ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
HybridGBN-SR: A Deep 3D/2D Genome Graph-Based Network for Hyperspectral Image Classification

The successful application of deep learning approaches in remote sensing image classification requires large hyperspectral image (HSI) datasets to learn discriminative spectral-spatial features simultaneously. To date, the HSI datasets available for image classification are relatively small to train deep learning methods. This study proposes a deep 3D/2D genome graph-based network (abbreviated as HybridGBN-SR) that is computationally efficient and not prone to overfitting even with extremely few training sample data. At the feature extraction level, the HybridGBN-SR utilizes the three-dimensional (3D) and two-dimensional (2D) Genoblocks trained using very few samples while improving HSI classification accuracy. The design of a Genoblock is based on a biological genome graph. From the experimental results, the study shows that our model achieves better classification accuracy than the compared state-of-the-art methods over the three publicly available HSI benchmarking datasets such as the Indian Pines (IP), the University of Pavia (UP), and the Salinas Scene (SA). For instance, using only 5% labeled data for training in IP, and 1% in UP and SA, the overall classification accuracy of the proposed HybridGBN-SR is 97.42%, 97.85%, and 99.34%, respectively, which is better than the compared state-of-the-art methods.
Autonomy, procedural and substantive: a discussion of the ethics of cognitive enhancement

As cognitive enhancement research advances, important ethical questions regarding individual autonomy and freedom are raised. Advocates of cognitive enhancement frequently adopt a procedural approach to autonomy, arguing that enhancers improve an individual's reasoning capabilities, which are quintessential to being an autonomous agent. On the other hand, critics adopt a more nuanced approach by considering matters of authenticity and self-identity, which go beyond the mere assessment of one's reasoning capacities. Both positions, nevertheless, require further philosophical scrutiny. In this paper, we investigate the ethics of cognitive enhancement through the lenses of political and philosophical arguments about autonomy and freedom. In so doing, we contend that a substantive, relational account of individual autonomy offers a more holistic understanding of the ethical concerns of cognitive enhancement.
Comparison of BinaxNOW and SARS-CoV-2 qRT-PCR Detection of the Omicron Variant from Matched Anterior Nares Swabs

The COVID-19 pandemic has increased use of rapid diagnostic tests (RDTs). In winter 2021 to 2022, the Omicron variant surge made it apparent that although RDTs are less sensitive than quantitative reverse transcription-PCR (qRT-PCR), the accessibility, ease of use, and rapid readouts made them a sought after and often sold-out item at local suppliers. Here, we sought to qualify the Abbott BinaxNOW RDT for use in our university testing program as a method to rule in positive or rule out negative individuals quickly at our priority qRT-PCR testing site. To perform this qualification study, we collected additional swabs from individuals attending this site. All swabs were tested using BinaxNOW. Initially as part of a feasibility study, test period 1 (n = 110) samples were stored cold before testing. In test period 2 (n = 209), samples were tested immediately. Combined, 102/319 samples tested severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) positive via qRT-PCR. All sequenced samples were Omicron (n = 92). We calculated 53.9% sensitivity, 100% specificity, a 100% positive predictive value, and an 82.2% negative predictive value for BinaxNOW (n = 319). Sensitivity would be improved (75.3%) by changing the qRT-PCR positivity threshold from a threshold cycle (CT) value of 40 to a CT value of 30. The receiver operating characteristic (ROC) curve shows that for qRT-PCR-positive CT values of between 24 and 40, the BinaxNOW test is of limited value diagnostically. Results suggest BinaxNOW could be used in our setting to confirm SARS-CoV-2 infection in individuals with substantial viral load, but a significant fraction of infected individuals would be missed if we used RDTs exclusively to rule out infection. IMPORTANCE Our results suggest BinaxNOW can rule in SARS-CoV-2 infection but would miss infections if RDTs were exclusively used.
[Retrospective Analysis of Common Faults and Maintenance Strategies of Medical Electronic Endoscope]

Medical electronic endoscope is one of the indispensable tools in medical diagnosis and treatment. With the development of science and technology, electronic endoscope has higher safety and accuracy than traditional optical endoscope. Due to the sophisticated construction and high price, hospitals spend a lot of money on maintenance every year. In order to prolong the working life of electronic endoscope, reduce the incidence of artificial failure and save hospital costs, this study made a retrospective analysis on the common faults of electronic endoscope, and summarized the maintenance strategies for reference.
2D and 3D crystallization of the wild-type IIC domain of the glucose PTS transporter from Escherichia coli

The bacterial phosphoenolpyruvate: sugar phosphotransferase system serves the combined uptake and phosphorylation of carbohydrates. This structurally and functionally complex system is composed of several conserved functional units that, through a cascade of phosphorylated intermediates, catalyze the transfer of the phosphate moiety from phosphoenolpyruvate to the substrate, which is bound to the integral membrane domain IIC. The wild-type glucose-specific IIC domain (wt-IIC(glc)) of Escherichia coli was cloned, overexpressed and purified for biochemical and functional characterization. Size-exclusion chromatography and scintillation-proximity binding assays showed that purified wt-IIC(glc) was homogenous and able to bind glucose. Crystallization was pursued following two different approaches: (i) reconstitution of wt-IIC(glc) into a lipid bilayer by detergent removal through dialysis, which yielded tubular 2D crystals, and (ii) vapor-diffusion crystallization of detergent-solubilized wt-IIC(glc), which yielded rhombohedral 3D crystals. Analysis of the 2D crystals by cryo-electron microscopy and the 3D crystals by X-ray diffraction indicated resolutions of better than 6Å and 4Å, respectively. Furthermore, a complete X-ray diffraction data set could be collected and processed to 3.93Å resolution. These 2D and 3D crystals of wt-IIC(glc) lay the foundation for the determination of the first structure of a bacterial glucose-specific IIC domain.
The effect of population model choice and network topologies on extinction time patterns in dendritic ecological networks

Models of populations in habitat networks are vital for understanding and linking processes and patterns across individuals, environments, ecological interactions, and population structures. River ecosystem models combine the physical structure of the networks with the biological processes of the organisms using structural and functional models, respectively. Previous studies on dendritic river networks have employed different functional (population) models and either directly claimed or implied that the results illustrate general properties of actual river systems. However, these studies have used different approaches and assumptions when modeling population characteristics and behavior, and it is possible that inferences regarding a system may vary based on the combination of functional model and the spatial structure of a network. This study aims to understand if different functional models in river systems produce substantially different model results and, therefore, whether conclusions are model-dependent. We compare variation in extinction time and occupancy proportion of river networks with linear, trellis, dendritic and ring-lattice topologies, using three population models (uniform, age-class and individual based) and one metapopulation-based (patch-occupancy) model. Dendritic, linear, and trellis structures did not show notable differences among extinction times for any of the four models. The difference between topologies was higher for the patch-occupancy model compared to the three population models. There were significant differences in the variations of patch-occupancy between the metapopulation and the population models, but the three population models of differing complexity produced broadly similar results. Therefore, if the occupancy data is obtained based on local subpopulations, spatial arrangement and connectivity does not appear to be the sole predictor of single-species metapopulation responses. We conclude that the outputs from functional models are robust to assumptions and varying levels of detail as long as they contain at least some detail at the level of individuals within habitat nodes. Also, if we are modeling network-scale populations, models that include at least some detailed information on individuals are a far better choice than considering populations implicitly.
Fast Online Set Intersection for Network Processing on FPGA

Online set intersection operations have been widely used in network processing tasks, such as Quality of Service differentiation, firewall processing, and packet/traffic classification. The major challenge for online set intersection is to sustain line-rate processing speed; accelerating set intersection using state-of-the-art hardware devices is of great interest to the research community. In this paper, we present a novel high-performance set intersection approach on FPGA. In our approach, each element in any set is represented by a combination of Group ID (GID) and Bit Stride (BS); all the sets are intersected using linear merge techniques and bitwise AND operations. We map our online set intersection algorithm onto hardware; this is done by constructing modular Processing Element (PE) and concatenating multiple PEs into a tree-based parallel architecture. In order to improve the throughput on a state-of-the-art FPGA, we feed all the inputs to FPGA in a streaming fashion with the help of the synchronization GIDs. Post place-and-route results show that, for a typical set intersection problem in network processing, our design can intersect eight sets, each of up to 32 K elements, at a throughput of 47: 4 Thousand Intersections Per Second (KIPS) and a latency of 94: 8 mu s per batch of inputs. Compared to the classic linear merge or bitwise AND techniques on state-of-the-art multi-core processors, our designs on FPGA achieves up to 66 x throughput improvement and 80 x latency reduction.
The state of the art for numerical simulations of the effect of the microstructure and its evolution in the metal-cutting processes

Microstructure features directly reflect mechanical responses during material deformation and can influence the surface integrity of machined components, which is of great interest to both academic communities and in-dustries. This paper summarizes the state of the art in modeling approaches for the effect of the microstructures and its evolution during metal-cutting processes. The aim of this paper is to analyze the advantages and drawbacks of current methods to improve modeling work and direct future orientations. First, dominant nu-merical modeling approaches for metal-cutting processes are reviewed. The finite element method (FEM) and mesh-free methods widely applied in professional research are discussed. Following this, approaches to modeling the effect of microstructures and their evolution are reviewed for two major categories-homogeneous field distribution and heterogeneous characteristics. Both the advantages and disadvantages of these two categories are analyzed and discussed in detail. Experimental techniques based on advanced characterization approaches to the validation of pertinent models are also discussed. Finally, a brief summary is presented and an outlook for future work is delineated.
Lithium-Salt-Containing High-Molecular-Weight Polystyrene-block-Polyethylene Oxide Block Copolymer Films

Ionic conductivity in relation to the morphology of lithium-doped high-molecular-weight polystyrene-block-polyethylene oxide (PS-b-PEO) diblock copolymer films was investigated as solid-state membranes for lithium-ion batteries. The tendency of the polyethylene (PEO) block to crystallize was highly suppressed by increasing both the salt-doping level and the temperature. The PEO crystallites completely vanished at a salt-doping ratio of Li/EO>0.08, at which the PEO segments were hindered from entering the crystalline unit of the PEO chain. A kinetically trapped lamella morphology of PS-b-PEO was observed, due to PEO crystallization. The increase in the lamella spacing with increasing salt concentration was attributed to the conformation of the PEO chain rather than the volume contribution of the salt or the previously reported increase in the effective interaction parameter. Upon loading the salt, the PEO chains changed from a compact/highly folded conformation to an amorphous/expanded-like conformation. The ionic conductivity was enhanced by amorphization of PEO and thereby the mobility of the PEO blocks increased upon increasing the salt-doping level.
Role of Post-translational Modification of Silent Mating Type Information Regulator 2 Homolog 1 in Cancer and Other Disorders

Silent mating type information regulator 2 homolog 1 (SIRT1), an NAD+-dependent histone/protein deacetylase, has multifarious physiological roles in development, metabolic regulation, and stress response. Thus, its abnormal expression or malfunction is implicated in pathogenesis of various diseases. SIRT1 undergoes post-translational modifications, including phosphorylation, oxidation/reduction, carbonylation, nitrosylation, glycosylation, ubiquitination/deubiquitination, SUMOylation etc. which can modulate its catalytic activity, stability, subcellular localization, and also binding affinity for substrate proteins. This short review highlights the regulation of SIRT1 post-translational modifications and their pathophysiologic implications.
The role of metal transporters in phytoremediation: A closer look at Arabidopsis

Pollution of the environment by heavy metals (HMs) has recently become a global issue, affecting the health of all living organisms. Continuous human activities (industrialization and urbanization) are the major causes of HM release into the environment. Over the years, two methods (physical and chemical) have been widely used to reduce HMs in polluted environment. However, these two methods are inefficient and very expensive to reduce the HMs released into the atmosphere. Alternatively, researchers are trying to remove the HMs by employing hyper-accumulator plants. This method, referred to phytoremediation, is highly efficient, cost-effective, and eco-friendly. Phytoremediation can be divided into five types: phytostabilization, phytodegradation, rhizofiltration, phytoextraction, and phytovolatilization, all of which contribute to HMs removal from the polluted environment. Brassicaceae family members (particularly Arabidopsis thaliana) can accumulate more HMs from the contaminated environment than those of other plants. This comprehensive review focuses on how HMs pollute the environment and discusses the phytoremediation measures required to reduce the impact of HMs on the environment. We discuss the role of metal transporters in phytoremediation with a focus on Arabidopsis. Then draw insights into the role of genome editing tools in enhancing phytoremediation efficiency. This review is expected to initiate further research to improve phytoremediation by biotechnological approaches to conserve the environment from pollution.
Tritium discharges in the environment and consequences: Recent advances and perspectives

Several publications have raised questions in France about the behavior of tritium in the environment and its impact on human health. In 2008, ASN asked two groups of experts to make a state of the art on this subject. An action plan based on the recommendations by these groups of experts was presented in the Tritium White Paper published in 2010. Since then, the follow-up committee has periodically addressed the identified topics. Metrological progresses, research work on the transfer and level of tritium activity in the environment were studied. The understanding of its toxicity has progressed. The operators of nuclear facilities have characterized the physicochemical forms of tritiated effluents existing in the discharges from their installations. Each year, ASN updates the inventory of tritium releases from nuclear facilities and the associated dosimetric impacts on the White Paper website. As the committee's actions concerning research subjects still in progress are now limited, ASN proposed to close the committee's work in its current form and to deal with research work not yet completed during a dedicated seminar organized by IRSN.
SiameseGAN: A Generative Model for Denoising of Spectral Domain Optical Coherence Tomography Images

Optical coherence tomography (OCT) is a standard diagnostic imaging method for assessment of ophthalmic diseases. The speckle noise present in the high-speed OCT images hampers its clinical utility, especially in Spectral-Domain Optical Coherence Tomography (SDOCT). In this work, a new deep generative model, called as SiameseGAN, for denoising Low signal-to-noise ratio (LSNR) B-scans of SDOCT has been developed. SiameseGAN is a Generative Adversarial Network (GAN) equipped with a siamese twin network. The siamese network module of the proposed SiameseGAN model helps the generator to generate denoised images that are closer to groundtruth images in the feature space, while the discriminator helps in making sure they are realistic images. This approach, unlike baseline dictionary learning technique (MSBTD), does not require an apriori high-quality image from the target imaging subject for denoising and takes less time for denoising. Moreover, various deep learning models that have been shown to be effective in performing denoising task in the SDOCT imaging were also deployed in this work. A qualitative and quantitative comparison on the performance of proposed method with these state-of-the-art denoising algorithms has been performed. The experimental results show that the speckle noise can be effectively mitigated using the proposed SiameseGAN along with faster denoising unlike existing approaches.
A 26.5 pA(rms) Neurotransmitter Front-End With Class-AB Background Subtraction

This paper presents an analog front-end (AFE) for fast-scan cyclic voltammetry (FSCV) with analog background subtraction using a pseudo-differential sensing scheme to cancel the large non-faradaic current before seeing the front-end. As a result, the AFE can be compact and low-power compared to conventional FSCV AFEs with dedicated digital back-ends to digitize and subtract the background from subsequent recordings. The reported AFE, fabricated in a 0.18-mu m CMOS process, consists of a class-AB common-mode rejection circuit, a low-input-impedance current conveyor, and a 1st-order current-mode delta-sigma (Delta Sigma) modulator with an infinite impulse response quantizer. This AFE achieves an effective dynamic range of 83 dB with a state-of-the-art 39.2 pA(rms) input-referred noise when loaded with a 1 nF input capacitance (26.5 pA(rms) open-circuit) across a 5 kHz bandwidth while consuming an average power of 3.7 mu W. This design was tested with carbon-fiber microelectrodes scanned at 300 V/s using flow-injection of dopamine, a key neurotransmitter.
The Relationship between Sustainable Built Environment, Art Therapy and Therapeutic Design in Promoting Health and Well-Being

At present, a smart city from the perspective of the United Nations Sustainable Development Goals (SDGs) emphasizes the importance of providing citizens with promising health and well-being. However, with the continuous impact of coronavirus disease 2019 (COVID-19) and the increase of city population, the health of citizens is facing new challenges. Therefore, this paper aims to assess the relationship between building, environment, landscape design, art therapy (AT), and therapeutic design (TD) in promoting health within the context of sustainable development. It also summarizes the existing applied research areas and potential value of TD that informs future research. This paper adopts the macro-quantitative and micro-qualitative research methods of bibliometric analysis. The results show that: the built environment and AT are related to sustainable development, and closely associated with health and well-being; the application of TD in the environment, architecture, space, and landscape fields promotes the realization of SDGs and lays the foundation for integrating digital technologies such as Building Information Modeling (BIM) into the design process to potentially solve the challenges of TD; and the principle of TD can consider design elements and characteristics from based on people's health needs to better promote human health and well-being.
Triplet Cross-Fusion Learning for Unpaired Image Denoising in Optical Coherence Tomography

Optical coherence tomography (OCT) is a widely-used modality in clinical imaging, which suffers from the speckle noise inevitably. Deep learning has proven its superior capability in OCT image denoising, while the difficulty of acquiring a large number of well-registered OCT image pairs limits the developments of paired learning methods. To solve this problem, some unpaired learning methods have been proposed, where the denoising networks can be trained with unpaired OCT data. However, majority of them are modified from the cycleGAN framework. These cycleGAN-based methods train at least two generators and two discriminators, while only one generator is needed for the inference. The dual-generator and dual-discriminator structures of cycleGAN-based methods demand a large amount of computing resource, which may be redundant for OCT denoising tasks. In this work, we propose a novel triplet cross-fusion learning (TCFL) strategy for unpaired OCT image denoising. The model complexity of our strategy is much lower than those of the cycleGAN-based methods. During training, the clean components and the noise components from the triplet of three unpaired images are cross-fused, helping the network extract more speckle noise information to improve the denoising accuracy. Furthermore, the TCFL-based network which is trained with triplets can deal with limited training data scenarios. The results demonstrate that the TCFL strategy outperforms state-of-the-art unpaired methods both qualitatively and quantitatively, and even achieves denoising performance comparable with paired methods. Code is available at: https://github.com/gengmufeng/TCFL-OCT.
In silico analysis of bacterial metabolism of glutamate and GABA in the gut in a rat model of obesity and type 2 diabetes

Dysbiosis of gut microbiota has adverse effects on host health. This study aimed to determine the effects of changes of faecal microbiota in obese and diabetic rats on the imputed production of enzymes involved in the metabolism of glutamate, gamma-aminobutyric acid (GABA), and succinate. The levels of glutamate decarboxylase, GABA transaminase, succinate-semialdehyde dehydrogenase, and methylisocitrate lyase were reduced or absent in diabetic rats compared with controls and obese rats. Glutamate decarboxylase (GAD) was significantly reduced in obese rats compared with control rats, while the other enzymes were unaltered; different bacterial taxa are suggested to be involved. Levels of bacterial enzymes were inversely correlated with the blood glucose level. These findings suggest that the absence of GABA and reduced succinate metabolism from gut microbiota contribute to the diabetic state in rats.