same amount of DNA. The ability of HS, HSPF, and HSPG to interact with DNA and protect it against plasm digestion was evaluated using agarose gel. Moreover, in vivo and in vitro biocompatibility of HSPF/DNA, HSPG/DNA, and HSPFG/DNA was investigated using MTT assay and calculating weight change and survival ratio of BALB/c mice, respectively. The results of agarose gel electrophoresis showed that HS, HSPF, and HSPG have the high ability to neutralize the negative charge of DNA and protect it against plasma degradation. The results of in vivo cytotoxicity assay revealed that the HSPF/DNA, HSPG/DNA, and HSPFG/DNA nanoparticles have good biocompatibility on female BALB/c mice. In vitro and in vivo transfection assays revealed that functionalization of the surface of HS using polyethylene glycol-folic acid (HSPF) and polyethylene glycol-glucose (HSPG) significantly increases gene delivery efficiency in vitro and in vivo. These results also showed that gene transfer using both HSPF and HSPG copolymers increases gene transfer efficiency compared to when only one of them is used. The HSPFG/DNA nanoparticles have a high potential for use in therapeutic applications because of their excellent biocompatibility and high gene transfer efficiency to breast cancer tissue.

The cultural transmission of tacit knowledge

A wide variety of cultural practices have a 'tacit' dimension, whose principles are neither obvious to an observer, nor known explicitly by experts. This poses a problem for cultural evolution: if beginners cannot spot the principles to imitate, and experts cannot say what they are doing, how can tacit knowledge pass from generation to generation? We present a domain-general model of 'tacit teaching', drawn from statistical physics, that shows how high-accuracy transmission of tacit knowledge is possible. It applies when the practice's underlying features are subject to interacting and competing constraints. Our model makes predictions for key features of the teaching process. It predicts a tell-tale distribution of teaching outcomes, with some students near-perfect performers while others receiving the same instruction are disastrously bad. This differs from standard cultural evolution models that rely on direct, high-fidelity copying, which lead to a much narrower distribution of mostly mediocre outcomes. The model also predicts generic features of the cultural evolution of tacit knowledge. The evolution of tacit knowledge is expected to be bursty, with long periods of stability interspersed with brief periods of dramatic change, and where tacit knowledge, once lost, becomes essentially impossible to recover.

Hand Gestures Recognition for Human-Machine Interfaces: A Low-Power Bio-Inspired Armband

Hand gesture recognition has recently increased its popularity as Human-Machine Interface (HMI) in the biomedical field. Indeed, it can be performed involving many different non-invasive techniques, e.g., surface ElectroMyoGraphy (sEMG) or PhotoPlethysmoGraphy (PPG). In the last few years, the interest demonstrated by both academia and industry brought to a continuous spawning of commercial and custom wearable devices, which tried to address different challenges in many application fields, from tele-rehabilitation to sign language recognition. In this work, we propose a novel 7-channel sEMG armband, which can be employed as HMI for both serious gaming control and rehabilitation support. In particular, we designed the prototype focusing on the capability of our device to compute the Average Threshold Crossing (ATC) parameter, which is evaluated by counting how many times the sEMG signal crosses a threshold during a fixed time duration (i.e., 130 ms), directly on the wearable device. Exploiting the event-driven characteristic of the ATC, our armband is able to accomplish the on-board prediction of common hand gestures requiring less power w.r.t. state of the art devices. At the end of an acquisition campaign that involved the participation of 26 people, we obtained an average classifier accuracy of 91.9% when aiming to recognize in real time 8 active hand gestures plus the idle state. Furthermore, with 2.92 mA of current absorption during active functioning and 1.34 ms prediction latency, this prototype confirmed our expectations and can be an appealing solution for long-term (up to 60 h) medical and consumer applications.

Erosion susceptibility mapping in the Central-Eastern Region of São Paulo in the last few decades

Soil degradation has become a critical global environmental challenge as a result of rapid population growth, intensified erosion, and increased global warming. Depletion of nutrients, decreased infiltration, availability of water in the subsoil, silting, and eutrophication of surface water resources are directly associated with soil degradation. Water erosion is one of the primary causes of erosion. The principal objective of this study was to understand how climate change and land use have affected susceptibility to erosion in the central-eastern region of the state of São Paulo, Brazil, over the past few decades. Using the technique of multicriteria decision analysis and comparison of thematic layers in pairs, different factors that contribute to soil erosion were integrated in a GIS environment to map erosion hotspots. The results indicated increasing very high, high, and medium erosion susceptibility class percentage. Slope and soil types were the most sensitive factors; however, changes in land use, in particular, increased land cultivation and expanded areas highly susceptible to erosion in late 2019. The results of this study will assist in implementing soil conservation practices in such areas, reducing soil degradation, and increasing productivity.

An Accelerated Procrustean Markov Process Model With Coherent Constraint for Non-Rigid Structure From Motion

Non-Rigid Structure from Motion (NRSfM) is the task of reconstructing the 3D point set of a non-rigid object from an ensemble of images with 2D correspondences, which has been a long-lasting challenging research topic. Compared to the state-of-the-art methods for NRSfM, the Procrustean Markov Process (PMP) model has obtained a relatively good performance. However, the estimation error and the convergence time of the PMP model will increase simultaneously when noise is present. To address this problem, in this paper, a coherent constraint is constructed to suppress the noise in the initialization step of the PMP algorithm. Moreover, an Accelerated Expectation Maximization (AEM) algorithm is devised to optimize the PMP estimation model. Experimental results on several widely used sequences demonstrate that our proposed algorithm achieves state-of-the-art performance, as well as its effectiveness and feasibility.

Zero-voltage-switching dc-dc converters with synchronous rectifiers

Active resonant tank (ART) cells are proposed in this paper to achieve zero-voltage-switching (ZVS) and eliminate body-diode conduction in dc-dc converters with synchronous rectifiers (SRs). In low-output-voltage dc-dc converters, SRs are widely utilized to reduce rectifier conduction loss and improve converter efficiency. However, during switches' transition, SRs' parasitic body diodes unavoidably carry load current, which decreases conversion efficiency because voltage drop across body diodes is much higher than that across SRs. Moreover, body diodes' reverse recovery leads to increased switching losses and electromagnetic interference. With the proposed cells of an ART, the body diode conduction of the SR is eliminated during the switching transition from a SR to an active switch, and thus body diode reverse-recovery-related switching and ringing losses are saved. An ART cell consists of a LC resonant tank and an auxiliary switch. A resonant tank cell is charged in a resonant manner and energy is stored in the capacitor of the tank. Prior to a switching transition from a SR to an active switch, the energy stored in the tank capacitor is released and converted to inductor current, which forces the SR current changes direction to avoid conduction of the body diode and related reverse recovery when the SR turns off. Moreover, at the help of energy released from the ART, the active switch's junction capacitance is discharged, which allows the active switch turns on at ZVS. Since energy commutation occurs only during switching transition, conduction loss in the ART cell is limited. Moreover, the auxiliary switch turns off at ZVS and the SR operates at ZVS. The concept of ART cells is generally introduced and detailed analysis is presented based on a synchronous buck converter. Experimental results show the proposed ART cell improves conversion efficiency due to the reduced switching loss, body diodes' conduction, and reverse-recovery losses.

Epigenetics in advanced renal cell carcinoma: Potential new targets

Renal cell carcinoma (RCC) is the seventh most frequently diagnosed tumor in adults in Europe and represents approximately 2.5 % of cancer deaths. In metastatic setting, clinical strategies including angiogenesis inhibition with tyrosine kinase inhibitors, as well as immunotherapy against immune checkpoint proteins, such as PD-1/PDL-1 and CTLA-4, have revolutionized the treatment landscape. Unfortunately, most patients progress to anti angiogenic and immunotherapy treatment. Epigenetic aberrations are commonly found in RCC, showing that changes in epigenetic modifications, like promoter methylation or abnormal microRNA expression, are key in the development of RCC due to gene expression alterations without changes in the genome sequence. Nowadays, new drugs in the field of epigenetics are able to modify gene expression to induce antitumoral effect in the tumor cell. In kidney cancer, drugs targeting epigenetics are in early development, but could be promising in the near future. In this review, we summarize the main epigenetic alterations found in RCC and their involvement in pathological signaling pathways, being a new potential target that could potentially be added to the treatment flow of patients with advanced RCC.

High-throughput engineering of cytoplasmic- and nuclear-replicating large dsDNA viruses by CRISPR/Cas9

The application of CRISPR/Cas9 to improve genome engineering efficiency for large dsDNA viruses has been extensively described, but a robust and versatile method for high-throughput generation of marker-free recombinants for a desired locus has not yet been reported. Cytoplasmic-replicating viruses use their own repair enzymes for homologous recombination, while nuclear-replicating viruses use the host repair machinery. This is translated into a wide range of Cas9-induced homologous recombination efficiencies, depending on the virus replication compartment and viral/host repair machinery characteristics and accessibility. However, the use of Cas9 as a selection agent to target parental virus genomes robustly improves the selection of desired recombinants across large dsDNA viruses. We used ectromelia virus (ECTV) and herpes simplex virus (HSV) type 1 and 2 to optimize a CRISPR/Cas9 method that can be used versatilely for efficient genome editing and selection of both cytoplasmic- and nuclear-replicating viruses. We performed a genome-wide genetic variant analysis of mutations located at predicted off-target sequences for 20 different recombinants, showing off-target-free accuracy by deep sequencing. Our results support this optimized method as an efficient, accurate and versatile approach to enhance the two critical factors of high-throughput viral genome engineering: generation and colour-based selection of recombinants. This application of CRISPR/Cas9 reduces the time and labour for screening of desired recombinants, allowing for high-throughput generation of large collections of mutant dsDNA viruses for a desired locus, optimally in less than 2 weeks.

Development of chitosan-polygalacturonic acid polyelectrolyte complex fibrous scaffolds using the hydrothermal treatment for bone tissue engineering

An ideal bone regeneration scaffold system needs to meet the high compressive properties of the bone. The stiffness of the scaffold extracellular matrix determines the cell's fate via cell adhesion migration and differentiation in-vitro and in-vivo. This study aims to investigate the effect of hydrothermal treatment on polyelectrolyte complex (PEC) fibrous biomaterials and its effect on scaffold morphology, cell viability, and function in-vitro. FTIR analysis revealed the ability of the thermal treatment to set the interaction of HAp with polymeric PEC fibers. FESEM analysis showed that with an increase in temperature, the interconnectivity and pore size increased (control-82.38 ± 12.92 μm; at 120°C-335.48 ± 85.10 μm). Mechanical tests showed that the scaffolds heated at 90°C showed the highest stiffness in both dry and wet states (dry state: 1.82 ± 0.07 MPa, wet state: 122 ± 1.78 kPa). Additionally, the hydrothermal treatment also improved the aqueous stability as well as swelling capacity. According to the experimental findings, hydrothermal treatment is a useful technique for crosslinker-free gelation with improved mechanical strength and nanofibrous structure. Furthermore, the cell adhesion, proliferation, and osteogenic differentiation of the MG63 cells on the hydrogel scaffolds in-vitro were evaluated by MTT assay, confocal imaging, alkaline phosphatase assay, and collagen estimation. The in-vitro study showed that scaffolds fabricated at 90°C promoted better MG63 cell attachment, proliferation, and differentiation. These results suggest the potential use of hydrothermal treated chitosan-polygalacturonic acid (PgA) fibrous scaffolds in bone tissue engineering.

Complete mitochondrial genome of the soft-shell clam Mya arenaria

We have sequenced and characterized the complete mitochondrial genome of the soft-shell clam, Mya arenaria, an important organism for environmental toxicology and aquaculture. Mya arenaria is located in the taxonomic order Myoida, which lacks any member with a completely annotated mitogenome. The M. arenaria mitochondrial genome is 17 947 bp in length. Like most marine bivalves, the circular mitogenome codes entirely on the heavy strand, with no introns. As with other bivalves, the gene order of the mitochondrion is highly rearranged. The mitogenome contains 12 protein-coding genes but ATP8 is missing, consistent with about half of all bivalve genera. Twenty-three tRNAs were identified. Phylogenetic analysis shows that M. arenaria is related most closely with the bivalves Sinonovacula constricta, and Moerella iridescens, of the infraclass Euheterodonta (unassigned). This, along with the close grouping of the phylogenetic trees, confirms a close tie between Myoida and Euheterodonta (unassigned).

The Process of Developing a Digital Repository for Online Teaching Using Design-Based Research

The Purdue Repository for Online Teaching and Learning (PoRTAL) was developed as an Open Educational Resource (OER) for graduate students and faculty in higher education settings to enhance their online teaching skills and strategies. The PoRTAL team used a design-based research approach (DBR; Wang & Hannafin, Educational Technology Research and Development, 53(4), 5-23, 2005). In this study context, we used Van Tiem et al.'s (2012) model to identify problems faced by instructors who struggled with or were new to online teaching from a Human Performance Technology (HPT) standpoint. To address the identified needs, we created resources for online teaching and embedded our research within practical activities to further study our design process. Our efforts resulted in an HPT-OER Model for Designing Digital Repositories. The purpose of this paper is to share the DBR process that we used to develop an OER repository within an HPT model.

Source Term Analysis of Xenon (STAX): An effort focused on differentiating man-made isotope production from nuclear explosions via stack monitoring

An overview of the hardware and software developed for the Source Term Analysis of Xenon (STAX) project is presented which includes the data collection from two stack monitoring systems installed at medical isotope production facilities, infrastructure to transfer data to a central repository, and methods for sharing data from the repository with users. STAX is an experiment to collect radioxenon emission data from industrial nuclear facilities with the goal of developing a better understanding of the global radioxenon background and the effect industrial radioxenon releases have on nuclear explosion monitoring. A final goal of this work is to utilize collected data along with atmospheric transport modeling to calculate the contribution of a peak or set of peaks detected by the International Monitoring System (IMS) to provide desired discriminating information to the International Data Centre (IDC) and National Data Centers (NDCs). Types of data received from the STAX equipment are shown and collected data was used for a case study to predict radioxenon concentrations at two IMS stations closest to the Institute for RadioElements (IRE) in Belgium. The initial evaluation of results indicate that the data is very valuable to the nuclear explosion monitoring community.

Investigating formulations of cellulose acetate plastics in the collections of the Art Institute of Chicago using pyrolysis gas chromatography mass spectrometry

A selective survey was undertaken of works of art made with cellulose acetate, primarily in the collections of the Art Institute of Chicago and dating from 1936 to 1976, using pyrolysis gas chromatography mass spectrometry to provide a detailed characterisation of the organic additives. The findings revealed a variety of plastic formulations used during this period; these are discussed in relation to the physical and chemical properties of individual plasticisers and their potential influence on the long-term stability of the objects. Several case studies are presented in detail in which the analyses provided specific insights into observed degradation phenomena: a 1936 sculpture by Cesar Domela, and the complex enclosure for a book by Jean-Pierre Duprey produced in 1970 in collaboration with Toyen (Marie Cerminova). The pyrolysis analyses were undertaken using a direct inlet technique that provided enhanced sensitivity with the use of extremely small (microgram-sized) samples.

Epidemiological, Clinical, and Oncological Outcomes of non-Alcohol Drinking and non-Smoking Laryngeal Squamous Cell Carcinoma Patients: A Distinct Entity

Purpose: To explore the discrepancy in clinicopathological and prognostic features between smoking and alcohol drinking (SA) and non-smoking and non-alcohol drinking (NSNA) patients with laryngeal squamous cell carcinoma (LSCC). Methods: This retrospective study including 1735 patients with LSCC was conducted from January 2005 to December 2010, which were categorized into 4 groups, NSNA group, smoking only group, alcohol-drinking only group, and SA group. We compared overall survival (OS) and disease-free survival (DFS) using the Kaplan-Meier method and indicated clinicopathological features by Cox proportional hazards regression models before and after propensity score matching (PSM). Results: A total of 415 patients (23.92%) were identified as NSNA. The SA group was predominantly patients ≤60 years old (46.63%) while the NSNA group was more older (58.07%). NSNA group was more likely to present at earlier disease stage and more female. No significant difference in OS (P = .685) and DFS (P = .976) was found between the 2 groups. In addition to age and recurrence and metastasis being common independent prognostic factors in terms of OS in both groups of patients, NSNA group also exhibited other factors, namely tumor area >3.7 cm2 and positive resection margin. For DFS, N + stage, tumor size >3.7 cm2, and positive resection margin were prognostic features specific to NSNA group. Conclusion: The outcome is similar in LSCC patients with and without SA. NSNA group shows a distinct profile from that found in SA group. Clinicopathological features from NSNA group should be considered for LSCC management.

Superpixels: An evaluation of the state-of-the-art

Superpixels group perceptually similar pixels to create visually meaningful entities while heavily reducing the number of primitives for subsequent processing steps. As of these properties, superpixel algorithms have received much attention since their naming in 2003 (Ren and Malik, 2003). By today, publicly available superpixel algorithms have turned into standard tools in low-level vision. As such, and due to their quick adoption in a wide range of applications, appropriate benchmarks are crucial for algorithm selection and comparison. Until now, the rapidly growing number of algorithms as well as varying experimental setups hindered the development of a unifying benchmark. We present a comprehensive evaluation of 28 state-of-the-art superpixel algorithms utilizing a benchmark focussing on fair comparison and designed to provide new insights relevant for applications. To this end, we explicitly discuss parameter optimization and the importance of strictly enforcing connectivity. Furthermore, by extending well-known metrics, we are able to summarize algorithm performance independent of the number of generated superpixels, thereby overcoming a major limitation of available benchmarks. Furthermore, we discuss runtime, robustness against noise, blur and affine transformations, implementation details as well as aspects of visual quality. Finally, we present an overall ranking of superpixel algorithms which redefines the state-of-the-art and enables researchers to easily select appropriate algorithms and the corresponding implementations which themselves are made publicly available as part of our benchmark at http://www.davidstutz.deiprojectsisuperpixel-benchmark/. (C) 2017 Elsevier Inc. All rights reserved.

Random Subspace Ensembles for fMRI Classification

Classification of brain images obtained through functional magnetic resonance imaging (fMRI) poses a serious challenge to pattern recognition and machine learning due to the extremely large feature-to-instance ratio. This calls for revision and adaptation of the current state-of-the-art classification methods. We investigate the suitability of the random subspace (RS) ensemble method for fMRI classification. RS samples from the original feature set and builds one (base) classifier on each subset. The ensemble assigns a class label by either majority voting or averaging of output probabilities. Looking for guidelines for setting the two parameters of the method-ensemble size and feature sample size-we introduce three criteria calculated through these parameters: usability of the selected feature sets, coverage of the set of "important" features, and feature set diversity. Optimized together, these criteria work toward producing accurate and diverse individual classifiers. RS was tested on three fMRI datasets from single-subject experiments: the Haxby et al. data (Haxby, 2001.) and two datasets collected in-house. We found that RS with support vector machines (SVM) as the base classifier outperformed single classifiers as well as some of the most widely used classifier ensembles such as bagging, AdaBoost, random forest, and rotation forest. The closest rivals were the single SVM and bagging of SVM classifiers. We use kappa-error diagrams to understand the success of RS.

Transmembrane proteins tetraspanin 4 and CD9 sense membrane curvature

Multiple membrane-shaping and remodeling processes are associated with tetraspanin proteins by yet unknown mechanisms. Tetraspanins constitute a family of proteins with four transmembrane domains present in every cell type. Prominent examples are tetraspanin4 and CD9, which are required for the fundamental cellular processes of migrasome formation and fertilization, respectively. These proteins are enriched in curved membrane structures, such as cellular retraction fibers and oocyte microvilli. The factors driving this enrichment are, however, unknown. Here, we revealed that tetraspanin4 and CD9 are curvature sensors with a preference for positive membrane curvature. To this end, we used a biomimetic system emulating membranes of cell retraction fibers and oocyte microvilli by membrane tubes pulled out of giant plasma membrane vesicles with controllable membrane tension and curvature. We developed a simple thermodynamic model for the partitioning of curvature sensors between flat and tubular membranes, which allowed us to estimate the individual intrinsic curvatures of the two proteins. Overall, our findings illuminate the process of migrasome formation and oocyte microvilli shaping and provide insight into the role of tetraspanin proteins in membrane remodeling processes.

Transmission of antibiotic resistance genes through mobile genetic elements in Acinetobacter baumannii and gene-transfer prevention

Antibiotic resistance is a major global public health concern. Acinetobacter baumannii is a nosocomial pathogen that has emerged as a global threat because of its high levels of resistance to many antibiotics, particularly those considered as last-resort antibiotics, such as carbapenems. Mobile genetic elements (MGEs) play an important role in the dissemination and expression of antibiotic resistance genes (ARGs), including the mobilization of ARGs within and between species. We conducted an in-depth, systematic investigation of the occurrence and dissemination of ARGs associated with MGEs in A. baumannii. We focused on a cross-sectoral approach that integrates humans, animals, and environments. Four strategies for the prevention of ARG dissemination through MGEs have been discussed: prevention of airborne transmission of ARGs using semi-permeable membrane-covered thermophilic composting; application of nanomaterials for the removal of emerging pollutants (antibiotics) and pathogens; tertiary treatment technologies for controlling ARGs and MGEs in wastewater treatment plants; and the removal of ARGs by advanced oxidation techniques. This review contemplates and evaluates the major drivers involved in the transmission of ARGs from the cross-sectoral perspective and ARG-transfer prevention processes.

Contrasting effects of chloride on growth, reproduction, and toxicant sensitivity in two genetically distinct strains of Hyalella azteca

The strain of Hyalella azteca (Saussure: Amphipoda) commonly used for aquatic toxicity testing in the United States has been shown to perform poorly in some standardized reconstituted waters frequently used for other test species. In 10-d and 42-d experiments, the growth and reproduction of the US laboratory strain of H. azteca was shown to vary strongly with chloride concentration in the test water, with declining performance observed below 15 mg/L to 20 mg/L. In contrast to the chloride-dependent performance of the US laboratory strain of H. azteca, growth of a genetically distinct strain of H. azteca obtained from an Environment Canada laboratory in Burlington, Ontario, Canada, was not influenced by chloride concentration. In acute toxicity tests with the US laboratory strain of H. azteca, the acute toxicity of sodium nitrate increased with decreasing chloride in a pattern similar not only to that observed for control growth, but also to previous acute toxicity testing with sodium sulfate. Subsequent testing with the Burlington strain showed no significant relationship between chloride concentration and the acute toxicity of sodium nitrate or sodium sulfate. These findings suggest that the chloride-dependent toxicity shown for the US laboratory strain may be an unusual feature of that strain and perhaps not broadly representative of aquatic organisms as a whole.

A 3D Probabilistic Deep Learning System for Detection and Diagnosis of Lung Cancer Using Low-Dose CT Scans

We introduce a new computer aided detection and diagnosis system for lung cancer screening with low-dose CT scans that produces meaningful probability assessments. Our system is based entirely on 3D convolutional neural networks and achieves state-of-the-art performance for both lung nodule detection and malignancy classification tasks on the publicly available LUNA16 and Kaggle Data Science Bowl challenges. While nodule detection systems are typically designed and optimized on their own, we find that it is important to consider the coupling between detection and diagnosis components. Exploiting this coupling allows us to develop an end-to-end system that has higher and more robust performance and eliminates the need for a nodule detection false positive reduction stage. Furthermore, we characterize model uncertainty in our deep learning systems, a first for lung CT analysis, and show that we can use this to provide well-calibrated classification probabilities for both nodule detection and patient malignancy diagnosis. These calibrated probabilities informed by model uncertainty can be used for subsequent risk-based decision making towards diagnostic interventions or disease treatments, as we demonstrate using a probability-based patient referral strategy to further improve our results.

Splenomegaly Segmentation on Multi-Modal MRI Using Deep Convolutional Networks

The findings of splenomegaly, abnormal enlargement of the spleen, is a non-invasive clinical biomarker for liver and spleen diseases. Automated segmentation methods are essential to efficiently quantify splenomegaly from clinically acquired abdominal magnetic resonance imaging (MRI) scans. However, the task is challenging due to: 1) large anatomical and spatial variations of splenomegaly; 2) large inter- and intra-scan intensity variations on multi-modal MRI; and 3) limited numbers of labeled splenomegaly scans. In this paper, we propose the Splenomegaly Segmentation Network (SS-Net) to introduce the deep convolutional neural network (DCNN) approaches in multi-modal MRI splenomegaly segmentation. Large convolutional kernel layers were used to address the spatial and anatomical variations, while the conditional generative adversarial networks were employed to leverage the segmentation performance of SS-Net in an end-to-end manner. A clinically acquired cohort containing both T1-weighted (T1w) and T2-weighted (T2w) MRI splenomegaly scans was used to train and evaluate the performance of multi-atlas segmentation (MAS), 2D DCNN networks, and a 3-D DCNN network. From the experimental results, the DCNN methods achieved superior performance to the state-of-the-art MAS method. The proposed SS-Net method has achieved the highest median and mean Dice scores among the investigated baseline DCNN methods.

Non-Intrusive Load Monitoring via Multi-Label Sparse Representation-Based Classification

This work follows the approach of multi-label classification for non-intrusive load monitoring (NILM). We modify the popular sparse representation based classification (SRC) approach (developed for single label classification) to solve multi-label classification problems. Results on benchmark REDD and Pecan Street dataset shows significant improvement over state-of-the-art techniques with small volume of training data.

Closed-Loop Attention Restoration Theory for Virtual Reality-Based Attentional Engagement Enhancement

Today, as media and technology multitasking becomes pervasive, the majority of young people face a challenge regarding their attentional engagement (that is, how well their attention can be maintained). While various approaches to improve attentional engagement exist, it is difficult to produce an effect in younger people, due to the inadequate attraction of these approaches themselves. Here, we show that a single 30-min engagement with an attention restoration theory (ART)-inspired closed-loop software program (Virtual ART) delivered on a consumer-friendly virtual reality head-mounted display (VR-HMD) could lead to improvements in both general attention level and the depth of engagement in young university students. These improvements were associated with positive changes in both behavioral (response time and response time variability) and key electroencephalography (EEG)-based neural metrics (frontal midline theta inter-trial coherence and parietal event-related potential P3b). All the results were based on the comparison of the standard Virtual ART tasks (control group, n = 15) and closed-loop Virtual ART tasks (treatment group, n = 15). This study provides the first case of EEG evidence of a VR-HMD-based closed-loop ART intervention generating enhanced attentional engagement.

2'-Fucosyllactose Promotes the Production of Short-Chain Fatty Acids and Improves Immune Function in Human-Microbiota-Associated Mice by Regulating Gut Microbiota

As a natural prebiotic in human milk, 2'-fucosyllactose (2'-FL) is actively used in infant formula (IF). However, the 2'-FL influence on the improvement of gut microbiota and the regulation of the immune function remains unknown. In this study, human microbiota-associated (HMA) mice were used to demonstrate that feeding 2'-FL-containing IF was comparable to human milk at levels of immune cytokines (IL-2, IL-9, IL-10, and sIgA) and short-chain fatty acids (SCFAs, i.e., acetate and propionate). In addition, 2'-FL increased the abundance of Blautia and Olsenella and improved the anti-inflammatory cytokine IL-10 levels. The abundance of Blautia and Olsenella positively correlated with the IL-10 levels. 2'-FL also decreased the abundance of Enterorhabdus and Lachnospiraceae_UCG-006 and elevated SCFA levels, showing a negative correlation between these genera and SCFAs. Our findings revealed that feeding 2'-FL-containing IF drives the levels of cytokines and SCFAs toward human milk levels by shaping the beneficial gut microbiota profile.

Dense semantic embedding network for image captioning

Recently, attributes that contain high-level semantic information of image are always used as a complementary knowledge to improve image captioning performance. However, the use of attributes in prior works cannot excavate the latent visual concepts effectively. At each time step, the semantic information which is sensitive to the predicted word could be different. In this paper, we propose a Dense Semantic Embedding Network (DSEN) for this task. The distinct operation of this network is to densely embed the attributes with the multi-modal of image and text at each step of word generation. The discriminative semantic information hidden in these attributes is formatted in form of global likelihood probabilities. As a result, this dense embedding can modulate the feature distributions of the image, text modals and the hidden states to explicit semantic representation. Furthermore, to improve the discrimination of attributes, a Threshold ReLU (TReLU) is proposed. In addition, a bidirectional LSTM structure is incorporated into the DSEN to capture both the previous and future contexts. Extensive experiments on the COCO and Flickr30K datasets achieve superior results when compared with the state-of-the-art models for the tasks of both image captioning and image-text cross modal retrieval. Most remarkably, our method obtains outstanding performance on the retrieval task, compared with the state-of-the-art models. (C) 2019 Published by Elsevier Ltd.

Evaluation of Antitumor and On-Target Activity of HDAC Inhibitors with the Zebrafish Embryo Xenograft Model

Reliable preclinical drug testing models for cancer research are urgently needed with zebrafish embryo models emerging as a powerful vertebrate model for xenotransplantation studies. Here, we describe the evaluation of toxicity, efficacy, and on-target activity of histone deacetylase (HDAC) inhibitors in a zebrafish embryo yolk sac xenotransplantation model of medulloblastoma and neuroblastoma cells. For this, we performed toxicity assays with our zebrafish drug library consisting of 28 clinically relevant targeted as well as chemotherapeutic drugs with zebrafish embryos. We further engrafted zebrafish embryos with fluorescently labeled pediatric tumor cells (SK-N-BE(2)-C, HD-MB03, or MED8A) and monitored the progression after HDAC inhibitor treatment of xenotransplanted tumors through tumor volume measurements with high-content confocal microscopy in a multi-well format. The on-target activity of HDAC inhibitors was verified through immunohistochemistry staining on paraffin-embedded early larvae. Overall, the zebrafish embryo xenotransplantation model allows for fast and cost-efficient in vivo evaluation of targeted drug toxicity, efficacy, and on-target activity in the field of precision oncology.

SRSF5-Mediated Alternative Splicing of M Gene is Essential for Influenza A Virus Replication: A Host-Directed Target Against Influenza Virus

Splicing of influenza A virus (IAV) RNA is an essential process in the viral life cycle that involves the co-opting of host factors. Here, it is demonstrated that induction of host serine and arginine-rich splicing factor 5 (SRSF5) by IAV facilitated viral replication by enhancing viral M mRNA splicing. Mechanistically, SRSF5 with its RRM2 domain directly bounds M mRNA at conserved sites (M mRNA position 163, 709, and 712), and interacts with U1 small nuclear ribonucleoprotein (snRNP) to promote M mRNA splicing and M2 production. Mutations introduced to the three binding sites, without changing amino acid code, significantly attenuates virus replication and pathogenesis in vivo. Likewise, SRSF5 conditional knockout in the lung protects mice against lethal IAV challenge. Furthermore, anidulafungin, an approved antifungal drug, is identified as an inhibitor of SRSF5 that effectively blocks IAV replication in vitro and in vivo. In conclusion, SRSF5 as an activator of M mRNA splicing promotes IAV replication and is a host-derived antiviral target.

GraphRegNet: Deep Graph Regularisation Networks on Sparse Keypoints for Dense Registration of 3D Lung CTs

In the last two years learning-based methods have started to show encouraging results in different supervised and unsupervised medical image registration tasks. Deep neural networks enable (near) real time applications through fast inference times and have tremendous potential for increased registration accuracies by task-specific learning. However, estimation of large 3D deformations, for example present in inhale to exhale lung CT or interpatient abdominal MRI registration, is still a major challenge for the widely adopted U-Net-like network architectures. Even when using multi-level strategies, current state-of-the-art DL registration results do not yet reach the high accuracy of conventional frameworks. To overcome the problem of large deformations for deep learning approaches, in this work, we present GraphRegNet, a sparse keypoint-based geometric network for dense deformable medical image registration. Similar to the successful 2D optical flow estimation of FlowNet or PWC-Net we leverage discrete dense displacement maps to facilitate the registration process. In order to cope with enormously increasing memory requirements when working with displacement maps in 3D medical volumes and to obtain a well-regularised and accurate deformation field we 1) formulate the registration task as the prediction of displacement vectors on a sparse irregular grid of distinctive keypoints and 2) introduce our efficient GraphRegNet for displacement regularisation, a combination of convolutional and graph neural network layers in a unified architecture. In our experiments on exhale to inhale lung CT registration we demonstrate substantial improvements (TRE below 1.4 mm) over other deep learning methods. Our code is publicly available at https://github.com/multimodallearning/graphregnet.

STUDY ON TRANSFORMATION OF INDOLE BY LACCASE IN THE PRESENCE OF PHENOL AND 2,2-AZINO-BIS (3-ETHYLBENZTHIAZOLINE-6-SULPHONIC ACID)

Transformation of indole by laccase in the presence of phenol using 2, 2-Azino-bis (3-ethylbenzthiazoline-6-sulphonic acid) (ABTS) as mediator was studied. The experiments were performed with different pH, enzyme dosage, ARTS and phenol concentration. The results showed that the degradation efficiency of indole was better in the acid solution. ARTS and phenol had significant effect on transformation of indole in the liquid substrate. Phenol could be oxidized by laccase substrate, and also enhance the degradation of indole. The intermediates of phenol could act as laccase mediator different from ABTS, which enhanced transformation of indole.

Practical Measurement of Changes in Leg Length Discrepancy After a Myofascial Release on the Thoracolumbar Fascia in Patients With Acute Low Back Pain: A Pilot Study

Background Recent work has examined an association between leg length discrepancy (LLD) and low back pain (LBP). Myofascial release (MFR) techniques are thought to be frequently applied in the treatment of chronic and acute LBP. The purpose of this study was to evaluate a practical measure of LLD and the feasibility of an MFR technique in a randomised controlled trial (RCT). Methods In 12 subjects (seven women and five men) with acute LBP and LLD greater than 3 mm, an MFR technique was performed on the thoracolumbar fascia. At the baseline, after the intervention, and at follow-up, LLD was measured using a cross-line laser and finger-to-floor distance, and the pain was measured with a visual analogue scale (VAS). Patients completed a questionnaire after follow-up to assess the acceptability of the study procedure. The therapist evaluated the methods in terms of their feasibility. Results LLD measurement and MFR treatment required little time and few resources. Participants agreed to the study procedure with moderate to high acceptance. The LLD decreased by 5 mm after treatment and by 4 mm at follow-up. The VAS showed a reduction in pain of 17.50 mm at follow-up but not immediately after treatment. Conclusion The measurement of LLD is applicable in daily osteopathic practice, but it cannot be assumed to be a valid method for an RCT. Validated methods such as video raster stereography are, therefore, recommended. Comprehensive RCTs to study the effects of MFR intervention on leg length are feasible.

MFNW: An MLC/TLC Flip-N-Write Architecture

The increased capacity of multi-level cells (MLC) and triple-level cells (TLC) in emerging non-volatile memory (NVM) technologies comes at the cost of higher cell write energies and lower cell endurance. In this article, we describe MFNW, a Flip-N-Write encoding that effectively reduces the write energy and improves the endurance of MLC NVMs. Two MFNW modes are analyzed: cell Hamming distance mode and energy Hamming distance mode. We derive an approximate model that accurately predicts the average number of cell writes that is proportional to the energy consumption, enabling word length optimization to maximize energy reduction subject to memory space overhead constraints. In comparison to state-of-the-art MLC NVM encodings, our simulation results indicate that MFNW achieves up to 7%-39% saving for 1.56%-50% NVM space overhead. Extra energy saving (up to 19%-47%) can be achieved for the same NVM space overhead using our proposed variations of MFNW, i.e., MFNW2 and MFNW3. For TLC NVMs, we propose TFNW that can achieve up to 53% energy saving in comparison to state-of-the-art TLC NVM encodings. Endurance simulations indicate that MFNW (TFNW) is capable of extending MLC (TLC) NVM life by up to 100% (87%).

EEG decoding method based on multi-feature information fusion for spinal cord injury

To develop an efficient brain-computer interface (BCI) system, electroencephalography (EEG) measures neuronal activities in different brain regions through electrodes. Many EEG-based motor imagery (MI) studies do not make full use of brain network topology. In this paper, a deep learning framework based on a modified graph convolution neural network (M-GCN) is proposed, in which temporal-frequency processing is performed on the data through modified S-transform (MST) to improve the decoding performance of original EEG signals in different types of MI recognition. MST can be matched with the spatial position relationship of the electrodes. This method fusions multiple features in the temporal-frequency-spatial domain to further improve the recognition performance. By detecting the brain function characteristics of each specific rhythm, EEG generated by imaginary movement can be effectively analyzed to obtain the subjects' intention. Finally, the EEG signals of patients with spinal cord injury (SCI) are used to establish a correlation matrix containing EEG channel information, the M-GCN is employed to decode relation features. The proposed M-GCN framework has better performance than other existing methods. The accuracy of classifying and identifying MI tasks through the M-GCN method can reach 87.456%. After 10-fold cross-validation, the average accuracy rate is 87.442%, which verifies the reliability and stability of the proposed algorithm. Furthermore, the method provides effective rehabilitation training for patients with SCI to partially restore motor function.

HARD-PnP: PnP Optimization Using a Hybrid Approximate Representation

This paper proposes a Hybrid Approximate Representation (HAR) based on unifying several efficient approximations of the generalized reprojection error (which is known as the gold standard for multiview geometry). The HAR is an over-parameterization scheme where the approximation is applied simultaneously in multiple parameter spaces. A joint minimization scheme "HAR-Descent" can then solve the PnP problem efficiently, while remaining robust to approximation errors and local minima. The technique is evaluated extensively, including numerous synthetic benchmark protocols and the real-world data evaluations used in previous works. The proposed technique was found to have runtime complexity comparable to the fastest Oonthorn techniques, and up to 10 times faster than current state of the art minimization approaches. In addition, the accuracy exceeds that of all 9 previous techniques tested, providing definitive state of the art performance on the benchmarks, across all 90 of the experiments in the paper and supplementary material, which can be found on the Computer Society Digital Library at http:w//doi.ieeecomputersociety.org/10.1109/TPAMI.2018.2806446.

Frequency of Nucleophosmin 1 Expression by Immunohistochemistry in Acute Myeloid Leukemia

Nucleophosmin (NPM1) mutation is one of the most common recurring genetic abnormalities seen in acute myeloid leukemia (AML). Immunohistochemistry serves as a cost effective and simple surrogate testing method for detection of NPM1 mutation. This study was conducted to evaluate the frequency of aberrant cytoplasmic nucleophosmin 1 expression in leukemic blast cells on formalin fixed bone marrow trephine biopsy (BMB) sections and also to correlate this data with the reference molecular method (reverse transcriptase-polymerase chain reaction; RT-PCR and gene sequencing), where available. Immunostains were performed using mouse anti-NPM1 monoclonal antibody on 71 paraffin embedded bone marrow biopsies (BMB) of patients with AML of any French-American-British (FAB) subtype. Results of immunohistochemistry (IHC) were then compared with the reference molecular method. The proportion of NPM1 expression by immunostaining in AML cases was found to be 17%. Twelve of the total 71 cases demonstrated cytoplasmic nucleophosmin (NPMc+) on immunostaining. Eleven of the positive cases that were correlated with the molecular standard demonstrated mutation in exon 12 of NPM1 gene. Cytoplasmic nucleophosmin expression by immunostaining was found to be in complete agreement with the standard molecular method. In a resource restricted setup, the information from this study might help in providing an inexpensive and accurate detection method to facilitate introduction of this marker in diagnostic and prognostic workup of AML especially in patients showing normal karyotype and no common recurrent translocations.

Pd Nanoparticle-Loaded Smart Microgel-Based Membranes as Reusable Catalysts

In this work, palladium-loaded smart membranes made by UV cross-linking of thermoresponsive microgels are prepared to obtain a reusable, catalytically active material which can, for example, be implemented in chemical reactors. The membranes are examined with respect to their coverage of a supporting mesh via atomic force microscopy measurements. Force indentation mapping was performed in the dried, collapsed state and in the swollen state in water to determine the Young modulus. Furthermore, we compare the catalytic activity of the membrane with the corresponding suspended colloidal nanoparticle microgel hybrids. For this purpose, the reduction of 4-nitrophenol is an established model reaction to quantify the catalytic activity by UV-vis spectroscopy. The membrane is embedded inside a continuous stirred tank reactor equipped for continuous monitoring of the reaction progress. Although catalysis with membranes shows lower catalytic activity than freely dispersed particles, membranes allow straightforward separation and recycling of the catalyst. The fabricated membranes in this work show no decrease in catalytic activity between several cycles, unlike free particles. The feasible and durable deposition of catalytically active inter-cross-linked microgel particles on commercial nylon meshes as supporting scaffolds, as demonstrated in this work, is promising for up-scaling of continuous industrial processes.

Feasibility, Acceptability, and Preliminary Impact of Full-Body Interaction on Computerized Cognitive Training Based on Instrumental Activities of Daily Living: A Pilot Randomized Controlled Trial with Chronic Psychiatric Inpatients

Objective: To conduct a pilot randomized control trial to assess the feasibility and acceptability of full-body interaction cognitive training (FBI-CT) inspired by instrumental activities of daily living in chronic psychiatric inpatients and to explore its preliminary impact on cognitive and noncognitive outcomes. Materials and Methods: Twenty psychiatric inpatients met the inclusion criteria and were randomly allocated to the FBI-CT group (n = 10) or the tablet-based CT group (T-CT) (n = 10). Neuropsychological assessments were performed at baseline, postintervention, and 3-month follow-up. Results: Both groups presented high completion rates at postintervention and follow-up. Participants reported high satisfaction following the interventions, with the FBI-CT group exhibiting slightly higher satisfaction. A within-group analysis showed significant improvements in the FBI-CT group for processing speed and sustained attention for short periods (P = 0.012), verbal memory (P = 0.008), semantic fluency (P = 0.027), depressive symptoms (P = 0.008), and quality of life (P = 0.008) at postintervention. At 3-month follow-up, this group maintained verbal memory improvements (P = 0.047) and depressive symptoms amelioration (P = 0.026). The T-CT group revealed significant improvements in sustained attention for long periods (P = 0.020), verbal memory (P = 0.014), and executive functions (P = 0.047) postintervention. A between-group analysis demonstrated that the FBI-CT group exhibited greater improvements in depressive symptoms (P = 0.042). Conclusions: Overall, we found support for the feasibility and acceptability of both training approaches. Our findings show promise regarding the preliminary impact of the FBI-CT intervention, but due to study limitations such as the small sample size, we cannot conclude that FBI-CT is a more effective approach than T-CT for enhancing cognitive and noncognitive outcomes of chronic psychiatric inpatients. Clinical trials (number: NCT05100849).

[Development and Application of the First Carbon Ion Therapy System in China]

At present, heavy ion is an ideal radiation for cancer treatment, and carbon ion is used in the treatment of many kinds of cancer due to its higher relative biological effect value. In 2019, Wuwei heavy ion center built the first medical heavy ion accelerator-carbon ion radiotherapy system in China, and obtained the registration license from the National Medical Products Administration, and officially received cancer patients in March 2020. This study introduced the development and application of the first carbon ion radiotherapy system in China.

Myotonia congenita: novel mutations in CLCN1 gene

Myotonia congenita belongs to the group of non-dystrophic myotonia caused by mutations of CLCN1gene, which encodes human skeletal muscle chloride channel 1. It can be inherited either in autosomal dominant (Thomsen disease) or recessive (Becker disease) forms. Here we have sequenced all 23 exons and exon-intron boundaries of the CLCN1 gene, in a panel of 5 unrelated Chinese patients with myotonia congenita (2 with dominant and 3 with recessive form). In addition, detailed clinical analysis was performed in these patients to summarize their clinical characteristics in relation to their genotypes. Mutational analyses revealed 7 different point mutations. Of these, we have found 3 novel mutations including 2 missense (R47W, V229M), one splicing (IVS19+2T>C), and 4 known mutations (Y261C,G523D, M560T, G859D). Our data expand the spectrum of CLCN1 mutations and provide insights for genotype-phenotype correlations of myotonia congenita in the Chinese population.

CR-FPN: channel relation feature pyramid network for object detection

Object detection is an important computer vision task, which aims to locate each object and classify them correctly. Since Convolution Neural Network (CNN) high-level feature map contains more semantic information and low-level feature map contains detail information which helps to locate precisely, fusing high-level feature maps with low-level feature maps is proven to be essential for multi-scale object detection. Recent work usually direct combine feature maps from different levels of CNN. However, as each feature map has different extents of discrimination, combining them directly may turn some valuable information into noise and further degrade performance. To address this issue, we focus on the channel relationship from different level of feature maps and propose a novel network, named Channel Relation Feature Pyramid Network (CR-FPN), that captures long-range relation of channel from different level of feature maps by similarity measure function and further to magnify the most relevant channels and suppress the irrelevant channels. Extensive experiments conducted on MS COCO and Pascal VOC datasets demonstrate the effectiveness of our CR-FPN also with competitive performance. Noting that our CR-FPN can apply to several typical state-of-the-art detectors. Using CR-FPN in a basic Cascade R-CNN detector, our method achieves state-of-the-art single model results on the COCO dataset.

Block Copolymer Self-Assembly Directed Synthesis of Porous Materials with Ordered Bicontinuous Structures and Their Potential Applications

Porous materials with their ordered bicontinuous structures have attracted great interest owing to ordered periodic structures as well as 3D interconnected network and pore channels. Bicontinuous structures may favor efficient mass diffusion to the interior of materials, thus increasing the utilization ratio of active sites. In addition, ordered bicontinuous structures confer materials with exceptional optical and magnetic properties, including tunable photonic bandgap, negative refraction, and multiple equivalent magnetization configurations. The attractive structural advantages and physical properties have inspired people to develop strategies for preparing bicontinuous-structured porous materials. Among a few synthetic approaches, the self-assembly of block copolymers represents a versatile strategy to prepare various bicontinuous-structured functional materials with pore sizes and lattice parameters ranging from 1 to 500 nm. This article overviews progress in this appealing area, with an emphasis on the synthetic strategies, the structural control (including topologies, pore sizes, and unit cell parameters), and their potential applications in energy storage and conversion, metamaterials, photonic crystals, cargo delivery and release, nanoreactors, and biomolecule selection.

Inhibitor of Glucosinolate Sulfatases as a Potential Friendly Insecticide to Control Plutella xylostella

The glucosinolate-myrosinase system is a two-component defense system characteristic of cruciferous plants. To evade the glucosinolate-myrosinase system, the crucifer specialist insect, Plutella xylostella, promptly desulfates the glucosinolates into harmless compounds by glucosinolate sulfatases (GSSs) in the gut. In this study, we identified an effective inhibitor of GSSs by virtual screening, molecular docking analysis, and in vitro enzyme inhibition assay. The combined effect of the GSS inhibitor with the plant glucosinolate-myrosinase system was assessed by the bioassay of P. xylostella. We show that irosustat is a GSS inhibitor and the inhibition of GSSs impairs the ability of P. xylostella to detoxify the glucosinolate-myrosinase system, leading to the systematic accumulation of toxic isothiocyanates in larvae, thereby severely affecting feeding, growth, survival, and reproduction of P. xylostella. While fed on the Arabidopsis mutants deficient in myrosinase or glucosinolates, irosustat had no significant negative effect on P. xylostella. These findings reveal that the GSS inhibitor is a novel friendly insecticide to control P. xylostella utilizing the plant glucosinolate-myrosinase system and promote the development of insecticide-plant chemical defense combination strategies.

Ultrasound Matrix ImaginguPart I: The Focused Reflection Matrix, the F-Factor and the Role of Multiple Scattering

This is the first article in a series of two dealing with a matrix approach for aberration quantification and correction in ultrasound imaging. Advanced synthetic beamforming relies on a double focusing operation at transmission and reception on each point of the medium. Ultrasound matrix imaging (UMI) consists in decoupling the location of these transmitted and received focal spots. The response between those virtual transducers form the so-called focused reflection matrix that actually contains much more information than a confocal ultrasound image. In this paper, a time-frequency analysis of this matrix is performed, which highlights the single and multiple scattering contributions as well as the impact of aberrations in the monochromatic and broadband regimes. Interestingly, this analysis enables the measurement of the incoherent input-output point spread function at any pixel of this image. A fitting process enables the quantification of the single scattering, multiple scattering and noise components in the image. From the single scattering contribution, a focusing criterion is defined, and its evolution used to quantify the amount of aberration throughout the ultrasound image. In contrast to the state-of-the-art coherence factor, this new indicator is robust to multiple scattering and electronic noise, thereby providing a contrasted map of the focusing quality at a much better transverse resolution. After a validation of the proof-of-concept based on time-domain simulations, UMI is applied to the in-vivo study of a human calf. Beyond this specific example, UMI opens a new route for speed-of-sound and scattering quantification in ultrasound imaging.

Ferulic acid relieved ulcerative colitis by inhibiting the TXNIP/NLRP3 pathway in rats

Ulcerative colitis (UC) is a disorder of the bowel that is characterized by a chronic inflammatory response. The traditional Chinese herbal medicine ferulic acid (FA) is known for its antioxidant, antiapoptotic, and antiinflammatory properties. However, its role in UC is still unclear. Thus, the current study was conducted to investigate the role of FA in UC. Rats were treated with 2,4,6-triabrobenzene sulfonic acid to induce UC and subjected to FA. Human intestinal microvascular endothelial cells (HIMECs) were treated with tumor necrosis factor-α (TNF-α) and pretreated with FA. Pathological changes in colonic tissues were visualized via hematoxylin-eosin staining. Enzyme linked immunosorbent assay was conducted to detect interleukin (IL)-6, IL-12, and IL-1β levels. Cell morphology was visualized by using a microscope, and viability was detected by using MTT. The percentage of apoptosis was detected via flow cytometry. Western blot analysis was performed to detect the expression of the apoptosis-related proteins thioredoxin-interacting protein (TXNIP) and NOD-like receptor pyrin domain-containing 3 (NLRP3). In vivo FA administration alleviated intestinal injury in UC rats and inhibited inflammatory factor levels (IL-6, IL-12, and IL-1β), apoptosis-related protein expression (caspase-1 and caspase-3) and the TXNIP/NLRP3 signaling pathway. In vitro, TNF-α treatment reduced HIMEC viability, increased cell apoptosis and inflammatory factor levels and activated the TXNIP/NLRP3 signaling pathway. However, FA treatment restored the viability of HIMECs, reduced TNF-α-induced cell apoptosis and inflammation and inhibited the TXNIP/NLRP3 signaling pathway. Furthermore, with increasing FA concentration, the effects were stronger. In summary, FA inhibits the inflammatory injury of endothelial cells in ulcerative colitis or alleviates TNF-α-induced HIMEC injury by inhibiting the TXNIP/NLRP3 signaling pathway.

Evaluating High Performance Pattern Matching on the Automata Processor

In this paper, we study the acceleration of applications that identify all the occurrences of thousands of string-patterns in an input data-stream using the Automata Processor (AP). For this evaluation, we use two applications from two fields, namely, cybersecurity and bioinformatics. The first application, called Fast-SNAP, scans network data for 4312 signatures of intrusion derived from the popular open-source Snort database. Using the resources of a single AP-board, Fast-SNAP can scan for all these signatures at 1 Gbps. The second application, called PROTOMATA, looks for all the occurrences of 1,309 motifs from the PROSITE database in protein sequences. PROTOMATA is up to 68 times faster than the state-of-the-art CPU implementation. As a comparison, we emulate the execution of the same NFAs by programming FPGAs using state-of-the-art techniques. We find that the performance derived by using the resources of a single AP-board, which houses 32 AP-chips, is comparable to that of the resources of five to six large FPGAs. The design techniques used in this paper are generic and may be applicable to the development of similar applications on the AP.

Adopting a "Both/And" Mindset to Address Relationship Violence and Sexual Misconduct (RVSM) in Institutions of Higher Education

Michigan State University (MSU) created a long-term, values-based strategic plan to increase help-seeking and reduce the incidence of relationship violence and sexual misconduct. Creating systemic change in institutions of higher education is challenging, particularly so in the wake of massive institutional crises and betrayal, as we had at MSU. In this paper, we address the challenges and critiques of our strategic planning efforts offered by esteemed scholar-activists: Jacobson López (2023), Hirsch and Khan (2023), McMahon (2023), and Boots et al. (2023).

Hydrogel-Based Smart Contact Lens for Highly Sensitive Wireless Intraocular Pressure Monitoring

Real-time intraocular pressure (IOP) monitoring plays a crucial role in glaucoma diagnosis and treatment. The wireless smart contact lens based on a flexible inductor-capacitor-resistor (LCR) sensor is chip-free and battery-free, demonstrating excellent application potential for physiological signal monitoring. To promote the use of LCR contact lenses for clinical IOP monitoring, reliable, comfortable contact lens materials should be used and excellent sensitivity needs to be realized. Here, we propose a method for producing hydrogel-based smart contact lenses for wireless IOP monitoring that uses the conformal stacking technique, solving the problems of swelling of the hydrogel and spherical integration of the pyramid-microstructured dielectric elastomer. The IOP of the in vitro porcine eye is successfully monitored owing to the high sensitivity of the spherical pyramid-microstructured capacitive pressure sensor and the hydrogel substrate. In addition, a glasses-integrated impedance-matching tunable reader for remote signal measurement is realized by enhancing the signal amplitude and increasing the reading distance, improving the portability of the signal measurement equipment. With the above improved designs, the wireless contact lens system has application potential for clinical IOP monitoring and shows substantial promise for next-generation daily ocular health management.

Novel approaches to human activity recognition based on accelerometer data

An increasing number of works have investigated the use of convolutional neural network (ConvNets) approaches to perform human activity recognition (HAR) based on wearable sensor data. These approaches present state-of-the-art results in HAR, outperforming traditional approaches, such as handcrafted methods and 1D convolutions. Motivated by this, in this work we propose a set of methods to enhance ConvNets for HAR. First, we propose a data augmentation which enables the ConvNets to learn more adequately the patterns of the signal. Second, we exploit the attitude estimation of the accelerometer data to devise a set of novel feature descriptors which allow the ConvNets to better discriminate the activities. Finally, we propose a novel ConvNet architecture to explore the patterns among the accelerometer axes throughout the layers that compose the network. We demonstrate that this is a simpler way of improving the activity recognition instead of proposing more complex architectures, serving as direction to future works with the purpose of building ConvNets architectures. The experimental results show that our proposed methods achieve notable improvements and outperform existing state-of-the-art methods.

Residential Electrical Load Monitoring and Modeling - State of the Art and Future Trends for Smart Homes and Grids

Building energy consumption accounts for a large fraction of the total global energy usage, and considerable energy savings are expected to be achieved in this respect through residential electrical load monitoring. Due to the limitations on the practical implementation of in-depth and expensive monitoring systems, non-intrusive load monitoring (NILM) is becoming a hot topic. In this paper, an overview of the state of the art residential electrical load monitoring is presented. Different from previous reviews, the applications of load monitoring are particularly addressed, based on which, technical challenges of load monitoring techniques, including NILM, are identified and thoroughly discussed, together with possible developments and trends predicted from the authors' perspective.

Parallel Computing of Graph-based Functions in ReRAM

Resistive Random Access Memory (ReRAM) is an emerging non-volatile memory technology. Besides its low power consumption and its high scalability, its inherent computation capabilities make ReRAM especially interesting for future computer architectures. Merging computations into the memory is a promising solution for overcoming the memory bottleneck. To perform computations in ReRAM, efficient synthesis strategies for Boolean functions have to be developed. In this article, we give a thorough presentation of how to employ parallel computing capabilities of ReRAM for the synthesis of functions given state-of-the-art graph-based representations AIGs or BDDs. Additionally, we introduce a new graph-based representation called m-And-Inverter Graph (m-AlGs), which allows us to fully exploit the computing capabilities of ReRAM. In the simulations, we show that our proposed approaches outperform state-of-the art synthesis strategies, and we show the superiority of m-AIGs over the standard AIG representation for ReRAM-based synthesis.

A Distributed Amplifier System for Bilayer Lipid Membrane (BLM) Arrays With Noise and Individual Offset Cancellation

Lipid bilayer membrane (BLM) arrays are required for high throughput analysis, for example drug screening or advanced DNA sequencing. Complex microfluidic devices are being developed but these are restricted in terms of array size and structure or have integrated electronic sensing with limited noise performance. We present a compact and scalable multichannel electrophysiology platform based on a hybrid approach that combines integrated state-of-the-art microelectronics with low-cost disposable fluidics providing a platform for high-quality parallel single ion channel recording. Specifically, we have developed a new integrated circuit amplifier based on a novel noise cancellation scheme that eliminates flicker noise derived from devices under test and amplifiers. The system is demonstrated through the simultaneous recording of ion channel activity from eight bilayer membranes. The platform is scalable and could be extended to much larger array sizes, limited only by electronic data decimation and communication capabilities.

Intra-tumoral infiltration of adipocyte facilitates the activation of antitumor immune response in pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is a highly fatal malignancy that is characterized by an immunosuppressive microenvironment. The immune suppression in PDAC is largely driven by heterogeneous stromal and tumor cells. However, how adipocyte in the tumor microenvironment (TME) is related to the immune cell infiltration in PDAC has rarely been published. We identified adipocytes by performing bioinformatics analyses, and explored the clinical outcomes and TME characters in PDAC with different levels of adipocyte infiltration. Interestingly, in contrast to adiposity, high adipocyte infiltration in the TME was related to significantly increased median overall survival and a lower total tumor mutational burden. Functionally, high adipocyte infiltration was associated with the immune response, particularly with the abundant cytokine infiltration in PDAC samples. Moreover, adipocyte infiltration in the TME was positively associated with anticancer signatures in the immune microenvironment. Immunohistochemistry and RT-PCR were performed with PDAC tissue samples from our center to study the expression of adipocytes in PDAC. The mature adipocytes were strongly associated with the immune composition and prognosis of patients with PDAC. Primary adipocytes were isolated from mice to construct a PDAC transplantation tumor model. In vivo experiments showed that adipocytes elicited increased CD8+ T cell infiltration and potent antitumor activity in tumor-bearing mice. Overall, we innovatively found that adipocytes facilitated the antitumor immune response in the TME by performing mouse experiments and analyzing PDAC samples. This study provides a new perspective on the activation of the immune microenvironment in PDAC.

Phylogeography, hybridization, and species discovery in the Etheostoma nigrum complex (Percidae: Etheostoma: Boleosoma)

The history of riverine fish diversification is largely a product of geographic isolation. Physical barriers that reduce or eliminate gene flow between populations facilitate divergence via genetic drift and natural selection, eventually leading to speciation. For freshwater organisms, diversification is often the product of drainage basin rearrangements. In young clades where the history of isolation is the most recent, evolutionary relationships can resemble a tangled web. One especially recalcitrant group of freshwater fishes is the Johnny Darter (Etheostoma nigrum) species complex, where traditional taxonomy and molecular phylogenetics indicate a history of gene flow and conflicting inferences of species diversity. Here we assemble a genomic dataset using double digest restriction site associated DNA (ddRAD) sequencing and use phylogenomic and population genetic approaches to investigate the evolutionary history of the complex of species that includes E. nigrum, E. olmstedi, E. perlongum, and E. susanae. We reveal and validate several evolutionary lineages that we delimit as species, highlighting the need for additional work to formally describe the diversity of the Etheostoma nigrum complex. Our analyses also identify gene flow among recently diverged lineages, including one instance involving E. susanae, a localized and endangered species. Phylogeographic structure within the Etheostoma nigrum species complex coincides with major geologic events, such as parallel divergence in river basins during Pliocene inundation of the Atlantic coastal plain and multiple northward post-glacial colonization routes tracking river basin rearrangements. Our study serves as a nuanced example of how low dispersal rates coupled with geographic isolation among disconnected river systems in eastern North America has produced one of the world's freshwater biodiversity hotspots.

Real-time image marbleization

We present a new real-time image marbleization method that converts an image into a marble-like appearance automatically. The approach models the marbleization process as a two-dimensional fluid dynamics problem, whereby color advection of an input image results in a marbleized image. During the fluid dynamics simulation, we add a pixel-level external force field which is tangent to salient features in the image. The forces are computed from the image characteristics without user intervention. A stylized image with marble-like appearance is easily created that maintains the basic shape of objects in the input image. The entire modeling framework is implemented on a graphics processing unit, thus enabling real-time visual feedback. This approach provides a new tool to design figurative marbling textures without mixing of colors, which are almost impossible with previous computer-generated marbling methods.

GA-CNN: Convolutional Neural Network Based on Geometric Algebra for Hyperspectral Image Classification

Convolutional neural networks (CNNs) have achieved state-of-the-art performance in hyperspectral images (HSIs) classification, which is widely used for the analysis of remotely sensed images. HSI includes spectral and spatial information from several hundreds of spectral data channels. Recent CNN models deal with various bands of HSIs as independent channels, which may lead to the loss of dependencies between different channels or the loss of associated information between each channel and the global. This article proposes a novel CNN model based on geometric algebra (GA), dubbed GA-CNN, to process the HSIs in a holistic way without losing the interrelationship among channels. Specifically, taking advantage of GA, different band images are represented as GA multivectors to capture the inherent structures and preserve the correlation of those channels. In particular, all the basic modules of our model, such as convolutional layers and the backpropagation algorithm, are extended to the GA domain. We evaluate the performance of the proposed GA-CNN model in classification tasks on four well-known HSI datasets. The experimental results indicate that our GA-CNN model outperforms traditional and state-of-the-art real-valued CNNs with higher classification accuracy and fewer model parameters.

Intensity and Compactness Enabled Saliency Estimation for Leakage Detection in Diabetic and Malarial Retinopathy

Leakage in retinal angiography currently is a key feature for confirming the activities of lesions in the management of a wide range of retinal diseases, such as diabetic maculopathy and paediatric malarial retinopathy. This paper proposes a new saliency-based method for the detection of leakage in fluorescein angiography. A superpixel approach is firstly employed to divide the image into meaningful patches (or superpixels) at different levels. Two saliency cues, intensity and compactness, are then proposed for the estimation of the saliency map of each individual superpixel at each level. The saliency maps at different levels over the same cues are fused using an averaging operator. The two saliency maps over different cues are fused using a pixel-wise multiplication operator. Leaking regions are finally detected by thresholding the saliency map followed by a graph-cut segmentation. The proposed method has been validated using the only two publicly available datasets: one for malarial retinopathy and the other for diabetic retinopathy. The experimental results show that it outperforms one of the latest competitors and performs as well as a human expert for leakage detection and outperforms several state-of-the-art methods for saliency detection.

Geochemical analysis of the painted panels at the "Genyornis" rock art site, Arnhem Land, Australia

The so-called "Genyornis" rockshelter site on the Arnhem Land plateau, northern Australia, features a painting of a large bird that some archaeologists and paleontologists have suggested could be an image of the megafaunal species Genyornis newtoni, until recently widely thought to have become extinct some 45,000 years ago. However, a recent archaeologicalegeomorphological study has concluded that the rock surface that contains the large, enigmatic bird painting only became exposed during overhang collapse between 13,739-13,976 cal BP, so the rock art on this panel can only be more recent than this age. It is, therefore, most unlikely to represent a Genyornis bird. Using a range of analytical techniques, here we report on the geochemistry (microstructure and chemistry) of the sequence of microlayers that makes up the "surface" of the rock at the "Genyornis" site. Our aim is to provide insights into the composition, preparation, source, application, weathering and preservation of pigment used in the making of the paintings, and to clarify the superimpositional order and relationships between the enigmatic large bird painting originally thought to possibly be of a Genyornis, and other nearby images. Micro-stratigraphic superimpositions show that the earliest painting was of a large anthropomorph, followed sometime later by the simultaneous painting of the large enigmatic ("Genyornis") bird image together with a barbed spear image embedded in it. This significantly alters the perception of the paintings on the wall of this rockshelter, with relegation of the anthropomorph to the background, followed by the image of a speared large bird. (C) 2016 Elsevier Ltd and INQUA. All rights reserved.

Crafting Sustainability: Handcraft in Contemporary Art and Cultural Sustainability in the Finnish Lapland

Crafting sustainability is discussed here with respect to the dimensions of handcraft traditions in contemporary art for promoting cultural sustainability in the Scandinavian North. Aspects of decolonization, cultural revitalisation, and intergenerational dialogue form an integral part of the negotiations around the need for cultural survival and renewal for a more sustainable future. These dimensions should also be considered in the development of the current education of art teachers. Learning traditional skills and applying them in contemporary art constitute an influential method when striving for cultural sustainability. This study examines three handcraft-based contemporary art cases through art-based action research conducted in the Finnish and the Swedish Lapland. The results show that handcraft-based contemporary art practices with place-specific intergenerational and intercultural approaches create an open space for dialogue where the values and the perceptions on cultural heritage can be negotiated.

Local adaptive approach toward segmentation of microscopic images of activated sludge flocs

Activated sludge process is a widely used method to treat domestic and industrial effluents. The conditions of activated sludge wastewater treatment plant (AS-WWTP) are related to the morphological properties of flocs (microbial aggregates) and filaments, and are required to be monitored for normal operation of the plant. Image processing and analysis is a potential time-efficient monitoring tool for AS-WWTPs. Local adaptive segmentation algorithms are proposed for bright-field microscopic images of activated sludge flocs. Two basic modules are suggested for Otsu thresholding-based local adaptive algorithms with irregular illumination compensation. The performance of the algorithms has been compared with state-of-the-art local adaptive algorithms of Sauvola, Bradley, Feng, and c-mean. The comparisons are done using a number of region-and nonregion-based metrics at different microscopic magnifications and quantification of flocs. The performance metrics show that the proposed algorithms performed better and, in some cases, were comparable to the state-of the-art algorithms. The performance metrics were also assessed subjectively for their suitability for segmentations of activated sludge images. The region-based metrics such as false negative ratio, sensitivity, and negative predictive value gave inconsistent results as compared to other segmentation assessment metrics. (C) 2015 SPIE and IS&T

Conductive Iodine-Doped Red Phosphorus Enabled Dendrite-Free Lithium Deposition on MXene Matrix

The highest theoretical capacity and lowest redox potential of lithium metal make lithium-based batteries the "holy grail" of the next-generation batteries. However, the uncontrollable dendrite growth and infinite volume change of lithium seriously hinder the real-world implementation of lithium-based batteries. Herein, a flexible MXene@iodine-doped red phosphorus (MXene@RP) paper with iodine-doped red phosphorous particles evenly distributed on the surface and interlayer of MXene matrix is designed by a simple vapor condensation reduction approach. The MXene@RP paper can be used as an efficient matrix to enable dendrite-free lithium deposition. On the one hand, the iodine doping alleviates the low conductivity shortcoming of red phosphorus, making it facilitate homogeneous lithium nucleation, thus promoting uniform lithium deposition and suppressing dendrite growth. On the other hand, the unique layered structure of conductive MXene paper provides ion transport channels and free spaces for lithium loading, alleviating the volume change induced structural damage. As a result, the MXene@RP paper with preloaded lithium exhibits long-term cycling stability. Particularly, a full cell based on Li-MXene@RP anode can maintain 81.4% of the initial capacity after 600 cycles at 4 C. The MXene@RP-based anode increases the potential applications of MXene and provides a guide for the design of dendrite-free lithium hosts.

"I miss my school!": Examining primary and secondary school students' social distancing and emotional experiences during the Covid-19 pandemic

With the rapid spread of Covid-19, countries around the world implemented strict protocols ordering schools to close. As a result, educational institutions were forced to establish a new form of schooling by implementing emergency remote education. Learning from home during the Covid-19 pandemic brought numerous changes, challenges, and stressors to students' daily lives. In this context, major concerns have been raised based on the reports of students' negative experiences resulting from social distancing and isolation. Given the impact of Covid-19 on many aspects of students' lives, in particular their social and school experiences, research that provides insights into the consequences of this health crisis for students' well-being has become important. This study aims to explore students' experiences of social distancing and its relation to their negative emotional experiences during Germany's first Covid-19-related school closure. Findings indicate that both primary and secondary students missed their friends and classmates and that primary school students perceived higher levels of social distancing. However, a linear regression analysis indicated that the older the students were, the more negatively affected they were by social distancing. The implications of the study's results and further lines of research are discussed.

Exploring Context and Content Links in Social Media: A Latent Space Method

Social media networks contain both content and context-specific information. Most existing methods work with either of the two for the purpose of multimedia mining and retrieval. In reality, both content and context information are rich sources of information for mining, and the full power of mining and processing algorithms can be realized only with the use of a combination of the two. This paper proposes a new algorithm which mines both context and content links in social media networks to discover the underlying latent semantic space. This mapping of the multimedia objects into latent feature vectors enables the use of any off-the-shelf multimedia retrieval algorithms. Compared to the state-of-the-art latent methods in multimedia analysis, this algorithm effectively solves the problem of sparse context links by mining the geometric structure underlying the content links between multimedia objects. Specifically for multimedia annotation, we show that an effective algorithm can be developed to directly construct annotation models by simultaneously leveraging both context and content information based on latent structure between correlated semantic concepts. We conduct experiments on the Flickr data set, which contains user tags linked with images. We illustrate the advantages of our approach over the state-of-the-art multimedia retrieval techniques.

Can Nature-Based Solutions (NBSs) for Stress Recovery in Green Hotels Affect Re-Patronage Intention?

Our research framework in this paper investigated natural-based solutions (NBSs) at green hotels. We employed attention restoration theory (ART) to test the mediating effect of perceived stress (PS), psychological wellness (PW), satisfaction (SA), and the moderating effect of health consciousness (HC) on re-patronage intentions (RI). Data were collected through a survey of 544 customers who frequently visited green hotels in Korea, and structural equation modeling (SEM) was used to test the research hypotheses. The findings generally supported the hypothesized associations of the study variables within our proposed theoretical framework (PS, PW, SF) in order of the mediating effect on RI and confirmed the moderating effect of HC. In addition, the study's results have important theoretical and practical implications for the environment. In the former case, our results demonstrate the application of ART and NBS by explaining the effect of the relationship among PS, PW, and SF on RI and confirm the mediating effect of the ART (PS, PW, SF) on RI, as demonstrated in previous studies. Moreover, in the latter case our results may encourage green hotels to participate in the prevention of environmental problems.

Inconsistency-Aware Uncertainty Estimation for Semi-Supervised Medical Image Segmentation

In semi-supervised medical image segmentation, most previous works draw on the common assumption that higher entropy means higher uncertainty. In this paper, we investigate a novel method of estimating uncertainty. We observe that, when assigned different misclassification costs in a certain degree, if the segmentation result of a pixel becomes inconsistent, this pixel shows a relative uncertainty in its segmentation. Therefore, we present a new semi-supervised segmentation model, namely, conservative-radical network (CoraNet in short) based on our uncertainty estimation and separate self-training strategy. In particular, our CoraNet model consists of three major components: a conservative-radical module (CRM), a certain region segmentation network (C-SN), and an uncertain region segmentation network (UC-SN) that could be alternatively trained in an end-to-end manner. We have extensively evaluated our method on various segmentation tasks with publicly available benchmark datasets, including CT pancreas, MR endocardium, and MR multi-structures segmentation on the ACDC dataset. Compared with the current state of the art, our CoraNet has demonstrated superior performance. In addition, we have also analyzed its connection with and difference from conventional methods of uncertainty estimation in semi-supervised medical image segmentation.

The complete mitochondrial genome and phylogenic analysis of Pseudobagrus vachelli

The complete mitochondrial genome of Pseudobagrus vachelli has been sequenced. The mitochondrial genome is 16 529 bp in length, with the base composition of 31.61% A, 26.88% T, 26.55% C, and 14.96% G, containing 2 ribosomal RNA genes, 13 protein-coding genes, 22 transfer RNA genes and a major non-coding control region (D-loop region). The gene order and orientation are similar with some typical fish species. The data will provide useful molecular information for phylogenetic studies concerning P. vachelli and its related species.

Modeling genetic benefits and financial costs of integrating biobanking into the conservation breeding of managed marsupials

Managed breeding programs are an important tool in marsupial conservation efforts but may be costly and have adverse genetic effects in unavoidably small captive colonies. Biobanking and assisted reproductive technologies (ARTs) could help overcome these challenges, but further demonstration of their potential is required to improve uptake. We used genetic and economic models to examine whether supplementing hypothetical captive populations of dibblers (Parantechinus apicalis) and numbats (Myrmecobius fasciatus) with biobanked founder sperm through ARTs could reduce inbreeding, lower required colony sizes, and reduce program costs. We also asked practitioners of the black-footed ferret (Mustela nigripes) captive recovery program to complete a questionnaire to examine the resources and model species research pathways required to develop an optimized biobanking protocol in the black-footed ferret. We used data from this questionnaire to devise similar costed research pathways for Australian marsupials. With biobanking and assisted reproduction, inbreeding was reduced on average by between 80% and 98%, colony sizes were on average 99% smaller, and program costs were 69- to 83-fold lower. Integrating biobanking made long-standing captive genetic retention targets possible in marsupials (90% source population heterozygosity for a minimum of 100 years) within realistic cost frameworks. Lessons from the use of biobanking technology that contributed to the recovery of the black-footed ferret include the importance of adequate research funding (US$4.2 million), extensive partnerships that provide access to facilities and equipment, colony animals, appropriate research model species, and professional and technical staff required to address knowledge gaps to deliver an optimized biobanking protocol. Applied research investment of A$133 million across marsupial research pathways could deliver biobanking protocols for 15 of Australia's most at-risk marsupial species and 7 model species. The technical expertise and ex situ facilities exist to emulate the success of the black-footed ferret recovery program in threatened marsupials using these research pathways. All that is needed now for significant and cost-effective conservation gains is greater investment by policy makers in marsupial ARTs.

Collective Intelligence Using 5G: Concepts, Applications, and Challenges in Sociotechnical Environments

Distributed intelligence is a well-known approach for optimizing interactions among numerous smart devices that interconnect and operate together as Internet of Things (IoT) systems. A modern form of human-machine collective intelligence emerges when humans interact with IoT systems in sociotechnical environments such as smart homes. Fifth-generation (5G) communication networks are designed for high-speed reliable wireless connectivity and expected to boost IoT and (distributed) collective intelligence by revolutionizing human-device-human interactions. In this paper, we contribute a comprehensive review of state-of-the-art sociotechnical environments that exhibit collective intelligence, supported by 5G-enabled IoT. We discuss the latest developments in 5G and their implications for collective intelligence. Further, we explain the key challenges for using 5G to support collective intelligence, e.g., data processing, security, and radio resource management. Finally, we describe four practical applications of collective intelligence to sociotechnical environments-road traffic control, unmanned aerial vehicles, electrical load demand response, and augmented democracy.

How to Represent Paintings: A Painting Classification Using Artistic Comments

The goal of large-scale automatic paintings analysis is to classify and retrieve images using machine learning techniques. The traditional methods use computer vision techniques on paintings to enable computers to represent the art content. In this work, we propose using a graph convolutional network and artistic comments rather than the painting color to classify type, school, timeframe and author of the paintings by implementing natural language processing (NLP) techniques. First, we build a single artistic comment graph based on co-occurrence relations and document word relations and then train an art graph convolutional network (ArtGCN) on the entire corpus. The nodes, which include the words and documents in the topological graph are initialized using a one-hot representation; then, the embeddings are learned jointly for both words and documents, supervised by the known-class training labels of the paintings. Through extensive experiments on different classification tasks using different input sources, we demonstrate that the proposed methods achieve state-of-art performance. In addition, ArtGCN can learn word and painting embeddings, and we find that they have a major role in describing the labels and retrieval paintings, respectively.

Phased arrays - Part II: Implementations, applications, and future trends

In Part I of this paper, we presented the basic architectures and theory for passive and active phased arrays. Here, we review array implementation, state-of-the-art applications, and identify future trends in phased-array technology.

Identification and validation of potential genotoxic impurities, 1,3-dichloro-2-propanol, and 2,3-dichloro-1-propanol, at subtle levels in a bile acid sequestrant, colesevelam hydrochloride, using hyphenated GC-MS technique

Potential genotoxic impurities (PGI) and N-nitrosamine impurities in active pharmaceutical ingredients (APIs) and their determination at low levels are substantial challenges for cholesterol-lowering agents in recent years. Herein we developed a robust, reliable, rapid, accurate and validated technique of gas chromatography equipped with a mass spectrometer (GC-MS) for quantifying subtle levels of 1,3-dichloro-2-propanol (PGI-I) and 2,3-dichloro-1-propanol (PGI-II) in colesevelam hydrochloride drug substance (bile acid sequestrant). The separation of colesevelam hydrochloride, PGI-I and PGI-II was executed with chromatographic technique using a capillary column, DB-624 measuring with 30 m × 0.32 mm × 1.8 μm specification of 6% cyanopropylphenyl-94% dimethylpolysiloxane copolymer and helium carrier gas. This developed technique gave a good intensity peak without any interference and extra masses at the retention times of 11.17 min for PGI-I and 11.59 min for PGI-II, which was adequate, with mass spectra (m/z) of 79 and 62, respectively. The method's sensitivity and linearity are demonstrated by its detection and quantification limits at subtle levels with correlation coefficients of 0.9965 for PGI-I and 0.9910 for PGI-II. The determination is mainly focused on improving sensitivity with the limits of detection and quantitation far below the specifications, which can support tighter limits. This results in a cost-effective and easily adoptable methodology having precise and accurate results in colesevelam hydrochloride API at subtle levels.

Multilayer X-ray optics

The principles, state of the art, and problems of multilayer X-ray optics are analysed. Among its applications, the projection X-ray lithography and mirrors for a repetitively pulsed capillary-discharge X-ray laser are considered.

Phospholipase Dε enhances Braasca napus growth and seed production in response to nitrogen availability

Phospholipase D (PLD), which hydrolyses phospholipids to produce phosphatidic acid, has been implicated in plant response to macronutrient availability in Arabidopsis. This study investigated the effect of increased PLDε expression on nitrogen utilization in Brassica napus to explore the application of PLDε manipulation to crop improvement. In addition, changes in membrane lipid species in response to nitrogen availability were determined in the oil seed crop. Multiple PLDε over expression (PLDε-OE) lines displayed enhanced biomass accumulation under nitrogen-deficient and nitrogen-replete conditions. PLDε-OE plants in the field produced more seeds than wild-type plants but have no impact on seed oil content. Compared with wild-type plants, PLDε-OE plants were enhanced in nitrate transporter expression, uptake and reduction, whereas the activity of nitrite reductase was higher under nitrogen-depleted, but not at nitrogen-replete conditions. The level of nitrogen altered membrane glycerolipid metabolism, with greater impacts on young than mature leaves. The data indicate increased expression of PLDε has the potential to improve crop plant growth and production under nitrogen-depleted and nitrogen-replete conditions.

Effects of multi-kinase inhibitors on the activity of cytochrome P450 2J2

1. Cytochrome P450 2J2 (CYP2J2) shows high expression in extrahepatic tissues, including the heart and kidney and in tumours. Inhibition of CYP2J2 has attracted attention for cancer treatment because it metabolises arachidonic acid (AA) to epoxyeicosatrienoic acid (EET), which inhibits apoptosis and promotes tumour growth. Multi-kinase inhibitor (MKI) is a molecular-targeted drug with antitumor activities. This study aimed to clarify the inhibitory effects of MKIs on CYP2J2 activity. We also investigated whether MKIs affected CYP2J2-catalysed EET formation from AA.2. Twenty MKIs showed different inhibitory potencies against astemizole O-demethylation in CYP2J2. In particular, apatinib, motesanib, and vatalanib strongly inhibited astemizole O-demethylation. These three MKIs exhibited competitive inhibition with inhibition constant (Ki) values of 9.3, 15.4, and 65.0 nM, respectively. Apatinib, motesanib, and vatalanib also inhibited CYP2J2-catalysed 14,15-EET formation from AA.3. In simulations of docking to CYP2J2, the U energy values of apatinib, motesanib, and vatalanib were low, and measured -84.5, -69.9, and -52.3 kcal/mol, respectively.4. In conclusion, apatinib, motesanib, and vatalanib strongly inhibited CYP2J2 activity, suggesting that the effects of a given CYP2J2 substrate may be altered upon the administration of these MKIs.

PCSCNet: Fast 3D semantic segmentation of LiDAR point cloud for autonomous car using point convolution and sparse convolution network

The autonomous car must recognize the driving environment quickly for safe driving. As the Light Detection And Range (LiDAR) sensor is widely used in the autonomous car, fast semantic segmentation of LiDAR point cloud, which is the point-wise classification of the point cloud within the sensor framerate, has attracted attention in recognition of the driving environment. Although the voxel and fusion-based semantic segmentation models are the state-of-the-art model in point cloud semantic segmentation recently, their real-time performance suffer from high computational load due to high voxel resolution. In this paper, we propose the fast voxel-based semantic segmentation model using Point Convolution and 3D Sparse Convolution (PCSCNet). The proposed model is designed to outperform at both high and low voxel resolution using point convolution-based feature extraction. Moreover, the proposed model accelerates the feature propagation using 3D sparse convolution after the feature extraction. The experimental results demonstrate that the proposed model outperforms the state-of-the-art real-time models in semantic segmentation of SemanticKITTI and

Computational biophysics approach towards the discovery of multi-kinase blockers for the management of MAPK pathway dysregulation

The MAPK pathway is important in human lung cancer and is improperly activated in a substantial proportion through number of ways. Strategies on dual-targeting RAF and MEK are an alternative option to diminish the limitations in this pathway inhibition. Hence, we implemented parallel pharmacophore screening of 11,808 DrugBank compounds against RAF and MEK. ADHRR and DHHRR were modeled as a pharmacophore hypothesis for RAF and MEK respectively. Importantly, these hypotheses resulted an AUC value of > 0.90 with the external data set. As a result of phase screening, glide docking, and prime-MM/GBSA scoring, it is determined that DB08424 and DB08907 have the best chances of acting as multi-kinase inhibitors. The pi-cation interaction with key amino acid residues of both target receptors may responsible for the stronger binding with these kinases. Cumulative 600 ns MD simulation studies validate the binding ability of these compounds. Significantly, the hit compounds resulted higher number of stable conformational state with less atomic movements than the reference compound against both targets. The anti-cancer efficacy of the lead compounds was validated through machine learning-based approaches. These findings suggest that DB08424 and DB08907 might be novel molecules to be explored further experimentally to block the MAPK signaling in lung cancer patients.

Hierarchical pattern matching for anomaly detection in time series

As companies rely on an ever increasing number of connected devices for their day to day operations, a need arises for automated anomaly detectors to constantly observe crucial device metrics in real time to prevent downtime and data loss. As production environments tend to monitor a huge amount of these metrics, it prevents current state-of-the-art techniques to be deployed as the required computational resources is too high. This paper proposes a lightweight anomaly detection method that can be deployed in these environments without a reduction in accuracy. The approach works fully online, and does not require an extensive history set to be kept in memory. The method is benchmarked on the publicly available Numenta dataset, as well as a network monitoring dataset from different environments provided by a network management solution vendor. These benchmarks show the proposed technique to be very competitive with the current state-of-the-art and exceeding it in production applicability.

Uncommon isolated distant subcutaneous tissue and skeletal muscle metastasis from oesophageal cancer diagnosed by PET/CT (18)F-FDG

Distant soft-tissue metastases (subcutaneous tissues and skeletal muscle) are extremely rare, particularly in oesophageal carcinoma. The case is described of a patient who was treated for oesophageal adenocarcinoma 2.5 years previously. A PET/CT was performed showing metastatic spread due to a solitary focus of increased tracer uptake corresponding to one subcutaneous node in the upper abdomen. An excisional biopsy showed a metastasis from the carcinoma. Restaging PET/CT (18)F-FDG study was performed 2 year later, demonstrating foci of increased uptake within several muscles as isolated distant haematogenous spread of metastases, histopathologically confirmed. As most of soft-tissue metastases are asymptomatic, the physicians should recommend a histopathological study of focal FDG uptake at subcutaneous tissues and/or skeletal muscles, because they may be the first sign of disease spread, so therapeutic management of these patients could be changed.

Catatonia in inpatients with psychiatric disorders: A comparison of schizophrenia and mood disorders

This study aimed to evaluate the symptom threshold for making the diagnosis of catatonia. Further the objectives were to (1) to study the factor solution of Bush Francis Catatonia Rating Scale (BFCRS); (2) To compare the prevalence and symptom profile of catatonia in patients with psychotic and mood disorders among patients admitted to the psychiatry inpatient of a general hospital psychiatric unit. 201 patients were screened for presence of catatonia by using BFCRS. By using cluster analysis, discriminant analysis, ROC curve, sensitivity and specificity analysis, data suggested that a threshold of 3 symptoms was able to correctly categorize 89.4% of patients with catatonia and 100% of patients without catatonia. Prevalence of catatonia was 9.45%. There was no difference in the prevalence rate and symptom profile of catatonia between those with schizophrenia and mood disorders (i.e., unipolar depression and bipolar affective disorder). Factor analysis of the data yielded 2 factor solutions, i.e., retarded and excited catatonia. To conclude this study suggests that presence of 3 symptoms for making the diagnosis of catatonia can correctly distinguish patients with and without catatonia. This is compatible with the recommendations of DSM-5. Prevalence of catatonia is almost equal in patients with schizophrenia and mood disorders.

A Comparative Study on Various State of the Art Face Recognition Techniques under Varying Facial Expressions

Through face we can know the emotions and feelings of a person. It can also be used to judge a person's mental aspect and psychomatic aspects. There are 5 state of the art approaches for recognizing faces under varying facial expressions. These 5 approaches are overlapping Discrete Cosine Transform (DCT), Hierarchical Dimensionality Reduction (HDR), Local and Global combined Computational Features (LGCF), Combined Statistical Moments (CSM), and Score Level Fusion Techniques (SLFT). Matlab code has been developed for all the 5 systems and tested using common set of train and test images. The train and test images are considered from standard public face databases ATT, JAFFE, and FEI. The key contribution of this article is, we have developed and analyzed the 5 state of the art approaches for recognizing faces under varying facial expressions using a common set of train and test images. This evaluation gives us the exact face recognition rates of the 5 systems under varying facial expressions. The face recognition rate of overlap DCT on ATT database was 95% and FEI 99% which was better than HDR, LGCM, CSM and SLFT. But the face recognition rate of CSM on JAFE database, which contains major facial expression variations, was 100% which was better than overlap DCT, HDR, LGCM, and SLFT.

Wetting Ridge-Guided Directional Water Self-Transport

Directional water self-transport plays a crucial role in diverse applications such as biosensing and water harvesting. Despite extensive progress, current strategies for directional water self-transport are restricted to a short self-driving distance, single function, and complicated fabrication methods. Here, a lubricant-infused heterogeneous superwettability surface (LIHSS) for directional water self-transport is proposed on polyimide (PI) film through femtosecond laser direct writing and lubricant infusion. By tuning the parameters of the femtosecond laser, the wettability of PI film can be transformed into superhydrophobic or superhydrophilic. After trapping water droplets on the superhydrophilic surface and depositing excess lubricant, the asymmetrical wetting ridge drives water droplets by an attractive capillary force on the LIHSS. Notably, the maximum droplet self-driving distance can approach ≈3 mm, which is nearly twice as long as the previously reported strategies for direction water self-transport. Significantly, it is demonstrated that this strategy makes it possible to achieve water self-transport, anti-gravity pumping, and chemical microreaction on a tilted LIHSS. This work provides an efficient method to fabricate a promising platform for realizing directional water self-transport.

Demonstration of pH imaging in acute stroke with endogenous ratiometric chemical exchange saturation transfer magnetic resonance imaging at 2 ppm

pH change is often considered a hallmark of metabolic disruption in diseases such as ischemic stroke and cancer. Chemical exchange saturation transfer (CEST) MRI, particularly amide proton transfer (APT), has emerged as a noninvasive pH imaging approach. However, there are changes in multipool CEST effects besides APT MRI. Our study investigated radiofrequency (RF) amplitude-based ratiometric CEST pH imaging in acute stroke. Briefly, adult male Wistar rats underwent CEST MRI under two RF saturation (B1 ) levels of 0.75 and 1.5 μT following middle cerebral artery occlusion. Magnetization transfer (MT), direct water saturation, CEST at 2 ppm (CEST@2 ppm), amine (2.75 ppm), and APT (3.5 ppm) effects were resolved with the multipool Lorentzian fitting approach. The ratiometric analysis was measured in the ischemic lesion and the contralateral normal area, which was also correlated with pH-specific MT and the relaxation normalized APT (MRAPT) index. MT, amine CEST effect, and their respective ratiometric indices did not show significant changes in ischemic regions (p > 0.05), as expected. Whereas APT decreased in the ischemic lesion for B1 of 1.5 μT (p < 0.01), the correlation between the amide ratio with MRAPT index was moderate (r = 0.52, p = 0.02). By comparison, the ischemic tissue showed a significantly increased CEST@2 ppm for both saturation levels from the contralateral normal area (p ≤ 0.01). Importantly, the CEST@2 ppm ratio decreased in the ischemic lesion (p < 0.01), which highly correlated with the MRAPT index (r = 0.93, p < 0.001). To summarize, our study demonstrated the feasibility of endogenous CEST@2 ppm ratiometric imaging of pH upon acute stroke, promising to detect pH changes in metabolic diseases.

State of the art in flow visualization in the environmental sciences

Flow plays a major role in environmental sciences, because many of the Earth's physical and biological processes involve movement. Yet, there are major differences between theoretically available and practically applied visualization techniques to represent flow. This paper surveys various techniques in computational and environmental flow visualization. Techniques from the computational flow visualization community are classified into geometric, texture-based, topology-based, and feature-based approaches. Environmental flow applications are categorized into four application domains (atmospheric science, ecology, geosciences, and urban environments). Computational and environmental visualization approaches are compared to exhibit gaps and suggest solutions on how to bridge the gap. Outcomes from this literature review will inform the development of strategic initiatives for both future flow visualization research and flow visualization in the environmental sciences.

Estimation of monkeypox spread in a nonendemic country considering contact tracing and self-reporting: A stochastic modeling study

In May 2022, monkeypox started to spread in nonendemic countries. To investigate contact tracing and self-reporting of the primary case in the local community, a stochastic model is developed. An algorithm based on Gillespie's stochastic chemical kinetics is used to quantify the number of infections, contacts, and duration from the arrival of the primary case to the detection of the index case (or until there are no more local infections). Different scenarios were set considering the delay in contact tracing and behavior of infectors. We found that the self-reporting behavior of a primary case is the most significant factor affecting outbreak size and duration. Scenarios with a self-reporting primary case have an 86% reduction in infections (average: 5-7, in a population of 10 000) and contacts (average: 27-72) compared with scenarios with a non-self-reporting primary case (average number of infections and contacts: 27-72 and 197-537, respectively). Doubling the number of close contacts per day is less impactful compared with the self-reporting behavior of the primary case as it could only increase the number of infections by 45%. Our study emphasizes the importance of the prompt detection of the primary case.

MPPT in Dynamic Condition of Partially Shaded PV System by Using WODE Technique

This paper introduces a humpback whale hunting behavior inspired whale optimization with differential evolution (WODE) technique-based tracking algorithm for the maximum power point tracking in the dynamic as well as the steady-state conditions of a partially shaded solar photovoltaic (PV) system. This "WODE" technique is used for quick and oscillation-free tracking of the global best peak position in a few steps. The unique advantage of this algorithm for maximum power point tracking in partially shaded condition is as, it is free from common and generalized problems of other evolutionary techniques, like longer convergence duration, a large number of search particles, steadystate oscillation, heavy computational burden, etc., which creates power loss and oscillations in output. This hybrid algorithm is tested in MATLAB simulation and verified on a developed hardware of the solar PV system, which consists of multiple peaks in voltage-power curve. Moreover, the tracking ability is compared with the state-of-the-art methods. The satisfactory steady-state and dynamic performances of the new hybrid technique under variable irradiance and temperature levels show the superiority over the state-of-the-art control methods.

X-ray spectrometry imaging and chemical speciation assisting to understand the toxic effects of copper oxide nanoparticles on zebrafish ( Danio rerio)

Currently, copper nanoparticles are used in various sectors of industry, agriculture, and medicine. To understand the effects induced by these nanoparticles, it is necessary to assess the environmental risk and safely expand their use. In this study, we evaluated the toxicity of copper oxide (nCuO) nanoparticles in Danio rerio adults, their distribution/concentration, and chemical form after exposure. This last assessment had never been performed on copper-exposed zebrafish. Such evaluation was done through the characterization of nCuO, acute exposure tests and analysis of distribution and concentration by microstructure X-ray fluorescence spectroscopy (µ-XRF) and atomic absorption spectroscopy (GF-AAS). Synchrotron X-ray absorption spectroscopy (XAS) was performed to find out the chemical form of copper in hotspots. The results show that the toxicity values of fish exposed to nCuO were 2.4 mg L-1 (25 nm), 12.36 mg L-1 (40 nm), 149.03 mg L-1 (80 nm) and 0.62 mg L-1 (CuSO4, used as a positive control). The total copper found in the fish was in the order of mg kg-1 and it was not directly proportional to the exposure concentration; most of the copper was concentrated in the gastric system. However, despite the existence of copper hotspots, chemical transformation of CuO into other compounds was not detected.

In vivo biocompatibility of SrO and MgO doped brushite cements

The addition of dopants in biomaterials has emerged as a critical regulator of bone formation and regeneration due to their imminent role in the biological process. The present work evaluated the role of strontium (Sr) and magnesium (Mg) dopants in brushite cement (BrC) on in vivo bone healing performance in a rabbit model. Pure, 1 wt% SrO (Sr-BrC), 1 wt% MgO (Mg-BrC), and a binary composition of 1.0 wt% SrO + 1.0 wt% MgO (Sr + Mg-BrC) BrCs were implanted into critical-sized tibial defects in rabbits for up to 4 months. The in vivo bone healing of three doped and pure BrC samples was examined and compared using sequential radiological examination, histological evaluations, and fluorochrome labeling studies. The results indicated excellent osseous tissue formation for Sr-BrC and Sr + Mg-BrC and moderate bone regeneration for Mg-BrC compared to pure BrC. Our findings indicated that adding small amounts of SrO, MgO, and binary dopants to the BrC can significantly influence new bone formation for bone tissue engineering.

Reweighted Infrared Patch-Tensor Model With Both Nonlocal and Local Priors for Single-Frame Small Target Detection

Many state-of-the-art methods have been proposed for infrared small target detection. They work well on the images with homogeneous backgrounds and high-contrast targets. However, when facing highly heterogeneous backgrounds, they would not perform very well, mainly due to: 1) the existence of strong edges and other interfering components, 2) not utilizing the priors fully. Inspired by this, we propose a novel method to exploit both local and nonlocal priors simultaneously. First, we employ a new infrared patch-tensor (IPT) model to represent the image and preserve its spatial correlations. Exploiting the target sparse prior and background nonlocal self-correlation prior, the target-background separation is modeled as a robust low-rank tensor recovery problem. Moreover, with the help of the structure tensor and reweighted idea, we design an entrywise local-structure-adaptive and sparsity enhancing weight to replace the globally constant weighting parameter. The decomposition could be achieved via the elementwise reweighted higher order robust principal component analysis with an additional convergence condition according to the practical situation of target detection. Extensive experiments demonstrate that our model outperforms the other state-of-the-arts, in particular for the images with very dim targets and heavy clutters.

Attention Network for Non-Uniform Deblurring

Recently, image deblurring task is valuable and challenging in computer vision. However, existing learning-based methods can not produce satisfactory results, such as lacking of salient structures and fine details. In this paper, we propose a solution to transform spatially variant blurry images into the photo-realistic sharp manifold. In this paper, we investigate an attention network for image deblurring. Instead of relying on local receptive fields to construct features by previous state-of-the-art methods, the non-local features for capturing long-range dependencies and the local features rely on receptive fields should be jointly considered. Therefore, we propose a novel dense feature fusion block that consists of a channel attention module and a pixel attention module. In addition, we further densely connected multiple dense feature fusion blocks to acquire high-order feature representation. Moreover, a scale attention module is further introduced for removing unfavorable features while retaining features that facilitate image recovery. Comprehensive experimental results show that our method is able to generate photo-realistic sharp images from real-world blurring images and outperforms state-of-the-art methods.

Unsupervised Semantic Similarity Computation between Terms Using Web Documents

In this work, Web-based metrics that compute the semantic similarity between words or terms are presented and compared with the state of the art. Starting from the fundamental assumption that similarity of context implies similarity of meaning, relevant Web documents are downloaded via a Web search engine and the contextual information of words of interest is compared (context-based similarity metrics). The proposed algorithms work automatically, do not require any human-annotated knowledge resources, e.g., ontologies, and can be generalized and applied to different languages. Context-based metrics are evaluated both on the Charles-Miller data set and on a medical term data set. It is shown that context-based similarity metrics significantly outperform co-occurrence-based metrics, in terms of correlation with human judgment, for both tasks. In addition, the proposed unsupervised context-based similarity computation algorithms are shown to be competitive with the state-of-the-art supervised semantic similarity algorithms that employ language-specific knowledge resources. Specifically, context-based metrics achieve correlation scores of up to 0.88 and 0.74 for the Charles-Miller and medical data sets, respectively. The effect of stop word filtering is also investigated for word and term similarity computation. Finally, the performance of context-based term similarity metrics is evaluated as a function of the number of Web documents used and for various feature weighting schemes.

Studying digital imagery of ancient paintings by mixtures of stochastic models

This paper addresses learning-based characterization of fine art painting styles. The research has the potential to provide a powerful tool to art historians for studying connections among artists or periods in the history of art. Depending on specific applications, paintings can be categorized in different ways. In this paper, we focus on comparing the painting styles. of artists. To profile the style of an artist, a mixture of stochastic models is estimated using training images. The two-dimensional (2-D) multiresolution hidden Markov model (MHMM) is used in the experiment. These models form an artist's distinct digital signature. For certain types of paintings, only strokes provide reliable information to distinguish artists. Chinese ink paintings are a prime example of the above phenomenon; they do not have colors or even tones. The 2-D MHMM analyzes relatively large regions in an image, which in turn makes it more likely to capture properties of the painting strokes. The mixtures of 2-D MHMMs established for artists can be further used to classify paintings and compare paintings or artists. We implemented and tested the system using high-resolution digital photographs of some of China's most renowned artists. Experiments have demonstrated good potential of our approach in automatic analysis of paintings. Our work can be applied to other domains.