ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
An Efficient Semi-Supervised Multi-label Classifier Capable of Handling Missing Labels

Multi-label classification has received considerable interest in recent years. Multi-label classifiers usually need to address many issues including: handling large-scale datasets with many instances and a large set of labels, compensating missing label assignments in the training set, considering correlations between labels, as well as exploiting unlabeled data to improve prediction performance. To tackle datasets with a large set of labels, embedding-based methods represent the label assignments in a low-dimensional space. Many state-of-the-art embedding-based methods use a linear dimensionality reduction to map the label assignments to a low-dimensional space. However, by doing so, these methods actually neglect the tail labels -labels that are infrequently assigned to instances. In this paper, we propose an embedding-based method that non-linearly embeds the label vectors using a stochastic approach, thereby predicting the tail labels more accurately. Moreover, the proposed method has excellent mechanisms for handling missing labels, dealing with large-scale datasets, as well as exploiting unlabeled data. Experiments on real-world datasets show that our method outperforms state-of-the-art multi-label classifiers by a large margin, in terms of prediction performance, as well as training time. Our implementation of the proposed method is available online at: https://github.com/Akbarnejad/ESMC_ Implementation.
Feeder cell-dependent primary culture of single blastula-derived embryonic cell lines from marine medaka (Oryzias dancena)

Fish embryonic stem cells (ESCs) are derived from blastomeres that have been cultured from blastula embryos. The most widely used method for derivation of fish ESCs is the culture of blastomeres that have been isolated from approximately 10 blastula embryos under feeder-free conditions. However, this method leads to intercellular genetic heterogeneity among the cultured cells, which is a major obstacle to the development of stable ESC culture conditions. In this study, to establish ESC lines with intercellular genetic homogeneity at the early stage of culture, we attempted to derive embryonic cell lines from single blastula-derived blastomeres of marine medaka (Oryzias dancena) in a feeder cell culture system. Using basic fibroblast growth factor-expressing feeder cells during primary culture, we successfully established 22 single blastula-derived embryonic cell lines that could be subcultured more than 20 times. In contrast, we were unable to efficiently derive cell lines using wild-type feeder cells and under feeder-free conditions. The established cell lines exhibited ESC-like cell characteristics in terms of alkaline phosphatase activity, pluripotency-related gene expression, and embryoid body formation. The results of this study will contribute to the development of methods for derivation of fish ESCs.
Modelling fingerprint ridge orientation using Legendre polynomials

The estimation of fingerprint ridge orientation is an essential step in every automatic fingerprint verification system. The importance of ridge orientation can be deflected from the fact that it is inevitably used for detecting, describing and matching fingerprint features such as minutiae and singular points. In this paper we propose a novel method for fingerprint ridge orientation modelling using Legendre polynomials. One of the main problems it addresses is smoothing orientation data while preserving details in high curvature areas, especially singular points. We show that singular points, which result in a discontinuous orientation field, can be modelled by the zero-poles of Legendre polynomials. The models parameters are obtained in a two staged optimization procedure. Another advantage of the proposed method is a very compact representation of the orientation field, using only 56 coefficients. We have carried out extensive experiments using a state-of-the-art fingerprint matcher and a singular point detector. Moreover, we compared the proposed method with other state-of-the-art fingerprint orientation estimation algorithms. We can report significant improvements in both singular point detection and matching rates. (C) 2009 Elsevier Ltd. All rights reserved.
The Importance of Corporate Reputation for Sustainable Supply Chains: A Systematic Literature Review, Bibliometric Mapping, and Research Agenda

Corporate Reputation (CR) is essential to value generation and is co-created between a company and its stakeholders, including supply chain actors. Consequently, CR is a critical and valuable resource that should be managed carefully along supply chains. However, the current CR literature is fragmented, and a general definition of CR is elusive. Besides, the academic CR debate largely lacks a supply chain perspective. This is not surprising, as it is very difficult to collect reliable data along supply chains. When supply chains span the globe, data collection is especially challenging, as the chain consists of multiple suppliers and subcontractors, positioned at different tier levels. Recognizing this, the paper examines firstly the current state of CR research through a systematic literature review from a business perspective. The review is combined with a bibliometric mapping approach to show the most influential research clusters, representative of CR research streams and their contributors. This process highlights that the connection between CR and supply chain issues represents a major research gap. Consequently, this paper introduces a research agenda connecting these the two traditionally separated research fields.
Exergy analysis of underground coal gasification with simultaneous storage of carbon dioxide

Various options are considered to reduce CO2 emissions when utilizing deep coal by applying underground coal gasification (UCG), i.e., in combination with carbonation of synthetic minerals (CaO), conventional UCG followed by ex-situ separation of CO2 and upgrading the product gas using naturally occurring minerals (wollastonite). A chemical equilibrium model was used to analyze the effect of process parameters on product composition and use it for an exergy analysis. The result is presented in terms of theoretical (ideal unit operations), practical (state of the art technology), and zero-emission (applying current CO2 capture and sequestration technology (CCS) to all sources of CO2 emission) recovery factors. The results show that underground gasification of deep coal can optimally extract 52-68% of the coal chemical exergy, but zero-emission extraction gives a negative recovery indicating that it is not practical with current state of the art CCS technology. Using in-situ CaO, which will enhance the H-2 production, is theoretically feasible with a recovery factor around 80%, but is not exergetically feasible with the current state of technology. Ex-situ upgrading of the conventional UCG product gas with wollastonite is exergetically feasible for both practical and zero-emission cases according to the equilibrium model. (C) 2012 Elsevier Ltd. All rights reserved.
Spontaneous retroperitoneal haemorrhage from pancreatoduodenal artery (PDA) rupture and associated complications

Spontaneous retroperitoneal haemorrhage (SRH) is rare. It may present with abdominal or back pain with or without haemodynamic instability. Aggressive resuscitation while investigating the cause of bleeding and providing haemostasis are the standard of care. Subsequent close monitoring is necessary to identify early complications.This study reports three patients who presented to our institution within the last 5 years with SRH from a ruptured pancreaticoduodenal artery (PDA) aneurysm. Each patient had a unique presentation, complications and treatment demonstrating the variability and complexity of SRH. One patient presented with sudden abdominal pain and hypovolaemic shock, underwent angioembolisation and had an eventful recovery. Another patient presented similarly and was treated via angioembolisation but experienced gastric outlet obstruction and obstructive jaundice requiring surgical haematoma evacuation. Another patient had an incidental finding of haemoperitoneum during laparoscopic cholecystectomy that was subsequently diagnosed as SRH resulting from a PDA aneurysm rupture secondary to medial arcuate ligament syndrome.
Alterations in gut microbiota are related to metabolite profiles in spinal cord injury

Studies have shown that gut microbiota metabolites can enter the central nervous system via the blood-spinal cord barrier and cause neuroinflammation, thus constituting secondary injury after spinal cord injury. To investigate the correlation between gut microbiota and metabolites and the possible mechanism underlying the effects of gut microbiota on secondary injury after spinal cord injury, in this study, we established mouse models of T8-T10 traumatic spinal cord injury. We used 16S rRNA gene amplicon sequencing and metabolomics to reveal the changes in gut microbiota and metabolites in fecal samples from the mouse model. Results showed a severe gut microbiota disturbance after spinal cord injury, which included marked increases in pro-inflammatory bacteria, such as Shigella, Bacteroides, Rikenella, Staphylococcus, and Mucispirillum and decreases in anti-inflammatory bacteria, such as Lactobacillus, Allobaculum, and Sutterella. Meanwhile, we identified 27 metabolites that decreased and 320 metabolites that increased in the injured spinal cord. Combined with pathway enrichment analysis, five markedly differential amino acids (L-leucine, L-methionine, L-phenylalanine, L-isoleucine and L-valine) were screened out, which play a pivotal role in activating oxidative stress and inflammatory responses following spinal cord injury. Integrated correlation analysis indicated that the alteration of gut microbiota was related to the differences in amino acids, which suggests that disturbances in gut microbiota might participate in the secondary injury through the accumulation of partial metabolites that activate oxidative stress and inflammatory responses. Findings from this study provide a new theoretical basis for improving the secondary injury after spinal cord injury through fecal microbial transplantation.
Haze Removal Using Radial Basis Function Networks for Visibility Restoration Applications

Restoration of visibility in hazy images is the first relevant step of information analysis in many outdoor computer vision applications. To this aim, the restored image must feature clear visibility with sufficient brightness and visible edges, while avoiding the production of noticeable artifacts. In this paper, we propose a haze removal approach based on the radial basis function (RBF) through artificial neural networks dedicated to effectively removing haze formation while retaining not only the visible edges but also the brightness of restored images. Unlike traditional haze-removal methods that consist of single atmospheric veils, the multiatmospheric veil is generated and then dynamically learned by the neurons of the proposed RBF networks according to the scene complexity. Through this process, more visible edges are retained in the restored images. Subsequently, the activation function during the testing process is employed to represent the brightness of the restored image. We compare the proposed method with the other state-of-the-art haze-removal methods and report experimental results in terms of qualitative and quantitative evaluations for benchmark color images captured in typical hazy weather conditions. The experimental results demonstrate that the proposed method is able to produce brighter and more vivid haze-free images with more visible edges than can the other state-of-the-art methods.
Silibinin Radiosensitizes EGF Receptor-knockdown Prostate Cancer Cells by Attenuating DNA Repair Pathways

Emergence of radioresistance in prostate cancer (PCa) cells is a major obstacle in cancer therapy and contributes to the relapse of the disease. EGF receptor (EGFR) signaling plays an important role in the development of radioresistance. Herein, we have assessed the modulatory effects of silibinin on radiation-induced resistance via DNA repair pathways in EGFR-knockdown DU145 cells. shRNA-based silencing of EGFR was done in radioresistant human PCa DU145 cells and effects of ionizing radiation (IR) and silibinin were assessed using clonogenic and trypan blue assays. Furthermore, radiosensitizing effects of silibinin on PCa in context with EGFR were analyzed using flow cytometry, comet assay, and immunoblotting. Silibinin decreased the colony formation ability with an increased death of DU145 cells exposed to IR (5 Gray), with a concomitant decrease in Rad51 protein expression. Silibinin (25 μM) augmented the IR-induced cytotoxic effect in EGFR-knockdown PCa cells, along with induction of G2/M phase cell cycle arrest. Further, we studied homologous recombination (HR) and non-homologous end joining (NHEJ) pathways in silibinin-induced DNA double-strand breaks in EGFR-knockdown DU145 cells. Silibinin down-regulated the expression of Rad51 and DNA-dependent protein kinase proteins without any considerable effect on Ku70 and Ku80 in IR-exposed EGFR-knockdown PCa cells. The pro-survival signaling proteins, phospho-extracellular signal-regulated kinases (ERK)1/2, phospho-Akt and phospho-STAT3 were decreased by silibinin in EGFR-deficient PCa cells. These findings suggest a novel mechanism of silibinin-induced radiosensitization of PCa cells by targeting DNA repair pathways, HR and NHEJ, and suppressing the pro-survival signaling pathways, ERK1/2, Akt and STAT3, in EGFR-knockdown PCa cells.
INFLUENCE OF ENVIRONMENTAL ISSUES INTO VISUAL ARTS CURRICULUM ON STUDENTS' ENVIRONMENTAL BEHAVIOUR

In recent years, when we are pursuing industrial development and economic growth, we do not know how to cherish our natural resources, and we are willing to destroy our nature, which leads to water pollution, soil loss, and extinction of animals and plants. We are facing the consequences of environmental pollution, and these natural counter-attacks remind us to pay attention to the crisis facing the environment. The most fundamental and efficient way is to start with education. Only by promoting environmental education we can change the way humans treat nature. In this study, the experimental design mode is used for experimental research. This study mainly focuses on Jiang Su Province Chang Zhou City College students as empirical objects, and a total of 222 students are experimental research objects, and experimental teaching research is conducted for 15 weeks, three hours a week (45 hours in total). In conclusion, it is expected that by integrating environmental issues into the visual arts curriculum, the school will implement relevant teaching activities in stages, to achieve the goal of environmental education and enable students to pay attention to, learn and participate in environmental issues for life.
Influenza subtype-specific maternal antibodies protect offspring against infection but inhibit vaccine-induced immunity and protection in mice

Following influenza A virus (IAV) infection or vaccination during pregnancy, maternal antibodies are transferred to offspring in utero and during lactation. The age and sex of offspring may differentially impact the transfer and effects of maternal immunity on offspring. To evaluate the effects of maternal IAV infection on immunity in offspring, we intranasally inoculated pregnant mice with sublethal doses of mouse-adapted (ma) H1N1, maH3N2, or media (mock) at embryonic day 10. In offspring of IAV-infected dams, maternal subtype-specific antibodies peaked at postnatal day (PND) 23, remained detectable through PND 50, and were undetectable by PND 105 in both sexes. When offspring were challenged with homologous IAV at PND 23, both male and female offspring had greater clearance of pulmonary virus and less morbidity and mortality than offspring from mock-inoculated dams. Inactivated influenza vaccination (IIV) against homologous IAV at PND 23 caused lower vaccine-induced antibody responses and protection following live virus challenge in offspring from IAV than mock-infected dams, with this effect being more pronounced among female than male offspring. At PND 105, there was no impact of maternal infection status, but vaccination induced greater antibody responses and protection against challenge in female than male offspring of both IAV-infected and mock-inoculated dams. To determine if maternal antibody or infection interfered with vaccine-induced immunity and protection in early life, offspring were vaccinated and challenged against a heterosubtypic IAV (i.e., different IAV group than dam) at PND 23 or 105. Heterosubtypic IAV maternal immunity did not affect antibody responses after IIV or protection after live IAV challenge of vaccinated offspring at either age. Subtype-specific maternal IAV antibodies, therefore, provide protection independent of offspring sex but interfere with vaccine-induced immunity and protection in offspring with more pronounced effects among females than males.
A Fully Embedded Adaptive Real-Time Hand Gesture Classifier Leveraging HD-sEMG and Deep Learning

This paper presents a real-time fine gesture recognition system for multi-articulating hand prosthesis control, using an embedded convolutional neural network (CNN) to classify hand-muscle contractions sensed at the forearm. The sensor consists in a custom non-intrusive, compact, and easy-to-install 32-channel high-density surface electromyography (HDsEMG) electrode array, built on a flexible printed circuit board (PCB) to allow wrapping around the forearm. The sensor provides a low-noise digitization interface with wireless data transmission through an industrial, scientific and medical (ISM) radio link. An original frequency-time-space cross-domain preprocessing method is proposed to enhance gesture-specific data homogeneity and generate reliable muscle activation maps, leading to 98.15% accuracy when using a majority vote over 5 subsequent inferences by the proposed CNN. The obtained real-time gesture recognition, within 100 to 200 ms, and CNN properties show reliable and promising results to improve on the state-of-the-art of commercial hand prostheses. Moreover, edge computing using a specialized embedded artificial intelligence (AI) platform ensures reliable, secure and low latency real-time operation as well as quick and easy access to training, fine-tuning and calibration of the neural network. Co-design of the signal processing, AI algorithms and sensing hardware ensures a reliable and power-efficient embedded gesture recognition system.
Cascade primer exchange reaction-based amplification strategy for sensitive and portable detection of amyloid β oligomer using personal glucose meters

Amyloid β oligomer (AβO) is an important biomarker for Almerzheimer's disease (AD) early diagnosis. In present study, cascade primer exchange reaction (PER) based amplification strategy was proposed for sensitive and portable detection of AβO using personal glucose meters (PGM). Two PER processes were employed here. In the primary PER, the hairpin template 1 (HT1) was designed with a primer binding domain, a primer extending domain and a blocking extending domain. The primers were designed to be modified on magbeads surface. Initially, the primer binding domain in HT1 was locked by AβO aptamer. When target AβO was present, aptamer bound with AβO and dissociated from HT1 to initiate the primary PER. The products acted as the primer of the secondary PER to hybridize with another hairpin template 2 (HT2), initiating the secondary PER and producing numerous ssDNA with repeated DNA-invertase binding sites. After binding with DNA-invertase, the obtained conjugates were magnetically separation to catalyze the conversion of sucrose to glucose, which were detected by a PGM. The strategy achieved a limit of detection of 0.22 pM with a linear ranged from 1 pM to 250 pM. Satisfactory reproducibility results were obtained in actual samples. This strategy provided a superior tool for sensitive and convenient detection of AβO, and showing a great potential in the early diagnosis of AD.
A Deep Reinforcement Learning Method for Pricing Electric Vehicles With Discrete Charging Levels

The effective pricing of electric vehicle (EV) charging by aggregators constitutes a key problem toward the realization of the significant EV flexibility potential in deregulated electricity systems and has been addressed by previous work through bi-level optimization formulations. However, the solution approach adopted in previous work cannot capture the discrete nature of the EV charging/discharging levels. Although reinforcement learning (RL) can tackle this challenge, state-of-the-art RL methods require discretization of state and/or action spaces and thus exhibit limitations in terms of solution optimality and computational requirements. This article proposes a novel deep reinforcement learning (DRL) method to solve the examined EV pricing problem, combining deep deterministic policy gradient (DDPG) principles with a prioritized experience replay (PER) strategy and setting up the problem in multi-dimensional continuous state and action spaces. Case studies demonstrate that the proposed method outperforms state-of-the-art RL methods in terms of both solution optimality and computational requirements and comprehensively analyze the economic impacts of smart-charging and vehicle-to-grid (V2G) flexibility on both aggregators and EV owners.
Drive-independent data recovery: The current state-of-the-art

The term "data recovery" herein refers to accessing logically and/or physically damaged storage media, for which no functioning backup exists. The state-of-the-art physical techniques for recovering data from failed hardware can all be described as "part replacement." To achieve high data density and high manufacturing yields, modern drives are "hyper-tuned" in the factory so that their data layout, zone frequencies, and various channel settings are optimized for each head, surface, and zone. This greatly complicates part re. placement because a transplanted headstack, for example, no longer matches the servo, preamp, and read channel parameters that were optimized for the original headstack. Methods and challenges are discussed for replacing, or "refreshing," firmware and system area information and for replacing all of the drive's electronics. The data recovery industry's point of view, limitations of current techniques, and some probable future directions in data recovery are also presented. It is predicted that data recovery will be more important in the future as drives are exposed to more extreme mobile environments. Drive manufacturers may be able to differentiate themselves from their competition by designing for recoverability.