ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Using species sensitivity distribution approach to assess the risks of commonly detected agricultural pesticides to Australia's tropical freshwater ecosystems

To assess the potential impacts of agricultural pesticides on tropical freshwater ecosystems, the present study developed temperature-specific, freshwater species protection concentrations (i.e., ecotoxicity threshold values) for 8 pesticides commonly detected in Australia's tropical freshwaters. Because relevant toxicity data for native tropical freshwater species to assess the ecological risks were mostly absent, scientifically robust toxicity data obtained at ≥20 °C were used for ecologically relevant taxonomic groups representing primary producers and consumers. Species sensitivity distribution (SSD) curves were subsequently generated for predicted chronic exposure using Burrlioz 2.0 software with mixed chronic and converted acute data relevant to exposure conditions at ≥20 °C. Ecotoxicity threshold values for tropical freshwater ecosystem protection were generated for ametryn, atrazine, diuron, metolachlor, and imidacloprid (all moderate reliability), as well as simazine, hexazinone, and tebuthiuron (all low reliability). Using these SSD curves, the retrospective risk assessments for recently reported pesticide concentrations highlight that the herbicides ametryn, atrazine, and diuron are of major concern for ecological health in Australia's tropical freshwater ecosystems. The insecticide imidacloprid also appears to pose an emerging threat to the most sensitive species in tropical freshwater ecosystems. The exposed temperature-specific approach may be applied to develop water quality guideline values for other environmental contaminants detected in tropical freshwater ecosystems until reliable and relevant toxicity data are generated using representative native species.
Single-walled carbon nanotube conjugated cytochrome c as exogenous nano catalytic medicine to combat intracellular oxidative stress

Mitochondrial dysfunction has been reported to be one of the main causes of many diseases including cancer, type2 diabetes, neurodegenerative disorders, cardiac ischemia, sepsis, muscular dystrophy, etc. Under in vitro conditions, Cytochrome C (Cyt C) maintains mitochondrial homeostasis and stimulates apoptosis, along with being a key participant in the life-supporting function of ATP synthesis. Hence, the medicinal importance of Cyt C as catalytic defense is immensely important in various mitochondrial disorders. Here, we have developed a nanomaterial via electrostatically conjugating oxidized single-wall carbon nanotube with Cyt C (Cyt C@cSWCNT) for the exogenous delivery of Cyt C. The chemical and morphological characterization of the developed Cyt C@cSWCNT was done using UV-vis, FTIR, XPS, powder XRD, TGA/DSC, TEM, etc. The developed Cyt C@cSWCNT exhibited bifunctional catalase and peroxidase activity with Km (∼ 642.7 μM and 351.6 μM) and Vmax (∼0.33 μM/s and 2.62 μM/s) values, respectively. Also, through this conjugation Cyt C was found to retain its catalytic activity even at 60 °C, excellent catalytic recyclability (at least up to 3 times), and wider pH activity (pH = 3 to 9). Cyt C@cSWCNT was found to promote intracellular ROS quenching and maintain mitochondrial membrane potential and cellular membrane integrity via Na+/K+ ion homeostasis during the H2O2 stress. Overall the present strategy provides an alternative approach for the exogenous delivery of Cyt C which can be used as nano catalytic medicine.
Deep representation for partially occluded face verification

By using deep learning-based strategy, the performance of face recognition tasks has been significantly enhanced. However, the verification and discrimination of the faces with occlusions still remain a challenge to most of the state-of-the-art approaches. Bearing this in mind, we propose a novel convolutional neural network which was designed specifically for the verification between the occluded and non-occluded faces for the same identity. It could learn both the shared and unique features based on a multiple network convolutional neural network architecture. The newly presented joint loss function and the corresponding alternating minimization approach were integrated to implement the training and testing of the presented convolutional neural network. Experimental results on the publicly available datasets (LFW 99.73%, YTF 97.30%, CACD 99.12%) show that the proposed deep representation approach outperforms the state-of-the-art face verification techniques.
Investigation of EEG-Based Biometric Identification Using State-of-the-Art Neural Architectures on a Real-Time Raspberry Pi-Based System

Despite the growing interest in the use of electroencephalogram (EEG) signals as a potential biometric for subject identification and the recent advances in the use of deep learning (DL) models to study neurological signals, such as electrocardiogram (ECG), electroencephalogram (EEG), electroretinogram (ERG), and electromyogram (EMG), there has been a lack of exploration in the use of state-of-the-art DL models for EEG-based subject identification tasks owing to the high variability in EEG features across sessions for an individual subject. In this paper, we explore the use of state-of-the-art DL models such as ResNet, Inception, and EEGNet to realize EEG-based biometrics on the BED dataset, which contains EEG recordings from 21 individuals. We obtain promising results with an accuracy of 63.21%, 70.18%, and 86.74% for Resnet, Inception, and EEGNet, respectively, while the previous best effort reported accuracy of 83.51%. We also demonstrate the capabilities of these models to perform EEG biometric tasks in real-time by developing a portable, low-cost, real-time Raspberry Pi-based system that integrates all the necessary steps of subject identification from the acquisition of the EEG signals to the prediction of identity while other existing systems incorporate only parts of the whole system.
Functionalized graphene quantum dots obtained from graphene foams used for highly selective detection of Hg2+ in real samples

Here we report the use of graphene quantum dots (GQDs), obtained from 3D graphene foam, functionalized with 8-hydroxyquinoline (8-HQ) for the sensitive and selective detection of Hg2+ via front-face fluorescence. The great surface area and active groups within the GQDs permitted the functionalization with 8-HQ to increase their selectivity toward the analyte of interest. The fluorescence probe follows the Stern-Volmer model, yielding a direct relationship between the degree of quenching and the concentration of the analyte. Diverse parameters, including the pH and the use of masking agents, were optimized in order to improve the selectivity toward Hg2+ down to a limit of detection of 2.4 nmol L-1. It is hereby demonstrated that the functionalized GQDs work perfectly fine under adverse conditions such as acidic pH and in the presence of a large number of cationic and anionic interferences for the detection of Hg2+ in real samples. Parallel measurements using cold vapor atomic fluorescence spectrometry also demonstrated an excellent correlation with the front-face fluorescence method applied here for real samples including tap, river, underground, and dam waters.
Relief of CoA sequestration and restoration of mitochondrial function in a mouse model of propionic acidemia

Propionic acidemia (PA, OMIM 606054) is a devastating inborn error of metabolism arising from mutations that reduce the activity of the mitochondrial enzyme propionyl-CoA carboxylase (PCC). The defects in PCC reduce the concentrations of nonesterified coenzyme A (CoASH), thus compromising mitochondrial function and disrupting intermediary metabolism. Here, we use a hypomorphic PA mouse model to test the effectiveness of BBP-671 in correcting the metabolic imbalances in PA. BBP-671 is a high-affinity allosteric pantothenate kinase activator that counteracts feedback inhibition of the enzyme to increase the intracellular concentration of CoA. Liver CoASH and acetyl-CoA are depressed in PA mice and BBP-671 treatment normalizes the cellular concentrations of these two key cofactors. Hepatic propionyl-CoA is also reduced by BBP-671 leading to an improved intracellular C3:C2-CoA ratio. Elevated plasma C3:C2-carnitine ratio and methylcitrate, hallmark biomarkers of PA, are significantly reduced by BBP-671. The large elevations of malate and α-ketoglutarate in the urine of PA mice are biomarkers for compromised tricarboxylic acid cycle activity and BBP-671 therapy reduces the amounts of both metabolites. Furthermore, the low survival of PA mice is restored to normal by BBP-671. These data show that BBP-671 relieves CoA sequestration, improves mitochondrial function, reduces plasma PA biomarkers, and extends the lifespan of PA mice, providing the preclinical foundation for the therapeutic potential of BBP-671.
A robust model for salient text detection in natural scene images using MSER feature detector and Grabcut

Visual attention models have been used to recognize the most prominent region in a natural scene. These regions are going to pull the human attention. The state-of-art models keep on under-predicting the significant image regions having text. These are specifically the regions with most noteworthy semantic significance in a natural scene and turn out to be useful for saliency-based applications like image classification and captioning. The text or character detection as a salient region in image remains a challenging research problem. Text contents within the scene convey vital information about the image. For example, signboard content conveys the important information for visually impaired person. In this paper, we have proposed a new model for salient text detection in a natural scene. In the proposed model, we integrate saliency model with the segmentation and text detection approach in a natural scene to generate the text saliency. The experimental outcomes in ROC curve and DET curves illustrate that the proposed model outperformed the state-of-art methods for detection of salient text content from a natural scene.
Dissected 3D CNNs: Temporal skip connections for efficient online video processing

Convolutional Neural Networks with 3D kernels (3D CNNs) currently achieve state-of-the-art results in video recognition tasks due to their supremacy in extracting spatiotemporal features within video frames. There have been many successful 3D CNN architectures surpassing state-of-the-art results successively. However, nearly all of them are designed to operate offline, creating several serious handicaps during online operation. Firstly, conventional 3D CNNs are not dynamic since their output features represent the complete input clip instead of the most recent frame in the clip. Secondly, they are not temporal resolution-preserving due to their inherent temporal downsampling. Lastly, 3D CNNs are constrained to be used with fixed temporal input size limiting their flexibility. In order to address these drawbacks, we propose dissected 3D CNNs, where the intermediate volumes of the network are dissected and propagated over depth (time) dimension for future calculations, substantially reducing the number of computations at online operation. For action classification, the dissected version of ResNet models performs 77%-90% fewer computations at online operation while achieving similar to 5% better classification accuracy on the Kinetics-600 dataset than conventional 3D-ResNet models. Moreover, the advantages of dissected 3D CNNs are demonstrated by deploying our approach onto several vision tasks, which consistently improved the performance.
AADG: Automatic Augmentation for Domain Generalization on Retinal Image Segmentation

Convolutional neural networks have been widely applied to medical image segmentation and have achieved considerable performance. However, the performance may be significantly affected by the domain gap between training data (source domain) and testing data (target domain). To address this issue, we propose a data manipulation based domain generalization method, called Automated Augmentation for Domain Generalization (AADG). Our AADG framework can effectively sample data augmentation policies that generate novel domains and diversify the training set from an appropriate search space. Specifically, we introduce a novel proxy task maximizing the diversity among multiple augmented novel domains as measured by the Sinkhorn distance in a unit sphere space, making automated augmentation tractable. Adversarial training and deep reinforcement learning are employed to efficiently search the objectives. Quantitative and qualitative experiments on 11 publicly-accessible fundus image datasets (four for retinal vessel segmentation, four for optic disc and cup (OD/OC) segmentation and three for retinal lesion segmentation) are comprehensively performed. Two OCTA datasets for retinal vasculature segmentation are further involved to validate cross-modality generalization. Our proposed AADG exhibits state-of-the-art generalization performance and outperforms existing approaches by considerable margins on retinal vessel, OD/OC and lesion segmentation tasks. The learned policies are empirically validated to be model-agnostic and can transfer well to other models. The source code is available at https://github.com/CRazorback/AADG.
Medical Assistance in Dying, Data and Casting Assertions Aside

Various assertions have been made regarding why eligibility for medical assistance in dying (MAiD) should be expanded. Examining these and the studies used to support them should clear the way for thoughtful data monitoring and research into why some patients make death hastening requests. This will not only improve MAiD practices in Canada, but will lead to better more effective palliative care for patients whose suffering leads them to covet death.
Dietary Intervention for Preventing Colorectal Cancer: A Practical Guide for Physicians

Colorectal cancer (CRC) is a disease with high prevalence and mortality. Estimated preventability for CRC is approximately 50%, indicating that altering modifiable factors, including diet and body weight, can reduce CRC risk. There is strong evidence that dietary factors including whole grains, high-fiber, red and processed meat, and alcohol can affect the risk of CRC. An alternative strategy for preventing CRC is use of a chemopreventive supplement that provides higher individual exposure to nutrients than what can be obtained from the diet. These include calcium, vitamin D, folate, n-3 polyunsaturated fatty acids, and phytochemicals. Several intervention trials have shown that these dietary chemopreventives have positive protective effects on development and progression CRC. Research on chemoprevention with phytochemicals that possess anti-inflammatory and/or, anti-oxidative properties is still in the preclinical phase. Intentional weight loss by bariatric surgery has not been effective in decreasing long-term CRC risk. Physicians should perform dietary education for patients who are at high risk of cancer for changing their dietary habits and behaviour. An increased understanding of the role of individual nutrients linked to the intestinal micro-environment and stages of carcinogenesis would facilitate the development of the best nutritional formulations for preventing CRC.
Impact of Economic Policy Uncertainty and Pandemic Uncertainty on International Tourism: What do We Learn From COVID-19?

Uncertainty is an overarching aspect of life that is particularly pertinent to the present COVID-19 pandemic crisis; as seen by the pandemic's rapid worldwide spread, the nature and level of uncertainty have possibly increased due to the possible disconnects across national borders. The entire economy, especially the tourism industry, has been dramatically impacted by COVID-19. In the current study, we explore the impact of economic policy uncertainty (EPU) and pandemic uncertainty (PU) on inbound international tourism by using data gathered from Italy, Spain, and the United States for the years 1995-2021. Using the Quantile on Quantile (QQ) approach, the study confirms that EPU and PU negatively affected inbound tourism in all states. Wavelet-based Granger causality further reveals bi-directional causality running from EPU to inbound tourism and unidirectional causality from PU to inbound tourism in the long run. The overall findings show that COVID-19 has had a strong negative effect on tourism. So resilient skills are required to restore a sustainable tourism industry.
Inspection and maintenance optimization for heterogeneity units in redundant structure with Non-dominated Sorting Genetic Algorithm III

Redundant structure has been widely deployed to improve system reliability, as when one unit fails, the system can continue to function by using another one. Most existing studies rely on the similar assumption that the heterogeneous units are subject to periodic inspections and identical in terms of their aging situations and the numbers of resisted shocks. In practice, it is often adequate to trigger a unit individually in the event of a single shock, which intensifies the degradation of that unit, accordingly, requiring a sooner inspection to ensure its safety. In this study, the stochastic dependency among units is addressed firstly by introducing a novel activation sequence. Secondly, an adaptive system-level inspection policy is proposed by prioritizing the unit with a worse state. Finally, we take advantage of Monte Carlo methods to simulate the whole process and estimate two objectives, referring to the average system unavailability and maintenance cost, in a designed service time. It is found that the two objectives are contradictory through numerical examples. The Non-dominated Sorting Genetic Algorithm III (NSGA-III) algorithm, therefore, has been employed to find the optimal solutions in system unavailability and cost, which provide clues for practitioners in decision-making.
Widespread Gene Expression Divergence in Butterfly Sensory Tissues Plays a Fundamental Role During Reproductive Isolation and Speciation

Neotropical Heliconius butterflies are well known for their intricate behaviors and multiple instances of incipient speciation. Chemosensing plays a fundamental role in the life history of these groups of butterflies and in the establishment of reproductive isolation. However, chemical communication involves synergistic sensory and accessory functions, and it remains challenging to investigate the molecular mechanisms underlying behavioral differences. Here, we examine the gene expression profiles and genomic divergence of three sensory tissues (antennae, legs, and mouthparts) between sexes (females and males) and life stages (different adult stages) in two hybridizing butterflies, Heliconius melpomene and Heliconius cydno. By integrating comparative transcriptomic and population genomic approaches, we found evidence of widespread gene expression divergence, supporting a crucial role of sensory tissues in the establishment of species barriers. We also show that sensory diversification increases in a manner consistent with evolutionary divergence based on comparison with the more distantly related species Heliconius charithonia. The findings of our study strongly support the unique chemosensory function of antennae in all three species, the importance of the Z chromosome in interspecific divergence, and the nonnegligible role of nonchemosensory genes in the divergence of chemosensory tissues. Collectively, our results provide a genome-wide illustration of diversification in the chemosensory system under incomplete reproductive isolation, revealing strong molecular separation in the early stage of speciation. Here, we provide a unique perspective and relevant view of the genetic architecture (sensory and accessory functions) of chemosensing beyond the classic chemosensory gene families, leading to a better understanding of the magnitude and complexity of molecular changes in sensory tissues that contribute to the establishment of reproductive isolation and speciation.
Optics and Photonics: Key Enabling Technologies

The fields of optics and photonics have experienced dramatic technical advances over the past several decades and have cemented themselves as key enabling technologies across many different industries. This paper explores past milestones, present state of the art, and future perspectives of several different topics, including: lasers, materials, devices, communications, bioimaging, displays, manufacturing, and industry evolution.
Periodicity-Oriented Data Analytics on Time-Series Data for Intelligence System

Periodic pattern mining models analyze patterns which occur periodically in a time-series database, such as sensor readings of smartphones and/or Internet of Things devices. The extracted patterns can be utilized for risk prediction, system management, and decision-making. In this article, we propose an efficient periodicity-oriented data analytics approach. It ignores intermediate events deliberately by adopting the concept of flexible periodic patterns, so it can be applied to more diverse real-life scenarios and systems. Moreover, the proposed approach adopts a novel symbol-centered data structure instead of existing data structures for state-of-the-art approaches of periodic pattern mining. Performance evaluations on real-life datasets, Diabetes, Oil Prices, and Bike Sharing, and requirements show that our approach has better runtime, memory usage, number of visited patterns, and sensitivity than efficient periodic pattern mining (EPPM) and flexible periodic pattern mining (FPPM), which are the state-of-the-art approaches in the same field. The experimental results show that the proposed algorithm will require less runtime and smaller memory than the existing algorithms on most data and requirements in real life.
MPC-CSAS: Multi-Party Computation for Real-Time Privacy-Preserving Speed Advisory Systems

As a part of Advanced Driver Assistance Systems (ADASs), Consensus-based Speed Advisory Systems (CSAS) have been proposed to recommend a common speed to a group of vehicles for specific application purposes, such as emission control and energy management. With Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I) technologies and advanced control theories in place, state-of-the-art CSAS can be designed to get an optimal speed in a privacy-preserving and decentralized manner. However, the current method only works for specific cost functions of vehicles, and its execution usually involves many algorithm iterations leading long convergence time. Therefore, the state-of-the-art design method is not applicable to a CSAS design which requires real-time decision making. In this article, we address the problem by introducing MPC-CSAS, a Multi-Party Computation (MPC) based design approach for privacy-preserving CSAS. Our proposed method is simple to implement and applicable to all types of cost functions of vehicles. Moreover, our simulation results show that the proposed MPC-CSAS can achieve very promising system performance in just one algorithm iteration without using extra infrastructure for a typical CSAS.