ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Novel concepts on mechanisms underlying Hepatitis Delta virus persistence and related pathogenesis

Hepatitis Delta virus is the smallest known human virus, exploiting the HBV surface proteins (HBsAg) for the release of its progeny and de novo entry into hepatocytes. Ever growing evidence have highlighted the existence of multiple mechanisms underlying HDV persistence including integrated HBV-DNA as a source of HBsAg production and the capability of the HDV genome to propagate through cell proliferation, thus supporting a potential HDV persistence even in the absence of HBV. Chronic HDV-infection causes the most severe form of viral hepatitis, leading to the development of cirrhosis in 15% of cases within 1-2 years and in 50%-60% of cases within 5-10 years. The rates of hepatocellular carcinoma and hepatic decompensation are also 2-3-fold higher than for HBV mono-infection. There is the evidence that persistent viral replication plays a key role in triggering liver injury, suggesting the existence of direct viral cytopathic properties that can modulate, synergistically with immune-responses, the progression towards end-stage liver diseases. All these aspects can be further exacerbated by the extraordinary degree of viral genetic variability that can promote HDV evasion from immune responses and has enabled viral differentiation into genotypes and subgenotypes with potential different pathobiological properties. In this light, this review aims at providing comprehensive insights of mechanisms (with a focus on virological factors) underlying HDV persistence and pathogenesis, critical in shaping the clinical outcome of the infection. Dissecting these mechanisms is pivotal to optimize therapeutic strategies aimed at fully counteracting this fascinating and fearsome virus.
A comment on two-locus epistatic interaction models for genome-wide association studies

Detection of epistatic interactions in genome-wide association studies is a computationally hard problem. Many detection algorithms have been proposed and will continue to be. Most of those algorithms measure their predictive power by running on simulated data many times under various disease models. However, we find that there have been subtle differences in interpreting the meaning of existing disease models among the previous studies on detection of epistatic interactions. We elucidate those differences and suggest that future studies on epistatic interactions in GWAS state explicitly which versions/interpretations are employed. We also provide a way to facilitate setting parameters of disease models.
Virtual Adversarial Training-Based Deep Feature Aggregation Network From Dynamic Effective Connectivity for MCI Identification

Dynamic functional connectivity (dFC) network inferred from resting-state fMRI reveals macroscopic dynamic neural activity patterns for brain disease identification. However, dFC methods ignore the causal influence between the brain regions. Furthermore, due to the complex non-Euclidean structure of brain networks, advanced deep neural networks are difficult to be applied for learning high-dimensional representations from brain networks. In this paper, a group constrained Kalman filter (gKF) algorithm is proposed to construct dynamic effective connectivity (dEC), where the gKF provides a more comprehensive understanding of the directional interaction within the dynamic brain networks than the dFC methods. Then, a novel virtual adversarial training convolutional neural network (VAT-CNN) is employed to extract the local features of dEC. The VAT strategy improves the robustness of the model to adversarial perturbations, and therefore avoids the overfitting problem effectively. Finally, we propose the high-order connectivity weight-guided graph attention networks (cwGAT) to aggregate features of dEC. By injecting the weight information of high-order connectivity into the attention mechanism, the cwGAT provides more effective high-level feature representations than the conventional GAT. The high-level features generated from the cwGAT are applied for binary classification and multiclass classification tasks of mild cognitive impairment (MCI). Experimental results indicate that the proposed framework achieves the classification accuracy of 90.9%, 89.8%, and 82.7% for normal control (NC) vs. early MCI (EMCI), EMCI vs. late MCI (LMCI), and NC vs. EMCI vs. LMCI classification respectively, outperforming the state-of-the-art methods significantly.
Asphalt production technology from engineering art to science

In the last third of the last century, important progress was made in developing the scientific basis for oxidized asphalt production technology. The classification of crude oils and mathematical model developed allow quantitatively predicting the basic parameters of asphalt production based on vacuum distillation and oxidation process using the reference properties of the crude oil.
Cube-Evo: A Query-Efficient Black-Box Attack on Video Classification System

The current progressive research in the domain of black-box adversarial attack enhances the reliability of deep neural network (DNN)-based video systems. Recent works mainly carry out black-box adversarial attacks on video systems by query-based parameter dimension reduction. However, the additional temporal dimension of video data leads to massive query consumption and low attack success rate. In this article, we embark on our efforts to design an effective adversarial attack on popular video classification systems. We deeply root the observations that the DNN-based systems are sensitive to adversarial perturbations with high frequency and reconstructed shape. Specifically, we propose a systematic attack pipeline Cube-Evo, aiming to reduce the search space dimension and obtain the effective adversarial perturbation via the optimal parameter group updating. We evaluate the proposed attack pipeline on two popular datasets: UCF101 and JESTER. Our attack pipeline reduces query consumption and achieves a high success rate on various DNN-based video classification systems. Compared with the state-of-the-art method Geo-Trap-Att, our pipeline averagely reduces 1.6x query consumption in untargeted attacks and 2.9x in targeted attacks. Besides, Cube-Evo improves 13% attack success rate on average, achieving new state-of-the-art results over diverse video classification systems.
CRISPRi screening identifies CASP8AP2 as an essential viability factor in lung cancer controlling tumor cell death via the AP-1 pathway

Since lung cancer remains the leading cause of cancer death globally, there is an urgent demand for novel therapeutic targets. We carried out a CRISPR interference (CRISPRi) loss-of-function screen for human lung adenocarcinoma (LUAD) targeting 2098 deregulated genes using a customized algorithm to comprehensively probe the functionality of every resolvable transcriptional start site (TSS). CASP8AP2 was identified as the only hit that significantly affected the viability of all eight screened LUAD cell lines while the viability of non-transformed lung cells was only moderately impacted. Knockdown (KD) of CASP8AP2 induced both autophagy and apoptotic cell death pathways. Systematic expression profiling linked the AP-1 transcription factor to the CASP8AP2 KD-induced cancer cell death. Furthermore, inhibition of AP-1 reverted the CASP8AP2 silencing-induced phenotype. Overall, the tailored CRISPRi screen profiled the impact of over 2000 genes on the survival of eight LUAD cell lines and identified the CASP8AP2 - AP-1 axis mediating lung cancer viability.
[Pandemic and value change?]

Recent sociological diagnoses suggest that profound social crises such as the COVID-19 pandemic challenge our value orientations and could change them even in the relatively short term. Based on this observation, we investigate whether significant shifts in value priorities according to the Shalom Schwartz scale took place in Austria in the period May 2020 to March/April 2021. The first two waves of the Values in Crisis panel study serve as data material. Two theoretical assumptions are central to the interpretation of the results: first, the thesis of a trend toward conservatism and second, the thesis of the effective power of political discourses in times of (re)emerging populism. The article also pays special attention to a methodological discussion of changes in the meaning of questionnaire items due to the COVID-19 pandemic.The empirical analyses confirm a clear stability of value orientations. Above all, the value of conformity has changed, becoming more important for a significant part of the population; at the same time, the desire for a hedonistic lifestyle lost some of its importance. Conformity became more important, particularly for voters of the governing political parties, while this trend was not apparent, especially among voters of the FPÖ. Since the observed shift in value priorities mainly concerns "pandemic-sensitive" value dimensions, the results suggest a short-term reaction to the crisis rather than a long-term change in values.
SYK coordinates neuroprotective microglial responses in neurodegenerative disease

Recent studies have begun to reveal critical roles for the brain's professional phagocytes, microglia, and their receptors in the control of neurotoxic amyloid beta (Aβ) and myelin debris accumulation in neurodegenerative disease. However, the critical intracellular molecules that orchestrate neuroprotective functions of microglia remain poorly understood. In our studies, we find that targeted deletion of SYK in microglia leads to exacerbated Aβ deposition, aggravated neuropathology, and cognitive defects in the 5xFAD mouse model of Alzheimer's disease (AD). Disruption of SYK signaling in this AD model was further shown to impede the development of disease-associated microglia (DAM), alter AKT/GSK3β-signaling, and restrict Aβ phagocytosis by microglia. Conversely, receptor-mediated activation of SYK limits Aβ load. We also found that SYK critically regulates microglial phagocytosis and DAM acquisition in demyelinating disease. Collectively, these results broaden our understanding of the key innate immune signaling molecules that instruct beneficial microglial functions in response to neurotoxic material.
Maximum Density Divergence for Domain Adaptation

Unsupervised domain adaptation addresses the problem of transferring knowledge from a well-labeled source domain to an unlabeled target domain where the two domains have distinctive data distributions. Thus, the essence of domain adaptation is to mitigate the distribution divergence between the two domains. The state-of-the-art methods practice this very idea by either conducting adversarial training or minimizing a metric which defines the distribution gaps. In this paper, we propose a new domain adaptation method named adversarial tight match (ATM) which enjoys the benefits of both adversarial training and metric learning. Specifically, at first, we propose a novel distance loss, named maximum density divergence (MDD), to quantify the distribution divergence. MDD minimizes the inter-domain divergence ("match" in ATM) and maximizes the intra-class density ("tight" in ATM). Then, to address the equilibrium challenge issue in adversarial domain adaptation, we consider leveraging the proposed MDD into adversarial domain adaptation framework. At last, we tailor the proposed MDD as a practical learning loss and report our ATM. Both empirical evaluation and theoretical analysis are reported to verify the effectiveness of the proposed method. The experimental results on four benchmarks, both classical and large-scale, show that our method is able to achieve new state-of-the-art performance on most evaluations.
Modeling and Evaluation of Carbon-Nanotube-Based Integrated Power Inductor for On-Chip Switching Power Converters

This paper presents a nanotechnology-based high-power-density and low-power-loss on-chip power inductor for dc-dc switching power converters. This power inductor utilizes a composite of bundled multiwalled (concentric) carbon nanotubes (BMWCNTs) and Fe (iron) in order to achieve high performance in a small size. Titanium (Ti) is then added as a coating material on the BMWCNT-based power inductor. The BMWCNT-based power inductor with a single layer and three turns occupies an area of 200 mu m x 200 mu m. It exhibits an inductance of 206 nH, a quality factor of 427 at 20 MHz, and a dc rated current of 100 mA. The power inductor size and the performance characteristics are competitive with state-of-the-art power inductors. The design, analysis, modeling, and simulation results of the BMWCNT-based power inductor are presented and compared with state-of-the-art conventional power inductors from the literature.
Hybrid histidine kinase HisK2301 modulates carotenoid production to counteract cold-induced oxidative stress in Rhodosporidium kratochvilovae YM25235 under low temperature

Hybrid histidine kinases (HHKs) are major sensor proteins for fungi that contribute to stress tolerance. In the present work, we investigated the roles and mechanisms of the HHK HisK2301 in cold-adapted Rhodosporidium kratochvilovae strain YM25235. The HisK2301 deletion strain was constructed by homologous recombination method and arranged for multiple stress tests. We analysed the content of carotenoid using UV-Vis and HPLC. Relative transcript levels of genes phytoene desaturase (RKCrtI) and phytoene synthase and lycopene cyclase (RKCrtYB) were analysed by RT-qPCR. Intracellular reactive oxygen species (ROS) generation was measured using 2',7'-dichlorodihydrofluorescein diacetate (DCFH-DA). Our results clearly indicated that YM25235 produces γ-carotene, torulene, β-carotene and torularhodin, with the latter two components strongly related to adapt to cold. HisK2301 is crucial for YM25235 adaptation to different types of stress such as cold, salt, osmotic and oxidative stress. Growth at low temperature clearly induced oxidative stress in YM25235, as more ROS accumulated at cold. During cold stress, HisK2301 is suggested to sense cold-induced ROS signals and then promote carotenoid production partially by RKCrtI and RKCrtYB to scavenge excessive ROS production. Such an inducible protective system may confer YM25235 fast response and better adaptation to cold stress. To conclude, our findings give the first insight into the effect of HisK2301 on carotenoid biosynthesis and cold-induced oxidative stress in fungi under low temperature and suggest the potential use of the cold-adapted HHK HisK2301 in industrial production of carotenoid.
Self-Supervised Attention Mechanism for Pediatric Bone Age Assessment With Efficient Weak Annotation

Pediatric bone age assessment (BAA) is a common clinical practice to investigate endocrinology, genetic and growth disorders of children. Different specific bone parts are extracted as anatomical Regions of Interest (RoIs) during this task, since their morphological characters have important biological identification in skeletal maturity. Following this clinical prior knowledge, recently developed deep learning methods address BAA with an RoI-based attention mechanism, which segments or detects the discriminative RoIs for meticulous analysis. Great strides have been made, however, these methods strictly require large and precise RoIs annotations, which limits the real-world clinical value. To overcome the severe requirements on RoIs annotations, in this paper, we propose a novel self-supervised learning mechanism to effectively discover the informative RoIs without the need of extra knowledge and precise annotation-only image-level weak annotation is all we take. Our model, termed PEAR-Net for Part Extracting and Age Recognition Network, consists of one Part Extracting (PE) agent for discriminative RoIs discovering and one Age Recognition (AR) agent for age assessment. Without precise supervision, the PE agent is designed to discover and extract RoIs fully automatically. Then the proposed RoIs are fed into AR agent for feature learning and age recognition. Furthermore, we utilize the self-consistency of RoIs to optimize PE agent to understand the part relation and select the most useful RoIs. With this self-supervised design, the PE agent and AR agent can reinforce each other mutually. To the best of our knowledge, this is the first end-to-end bone age assessment method which can discover RoIs automatically with only image-level annotation. We conduct extensive experiments on the public RSNA 2017 dataset and achieve state-of-the-art performance with MAE 3.99 months. Project is available at http://imcc.ustc.edu.cn/project/ssambaa/.
Circadian clock-mediated nuclear receptors in cancer

Circadian system coordinates the daily periodicity of physiological and biochemical functions to adapt to environmental changes. Circadian disruption has been identified to increase the risk of cancer and promote cancer progression, but the underlying mechanism remains unclear. And further mechanistic understanding of the crosstalk between clock components and cancer is urgent to achieve clinical anticancer benefits from chronochemotherapy. Recent studies discover that several nuclear receptors regulating circadian clock, also play crucial roles in mediating multiple cancer processes. In this review, we aim to summarize the latest developments of clock-related nuclear receptors in cancer biology and dissect mechanistic insights into how nuclear receptors coordinate with circadian clock to regulate tumorigenesis and cancer treatment. A better understanding of circadian clock-related nuclear receptors in cancer could help prevent tumorigenesis and improve anticancer efficacy.
CARA: Connectivity-Aware Relay Algorithm for Multi-Robot Expeditions

The exploration of unknown environments is an essential application of multi-robot systems, particularly in critical missions, such as hazard detection and search and rescue. These missions share the need to reach full coverage of the explorable space in the shortest time possible. To minimize the completion time, robots in the fleet must be able to reliably exchange information about the environment with one another. One of the main methods to expand coverage is by placing relays. Existing relay-placement algorithms tend to either require prior knowledge of the environment, or they rely on maintaining specific distances between the relays and the rest of the robots. These approaches lack flexibility and adaptability to the environment. This paper introduces the "Connectivity-Aware Relay Algorithm" (CARA), a dynamic context-aware relay-placement algorithm that does not require any prior knowledge of the environment. We compare CARA against a state-of-the-art distance-based relay-placement algorithm. Our results demonstrate that CARA outperformed the state-of-the-art algorithm in terms of the time to completion by a factor of 10 as it placed, on average, half the number of relays.
Plasma-processed CoSn/RGO nanocomposite: A low-cost and sustainable counter electrode for dye-sensitized solar cells

The high cost of state-of-the-art Pt counter electrodes (CEs) hinders the large-scale applications of dye-sensitized solar cells (DSCs). The development of Pt-free catalysts while maintaining state-of-the-art catalytic activity for CE materials is one mean to reduce costs. Here, CoxSn1-x/reduced graphene oxide (RGO) (0 <= x <= 1) nanohybrids were synthesized and employed as inexpensive, stable, and earth-abundant CEs in DSCs. The synthesis was performed through the plasma-assisted reduction of the oxygen functional groups of the graphene oxide along with the immobilization of bimetallic nanoparticles (NPs) on the surface of RGO. The optimization of the composition of the alloy NPs for the highest efficiency of DSC yields the Co0.9Sn0.1/RGO nanocomposite. The highest device performance correlates well with the experimentally obtained lowest charge transfer resistance in conjunction with the highest electrocatalytic activity of the Co0.9Sn0.1/RGO CE. The DSC employed the synthesized CE showed good stability over long term operation. Both the developed CoSn/RGO nanohybrids and the strategy used for their synthesis are cost-effective. Our results provide economically implementable and green nanotechnology for efficient and stable DSCs required for commercialization.
Hybrid Joint Diagonalization Algorithms

This letter deals with a hybrid joint diagonalization problem considering both Hermitian and transpose congruence. Such problem can be encountered in certain noncircular signal analysis applications including blind source separation. We introduce new Jacobi-like algorithms using Givens or a combination of Givens and hyperbolic rotations. These algorithms are compared with state-of-the-art methods and their performance gain, especially in the high dimensional case, is assessed through simulation experiments including examples related to blind separation of noncircular sources.
The timed up and go test for lumbar degenerative disc disease

We report on the use and performance of an objective measure of functional impairment, the timed up and go (TUG) test, in clinical practice for patients with lumbar degenerative disc disease (DDD). We illustrate nine representative patients with lumbar DDD, who were selected from an ongoing prospective study, to report our clinical experience with the TUG test. In addition, a preliminary sample of 30 non-selected consecutive patients is presented. The following parameters were assessed preoperatively, and 3 days and 6 weeks postoperatively: back and leg pain using the visual analogue scale (VAS); functional impairment using the Oswestry disability index (ODI) and Roland-Morris disability index (RMDI); health-related quality of life using the EuroQol 5D (EQ5D) and Short-Form 12 (SF-12). The TUG test results improved by 2.6 and 5.4s after 3 days and 6 weeks compared to the baseline assessment. The mean VAS for back and leg pain decreased by 2.3 and 5.3, respectively, after 3 days, and by 2.7 and 4.6 after 6 weeks. The mean RMDI and ODI decreased by 3.4 and 23.3, respectively, after 3 days, and by 7.0 and 28.0 after 6 weeks. The mean EQ5D increased by 0.38 after 3 days and 0.358 after 6 weeks. The mean SF-12 mental component scale decreased by 0.2 after 3 days and increased by 5.6 after 6 weeks, whereas the mean SF-12 physical component scale increased by 6.4 after 3 days and by 9.8 after 6 weeks. The TUG test proved to be a useful, easy to use tool that could add a new, objective dimension to the armamentarium of clinical tests for the diagnosis and management of DDD. From our preliminary experience, we conclude that the TUG test accurately reflects a patient's objective functional impairment before and after surgery.
GAD: A Global-Aware Diversity-Based Template Matching Method

A novel similarity measurement metric, global-aware diversity (GAD), between a template and an image is proposed in this article, which can be efficiently utilized in a template matching method. Unlike the existing nearest neighbor field (NNF)-based methods, which use either the local or global diversity alone, GAD utilizes the global context to guide the local diversity. We verify that the global context is important for template matching task and can largely remove the background and outliers. The state-of-the-art NNF-based methods in general are not efficient enough as resolution grows. Thus, we also elaborately design a very efficient algorithm of GAD to reduce the complexity from . First, the GAD algorithm has been evaluated on two challenging benchmark datasets best-buddies similarity (BBS) and TinyTLP. Experiments show that the GAD algorithm achieves the state-of-the-art performance. As GAD does not impose any prior on unseen data, it is more insensitive to large rotation or deformation than the deformation-based algorithms. Besides, the GAD algorithm can run at 150 ms on average with high accuracy on the dataset of resolution 1280 x 780.