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Emerging and Versatile Platforms of Metal-Ion-Doped Carbon Dots for Biosensing, Bioimaging, and Disease Therapy

Metal ions possess abundant electrons and unoccupied orbitals, as well as large atomic radii, whose doping into carbon dots (CDs) is a facile strategy to endow CDs with additional physicochemical characteristics. After being doped with metal ions, CDs reveal obvious changes in their optical, electronic, and magnetic properties by adjustments to their electron density distribution and the energy gaps, leading them to be promising and competitive candidates as labeling probes, imaging agents, catalysts, nanodrugs, and so on. In this review, we summarize the fabrication methods of metal-ion-doped CDs (M-CDs), and highlight their biological applications including biosensing, bioimaging, tumor therapy, and anti-microbial treatment. Finally, the challenging future perspectives of M-CDs are analyzed. We hope this review will provide inspiration for further development of M-CDs in various biological aspects, and help readers who are interested in M-CDs and their biological applications.

The small heat shock protein, HSP30, is associated with aggresome-like inclusion bodies in proteasomal inhibitor-, arsenite-, and cadmium-treated Xenopus kidney cells

In the present study, treatment of Xenopus laevis A6 kidney epithelial cells with the proteasomal inhibitor, MG132, or the environmental toxicants, sodium arsenite or cadmium chloride, induced the accumulation of the small heat shock protein, HSP30, in total and in both soluble and insoluble protein fractions. Immunocytochemical analysis revealed the presence of relatively large HSP30 structures primarily in the perinuclear region of the cytoplasm. All three of the stressors promoted the formation of aggresome-like inclusion bodies as determined by immunocytochemistry and laser scanning confocal microscopy using a ProteoStat aggresome dye and additional aggresomal markers, namely, anti-γ-tubulin and anti-vimentin antibodies. Further analysis revealed that HSP30 co-localized with these aggresome-like inclusion bodies. In most cells, HSP30 was found to envelope or occur within these structures. Finally, we show that treatment of cells with withaferin A, a steroidal lactone with anti-inflammatory, anti-tumor, and proteasomal inhibitor properties, also induced HSP30 accumulation that co-localized with aggresome-like inclusion bodies. It is possible that proteasomal inhibitor or metal/metalloid-induced formation of aggresome-like inclusion bodies may sequester toxic protein aggregates until they can be degraded. While the role of HSP30 in these aggresome-like structures is not known, it is possible that they may be involved in various aspects of aggresome-like inclusion body formation or transport.

Motion Blur Kernel Estimation via Deep Learning

The success of the state-of-the-art deblurring methods mainly depends on the restoration of sharp edges in a coarse-to-fine kernel estimation process. In this paper, we propose to learn a deep convolutional neural network for extracting sharp edges from blurred images. Motivated by the success of the existing filtering-based deblurring methods, the proposed model consists of two stages: suppressing extraneous details and enhancing sharp edges. We show that the two-stage model simplifies the learning process and effectively restores sharp edges. Facilitated by the learned sharp edges, the proposed deblurring algorithm does not require any coarse-to-fine strategy or edge selection, thereby significantly simplifying kernel estimation and reducing computation load. Extensive experimental results on challenging blurry images demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods on both synthetic and real-world images in terms of visual quality and run-time.

Polymeric stabilizers for protection of soil and ground against wind and water erosion

The article is devoted to the design, development and application of a new generation of binders for various dispersed systems, including soil, ground, sand, waste rock and others. The binders are formed by interaction of oppositely charged polyelectrolytes, both chemically stable and (bio)degradable. The fundamental aspects of interpolyelectrolyte reactions are discussed; the IPC structure and properties of the resulting interpolyelectrolyte complexes (IPCs) allow considering them as unique and universal binders. Numerous results of laboratory experiments and field trials of the IPC formulations are presented. In particular, large-scale tests have been done in the Chernobyl accident zone where the IPC binders were shown to be effective means to suppress water and wind erosion thereby preventing a spread of radioactive particles (radionuclides) from contaminated sites. Ecologically friendly IPC compositions are described, including those based on commercially available polymers; prospects for improving their efficiency and extending the range of their possible use are discussed.

Contralaterally Enhanced Networks for Thoracic Disease Detection

Identifying and locating diseases in chest X-rays are very challenging, due to the low visual contrast between normal and abnormal regions, and distortions caused by other overlapping tissues. An interesting phenomenon is that there exist many similar structures in the left and right parts of the chest, such as ribs, lung fields and bronchial tubes. This kind of similarities can be used to identify diseases in chest X-rays, according to the experience of broad-certificated radiologists. Aimed at improving the performance of existing detection methods, we propose a deep end-to-end module to exploit the contralateral context information for enhancing feature representations of disease proposals. First of all, under the guidance of the spine line, the spatial transformer network is employed to extract local contralateral patches, which can provide valuable context information for disease proposals. Then, we build up a specific module, based on both additive and subtractive operations, to fuse the features of the disease proposal and the contralateral patch. Our method can be integrated into both fully and weakly supervised disease detection frameworks. It achieves 33.17 AP50 on a carefully annotated private chest X-ray dataset which contains 31,000 images. Experiments on the NIH chest X-ray dataset indicate that our method achieves state-of-the-art performance in weakly-supervised disease localization.

Biodeterioration of Concrete Sewer Pipes: State of the Art and Research Needs

Biodeterioration of concrete sewers is a common problem that results in concrete disintegration and significant damage. Two stages are normally identifiable in the process: an initiation stage, during which the concrete pore water pH is reduced from an initial value of over 12 to a value of about 9 by ingress of hydrogen sulfide gas and carbon dioxide (carbonation) and subsequent reaction with the hydrated cement paste; and a second stage, active biodeterioration, during which microorganisms excrete sulfuric acid that attacks the hydrated cement paste (HCP). Various national codes and standards provide some guidance to mitigate this problem. Sulfur-oxidizing microorganisms growing on the sewer walls have been isolated as the main culprit, but other phenomena come into play before biodeterioration occurs. Steady-state biodeterioration rates of 3 mm/year have been proposed in the literature. A detailed literature review was conducted to evaluate the various aspects of concrete biodeterioration and determine further research needs. This state-of-the-art report summarizes the deterioration mechanisms involved in biodeterioration of concrete sewers and the work necessary to complete the modeling of this phenomenon. A conceptual model of the processes is presented. (C) 2011 American Society of Civil Engineers.

Telling impressions: Breuil's 1929 visit to rock art and Stone Age sites near Kimberley, South Africa

Stone Age and rock art sites that Breuil would visit near Kimberley, South Africa, during and after the 1929 conference of the British and South African Associations for the Advancement of Science, were amongst the first he would see in Africa. Breuil's published observations and an autobiographical account afford a keyhole view on his initial thoughts with hints of ways these would develop, alongside and in relation to the perspectives of contemporaries. The roles of Breuil's field and editorial helpers are highlighted for the insights they provide on his modus operandi. Pratt's concept of 'discursive polyphony' is advanced as a useful frame for understanding the range of shifting perspectives that are entangled in these histories. Opportunities are sought, in discussion, to reflect on the Breuil legacy in light of subsequent work in the region.

Haploidentical transplant for paediatric patients with severe thalassaemia using post-transplant cyclophosphamide and methotrexate: A prospectively registered multicentre trial from the Bone Marrow Failure Working Group of Hunan Province, China

Haploidentical transplantation strategies for patients with transfusion-dependent thalassaemia (TD-TM) remain to be investigated. In this study, 54 paediatric patients with TD-TM were treated with a novel approach using post-transplant cyclophosphamide (PTCy) and low-dose methotrexate (LD-MTX), following a myeloablative regimen. The incidence of neutrophil and platelet engraftment was 96.3% ± 2.6% and 94.4% ± 3.1% respectively. The cumulative incidence of grades II-III acute graft-versus-host disease (GVHD) was 13.8% ± 4.8% at 100 days. At three years, the cumulative incidence of chronic GVHD was 28.5% ± 8.5%. With a median follow-up of 520 days (132-1325 days), the overall survival (OS) and event-free survival (EFS) were 98.1% ± 1.8% and 90.7% ± 3.9% respectively. Compared with the low-dose cyclophosphamide (CTX) conditioning regimen (120 mg/kg), the high-CTX regimen (200 mg/kg) achieved a higher incidence of stable engraftment (100% vs 66.7% ± 15.7%, p = 0.003), a comparable incidence of grades II-III acute GVHD, a lower incidence of chronic GVHD (20.2% ± 8.3% vs 66.6% ± 19.2%, p = 0.011), and better overall survival (100% vs 88.9% ± 10.5%, p = 0.025) as well as EFS (95.6% ± 3.1% vs 66.7% ± 15.7%, p = 0.008). Our results using unmanipulated haploidentical grafts and PTCy with LD-MTX in TD-TM are encouraging. (chictr.org.cn ChiCTR1800017969).

A comparative economic analysis of torrefied pellet production based on state-of-the-art pellets

Torrefied pellets have fuel properties superior to those of conventional wood pellets and potentially allow greater rates of co-firing and thus larger reductions in net CO2 emissions. Despite the growing amount of scientific output on torrefaction, the economic feasibility of torrefied pellet production is still a topic of considerable uncertainty. This is an obstacle for decision makers looking to implement sustainable energy policies. This paper compares the economics of torrefied pellets to conventional wood pellets. Working backwards from demonstrated pellet properties, this work attempts to answer the following question: Based on state-of-the-art torrefied pellets, what would be the maximum capital investment required for a torrefied pellet plant so that production is economically viable? Herein, the production costs of torrefied pellets are calculated based on inputs in production. The market value of the produced pellets is estimated and a cash-flow analysis is carried out. Three economic indicators are calculated and compared for a torrefied and conventional pellet production scenario. A sensitivity analysis is carried out for selected process inputs and the cost of CO2 through co-firing pellets is estimated. The results indicate that state-of-the-art torrefied pellet production cannot compete with conventional pellets even with transatlantic product transport distances. A high capital investment cost and a low heating value are the main barriers to economic feasibility of state-of-the-art torrefied pellets. (C) 2016 Elsevier Ltd. All rights reserved.

Socio-technical energy scenarios: state-of-the-art and CIB-based approaches

Energy conversion is a major source of greenhouse gas (GHG) emissions, and energy transition scenarios are a key tool for gaining a greater understanding of the possible pathways toward climate protection. There is consensus in energy research that political and societal framework conditions will play a pivotal role in shaping energy transitions. In energy scenario construction, this perspective is increasingly acknowledged through the approach of informing model-based energy analysis with storylines about societal futures, an exercise we call "socio-technical energy scenario construction" in this article. However, there is a dispute about how to construct the storylines in a traceable, consistent, comprehensive, and reproducible way. This study aims to support energy researchers considering the use of the concept of socio-technical scenarios in two ways: first, we provide a state-of-the-art analysis of socio-technical energy scenario construction by comparing 16 studies with respect to five categories. Second, we address the dispute regarding storyline construction in energy research and examine 13 reports using the Cross-Impact Balances method. We collated researcher statements on the strengths and challenges of this method and identified seven categories of promises and challenges each.

Multiorientation scene text detection via coarse-to-fine supervision-based convolutional networks

Text detection in natural scenes has long been an open challenge and a lot of approaches have been presented, in which the deep learning-based methods have achieved state-of-the-art performance. However, most of them merely use coarse-level supervision information, limiting the detection effectiveness. We propose a deep method utilizing coarse-to-fine supervisions for multiorientation scene text detection. The coarse-to-fine supervisions are generated in three levels: coarse text region (TR), text central line, and fine character shape. With these multiple supervisions, the multiscale feature pyramids and deeply supervised nets are integrated in a unified architecture, and the corresponding convolutional kernels are learned jointly. An effective top-down pipeline is developed to obtain more precise text segmentation regions and their relationship from coarse TR. In addition, the proposed method can handle texts in multiple orientations and languages. Four public datasets, i.e., ICDAR2013, MSRA-TD500, USTB, and street view text dataset, are used to evaluate the performance of our proposed method. The experimental results show that our method achieves the state-of-the-art performance. (C) 2018 SPIE and IS&T

Comparison of efficacy, safety and satisfaction of latanoprost versus minoxidil, betamethasone and in combination in patients with alopecia areata: A blinded multiple group randomized controlled trial

Alopecia areata (AA), a polygenic and chronic autoimmune disease and there is no definitive cure. We aimed to evaluate latanoprost effects in patients with AA. In this controlled randomized double-blind clinical trial, we enrolled patients with AA randomly assigned to six groups of 18; Group 1 received latanoprost eye drops; group 2 minoxidil 5% solution; group 3 latanoprost eye drops and minoxidil 5% solution; group 4 betamethasone and minoxidil 5% solution; group 5 betamethasone solution and latanoprost eye drops; group 6 (the control group) betamethasone solution. The alopecia severity in patients before and after treatment was assessed by severity of alopecia tool (SALT). One hundred and eight patients, 50% male (mean age: 32.6 ± 10.4) were studied. The overall SALT score decreased in all. After 2 weeks, patients receiving betamethason-minoxidil and betamethason-latanoprost showed more decline in their SALT than other groups. In final, there was statistically significant difference among betamethasone-latanoprost group with minoxidil, betamethasone-minoxidil and betamethasone groups. Regrowth was higher in latanoprost and betamethasone-latanoprost groups than minoxidil. Topical latanoprost added to therapeutic efficacy of topical betamethason and minoxidil in treating patchy AA, suggesting it being beneficial and safe adjuvant therapy and add to efficacy of topical treatments without any adverse effects.

BRAPT: A New Metric for Translation Evaluation Based on Psycholinguistic Perspectives

There are some metrics to evaluate automatic text translations in the literature. However, the state-of-the-art of these metrics still has limitations. One of them is the dependence of an exact and ordered pairing of words for evaluating similarity among texts. Another, is the non-consideration of the semantics of the text in such comparison. Previous studies point out the need to analyze the semantics of words in the evaluation of translations. In this scenario, this paper presents a novel metric capable of evaluating the differences in automatic text translations that takes into account the semantics of the words presented in the texts. As a proof of concept, we selected ten journalistic texts written in English. These texts have been translated to Portuguese by specialists and by automatic text translation tools. Experimental results show the potential of the proposed metric in evaluating these translations, indicating it can perform better than the state-of-the-art metric.

Highly fluorescent nickel based metal organic framework for enhanced sensing of Fe3+ and Cr2O72- ions

Heavy metal contamination has sparked widespread concern among the populace. The significant issues necessitate the creation of high-performance fluorescent pigments that can identify harmful elements in water. The present study deals with metal organic framework [MOF] based on nickel [Ni-BDC MOF]. The Ni-BDC MOF was prepared by facile solvothermal method using nickel nitrate hexahydrate and terephthalic acid ligand as precursors. The MOF was characterized by various techniques in order to examine the crystal, morphological, structural, composition, thermal and optical properties. The detailed characterizations revealed that the synthesized Ni-BDC MOF are well-crystalline with high purity and possessing 3D rhombohedral microcrystals with rough surface. The MOF demonstrate good luminescence performance and excellent water stability. According to the Stern Volmer plot, the tests set up under optimized conditions demonstrate a linear correlation between the fluorescence intensity and concentration of both ions, i.e. Fe3+, and Cr2O72- ions. The linear range and detection limit for Fe3+ and Cr2O72- were found to be 0-1.4 nM and 0.159 nM, and 0-1 nM and 0.120 nM, respectively. The mechanisms for the selective detection of cations and anions were also explored. The recyclability for the prepared MOF was checked up to five cycles which showed excellent stability with just a slight reduction in efficiency. The constructed sensor was also used to assess the presence of Fe3+ and Cr2O72- ions in actual water samples. The results of the different experiments revealed that the prepared MOF is a good material for detecting Fe3+ and Cr2O72- ions.

Predictive Set Point Modulation Charging of Autonomous Rail Transit Vehicles

Autonomous rail transit (ART) vehicle is a new type of urban rail transportation, which has good development prospects. It is powered by onboard supercapacitors, which are charged at midway stations. It requires short charging time and fast charging speed. Usually, multiple chargers are used in parallel for charging. However, this will cause an overshoot phenomenon during charging, and the overshoot of multiple chargers will be superimposed on the supercapacitor, affecting the stability and life of both supercapacitors and chargers. In this paper, we propose a predictive set point modulation charging method, which can reduce the system's overshoot and increase the reliability of the system. First, the state-space averaging method is used to establish the electronic physical model of the multicharger system. Secondly, a predictive set point modulation charging control method is designed, and the closed-loop model of the proposed charging system is developed using the buck diagram. The effectiveness of the proposed method is verified through extensive simulation and experiments. The experimental results show that compared with the classical design method, the proposed method can effectively suppress the current overshoot.

Adopting the YOLOv4 Architecture for Low-Latency Multispectral Pedestrian Detection in Autonomous Driving

Detecting pedestrians in autonomous driving is a safety-critical task, and the decision to avoid a a person has to be made with minimal latency. Multispectral approaches that combine RGB and thermal images are researched extensively, as they make it possible to gain robustness under varying illumination and weather conditions. State-of-the-art solutions employing deep neural networks offer high accuracy of pedestrian detection. However, the literature is short of works that evaluate multispectral pedestrian detection with respect to its feasibility in obstacle avoidance scenarios, taking into account the motion of the vehicle. Therefore, we investigated the real-time neural network detector architecture You Only Look Once, the latest version (YOLOv4), and demonstrate that this detector can be adapted to multispectral pedestrian detection. It can achieve accuracy on par with the state-of-the-art while being highly computationally efficient, thereby supporting low-latency decision making. The results achieved on the KAIST dataset were evaluated from the perspective of automotive applications, where low latency and a low number of false negatives are critical parameters. The middle fusion approach to YOLOv4 in its Tiny variant achieved the best accuracy to computational efficiency trade-off among the evaluated architectures.

A brief morning rest period benefits cardiac repair in pressure overload hypertrophy and postmyocardial infarction

Rest has long been considered beneficial to patient healing; however, remarkably, there are no evidence-based experimental models determining how it benefits disease outcomes. Here, we created an experimental rest model in mice that briefly extends the morning rest period. We found in 2 major cardiovascular disease conditions (cardiac hypertrophy, myocardial infarction) that imposing a short, extended period of morning rest each day limited cardiac remodeling compared with controls. Mechanistically, rest mitigates autonomic-mediated hemodynamic stress on the cardiovascular system, relaxes myofilament contractility, and attenuates cardiac remodeling genes, consistent with the benefits on cardiac structure and function. These same rest-responsive gene pathways underlie the pathophysiology of many major human cardiovascular conditions, as demonstrated by interrogating open-source transcriptomic data; thus, patients with other conditions may also benefit from a morning rest period in a similar manner. Our findings implicate rest as a key driver of physiology, creating a potentially new field - as broad and important as diet, sleep, or exercise - and provide a strong rationale for investigation of rest-based therapy for major clinical diseases.

Copper-Catalyzed 1,2-Dicarbonylative Cyclization of Alkenes with Alkyl Bromides via Radical Cascade Process

Herein, we developed a new procedure on 1,2-dicarbonylative cyclization of 4-aryl-1-butenes with alkyl bromides. Using simple copper catalyst, two molecules of carbon monoxide were introduced into the double bond with the formation of four new C-C bonds and a new ring. Various α-tetralones and 2,3-dihydroquinolin-4-ones were formed in moderate to good yields.

Inter-Dimensional Correlations Aggregated Attention Network for Action Recognition

Recently, 3D Convolutional Neural Network (3D-CNN) models with attention mechanisms have been widely studied in action recognition tasks. Although most of these methods explore the spatial, temporal and channel attention for action recognition, the inter-correlations over spatial, temporal and channel are not fully exploited. In this paper, we introduce a novel inter-dimensional correlations aggregated attention (ICAA) network that extracts inter-correlations between two dimensions in spatial, temporal and channel, and inter-spatial-temporal-channel correlations to obtain more comprehensive correlations. The proposed ICAA module can be wrapped as a generic module easily plugged into the state-of-the-art 3D-CNN models as well as multi-stream architectures for video action recognition. We extensively evaluate our method on action recognition tasks over UCF-101 and HMDB-51 datasets, and the experimental results demonstrate that adding our ICAA module can obtain state-of-the-art performance on UCF-101 and HMDB-51, which has the performance of 98.4% and 81.9% respectively, and achieve significant improvement compared against original models.

Introducing Patents with Indirect Connection (PIC) for Establishing Patent Strategies

A patent system requires novelty and progressiveness so that new patents do not infringe on the rights of prior art. Patent investigation including a prior art search is essential to the process of commercialization of technology. In general, patent investigation has been conducted by experts based on their qualitative judgement. However, the number of patents has increased so fast that it has become difficult to handle the quantitative burdens of the search with a conventional approach. There have been previous studies dealing with patent investigation to find similar technologies. They had limitations as they did not utilize the citation relationship and similarity between patents in a comprehensive way. In addition, they could not properly reflect the sequential citation relationship of patents though this is effective in discovering similar patents. In this study, we propose an efficient methodology to discover similar technologies by comprehensively considering the similarity and citation relationship between patents. In particular, we intended to reflect the citation sequence and indirect citation relationship in the process of searching for similar patents. For this, we introduced the concept of "patents with indirect connections" (PICs) and devised an algorithm to efficiently detect patent pairs having such a relationship. The proposed methodology of this study contributes to preventing patent litigation in advance by discovering patents with such potential risks. It is expected that this method will provide patent applicants with the opportunity to establish appropriate strategies against competitors with similar technologies. In order to examine the practical applicability of the proposed method, Korean patents related to machine learning and deep learning were collected. As a result of the experiment, it was possible to identify 24 pairs of similar patents without a direct citation relationship and derive appropriate counter strategies.

Students' continuance intention to use MOOCs: empirical evidence from India

In recent years, there has been an increasing interest in understanding the Massive open online courses (MOOCs) due to its gaining popularity. Even though the number of online platforms and programs has grown during the COVID-19 pandemic, there is still a high rate of dropout and non-completion. In this work, the expectation-confirmation model is combined with MOOC features such as perceived openness, perceived reputation, and other factors i.e., perceived enjoyment, and perceived computer self-efficacy to investigate the learner's continued intention to use MOOC. A survey was undertaken and the data was collected from 383 students pursuing their degrees (undergraduate and post-graduate) in Karnataka state, India. The collected data were analyzed with structural equation modelling in Smart PLS 3. The study confirms a significant influence of confirmation and perceived usefulness on satisfaction, and direct significant influence of perceived computer self-efficacy, satisfaction, and perceived usefulness on continuance intention. Also, the results demonstrated the significant influence of confirmation on perceived enjoyment and usefulness and the effect of computer self-efficacy on usefulness. The findings in this study indicate that the MOOC platforms should focus on confirming learner expectations and the usefulness of courses to ensure student satisfaction and continuance of courses.

Relationships between paranoid thinking, self-esteem and the menstrual cycle

This study aimed to investigate whether paranoid experiences and levels of self-esteem fluctuate over the menstrual cycle and whether levels of self-esteem are lower when perceived persecution is felt to be deserved. Measures of anxiety, depression, persecution, deservedness and self-esteem were completed on-line by 278 women over their menstrual cycle. Responses were compared at the paramenstrual (3 days before and after menses onset) and mid-cycle phase. At the paramenstrual phase persecution, negative self-esteem, anxiety and depression were higher and positive self-esteem was lower than at mid-cycle. A greater proportion of women experienced persecution as deserved at the paramenstrual phase. This was associated with higher depression and negative self-esteem scores. Increased levels of deservedness significantly strengthened the relationship between persecution and negative, but not positive, self-esteem. These findings suggest that the paramenstrual phase is a time of vulnerability to increased paranoid experiences, an increased likelihood that feelings of persecution will feel deserved and lowered self-esteem. The findings support the view that interpersonal sensitivities may be key to menstrual cycle symptoms and have an impact on relationships. Further, the study illustrated that ideas developed for psychosis could make a valuable contribution to understanding and managing this aspect of menstruation-related distress.

Therapeutic Options for Brain Metastases in Gynecologic Cancers

Brain metastases (BM) are rare in gynecologic cancers. Overall BM confers a poor prognosis but other factors such as number of brain lesions, patient age, the presence of extracranial metastasis, the Karnofsky Performance Status (KPS) score, and the type of primary cancer also impact prognosis. Taking a patient's whole picture into perspective is crucial in deciding the appropriate management strategy. The management of BM requires an interdisciplinary approach that frequently includes oncology, neurosurgery, radiation oncology and palliative care. Treatment includes both direct targeted therapies to the lesion(s) as well as management of the neurologic side effects caused by mass effect. There is limited evidence of when screening for BM in the gynecology oncology patient is warranted but it is recommended that any cancer patient with new focal neurologic deficit or increasing headaches should be evaluated. The primary imaging modality for detection of BM is MRI, but other imaging modalities such as CT and PET scan can be used for certain scenarios. New advances in radiation techniques, improved imaging modalities, and systemic therapies are helping to discover BM earlier and provide treatments with less detrimental side effects.

Erythema induratum of bazin - Skin lesions with pyrexia of unknown origin undiagnosed for 4 years

Erythema induratum of Bazin is characterized by chronic, tender, erythematous, indurated subcutaneous nodules on the lower extremities caused by tuberculin hypersensitivity. A 21-year-old woman presented with recurrent episodes of multiple erythematous scaly lesions over both lower limbs below the knee and low-grade fever for 4 years. She was treated from various outside hospitals with multiple courses of antibiotics and nonsteroidal anti-inflammatory drugs without improvement. The histopathology of the skin lesion was suggestive of erythema induratum. She had complete resolution of her skin lesions and fever following 6 months of treatment with antitubercular drugs. A delay in the diagnosis of rare presentations of tuberculosis can result in the administration of ineffective and potentially damaging treatments.

Effect of dietary replacement of fish meal by poultry by-product meal on the growth performance, immunity, and intestinal health of juvenile red swamp crayfish, procambarus clarkia

The present study was conducted to investigate the dietary replacement of fish meal with poultry by-product meal (PBM) on the growth performance, immunity, antioxidant properties, and intestinal health of red swamp crayfish (Procambarus clarkia). A diet containing 20% fish meal (FM) and complex plant ingredients as the main protein resources was set as the FM group (crude protein 32%, crude lipid 6%). Four diets replacing 25%, 50%, 75%, and 100% fish meal of the FM diet with PBM were set as the PBM25, PBM50, PBM75, and PBM100 groups, respectively. Compared to the FM group, the PBM100 diet significantly decreased growth performance and feed utilization of crayfish, while markedly increasing the activity of serum aspartate aminotransferase. The immune response was depressed in crayfish fed the PBM100 diet as the activities of serum lysozyme and phenoloxidase, gene expression of anti-lipopolysaccharide factors (alf), cyclophilin A (cypa), crustin, and hemocyanin-1 (hep-1) in hepatopancreas were remarkably decreased. The activities of antioxidases and expression of antioxidant-relevant genes in the hepatopancreas were not influenced by PBM inclusion. Crayfish fed different diets exhibited no obvious symptoms of enteritis, but the PBM100 diet destructed intestinal morphology by significantly decreasing the average length of longitudinal ridges. The α-diversity and overall community structure were not significantly influenced but variations were found in the relative abundance of some genera by PBM inclusion. In summary, CAP could successfully replace 75% dietary FM in a basal diet containing 20% fish meal, while higher CAP level compromised growth performance, immunity, and intestinal histology of crayfish.

Catch Effectiveness Revealed by Site-Related Differences in Capture-Mark-Recapture Methods: A Butterfly Metapopulation Study

Understanding metapopulation structures is very important in the context of ecological studies and conservation. Crucial in this respect are the abundances of both the whole metapopulation and its constituent subpopulations. In recent decades, capture-mark-recapture studies have been considered the most reliable means of calculating such abundances. In butterfly studies, individual insects are usually caught with an entomological net. But the effectiveness of this method can vary for a number of reasons: differences between fieldworkers, in time, between sites etc. This article analyses catch effectiveness data with respect to two subpopulations of the Apollo butterfly (Parnassius apollo) metapopulation in the Pieniny National Park (Polish Carpathians). The results show that this parameter varied significantly between sites, probably because of differences in microrelief and plant cover. In addition, a method is proposed that will include information on catch effectiveness for estimating the sizes of particular subpopulations and will help to elucidate the structure of the entire metapopulation.

State-Aware Compositional Learning Toward Unbiased Training for Scene Graph Generation

How to avoid biased predictions is an important and active research question in scene graph generation (SGG). Current state-of-the-art methods employ debiasing techniques such as resampling and causality analysis. However, the role of intrinsic cues in the features causing biased training has remained under-explored. In this paper, for the first time, we make the surprising observation that object identity information, in the form of object label embeddings (e.g. GLOVE), is principally responsible for biased predictions. We empirically observe that, even without any visual features, a number of recent SGG models can produce comparable or even better results solely from object label embeddings. Motivated by this insight, we propose to leverage a conditional variational auto-encoder to decouple the entangled visual features into two meaningful components: the object's intrinsic identity features and the extrinsic, relation-dependent state feature. We further develop two compositional learning strategies on the relation and object levels to mitigate the data scarcity issue of rare relations. On the two benchmark datasets Visual Genome and GQA, we conduct extensive experiments on the three scenarios, i.e., conventional, few-shot and zero-shot SGG. Results consistently demonstrate that our proposed Decomposition and Composition (DeC) method effectively alleviates the biases in the relation prediction. Moreover, DeC is model-free, and it significantly improves the performance of recent SGG models, establishing new state-of-the-art performance.

Hi-Net: Hybrid-Fusion Network for Multi-Modal MR Image Synthesis

Magnetic resonance imaging (MRI) is a widely used neuroimaging technique that can provide images of different contrasts (i.e., modalities). Fusing this multi-modal data has proven particularly effective for boosting model performance in many tasks. However, due to poor data quality and frequent patient dropout, collecting all modalities for every patient remains a challenge. Medical image synthesis has been proposed as an effective solution, where any missing modalities are synthesized from the existing ones. In this paper, we propose a novel Hybrid-fusion Network (Hi-Net) for multi-modal MR image synthesis, which learns a mapping from multi-modal source images (i.e., existing modalities) to target images (i.e., missing modalities). In our Hi-Net, a modality-specific network is utilized to learn representations for each individual modality, and a fusion network is employed to learn the common latent representation of multi-modal data. Then, a multi-modal synthesis network is designed to densely combine the latent representation with hierarchical features from each modality, acting as a generator to synthesize the target images. Moreover, a layer-wise multi-modal fusion strategy effectively exploits the correlations among multiple modalities, where a Mixed Fusion Block (MFB) is proposed to adaptively weight different fusion strategies. Extensive experiments demonstrate the proposed model outperforms other state-of-the-art medical image synthesis methods.

Recent advances in high performance conducting solid polymer electrolytes for lithium-ion batteries

Lithium-ion batteries (LIBs) as an important energy storage technology have found more and more applications in electric vehicles, electronic devices as well as grid energy storage systems. However, due to the safety issues from the flammability of combustible organic liquid electrolytes, recently solid polymer electrolytes (SPEs) have attracted more interest. Conventional SPEs are dual-ion conductors where both anions and cations possess slightly free movement and favor the concentration polarization phenomena, which may reduce their performances and lifetime. To overcome these issues, the conducting solid polymer electrolytes (CSPEs) are developed where the anions are linked via covalent bonds to the polymeric and inorganic backbones or anion acceptors, which may greatly accelerate the transportation of lithium ions and increase their lithium-ion transference number (LTN) and ionic conductivity as well as reduce the formation of concentration polarization and Li dendrite growth. Herein, a comprehensive overview of the latest development in CSPEs for LIBs is described, which emphasizes the correlations among their synthetic strategies, structures, and electrochemical performances. Finally, the state of the art, future perspectives, and core challenges are provided for designing the next generation of CSPEs in high performance LIBs.

GROUPS-NET: Group meetings aware routing in multi-hop D2D networks

Device-to-device (D2D) communication will allow direct transmission between nearby mobile devices in the next generation cellular networks. A fundamental problem in multi-hop D2D networks is the design of forwarding algorithms that achieve, at the same time, high delivery ratio and low network overhead. In this work, we study group meetings' properties by looking at their structure and regularity with the goal of applying such knowledge in the design of a forwarding algorithm for D2D multi-hop networks. We introduce a forwarding protocol, namely GROUPS-NET, which is aware of social group meetings and their evolution over time. Our algorithm is parameter-calibration free and does not require any knowledge about the social network structure of the system. In particular, different from the state of the art algorithms, GROUPS-NET does not need communities' detection, which is a complex and expensive task. We validate our algorithm by using different publicly available data sources. In real-world large scale scenarios, our algorithm achieves approximately the same delivery ratio of the state-of-the-art solution with 40% less network overhead. (C) 2017 Elsevier B.V. All rights reserved.

Power Electronic Converters in Electric Aircraft: Current Status, Challenges, and Emerging Technologies

The electric revolution is underway in the transportation sector, and the aviation industry is poised to embrace fundamental disruption. Moving to electric aircraft brings undeniable benefits in terms of environmental impact, cost savings, maintenance, noise pollution, and safety. Nevertheless, several technical challenges are yet to be overcome to build electric airplanes that meet public needs while gaining acceptance and trust. From urban air mobility to long-haul flight applications, hundreds of projects are under research to push toward more electrification. At the heart of each aircraft architecture, power electronics plays a crucial role in the new era of transportation. This article aims to provide a comprehensive analysis of state-of-the-art power electronics in electric aircraft. A review of the current status of aircraft electrification will be provided, and technology surveys of power electronic converters will be detailed. Challenges for forthcoming power electronics in response to the future trends of the electrical network will be explained. Finally, emerging technologies regarding wide bandgap devices, advanced topologies and control, thermal management, passive components, and system integration will be discussed.

Lagging X chromatids specify the orientation of asymmetric organelle partitioning in XX spermatocytes of Auanema rhodensis

The unequal partitioning of molecules and organelles during cell division results in daughter cells with different fates. An extreme example is female meiosis, in which consecutive asymmetric cell divisions give rise to 1 large oocyte and 2 small polar bodies with DNA and minimal cytoplasm. Here, we test the hypothesis that during an asymmetric cell division during spermatogenesis of the nematode Auanema rhodensis, the late segregating X chromatids orient the asymmetric partitioning of cytoplasmic components. In previous studies, the secondary spermatocytes of wild-type XO males were found to divide asymmetrically to generate functional spermatids that inherit components necessary for sperm viability and DNA-containing residual bodies that inherit components to be discarded. Here we extend that analysis to 2 novel contexts. First, the isolation and analysis of a strain of mutant XX pseudomales revealed that such animals have highly variable patterns of X-chromatid segregation. The pattern of late segregating X chromatids nevertheless predicted the orientation of organelle partitioning. Second, while wild-type XX hermaphrodites were known to produce both 1X and 2X sperm, here, we show that spermatocytes within specific spermatogonial clusters exhibit 2 different patterns of X-chromatid segregation that correlate with distinct patterns of organelle partitioning. Together this analysis suggests that A. rhodensis has coopted lagging X chromosomes during anaphase II as a mechanism for determining the orientation of organelle partitioning.

Hepatitis B virus (HBV) codon adapts well to the gene expression profile of liver cancer: an evolutionary explanation for HBV's oncogenic role

Due to the evolutionary arms race between hosts and viruses, viruses must adapt to host translation systems to rapidly synthesize viral proteins. Highly expressed genes in hosts have a codon bias related to tRNA abundance, the primary RNA translation rate determinant. We calculated the relative synonymous codon usage (RSCU) of three hepatitis viruses (HAV, HBV, and HCV), SARS-CoV-2, 30 human tissues, and hepatocellular carcinoma (HCC). After comparing RSCU between viruses and human tissues, we calculated the codon adaptation index (CAI) of viral and human genes. HBV and HCV showed the highest correlations with HCC and the normal liver, while SARS-CoV-2 had the strongest association with lungs. In addition, based on HCC RSCU, the CAI of HBV and HCV genes was the highest. HBV and HCV preferentially adapt to the tRNA pool in HCC, facilitating viral RNA translation. After an initial trigger, rapid HBV/HCV translation and replication may change normal liver cells into HCC cells. Our findings reveal a novel perspective on virus-mediated oncogenesis.

Sustainable Management of Contemporary Art Galleries: A Delphi Survey for the Spanish Art Market

The art market operates in a very different way from conventional economic markets, ranging from its behaviors of supply and demand, the trading of goods, and the economic agents intervening in it. In addition, it is a highly unregulated market, with very little standardized information in economic terms. This paper focuses on art galleries, which are the most influential intermediaries in the Spanish primary contemporary fine-art market and perform a role that goes beyond the mere distribution of works of art. This study develops and applies a prospective methodology based on the subjective information compiled by experts, known as the Delphi method, to identify and evaluate the factors that determine the current situation and future outlook for Spanish contemporary art galleries. The results show, on one hand, that the method employed constitutes a valid option to provide reliable information. In addition, they show that the survival of these organizations will depend on their ability to adapt to the changing conditions of the economic environment, reactivating and internationalizing demand, and redirecting their business model towards sustainable management by implementing appropriate business management models and techniques.

Study on Ocean Intelligence Service Security Utilizing Blockchain Technology

The environment of ocean having special conditions is at risk of frequent accidents. For safer use of ocean environment, the whole world is attempting to graft the state-of-the-art technology with the introduction of lots of institutions such as international treaties. In the meantime, with the development of the state-of-the-art IT technology and increase in superhigh speed network establishment, the era of the Internet of Things is opening, and there are increasing cases of fusion with IT in the ocean area. Accordingly, at a time when security for data is especially required, blockchain technology is one of the major alternatives. This study examined application possibility and limits of blockchain technology through ocean space information's security treatment experiment utilizing blockchain technology. For the experiment, GML data which has S-10X electric navigation chart standard structure was used. The experiment results suggested that descrambling of ocean space information utilizing blockchain technology was successfully conducted for safe protection, and it is expected that it would be utilized for ocean space information of other types.

Incorporating Non-Convex Operating Characteristics Into Bi-Level Optimization Electricity Market Models

Bi-level optimization constitutes the most popular mathematical methodology for modeling the deregulated electricity market. However, state-of-the-art models neglect the physical non-convex operating characteristics of market participants, due to their inherent inability to capture binary decision variables in their representation of the market clearing process, rendering them problematic in modeling markets with complex bidding and unit commitment (UC) clearing mechanisms. This paper addresses this fundamental limitation by proposing a novel modeling approach enabling incorporation of these non-convexities into bi-level optimization market models, which is based on the relaxation and primal-dual reformulation of the original, non-convex lower level problem and the penalization of the associated duality gap. Case studies demonstrate the ability of the proposed approach to closely approximate the market clearing solution of the actual UC clearing algorithm and devise more profitable bidding decisions for strategic producers than the state-of-the-art bi-level optimization approach, and reveal the potential of strategic behavior in terms of misreporting non-convex operating characteristics.

State of the Art in Holographic Displays: A Survey

True-3D imaging and display systems are based on physical duplication of light distribution. Holography is a true-3D technique. There are significant developments in electro-holographic displays in recent years. Liquid crystal, liquid crystal on silicon, optically addressed, mirror-based, holographic polymer-dispersed, and acousto-optic devices are used as holographic displays. There are complete electro-holographic display systems and some of them are already commercialized.

Estimating healthcare expenditures after becoming divorced or widowed using propensity score matching

Becoming divorced or widowed are stressful life events experienced by a substantial part of the population. While marital status is a significant predictor in many studies on healthcare expenditures, effects of a change in marital status, specifically becoming divorced or widowed, are less investigated. This study combines individual health claims data and registered sociodemographic characteristics from all Dutch inhabitants (about 17 million) to estimate the differences in healthcare expenditure for individuals whose marital status changed (n = 469,901) compared to individuals who remained married, using propensity score matching and generalized linear models. We found that individuals who were (long-term) divorced or widowed had 12-27% higher healthcare expenditures (RR = 1.12, 95% CI 1.11-1.14; RR = 1.27, 95% CI 1.26-1.29) than individuals who remained married. Foremost, this could be attributed to higher spending on mental healthcare and home care. Higher healthcare expenditures are observed for both divorced and widowed individuals, both recently and long-term divorced/widowed individuals, and across all age groups, income levels and educational levels.

Experiences of romantic relationships among early adults who do not turn to their long-term partner when in need of love and support

As most early adults in long-term romantic relationships rely on their partner to fulfill their relational needs, relationships with romantic partners are very important to many people at this time of life. However, there is a group of individuals for whom their long-term partner is not the most important person when they need love and support. This study explored experiences of romantic relationships among early adults who do not turn to their long-term partner to meet these needs. Twelve individuals in early adulthood (Mage = 33.3 years; SD = 0.54) were selected from a larger community sample (N = 124) based on their answers on a questionnaire about who they turn to when in need of love and support. A thematic analysis of their answers in interviews about romantic relationships resulted in three main themes: Distancing attitudes toward romantic relationships, Desirable aspects of romantic relationships, and Not thinking about romantic relationships. The results show that these early adults' reflections on romantic relationships were characterized by ambivalence, striving toward independence, and relating to their partner as a person to have fun with rather than someone to share a deep connection with. Participants also expressed disagreement with what they perceived as norms concerning romantic relationships. Taken together, this study illustrates that the experiences of early adults who do not turn to their long-term partner to meet needs of love and support are characterized by a complex interaction between contradictory feelings, values, and behaviors.

Fast Blind Image Deblurring Using Smoothing-Enhancing Regularizer

Blind deconvolution is a highly ill-posed problem for the restoration of degraded images and requires prior knowledge or regularization. Recently, various priors have been proposed and the models based on these priors have achieved state-of-the-art performances. In this paper, we present a blind image deblurring method based on a computationally efficient and effective image regularizer. The proposed regularizer is motivated by the fact that the success of recent priors mainly stems from their properties, which implicitly generate an unnatural latent image suppressing insignificant structures and preserving only salient edges. These salient edges guide the models to estimate an accurate kernel. In this paper, the proposed regularizer termed smoothing-enhancing regularizer, not only assures that only salient structures in the image are preserved but also enhances these salient structures to help the model estimate the more accurate kernel. To efficiently solve the proposed model, we develop an efficient numerical approach based on the half-quadratic splitting algorithm and the lagged-fixed-point iteration scheme. The optimization scheme only requires a few additional shrinkage operations compared with the original half-quadratic splitting algorithm, making our method much faster than recent leading methods. The qualitative and quantitative experimental results show that our algorithm achieves the state-of-the-art results and can be extended to other challenging deblurring tasks, such as those involving text, face, and low-illuminated images. Furthermore, the proposed method is much more computationally efficient than the recent state-of-the-art algorithms with up to more than 10x faster execution time.

Semi-supervised robust deep neural networks for multi-label image classification

This paper introduces a robust method for semi-supervised training of deep neural networks for multi-label image classification. To this end, a ramp loss is utilized since it is more robust against noisy and incomplete image labels compared to the classic hinge loss. The proposed method allows for learning from both labeled and unlabeled data in a semi-supervised setting. This is achieved by propagating labels from the labeled images to their unlabeled neighbors in the feature space. Using a robust loss function becomes crucial here, as the initial label propagations may include many errors, which degrades the performance of non-robust loss functions. In contrast, the proposed robust ramp loss restricts extreme penalties from the samples with incorrect labels, and the label assignment improves in each iteration and contributes to the learning process. The proposed method achieves state-of-the-art results in semi-supervised learning experiments on the CIFAR-10 and STL-10 datasets, and comparable results to the state-of the-art in supervised learning experiments on the NUS-WIDE and MS-COCO datasets. Experimental results also verify that our proposed method is more robust against noisy image labels as expected. (C) 2019 Elsevier Ltd. All rights reserved.

COVID-19 Vaccination-Induced Cholangiopathy and Autoimmune Hepatitis: A Series of Two Cases

The coronavirus disease 2019 (COVID-19) pandemic has been associated with significant morbidity and mortality. Following the introduction of vaccines, various side effects have been reported. Whilst those reported may be attributed to the vaccine itself, at times, it may simply incite an immunological phenomenon. We present a case series of two patients who presented with symptoms of yellowing of the eyes and the skin along with fatigue, and tiredness, following vaccination for COVID-19. The diagnosis of post COVID-19-vaccination related hepatitis is one of the fewer, less understood, yet reported side effects associated with significant morbidity. The diagnosis of COVID-19 vaccination-related cholangitis is an outcome reported here for the first time to the best of our knowledge. It was alarming that both patients did not have any significant past history of medical ailments. A prompt assessment followed by investigations including liver biopsy assisted in a timely understanding of the phenomenon with complete resolution of the symptoms.

Super-resolution using neural networks based on the optimal recovery theory

An optimal recovery based neural-network Super Resolution algorithm is developed. The proposed method is computationally less expensive and outputs images with high subjective quality, compared with previous neural-network or optimal recovery algorithms. It is evaluated on classical SR test images with both generic and specialized training sets, and compared with other state-of-the-art methods. Results show that our algorithm is among the state-of-the-art, both in quality and efficiency.

Graph-Based Intercategory and Intermodality Network for Multilabel Classification and Melanoma Diagnosis of Skin Lesions in Dermoscopy and Clinical Images

The identification of melanoma involves an integrated analysis of skin lesion images acquired using clinical and dermoscopy modalities. Dermoscopic images provide a detailed view of the subsurface visual structures that supplement the macroscopic details from clinical images. Visual melanoma diagnosis is commonly based on the 7-point visual category checklist (7PC), which involves identifying specific characteristics of skin lesions. The 7PC contains intrinsic relationships between categories that can aid classification, such as shared features, correlations, and the contributions of categories towards diagnosis. Manual classification is subjective and prone to intra- and interobserver variability. This presents an opportunity for automated methods to aid in diagnostic decision support. Current state-of-the-art methods focus on a single image modality (either clinical or dermoscopy) and ignore information from the other, or do not fully leverage the complementary information from both modalities. Furthermore, there is not a method to exploit the 'intercategory' relationships in the 7PC. In this study, we address these issues by proposing a graph-based intercategory and intermodality network (GIIN) with two modules. A graph-based relational module (GRM) leverages intercategorical relations, intermodal relations, and prioritises the visual structure details from dermoscopy by encoding category representations in a graph network. The category embedding learning module (CELM) captures representations that are specialised for each category and support the GRM. We show that our modules are effective at enhancing classification performance using three public datasets (7PC, ISIC 2017, and ISIC 2018), and that our method outperforms state-of-the-art methods at classifying the 7PC categories and diagnosis.

Identification of the novel KIR3DL1*00703 allele in a Chinese Han individual

The novel KIR3DL1*00703 allele differs from the closest allele KIR3DL1*00701 by a single silent mutation.

Attention and boundary guided salient object detection

In recent years, fully convolutional neural network (FCN) has broken all records in various vision task. It also achieves great performance in salient object detection. However, most of the state-of-the-art methods have suffered from the challenge of precisely segmenting the entire salient object with uniform region and explicit boundary and effectively suppressing the backgrounds on complex images. There is still a large room for improvement over the FCN-based saliency detection approaches. In this paper, we propose an attention and boundary guided deep neural network for salient object detection to better locate and segment the salient objects with uniform interior and explicit boundary. A channel-wise attention module is utilized to emphasize the important regions, which selects the important feature channels and assigns large weights to them. A boundary information localization module is proposed for suppressing the irrelevant boundary information to better locate and explore the useful structure of objects. The proposed approach achieves state-of-the-art performance on four well-known benchmark datasets. (C) 2020 Elsevier Ltd. All rights reserved.

Development of a Punch-O-Meter for Sport Karate Training

In karate sparring (kumite), punches are used more than kicks to score points. Among these punches, gyaku tsuki is a very commonly used punch. The objective of the punch is to hit the target at a medium range in a very short time, producing a maximum force. In this study, we proposed the development of a novel standalone Punch-O-Meter system to measure the speed and the force generated by a punch.

Lesion-Harvester: Iteratively Mining Unlabeled Lesions and Hard-Negative Examples at Scale

The acquisition of large-scale medical image data, necessary for training machine learning algorithms, is hampered by associated expert-driven annotation costs. Mining hospital archives can address this problem, but labels often incomplete or noisy, e.g., 50% of the lesions in DeepLesion are left unlabeled. Thus, effective label harvesting methods are critical. This is the goal of our work, where we introduce Lesion-Harvester-a powerful system to harvest missing annotations from lesion datasets at high precision. Accepting the need for some degree of expert labor, we use a small fully-labeled image subset to intelligently mine annotations from the remainder. To do this, we chain together a highly sensitive lesion proposal generator (LPG) and a very selective lesion proposal classifier (LPC). Using a new hard negative suppression loss, the resulting harvested and hard-negative proposals are then employed to iteratively finetune our LPG. While our framework is generic, we optimize our performance by proposing a new 3D contextual LPG and by using a global-local multi-view LPC. Experiments on DeepLesion demonstrate that Lesion-Harvester can discover an additional 9,805 lesions at a precision of 90%. We publicly release the harvested lesions, along with a new test set of completely annotated DeepLesion volumes. We also present a pseudo 3D IoU evaluation metric that corresponds much better to the real 3D IoU than current DeepLesion evaluation metrics. To quantify the downstream benefits of Lesion-Harvester we show that augmenting the DeepLesion annotations with our harvested lesions allows state-of-the-art detectors to boost their average precision by 7 to 10%.

A Free-Standing Polyaniline/Silicon Nanowire Forest as the Anode for Lithium-ion Batteries

Despite its high theoretical capacity, silicon anode has limited intrinsic conductivity and experiences significant volume changes during charge-discharge. To overcome these issues, facile metal-assisted chemical etching and in-situ polymerization of aniline are employed to produce a dense 1D polyaniline/silicon nanowire forest without noticeable agglomeration as a free-standing anode for lithium-ion batteries. This hybrid electrode possesses high cycling performance, delivering a stable capacity capped at 2 mAh cm-2 for 346 cycles of charge-discharge. Maximum capacity of 2 mAh cm-2 is also achievable at high-rate cell testing of 2 mA cm-2 , which cannot be obtained by the anode with plain silicon wafer and silicon nanowire only. The introduction of polyaniline on the silicon nanowire is shown to reduce the solid electrolyte interface (SEI) resistance, stabilize the SEI layer, further alleviate the effect of volume changes, and boost the conductivity of the hybrid anode, resulting in the high electrochemical performance of the anode.

State of the art and future perspectives of thermophilic anaerobic digestion

The slate of the art of thermophilic digestion is discussed. Thermophilic digestion is a well established technology in Europe for treatment of mixtures of waste in common large scale biogas plants or for treatment of the organic fraction of municipal solid waste. Due to a large number of failures over time with thermophilic digestion of sewage sludge this process has lost its appeal in the USA. New demands on sanitation of biosolids before land use will, however, bring the attention back to the use of elevated temperatures during sludge stabilization. In the paper we show how the use of a start-up strategy based on the actual activity of key microbes can be used to ensure proper and fast transfer of mesophilic digesters into thermophilic operation. Extreme thermophilic temperatures of 65degreesC or more may be necessary in the future to meet the demands for full sanitation of the waste material before final disposal. We show data of anaerobic digestion at extreme thermophilic temperatures.

The care of infants with rapid weight gain: Should we be doing more?

Rapid weight gain (RWG) during infancy is a known risk factor for later childhood obesity. It can be measured using a range of definitions across various time periods in the first 2 years of life. In recent years, some early childhood obesity prevention trials have included a focus on preventing RWG during infancy, with modest success. Overall, RWG during infancy remains common, yet little work has examined whether infants with this growth pattern should receive additional care when it is identified in health-care settings. In this viewpoint, we contend that RWG during infancy should be routinely screened for in health-care settings, and when identified, viewed as an opportunity for health-care professionals to instigate non-stigmatising discussions with families about RWG and general healthy practices for their infants. If families wish to engage, we suggest that six topics from early life obesity prevention studies (breastfeeding, formula feeding, complementary feeding, sleep, responsive parenting, and education around growth charts and monitoring) could form the foundations of conversations to help them establish and maintain healthy habits to support their infant's health and well-being and potentially lower the risk of later obesity. However, further work is needed to develop definitive guidelines in this area, and to address other gaps in the literature, such as the current lack of a standardised definition for RWG during infancy and a clear understanding of the time points over which it should be measured.

Near Infrared Spectroscopy Detection and Quantification of Herbal Medicines Adulterated with Sibutramine

There is an increasing demand for herbal medicines in weight loss treatment. Some synthetic chemicals, such as sibutramine (SB), have been detected as adulterants in herbal formulations. In this study, two strategies using near infrared (NIR) spectroscopy have been developed to evaluate potential adulteration of herbal medicines with SB: a qualitative screening approach and a quantitative methodology based on multivariate calibration. Samples were composed by products commercialized as herbal medicines, as well as by laboratory adulterated samples. Spectra were obtained in the range of 14,000-4000 per cm. Using PLS-DA, a correct classification of 100% was achieved for the external validation set. In the quantitative approach, the root mean squares error of prediction (RMSEP), for both PLS and MLR models, was 0.2% w/w. The results prove the potential of NIR spectroscopy and multivariate calibration in quantifying sibutramine in adulterated herbal medicines samples.

Sparse Representation Using Stepwise Tikhonov Regularization With Offline Computations

This letter describes a novel algorithm for sparse reconstruction. The method uses offline computations to reduce the computational burden of online execution. The approach relies on the recently proposed stepwise Tikhonov regularization (STIR) method to implement forward selection procedures such as orthogonal least squares (OLS), orthogonal matching pursuit (OMP), and STIR. Numerical simulations show the efficacy of the proposed approach, which is competitive against state-of-the-art implementation of OLS and OMP.

Effect of the photoperiod on bud dormancy in Liriodendron chinense

Bud dormancy and its release are complex physiological phenomena in plants. The molecular mechanisms of bud dormancy in Liriodendron chinense are mainly unknown. Here, we studied bud dormancy and the related physiological and molecular phenomena in Liriodendron under long-day (LD) and short-day (SD). Bud burst was released faster under LD than under SD. Abscisic acid (ABA), superoxide dismutase (SOD), catalase (CAT), and glutathione reductase (GR) activities were increased significantly under LD in Liriodendron buds. In contrast, the contents of gibberellic acid (GA3), ascorbic acid (AsA), glutathione (GSH), malondialdehyde (MDA), and ascorbate peroxidase (APX) activity decreased under LD but increased under SD. Differentially expressed genes (DEGs) were up-regulated under LD and down-regulated under SD and these changes correspondingly promoted (LD) or repressed (SD) cell division and the number and/or size of cells in the bud. Transcriptomic analysis of Liriodendron buds under different photoperiods identified 187 DEGs enriched in several pathways such as flavonoid biosynthesis and phenylpropanoid biosynthesis, plant hormone and signal transduction, etc. that are associated with antioxidant enzymes, non-enzymatic antioxidants, and subsequently promote the growth of the buds. Our findings provide novel insights into regulating bud dormancy via flavonoid and phenylpropanoid biosynthesis, plant hormone and signal transduction pathways, and ABA content. These physiological and biochemical traits would help detect bud dormancy in plants.

Surface integrity in metal machining - Part I: Fundamentals of surface characteristics and formation mechanisms

The surface integrity of machined metal components is critical to their in-service functionality, longevity and overall performance. Surface defects induced by machining operations vary from the nano to macro scale, which cause microstructural, mechanical and chemical effects. Hence, they require advanced evaluation and post processing techniques. While surface integrity varies significantly across the range of machining processes, this paper explores the state-of-the-art of surface integrity research with an emphasis on their governing mechanisms and emerging evaluation approaches. In this review, removal mechanisms are grouped by their primary energy transfer mechanisms; mechanical, thermal and chemical based. Accordingly, the resultant multi-scale phenomena associated with metal machining are analyzed. The contribution of these material removal mechanisms to the workpiece surfaces/subsurface characteristics is reviewed. Post-processing options for the mitigation of induced surface defects are also discussed.

DICDNet: Deep Interpretable Convolutional Dictionary Network for Metal Artifact Reduction in CT Images

Computed tomography (CT) images are often impaired by unfavorable artifacts caused by metallic implants within patients, which would adversely affect the subsequent clinical diagnosis and treatment. Although the existing deep-learning-based approaches have achieved promising success on metal artifact reduction (MAR) for CT images, most of them treated the task as a general image restoration problem and utilized off-the-shelf network modules for image quality enhancement. Hence, such frameworks always suffer from lack of sufficient model interpretability for the specific task. Besides, the existing MAR techniques largely neglect the intrinsic prior knowledge underlying metal-corrupted CT images which is beneficial for the MAR performance improvement. In this paper, we specifically propose a deep interpretable convolutional dictionary network (DICDNet) for the MAR task. Particularly, we first explore that the metal artifacts always present non-local streaking and star-shape patterns in CT images. Based on such observations, a convolutional dictionary model is deployed to encode the metal artifacts. To solve the model, we propose a novel optimization algorithm based on the proximal gradient technique. With only simple operators, the iterative steps of the proposed algorithm can be easily unfolded into corresponding network modules with specific physical meanings. Comprehensive experiments on synthesized and clinical datasets substantiate the effectiveness of the proposed DICDNet as well as its superior interpretability, compared to current state-of-the-art MAR methods. Code is available at https://github.com/hongwang01/DICDNet.

'State-of-the-art' of building integrated photovoltaic products

During the last decades, the photovoltaic (PV) modules and their associated architectural materials are increasingly being incorporated into the construction of the building envelope such as facade, roof and skylights in the urban centers. This paper analyzes the-state-of-the-art of the PV elements and construction materials which are advertised as BIPV-products at the most important companies in the world. For this purpose 136 companies and 445 PV elements have been investigated and analyzed from a technical and architectural point of view. Also, the study has been divided into two main groups according to industry which producing the product: BIPV-Modules, which comes from the PV modules manufacturers and consist of standard PV-modules with some variations in its aesthetic features, support or dimensions; and PV-Constructions Elements, which consist of conventional constructive elements with architectural features intentionally manufactured for photovoltaic integration. In advance for conclusions, the solar tile is the most common PV-constructions element, the Si-crystalline is the most widely used PV technology, and the BIPV-urban furniture is the fastest growing market experienced in recent years. However, it is clear the absences of innovative elements which meet at the same time both the constructive purpose as the quality standards of PV technology. (C) 2013 Elsevier Ltd. All rights reserved.

Portable Sequentially Shifted Excitation Raman Spectroscopy to Examine Historic Powders Enclosed in Glass Vials

Raman spectroscopy (RS) is a powerful non-invasive tool for the characterization of materials. However, the fluorescence effect often hampers the detectability of the relatively weak vibrational Raman signal. Several approaches were exploited to overcome this limit. This work, in particular, evaluates the performance of an in situ portable sequentially shifted excitation (SSE (TM)) Raman spectrometer applied to the examination of artistic historical pigment powders enclosed in glass vials. The explored handheld spectrometer employs a dual, temperature-shifted, 785 nm and 852 nm laser excitation to optimize both spectral coverage and fluorescence subtraction. The study demonstrates the feasibility of the SSE RS approach for non-invasive identification of art materials, and its applicability in complex situations where the examined material cannot be removed from its container. Laboratory measurements using benchtop dispersive micro-Raman spectroscopy at 785 nm are reported for comparison.

Natural Language Processing in Nephrology

Unstructured data in the electronic health records contain essential patient information. Natural language processing (NLP), teaching a computer to read, allows us to tap into these data without needing the time and effort of manual chart abstraction. The core first step for all NLP algorithms is preprocessing the text to identify the core words that differentiate the text while filtering out the noise. Traditional NLP uses a rule-based approach, applying grammatical rules to infer meaning from the text. Newer NLP approaches use machine learning/deep learning which can infer meaning without explicitly being programmed. NLP use in nephrology research has focused on identifying distinct disease processes, such as CKD, and extraction of patient-oriented outcomes such as symptoms with high sensitivity. NLP can identify patient features from clinical text associated with acute kidney injury and progression of CKD. Lastly, inclusion of features extracted using NLP improved the performance of risk-prediction models compared to models that only use structured data. Implementation of NLP algorithms has been slow, partially hindered by the lack of external validation of NLP algorithms. However, NLP allows for extraction of key patient characteristics from free text, an infrequently used resource in nephrology.

FloVasion: Towards Detection of non-sensitive Variable Based Evasive Information-Flow in Android Apps

Smartphones are enriched by applications (apps) available through the mobile ecosystem. Various studies have reported that apps leaking sensitive user and device information are the primary target of cyber criminals. Existing program analysis tools can detect such information leakage flows. Reverse engineering tools are deployed to determine app information-flow via control and data-flow analysis. Malware authors employ information-flow based evasion techniques while leaking privacy sensitive data. In this paper, we discuss five novel app attacks that evade information flow analysis and leak sensitive device and user information (e.g. IMEI, SIM details, Location details, and user contacts). These attacks circumvent state-of-the-art analysis tools. We show that sensitive information can be leaked via non-sensitive variables, or by performing runtime inspection of classes and fields. We analyzed the proposed novel attack apps against some of the most promising state-of-the-art static analysis tools such as FlowDroid, DroidSafe, and dynamic analysis tools such as TaintDroid. Furthermore, we evaluated Play-Protect i.e. default on-device anti-malware, AVL Antivirus, and some other top commercial products against proposed novel app attacks. We demonstrate that existing tools are vulnerable to proposed attacks. Finally, this paper proposes AspectJ based runtime monitor as a possible solution that can be incorporated in the state-of-the-art app analysis techniques to detect information flow misuse.

Toward Real Hyperspectral Image Stripe Removal via Direction Constraint Hierarchical Feature Cascade Networks

In hyperspectral imaging (HSI), stripe noise is one of the most common noise types that adversely affects its application. Convolutional neural networks (CNNs) have contributed to state-of-the-art performance in HSI destriping given their powerful feature extraction and learning capabilities. However, it is difficult to obtain paired training samples for real data. Most CNN destriping methods construct a paired training dataset with simulated stripe noise for network training. However, when the stripe noise of real data is complex, destriping performance of the model is constrained. To solve this problem, this study proposes a real HSI stripe removal method using a toward real HSI stripe removal via direction constraint hierarchical feature cascade network (TRS-DCHC). TRS-DCHC uses the stripe noise extract subnetwork to extract stripe patterns from real stripe-containing HSI data and incorporates clean images to form paired training samples. The destriping subnetwork advantageously utilizes a wavelet transform to explicitly decompose stripe and stripe-free components. It also adopts multi-scale feature dense connections and feature fusion to enrich feature information and deeply mine the discriminate features of stripe and stripe-free components. Our experiments on both simulated and real data of various loads showed that TRS-DCHC features better performance in both simulated and real data compared with state-of-the-art method.

A Minimum-Skew Clock Tree Synthesis Algorithm for Single Flux Quantum Logic Circuits

This article presents a synchronous minimum-skew clock tree synthesis algorithm for single flux quantum circuits considering splitter delays and placement blockages. The proposed methodology improves the state-of-the-art by accounting for splitter delays and creating a fully balanced clock tree structure in which the number of clock splitters from the clock source to all the sink nodes is identical. Additionally, a mixed integer linear programming based algorithm is presented that removes the overlaps among the clock splitters and placed cells (i.e., placement blockages) and minimizes the clock skew, simultaneously. Using the proposed method, the average clock skew for 17 benchmark circuits is 4.6 ps, improving the state-of-the-art algorithm by 70%. Finally, a clock tree synthesis algorithm for imbalanced topologies is presented that reduces the clock skew and the number of clock splitters in the clock network by 56% and 37%, respectively, compared with a fully balanced clock tree solution.

Glutamine synthetase limits β-catenin-mutated liver cancer growth by maintaining nitrogen homeostasis and suppressing mTORC1

Glutamine synthetase (GS) catalyzes de novo synthesis of glutamine that facilitates cancer cell growth. In the liver, GS functions next to the urea cycle to remove ammonia waste. As a dysregulated urea cycle is implicated in cancer development, the impact of GS's ammonia clearance function has not been explored in cancer. Here, we show that oncogenic activation of β-catenin (encoded by CTNNB1) led to a decreased urea cycle and elevated ammonia waste burden. While β-catenin induced the expression of GS, which is thought to be cancer promoting, surprisingly, genetic ablation of hepatic GS accelerated the onset of liver tumors in several mouse models that involved β-catenin activation. Mechanistically, GS ablation exacerbated hyperammonemia and facilitated the production of glutamate-derived nonessential amino acids, which subsequently stimulated mechanistic target of rapamycin complex 1 (mTORC1). Pharmacological and genetic inhibition of mTORC1 and glutamic transaminases suppressed tumorigenesis facilitated by GS ablation. While patients with hepatocellular carcinoma, especially those with CTNNB1 mutations, have an overall defective urea cycle and increased expression of GS, there exists a subset of patients with low GS expression that is associated with mTORC1 hyperactivation. Therefore, GS-mediated ammonia clearance serves as a tumor-suppressing mechanism in livers that harbor β-catenin activation mutations and a compromised urea cycle.

Rare Co-Existent Dermatitis Herpetiformis and Psoriasis in a Child: A Causal Relationship?

Dermatitis herpetiformis (DH) is an auto-inflammatory skin disease that is linked to gluten sensitivity and is related to celiac disease (CD). Psoriasis is an inflammatory skin disorder found to have an association with the celiac disease, according to various genetic and epidemiological studies. We report a 12-year-girl who presented with multiple tense blisters along with red raised, scaly and itchy lesions over her body. She was a known case of psoriasis and was diagnosed as dermatitis herpetiformis in an immunofluorescence study. In this case report, we want to highlight the fact that the co-existence of dermatitis herpetiformis and psoriasis could be more than a mere coincidence. In our patient's previously uncontrolled psoriasis and dermatitis herpetiformis both improved after a gluten-free diet along with systemic therapy.

Science Walden: Exploring the Convergence of Environmental Technologies with Design and Art

Science Walden, which is inspired by two prominent literary works, namely, Walden by Henry David Thoreau (1817-1862) and Walden Two by Burrhus Frederic Skinner (1904-1990), is aimed at establishing a community that embodies humanistic values while embracing scientific advancement to produce renewable energy and water sources. This study attempts to capitalize on feces standard money (FSM) and artistic collaboration between scientists and artists as a means of achieving the forms of life depicted in Walden and Walden Two. On our campus, we designed and built a pavilion that serves as a laboratory where scientific advantages, design, and art are merged. In the pavilion, feces are processed in reactors and facilities for sustainable energy production, and rainwater is harvested and treated for use in daily life. Our application of design and art contributes to easing interaction between the general public and scientists because it visualizes an ambiguous theory and concretizes it into an understandable image.

AEDCN-Net: Accurate and Efficient Deep Convolutional Neural Network Model for Medical Image Segmentation

Image segmentation was significantly enhanced after the emergence of deep learning (DL) methods. In particular, deep convolutional neural networks (DCNNs) have assisted DL-based segmentation models to achieve state-of-the-art performance in fields critical to human beings, such as medicine. However, the existing state-of-the-art methods often use computationally expensive operations to achieve high accuracy and lightweight networks often lack a precise medical image segmentation. Therefore, this study proposes an accurate and efficient DCNN model (AEDCN-Net) based on an elaborate preprocessing step and a resourceful model architecture. The AEDCN-Net exploits bottleneck, atrous, and asymmetric convolution-based residual skip connections in the encoding path that reduce the number of trainable parameters and floating point operations (FLOPs) to learn feature representations with a larger receptive field. The decoding path employs the nearest-neighbor based upsampling method instead of a computationally resourceful transpose convolution operation that requires an extensive number of trainable parameters. The proposed method attains a superior performance in both computational time and accuracy compared to the existing state-of-the-art methods. The results of benchmarking using four real-life medical image datasets specifically illustrate that the AEDCN-Net has a faster convergence compared to the computationally expensive state-of-the-art models while using significantly fewer trainable parameters and FLOPs that result in a considerable speed-up during inference. Moreover, the proposed method obtains a better accuracy in several evaluation metrics compared with the existing lightweight and efficient methods.

Hardware-Efficient Post-Processing Architectures for True Random Number Generators

In this brief we present novel post-processing modules for use in true random number generators (TRNGs). These modules are based on mathematical constructs called strong blenders, which provide theoretical guarantees for the randomness of the output. We start by pointing out problems with current post-processing methods used in state-of-the-art TRNG designs. We present three novel hardware-efficient architectures and provide guidelines for choosing the design parameters.

FPGA implementation of a critical railway interlocking system

An interlocking is a railway system that automatically controls that changes in routes are safely managed, avoidingtrain crashes and derailments. This article presents an analysis of the state of the art technology used in several commercial interlocking equipment. Based on this analysis, design and implementation of an interlocking system architecture in FPGA technology is proposed. Appropriate techniques are applied to achieve the required level of security. The scalability of the design is also analyzed and conclusions are presented.

An end to end trained hybrid CNN model for multi-object tracking

A robust MOT (multi-object tracking) is very crucial for computer vision applications such as crowd density estimation and autonomous vehicles. Most of the existing mot approaches perform object tracking in a two task manner such as motion estimation and Re-identification but these approaches pose some drawbacks like the model is not end-to-end trained, the Re-Id required lots of identity switches thus incurred computational overhead and the performance further degrades in complex crowd scenarios. To overcome such drawbacks we are motivated to design an end-to-end trained DNN for MOT. The proposed model utilizes a matching technique that utilizes the relative scale between the boundary boxes and relative position calculates the relative distance between the objects for MOT. To solve the problems, we proposed a matching technique that poses two subtasks to efficiently scale up a single shot DNN tracking approach for an indefinite number of objects in the video frames. The proposed method uses a relative scale and relative position to matching between the detected and targeted objects. The achieved state-of-the-art results of the tasks allow to obtain high accuracy of tracking with detection and surpasses existing state-of-the-art methods by a huge margin on various public datasets.

Enabling a Single Deep Learning Model for Accurate Gland Instance Segmentation: A Shape-Aware Adversarial Learning Framework

Segmenting gland instances in histology images is highly challenging as it requires not only detecting glands from a complex background but also separating each individual gland instance with accurate boundary detection. However, due to the boundary uncertainty problem in manual annotations, pixel-to-pixel matching based loss functions are too restrictive for simultaneous gland detection and boundary detection. State-of-the-art approaches adopted multi-model schemes, resulting in unnecessarily high model complexity and difficulties in the training process. In this paper, we propose to use one single deep learning model for accurate gland instance segmentation. To address the boundary uncertainty problem, instead of pixel-to-pixel matching, we propose a segment-level shape similarity measure to calculate the curve similarity between each annotated boundary segment and the corresponding detected boundary segment within a fixed searching range. As the segment-level measure allows location variations within a fixed range for shape similarity calculation, it has better tolerance to boundary uncertainty and is more effective for boundary detection. Furthermore, by adjusting the radius of the searching range, the segment-level shape similarity measure is able to deal with different levels of boundary uncertainty. Therefore, in our framework, images of different scales are down-sampled and integrated to provide both global and local contextual information for training, which is helpful in segmenting gland instances of different sizes. To reduce the variations of multi-scale training images, by referring to adversarial domain adaptation, we propose a pseudo domain adaptation framework for feature alignment. By constructing loss functions based on the segment-level shape similarity measure, combining with the adversarial loss function, the proposed shape-aware adversarial learning framework enables one single deep learning model for gland instance segmentation. Experimental results on the 2015 MICCAI Gland Challenge dataset demonstrate that the proposed framework achieves state-of-the-art performance with one single deep learning model. As the boundary uncertainty problem widely exists in medical image segmentation, it is broadly applicable to other applications.

Scene Classification via a Gradient Boosting Random Convolutional Network Framework

Due to the recent advances in satellite sensors, a large amount of high-resolution remote sensing images is now being obtained each day. How to automatically recognize and analyze scenes from these satellite images effectively and efficiently has become a big challenge in the remote sensing field. Recently, a lot of work in scene classification has been proposed, focusing on deep neural networks, which learn hierarchical internal feature representations from image data sets and produce state-of-the-art performance. However, most methods, including the traditional shallow methods and deep neural networks, only concentrate on training a single model. Meanwhile, neural network ensembles have proved to be a powerful and practical tool for a number of different predictive tasks. Can we find a way to combine different deep neural networks effectively and efficiently for scene classification? In this paper, we propose a gradient boosting random convolutional network (GBRCN) framework for scene classification, which can effectively combine many deep neural networks. As far as we know, this is the first time that a deep ensemble framework has been proposed for scene classification. Moreover, in the experiments, the proposed method was applied to two challenging high-resolution data sets: 1) the UC Merced data set containing 21 different aerial scene categories with a submeter resolution and 2) a Sydney data set containing eight land-use categories with a 1.0-m spatial resolution. The proposed GBRCN framework outperformed the state-of-the-art methods with the UC Merced data set, including the traditional single convolutional network approach. For the Sydney data set, the proposed method again obtained the best accuracy, demonstrating that the proposed framework can provide more accurate classification results than the state-of-the-art methods.

Deep Symmetric Adaptation Network for Cross-Modality Medical Image Segmentation

Unsupervised domain adaptation (UDA) methods have shown their promising performance in the cross-modality medical image segmentation tasks. These typical methods usually utilize a translation network to transform images from the source domain to target domain or train the pixel-level classifier merely using translated source images and original target images. However, when there exists a large domain shift between source and target domains, we argue that this asymmetric structure, to some extent, could not fully eliminate the domain gap. In this paper, we present a novel deep symmetric architecture of UDA for medical image segmentation, which consists of a segmentation sub-network, and two symmetric source and target domain translation sub-networks. To be specific, based on two translation sub-networks, we introduce a bidirectional alignment scheme via a shared encoder and two private decoders to simultaneously align features 1) from source to target domain and 2) from target to source domain, which is able to effectively mitigate the discrepancy between domains. Furthermore, for the segmentation sub-network, we train a pixel-level classifier using not only original target images and translated source images, but also original source images and translated target images, which could sufficiently leverage the semantic information from the images with different styles. Extensive experiments demonstrate that our method has remarkable advantages compared to the state-of-the-art methods in three segmentation tasks, such as cross-modality cardiac, BraTS, and abdominal multi-organ segmentation.

Self-coordinated nanozyme on Cu3BiS3 nanorods for high-performance aptasensing

A novel strategy is reported to access high-performance nanozymes via the self-coordination of ferrocyanides ([Fe(CN)6]4-) onto the surface of the Cu3BiS3 (CBS) nanorods. Notably, the in situ formed nanozymes had high catalytic activity, good stability, low cost, and easy mass production. The formed nanozyme catalyzed the oxidation of the typical chromogenic substrate of 3,3',5,5'-tetramethylbenzidine (TMB) with a distinctive absorption peak at 652 nm, accompanied by a blue color development. Moreover, the attachment of deoxyribonucleoside 5'-monophosphates (dNMP) beforehand onto the surface of CBS prevented coordination of ferrocyanides and resulted in the tunable formation of the nanozyme, thereby enabling the construction of an exquisite biosensing platform. Taking the aptasensing of chloramphenicol (CAP) as an example, the engineered nanozyme allowed the construction of a homogenous, label-free, and high-performance bioassay in terms of its convenience and high sensitivity. Under the optimal conditions, changes in the absorption intensity at 652 nm for the oxidized TMB provides a good linear correlation with the logarithm of CAP concentrations in the range 0.1 pM to 100 nM, and the limit of detection was 0.033 pM (calculated from 3σ/s). Considering a vast number of bioreactions can be connected to dNMP production, we expect the engineerable nanozyme as a universal signal transduction scaffold for versatile applications in bioassays. Through the attachment of deoxyribonucleoside 5'-monophosphate (dNMP) on the surface of CBS to regulate the generation of self-coordinated nanozyme CBS/BiHCF, a homogeneous, label-free, and high-performance universal aptasensing platform was constructed.

Analysis of War and Conflict Effect on the Transmission Dynamics of the Tenth Ebola Outbreak in the Democratic Republic of Congo

The tenth Ebola outbreak in the Democratic Republic of Congo (DRC) that occurred from 2018 to 2020 was exacerbated by long-lasting conflicts and war in the region. We propose a deterministic model to investigate the impact of such disruptive events on the transmission dynamics of the Ebola virus disease. It is an extension of the classical susceptible-infectious-recovered model, enriched by an additional class of contaminated environment to account for indirect transmission as well as two classes of hospitalized individuals and patients who escape from the healthcare facility due to violence and attacks perpetrated by armed groups, rebels, etc. The model is formulated using two patches, namely Patch 1 consisting of the three affected eastern provinces in DRC and Patch 2, a war- and conflict-free area consisting of the go-to neighboring provinces for escaped patients. We introduce two key parameters, the escaping rate from hospitals and the destruction of hospitals, in terms of which the effect of war and conflicts is measured. The model is fitted and parameterized using the cumulative mortality data from the region. The basic reproduction number [Formula: see text] is computed and found to have a complex expression due to the high nonlinearity of the model. By using, not a Lyapunov function, but a decomposition theorem in Castillo-Chavez et al.(in Castillo-Chavez et al. (eds) Mathematical approaches for emerging and reemerging infectious diseases: an introduction, vol 126. Springer Science & Business Media, Berlin, 2002), it is shown that the disease-free equilibrium is globally asymptotically stable when [Formula: see text] and unstable when [Formula: see text]. A nonstandard finite difference scheme which replicates the dynamics of the continuous model is designed. In particular, a discrete counterpart of the above-mentioned theorem on the global asymptotic stability of the disease-free equilibrium is investigated. Numerical experiments are presented to support the theoretical results. When [Formula: see text], the numerical simulations suggest that there exists for the full model a unique globally asymptotically stable interior endemic equilibrium point, while it is shown theoretically and computationally that the model possesses at least a one Patch 1 and a one Patch 2 boundary equilibria (i.e., Patch 2 and Patch 1 disease-free equilibrium) points, which are locally asymptotically stable. Some recommendations to tackle Ebola in a conflict zone are stated.

Validation of the names Cyanobacterium and Cyanobacterium stanieri, and proposal of Cyanobacteriota phyl. nov

The decision by the International Committee on Systematics of Prokaryotes (ICSP) to place the rank of phylum under the rules of the International Code of Nomenclature of Prokaryotes (ICNP), with phylum names ending in -ota based on the name of a type genus, enables the valid publication of the phylum name Cyanobacteriota with Cyanobacterium as the type genus. The names Cyanobacterium and its type species Cyanobacterium stanieri were effectively published in 1983 by Rippka and Cohen-Bazire, but the names were not validly published under the rules of the ICNP (then named the International Code of Nomenclature of Bacteria) or the rules of the ICN (International Code of Nomenclature for algae, fungi, and plants, then named the International Code of Botanical Nomenclature). We here propose the names Cyanobacterium gen. nov and Cyanobacterium stanieri sp. nov. for valid publication under the provisions of the ICN. Upon validation these names are also validly published under the ICNP according to General Consideration 5 and Rule 30. We also propose the phylum name Cyanobacteriota phyl. nov. under the rules of the ICNP.

Variational single image interpolation with time-varying regularization

Single image interpolation has wide applications in digital photography and image display. Most single image interpolation approaches achieve state-of-the-art performance at the expense of very high computation time. While efficient alternatives exist, they do not reach the same level of image quality. In this paper, we propose an image interpolation method offering both high computational efficiency and high interpolation quality. We exploit a newly-developed variational framework with time-varying regularization, i.e., the parameters of the regularization are allowed to change with time, making it different to conventional variational problems with time-independent regularization parameters. These time-varying parameters are learned from training samples. We train the model parameters for the problem of single image interpolation. Experiments show that the trained models lead to promising quality of the interpolated images in terms of quantitative measurements (e.g., PSNR and SSIM), compared with the state-of-the-art approaches. Meanwhile, high computational efficiency is obtained. (C) 2017 Elsevier B.V. All rights reserved.

Profiling and occupational health risk assessment study on coal ashes in terms of polycyclic aromatic hydrocarbons (PAHs)

Profiling and cancer risk assessment on the polycyclic aromatic hydrocarbons (PAHs) content of coal ashes produced by the major coal combustion plants from the eastern coalfield region in India was conducted. Thirteen PAHs were detected on coal ashes collected from ash deposition sites of major thermal power plants and the profiling of the PAHs was done. Benzo[a]pyrene equivalents (BaPeq) for individual PAHs were calculated and applied to the probabilistic assessment model from US EPA (1989). Monte Carlo simulations were conducted to assess the risk of inhabitants exposed to PAHs through the dust of the coal ash deposition site. In fly ash, the range of total amount of carcinogenic PAHs was from 3.50 to 6.72 µg g-1 and for the bottom ash, the range was 8.49 to 14.91 µg g-1. Bottom ashes were loaded with ample amounts of 5- and 6-ring carcinogenic PAHs, whereas fly ashes were dominated by medium molecular weight PAHs. The simulated mean cancer risks from fly ashes were 2.187 E-06 for children and 3.749 E-06 for adults. For the case of bottom ash, the mean risks were 1.248 E-05 and 2.173 E-05 respectively for children and adults. Among all the three exposure routes, dermal contact was the major and caused 81% of the total cancer risk. The most sensitive parameters were exposure duration and relative skin adherence factor for soil, which contributed the most to total variation. The 90% risks calculated from the bottom ashes (2.617 E-05 for children and 4.803 E-05 for adults) are marginally above the acceptable limit (>1.000 E-06) according to US EPA. In this study, a comprehensive risk assessment on carcinogenic PAHs present in coal ashes was done for the first time that may be helpful to develop potential strategies against occupational cancer risk.

Cine Cardiac MRI Motion Artifact Reduction Using a Recurrent Neural Network

Cine cardiac magnetic resonance imaging (MRI) is widely used for the diagnosis of cardiac diseases thanks to its ability to present cardiovascular features in excellent contrast. As compared to computed tomography (CT), MRI, however, requires a long scan time, which inevitably induces motion artifacts and causes patients' discomfort. Thus, there has been a strong clinical motivation to develop techniques to reduce both the scan time and motion artifacts. Given its successful applications in other medical imaging tasks such as MRI super-resolution and CT metal artifact reduction, deep learning is a promising approach for cardiac MRI motion artifact reduction. In this paper, we propose a novel recurrent generative adversarial network model for cardiac MRI motion artifact reduction. This model utilizes bi-directional convolutional long short-term memory (ConvLSTM) and multi-scale convolutions to improve the performance of the proposed network, in which bi-directional ConvLSTMs handle long-range temporal features while multi-scale convolutions gather both local and global features. We demonstrate a decent generalizability of the proposed method thanks to the novel architecture of our deep network that captures the essential relationship of cardiovascular dynamics. Indeed, our extensive experiments show that our method achieves better image quality for cine cardiac MRI images than existing state-of-the-art methods. In addition, our method can generate reliable missing intermediate frames based on their adjacent frames, improving the temporal resolution of cine cardiac MRI sequences.

Coordination Switch Drives Selective C-S Bond Formation by the Non-Heme Sulfoxide Synthases

The non-heme iron ergothioneine synthase (EgtB) is a sulfoxide synthase that catalyzes oxidative C-S bond formation in the synthesis of ergothioneine, which plays roles against oxidative stress in cells. Despite extensive experimental and computational studies of the catalytic mechanisms of EgtB, the root causes for the selective C-S bond formation remain elusive. Using quantum mechanics/molecular mechanics (QM/MM) calculations, we show herein that a coordination switch of the sulfoxide intermediate is involved in the catalysis of the non-heme iron EgtB. This coordination switch from the S to the O atom is driven by the S/π electrostatic interactions, which efficiently promotes the observed stereoselective C-S bond formation while bypassing cysteine dioxygenation. The present mechanism is in agreement with all available experimental data, including regioselectivity, stereoselectivity and KIE results. This match underscores the critical role of coordination switching in the catalysis of non-heme enzymes.

Toward Large-Pixel Number High-Speed Imaging Exploiting Time and Space Sparsity

In this paper, we propose an algorithm that enhances the number of pixels for high-speed imaging. High-speed cameras have a principle problem that the number of pixels reduces when the number of frames per second (fps) increases. To enhance the number of pixels, we suppose an optical structure that block-randomly selects some percent of pixels in an image. Then, we need to reconstruct the entire image. For this, a state-of-the-art method takes three-dimensional reconstruction strategy, which requires a heavy computational cost in terms of time. To reduce the cost, the proposed method reconstructs the entire image frame-by-frame using a new cost function exploiting two types of sparsity. One is within each frame and the other is induced from the similarity between adjacent frames. The latter further means not only in the image domain, but also in a sparsifying transformed domain. Since the cost function we define is convex, we can find the optimal solution using a convex optimization technique with small computational cost. We conducted simulations using grayscale image sequences. The results show that the proposed method produces a sequence, mostly the same quality as the state-of-the-art method, with dramatically less computational time.

Today and tomorrow of analyses' methodology of electric power system reliability

In this paper the review of the state-of-art of electric power system reliability analyses methodology as well as the new trends are given. The definition and the relationships between electric power system reliability and electric energy security and quality of electricity supply are presented. The available models, methods and computer tools are discussed. (Today and tomorrow of analyses' methodology of electric power system reliability).

Stochastic Computing Improves the Timing-Error Tolerance and Latency of Turbo Decoders: Design Guidelines and Tradeoffs

Stochastic computing has been recently proposed for the hardware implementation of both low-density parity-check (LDPC) decoders and turbo decoders, which facilitate near-optimal error correction capabilities in wireless communication applications. Previous contributions have demonstrated that stochastic LDPC decoders offer an attractive tradeoff between their error correction capabilities, hardware performance, and timing-error tolerance. Motivated by this, we propose a pair of stochastic turbo decoder (S Ill) designs having significantly enhanced timing-error tolerance and significantly reduced processing latency. Moreover, we characterize the tradeoffs between chip area, energy efficiency, latency, throughput, and error correction capabilities of both the timing-error-tolerant S ID and of the reduced-latency S ID. We demonstrate that our proposed timing-error-tolerant S ID operated at 1.20 V, with a clock period of 2.2 ns and in the presence of a three-standard deviation power supply variation of 7%, exhibits an unimpaired performance, compared with the state-of-the-art S ID, operated at 1.20 V and 4 ns and with no power supply variations. This corresponds processing throughput improvement by a factor of 2.42 and energy consumption reduction by a factor of 4. Finally, we demonstrate that our proposed reduced-latency S ID has a processing latency that is an order of magnitude lower than that of the state-of-the-art STD. This is despite reducing the chip area by a factor of 4, increasing the processing throughput by a factor of 65, while consuming only 0.005 times the energy of the state-of-the-art S ID, when using binary phase-shift keying for communication over an additive white Gaussian noise channel having E-b/N-0 = 3 dB.

The Internet of Audio Things: State of the Art, Vision, and Challenges

The Internet of Audio Things (IoAuT) is an emerging research field positioned at the intersection of the Internet of Things, sound and music computing, artificial intelligence, and human-computer interaction. The IoAuT refers to the networks of computing devices embedded in physical objects (Audio Things) dedicated to the production, reception, analysis, and understanding of audio in distributed environments. Audio Things, such as nodes of wireless acoustic sensor networks, are connected by an infrastructure that enables multidirectional communication, both locally and remotely. In this article, we first review the state of the art of this field, then we present a vision for the IoAuT and its motivations. In the proposed vision, the IoAuT enables the connection of digital and physical domains by means of appropriate information and communication technologies, fostering novel applications and services based on auditory information. The ecosystems associated with the IoAuT include interoperable devices and services that connect humans and machines to support human-human and human-machines interactions. We discuss the challenges and implications of this field, which lead to future research directions on the topics of privacy, security, design of Audio Things, and methods for the analysis and representation of audio-related information.

Variational EM method for blur estimation using the spike-and-slab image prior

Most state-of-the-art blind image deconvolution methods rely on the Bayesian paradigm to model the deblurring problem and estimate both the blur kernel and latent image. It is customary to model the image in the filter space, where it is supposed to be sparse, and utilize convenient priors to account for this sparsity. In this paper, we propose the use of the spike-and-slab prior together with an efficient variational Expectation Maximization (EM) inference scheme to estimate the blur in the image. The spike-and-slab prior, which constitutes the gold standard in sparse machine learning, selectively shrinks irrelevant variables while mildly regularizing the relevant ones. The proposed variational Expectation Maximization algorithm is more efficient than usual Markov Chain Monte Carlo (MCMC) inference and, also, proves to be more accurate than the standard mean-field variational approximation. Additionally, all the prior model parameters are estimated by the proposed scheme. After blur estimation, a non blind restoration method is used to obtain the actual estimation of the sharp image. We investigate the behavior of the prior in the experimental section together with a series of experiments with synthetically generated and real blurred images that validate the method's performance in comparison with state-of-the-art blind deconvolution techniques. (C) 2019 Elsevier Inc. All rights reserved.

Benign acute childhood myositis following human Influenza B virus infection - A case series

Influenza virus primarily affects ciliated cells of respiratory epithelium. Humans do not have innate immunity for these viruses and are vulnerable to get attacked. Benign acute childhood myositis usually occurs at the early convalescent phase of a influenza viral illness when fever, cough, myalgia, nasal discharge are the initial presentation.

Weld defect classification in radiographic images using unified deep neural network with multi-level features

Deep neural network (DNN) exhibits state-of-the-art performance in many fields including weld defect classification. However, there is still a large room for improving the classification performance over the generic DNN models. In this paper, a unified deep neural network with multi-level features is proposed for weld defect classification. Firstly, we define 11 weld defect features as inputs of our proposed classification model. Not limited to geometric and intensity features, 4 features based on the intensity contrast between weld defect and its background are proposed in this paper. Secondly, we construct a novel deep learning framework: a unified deep neural network, where multi-level features of each hidden layer are fused by the last hidden layer to predict the type of weld defect comprehensively. In addition, we investigate pre-training and fine-turning strategies to get better generalization performance with small dataset. Comparing with other classification methods like SVM and generic DNN model, our framework takes full advantage of multi-level features extracted from each hidden layer, an outstanding performance is shown where the classification accuracy is improved by 3.18% and 4.33% on the test dataset, to reach 91.36%.

BitCluster: Fine-Grained Weight Quantization for Load-Balanced Bit-Serial Neural Network Accelerators

Convolutional neural network (CNN) has demonstrated great success in pattern recognition scenarios at the cost of nearly billions of parameters and consequent convolution operations. Various dedicated hardware designs are proposed to accelerate the CNN computation in more energy-efficient manners. Especially, the bit-serial accelerator (BSA) is one of the most effective approaches on resource-limited platforms by eliminating zero-bit computations. However, the irregular distribution and varying number of effectual (nonzero) bits in weights significantly cause hardware underutilization, impeding further performance improvement of state-of-the-art BSAs. To address this issue, BitCluster, a hardware-friendly quantization method, is proposed to make each weight with the identical number of effectual bits for load-balanced computation. Considering distinct sensitivities to weight precision in different neural layers, layer-level BitCluster is proposed to design further for fine-grained weight quantization. It systematically determines the layerwise quantization configurations, which significantly improve the overall performance with <1% accuracy loss. BitCluster is comprehensively evaluated on a BitCluster-compatible BSA design by taking six mainstream CNN models as benchmarks. The experimental results show that the BitCluster-based BSA achieves 1.6x higher hardware utilization and 3.4x speedup on average than state-of-the-art BSAs, with 5x better energy efficiency on average.

Towards Ultrasound Everywhere: A Portable 3D Digital Back-End Capable of Zone and Compound Imaging

Ultrasound imaging is a ubiquitous diagnostic technique, but does not fit the requirements of the telemedicine approach, because it relies on the real-time manipulation and image recognition skills of a trained expert, called sonographer. Sonographers are only available in hospitals and clinics, negating or at least delaying access to ultrasound scans in many locales-rural areas, developing countries-as well as in medical rescue operations. Telesonography would require an advanced imager that supports three-dimensional (3-D) acquisition; this would allow untrained operators to acquire broad scans and upload them remotely for diagnosis. Such advanced imagers do exist, but do not meet several other requirements for telesonography, such as being portable, inexpensive, and sufficiently low power to enable battery operation. In thiswork, we present our prototype of the first portable 3-D digital ultrasound back-end system. The prototype is implemented in a single midrange Xilinx field programmable gate array (FPGA), for an estimated power consumption of 5 W. The device supports up to 1024 input channels, which is state of the art and could be scaled further, and supports multiple image reconstruction modes. We evaluate the resource utilization of the FPGA and provide various quality metrics to ascertain the output image quality.

Fuzzy-Coded Space-Frequency Quantization for SAR Data Compression

In this letter, we propose a new image coding technique, which is a combination of space frequency quantization and a context-based modeling using fuzzy logic. The compression results showed that the proposed coder outperforms the state-of-the-art coders in the rate-distortion sense for compression of processed synthetic aperture radar amplitude data.

Steven-Johnson Syndrome: A Rare but Serious Adverse Event of Nivolumab Use in a Patient With Metastatic Gastric Adenocarcinoma

Nivolumab is a humanized monoclonal anti-programmed cell death receptor-1 (PD-1) antibody that has been authorized for use in the treatment of advanced malignancies. Cutaneous reactions are the most common immune-related adverse events reported with anti-PD-1 agents, and they range broadly from mild localized reactions to rarely severe or life-threatening systemic dermatoses. The occurrence of Steven-Johnson syndrome (SJS) or toxic epidermal necrolysis (TEN) with nivolumab use is an exceedingly rare phenomenon that was only documented in a handful of cases in the current literature, but it deserves careful attention as SJS/TEN may be associated with fatal outcomes. We present a case of nivolumab-induced SJS/TEN in a middle-aged female patient with metastatic gastric adenocarcinoma that was successfully treated with immunosuppressive therapy and supportive care. Prompt recognition of SJS/TEN with discontinuation of nivolumab is warranted when SJS/TEN is suspected clinically. Multidisciplinary management in a specialized burn unit is the key to improving outcomes of SJS/TEN.

Local Information Assisted Attention-Free Decoder for Audio Captioning

Automated audio captioning aims to describe audio data with captions using natural language. Existing methods often employ an encoder-decoder structure, where the attention-based decoder (e.g., Transformer decoder) is widely used and achieves state-of-the-art performance. Although this method effectively captures global information within audio data via the self-attention mechanism, it may ignore the event with short time duration, due to its limitation in capturing local information in an audio signal, leading to inaccurate prediction of captions. To address this issue, we propose a method using the pretrained audio neural networks (PANNs) as the encoder and local information assisted attention-free Transformer (LocalAFT) as the decoder. The novelty of our method is in the proposal of the LocalAFT decoder, which allows local information within an audio signal to be captured while retaining the global information. This enables the events of different duration, including short duration, to be captured for more precise caption generation. Experiments show that our method outperforms the state-of-the-art methods in Task 6 of the DCASE 2021 Challenge with the standard attention-based decoder for caption generation.

Associations of genome-wide cell-free DNA fragmentation profiles with blood biochemical and hematological parameters in healthy individuals

Cell-free DNA (cfDNA), as a non-invasive approach, has been introduced in a wide range of applications, including cancer diagnosis/ monitoring, prenatal testing, and transplantation monitoring. Yet, studies of cfDNA fragmentomics in physiological conditions are lacking. In this study, we aim to explore the correlation of fragmentation patterns of cfDNA with blood biochemical and hematological parameters in healthy individuals. We addressed the impact of physiological variables and abnormal blood biochemical and hematological parameters on cfDNA fragment size distribution. We also figured and validated that hematological inflammation markers, including leukocyte, lymphocyte, neutrophil, and platelet distribution width as well as aspartate transaminase levels were significantly correlated with the genome-wide cfDNA fragmentation pattern. Our findings suggest that cfDNA fragmentation profiles were associated with physiological parameters related to cardiovascular risk factors, inflammatory response and hepatocyte injury, which may provide insights for further research on the potential role of cfDNA fragmentation in diagnosis and monitor of several disease.

Image compression using adaptive multiresolution image decomposition algorithm

With the growth of modern digital technologies, demand for transmission multimedia and digital images, which require more storage space and transmission bandwidth, has been increased rapidly. Hence, developing new image compression techniques for reducing data size without degrading the quality of the image, has gained a lot of interest recently. In this study, an adaptive multiresolution image decomposition (AMID) algorithm is proposed and its application for image compression is explored. The developed algorithm is capable of decomposing an image along the vertical, horizontal, and diagonal directions using the pyramidal multiresolution scheme. Compared to the wavelet transform, the AMID can be used for decimation with the guarantees of perfect signal reconstruction. Furthermore, the application of the AMID for image compression is explored and its performance is compared with the state-of-the-art image compression techniques. The performance of compression method is evaluated using peak signal-to-noise ratio and compression ratio. Experimental results have shown promising performance compared with the results of using other image compression approaches.

Differentiation therapy: Unlocking phenotypic plasticity of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors. The current treatment of HCC mainly includes surgery, chemotherapy, and liver transplantation. HCC differentiation therapy aims to restore tumor cells' normal liver characteristics and unlock their phenotypic plasticity. Understanding the molecular and signaling pathways that control HCC differentiation can help identify new targets for inducing differentiation and provide ideas for drug design. Downregulation of liver enriched transcription factors, imbalanced signal pathway, and dysregulated microRNA play essential roles in regulating the differentiation state of HCC. Restoring normal expression levels of these molecules could induce the tumor cells to differentiate into hepatocyte-like cells (HLCs) and suppress the malignant tumor phenotype. The strategies for inducing HLCs from induced pluripotent stem cells, fibroblasts, and other somatic cells provide a reference for the induced differentiation of liver cancer. The differentiation therapy is expected to be a promising and effective treatment for HCC.

Dual-Bit-Wise Stochastic Decoding for Polar Codes

codes, being the first class of codes with provable capacity-achieving property, have aroused extensive attention. The belief propagation (BP) algorithm, which is one of the popular decoding approaches for polar codes, possesses inherent high parallelism but also high computational complexity. With low complexity and high fault tolerance, stochastic computing has been well studied and applied to BP decoding for polar codes. However, existing stochastic BP decoders suffer from high decoding latency. In this paper, firstly, a novel dual-bit-wise iterative message update method is proposed for stochastic BP, reducing the decoding latency by more than half. Based on the dual-bit-wise decoding, more special nodes in the factor graph are investigated and then fully exploited to pursue lower decoding complexity than prior arts. Moreover, a well-optimized architecture is developed for the proposed stochastic decoder, where the random number generator (RNG), tracking forecast memory (TFM), and hard decision unit are intelligently designed for reduced hardware cost. Experimental results demonstrate that the presented decoder can exhibit 61.7% lower decoding latency than the state-of-the-art works. Besides, the throughput and hardware efficiency can be improved by nearly 2.8x and 2.7x, respectively.

Interval between prior SARS-CoV-2 infection and booster vaccination impacts magnitude and quality of antibody and B cell responses

SARS-CoV-2 mRNA booster vaccines provide protection from severe disease, eliciting strong immunity that is further boosted by previous infection. However, it is unclear whether these immune responses are affected by the interval between infection and vaccination. Over a 2-month period, we evaluated antibody and B cell responses to a third-dose mRNA vaccine in 66 individuals with different infection histories. Uninfected and post-boost but not previously infected individuals mounted robust ancestral and variant spike-binding and neutralizing antibodies and memory B cells. Spike-specific B cell responses from recent infection (<180 days) were elevated at pre-boost but comparatively less so at 60 days post-boost compared with uninfected individuals, and these differences were linked to baseline frequencies of CD27lo B cells. Day 60 to baseline ratio of BCR signaling measured by phosphorylation of Syk was inversely correlated to days between infection and vaccination. Thus, B cell responses to booster vaccines are impeded by recent infection.

Robust Adaptive Extended Kalman Filtering for Real Time MR-Thermometry Guided HIFU Interventions

Real time magnetic resonance (MR) thermometry is gaining clinical importance formonitoring and guiding high intensity focused ultrasound (HIFU) ablations of tumorous tissue. The temperature information can be employed to adjust the position and the power of the HIFU system in real time and to determine the therapy endpoint. The requirement to resolve both physiological motion of mobile organs and the rapid temperature variations induced by state-of-the-art high-power HIFU systems require fast MRI-acquisition schemes, which are generally hampered by low signal-to-noise ratios (SNRs). This directly limits the precision of real time MR-thermometry and thus in many cases the feasibility of sophisticated control algorithms. To overcome these limitations, temporal filtering of the temperature has been suggested in the past, which has generally an adverse impact on the accuracy and latency of the filtered data. Here, we propose a novel filter that aims to improve the precision of MR-thermometry while monitoring and adapting its impact on the accuracy. For this, an adaptive extended Kalman filter using a model describing the heat transfer for acoustic heating in biological tissues was employed together with an additional outlier rejection to address the problem of sparse artifacted temperature points. The filter was compared to an efficient matched FIR filter and outperformed the latter in all tested cases. The filter was first evaluated on simulated data and provided in the worst case (with an approximate configuration of the model) a substantial improvement of the accuracy by a factor 3 and 15 during heat up and cool down periods, respectively. The robustness of the filter was then evaluated during HIFU experiments on a phantom and in vivo in porcine kidney. The presence of strong temperature artifacts did not affect the thermal dose measurement using our filter whereas a high measurement variation of 70% was observed with the FIR filter.

FLASH X-ray spares intestinal crypts from pyroptosis initiated by cGAS-STING activation upon radioimmunotherapy

DNA-damaging treatments such as radiotherapy (RT) have become promising to improve the efficacy of immune checkpoint inhibitors by enhancing tumor immunogenicity. However, accompanying treatment-related detrimental events in normal tissues have posed a major obstacle to radioimmunotherapy and present new challenges to the dose delivery mode of clinical RT. In the present study, ultrahigh dose rate FLASH X-ray irradiation was applied to counteract the intestinal toxicity in the radioimmunotherapy. In the context of programmed cell death ligand-1 (PD-L1) blockade, FLASH X-ray minimized mouse enteritis by alleviating CD8+ T cell-mediated deleterious immune response compared with conventional dose rate (CONV) irradiation. Mechanistically, FLASH irradiation was less efficient than CONV X-ray in eliciting cytoplasmic double-stranded DNA (dsDNA) and in activating cyclic GMP-AMP synthase (cGAS) in the intestinal crypts, resulting in the suppression of the cascade feedback consisting of CD8+ T cell chemotaxis and gasdermin E-mediated intestinal pyroptosis in the case of PD-L1 blocking. Meanwhile, FLASH X-ray was as competent as CONV RT in boosting the antitumor immune response initiated by cGAS activation and achieved equal tumor control in metastasis burdens when combined with anti-PD-L1 administration. Together, the present study revealed an encouraging protective effect of FLASH X-ray upon the normal tissue without compromising the systemic antitumor response when combined with immunological checkpoint inhibitors, providing the rationale for testing this combination as a clinical application in radioimmunotherapy.

A Logarithmic Opinion Pool Based STAPLE Algorithm for the Fusion of Segmentations With Associated Reliability Weights

Pelvic floor dysfunction is common in women after childbirth and precise segmentation of magnetic resonance images (MRI) of the pelvic floor may facilitate diagnosis and treatment of patients. However, because of the complexity of its structures, manual segmentation of the pelvic floor is challenging and suffers fromhigh inter and intra-rater variability of expert raters. Multiple template fusion algorithms are promising segmentation techniques for these types of applications, but they have been limited by imperfections in the alignment of templates to the target, and by template segmentationerrors. A number of algorithms sought to improve segmentation performance by combining image intensities and template labels as two independent sources of information, carrying out fusion through local intensity weighted voting schemes. This class of approach is a form of linear opinion pooling, and achieves unsatisfactory performance for this application. We hypothesized that better decision fusion could be achieved by assessing the contribution of each template in comparison to a reference standard segmentation of the target image and developed a novel segmentation algorithm to enable automatic segmentation of MRI of the female pelvic floor. The algorithm achieves high performance by estimating and compensating for both imperfect registration of the templates to the target image and template segmentation inaccuracies. A local image similarity measure is used to infer a local reliability weight, which contributes to the fusion through a novel logarithmic opinion pooling. We evaluated our new algorithm in comparison to nine state-of-the-art segmentation methods and demonstrated our algorithm achieves the highest performance.

An integrated approach for process monitoring using wavelet analysis and competitive neural network

A novel framework involving both a detection module and a classification module is proposed for the recognition of the six main types of process signals. In particular, a multi-scale wavelet filter is used for denoising and its performance is compared with that of single-scale linear filters. Moreover, two kinds of competitive neural networks, based on learning vector quantization (LVQ) and adaptive resonance theory ( ART), are adopted for the task of pattern classification and benchmarking. Our results show that denoising through a wavelet filter is best for pattern classification, and the classification accuracy with respect to six predefined categories using a LVQ-X network is a little better than using an ART network. However, when an unexpected novel pattern occurs within the process, LVQ will force the novel pattern to be classified into one of those predefined categories that is most similar to the novel pattern. On the contrary, ART will automatically construct a new class when the similarity measured between the novel pattern and the most similar category is too small to be incorporated. Therefore, under the consideration of the stability - plasticity dilemma, our simplified ART network based on multi-scale wavelet denoising provides a more promising way to adapt unexpected novel patterns.