ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Current advances in membrane technologies for produced water desalination

The limited clean water resource has urged the need to extract fresh water from non-conventional sources. Produced water (PW) is a potential source of fresh water, particularly for oil-producing countries experiencing water stress issue. The engagement of membrane-based desalination technology in the field of PW treatment brings about new possibility of water reclamation. This review highlights the state-of-the-art membrane-based technologies including both stand-alone and their hybrid system, for PW treatment and reclamation. While reverse osmosis continues as a reliable option for PW treatment, emerging technologies like forward osmosis and membrane distillation have attained increasing attentions from the desalination community. To pave a way for practical applications of membrane technologies for PW desalination, the recent advances in term of membrane properties improvement, operating condition optimization, and membrane cleaning protocols are reviewed. Finally, the challenges and future outlooks of current PW desalination technologies are also highlighted.
Impact of Definitive Surgery for Graves' Disease on Adolescent Disease-Specific Quality of Life and Psychosocial Functioning

Introduction: Pediatric Graves' disease (GD) is associated with hyperthyroid symptoms that impact psychosocial and physical functioning. Total thyroidectomy (TT) is a definitive treatment option that replaces antithyroid medication. While studies have examined health-related quality of life (QOL) in adults, there are no data describing impacts of TT in pediatrics. In this prospective longitudinal study, we explored the impact of TT on disease-specific QOL and satisfaction with TT and scar appearance in adolescent patients with GD undergoing TT. Methods: Patients 12-19 years old pursuing TT for GD and their parents were recruited to complete surveys before and at least 6 months after TT. Surveys assessed motivations for pursuing TT, QOL, perceived stigmatization, self-esteem, scar appearance, and surgery satisfaction. Paired scores were compared using Wilcoxon signed-rank tests, and subscore associations were assessed using Spearman association tests. Results: Thirty-seven patient-parent dyads completed baseline surveys, including 20 patient-parent dyads completing pre- and post-TT surveys. At baseline, patients reported physical and cognitive symptomology, including tiredness, anxiety, and emotional susceptibility through ThyPRO. Psychosocial functioning at school was low through PedsQL. Disease-specific QOL significantly improved after TT, with notable improvements associated with resolution of goiter (median change = -26.14, p = 0.003), hyperthyroid symptoms (median change = -43.75, p = 0.002), tiredness (median change = -26.79, p = 0.017), cognitive impairment (median change = -14.58, p = 0.035), anxiety (median change = -33.33, p = 0.010), and emotional susceptibility (median change = -28.99, p = 0.035). Physical (median change = 18.75, p = 0.005) and school-related functioning (median change = 30.00, p = 0.002) also significantly improved post-TT. Reported GD-associated eye symptomology (thyroid eye disease) was the second lowest scoring ThyPRO subscore at baseline and improved after surgery (median change = 14.06, p = 0.03). Families reported median recovery by two months, high satisfaction with the outcomes of TT, and minimal concerns over scar appearance. No permanent surgical complications (i.e., recurrent laryngeal nerve damage or hypoparathyroidism) were sustained. Conclusions: In the setting of a high-volume surgeon with low complication rates, TT for GD in pediatric populations may have substantial beneficial effects on disease-specific QOL and psychosocial functioning, with minimal adverse complaints about scar appearance.
Collocation for Diffeomorphic Deformations in Medical Image Registration

Diffeomorphic deformation is a popular choice in medical image registration. A fundamental property of diffeomorphisms is invertibility, implying that once the relation between two points A to B is found, then the relation B to A is given per definition. Consistency is a measure of a numerical algorithm's ability to mimic this invertibility, and achieving consistency has proven to be a challenge for many state-of-the-art algorithms. We present CDD (Collocation for Diffeomorphic Deformations), a numerical solution to diffeomorphic image registration, which solves for the Stationary Velocity Field (SVF) using an implicit A-stable collocation method. CDD guarantees the preservation of the diffeomorphic properties at all discrete points and is thereby consistent to machine precision. We compared CDD's collocation method with the following standard methods: Scaling and Squaring, Forward Euler, and Runge-Kutta 4, and found that CDD is up to 9 orders of magnitude more consistent. Finally, we evaluated CDD on a number of standard bench-mark data sets and compared the results with current state-of-the-art methods: SPM-DARTEL, Diffeomorphic Demons and SyN. We found that CDD outperforms state-of-the-art methods in consistency and delivers comparable or superior registration precision.
A generic framework for hierarchical de novo protein design

De novo protein design enables the exploration of novel sequences and structures absent from the natural protein universe. De novo design also stands as a stringent test for our understanding of the underlying physical principles of protein folding and may lead to the development of proteins with unmatched functional characteristics. The first fundamental challenge of de novo design is to devise "designable" structural templates leading to sequences that will adopt the predicted fold. Here, we built on the TopoBuilder (TB) de novo design method, to automatically assemble structural templates with native-like features starting from string descriptors that capture the overall topology of proteins. Our framework eliminates the dependency of hand-crafted and fold-specific rules through an iterative, data-driven approach that extracts geometrical parameters from structural tertiary motifs. We evaluated the TopoBuilder framework by designing sequences for a set of five protein folds and experimental characterization revealed that several sequences were folded and stable in solution. The TopoBuilder de novo design framework will be broadly useful to guide the generation of artificial proteins with customized geometries, enabling the exploration of the protein universe.
Advances in large-area Hg1-xCdxTe photovoltaic detectors for remote-sensing applications

State-of-the-art large-area photovoltaic (PV) detectors fabricated in HgCdTe grown by molecular beam epitaxy (MBE) have been demonstrated for the Crosstrack Infrared Sounder (CrIS) instrument. Large-area devices (1 mm in diameter) yielded excellent electrical and optical performance operating at 81 K for lambda(c) similar to 15 mum, at 98 K for lambda(c) similar to 9 mum, and lambda(c) similar to 5-mum spectral cutoffs. Fabricated detectors have near-theoretical electrical performance, and Anti Reflection coated quantum efficiency (QE) is greater than 0.70. Measured average R(0)A at 98 K is 2.0E7 Omegacm(2), and near-theoretical QEs greater than 0.90 were obtained on detectors with lambda(c) similar to 5-mum spectral cutoffs. These state-of-the-art large-area PV detector results reflect high-quality HgCdTe grown by MBE on CdZnTe substrates in all three spectral bands of interest.
Willingness to pay for policies to reduce health risks from COVID-19: Evidence from U.S. professional sports

Airborne transmission of the COVID-19 virus increased the need for health policies to reduce transmission in congregate settings associated with minimal risk before the pandemic. While a large literature estimates tradeoffs between policies designed to reduce negative health outcomes, no empirical research addresses consumer willingness to pay (WTP) for health policies designed to reduce airborne virus transmission. Using survey data from 1381 fans of professional sports, we estimate consumers' WTP for reduced likelihood of coronavirus transmission through mask and social distancing policies using a stated preference approach. The results indicate increased attendance likelihood if the venue requires masks and limits attendance, with significant heterogeneity in WTP across risk scenarios and sports. We characterize consumers as casual fans who prefer a mask requirement but are indifferent to capacity constraints, strong fans who are anti-maskers and prefer capacity constraints, and a second group of casual fans with positive WTP under both mask and limited capacity requirements. For example, casual fans' WTP for masking, $38 per National Basketball Association (NBA) game attended, is more than double their WTP for capacity constraints only. Strong fans' WTP for attending capacity constrained NBA games was $490, more than 400% higher than the pre-pandemic average WTP of $105.
Analyzing Dynamical Brain Functional Connectivity as Trajectories on Space of Covariance Matrices

Human brain functional connectivity (FC) is often measured as the similarity of functional MRI responses across brain regions when a brain is either resting or performing a task. This paper aims to statistically analyze the dynamic nature of FC by representing the collective time-series data, over a set of brain regions, as a trajectory on the space of covariance matrices, or symmetric-positive definite matrices (SPDMs). We use a recently developed metric on the space of SPDMs for quantifying differences across FC observations, and for clustering and classification of FC trajectories. To facilitate large scale and high-dimensional data analysis, we propose a novel, metric-based dimensionality reduction technique to reduce data from large SPDMs to small SPDMs. We illustrate this comprehensive framework using data from the Human Connectome Project (HCP) database for multiple subjects and tasks, with task classification rates that match or outperform state-of-the-art techniques.
Divergent semantic integration (DSI): Extracting creativity from narratives with distributional semantic modeling

We developed a novel conceptualization of one component of creativity in narratives by integrating creativity theory and distributional semantics theory. We termed the new construct divergent semantic integration (DSI), defined as the extent to which a narrative connects divergent ideas. Across nine studies, 27 different narrative prompts, and over 3500 short narratives, we compared six models of DSI that varied in their computational architecture. The best-performing model employed Bidirectional Encoder Representations from Transformers (BERT), which generates context-dependent numerical representations of words (i.e., embeddings). BERT DSI scores demonstrated impressive predictive power, explaining up to 72% of the variance in human creativity ratings, even approaching human inter-rater reliability for some tasks. BERT DSI scores showed equivalently high predictive power for expert and nonexpert human ratings of creativity in narratives. Critically, DSI scores generalized across ethnicity and English language proficiency, including individuals identifying as Hispanic and L2 English speakers. The integration of creativity and distributional semantics theory has substantial potential to generate novel hypotheses about creativity and novel operationalizations of its underlying processes and components. To facilitate new discoveries across diverse disciplines, we provide a tutorial with code (osf.io/ath2s) on how to compute DSI and a web app ( osf.io/ath2s ) to freely retrieve DSI scores.
Acoustic Resonance Testing of Small Data on Sintered Cogwheels

Based on the fact that cogwheels are indispensable parts in manufacturing, we present the acoustic resonance testing (ART) of small data on sintered cogwheels for quality control in the context of non-destructive testing (NDT). Considering the lack of extensive studies on cogwheel data by means of ART in combination with machine learning (ML), we utilize time-frequency domain feature analysis and apply ML algorithms to the obtained feature sets in order to detect damaged samples in two ways: one-class and binary classification. In each case, despite small data, our approach delivers robust performance: All damaged test samples reflecting real-world scenarios are recognized in two one-class classifiers (also called detectors), and one intact test sample is misclassified in binary ones. This shows the usefulness of ML and time-frequency domain feature analysis in ART on a sintered cogwheel dataset.
Expansion and Neofunctionalization of Actinoporin-like Genes in Mediterranean Mussel (Mytilus galloprovincialis)

Pore-forming toxins are an important component of the venom of many animals. Actinoporins are potent cytolysins that were first detected in the venom of sea anemones; however, they are occasionally found in animals other than cnidarians and are expanded in a few predatory gastropods. Here, we report the presence of 27 unique actinoporin-like genes with monophyletic origin in Mytilus galloprovincialis, which we have termed mytiporins. These mytiporins exhibited a remarkable level of molecular diversity and gene presence-absence variation, which warranted further studies aimed at elucidating their functional role. We structurally and functionally characterized mytiporin-1 and found significant differences from the archetypal actinoporin fragaceatoxin C. Mytiporin-1 showed weaker permeabilization activity, no specificity towards sphingomyelin, and weak activity in model lipid systems with negatively charged lipids. In contrast to fragaceatoxin C, which forms octameric pores, functional mytiporin-1 pores on negatively charged lipid membranes were hexameric. Similar hexameric pores were observed for coluporin-26 from Cumia reticulata and a conoporin from Conus andremenezi. This indicates that also other molluscan actinoporin-like proteins differ from fragaceatoxin C. Although the functional role of mytiporins in the context of molluscan physiology remains to be elucidated, the lineage-specific gene family expansion event that characterizes mytiporins indicates that strong selective forces acted on their molecular diversification. Given the tissue distribution of mytiporins, this process may have broadened the taxonomic breadth of their biological targets, which would have important implications for digestive processes or mucosal immunity.
Bright versus dim ambient light affects subjective well-being but not serotonin-related biological factors

Light falling on the retina is converted into an electrical signal which stimulates serotonin synthesis. Previous studies described an increase of plasma and CNS serotonin levels after bright light exposure. Ghrelin and leptin are peptide hormones which are involved in the regulation of hunger/satiety and are related to serotonin. Neopterin and kynurenine are immunological markers which are also linked to serotonin biosynthesis. In this study, 29 healthy male volunteers were exposed to bright (5000lx) and dim (50lx) light conditions for 120min in a cross-over manner. Subjective well-being and hunger as well as various serotonin associated plasma factors were assessed before and after light exposure. Subjective well-being showed a small increase under bright light and a small decrease under dim light, resulting in a significant interaction between light condition and time. Ghrelin concentrations increased significantly under both light conditions, but there was no interaction between light and time. Correspondingly, leptin decreased significantly under both light conditions. Hunger increased significantly with no light-time interaction. We also found a significant decrease of neopterin, tryptophan and tyrosine levels, but no interaction between light and time. In conclusion, ambient light was affecting subjective well-being rather than serotonin associated biological factors.
A Survey on Adaptive Random Testing

Random testing (RT) is a well-studied testing method that has been widely applied to the testing of many applications, including embedded software systems, SQL database systems, and Android applications. Adaptive random testing (ART) aims to enhance RT's failure-detection ability by more evenly spreading the test cases over the input domain. Since its introduction in 2001, there have been many contributions to the development of ART, including various approaches, implementations, assessment and evaluation methods, and applications. This paper provides a comprehensive survey on ART, classifying techniques, summarizing application areas, and analyzing experimental evaluations. This paper also addresses some misconceptions about ART, and identifies open research challenges to be further investigated in the future work.
'Don't think that we die from AIDS': Invisibilised uncertainty and global transgender health

The invisibilisation of social groups in health research and survey data is a source of medical uncertainty, long seen as a hallmark of the medical field. However, scholarship has not thoroughly assessed how medical uncertainty is structured by state-level processes and global health agendas, especially for people beyond the Global North. This article introduces invisibilised uncertainty as a type of medical uncertainty structured by global organisational and state-level priorities, which can invisibilise social groups and health problems from research and data collection, exacerbating medical uncertainty and health disparities for people worldwide. Based on 14 months of fieldwork in Thailand and in-depth interviews with 62 participants, the article illuminates how state-level processes and global clinical research agendas have structured knowledge gaps and uncertainties for Thai transgender women. As omissions in health research and data collection become embodied on a world scale, the article expands our understandings of how gendered health disparities are structured nationally and globally. It advances a sociology of medical ignorance by analysing the uneven landscape of holistic transgender health research, parsing how institutional dynamics can prioritise or invisibilise people and health issues in research and data, and structure uncertainties.
Use of Telehealth During the COVID-19 Pandemic Among Practicing Maternal-Fetal Medicine Clinicians

Background: Limited knowledge exists about the drivers of telehealth use among obstetricians during COVID-19 in the United States. We investigated the use of live video visits by Maternal-Fetal Medicine (MFM) clinicians, the factors associated with use and interest in future use. Methods: We drew survey data from 373 clinicians on two outcomes: (1) use of any (vs. no) live video visits during COVID-19 and (2) among users, the extent of live video use. Bivariate and multivariate logistic regressions quantified the association between predisposing (demographic and practice setting characteristics) and enabling factors (prepandemic telehealth use, structural and perceived patient barriers) and each outcome. Results: During the pandemic, 88% reported any use, a jump from 29% prepandemic utilization. Users (vs. nonusers) were younger (p = 0.02); tended to provide comprehensive prenatal care (p = 0.01) and/or inpatient care (p = 0.02), practice in university settings (p = 0.01), engage in various telehealth modalities prepandemic (p ≤ 0.01), and to perceive challenges with technical (p < 0.01), reimbursement (p = 0.05), and patient barriers to internet or data plan access (p ≤ 0.001). After adjusting for covariates, only prepandemic communication through patient portal (adjusted odds ratio [aOR] = 3.85; 95% confidence interval [CI] = 1.33-11.12), perceived patient access barriers (aOR = 5.27; 95% CI = 1.95-14.23), and practice in multiple versus university settings (aOR = 0.18; 95% CI = 0.06-0.56) remained significantly associated with use. Approximately 44% were high users. Prepandemic ultrasound use (aOR = 1.92; 95% CI = 1.17-3.16), perceived patient access barriers (aOR = 1.85; 95% CI = 1.12-3.06) and Midwest versus North practice location (aOR = 0.46; 95% CI = 0.21-0.98) predicted high use. Among high users, 99% wanted to continue offering video visits. Conclusions: We found widespread use of live video obstetric care by MFM clinicians and continued interest in use postpandemic.
A Self-Play and Sentiment-Emphasized Comment Integration Framework Based on Deep Q-Learning in a Crowdsourcing Scenario

Crowdsourcing is a hotspot research field which can facilitate machine learning by collecting labels to train models. Consequently, the state-of-the-art research efforts in crowdsourcing focus on truth inference or label integration, to remove inconsistent labels or to alleviate biased labeling. In turn, the integrated labels will be used to fine-tune machine learning models. Particularly, in this paper, we change the target of truth inference in crowdsourcing from discrete labels to multiple comments given by online participants, that is, the integration of the crowdsourced comments. For such a goal, we propose a Self-play and Sentiment-Emphasized Comment Integration Framework (SSECIF), based on deep Q-learning, with three unique features. First, our framework SSECIF can generate the comment integration in a totally self-play way, without relying on the ground truth generated by human effort. Second, the integrated comment generated by SSECIF can include salient content with low redundancy. Third, the proposed framework SSECIF has emphasized, with a higher intensity, the sentiment in the integrated comment, in order to reflect the attitude or opinion more obviously. Extensive evaluation on real-world datasets demonstrates that SSECIF has achieved the best overall performance in terms of both effectiveness and efficiency, compared with the state-of-the-art methods.
Exploiting Lightweight Statistical Learning for Event-Based Vision Processing

This paper presents a lightweight statistical learning framework potentially suitable for low-cost event-based vision systems, where visual information is captured by a dynamic vision sensor (DVS) and represented as an asynchronous stream of pixel addresses (events) indicating a relative intensity change on those locations. A simple random ferns classifier based on randomly selected patch-based binary features is employed to categorize pixel event flows. Our experimental results demonstrate that compared to existing event-based processing algorithms, such as spiking convolutional neural networks (SCNNs) and the state-of-the-art bag-of-events (BoE)-based statistical algorithms, our framework excels in high processing speed (2x faster than the BoE statistical methods and >100x faster than previous SCNNs in training speed) with extremely simple online learning process, and achieves state-of-the-art classification accuracy on four popular address-event representation data sets: MNIST-DVS, Poker-DVS, Posture-DVS, and CIFAR10-DVS. Hardware estimation shows that our algorithm will be preferable for low-cost embedded system implementations.
Total generalized variation and shearlet transform based Poissonian image deconvolution

Integrating the advantages of total generalized variation and shearlet transform, this article introduces a hybrid regularizers scheme for deconvolving Poissonian image. Computationally, a highly efficient alternating minimization algorithm associated with variable splitting approach is described to obtain the optimal solution in detail. Illustrationally, in comparison with several current state-of-the-art numerical methods, numerical simulations consistently demonstrate the outstanding performance of our proposed approach to deblurring Poissonian image, in terms of both restoration accuracy and feature-preserving ability.