ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Framework and baseline examination of the German National Cohort (NAKO)

The German National Cohort (NAKO) is a multidisciplinary, population-based prospective cohort study that aims to investigate the causes of widespread diseases, identify risk factors and improve early detection and prevention of disease. Specifically, NAKO is designed to identify novel and better characterize established risk and protection factors for the development of cardiovascular diseases, cancer, diabetes, neurodegenerative and psychiatric diseases, musculoskeletal diseases, respiratory and infectious diseases in a random sample of the general population. Between 2014 and 2019, a total of 205,415 men and women aged 19-74 years were recruited and examined in 18 study centres in Germany. The baseline assessment included a face-to-face interview, self-administered questionnaires and a wide range of biomedical examinations. Biomaterials were collected from all participants including serum, EDTA plasma, buffy coats, RNA and erythrocytes, urine, saliva, nasal swabs and stool. In 56,971 participants, an intensified examination programme was implemented. Whole-body 3T magnetic resonance imaging was performed in 30,861 participants on dedicated scanners. NAKO collects follow-up information on incident diseases through a combination of active follow-up using self-report via written questionnaires at 2-3 year intervals and passive follow-up via record linkages. All study participants are invited for re-examinations at the study centres in 4-5 year intervals. Thereby, longitudinal information on changes in risk factor profiles and in vascular, cardiac, metabolic, neurocognitive, pulmonary and sensory function is collected. NAKO is a major resource for population-based epidemiology to identify new and tailored strategies for early detection, prediction, prevention and treatment of major diseases for the next 30 years.
A Graph Lattice Approach to Maintaining and Learning Dense Collections of Subgraphs as Image Features

Effective object and scene classification and indexing depend on extraction of informative image features. This paper shows how large families of complex image features in the form of subgraphs can be built out of simpler ones through construction of a graph lattice-a hierarchy of related subgraphs linked in a lattice. Robustness is achieved by matching many overlapping and redundant subgraphs, which allows the use of inexpensive exact graph matching, instead of relying on expensive error-tolerant graph matching to a minimal set of ideal model graphs. Efficiency in exact matching is gained by exploitation of the graph lattice data structure. Additionally, the graph lattice enables methods for adaptively growing a feature space of subgraphs tailored to observed data. We develop the approach in the domain of rectilinear line art, specifically for the practical problem of document forms recognition. We are especially interested in methods that require only one or very few labeled training examples per category. We demonstrate two approaches to using the subgraph features for this purpose. Using a bag-of-words feature vector we achieve essentially single-instance learning on a benchmark forms database, following an unsupervised clustering stage. Further performance gains are achieved on a more difficult dataset using a feature voting method and feature selection procedure.
Variable Parallelism Cyclic Redundancy Check Circuit for 3GPP-LTE/LTE-Advanced

Cyclic Redundancy Check (CRC) is often employed in data storage and communications to detect errors. The 3GPP-LTE wireless communication standard uses a 24-bit CRC with every turbo coded frame, thus, the CRC can be exploited to detect residual errors and to enable early stopping of iterations as well. The current state of the art lacks specific CRC implementations for this standard, and most current solutions adopt a fixed degree of parallelism, unsuitable for many turbo decoder architectures. This work proposes a variable parallelism circuit targeting the 3GPP-LTE/LTE-Advanced 24-bit CRC, that can adapt to input data of different sizes. Low complexity is achieved through careful functional sharing among the various parallelisms: comparison with the state of the art shows comparable or superior speed and extremely low complexity.
Hybrid SDN evolution: A comprehensive survey of the state-of-the-art

Software-Defined Networking (SDN) is an evolutionary networking paradigm which has been adopted by large network and cloud providers, among which are Tech Giants. However, embracing a new and futuristic paradigm as an alternative to well-established and mature legacy networking paradigm requires a lot of time along with considerable financial resources and technical expertise. Consequently, many enterprises cannot afford it. A compromise solution then is a hybrid networking environment (a.k.a. Hybrid SDN (hSDN)) in which SDN functionalities are leveraged while existing traditional network infrastructures are acknowledged. Recently, hSDN has been seen as a viable networking solution for a diverse range of businesses and organizations. Accordingly, the body of literature on hSDN research has improved remarkably. On this account, we present this paper as a comprehensive state-of-the-art survey which expands upon hSDN from many different perspectives.
Different degree of loss-of-function among four missense mutations in the EDAR gene responsible for autosomal recessive hypohidrotic ectodermal dysplasia may be associated with the phenotypic severity

Hypohidrotic ectodermal dysplasia is a rare condition characterized by hypohidrosis, hypodontia, and hypotrichosis. The disease can show X-linked recessive, autosomal dominant or autosomal recessive inheritance trait. Of these, the autosomal forms are caused by mutations in either EDAR or EDARADD. To date, the underlying pathomechanisms or genotype-phenotype correlations for autosomal forms have not completely been disclosed. In this study, we performed a series of in vitro studies for four missense mutations in the death domain of EDAR protein: p.R358Q, p.G382S, p.I388T, and p.T403M. The results revealed that p.R358Q- and p.T403M-mutant EDAR showed different expression patterns from wild-type EDAR in both western blots and immunostainings. NF-κB reporter assays demonstrated that all the mutant EDAR showed reduced activation of NF-κB, but the reduction by p.G382S- and p.I388T-mutant EDAR was moderate. Co-immunoprecipitation assays showed that p.R358Q- and p.T403M-mutant EDAR did not bind with EDARADD at all, whereas p.G382S- and p.I388T-mutant EDAR maintained the affinity to some extent. Furthermore, we demonstrated that all the mutant EDAR proteins analyzed aberrantly bound with TRAF6. Sum of the data suggest that the degree of loss-of-function is different among the mutant EDAR proteins, which may be associated with the severity of the disease.
Quinary, Senary, and Septenary High Entropy Alloy Nanoparticle Catalysts from Core@Shell Nanoparticles and the Significance of Intraparticle Heterogeneity

Colloidally prepared core@shell nanoparticles (NPs) were converted to monodisperse high entropy alloy (HEA) NPs by annealing, including quinary, senary, and septenary phases comprised of PdCuPtNi with Co, Ir, Rh, Fe, and/or Ru. Intraparticle heterogeneity, i.e., subdomains within individual NPs with different metal distributions, was observed for NPs containing Ir and Ru, with the phase stabilities of the HEAs studied by atomistic simulations. The quinary HEA NPs were found to be durable catalysts for the oxygen reduction reaction, with all but the PdCuPtNiIr NPs presenting better activities than commercial Pt. Density functional theory (DFT) calculations for PdCuPtNiCo and PdCuPtNiIr surfaces (the two extremes in performance) found agreement with experiment by weighting the adsorption energy contributions by the probabilities of each active site based on their DFT energies. This finding highlights how intraparticle heterogeneity, which we show is likely overlooked in many systems due to analytical limitations, can be leveraged toward efficient catalysis.
Learning Saliency From Single Noisy Labelling: A Robust Model Fitting Perspective

The advances made in predicting visual saliency using deep neural networks come at the expense of collecting large-scale annotated data. However, pixel-wise annotation is labor-intensive and overwhelming. In this paper, we propose to learn saliency prediction from a single noisy labelling, which is easy to obtain (e.g., from imperfect human annotation or from unsupervised saliency prediction methods). With this goal, we address a natural question: Can we learn saliency prediction while identifying clean labels in a unified framework? To answer this question, we call on the theory of robust model fitting and formulate deep saliency prediction from a single noisy labelling as robust network learning and exploit model consistency across iterations to identify inliers and outliers (i.e., noisy labels). Extensive experiments on different benchmark datasets demonstrate the superiority of our proposed framework, which can learn comparable saliency prediction with state-of-the-art fully supervised saliency methods. Furthermore, we show that simply by treating ground truth annotations as noisy labelling, our framework achieves tangible improvements over state-of-the-art methods.
Intranasal vaccination of recombinant H5N1 HA1 proteins fused with foldon and Fc induces strong mucosal immune responses with neutralizing activity: Implication for developing novel mucosal influenza vaccines

The highly pathogenic avian influenza (HPAI) H5N1 virus remains a threat to public health because of its continued spread in poultry in some countries and its ability to infect humans with high mortality rate, calling for the development of effective and safe vaccines against H5N1 infection. Here, we constructed 4 candidate vaccines by fusing H5N1 hemagglutinin 1 (HA1) with foldon (HA1-Fd), human IgG Fc (HA1-Fc), foldon and Fc (HA1-FdFc) or His-tag (HA1-His). We then compared their ability to induce mucosal immune responses and neutralizing antibodies in the presence or absence of Poly(I:C) and CpG adjuvants via the intranasal route. Without an adjuvant, HA1-FdFc could elicit appreciable humoral immune responses and local mucosal IgA antibodies in immunized mice, while other vaccine candidates only induced background immune responses. In the presence of Poly(I:C) and CpG, both HA1-Fd and HA1-Fc elicited much higher levels of serum IgG and local mucosal IgA antibodies than HA1-His. Poly(I:C) and CpG could also augment the neutralizing antibody responses induced by these 4 vaccine candidates in the order of HA1-FdFc > HA1-Fc > HA1-Fd > HA1-His. These results suggest that both Fd and Fc potentiate the immunogenicity of the recombinant HA1 protein and that Poly(I:C) and CpG serve as efficient mucosal adjuvants in promoting efficacy of these vaccine candidates to induce strong systemic and local antibody responses and potent neutralizing antibodies, providing a useful strategy to develop effective and safe mucosal H5N1 vaccines.
Using multisines to measure state-of-the-art analog-to-digital converters

Multisines have proven to be very useful for fast and accurate measurements, particularly when studying the nonlinear behavior of analog devices under test. This paper will deal with some problems that can arise when designing experiments for measuring state-of-the-art analog-to-digital converters (ADCs) with multisines. Special care has been taken to the measurement setup, particularly with the generation and verification of the test signals. The verification of the test signals requires either a more performant ADC or alternative measurement methods such as a spectrum analyzer. By means of real measurement examples, it is shown that most existing techniques used for analog measurements can be reused after an adaptation to the new proposed measurement setup. It will, e.g., be shown that the loss of information introduced by using a spectrum analyzer leads to bounds onto the nonlinear distortions instead of the absolute value.
An adversarial framework for op en-set human action recognition using skeleton data

Human action recognition is a fundamental problem which is applied in various domains, and it is widely studied in the literature. Majority of the studies model action recognition as a closed-set problem. However, in real-life applications it usually arises as an op en-set problem where a set of actions are not available during training but are introduced to the system during testing. In this study, we propose an op en-set action recognition system, human action recognition and novel action detection system (HARNAD), which consists of two stages and uses only 3D skeleton information. In the first stage, HARNAD recognizes a given action and in the second stage it decides whether the action really belongs to one of the a priori known classes or if it is a novel action. We evaluate the performance of the system experimentally both in terms of recognition and novelty detection. We also compare the system performance with state-of-the-art op en-set recognition methods. Our experiments show that HARNAD is compatible with state-of-the-art methods in novelty detection, while it is superior to those methods in recognition.
Thermal-Stable Separators: Design Principles and Strategies Towards Safe Lithium-Ion Battery Operations

Lithium-ion batteries (LIBs) are momentous energy storage devices, which have been rapidly developed due to their high energy density, long lifetime, and low self-discharge rate. However, the frequent occurrence of fire accidents in laptops, electric vehicles, and mobile phones caused by thermal runaway of the inside batteries constantly reminds us of the urgency in pursuing high-safety LIBs with high performance. To this end, this Review surveyed the state-of-the-art developments of high-temperature-resistant separators for highly safe LIBs with excellent electrochemical performance. Firstly, the basic properties of separators (e. g., thickness, porosity, pore size, wettability, mechanical strength, and thermal stability) in constructing commercialized LIBs were introduced. Secondly, the working mechanisms of advanced separators with different melting points acting in the thermal runaway stage were discussed in terms of improving battery safety. Thirdly, rational design strategies for constructing high-temperature-resistant separators for LIBs with high safety were summarized and discussed, including graft modification, blend modification, and multilayer composite modification strategies. Finally, the current obstacles and future research directions in the field of high-temperature-resistant separators were highlighted. These design ideas are expected to be applied to other types of high-temperature-resistant energy storage systems working under extreme conditions.
Radiation effects in low dielectric constant methyl-silsesquioxane films

The use of state-of-the-art microelectronic devices in space radiation environments faces new challenges with the adoption of low dielectric constant (low-k) materials as interlevel dielectrics. This is demonstrated in a preliminary study of methyl-silsesquioxane low-k films. We report radiation damage, induced by a 2-keV low- current-density (similar to2 x 10(6) s(-1) cm(-2)) positron beam, and observed by positron annihilation spectroscopy.
Proximal tubular Bmal1 protects against chronic kidney injury and renal fibrosis by maintaining of cellular metabolic homeostasis

Recent studies suggest that deletion of the core clock gene Bmal1 in the kidney has a significant influence on renal physiological functions. However, the role of renal Bmal1 in chronic kidney disease (CKD) remains poorly understood. Here by generating mice lacking Bmal1 in proximal tubule (Bmal1flox/flox-KAP-Cre+, ptKO) and inducing CKD with the adenine diet model, we found that lack of Bmal1 in proximal tubule did not alter renal water and electrolyte homeostasis. However, adenine-induced renal injury indexes, including blood urea nitrogen, serum creatinine, and proteinuria, were markedly augmented in the ptKO mice. The ptKO kidneys also developed aggravated tubulointerstitial fibrosis and epithelial-mesenchymal transformation. Mechanistically, RNAseq analysis revealed significant downregulation of the expression of genes related to energy and substance metabolism, in particular fatty acid oxidation and glutathione/homocysteine metabolism, in the ptKO kidneys. Consistently, the renal contents of ATP and glutathione were markedly reduced in the ptKO mice, suggesting the disruption of cellular metabolic homeostasis. Moreover, we demonstrated that Bmal1 can activate the transcription of cystathionine β-synthase (CBS), a key enzyme for homocysteine metabolism and glutathione biosynthesis, through direct recruitment to the E-box motifs of its promoter. Supporting the in vivo findings, knockdown of Bmal1 in cultured proximal tubular cells inhibited CBS expression and amplified albumin-induced cell injury and fibrogenesis, while glutathione supplementation remarkably reversed these changes. Taken together, we concluded that deletion of Bmal1 in proximal tubule may aggravate chronic kidney injury and exacerbate renal fibrosis, the mechanism is related to suppressing CBS transcription and disturbing glutathione related metabolic homeostasis. These findings suggest a protective role of Bmal1 in chronic tubular injury and offer a novel target for treating CKD.
Automatic Knowledge Discovery in Lecturing Videos via Deep Representation

In recent years, e-learning systems such as massive open online courses (MOOC) have been widely employed in academic institutions and have shown its power in enhancing the students' learning ability. Automatic detection and classification of knowledge points in lecture video would significantly enhance the performance of the online learning platform. Most of the previously presented approach for knowledge discovery focused on the text and audio documents, whereas the identification of knowledge points in videos still remains a challenge. To bridge this gap, we proposed a novel convolutional neural network which was designed for the characteristics of lecture video. It could both extract the temporal-spatial and semantic information from the multimedia record. To evaluate the performance of the proposed technique, we conducted comparison experiments between the state-of-the-art methods and ours. The experimental results demonstrated that the presented approach outperformed the state-of-the-art techniques and could be potentially invaluable for the accurate discovery of knowledge points within videos.
Microfluidics on foil: state of the art and new developments

The concept of microfluidics on foil opens up new opportunities for combining the advantages of having a flexible substrate with reel-to-reel processing, which has the potential to be the basis for extremely cheap micro products. To reach this goal, foil substrates must be combined with micro-manufacturing technologies that are well adapted to these substrates. Some technologies are already available, some are the subject of current research, and some still have to be conceived. In the current paper, technologies such as reel-to-reel embossing, reel-to-reel laminating, and laser ablation/cutting as well as laser welding will be discussed. The discussions include a brief review of the state of the art as well as a report on latest research results stemming from research by the present authors. Furthermore, this paper shows the vision of what can be achieved if foil-based technologies, such as polymer (opto-) electronics and microfluidics are combined. A polymer electronics-based alcohol sensor is presented as an example system.
Machine learning based false data injection in smart grid

Smart Grid is the seamless integration of advance digital communication network, state of the art control technologies, and power system infrastructure working together as an entity to ensure the reliability, sustainability, and stability of the power infrastructure. Digital communication network with is the key to the reliability of Smart Grid as all control actions are deemed upon the data transmitted by a communication network. With false data, however, the same digital communication network can lead to anomalies like abnormal disruptions, load shedding, malicious attacks and power theft. Robust False data injection attack methods proposed till now demand for the complete knowledge of interconnected power grid network topology. In this paper, three network topology independent techniques for false data injection into the smart grid are proposed based on linear regression, linear regression with time stamp, and by using delta thresholds. To make injected false data more unlikely to be detected, it is constructed to fill up the missing measurements in real-time data. The robustness of proposed attack algorithms are stated by state-of-the-art defence techniques, i.e. Bad Data Detection, AC State estimation, Support Vector Machine, and Temporal Behaviours based False data detection.
Mangrove fruit ( Bruguiera gymnorhiza) increases circulating GLP-1 and PYY, modulates lipid profiles, and reduces systemic inflammation by improving SCFA levels in obese wistar rats

Bruguiera gymnorhiza (BG) has potential as a functional food because of its dietary fibre content and bioactive components such as flavonoids and phenolic compounds. However, it is not studied in the context of diet-related disease prevention. In the present study, we aimed to investigate the effects of Bruguiera gymnorhiza fruit flour (BGF) on satiety hormone, lipid profile, systemic inflammation, body weight, and caecum SCFA levels in diet-induced obese rats. A total of 28 obese male Wistar rats were divided into four groups. Group 1 (K1) was given a standard chow, group 2 (K2) standard chow + orlistat, group 3 (P1) standard chow + BGF 2 g/200 g BW/day, and group 4 (P2) standard chow + BGF 4 g/200 g BW/day for 28 days. The levels of GLP-1, PYY, total cholesterol (TC), triglyceride (TG), HDL, IL-6, TNF-α, and body weight were measured before and after the intervention; meanwhile, the caecum SCFA levels were assessed only after the intervention. In this study, BGF intervention increased the dose-dependent plasma GLP-1 and PYY levels (P < 0.000). In addition, BGF intervention also decreased lipid profiles (TC & TG) (P < 0.000, respectively) and systemic inflammation in a dose-dependent manner. Finally, acetate, propionate, and total SCFA concentrations were higher in the BGF intervention group (P2) compared to the other groups (p < 0.05). The SCFA levels were associated with satiety hormones, lipids, and systemic inflammation (P < 0.05). The BGF intervention improved satiety hormone, lipid profile, systemic inflammation, and SCFA levels.