ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Elevated frequencies of micronucleated erythrocytes in infants exposed to zidovudine in utero and postpartum to prevent mother-to-child transmission of HIV

Zidovudine-based antiretroviral therapies (ARTs) for treatment of HIV-infected pregnant women have markedly reduced mother-to-child transmission of the human immunodeficiency virus (HIV-1) from similar to 25% to < 1%. However, zidovudine (ZDV; AZT), a nucleoside analogue, induces chromosomal damage, gene mutations, and cancer in animals following direct or transplacental exposure. To determine if chromosomal damage is induced by ZDV in infants exposed transplacentally, we evaluated micronucleated reticulocyte frequencies (%MN-RET) in 16 HIV-infected ART-treated mother-infant pairs. Thirteen women received prenatal ART containing ZDV; three received ART without ZDV. All infants received ZDV for 6 weeks postpartum. Venous blood was obtained from women at delivery and from infants at 1-3 days, 4-6 weeks, and 4-6 months of life; cord blood was collected immediately after delivery. Ten cord blood samples (controls) were obtained from infants of HIV-uninfected women who did not receive ART. %MN-RET was measured using a single laser 3-color flow cytometric system. Tenfold increases in %MNRET were seen in women and infants who received ZDV-containing ART prenatally; no increases were detected in three women and infants who received prenatal ART without ZDV. Specifically, mean %MNRET in cord blood of ZDV-exposed infants was 1.67 +/- 0.34 compared with 0.16 +/- 0.06 in non-ZDV ART-exposed infants (P=0.006) and 0.12 +/- 0.02 in control cord bloods (P < 0.0001). %MN-RET in ZDV-exposed newborns decreased over the first 6 months of life to levels comparable to cord blood controls. These results demonstrate that transplacental ZDV exposure is genotoxic in humans. Long-term monitoring of HIV-uninfected ZDV-exposed infants is recommended to ensure their continued health. Environ. Mol. Mutagen. 48:322-329, 2007. Published 2007 Wiley-Liss, Inc.
Side Lobe Suppression of Concentric Circular Antenna Array Using Social Spider Algorithm

This paper presents an efficient method to improve the far-field radiation pattern of concentric circular antenna array (CCAA) design using two stochastic optimization algorithms known as social spider algorithm (SSA) and modified social spider algorithm (MSSA). Low side lobe level (SLL) plays a crucial role in reducing the interference with the other frequency components along the entire side lobes of the far-field radiation pattern. SSA and MSAA are the state-of-the-art evolutionary optimization techniques which are applied here to determine the optimal current amplitude and the inter-element distance between two consecutive antennae of the 3-ring CCAA. In this paper, the optimal results achieved by using SSA, MSSA for (4, 6, 8) elements and (8, 10, 12) elements 3-ring CCAAs, with and without centre elements are reported. The results achieved by employing SSA and MSSA show a considerable improvement in SLL reduction as compared to the uniform and the other array patterns reported in the state-of-the-art literature.
An automated method for mining high-quality assertion sets

Assertion-Based Verification (ABV) is one of the promising ways of functional verification. The efficiency of ABV largely depends on the quality of the assertions in terms of how accurately they capture the consistency between implementation and specification. To this end, several assertion miners have been developed to automatically generate assertions. However, existing automatic assertion miners typically generate a huge amount of assertions which can lead to overhead in the verification process. Assertion evaluation, on the other hand, has recently appeared to evaluate and select high-quality assertions among the huge generated assertion set. These methods typically measure the quality of an assertion based on different metrics. These metrics nonetheless, consider dissimilar and distinct aspects which lead to difficulties in deciding what metric should influence more in assertion evaluation. Thereby, to exceed the state-of-the-art, a flow is proposed in which an assertion miner and an assertion evaluator are introduced. The assertion miner is capable of generating a set of readable and compact assertions. The assertion evaluator instead estimates the quality of the assertion set with a data-mining-based algorithm called dominance. Dominance is able to analyze the outcome of different metrics to unify them. Experimental results present the effectiveness of the proposed flow by comparing them to the state-of-the-art.
Hardware Division by Small Integer Constants

This article studies the design of custom circuits for division by a small positive constant. Such circuits can be useful for specific FPGA and ASIC applications. The first problem studied is the Euclidean division of an unsigned integer by a constant, computing a quotient and remainder. Several new solutions are proposed and compared against the state-of-the-art. As the proposed solutions use small look-up tables, they match well with the hardware resources of an FPGA. The article then studies whether the division by the product of two constants is better implemented as two successive dividers or as one atomic divider. It also considers the case when only a quotient or only a remainder is needed. Finally, it addresses the correct rounding of the division of a floating-point number by a small integer constant. All these solutions, and the previous state-of-the-art, are compared in terms of timing, area, and area-timing product. In general, the relevance domains of the various techniques are different on FPGA and on ASIC.
Dietary restriction and medical therapy drives PPARα-regulated improvements in early diabetic kidney disease in male rats

The attenuation of diabetic kidney disease (DKD) by metabolic surgery is enhanced by pharmacotherapy promoting renal fatty acid oxidation (FAO). Using the Zucker Diabetic Fatty and Zucker Diabetic Sprague Dawley rat models of DKD, we conducted studies to determine if these effects could be replicated with a non-invasive bariatric mimetic intervention. Metabolic control and renal injury were compared in rats undergoing a dietary restriction plus medical therapy protocol (DMT; fenofibrate, liraglutide, metformin, ramipril, and rosuvastatin) and ad libitum-fed controls. The global renal cortical transcriptome and urinary 1H-NMR metabolomic profiles were also compared. Kidney cell type-specific and medication-specific transcriptomic responses were explored through in silico deconvolution. Transcriptomic and metabolomic correlates of improvements in kidney structure were defined using a molecular morphometric approach. The DMT protocol led to ∼20% weight loss, normalized metabolic parameters and was associated with reductions in indices of glomerular and proximal tubular injury. The transcriptomic response to DMT was dominated by changes in fenofibrate- and peroxisome proliferator-activated receptor-α (PPARα)-governed peroxisomal and mitochondrial FAO transcripts localizing to the proximal tubule. DMT induced urinary excretion of PPARα-regulated metabolites involved in nicotinamide metabolism and reversed DKD-associated changes in the urinary excretion of tricarboxylic acid (TCA) cycle intermediates. FAO transcripts and urinary nicotinamide and TCA cycle metabolites were moderately to strongly correlated with improvements in glomerular and proximal tubular injury. Weight loss plus pharmacological PPARα agonism is a promising means of attenuating DKD.
Performance and methane potential of up-flow anaerobic sludge blanket treating thermal hydrolyzed sludge dewatering liquor

Thermal hydrolysis pretreatment could release organic sufficiently from solid into liquid phase to accelerate the high solid sludge anaerobic digestion. Thus, up-flow anaerobic sludge blanket (UASB) could be a promising energy recovery process to treat thermal hydrolyzed sludge dewatering liquor with significantly augmented the organic loading rate (OLR). In this study, its performance was investigated using a lab-scale UASB to treat sludge dewatering liquor after 165 °C, 30 min thermal hydrolysis pretreatment. The results show that 85.57% of the organic in thermal hydrolyzed sludge dewatering liquor could be converted to methane. The UASB adapts to high OLR stably, and the COD removal efficiency was 71.98 ± 1.95% at OLR of 18.35 ± 0.78 kgCOD·(m3·d)-1, and the gap between the maximum potential and experimental methane production yields could be observed during different OLRs. It could be explained as the methanogenesis rate decreased due to the shift of dominant pathway from acetoclastic methanogenesis to syntrophic acetate oxidation following hydrogenotrophic methanogenesis. Methanospirillum became the dominant methanogen with the increase of OLR. In addition, the methane production yield and rate would be hindered till the ammonia nitrogen concentration exceeds 4 g·L-1. Direct interspecies electron transfer could be promising methods to improve UASB performance treating thermal hydrolyzed dewatering liquor.
HM-Net: A Regression Network for Object Center Detection and Tracking on Wide Area Motion Imagery

Wide Area Motion Imagery (WAMI) yields high resolution images with a large number of extremely small objects. Target objects have large spatial displacements throughout consecutive frames. This nature of WAMI images makes object tracking and detection challenging. In this paper, we present our deep neural network-based combined object detection and tracking model, namely, Heat Map Network (HM-Net). HM-Net is significantly faster than state-of-the-art frame differencing and background subtraction-based methods, without compromising detection and tracking performances. HM-Net follows object center-based joint detection and tracking paradigm. Simple heat map-based predictions support unlimited number of simultaneous detections. The proposed method uses two consecutive frames and the object detection heat map obtained from the previous frame as input, which helps HM-Net monitor spatio-temporal changes between frames and keep track of previously predicted objects. Although reuse of prior object detection heat map acts as a vital feedback-based memory element, it can lead to unintended surge of false positive detections. To increase robustness of the method against false positives and to eliminate low confidence detections, HM-Net employs novel feedback filters and advanced data augmentations. HM-Net outperforms state-of-the-art WAMI moving object detection and tracking methods on WPAFB dataset with its 96.2% F1 and 94.4% mAP detection scores, while achieving 61.8 % mAP tracking score on the same dataset. This performance corresponds to an improvement of 2.1% for F1, 6.1% for mAP scores on detection, and 9.5% for mAP score on tracking over state-of-the-art.
The critical role of the endolysosomal system in cerebral ischemia

Cerebral ischemia is a serious disease that triggers sequential pathological mechanisms, leading to significant morbidity and mortality. Although most studies to date have typically focused on the lysosome, a single organelle, current evidence supports that the function of lysosomes cannot be separated from that of the endolysosomal system as a whole. The associated membrane fusion functions of this system play a crucial role in the biodegradation of cerebral ischemia-related products. Here, we review the regulation of and the changes that occur in the endolysosomal system after cerebral ischemia, focusing on the latest research progress on membrane fusion function. Numerous proteins, including N-ethylmaleimide-sensitive factor and lysosomal potassium channel transmembrane protein 175, regulate the function of this system. However, these proteins are abnormally expressed after cerebral ischemic injury, which disrupts the normal fusion function of membranes within the endolysosomal system and that between autophagosomes and lysosomes. This results in impaired "maturation" of the endolysosomal system and the collapse of energy metabolism balance and protein homeostasis maintained by the autophagy-lysosomal pathway. Autophagy is the final step in the endolysosomal pathway and contributes to maintaining the dynamic balance of the system. The process of autophagosome-lysosome fusion is a necessary part of autophagy and plays a crucial role in maintaining energy homeostasis and clearing aging proteins. We believe that, in cerebral ischemic injury, the endolysosomal system should be considered as a whole rather than focusing on the lysosome. Understanding how this dynamic system is regulated will provide new ideas for the treatment of cerebral ischemia.
Long-Term Prostaglandin E1 Infusion for Newborns with Critical Congenital Heart Disease

Prostaglandin E1 is crucial for keeping the patent ductus arteriosus in critical congenital heart disease for the survival and palliation of particularly prematurely born babies until a cardiosurgical intervention is available. In this study, the side effects of prostaglandin E1 in newborns with critical congenital heart disease and clinical outcomes were evaluated. Thirty-five newborns diagnosed with critical congenital heart disease were treated with prostaglandin E1 between January 2012 and September 2014 at our hospital. Patient charts were examined for prostaglandin E1 side effects (metabolic, gastric outlet obstruction, apnea), clinical status, and prognosis. Acquired data were analyzed in the SPSS 20.0 program. Patients with birth weight under 2500 g needed more days of prostaglandin E1 infusion than ones with birthweight over 2500 g (P = 0.016). The ratio of patients with birth weight under 2500 g who received prostaglandin E1 longer than 7 days was higher than the patients with birth weight over 2500 g (P = 0.02). Eighteen side effects were encountered in 11 of 35 patients (31%). Of these side effects, 1 patient had 4, 4 patients had 2, and 6 patients had only 1 side effect. Discontinuation of the therapy was never needed. Prostaglandin E1 is an accepted therapy modality for survival and outcome in critical congenital heart disease in particularly low-birth-weight babies until a surgical intervention is available. Side effects are not less encountered but are almost always manageable, and discontinuation is not needed.
High-throughput-derived biologically-inspired features for unconstrained face recognition

Many modern computer vision algorithms are built atop of a set of low-level feature operators (such as SIFT [23,24]; HOG [8,3]; or LBP [1,2]) that transform raw pixel values into a representation better suited to subsequent processing and classification. While the choice of feature representation is often not central to the logic of a given algorithm, the quality of the feature representation can have critically important implications for performance. Here, we demonstrate a large-scale feature search approach to generating new, more powerful feature representations in which a multitude of complex, nonlinear, multilayer neuromorphic feature representations are randomly generated and screened to find those best suited for the task at hand. In particular, we show that a brute-force search can generate representations that, in combination with standard machine learning blending techniques, achieve state-of-the-art performance on the Labeled Faces in the Wild (LFW) [19] unconstrained face recognition challenge set. These representations outperform previous state-of-the-art approaches, in spite of requiring less training data and using a conceptually simpler machine learning backend. We argue that such large-scale-search-derived feature sets can play a synergistic role with other computer vision approaches by providing a richer base of features with which to work. (C) 2012 Elsevier B.V. All rights reserved.
Bidirectional LSTM-RNN-based hybrid deep learning frameworks for univariate time series classification

Time series classification (TSC) has been around for recent decades as a significant research problem for industry practitioners as well as academic researchers. Due to the rapid increase in temporal data in a wide range of disciplines, an incredible amount of algorithms have been proposed. This paper proposes robust approaches based on state-of-the-art techniques, bidirectional long short-term memory (BiLSTM), fully convolutional network (FCN), and attention mechanism. A BiLSTM considers both forward and backward dependencies, and FCN is proven to be good at feature extraction as a TSC baseline. Therefore, we augment BiLSTM and FCN in a hybrid deep learning architecture, BiLSTM-FCN. Moreover, we similarly explore the use of the attention mechanism to check its efficiency on BiLSTM-FCN and propose another model ABiLSTM-FCN. We validate the performance on 85 datasets from the University of California Riverside (UCR) univariate time series archive. The proposed models are evaluated in terms of classification testing error and f1-score and also provide performance comparison with various existing state-of-the-art techniques. The experimental results show that our proposed models perform comprehensively better than the existing state-of-the-art methods and baselines.
Simultaneously Stabilizing Both Electrodes and Electrolytes by a Self-Separating Organometallics Interface for High-Performance Zinc-Ion Batteries at Wide Temperatures

Rechargeable aqueous zinc-ion batteries are of great potential as one of the next-generation energy-storage devices due to their low cost and high safety. However, the development of long-term stable electrodes and electrolytes still suffers from great challenges. Herein, a self-separation strategy is developed for an interface layer design to optimize both electrodes and electrolytes simultaneously. Specifically, the coating with an organometallics (sodium tricyanomethanide) evolves into an electrically responsive shield layer composed of nitrogen, carbon-enriched polymer network, and sodium ions, which not only modulates the zinc-ion migration pathways to inhibit interface side reactions but also adsorbs onto Zn perturbations to induce planar zinc deposition. Additionally, the separated ions from the coating can diffuse to the electrolyte to affect the Zn2+ solvation structure and maintain the cathode structural stability by forming a stable cathode-electrolyte interface and sodium ions' equilibrium, confirmed by in situ spectroscopy and electrochemical analysis. Due to these unique advantages, the symmetric zinc batteries exhibit an extralong cycling lifespan of 3000 h and rate performance at 20 mA cm-2 at wide temperatures. The efficiency of the self-separation strategy is further demonstrated in practical full batteries with an ultralong lifespan over 10 000 cycles from -35 to 60 °C.
An Efficient Optimization Framework for Multi-Region Segmentation Based on Lagrangian Duality

We introduce a multi-region model for simultaneous segmentation of medical images. In contrast to many other models, geometric constraints such as inclusion and exclusion between the regions are enforced, which makes it possible to correctly segment different regions even if the intensity distributions are identical. We efficiently optimize the model using a combination of graph cuts and Lagrangian duality which is faster and more memory efficient than current state of the art. As the method is based on global optimization techniques, the resulting segmentations are independent of initialization. We apply our framework to the segmentation of the left and right ventricles, myocardium and the left ventricular papillary muscles in magnetic resonance imaging and to lung segmentation in full-body X-ray computed tomography. We evaluate our approach on a publicly available benchmark with competitive results.
Biogeography-based optimization scheme for solving the coverage and connected node placement problem for wireless sensor networks

In wireless sensor networks, coverage and connectivity are the fundamental problems for monitoring the targets and guaranteed information dissemination to the far away base station from each node which covers the target. This problem has been proved NP-complete problem, where a set of target points are given, the objective is to find optimal number of suitable positions to organize sensor nodes such that it must satisfy both k-coverage and m-connectivity requirements. In this paper, a biogeography-based optimization (BBO) scheme is used to solve this problem. The proposed BBO-based scheme provides an efficient encoding scheme for the habitat representation and formulates an objective function along with the BBO's migration and mutation operators. Simulation results show the performance of the proposed scheme to find approximate optimal number of suitable positions under different combinations of k and m. In addition, a comparative study with state-of-art schemes has also been done and its analysis confirms the superiority of the proposed BBO-based scheme over state-of-art schemes.
VOC exposures in a mixed-use university art building

Despite a sizable educational art enterprise in the United States there is a dearth of rigorously performed studies of exposures to persons engaged in such activities. Exposures to 45 EPA-designated volatile organic compounds were examined in printmakers in a mixed-use university art school served by a 100% exhausted mechanical ventilation system. Personal exposures (n=90) were compared with area concentrations (n=36) in the studio area and at a second location at the same facility. For personal exposure assessments a cohort of 12 students wore passive dosimeters twice weekly over a 6-week period. Numerous compounds were found, the most prevalent being toluene at an average concentration of 64.6 mug/m(3) (17.1 ppb; range <1-319 ppb); 1,1,1, trichloroethane at 40.5 mug/m(3) (7.5 ppb; range <11-211 ppb); xylenes at 8.0 mug/m(3) (1.8 ppb; range <1-43 ppb); 1,3,5-trimethyl benzene at 6.2 mug/m(3) (1.3 ppb; range <.3-32 ppb); propyl benzene at 5.0 mug/m(3) (1.0 ppb; range <.5-27 ppb); methylene chloride at 4.9 mug/m(3) (1.4 ppb; range <1-10 ppb); and ethyl benzene at 4.5 mug/m(3) (1.1 ppb; range <.4-23 ppb). Personal exposures were considerably higher than average area air concentrations, with the exception of methylene chloride concentrations, which were five times higher at the print cleaning operation. Floors where solvents were not used had no detectable exposures (typical lower limit of detection 1 ppb) and were free of solvent odors. Despite frequent solvent contact with skin, personal protective equipment was seldom used. Results indicate that in mixed-use facilities such as this, nonrecirculating general ventilation systems can effectively eliminate indoor air quality issues between floors, despite perceptible odors on solvent use floors. For total exposure assessments in such processes, contact exposures from printmaking solvents during cleaning procedures are a potentially important consideration.