ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Malawi Stories: mapping an art-science collaborative process

This paper outlines a project drawing together an artist working on creative GIS, a geomatics scholar, an NGO leader, a rural geographer and soil scientist, an environmental geochemist, and a political geographer. With a shared interest in the social and physical processes affecting people's lives in Malawi, and the possibilities for interdisciplinary collaboration, the team engaged in practice-based mapping of our data sources and respective methodologies. The project relates to two sites in Malawi: Tikondwe Freedom Gardens and the Likangala River. The paper details our practices as we shared, debated, and repurposed our data as a means of situating these practices and data. Using paper and pen, whiteboard, PowerPoint, and web-design software, we note here our effort to map a 'space of experimentation' highlighting, and reflecting on, our diverse disciplinary orientations, training, instrumentation, recording, and reporting procedures, as well as bodily practices that enable and give animation to these factors.
Microbial ecology of the Southern Ocean

The Southern Ocean (SO) distributes climate signals and nutrients worldwide, playing a pivotal role in global carbon sequestration. Microbial communities are essential mediators of primary productivity and carbon sequestration, yet we lack a comprehensive understanding of microbial diversity and functionality in the SO. Here, we examine contemporary studies in this unique polar system, focusing on prokaryotic communities and their relationships with other trophic levels (i.e. phytoplankton and viruses). Strong seasonal variations and the characteristic features of this ocean are directly linked to community composition and ecosystem functions. Specifically, we discuss characteristics of SO microbial communities and emphasise differences from the Arctic Ocean microbiome. We highlight the importance of abundant bacteria in recycling photosynthetically derived organic matter. These heterotrophs appear to control carbon flux to higher trophic levels when light and iron availability favour primary production in spring and summer. Conversely, during winter, evidence suggests that chemolithoautotrophs contribute to prokaryotic production in Antarctic waters. We conclude by reviewing the effects of climate change on marine microbiota in the SO.
Toward a Universal Synthetic Speech Spoofing Detection Using Phase Information

In the field of speaker verification (SV) it is nowadays feasible and relatively easy to create a synthetic voice to deceive a speech driven biometric access system. This paper presents a synthetic speech detector that can be connected at the front-end or at the back-end of a standard SV system, and that will protect it from spoofing attacks coming from state-of-the-art statistical Text to Speech (TTS) systems. The system described is a Gaussian Mixture Model (GMM) based binary classifier that uses natural and copy-synthesized signals obtained from the Wall Street Journal database to train the system models. Three different state-of-the-art vocoders are chosen and modeled using two sets of acoustic parameters: 1) relative phase shift and 2) canonical Mel Frequency Cepstral Coefficients (MFCC) parameters, as baseline. The vocoder dependency of the system and multivocoder modeling features are thoroughly studied. Additional phase-aware vocoders are also tested. Several experiments are carried out, showing that the phase-based parameters perform better and are able to cope with new unknown attacks. The final evaluations, testing synthetic TTS signals obtained from the Blizzard challenge, validate our proposal.
Stabilizing peer-to-peer energy trading in prosumer coalition through computational efficient pricing

Load balancing issues in distribution networks have emerged alongside the large-scale deployment of distributed renewable generation sources. In light of this challenge, peer-to-peer (P2P) energy trading constitutes a promising approach for delivering secure and economic supply-demand balance when faced with variable load and intermittent renewable generation through matching energy demand and supply locally. However, state-of-the-art mechanisms for governing P2P energy trading either fail to suitably incentivize prosumers to participate in P2P trading or suffer severely from the curse of dimensionality with their computational complexity increase exponentially with the number of prosumers. In this paper, a P2P energy trading mechanism based on cooperative game theory is proposed to establish a grand energy coalition of prosumers and a computationally efficient pricing algorithm is developed to suitably incentivize prosumers for their sustainable participation in the grand coalition. The performance of the proposed algorithm is demonstrated by comparing it to state-of-the-art mechanisms through numerous case studies in a real-world scenario. The superior computational performance of the proposed algorithm is also validated.
An Efficient Audio Coding Scheme for Quantitative and Qualitative Large Scale Acoustic Monitoring Using the Sensor Grid Approach

The spreading of urban areas and the growth of human population worldwide raise societal and environmental concerns. To better address these concerns, the monitoring of the acoustic environment in urban as well as rural or wilderness areas is an important matter. Building on the recent development of low cost hardware acoustic sensors, we propose in this paper to consider a sensor grid approach to tackle this issue. In this kind of approach, the crucial question is the nature of the data that are transmitted from the sensors to the processing and archival servers. To this end, we propose an efficient audio coding scheme based on third octave band spectral representation that allows: (1) the estimation of standard acoustic indicators; and (2) the recognition of acoustic events at state-of-the-art performance rate. The former is useful to provide quantitative information about the acoustic environment, while the latter is useful to gather qualitative information and build perceptually motivated indicators using for example the emergence of a given sound source. The coding scheme is also demonstrated to transmit spectrally encoded data that, reverted to the time domain using state-of-the-art techniques, are not intelligible, thus protecting the privacy of citizens.
A clustering fuzzy approach for image segmentation

Segmentation is a fundamental step in image description or classification. In recent years, several computational models have been used to implement segmentation methods but without establishing a single analytic solution. However, the intrinsic properties of neural networks make them an interesting approach, despite some measure of inefficiency. This paper presents a clustering approach for image segmentation based on a modified fuzzy approach for image segmentation (ART) model. The goal of the proposed approach is to find a simple model able to instance a prototype for each cluster avoiding complex post-processing phases. Results and comparisons with other similar models presented in the literature (like self-organizing maps and original fuzzy ART) are also discussed. Qualitative and quantitative evaluations confirm the validity of the approach proposed. (C) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
Multiscale texture retrieval based on low-dimensional and rotation-invariant features of curvelet transform

Multiscale-based texture retrieval algorithms use low-dimensional feature sets in general. However, they do not have as good retrieval performances as those of the state-of-the-art techniques in the literature. The main motivation of this study is to use low-dimensional multiscale features to provide comparable retrieval performances with the state-of-the-art techniques. The proposed features of this study are low-dimensional, robust against rotation, and have better performance than the earlier multiresolution-based algorithms and the state-of-the-art techniques with low-dimensional feature sets. They are obtained through curvelet transformation and have considerably small dimensions. The rotation invariance is provided by applying a novel principal orientation alignment based on cross energies of adjacent curvelet blocks. The curvelet block pair with the highest cross energy is marked as the principle orientation, and the rest of the blocks are cycle-shifted around the principle orientation. Two separate rotation-invariant feature vectors are proposed and evaluated in this study. The first feature vector has 84 elements and contains the mean and standard deviation of curvelet blocks at each angle together with a weighting factor based on the spatial support of the curvelet coefficients. The second feature vector has 840 elements and contains the kernel density estimation (KDE) of curvelet blocks at each angle. The first and the second feature vectors are used in the classification of textures based on nearest neighbor algorithm with Euclidian and Kullback-Leibler distance measures, respectively. The proposed method is evaluated on well-known databases such as, Brodatz, TC10, TC12-t184, and TC12-horizon of Outex, UIUCTex, and KTH-TIPS. The best performance is obtained for kernel density feature vector. Mean and standard deviation feature vector also provides similar performance and has less complexity due to its smaller feature dimension. The results are reported as both precision-recall curves and classification rates and compared with the existing state-of-the-art texture retrieval techniques. It is shown through several experiments that the proposed rotation-invariant feature vectors outperform earlier multiresolution-based ones and provide comparable performances with the rest of the literature even though they have considerably small dimensions.
Detection of the protistan parasite, Haplosporidium costale in Crassostrea gigas oysters from the French coast: A retrospective study

The parasite Haplosporidium costale is known to infect and cause mortality in the oyster Crassostrea virginica in the USA. Decades after its first description in the 1960s, this parasite was detected in Crassostrea gigas in the USA and China. However, it presented a low prevalence and no mortality was associated with it. More recently, in 2019, H. costale was detected in France in a batch of moribund oysters. In order to observe how long this parasite has been present on French coasts, from Normandy to Thau lagoon, a retrospective investigation was conducted on 871 adult and spat oyster batches from 2004 to 2020. To allow rapid detection on a large panel of samples, a real-time PCR for the H. costale actin gene was developed. This method allowed the detection of H. costale DNA in adults from 2005 and in spat from 2008. The H. costale prevalence in spat appeared higher than in adults over the years studied, 14.59 % compared to 6.50 %, respectively. All samples presenting positive results were then sequenced on two targets, H. costale rRNA and actin genes. The actin gene sequencing highlighted the presence of two H. costale strains. Adult C. gigas as well as spat batches coming from hatcheries and DNA controls from C. virginica all presented with the Profile 1 H. costale strain. The Profile 2 H. costale strain was detected only in C. gigas spat coming from natural sources. These observations suggest a correlation between the origin of oysters and H. costale strains which may have been caused by commercial imports between Japan, USA and France back to the 1970s. Over the positive samples studied, only few batches (n = 3) suffered mortalities which could be hypothesized to be caused by H. costale, all presenting the Profile 1 H. costale strain.
Snow Avalanche Segmentation in SAR Images With Fully Convolutional Neural Networks

Knowledge about frequency and location of snow avalanche activity is essential for forecasting and mapping of snow avalanche hazard. Traditional field monitoring of avalanche activity has limitations, especially when surveying large and remote areas. In recent years, avalanche detection in Sentinel-1 radar satellite imagery has been developed to improve monitoring. However, the current state-of-the-art detection algorithms, based on radar signal processing techniques, are still much less accurate than human experts. To reduce this gap, we propose a deep learning architecture for detecting avalanches in Sentinel-1 radar images. We trained a neural network on 6345 manually labeled avalanches from 117 Sentinel-1 images, each one consisting of six channels that include backscatter and topographical information. Then, we tested our trained model on a new synthetic aperture radar image. Comparing to the manual labeling (the gold standard), we achieved an F1 score above 66%, whereas the state-of-the-art detection algorithm sits at an F1 score of only 38%. A visual inspection of the results generated by our deep learning model shows that only small avalanches are undetected, whereas some avalanches that were originally not labeled by the human expert are discovered.
NICUface: Robust Neonatal Face Detection in Complex NICU Scenes

The development of non-contact patient monitoring applications for the neonatal intensive care unit (NICU) is an active research area, particularly in facial video analysis. Recent studies have used facial video data to estimate vital signs, assess pain from facial expression, differentiate sleep-wake status, detect jaundice, and in face recognition. These applications depend on an accurate definition of the patient's face as a region of interest (ROI). Most studies have required manual ROI definition, while others have leveraged automated face detectors developed for adult patients, without systematic validation for the neonatal population. To overcome these issues, this paper first evaluates the state-of-the-art in face detection in the NICU setting. Finding that such methods often fail in complex NICU environments, we demonstrate how fine-tuning can increase neonatal face detector robustness, resulting in our NICUface models. A large and diverse neonatal dataset was gathered from actual patients admitted to the NICU across three studies and gold standard face annotations were completed. In comparison to state-of-the-art face detectors, our NICUface models address NICU-specific challenges such as ongoing clinical intervention, phototherapy lighting, occlusions from hospital equipment, etc. These analyses culminate in the creation of robust NICUface detectors with improvements on our most challenging neonatal dataset of +36.14, +35.86, and +32.19 in AP30, AP50, and mAP respectively, relative to state-of-the-art CE-CLM, MTCNN, img2pose, RetinaFace, and YOLO5Face models. Face orientation estimation is also addressed, leading to an accuracy of 99.45%. Fine-tuned NICUface models, gold-standard face annotation data, and the face orientation estimation method are also released here.
An Application of Tumor-Associated Macrophages as Immunotherapy Targets: Sialic Acid-Modified EPI-Loaded Liposomes Inhibit Breast Cancer Metastasis

Breast cancer metastasis is an important cause of death in patients with breast cancer and is closely related to circulating tumor cells (CTCs) and the metastatic microenvironment. As the most infiltrating immune cells in the tumor microenvironment (TME), tumor-associated macrophages (TAMs), which highly express sialic acid (SA) receptor (Siglec-1), are closely linked to tumor progression and metastasis. Furthermore, the surface of CTCs also highly expressed receptor (Selectin) for SA. A targeting ligand (SA-CH), composed of SA and cholesterol, was synthesized and modified on the surface of epirubicin (EPI)-loaded liposomes (EPI-SL) as an effective targeting delivery system. Liposomes were evaluated for characteristics, stability, in vitro release, cytotoxicity, cellular uptake, pharmacokinetics, tumor targeting, and pharmacodynamics. In vivo and in vitro experiments showed that EPI-SL enhanced EPI uptake by TAMs. In addition, cellular experiments showed that EPI-SL could also enhance the uptake of EPI by 4T1 cells, resulting in cytotoxicity second only to that of EPI solution. Pharmacodynamic experiments have shown that EPI-SL has optimal tumor inhibition with minimal toxicity, which can be ascribed to the fact that EPI-SL can deliver drugs to tumor based on TAMs and regulate TME through the depletion of TAMs. Our study demonstrated the significant potential of SA-modified liposomes in antitumor metastasis. Schematic diagram of the role of SA-CH modified EPI-loaded liposomes in the model of breast cancer metastasis.
Abundance, composition, and distribution of microplastics in intertidal sediment and soft tissues of four species of Bivalvia from Southeast Brazilian urban beaches

In coastal areas, microplastics (MPs) can deposit in sediment, allowing it to be ingested by benthic organisms, like mussels, thus creating a possible transfer to humans. The aim of this study is to evaluate MPs pollution in sediment as a function of shoreline elevation in two urbanized beaches and to evaluate the abundance/frequency of MPs in 4 different species of bivalves commonly used in the human diet, such as the oyster Crassostrea brasiliana, the mussels Mytella strigata and Perna perna and the clam Tivela mactroides, and identify the polymers via μ-FTIR technique. A total of 3337 MPs were found in this study, of which 1488 were found in the sediment at the five sites analyzed, and 1849 in the bivalve tissues at the two sampling sites. MPs contamination was observed in all sediment samples and species of the pool and in each of the 10 specimens of the four species. Thus, the frequency of contamination by MPs reached 100 % for the analyzed samples. The number of filaments is higher than fragments in sediment samples and in each bivalve species. Regarding types and colors, the blue were greater than fragment-type in sediments and samples. In an effort to classify the polymers via μ-FTIR, our study was able to identify polypropylene, polyethylene and polyethylene terephthalate, besides a great number of cellulose fibers.
Early-immediate gene Egr1 is associated with TGFβ1 regulation of epigenetic reader Bromodomain-containing protein 4 via the canonical Smad3 signaling in hepatic stellate cells in vitro and in vivo

Upon chronic damage to the liver, multiple cytokines stimulate hepatic stellate cells (HSCs), causing the alterations of gene expression profiles and thus leading to HSC activation, a key step in liver fibrogenesis. Activated HSCs are the dominant contributors to liver fibrosis. Bromodomain containing protein 4 (BrD4), an important epigenetic reader, was demonstrated to concentrate on hundreds of enhancers associated with genes involved in multiple profibrotic pathways, thereby directing HSC activation and the fibrotic responses. The present studies were designed to examine the effect of transforming growth factor beta-1 (TGFβ1), the most potent pro-fibrotic cytokine, on BrD4 expression in HSCs and, if so, elucidated the underlying mechanisms in vitro and in vivo. The experiments employed the heterogeneous TGFβ1 knockout (TGFβ1+/- ) mice, gene knockdown in vivo, and a model of thioacetamide (TAA)-induced liver injury. The results revealed that TGFβ1 enhanced BrD4 expression in HSCs, which was mediated, at least, by Smad3 signaling and early-immediate gene Egr1 (early growth response-1). TGFβ1-induced Smad3 signaling increased Egr1 expression and promoted Egr1 binding to BrD4 promoter at a site around -111 bp, promoting BrD4 expression. Egr1 knockdown reduced BrD4 expression in HSCs in a mouse model of TAA-induced liver injury and lessened liver fibrosis. Double fluorescence staining demonstrated a strong increase in BrD4 expression in activated HSCs in fibrotic areas of the human livers, paralleling the upregulation of p-Smad3 and Egr1. This research suggested novel molecular events underlying the roles of the master pro-fibrotic cytokine TGFβ1 in HSC activation and liver fibrogenesis.
Three-Dimensional Covalent Organic Frameworks: From Synthesis to Applications

Three-dimensional covalent organic frameworks (3D COFs) with spatially periodic networks demonstrate significant advantages over their 2D counterparts, including enhanced specific surface areas, interconnected channels, and more sufficiently exposed active sites. Nevertheless, research on these materials has met an impasse due to serious problems in crystallization and stability, which must be solved for practical applications. In this Minireview, we first summarize some strategies for preparing functional 3D COFs, including crystallization techniques and functionalization methods. Hereafter, applications of these functional materials are presented, covering adsorption, separation, catalysis, fluorescence, sensing, and batteries. Finally, the future challenges and perspectives for the development of 3D COFs are discussed.
Triclosan Promotes Conjugative Transfer of Antibiotic Resistance Genes to Opportunistic Pathogens in Environmental Microbiome

Although triclosan, as a widely used antiseptic chemical, is known to promote the transmission of antibiotic resistance to diverse hosts in pure culture, it is still unclear whether and how triclosan could affect the transmission of broad-host-range plasmids among complex microbial communities. Here, bacterial culturing, fluorescence-based cell sorting, and high-throughput 16S rRNA gene amplicon sequencing were combined to investigate contributions of triclosan on the transfer rate and range of an IncP-type plasmid from a proteobacterial donor to an activated sludge microbiome. Our results demonstrate that triclosan significantly enhances the conjugative transfer of the RP4 plasmid among activated sludge communities at environmentally relevant concentrations. High-throughput 16S rRNA gene sequencing on sorted transconjugants demonstrates that triclosan not only promoted the intergenera transfer but also the intragenera transfer of the RP4 plasmid among activated sludge communities. Moreover, triclosan mediated the transfer of the RP4 plasmid to opportunistic human pathogens, for example, Legionella spp. The mechanism of triclosan-mediated conjugative transfer is primarily associated with excessive oxidative stress, followed by increased membrane permeability and provoked SOS response. Our findings offer insights into the impacts of triclosan on the dissemination of antibiotic resistance in the aquatic environmental microbiome.
Accelerating Semi-Supervised Text Classification by K-Way Projecting Networks

The state of the art semi-supervised learning framework has greatly shown its potential in making deep and complex language models such as BERT highly effective for text classification tasks when labeled data is limited. However, the large size and low inference speed of such models may hinder their application on resources-limited or real-time use cases. In this paper, we propose a new approach in semi-supervised learning framework to distill large complex teacher model into a fairly lightweight student model which has the ability of acquiring knowledge from different layers of teacher with the usage of K -way projecting networks. Across four English datasets in text classification benchmarks and one dataset collected from an Chinese online course, our experiment shows that this student model achieves comparable results with the state of the art Transformer-based semi-supervised text classification methods, while using only 0.156MB parameters and having an inference speed 785 times faster than the teacher model.