ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Identifying the geochemical evolution and controlling factors of the shallow groundwater in a high fluoride area, Feng County, China

Understanding how groundwater is formed and evolves is critical for water resource exploitation and utilization. In this study, hydrochemistry and stable isotope tracing techniques were adopted to determine the key factors influencing groundwater chemical evolution in Feng County. A total of fourteen wells and five surface water samples were investigated in November 2021. The δD and δ18O compositions show that both surface water and groundwater are recharged from atmospheric precipitation. The dominating order of cations and anions in groundwater appears to be Na+ > Mg2+ > Ca2+ > K+ and HCO3- > SO42- > Cl- > NO3- > F-, respectively. The groundwater hydrochemical facies are mainly characterized by HCO3-Ca-Mg and SO4-Cl-Na types. The chemical evolution of groundwater is dominated by water-rock interaction and cation exchange reactions. The major ions in groundwater are mainly controlled by various geogenic processes including halite, gypsum, calcite, dolomite, Glauber's salt, feldspar, and fluorite dissolution/precipitation. Furthermore, the abundant fluoride-bearing sediments, together with low Ca2+, promote the formation of high F- groundwater. Approximately 85.7% and 28.6% of groundwater samples exceeded the permissible limit for F- and NO3- respectively. Apart from geogenic F-, human interventions (i.e., industrial fluoride-containing wastewater discharge and agricultural phosphate fertilizer uses) also regulate the F- enrichment in the shallow groundwater. Nitrate pollution of the groundwater may be attributed to domestic waste and animal feces. Our findings could provide valuable information for the sustainable exploitation of groundwater in the study area and the development of effective management strategies by the authorities.
Are androstenedione, dihydrotestosterone, thyroid-stimulating hormone, insulin-like growth factor I, and insulin-like growth factor binding protein 3 necessary for isolated micropenis healthy boys' evaluation without any phenotypic abnormalities? A cross-sectional study

The study aimed to familiarise primary care physicians and specialists with the minimum hormonal diagnostic tests necessary to assay isolated micropenis in healthy children without any phenotypic abnormality. Children aged 6-15 years (mean 11.6 ± 1.68) were assessed from May 2010 to September 2021 (N = 247). Multiple regression analysis showed correlations between stretched penile length (SPL) and hormonal assays as follows: follicle-stimulating hormone (FSH): r = 0.097, p = 0.035; luteinizing hormone (LH): r = 0.139, p = 0.012, thyroid-stimulating hormone (TSH): r = -0.001, p = 0.321; testosterone (T): r = 0.118, p = 0.004; dihydrotestosterone (DHT): r = 0.002, p = 0.243; androstenedione (Δ4And): r = -0.004, p = 0.502; insulin-like growth factor I (IGF-I): r = -0.003, p = 0.062; and IFG-binding protein 3 (IGF-BP3 ): r = 0.052, p = 0.051. The most hormonal disorder was testosterone deficiency. TSH, Δ4And, and DHT were normal in all boys. SPL was significantly correlated with FSH, LH, and T, but there was no significant correlation between SPL and TSH, DHT, Δ4And, IGF-I, and IGF-BP3 . Whenever the isolated micropenis is seen without other anomalies, it is sufficient to assay testosterone, FSH, and LH in the first step.
Incidental Discovery of Nonrotation in a Patient With Nonspecific Abdominal Pain: A Surgical Diagnostic Dilemma

Intestinal nonrotation is a subtype of malrotation occurring when the midgut fails to rotate before returning to the peritoneal cavity between weeks 8-10 of development. Though sometimes presenting as volvulus during the neonatal period, a subset of patients remains asymptomatic and are identified incidentally as adults. When patients with intestinal nonrotation present with abdominal symptoms, there exists a diagnostic dilemma for the treating surgeon. We present the case of a patient who presented with acute abdominal pain and vomiting, with radiographic findings of intestinal nonrotation and no other acute pathology. Symptoms spontaneously resolved with conservative management for likely etiology of viral gastroenteritis. At the one-month follow-up, the patient had no residual or recurrent symptoms, with no further interventions planned.
From the Women's Health Initiative to the combination of estrogen and selective estrogen receptor modulators to avoid progestin addition

The female life expectancy rose from an average of 48 years to over 80 years in a century. The decline in the endogenous production of estrogen (especially the main circulating physiological hormone, 17β-estradiol, E2) at menopause (51 years on average) often leads to functional disorders affecting the quality of life. Estrogen deficiency impacts different tissues and results in an increase of various diseases, such as osteoporosis or cardiovascular diseases. Hormone therapy (HT) for menopause is a rather new challenge which experienced vagaries following the women's health initiative (WHI) study conducted in largely post-menopausal women. In the first part of this review, we will try to summarize the main conclusions of the WHI trials, in particular the timing effect as well as the deleterious impact of the associated progestin, medroxyprogesterone acetate (MPA). Hormone therapy, particularly the conjugated equine estrogen (CEE) combined with the MPA favor the occurrence of breast cancer, whereas conversely selective estrogen receptor modulators (SERMs, such as tamoxifen or raloxifene) that block the activity of estrogen receptor alpha (ERa) prevent the risk of recurrence of ERa-positive breast cancers. A new strategy of ERa modulation called tissue selective estrogen complex (TSEC), combines (1) CEE to maintain the benefits of estrogen (climacteric symptoms and prevention of osteoporosis) and (2) bazedoxifene, which is not only a SERM, but which also induces a rapid degradation of ERa in the uterus and in the breast, thereby prevents the stimulatory effects of estrogens on epithelial proliferation of these two sex targets. In the second part of this review, we will summarize the recognized benefits of the TSEC approach, and our current knowledge of its potential benefits and risks.
A Low Complexity Design Framework for NFC-RFID Inductive Coupled Antennas

Inductive Wireless Power and Data Transfer (WPDT) technology has become a vital enabler to the globalisation of Internet of Things. Driven by an increasing demand for data within applications and by the need to reduce the devices footprint by transmitting data and power with the same antenna, power transfer efficiency has become a barrier to WPDT systems' performance. To overcome the limitations of power transfer efficiency, current research focuses on the design of efficient integrated circuits and does not consider the challenges of inductive antennas' design and system integration. Hence, current system integration methods used in industry to design receivers for WPDT applications still require expensive experimental benchmarking of antennas. This paper introduces a new framework for inductive WPDT systems integration that focuses on the design of inductive coils and tuning capacitances. First, this framework proposes a new planar rectangular coil inductance formula that achieves an average error of 11% based on the testing of one hundred of coils, which out performs the current state of the art. Then, based on a detailed electrical model of both transmitter and receiver of WPDT systems, our design framework computes the coils geometric parameters and tuning capacitances that will optimize the overall efficiency of the WPDT system. Unlike state of the art design approaches, the main advantage of this framework is that it does not require expensive benchmarking of inductive antennas to find the optimal antenna. Verification of our design framework was achieved through a comparative analysis for Very High Bit Rate 13.56 MHz RFID applications. Results indicate an improvement of more than 15% in overall system power transfer efficiency compared to current state of the art methods within a comparatively more cost effective framework. A sensitivity analysis provides an insight and practical guide to implications of manufacturing variances in component parameters.
Estimation of SARS-CoV-2 Neutralizing Activity and Protective Immunity in Different Vaccine Types Using Three Surrogate Virus Neutralization Test Assays and Two Semiquantitative Binding Assays Targeting the Receptor-Binding Domain

Estimating neutralizing activity in vaccinees is crucial for predicting the protective effect against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). As the plaque reduction neutralization test (PRNT) requires a biosafety level 3 facility, it would be advantageous if surrogate virus neutralization test (sVNT) assays and binding assays could predict neutralizing activity. Here, five different assays were evaluated with respect to the PRNT in vaccinees: three sVNT assays from GenScript, Boditech Med, and SD Biosensor and two semiquantitative binding assays from Roche and Abbott. The vaccinees were subjected to three vaccination protocols: homologous ChAdOx1, homologous BNT162b2, and heterologous administration. The ability to predict a 50% neutralizing dose (ND50) of ≥20 largely varied among the assays, with the binding assays showing substantial agreement (kappa, ~0.90) and the sVNT assays showing relatively poor performance, especially in the ChAdOx1 group (kappa, 0.33 to 0.97). The ability to predict an ND50 value of ≥118.25, indicating a protective effect, was comparable among different assays. Applying optimal cutoffs based on Youden's index, the kappa agreements were greater than 0.60 for all assays in the total group. Overall, relatively poor performance was demonstrated in the ChAdOx1 group, owing to low antibody titers. Although there were intra-assay differences related to the vaccination protocols, as well as interassay differences, all assays demonstrated fair performance in predicting the protective effect using the new cutoffs. This study demonstrates the need for a different cutoff for each assay to appropriately determine a higher neutralizing titer and suggests the clinical feasibility of using various assays for estimation of the protective effect. IMPORTANCE The coronavirus disease 2019 (COVID-19) pandemic continues to last, despite high COVID-19 vaccination rates. As many people experience breakthrough infection after prior infection and/or vaccination, estimating the neutralization activity and predicting the protective effect are major issues of concern. However, since standard neutralization tests are not available in most clinical laboratories, it would be beneficial if commercial assays could predict these aspects. In this study, we evaluated the performance of three sVNT assays and two semiquantitative binding assays targeting the receptor-binding domain with respect to the PRNT. Our results suggest that these assays could be used for predicting the protective effect by adjusting the cutoffs.
Radiocarbon and U-series age constraints for the Lateglacial rock art of Sicily

The presence of rock and portable art on Sicily has been recognized since World War II. This record has been unanimously attributed to the Upper Palaeolithic in the published literature, based almost uniquely on stylistic reasoning. Here we present the first absolute dates in direct association with the Sicilian art record. These data provide new insights into the life of Southern European hunter-gatherers and their relationships with coeval groups from Western Europe, contributing a fresh perspective on the ongoing discussion about the development and co-existence of different art traditions in Europe during the final phases of the Pleistocene and at the beginning of the Holocene. (C) 2020 Elsevier Ltd. All rights reserved.
Cervix type detection using a self-supervision boosted object detection technique

Cervical cancer accounts for a large number of fatalities among cancer patients. It is ranked fourth in the total cancer patients and total number of deaths due to cancer. Developing countries account for 70% of the cases and 90% of the fatalities. Contemporary techniques used for screening cervical cancer are PAP smear test and HPV DNA test. Today there are treatments that can successfully prevent cervical cancer if detected at an early stage. Understanding the cervix type is very important for treatment; computational methods can help us classify the cervix type from cervical images. In this study, we propose an ROI proposal network EfficientCenterDet and a self-supervision boosted training trick that improves the performance of the network with relatively less labeled data. We use 6114 unlabeled images to perform a pretraining task and 1166 labeled images to retrain the ROI proposal network. The proposed model matches the state-of-the-art IOU of FasterRCNN on the ISIC skin lesions dataset while using one-third of the number of parameters used in FasterRCNN. On MobileODT cervical data, our self-supervision boosted model achieves 0.632 IOU, a 10% boost over the state-of-the-art FasterRCNN. Introducing an ensembled EfficientNet B4, the cervix type classification stage achieved an accuracy of 87%.
High Energy Storage Performance in La-Doped AgNbO3 Ceramics via Tape Casting

AgNbO3-based Pb-free antiferroelectric (AFE) ceramics have attracted increasing interest owing to their excellent potential in energy storage applications. Herein, a high recoverable energy storage density (Wrec) of 7.62 J/cm3 is realized in La-doped AgNbO3 ceramics prepared via tape casting. The high Wrec is attributed to high breakdown strength Eb of 380 kV/cm induced by dense microstructure as well as fine grain size and enhanced AFE stability stemming from M2 phase and reduced tolerance factor t. The high Wrec exceeding 6 J/cm3 was maintained in a wide temperature range of 20-150 °C and exhibited frequency stability with less than 8% variation in a range of 1-200 Hz. The discharge energy density Wd exhibited temperature stability at 30-110 °C with less than 9% variation. Our research provides a good method for producing AgNbO3-based ceramics having high energy storage performances.
Semi-Supervised Segmentation of Radiation-Induced Pulmonary Fibrosis From Lung CT Scans With Multi-Scale Guided Dense Attention

Computed Tomography (CT) plays an important role in monitoring radiation-induced Pulmonary Fibrosis (PF), where accurate segmentation of the PF lesions is highly desired for diagnosis and treatment follow-up. However, the task is challenged by ambiguous boundary, irregular shape, various position and size of the lesions, as well as the difficulty in acquiring a large set of annotated volumetric images for training. To overcome these problems, we propose a novel convolutional neural network called PF-Net and incorporate it into a semi-supervised learning framework based on Iterative Confidence-based Refinement And Weighting of pseudo Labels (I-CRAWL). Our PF-Net combines 2D and 3D convolutions to deal with CT volumes with large inter-slice spacing, and uses multi-scale guided dense attention to segment complex PF lesions. For semi-supervised learning, our I-CRAWL employs pixel-level uncertainty-based confidence-aware refinement to improve the accuracy of pseudo labels of unannotated images, and uses image-level uncertainty for confidence-based image weighting to suppress low-quality pseudo labels in an iterative training process. Extensive experiments with CT scans of Rhesus Macaques with radiation-induced PF showed that: 1) PF-Net achieved higher segmentation accuracy than existing 2D, 3D and 2.5D neural networks, and 2) I-CRAWL outperformed state-of-the-art semi-supervised learning methods for the PF lesion segmentation task. Our method has a potential to improve the diagnosis of PF and clinical assessment of side effects of radiotherapy for lung cancers.
Language Recognition with Language Total Variability

In this paper, we try to introduce the idea of total variability used in speaker recognition to language recognition. In language total variability, we propose two new recognition systems, Language-independent total variability recognition system (LITV) and Language-dependent total variability recognition system (LDTV). Our experiments show that language-independent total factor vector includes the language dependent information, what's more, language-dependent total factor vector contains more language dependent information. These two systems LITV and LDTV can achieve performance similar to that obtained with state-of-the-art approaches. Experiment results on 2007 National Institute of Standards and Technology (NIST) Language Recognition Evaluation (LRE) databases show LDTV gains relative improvement in Equal error rate (EER) of 23.2% and in minimum Decision cost value (minDCF) of 14.2% comparing to LITV in 30-second tasks, and we can obtain further improvement by combining these two new systems with state-of-the-art systems. It leads to relative improvement of 21.1% in EER and 23.1% in minDCF comparing with the performance of the combination of the MMI and the GMM-SVM systems.
Multi-Area Distribution System State Estimation Using Decentralized Physics-Aware Neural Networks

The development of active distribution grids requires more accurate and lower computational cost state estimation. In this paper, the authors investigate a decentralized learning-based distribution system state estimation (DSSE) approach for large distribution grids. The proposed approach decomposes the feeder-level DSSE into subarea-level estimation problems that can be solved independently. The proposed method is decentralized pruned physics-aware neural network (D-P2N2). The physical grid topology is used to parsimoniously design the connections between different hidden layers of the D-P2N2. Monte Carlo simulations based on one-year of load consumption data collected from smart meters for a three-phase distribution system power flow are developed to generate the measurement and voltage state data. The IEEE 123-node system is selected as the test network to benchmark the proposed algorithm against the classic weighted least squares and state-of-the-art learning-based DSSE approaches. Numerical results show that the D-P2N2 outperforms the state-of-the-art methods in terms of estimation accuracy and computational efficiency.
Early outcomes of the treatment of aortic coarctation with BeGraft aortic stent in children and young adults

We report our experience and early outcomes of using the BeGraft aortic stent in children, adolescents, and young adults. BeGraft aortic stent (Bentley InnoMed, Hechingen, Germany) requires a smaller long sheath compared to other covered stents, and it has a low profile and adequate radial power. With these features, it can overcome some limitations in the treatment of coarctation, especially in children. This is a single centre retrospective analysis of 11 implanted BeGraft aortic stents in coarctation of the aorta between July 2020 and November 2021. The eleven stents were successfully implanted in 11 patients (10 males). The median age of the patients was 13.7 years (interquartile range 12-16 years), and the median weight was 43 kg (interquartile range 35-62 kg). In five patients, after the stents were opened completely by the first balloon, they were exchanged with a Z-MED II™ balloon, 1-3 mm larger in diameter, and the stents were redilated. The median catheter-derived systolic peak-to-peak pressure gradient was 23 mm Hg (interquartile range 16-37 mmHg) before the procedure and 3 mm Hg (interquartile range 1-5 mm Hg) after the procedure. Except for the partial femoral artery thrombosis in two patients, no other procedural complications were observed in our study. The median follow-up duration was 5 months (interquartile range 2-12 months). During follow-up, only one patient (9%) had stent narrowing that required dilation. Our initial results and short-term follow-up showed that the BeGraft aortic stent implantation and redilation can be performed effectively, safely, and successfully in the treatment of coarctation of the aorta.
Deep Diffusion MRI Registration (DDMReg): A Deep Learning Method for Diffusion MRI Registration

In this paper, we present a deep learning method, DDMReg, for accurate registration between diffusion MRI (dMRI) datasets. In dMRI registration, the goal is to spatially align brain anatomical structures while ensuring that local fiber orientations remain consistent with the underlying white matter fiber tract anatomy. DDMReg is a novel method that uses joint whole-brain and tract-specific information for dMRI registration. Based on the successful VoxelMorph framework for image registration, we propose a novel registration architecture that leverages not only whole brain information but also tract-specific fiber orientation information. DDMReg is an unsupervised method for deformable registration between pairs of dMRI datasets: it does not require nonlinearly pre-registered training data or the corresponding deformation fields as ground truth. We perform comparisons with four state-of-the-art registration methods on multiple independently acquired datasets from different populations (including teenagers, young and elderly adults) and different imaging protocols and scanners. We evaluate the registration performance by assessing the ability to align anatomically corresponding brain structures and ensure fiber spatial agreement between different subjects after registration. Experimental results show that DDMReg obtains significantly improved registration performance compared to the state-of-the-art methods. Importantly, we demonstrate successful generalization of DDMReg to dMRI data from different populations with varying ages and acquired using different acquisition protocols and different scanners.
Adaptor complex-mediated trafficking of Newcastle disease virus fusion protein is regulated by the YLMY motif of its cytoplasmic tail

Previously, we reported that the mediation of Newcastle disease virus (NDV) pathogenicity by the 524YLMY527 motif depends mainly on the regulation of F protein transport to the cell surface. The virus and host determinants that govern this intracellular trafficking remain unknown. Here, we confirmed that host adaptor protein (AP) complexes are involved in NDV infection using small interfering RNA. The transport of viral F protein to the cell surface depends on host transport proteins. We observed that the trends for host expression of AP complexes AP1M1 and AP2M1 were similar to those of mutated F proteins, especially in the membrane protein. NDV F protein interacted with AP1M1 and AP2M1, and the YLMY motif influenced this interaction. Knockdown of AP1M1 or AP2M1 suppressed the intracellular and extracellular virus titre of mutated-YLMY-motif NDVs, especially rSG10*-F/Y527A and rSG10*-F/Y524AY527A, to varying degrees. Therefore, the YLMY motif regulates AP-mediated viral F protein transportation from the cytoplasm to the cell surface and subsequently affects viral titer. We further found that the YLMY-motif mutants were differently associated with the process of AAK1 and GAK kinase-mediated AP - viral F protein interaction. These data demonstrate that the essential YLMY motif located in the NDV F protein cytoplasmic tail recruits AP to direct the F protein to the cell surface, which is necessary for its ability to affect virus budding. This study provides support for a deeper understanding of virus and host determinants that facilitate virus trafficking, which can be exploited in the design of novel antiviral therapies.