ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Hand Gestures Recognition for Human-Machine Interfaces: A Low-Power Bio-Inspired Armband

Hand gesture recognition has recently increased its popularity as Human-Machine Interface (HMI) in the biomedical field. Indeed, it can be performed involving many different non-invasive techniques, e.g., surface ElectroMyoGraphy (sEMG) or PhotoPlethysmoGraphy (PPG). In the last few years, the interest demonstrated by both academia and industry brought to a continuous spawning of commercial and custom wearable devices, which tried to address different challenges in many application fields, from tele-rehabilitation to sign language recognition. In this work, we propose a novel 7-channel sEMG armband, which can be employed as HMI for both serious gaming control and rehabilitation support. In particular, we designed the prototype focusing on the capability of our device to compute the Average Threshold Crossing (ATC) parameter, which is evaluated by counting how many times the sEMG signal crosses a threshold during a fixed time duration (i.e., 130 ms), directly on the wearable device. Exploiting the event-driven characteristic of the ATC, our armband is able to accomplish the on-board prediction of common hand gestures requiring less power w.r.t. state of the art devices. At the end of an acquisition campaign that involved the participation of 26 people, we obtained an average classifier accuracy of 91.9% when aiming to recognize in real time 8 active hand gestures plus the idle state. Furthermore, with 2.92 mA of current absorption during active functioning and 1.34 ms prediction latency, this prototype confirmed our expectations and can be an appealing solution for long-term (up to 60 h) medical and consumer applications.
Erosion susceptibility mapping in the Central-Eastern Region of São Paulo in the last few decades

Soil degradation has become a critical global environmental challenge as a result of rapid population growth, intensified erosion, and increased global warming. Depletion of nutrients, decreased infiltration, availability of water in the subsoil, silting, and eutrophication of surface water resources are directly associated with soil degradation. Water erosion is one of the primary causes of erosion. The principal objective of this study was to understand how climate change and land use have affected susceptibility to erosion in the central-eastern region of the state of São Paulo, Brazil, over the past few decades. Using the technique of multicriteria decision analysis and comparison of thematic layers in pairs, different factors that contribute to soil erosion were integrated in a GIS environment to map erosion hotspots. The results indicated increasing very high, high, and medium erosion susceptibility class percentage. Slope and soil types were the most sensitive factors; however, changes in land use, in particular, increased land cultivation and expanded areas highly susceptible to erosion in late 2019. The results of this study will assist in implementing soil conservation practices in such areas, reducing soil degradation, and increasing productivity.
An Accelerated Procrustean Markov Process Model With Coherent Constraint for Non-Rigid Structure From Motion

Non-Rigid Structure from Motion (NRSfM) is the task of reconstructing the 3D point set of a non-rigid object from an ensemble of images with 2D correspondences, which has been a long-lasting challenging research topic. Compared to the state-of-the-art methods for NRSfM, the Procrustean Markov Process (PMP) model has obtained a relatively good performance. However, the estimation error and the convergence time of the PMP model will increase simultaneously when noise is present. To address this problem, in this paper, a coherent constraint is constructed to suppress the noise in the initialization step of the PMP algorithm. Moreover, an Accelerated Expectation Maximization (AEM) algorithm is devised to optimize the PMP estimation model. Experimental results on several widely used sequences demonstrate that our proposed algorithm achieves state-of-the-art performance, as well as its effectiveness and feasibility.
Zero-voltage-switching dc-dc converters with synchronous rectifiers

Active resonant tank (ART) cells are proposed in this paper to achieve zero-voltage-switching (ZVS) and eliminate body-diode conduction in dc-dc converters with synchronous rectifiers (SRs). In low-output-voltage dc-dc converters, SRs are widely utilized to reduce rectifier conduction loss and improve converter efficiency. However, during switches' transition, SRs' parasitic body diodes unavoidably carry load current, which decreases conversion efficiency because voltage drop across body diodes is much higher than that across SRs. Moreover, body diodes' reverse recovery leads to increased switching losses and electromagnetic interference. With the proposed cells of an ART, the body diode conduction of the SR is eliminated during the switching transition from a SR to an active switch, and thus body diode reverse-recovery-related switching and ringing losses are saved. An ART cell consists of a LC resonant tank and an auxiliary switch. A resonant tank cell is charged in a resonant manner and energy is stored in the capacitor of the tank. Prior to a switching transition from a SR to an active switch, the energy stored in the tank capacitor is released and converted to inductor current, which forces the SR current changes direction to avoid conduction of the body diode and related reverse recovery when the SR turns off. Moreover, at the help of energy released from the ART, the active switch's junction capacitance is discharged, which allows the active switch turns on at ZVS. Since energy commutation occurs only during switching transition, conduction loss in the ART cell is limited. Moreover, the auxiliary switch turns off at ZVS and the SR operates at ZVS. The concept of ART cells is generally introduced and detailed analysis is presented based on a synchronous buck converter. Experimental results show the proposed ART cell improves conversion efficiency due to the reduced switching loss, body diodes' conduction, and reverse-recovery losses.
Epigenetics in advanced renal cell carcinoma: Potential new targets

Renal cell carcinoma (RCC) is the seventh most frequently diagnosed tumor in adults in Europe and represents approximately 2.5 % of cancer deaths. In metastatic setting, clinical strategies including angiogenesis inhibition with tyrosine kinase inhibitors, as well as immunotherapy against immune checkpoint proteins, such as PD-1/PDL-1 and CTLA-4, have revolutionized the treatment landscape. Unfortunately, most patients progress to anti angiogenic and immunotherapy treatment. Epigenetic aberrations are commonly found in RCC, showing that changes in epigenetic modifications, like promoter methylation or abnormal microRNA expression, are key in the development of RCC due to gene expression alterations without changes in the genome sequence. Nowadays, new drugs in the field of epigenetics are able to modify gene expression to induce antitumoral effect in the tumor cell. In kidney cancer, drugs targeting epigenetics are in early development, but could be promising in the near future. In this review, we summarize the main epigenetic alterations found in RCC and their involvement in pathological signaling pathways, being a new potential target that could potentially be added to the treatment flow of patients with advanced RCC.
High-throughput engineering of cytoplasmic- and nuclear-replicating large dsDNA viruses by CRISPR/Cas9

The application of CRISPR/Cas9 to improve genome engineering efficiency for large dsDNA viruses has been extensively described, but a robust and versatile method for high-throughput generation of marker-free recombinants for a desired locus has not yet been reported. Cytoplasmic-replicating viruses use their own repair enzymes for homologous recombination, while nuclear-replicating viruses use the host repair machinery. This is translated into a wide range of Cas9-induced homologous recombination efficiencies, depending on the virus replication compartment and viral/host repair machinery characteristics and accessibility. However, the use of Cas9 as a selection agent to target parental virus genomes robustly improves the selection of desired recombinants across large dsDNA viruses. We used ectromelia virus (ECTV) and herpes simplex virus (HSV) type 1 and 2 to optimize a CRISPR/Cas9 method that can be used versatilely for efficient genome editing and selection of both cytoplasmic- and nuclear-replicating viruses. We performed a genome-wide genetic variant analysis of mutations located at predicted off-target sequences for 20 different recombinants, showing off-target-free accuracy by deep sequencing. Our results support this optimized method as an efficient, accurate and versatile approach to enhance the two critical factors of high-throughput viral genome engineering: generation and colour-based selection of recombinants. This application of CRISPR/Cas9 reduces the time and labour for screening of desired recombinants, allowing for high-throughput generation of large collections of mutant dsDNA viruses for a desired locus, optimally in less than 2 weeks.
Development of chitosan-polygalacturonic acid polyelectrolyte complex fibrous scaffolds using the hydrothermal treatment for bone tissue engineering

An ideal bone regeneration scaffold system needs to meet the high compressive properties of the bone. The stiffness of the scaffold extracellular matrix determines the cell's fate via cell adhesion migration and differentiation in-vitro and in-vivo. This study aims to investigate the effect of hydrothermal treatment on polyelectrolyte complex (PEC) fibrous biomaterials and its effect on scaffold morphology, cell viability, and function in-vitro. FTIR analysis revealed the ability of the thermal treatment to set the interaction of HAp with polymeric PEC fibers. FESEM analysis showed that with an increase in temperature, the interconnectivity and pore size increased (control-82.38 ± 12.92 μm; at 120°C-335.48 ± 85.10 μm). Mechanical tests showed that the scaffolds heated at 90°C showed the highest stiffness in both dry and wet states (dry state: 1.82 ± 0.07 MPa, wet state: 122 ± 1.78 kPa). Additionally, the hydrothermal treatment also improved the aqueous stability as well as swelling capacity. According to the experimental findings, hydrothermal treatment is a useful technique for crosslinker-free gelation with improved mechanical strength and nanofibrous structure. Furthermore, the cell adhesion, proliferation, and osteogenic differentiation of the MG63 cells on the hydrogel scaffolds in-vitro were evaluated by MTT assay, confocal imaging, alkaline phosphatase assay, and collagen estimation. The in-vitro study showed that scaffolds fabricated at 90°C promoted better MG63 cell attachment, proliferation, and differentiation. These results suggest the potential use of hydrothermal treated chitosan-polygalacturonic acid (PgA) fibrous scaffolds in bone tissue engineering.
Complete mitochondrial genome of the soft-shell clam Mya arenaria

We have sequenced and characterized the complete mitochondrial genome of the soft-shell clam, Mya arenaria, an important organism for environmental toxicology and aquaculture. Mya arenaria is located in the taxonomic order Myoida, which lacks any member with a completely annotated mitogenome. The M. arenaria mitochondrial genome is 17 947 bp in length. Like most marine bivalves, the circular mitogenome codes entirely on the heavy strand, with no introns. As with other bivalves, the gene order of the mitochondrion is highly rearranged. The mitogenome contains 12 protein-coding genes but ATP8 is missing, consistent with about half of all bivalve genera. Twenty-three tRNAs were identified. Phylogenetic analysis shows that M. arenaria is related most closely with the bivalves Sinonovacula constricta, and Moerella iridescens, of the infraclass Euheterodonta (unassigned). This, along with the close grouping of the phylogenetic trees, confirms a close tie between Myoida and Euheterodonta (unassigned).
The Process of Developing a Digital Repository for Online Teaching Using Design-Based Research

The Purdue Repository for Online Teaching and Learning (PoRTAL) was developed as an Open Educational Resource (OER) for graduate students and faculty in higher education settings to enhance their online teaching skills and strategies. The PoRTAL team used a design-based research approach (DBR; Wang & Hannafin, Educational Technology Research and Development, 53(4), 5-23, 2005). In this study context, we used Van Tiem et al.'s (2012) model to identify problems faced by instructors who struggled with or were new to online teaching from a Human Performance Technology (HPT) standpoint. To address the identified needs, we created resources for online teaching and embedded our research within practical activities to further study our design process. Our efforts resulted in an HPT-OER Model for Designing Digital Repositories. The purpose of this paper is to share the DBR process that we used to develop an OER repository within an HPT model.
Source Term Analysis of Xenon (STAX): An effort focused on differentiating man-made isotope production from nuclear explosions via stack monitoring

An overview of the hardware and software developed for the Source Term Analysis of Xenon (STAX) project is presented which includes the data collection from two stack monitoring systems installed at medical isotope production facilities, infrastructure to transfer data to a central repository, and methods for sharing data from the repository with users. STAX is an experiment to collect radioxenon emission data from industrial nuclear facilities with the goal of developing a better understanding of the global radioxenon background and the effect industrial radioxenon releases have on nuclear explosion monitoring. A final goal of this work is to utilize collected data along with atmospheric transport modeling to calculate the contribution of a peak or set of peaks detected by the International Monitoring System (IMS) to provide desired discriminating information to the International Data Centre (IDC) and National Data Centers (NDCs). Types of data received from the STAX equipment are shown and collected data was used for a case study to predict radioxenon concentrations at two IMS stations closest to the Institute for RadioElements (IRE) in Belgium. The initial evaluation of results indicate that the data is very valuable to the nuclear explosion monitoring community.
Investigating formulations of cellulose acetate plastics in the collections of the Art Institute of Chicago using pyrolysis gas chromatography mass spectrometry

A selective survey was undertaken of works of art made with cellulose acetate, primarily in the collections of the Art Institute of Chicago and dating from 1936 to 1976, using pyrolysis gas chromatography mass spectrometry to provide a detailed characterisation of the organic additives. The findings revealed a variety of plastic formulations used during this period; these are discussed in relation to the physical and chemical properties of individual plasticisers and their potential influence on the long-term stability of the objects. Several case studies are presented in detail in which the analyses provided specific insights into observed degradation phenomena: a 1936 sculpture by Cesar Domela, and the complex enclosure for a book by Jean-Pierre Duprey produced in 1970 in collaboration with Toyen (Marie Cerminova). The pyrolysis analyses were undertaken using a direct inlet technique that provided enhanced sensitivity with the use of extremely small (microgram-sized) samples.
Epidemiological, Clinical, and Oncological Outcomes of non-Alcohol Drinking and non-Smoking Laryngeal Squamous Cell Carcinoma Patients: A Distinct Entity

Purpose: To explore the discrepancy in clinicopathological and prognostic features between smoking and alcohol drinking (SA) and non-smoking and non-alcohol drinking (NSNA) patients with laryngeal squamous cell carcinoma (LSCC). Methods: This retrospective study including 1735 patients with LSCC was conducted from January 2005 to December 2010, which were categorized into 4 groups, NSNA group, smoking only group, alcohol-drinking only group, and SA group. We compared overall survival (OS) and disease-free survival (DFS) using the Kaplan-Meier method and indicated clinicopathological features by Cox proportional hazards regression models before and after propensity score matching (PSM). Results: A total of 415 patients (23.92%) were identified as NSNA. The SA group was predominantly patients ≤60 years old (46.63%) while the NSNA group was more older (58.07%). NSNA group was more likely to present at earlier disease stage and more female. No significant difference in OS (P = .685) and DFS (P = .976) was found between the 2 groups. In addition to age and recurrence and metastasis being common independent prognostic factors in terms of OS in both groups of patients, NSNA group also exhibited other factors, namely tumor area >3.7 cm2 and positive resection margin. For DFS, N + stage, tumor size >3.7 cm2, and positive resection margin were prognostic features specific to NSNA group. Conclusion: The outcome is similar in LSCC patients with and without SA. NSNA group shows a distinct profile from that found in SA group. Clinicopathological features from NSNA group should be considered for LSCC management.
Superpixels: An evaluation of the state-of-the-art

Superpixels group perceptually similar pixels to create visually meaningful entities while heavily reducing the number of primitives for subsequent processing steps. As of these properties, superpixel algorithms have received much attention since their naming in 2003 (Ren and Malik, 2003). By today, publicly available superpixel algorithms have turned into standard tools in low-level vision. As such, and due to their quick adoption in a wide range of applications, appropriate benchmarks are crucial for algorithm selection and comparison. Until now, the rapidly growing number of algorithms as well as varying experimental setups hindered the development of a unifying benchmark. We present a comprehensive evaluation of 28 state-of-the-art superpixel algorithms utilizing a benchmark focussing on fair comparison and designed to provide new insights relevant for applications. To this end, we explicitly discuss parameter optimization and the importance of strictly enforcing connectivity. Furthermore, by extending well-known metrics, we are able to summarize algorithm performance independent of the number of generated superpixels, thereby overcoming a major limitation of available benchmarks. Furthermore, we discuss runtime, robustness against noise, blur and affine transformations, implementation details as well as aspects of visual quality. Finally, we present an overall ranking of superpixel algorithms which redefines the state-of-the-art and enables researchers to easily select appropriate algorithms and the corresponding implementations which themselves are made publicly available as part of our benchmark at http://www.davidstutz.deiprojectsisuperpixel-benchmark/. (C) 2017 Elsevier Inc. All rights reserved.
Random Subspace Ensembles for fMRI Classification

Classification of brain images obtained through functional magnetic resonance imaging (fMRI) poses a serious challenge to pattern recognition and machine learning due to the extremely large feature-to-instance ratio. This calls for revision and adaptation of the current state-of-the-art classification methods. We investigate the suitability of the random subspace (RS) ensemble method for fMRI classification. RS samples from the original feature set and builds one (base) classifier on each subset. The ensemble assigns a class label by either majority voting or averaging of output probabilities. Looking for guidelines for setting the two parameters of the method-ensemble size and feature sample size-we introduce three criteria calculated through these parameters: usability of the selected feature sets, coverage of the set of "important" features, and feature set diversity. Optimized together, these criteria work toward producing accurate and diverse individual classifiers. RS was tested on three fMRI datasets from single-subject experiments: the Haxby et al. data (Haxby, 2001.) and two datasets collected in-house. We found that RS with support vector machines (SVM) as the base classifier outperformed single classifiers as well as some of the most widely used classifier ensembles such as bagging, AdaBoost, random forest, and rotation forest. The closest rivals were the single SVM and bagging of SVM classifiers. We use kappa-error diagrams to understand the success of RS.
Transmembrane proteins tetraspanin 4 and CD9 sense membrane curvature

Multiple membrane-shaping and remodeling processes are associated with tetraspanin proteins by yet unknown mechanisms. Tetraspanins constitute a family of proteins with four transmembrane domains present in every cell type. Prominent examples are tetraspanin4 and CD9, which are required for the fundamental cellular processes of migrasome formation and fertilization, respectively. These proteins are enriched in curved membrane structures, such as cellular retraction fibers and oocyte microvilli. The factors driving this enrichment are, however, unknown. Here, we revealed that tetraspanin4 and CD9 are curvature sensors with a preference for positive membrane curvature. To this end, we used a biomimetic system emulating membranes of cell retraction fibers and oocyte microvilli by membrane tubes pulled out of giant plasma membrane vesicles with controllable membrane tension and curvature. We developed a simple thermodynamic model for the partitioning of curvature sensors between flat and tubular membranes, which allowed us to estimate the individual intrinsic curvatures of the two proteins. Overall, our findings illuminate the process of migrasome formation and oocyte microvilli shaping and provide insight into the role of tetraspanin proteins in membrane remodeling processes.
Transmission of antibiotic resistance genes through mobile genetic elements in Acinetobacter baumannii and gene-transfer prevention

Antibiotic resistance is a major global public health concern. Acinetobacter baumannii is a nosocomial pathogen that has emerged as a global threat because of its high levels of resistance to many antibiotics, particularly those considered as last-resort antibiotics, such as carbapenems. Mobile genetic elements (MGEs) play an important role in the dissemination and expression of antibiotic resistance genes (ARGs), including the mobilization of ARGs within and between species. We conducted an in-depth, systematic investigation of the occurrence and dissemination of ARGs associated with MGEs in A. baumannii. We focused on a cross-sectoral approach that integrates humans, animals, and environments. Four strategies for the prevention of ARG dissemination through MGEs have been discussed: prevention of airborne transmission of ARGs using semi-permeable membrane-covered thermophilic composting; application of nanomaterials for the removal of emerging pollutants (antibiotics) and pathogens; tertiary treatment technologies for controlling ARGs and MGEs in wastewater treatment plants; and the removal of ARGs by advanced oxidation techniques. This review contemplates and evaluates the major drivers involved in the transmission of ARGs from the cross-sectoral perspective and ARG-transfer prevention processes.