ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Sequential beat-to-beat P and T wave delineation and waveform estimation in ECG signals: Block Gibbs sampler and marginalized particle filter

For ECG interpretation, the detection and delineation of P and T waves are challenging tasks. This paper proposes sequential Bayesian methods for simultaneous detection, threshold-free delineation, and waveform estimation of P and T waves on a beat-to-beat basis. By contrast to state-of-the-art methods that process multiple-beat signal blocks, the proposed Bayesian methods account for beat-to-beat waveform variations by sequentially estimating the waveforms for each beat. Our methods are based on Bayesian signal models that take into account previous beats as prior information. To estimate the unknown parameters of these Bayesian models, we first propose a block Gibbs sampler that exhibits fast convergence in spite of the strong local dependencies in the ECG signal. Then, in order to take into account all the information contained in the past rather than considering only one previous beat, a sequential Monte Carlo method is presented, with a marginalized particle filter that efficiently estimates the unknown parameters of the dynamic model. Both methods are evaluated on the annotated QT database and observed to achieve significant improvements in detection rate and delineation accuracy compared to state-of-the-art methods, thus providing promising approaches for sequential P and T wave analysis. (C) 2014 Elsevier B.V. All rights reserved.
Internalized HIV-related stigma in women of color obtaining care at an HIV specialty center in Los Angeles County, California

The current study examined the role of internalized HIV-related stigma in antiretroviral therapy adherence, viral load, and retention in care among women of color living with HIV in Los Angeles County, California. African American and Hispanic/Latino women 18 years of age and older completed a one-time brief survey between September 2017 and February 2018. Descriptive statistics, and univariable and multivariable logistic regressions were used to analyze the data. Seventy-six participants enrolled in the study and 74 completed the entire survey. Seventy-six percent of respondents were Hispanic/Latino, 24% were African American, 71% were unemployed, and 54% had less than a high school education. Thirty-five percent were defined as having "high" stigma with a score in the upper quartile of the scale. Being unemployed, having a high school education or less, and not meeting the Health Resources and Services Administration's annual retention in care measure were associated with "high" stigma. When controlling for education and employment status, those reporting "high" stigma vs. "low" stigma were 18.8 times more likely to not meet the criteria for annual retention in care (OR = 18.8, 95% CI = 1.9-189.2, p = 0.013). Stigma-reduction interventions targeting healthcare settings may be necessary to improve patient retention and engagement in care.
Structure-Guided Segmentation for 3D Neuron Reconstruction

Digital reconstruction of neuronal morphologies in 3D microscopy images is critical in the field of neuroscience. However, most existing automatic tracing algorithms cannot obtain accurate neuron reconstruction when processing 3D neuron images contaminated by strong background noises or containing weak filament signals. In this paper, we present a 3D neuron segmentation network named Structure-Guided Segmentation Network (SGSNet) to enhance weak neuronal structures and remove background noises. The network contains a shared encoding path but utilizes two decoding paths called Main Segmentation Branch (MSB) and Structure-Detection Branch (SDB), respectively. MSB is trained on binary labels to acquire the 3D neuron image segmentation maps. However, the segmentation results in challenging datasets often contain structural errors, such as discontinued segments of the weak-signal neuronal structures and missing filaments due to low signal-to-noise ratio (SNR). Therefore, SDB is presented to detect the neuronal structures by regressing neuron distance transform maps. Furthermore, a Structure Attention Module (SAM) is designed to integrate the multi-scale feature maps of the two decoding paths, and provide contextual guidance of structural features from SDB to MSB to improve the final segmentation performance. In the experiments, we evaluate our model in two challenging 3D neuron image datasets, the BigNeuron dataset and the Extended Whole Mouse Brain Sub-image (EWMBS) dataset. When using different tracing methods on the segmented images produced by our method rather than other state-of-the-art segmentation methods, the distance scores gain 42.48% and 35.83% improvement in the BigNeuron dataset and 37.75% and 23.13% in the EWMBS dataset.
Query Reorganization Algorithms for Efficient Boolean Information Filtering

In the information filtering paradigm, clients subscribe to a server with continuous queries that express their information needs and get notified every time appropriate information is published. To perform this task in an efficient way, servers employ indexing schemes that support fast matches of the incoming information with the query database. Such indexing schemes involve (i) main-memory trie-based data structures that cluster similar queries by capturing common elements between them and (ii) efficient filtering mechanisms that exploit this clustering to achieve high throughput and low filtering times. However, state-of-the-art indexing schemes are sensitive to the query insertion order and cannot adopt to an evolving query workload, degrading the filtering performance over time. In this paper, we present an adaptive trie-based algorithm that outperforms current methods by relying on query statistics to reorganise the query database. Contrary to previous approaches, we show that the nature of the constructed tries, rather than their compactness, is the determining factor for efficient filtering performance. Our algorithm does not depend on the order of insertion of queries in the database, manages to cluster queries even when clustering possibilities are limited, and achieves more than 96 percent filtering time improvement over its state-of-the-art competitors. Finally, we demonstrate that our solution is easily extensible to multi-core machines.
Endoscopic and external dacryocystorhinostomy: A therapeutic proposal for distal acquired lacrimal obstructions

Endoscopic (END-DCR) and external dacryocystorhinostomies (EXT-DCR) are nowadays considered the gold standard techniques for non-oncologic distal acquired lacrimal disorders (DALO). However, no unanimous consensus has been achieved on which of these surgeries is the most suitable to the individual patient. Herein, we review the available literature of the last 30 years with the aim of defining a simple and reproduceable treatment algorithm to treat DALO. A search of PubMed, EMBASE, Scopus and Cochrane databases was last performed in December 2021 to examine evidence regarding the role of END-DCR and EXT-DCR in primary and revision surgeries. If considered primary surgeries, END-DCR should be preferred in case of intranasal comorbidities, given the possibility to directly visualize and treat potential intranasal pathologies. Conversely, EXT-DCR should be chosen in case of need/preference for local anesthesia, given the major historical experience and wider surgical field that helps to resolve intra-operatory complications (e.g., bleeding) in an uncollaborative patient. In the absence of the abovementioned conditions, the decision of one or other approach should be discussed with the patient. In recurrent cases, END-DCR should be considered the treatment of choice given the major likelihood to visualize the causes of primary failure and directly resolve it. In conclusion, END-DCR should be considered the treatment of choice in revision cases or in primary ones associated with intranasal pathologies, whereas EXT-DCR should be chosen if local anesthesia is needed. In the absence of these scenarios, it is still open to debate which of these two approaches should be used.
Inductive Power Transfer for Automotive Applications: State-of-the-Art and Future Trends

The paper discusses the development status of the inductive power transmission for automotive applications. This technology is, in fact, gaining the interest of electric vehicle manufacturers as an effective strategy to improve themarket penetration of electric mobility. Starting from the origin of this technology, the paper presents an overview of the current state of the art as well as the current research and industrial projects. Particular attention is devoted to the description of a prototypal system for the dynamic inductive power transmission whose goal is to extend the battery range by a fast partial recharging during the movement of the vehicle.
Optimization, validation and application of a high-throughput 96-well elution protocol for the quantification of psychoactive substances in influent wastewater

Wastewater-based epidemiology (WBE) is based on the analysis of human metabolic excretion products (biomarkers) of xenobiotics in wastewater, to gain information about various lifestyles and health aspects of a population in an evidence-based manner. Due to the complex wastewater matrix and trace level occurrence of human biomarkers in the sewage network, it is crucial to have sensitive analytical procedures available. Additionally, to improve the value of WBE as a complementary epidemiological source, there is increasing pressure on the analysis of more compounds, more locations and more samples. A high-throughput method based on 96-well Oasis MCX solid-phase extraction (SPE), requiring less influent wastewater (2 mL), was developed in accordance with the European Medicines Agency guidelines. Validation was successful for 28 parent drugs and metabolites of antidepressants, opioids and drugs of abuse. The selection of biomarkers and quantification limit was chosen to be relevant for WBE and was predominantly 10 ng/L or below. The final method was successfully applied to 24-h composite samples of October 2019 (n = 27), obtained from an urban wastewater treatment plant in Leuven (Belgium).
Dense depth maps from correspondences derived from perceived motion

Many computer vision applications require finding corresponding points between images and using the corresponding points to estimate disparity. Today's correspondence finding algorithms primarily use image features or pixel intensities common between image pairs. Some 3-D computer vision applications, however, do not produce the desired results using correspondences derived from image features or pixel intensities. Two examples are the multimodal camera rig and the center region of a coaxial camera rig. We present an image correspondence finding technique that aligns pairs of image sequences using optical flow fields. The optical flow fields provide information about the structure and motion of the scene, which are not available in still images but can be used in image alignment. We apply the technique to a dual focal length stereo camera rig consisting of a visible light-infrared camera pair and to a coaxial camera rig. We test our method on real image sequences and compare our results with the state-of-the-art multimodal and structure from motion (SfM) algorithms. Our method produces more accurate depth and scene velocity reconstruction estimates than the state-of-the-art multimodal and SfM algorithms. (C) 2017 SPIE and IS&T
Zavarzinia marina sp. nov., a novel hydrocarbon-degrading bacterium isolated from deep chlorophyll maximum layer seawater of the West Pacific Ocean and emended description of the genus Zavarzinia

A Gram-stain-negative, motile, non-spore-forming, strictly aerobic and rod-shaped bacterial strain, Adcm-6AT, was isolated from a seawater sample collected from the deep chlorophyll maximum layer in the West Pacific Ocean. Strain Adcm-6AT grew at 20-37 °C (optimum, 28-32 °C), at pH 6-11 (pH 7) and in the presence of 0-6 % (1-2 %) NaCl (w/v). Phylogenetic analysis based on 16S rRNA gene sequences indicated that it belonged to the genus Zavarzinia and had 97.7 and 96.9 % sequence similarity to Zavarzinia compransoris DSM 1231T and Zavarzinia aquatilis JCM 32263T, respectively. Digital DNA-DNA hybridization and average nucleotide identity values between strain Adcm-6AT and the two type strains were 22.2-22.9 % and 79.7-80.4 %, respectively. The principal fatty acids were C19:0 cyclo ω8c, summed feature 8 (C18:1 ω6c and/or C18:1 ω7c) and C16:0. The predominant respiratory quinone was Q-10. The polar lipids were diphosphatidylglycerol, two phosphatidylethanolamines, two phosphatidyglycerols and an unidentified lipid. The genomic DNA G+C content of strain Adcm-6AT was 67.7 %. Based on phylogenetic analysis and genomic-based relatedness indices, as well as phenotypic and genotypic characteristics, strain Adcm-6AT represents a novel species within the genus Zavarzinia, for which the name Zavarzinia marina sp. nov. is proposed. The type strain is Adcm-6AT (=MCCC M24951T=KCTC 82849T).
Intramedullary spinal metastasis of a carcinoid tumor

We report an intramedullary spinal cord metastasis from a bronchial carcinoid, and discuss its mechanisms and management. Intramedullary spinal cord metastases from any cancer are rare, and bronchial carcinoids account for only a small fraction of lung cancers. To our knowledge, an intramedullary spinal cord metastasis from a bronchial carcinoid has been described only once previously.
Vibrational Properties of LaNb0.8 M0.2 O4-δ (M=As, Sb, V, and Ta)

LaNb0.8 M0.2 O4-δ (where M=As, Sb, V, and Ta) oxides with pentavalent elements of different ionic sizes were synthesized by a solid-state reaction method. The vibrational properties of these oxides have been investigated. These studies revealed that the substituent element influences both Debye temperature value as well as the Raman active vibrational modes. Additionally, the low-temperature vibrational properties of LaNb0.8 Sb0.2 O4-δ have been determined to show the phase transition occurrence at 260 K which is lower than previously reported.
[Ideas and Briefing about Regulatory Requirements for Laboratory Developed Tests in the US]

As a special kind of in vitro diagnostic devices（IVDs）, laboratory developed tests（LDTs） are of great significance to the development of clinical laboratories. This study aims to explore the regulatory requirements ideas of LDTs. By introducing the development of LDTs and the changing of regulatory requirements in the United States, combing the current regulatory framework and discussing relevant ideas in the regulatory requirements of LDTs.
VQA as a factoid question answering problem: A novel approach for knowledge-aware and explainable visual question answering

With recent advancements in machine perception and scene understanding, Visual Question Answering (VQA) has garnered much attraction from researchers in the direction of training neural models for jointly analyzing, grounding and reasoning over the multi-modal space of image visual context and natural language in order to answer natural language questions pertaining to the image contents. However, though recent works have achieved significant improvement over state-of-art models for answering questions that are answerable by solely referring to the visual context of the image, such models are often limited, being incapable of tackling questions involving external world knowledge beyond the visible contents. Though recently, research has been driven towards tackling external knowledge based VQA as well, there is significant room for improvement as limited studies exist in this area. Inspired by the aforementioned challenges involved, this paper is aimed at answering free form and open ended natural language questions, not limited to visual context of an image, but external world knowledge as well. With this motive, inspired by human cognitive abilities of comprehending and reasoning answers when given a set of facts, this paper proposes a novel model architecture to model VQA as a factoid question answering problem, leveraging state-of-the-art deep learning techniques for reasoning and inferring answers to free form questions, in an attempt of improving the state-of-art in open ended visual question answering. (c) 2021 Elsevier B.V. All rights reserved.
Multi-Scale Dynamic Graph Convolution Network for Point Clouds Classification

Point clouds provide an efficient way for 3D geometric object representation. In order to deal with the classification and segmentation of point cloud, it is very important to design an efficient and intelligent model that can directly affect point cloud. Due to the irregularity of the data format, traditional convolutional neural networks cannot be applied to point clouds processing directly. Graph convolution network (GCN) has attracted more and more attention in recent years, especially in the field of non-Euclidean data processing. Point clouds processing with GCN models is an efficient and suitable method, a lot of GCN models have achieved state-of-the-art performance on irregular data processing challenges. In this paper, we propose a Multi-scale Dynamic GCN model for point clouds classification, a Farthest Point Sampling method is applied in our model firstly to efficiently cover the entire point set, it uses different scale k-NN group method to locate on k nearest neighborhood for each central node, Edge Convolution (EdgeConv) operation is used to extract and aggregate local features between neighbor connected nodes and central node. We use ModelNet40, ModelNet10 and ShapeNet part dataset to classify point clouds and segment them semantically. Experiments show that our model achieves a better performance on classification accuracy and model complexity than other state-of-the-art models.
Ensemble Optimization for Invasive Ductal Carcinoma (IDC) Classification Using Differential Cartesian Genetic Programming

The high cost of acquiring annotated histological slides for breast specimens entails exploiting an ensemble of models appropriately trained on small datasets. Histological Image Classification ensembles strive to accurately detect abnormal tissues in the breast samples by determining the correlation between the predictions of its weak learners. Nonetheless, the state-of-the-art ensemble methods, such as boosting and bagging, count merely on manipulating the dataset and lack intelligent ensemble decision making. Furthermore, the methods mentioned above are short of the diversity of the weak models of the ensemble. Likewise, other commonly used voting strategies, such as weighted averaging, are limited to how the classifiers' diversity and accuracy are balanced. Hence, In this paper, we assemble a Neural Network ensemble that integrates the models trained on small datasets by employing biologically-inspired methods. Our procedure is comprised of two stages. First, we train multiple heterogeneous pre-trained models on the benchmark Breast Histopathology Images for Invasive Ductal Carcinoma (IDC) classification dataset. In the second meta-training phase, we utilize the differential Cartesian Genetic Programming (dCGP) to generate a Neural Network that merges the trained models optimally. We compared our empirical outcomes with other state-of-the-art techniques. Our results demonstrate that improvising a Neural Network ensemble using Cartesian Genetic Programming transcended formerly published algorithms on slim datasets.
Resistance in health and healthcare: Applying Essex conceptualisation to a multiphased study on the experiences of Australian nurses and midwives who provide abortion care to people victimised by gender-based violence

In this article, we explore the act of resistance by nurses and midwives at the nexus of abortion care and gender-based violence. We commence with a brief overview of a multiphased extended grounded theory doctoral project that analysed the individual, situational and socio-political experiences of Australian nurses and midwives who provide abortion care to people victimised by gender-based violence. We then turn to Essex's conceptualisation of resistance in health and healthcare and draw upon these concepts to tell a unifying and cohesive story about how nurses and midwives exercise their politics. Vignettes taken from the three study phases are provided for demonstrative purposes. Finally, we discuss the potential of resistance in health and healthcare as a postmodern feminist research tool to analyse acts by nurses and midwives that could be categorised as political.
Characterization of hepatic inflammatory changes in a C57BL/6J mouse model of alpha1-antitrypsin deficiency

Alpha-1 antitrypsin deficiency (AATD) is a genetic disease caused by a hepatic accumulation of mutant alpha-1 antitrypsin (ZAAT). Individuals with AATD are prone to develop a chronic liver disease that remains undiagnosed until late stage of the disease. Here, we sought to characterize the liver pathophysiology of a human transgenic mouse model for AATD with a manifestation of liver disease compared with normal transgenic mice model. Male and female transgenic mice for normal (Pi*M) and mutant variant (Pi*Z) human alpha-1 antitrypsin at 3 and 6 mo of age were subjected to this study. The progression of hepatic ZAAT accumulation, hepatocyte injury, steatosis, liver inflammation, and fibrotic features were monitored by performing an in vivo study. We have also performed a Next-Gene transcriptomic analysis of the transgenic mice liver tissue 16 h after lipopolysaccharide (LPS) administration to delineate liver inflammatory response in Pi*Z mice as compared with Pi*M. Our results show hepatic ZAAT accumulation, followed by hepatocyte ballooning and liver steatosis developed at 3 mo in Pi*Z mice compared with the mice carrying normal variant of human alpha-1 antitrypsin. We observed higher levels of hepatic immune cell infiltrations in both 3- and 6-mo-old Pi*Z mice compared with Pi*M as an indication of liver inflammation. Liver fibrosis was observed as accumulation of collagen in 6-mo-old Pi*Z liver tissues compared with Pi*M control mice. Furthermore, the transcriptomic analysis revealed a dysregulated liver immune response to LPS in Pi*Z mice compared with Pi*M. Of particular interest for translational work, this study aims to establish a mouse model of AATD with a strong manifestation of liver disease that will be a valuable in vivo tool to study the pathophysiology of AATD-mediated liver disease. Our data suggest that the human transgenic mouse model of AATD could provide a suitable model for the evaluation of therapeutic approaches and preventive reagents against AATD-mediated liver disease.NEW & NOTEWORTHY We have characterized a mouse model of human alpha-1 antitrypsin deficiency with a strong manifestation of liver disease that can be used as an in vivo tool to test preventive and therapeutic reagents. Our data explores the altered immunophenotype of alpha-1 antitrypsin-deficient liver macrophages and suggests a relationship between acute inflammation, immune response, and fibrosis.