ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Review and Comparison of Control Strategies in Active Power Decoupling

The active power-decoupling (APD) method is an effective solution to handle the inherent double-line frequency ripple power in single-phase power systems. It removes the bulky passive devices and facilitates the improvement of the system power density and even the reliability. This article provides a comprehensive review of the prior-art and state-of-the-art control strategies in APD. They are categorized into four groups according to the basic control ideas of "power balance," "harmonic suppression," "volt-second balance/charge balance," and "virtual impedance." And the specific control strategies under each control idea are discussed and compared. The pros and cons of each control idea are also presented . Finally, this article draws a sketch of the global trends in APD control.
Alpha-Particle Emission Energy Spectra From Materials Used for Solder Bumps

The emitted alpha particle energy distribution from solder bumps can show substantial surface emission which has a large impact on the modeled SEU rate. State-of-the art alpha-particle detectors are required to measure the low emissivity and energy distribution.
National Email Communication Platforms May Indicate or Contribute to Gender Disparities: Preliminary Analysis of an Academic Medicine Listserv

Background: Gender disparities are well documented in the academic medicine literature and have been shown to impact representation, rank, and leadership opportunities for women. Social media platforms, including electronic mailing lists (listservs), may contribute to disparities by differentially highlighting or promoting individuals' work in academic and public health settings. Because of this, they provide a record by which to assess the presence of gender disparities; therefore, they become tools to identify gender differences in the frequency or pattern of representation. This study examines the representation of women in academic medicine electronic communications by analyzing weekly email listserv announcements of the American Association of Medical Colleges (AAMC). Materials and Methods: A mixed methods approach was used to analyze listserv communications during two time periods, 2012-2014 and 2018-2019. Each email contained multiple announcements. Individual achievement messages were selected, categorized by gender, and coded with one of three action categories: departures, appointments, and other mentions. Additionally, each notice was coded by professional setting (media, professional organizations, medical school/research, health care systems, public health, and government). Results: We analyzed a total of 5701 announcements in the AAMC communication listserv. Men represented 73.2% (N = 4171) and women 26.8% (N = 1530) of the total announcements. During 2012-2014, 24.0% of announcements were about women, while in the 2018-2019 sample, 35.7% of announcements were about women (p < 0.001). Overall, women were underrepresented in departure-focused messages compared to messages with an appointment or other focus in the sample. The prevalence of women in announcements from the 2012-2014 and 2018-2019 samples also varied based on setting. Conclusions: Findings support the presence of gender disparities in these sets of listserv communications. While social media overall is not considered to be a source of complete information, this study analyzed the same listserv communication by the same organization over the entire period, thereby providing a window into the frequency and type of representation of women's professional activity in academic medicine.
Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets

Under the global pandemic of COVID-19, the use of artificial intelligence to analyze chest X-ray (CXR) image for COVID-19 diagnosis and patient triage is becoming important. Unfortunately, due to the emergent nature of the COVID-19 pandemic, a systematic collection of CXR data set for deep neural network training is difficult. To address this problem, here we propose a patch-based convolutional neural network approach with a relatively small number of trainable parameters for COVID-19 diagnosis. The proposed method is inspired by our statistical analysis of the potential imaging biomarkers of the CXR radiographs. Experimental results show that our method achieves state-of-the-art performance and provides clinically interpretable saliency maps, which are useful for COVID-19 diagnosis and patient triage.
Usability testing of three visual HMIs for assisted driving: How design impacts driver distraction and mental models

There is a variety of visual human-machine interfaces (HMI) designed across vehicle manufacturers that support drivers while supervising driving automation features, such as adaptive cruise control (ACC). These various designs communicate the same limited amount of information to drivers about their ACC system and it is unclear which HMI designs impact driver distraction the least or how their design could be modified to help drivers develop more accurate mental models of their ACC system. Using a user-centred design (UCD) approach, we designed a speedometer to inform drivers about some of the system's capabilities and then invited 23 drivers to use ACC in a low-fidelity driving simulator to compare the usability of three HMIs using eye-tracking, response times, and qualitative data. Our attempt at designing an intuitive and more informative speedometer received mixed results, but design recommendations are given regarding the indication of the set target speed, set time gap between vehicles (headway distance), and system mode (conventional or adaptive cruise). Practitioner summary: Manufacturers' heterogeneous designs of their visual HMIs for the ACC systems may impact driver distraction in different ways. We used usability testing to compare three HMIs in a driving simulator and make several design recommendations to indicate speed, time gap, and system mode in a more efficient way. Abbreviations: ACC: adaptive cruise control; ADAS: advanced driving assistance system; HMI: human-machine interface; ISO: international organisation for standardization; OEM: original equipment manufacturer; RSME: rating scale of mental effort; RT: response time; R-TLX: raw task load index; SUS: system usability scale; TGT: total glance time; UCD: user-centred design; UX: user experience; xTGT: extended total glance time.
How two extraembryonic epithelia became one: serosa and amnion features and functions of Drosophila's amnioserosa

The conservation of gene networks that specify and differentiate distinct tissues has long been a subject of great interest to evolutionary developmental biologists, but the question of how pre-existing tissue-specific developmental trajectories merge is rarely asked. During the radiation of flies, two extraembryonic epithelia, known as serosa and amnion, evolved into one, called amnioserosa. This unique extraembryonic epithelium is found in fly species of the group Schizophora, including the genetic model organism Drosophila melanogaster, and has been studied in depth. Close relatives of this group develop a serosa and a rudimentary amnion. The scuttle fly Megaselia abdita has emerged as an excellent model organism to study this extraembryonic tissue organization. In this review, development and functions of the extraembryonic tissue complements of Drosophila and Megaselia are compared. It is concluded that the amnioserosa combines cells, genetic pathway components and functions that were previously associated either with serosa development or amnion development. The composite developmental trajectory of the amnioserosa raises the question of whether merging tissue-specific gene networks is a common evolutionary process. This article is part of the theme issue 'Extraembryonic tissues: exploring concepts, definitions and functions across the animal kingdom'.
Mitochondrial DNA maintenance in Drosophila melanogaster

All 37 mitochondrial DNA (mtDNA)-encoded genes involved with oxidative phosphorylation and intramitochondrial protein synthesis, and several nuclear-encoded genes involved with mtDNA replication, transcription, repair and recombination are conserved between the fruit fly Drosophila melanogaster and mammals. This, in addition to its easy genetic tractability, has made Drosophila a useful model for our understanding of animal mtDNA maintenance and human mtDNA diseases. However, there are key differences between the Drosophila and mammalian systems that feature the diversity of mtDNA maintenance processes inside animal cells. Here, we review what is known about mtDNA maintenance in Drosophila, highlighting areas for which more research is warranted and providing a perspective preliminary in silico and in vivo analyses of the tissue specificity of mtDNA maintenance processes in this model organism. Our results suggest new roles (or the lack thereof) for well-known maintenance proteins, such as the helicase Twinkle and the accessory subunit of DNA polymerase γ, and for other Drosophila gene products that may even aid in shedding light on mtDNA maintenance in other animals. We hope to provide the reader some interesting paths that can be taken to help our community show how Drosophila may impact future mtDNA maintenance research.
SRI3D: Two-stream inflated 3D ConvNet based on sparse regularization for action recognition

Although most state-of-the-art action recognition models have adopted a two-stream 3D convolutional structure as a backbone network, few works have studied the impact of loss functions on action recognition models. In addition, sparsity is used as a key prior knowledge in many fields. However, as far as is known, no one has studied the influence of the sparsity of network output on the output of deep learning-based action recognition models. Therefore, this paper proposes a novel two-stream inflated 3D ConvNet based on the sparse regularization (SRI3D) model for action recognition. In order to allow the network to learn the sparsity of output, the l(1) norm is embedded in the loss function in regularization form in a plug-and-play manner. It can make the classification result after the fusion of the two-stream network only be the category with the highest confidence in one of the streams and not the other cases. The proposed loss function based on sparse regularization makes the output vector of the neural network as sparse as possible so that the classification results will not be ambiguous. Experimental results show that compared with other state-of-the-art models, this SRI3D has a competitive advantage on Kinetics-400, Something-Something V2, UCF-101 and HMDB-51.
Binding interaction of SGLT with sugar and thiosugar by the molecular dynamics simulation

The human sodium-glucose co-transporter 2 (hSGLT2) is a transporter responsible for reabsorption of glucose in the proximal convoluted tubule of the kidney. hSGLT2 inhibitors, including luseogliflozin, have been developed as drugs for type 2 diabetes mellitus. Only luseogliflozin contains a thiosugar ring in its chemical structure, while other hSGLT2 inhibitors contain glucose rings. Consequently, we focused on the binding interactions of hSGLT2 with sugars and thiosugars. We first revealed that the binding affinities of thiosugars are stronger than those of sugars through molecular dynamics simulations of Vibrio parahaemolyticus, sodium-galactose co-transporter, and human hSGLT2. We then demonstrated that Na(+) dissociates from the protein to the cytoplasmic solution more slowly in the thiosugar system than in the sugar system. These differences between sugars and thiosugars are discussed on the basis of the different binding modes due to the atom at the 5-position of the sugar and thiosugar rings. Finally, as a result of Na(+) dissociation, we suggest that the dissociation of thiosugars is slower than that of sugars.
Occurrence and ecological risks of brominated flame retardants and dechlorane plus in sediments from the Pearl River Estuary and Daya Bay, South China

Considering the phasing-out of polybrominated diphenyl ethers (PBDEs), environmental concerns of PBDE alternatives and dechlorane plus (DP) are rising. Accordingly, this study investigates occurrence and ecological risks of PBDEs, PBDE alternatives and DPs in sediments of two littoral regions, the Pearl River Estuary (PRE) and Daya Bay (DYB), in southern China. Total PBDEs concentrations in surface sediments of the PRE and DYB were in the range (mean) of 0.30-28.7 (8.71) and 0.29-43.4 (6.05) ng/g dw, respectively. DP levels in surface sediments of the PRE (0.004-0.27 ng/g dw) were significantly higher than those in the DYB (0.005-0.24 ng/g dw) (p < 0.05). BDE 209 was the predominant component, followed by DBDPE, exhibiting regional variations in BFRs usage. Vertical profiles of BFRs and DP in the PRE and DYB sediment cores exhibited clear anthropogenic influences. Risk quotients suggest critical ecological risks of tetra-, penta- and deca-BDE congeners in all the surface sediments.
Efficient Hardware Implementation of Real-Time Low-Power Movement Intention Detector System Using FFT and Adaptive Wavelet Transform

The brain-computer interfacing (BCI), a platform to extract features and classify different motor movement tasks from noisy and highly correlated electroencephalogram signals, is limited mostly by the complex and power-hungry algorithms. Among different techniques recently devised to tackle this issue, real-time onset detection, due to its negligible delay and minimal power overhead, is the most efficient one. Here, we propose a novel algorithm that outperforms the state-of-the-art design by sixfold in terms of speed, without sacrificing the accuracy for a real-time, hand movement intention detection based on the adaptive wavelet transform with only 1 s detection delay and maximum sensitivity of 88% and selectivity of 78% (only 7% loss of sensitivity).
Kronecker-Markov Prior for Dynamic 3D Reconstruction

Recovering dynamic 3D structures from 2D image observations is highly under-constrained because of projection andmissing data, motivating the use of strong priors to constrain shape deformation. In this paper, we empirically show that the spatiotemporal covariance of natural deformations is dominated by a Kronecker pattern. We demonstrate that this pattern arises as the limit of a spatiotemporal autoregressive process, and derive a Kronecker Markov Random Field as a prior distribution over dynamic structures. This distribution unifies shape and trajectory models of prior art and has the individual models as its marginals. The key assumption of the Kronecker MRF is that the spatiotemporal covariance is separable into the product of a temporal and a shape covariance, and can therefore bemodeled using thematrix normal distribution. Analysis on motion capture data validates that this distribution is an accurate approximation with significantly fewer free parameters. Using the trace-norm, we present a convex method to estimate missing data from a single sequence when the marginal shape distribution is unknown. The Kronecker-Markov distribution, fit to a single sequence, outperforms state-of-the-art methods at inferring missing 3D data, and additionally provides covariance estimates of the uncertainty.
Kernel Regression for Matrix-Variate Gaussian Distributed Signals Over Sample Graphs

Recent advances of kernel regression assume that target signals lie over a feature graph such that their values can be predicted with the assistance of the graph learned from training data. In this article, we propose a novel kernel regression framework whose outputs follow a matrix-variate Gaussian distribution (MVGD) such that the kernel matrix can he viewed as the column covariance matrix of outputs, and the hyperparameters of a chosen kernel can be optimized using gradient methods. Furthermore, in contrast to the state-of-the-art kernel regression algorithms over graph (KRG), a sample graph of target outputs is introduced to work with regression coefficients and hyperparameters of a chosen kernel in our algorithms. The proposed KRG framework is decomposed into two stages, including the estimation of row and column covariance matrices of MVGD and graph learning along with the estimation of regression coefficients. Numerical approaches are developed to tackle the corresponding optimization problems. Experimental results over synthetic and real-world datasets demonstrate that the performance of the proposed algorithms is superior to that of the state-of-the-art methods.
Random GUI Testing of Android Application Using Behavioral Model

Automated GUI testing based on behavioral model is one of the most efficient testing approaches. By mining user usage, test scenarios can be generated based on statistical models such as Markov chain. However, these works require static analysis before starting the exploration which requires too much prerequisites and time. To address these challenges, we propose a behavioral-based GUI testing approach for mobile applications that achieves faster and higher coverage. The proposed approach does not conduct static analysis. It creates a behavioral model from usage logs by applying a statistical model. The events within the behavioral model are mapped to GUI components in a GUI tree. Finally, it updates the model dynamically to increase the probability of an event that rarely or never occurs when users use the application. The proposed approach was evaluated on four open-source Android applications, and compared with the state-of-the-art tools and manual testing. The main evaluation criteria are code coverage and ability to and errors. The proposed approach performed better than the current state-of-the-art automated testing tools in most aspects.
Characteristics and Long-Term Outcome of Cerebellar Strokes in a Single Health Care Facility in Mexico

Objective The purpose of this study was to analyze and discuss the clinical characteristics, long-term outcome, and prognostic factors of cerebellar strokes treated in a single health care facility in Mexico. Methods We retrospectively reviewed the medical records of adult patients admitted to our hospital with diagnosis of cerebellar ischemic and hemorrhagic stroke between 2018 and 2020. Baseline data included sociodemographic and radiological variables, treatment (surgical versus conservative), and Glasgow Coma Scale on arrival (GCSOA). The final neurological outcome was evaluated with the Glasgow Outcome Scale (GOS) six months after hospital discharge. Results Ten patients (seven male and three female) with a mean age of 57.9 ± 9.3 years were included, six with cerebellar ischemic infarction and four with cerebellar hemorrhage. Out of the 10 patients, four underwent surgery (suboccipital decompressive craniectomy {SDC} ± ventriculostomy). The outcome was favorable in four cases (40%) and unfavorable in six (60%). Patients who underwent surgical treatment fared worse with all four cases associating poor outcome. The comparison between good and poor outcome groups showed significant differences in the presence of obstructive hydrocephalus (one versus six, p = 0.05) and poorer GCSOA (6.16 ± 1.72 versus 12.5 ± 3.6, p = 0.05), associating poorer outcome. Conclusion There is still controversy regarding the appropriate management of cerebellar strokes. The presence of obstructive hydrocephalus and poorer GCSOA are associated to worse outcomes.
Evidence for a fragile X messenger ribonucleoprotein 1 (FMR1) mRNA gain-of-function toxicity mechanism contributing to the pathogenesis of fragile X-associated premature ovarian insufficiency

Fragile X-associated premature ovarian insufficiency (FXPOI) is among a family of disorders caused by expansion of a CGG trinucleotide repeat sequence located in the 5' untranslated region (UTR) of the fragile X messenger ribonucleoprotein 1 (FMR1) gene on the X chromosome. Women with FXPOI have a depleted ovarian follicle population, resulting in amenorrhea, hypoestrogenism, and loss of fertility before the age of 40. FXPOI is caused by expansions of the CGG sequence to lengths between 55 and 200 repeats, known as a FMRI premutation, however the mechanism by which the premutation drives disease pathogenesis remains unclear. Two main hypotheses exist, which describe an mRNA toxic gain-of-function mechanism or a protein-based mechanism, where repeat-associated non-AUG (RAN) translation results in the production of an abnormal protein, called FMRpolyG. Here, we have developed an in vitro granulosa cell model of the FMR1 premutation by ectopically expressing CGG-repeat RNA and FMRpolyG protein. We show that expanded CGG-repeat RNA accumulated in intranuclear RNA structures, and these aggregates were able to cause significant granulosa cell death independent of FMRpolyG expression. Using an innovative RNA pulldown, mass spectrometry-based approach we have identified proteins that are specifically sequestered by CGG RNA aggregates in granulosa cells in vitro, and thus may be deregulated as consequence of this interaction. Furthermore, we have demonstrated reduced expression of three proteins identified via our RNA pulldown (FUS, PA2G4 and TRA2β) in ovarian follicles in a FMR1 premutation mouse model. Collectively, these data provide evidence for the contribution of an mRNA gain-of-function mechanism to FXPOI disease biology.
Anthrax Meningoencephalitis and Intracranial Hemorrhage

The neurological sequelae of Bacillus anthracis infection include a rapidly progressive fulminant meningoencephalitis frequently associated with intracranial hemorrhage, including subarachnoid and intracerebral hemorrhage. Higher mortality than other forms of bacterial meningitis suggests that antimicrobials and cardiopulmonary support alone may be insufficient and that strategies targeting the hemorrhage might improve outcomes. In this review, we describe the toxic role of intracranial hemorrhage in anthrax meningoencephalitis. We first examine the high incidence of intracranial hemorrhage in patients with anthrax meningoencephalitis. We then review common diseases that present with intracranial hemorrhage, including aneurysmal subarachnoid hemorrhage and spontaneous intracerebral hemorrhage, postulating applicability of established and potential neurointensive treatments to the multimodal management of hemorrhagic anthrax meningoencephalitis. Finally, we examine the therapeutic potential of minocycline, an antimicrobial that is effective against B. anthracis and that has been shown in preclinical studies to have neuroprotective properties, which thus might be repurposed for this historically fatal disease.
Decolorization of Dye-Containing Textile Industry Effluents Using Ganoderma Lucidum IBL-05 in Still Cultures

A locally isolated white rot fungus Ganoderma lucidum IBL-05 was used for development of a bioremediation process for original textile industry effluents. Dye-containing effluents of different colors were collected from the Arzoo (maroon), Ayesha (yellow), Ittemad (green), Crescent (navy blue) and Magna ( yellowish) textile industries of Faisalabad, Pakistan. G. lucidum IBL-05 was screened for its decolorization potential on all the effluents. Maximum decolorization (49.5%) was observed in the case of the Arzoo textile industry (ART) effluent (lambda(max) = 515 nm) on the 10th day of incubation. Therefore, the ART effluent was selected for optimization of its decolorization process. Process optimization could improve color removal efficiency of the fungus to 95% within only 2 days, catalyzed by manganese peroxidase (1295 U/mL) as the main enzyme activity at pH 3 and 35 degrees C using 1% starch supplemented Kirk's basal medium. Nitrogen addition inhibited enzyme formation and effluent decolorization. The economics and effectiveness of the process can be improved by further process optimization. Water Environ. Res., 82, 357 (2010).