ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Conjoint belief adaption functionality for statistically secure communication paradigm for CR-IIoTs

In an infrastructure-critical manufacturing industry, continuous monitoring of the assembly lines with internet-integrated sensors in an unforeseeable, stochastic, and harsh environment becomes indispensable for real-time fault detection, location, and isolation. State-of-the-art literature suggest that the Cognitive Radio-based Industrial Internet of Things (CR-IIoTs), a spectrum aware wireless communication paradigm, is best suited for monitoring as they are proficient in overcoming spectrum scarcity in a 2.4-GHz industrial, scientific, and medical (ISM) band and guarantee a threshold Quality of Service (QoS) in hostile environs. Nonetheless, CR-IIoTs and the Cognitive Radio-based network archetype are susceptible to internal and external security threats at the application layer and vulnerable to Spectrum Sensing Data Falsification (SSDF) at Data Link Layer (DLL). In CR-IIoTs, cooperative spectrum sensing (CSS) is a competent technique to ameliorate the performance of Primary User (PU) detection and optimizes the idle licensed spectrum bands access management. In this paper, a novel belief adaption framework in conjunction with an optimized secure communication scheme (termed as CBAF) based on the Certificateless-Public Key Cryptography (CL-PKC) is proposed for robust CSS in CR-IIoTs. CBAF proficiently provides full-Perfect Forward Secrecy, Ephemeral Secret Leakage-Resilience, Public Key Replacement-Resilience Malicious but Passive KGC-Resilience, and Key Escrow Resilience. Simulation analysis pertaining to network performance indices ascertains the efficacy of CBAF, as it outperforms the state-of-the-art in throughput, packet delivery ratio, and communication overhead by 8.5%, 6.7%, and 19.9% respectively. Under internal attacks, CBAF outperforms the best from state-of-the-art by 11.3% in On-Off attack, 19.7% in Data Tampering attack, 21.1% in SDF attack, and 13.4% in DDoS attack.
Protective effect of Secukinumab on severe sepsis model rats by neutralizing IL-17A to inhibit IKBα/NFκB inflammatory signal pathway

Secukinumab is a specific neutralizing antibody for IL-17A. At present, numerous studies have confirmed the important role of IL-17A in sepsis, but the role of secukinumab in sepsis has not been studied. The present study explored the protective effect and underlying mechanism of secukinumab in severe sepsis model rats. We established a severe sepsis rat model using cecal ligation and puncture (CLP). The optimal dose of secukinumab was determined by observing the 7-day survival rate of severe sepsis model rats. The expression levels of TNF-α, IL-6, and IL-17A in plasma and lung tissue were determined by enzyme-linked immunosorbent assay. The degree of pathological damage to lung tissue was evaluated by hematoxylin-eosin (H-E) staining and pathological damage scale. The expressions of IKBα/NFκB pathway proteins and downstream-related inflammatory factors were detected by western blotting and real-time quantitative polymerase chain reaction (RT-qPCR). Our results show that high-dose secukinumab can inhibit the activation of the IKBα/NFκB inflammatory pathway by neutralizing IL-17A and reducing the gene expression of pathway-related inflammatory cytokines, thereby reducing the levels of inflammatory cytokines in lung tissue and plasma, thereby reducing the damage of lung tissue in severe sepsis model rats and improving the systemic inflammatory response.
CA-Net: Comprehensive Attention Convolutional Neural Networks for Explainable Medical Image Segmentation

Accurate medical image segmentation is essential for diagnosis and treatment planning of diseases. Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance for automatic medical image segmentation. However, they are still challenged by complicated conditions where the segmentation target has large variations of position, shape and scale, and existing CNNs have a poor explainability that limits their application to clinical decisions. In this work, we make extensive use of multiple attentions in a CNN architecture and propose a comprehensive attention-based CNN (CA-Net) for more accurate and explainable medical image segmentation that is aware of the most important spatial positions, channels and scales at the same time. In particular, we first propose a joint spatial attention module to make the network focus more on the foreground region. Then, a novel channel attention module is proposed to adaptively recalibrate channel-wise feature responses and highlight the most relevant feature channels. Also, we propose a scale attention module implicitly emphasizing the most salient feature maps among multiple scales so that the CNN is adaptive to the size of an object. Extensive experiments on skin lesion segmentation from ISIC 2018 and multi-class segmentation of fetal MRI found that our proposed CA-Net significantly improved the average segmentation Dice score from 87.77% to 92.08% for skin lesion, 84.79% to 87.08% for the placenta and 93.20% to 95.88% for the fetal brain respectively compared with U-Net. It reduced the model size to around 15 times smaller with close or even better accuracy compared with state-of-the-art DeepLabv3+. In addition, it has a much higher explainability than existing networks by visualizing the attention weight maps. Our code is available at https://github.com/HiLab-git/CA-Net.
A Transcriptomic Analysis of Higher-Order Ecological Interactions in a Eukaryotic Model Microbial Ecosystem

Nonlinear ecological interactions within microbial ecosystems and their contribution to ecosystem functioning remain largely unexplored. Higher-order interactions, or interactions in systems comprised of more than two members that cannot be explained by cumulative pairwise interactions, are particularly understudied, especially in eukaryotic microorganisms. The wine fermentation ecosystem presents an ideal model to study yeast ecosystem establishment and functioning. Some pairwise ecological interactions between wine yeast species have been characterized, but very little is known about how more complex, multispecies systems function. Here, we evaluated nonlinear ecosystem properties by determining the transcriptomic response of Saccharomyces cerevisiae to pairwise versus tri-species culture. The transcriptome revealed that genes expressed during pairwise coculture were enriched in the tri-species data set but also that just under half of the data set comprised unique genes attributed to a higher-order response. Through interactive protein-association network visualizations, a holistic cell-wide view of the gene expression data was generated, which highlighted known stress response and metabolic adaptation mechanisms which were specifically activated during tri-species growth. Further, extracellular metabolite data corroborated that the observed differences were a result of a biotic stress response. This provides exciting new evidence showing the presence of higher-order interactions within a model microbial ecosystem. IMPORTANCE Higher-order interactions are one of the major blind spots in our understanding of microbial ecosystems. These systems remain largely unpredictable and are characterized by nonlinear dynamics, in particular when the system is comprised of more than two entities. By evaluating the transcriptomic response of S. cerevisiae to an increase in culture complexity from a single species to two- and three-species systems, we were able to confirm the presence of a unique response in the more complex setting that could not be explained by the responses observed at the pairwise level. This is the first data set that provides molecular targets for further analysis to explain unpredictable ecosystem dynamics in yeast.
How can the UK statutory Cryptosporidium monitoring be used for Quantitative Risk Assessment of Cryptosporidium in drinking water?

Quantitative Microbiological Risk Assessment (QMRA) is increasingly being used to complement traditional verification of drinking water safety through the absence of indicator bacteria. However, the full benefit of QMRA is often not achieved because of a lack of appropriate data on the fate and behaviour of pathogens. In the UK statutory monitoring for Cryptosporidium has, provided a unique dataset of pathogens directly measured in large volumes of treated drinking water. Using this data a QMRA was performed to determine the benefits and limitations of such state-of-the-art monitoring for risk assessment. Estimates of the risk of infection at the 216 assessed treatment sites ranged from 10(-6.5) to 10(-2.5) person(-1) d(-1). In addition, Cryptosporidium monitoring data in source water was collected at eight treatment sites to determine how Cryptosporidium removal could be quantified for QMRA purposes. Cryptosporidium removal varied from 1.8 to 5.2 log units and appeared to be related to source water Cryptosporidium concentration. Application of general removal credits can either over- or underestimate Cryptosporidium removal by full-scale sedimentation and filtration. State-of-the-art pathogen monitoring can identify poorly performing systems, although it is ineffective to verify drinking water safety to the level of 10(-4) infections person(-1) yr(-1).
Dexmedetomidine attenuates renal ischemia-reperfusion injury through activating PI3K/Akt-eNOS signaling via α2 adrenoreceptors in renal microvascular endothelial cells

Renal microvascular endothelial cells (RMECs), which are closely related to regulation of vascular reactivity and modulation of inflammation, play a crucial role in the process of renal ischemia and reperfusion (I/R) injury. Previous studies have reported the protective effects of dexmedetomidine (DEX) against renal I/R injury, but little is known about the role of DEX on RMECs. This study aimed to investigate whether DEX alleviated renal I/R injury via acting on the RMECs. Mice underwent bilateral renal artery clamping for 45 min followed by reperfusion for 48 h, and the cultured neonatal mice RMECs were subjected to hypoxia for 1 h followed by reoxygenation (H/R) for 24 h. The results suggest that DEX alleviated renal I/R injury in vivo and improved cell viability of RMECs during H/R injury in vitro. Gene sequencing revealed that the PI3K/Akt was the top enriched signaling pathway and the endothelial cells were widely involved in renal I/R injury. DEX activated phosphorylation of PI3K and Akt, increased eNOS expression, and attenuated inflammatory responses. In addition, the results confirmed the distribution of α2 adrenoreceptor (α2 -AR) in RMECs. Furthermore, the protective effects of DEX against renal I/R injury were abolished by α2 -AR antagonist (atipamezole), which was partly reversed by the PI3K agonist (740 Y-P). These findings indicated that DEX protects against renal I/R injury by activating the PI3K/Akt-eNOS pathway and inhibiting inflammation responses via α2 -AR in RMECs.
Interior Design with Consumers' Perception about Art, Brand Image, and Sustainability

In this study, the main research purpose was to determine whether artistic components of interior design in a store lead consumers to have different perceptions of the store. There were three main research questions. The first was whether consumers perceived the artistic components in a store visually. Second, based on the first research question, this study explored whether the artistic displays at the show window, around the furniture, and around the stairs were associated with consumers' perceptions of the store as environmental-friendly. The third research question explored how the consumers' perceptions of artistic and environment-friendly components were associated with the conventional marketing performance of the store. The 2 Stages Probit Least Squares (2SPLS) method was utilized to answer the first and second research questions and the 2 Stage Least Squares (2SLS) method was utilized for the third research question. Findings indicated that consumers had significant emotional responses from seeing artistic components in a store. In addition, these perceived art elements were associated with marketing performances, including pro-environmental perception, store differentiation, brand image, and consumer satisfaction. The practical implications were included in the discussion.
Biochemical studies highlight determinants for metal selectivity in the Escherichia coli periplasmic solute binding protein NikA

Nickel is an essential micronutrient for the survival of many microbes. On account of the toxicity of nickel and its scarcity in the environment, microbes have evolved specific systems for uptaking and delivering nickel to enzymes. NikA, the solute binding protein for the ATP-binding cassette (ABC) importer NikABCDE, plays a vital role in the nickel homeostasis of Escherichia coli by selectively binding nickel over other metals in the metabolically complex periplasm. While the endogenous ligand for NikA is known to be the Ni(II)-(L-His)2 complex, the molecular basis by which NikA selectively binds Ni(II)-(L-His)2 is unclear, especially considering that NikA can bind multiple metal-based ligands with comparable affinity. Here we show that, regardless of its promiscuous binding activity, NikA preferentially interacts with Ni(II)-(L-His)2, even over other metal-amino acid ligands with an identical coordination geometry for the metal. Replacing both the Ni(II) and the L-His residues in Ni(II)-(L-His)2 compromises binding of the ligand to NikA, in part because these alterations affect the degree by which NikA closes around the ligand. Replacing H416, the only NikA residue that ligates the Ni(II), with other potential metal-coordinating amino acids decreases the binding affinity of NikA for Ni(II)-(L-His)2 and compromises uptake of Ni(II) into E. coli cells, likely due to altered metal selectivity of the NikA mutants. Together, the biochemical and in vivo studies presented here define key aspects of how NikA selects for Ni(II)-(L-His)2 over other metal complexes, and can be used as a reference for studies into the metal selectivity of other microbial solute binding proteins.
Does Public Participation Matter to Planning? Urban Sculpture Reception in the Context of Elite-Led Planning in Shanghai

Scholars have long debated how effective public participation is in urban planning. While most research was designed to assess the effect of public participation, the knowledge gap concerns whether urban planning would receive negative reception without public participation due to failure in managing people's emotions. One of the underlying reasons is that public participation is crucial to public emotion management. In this paper, we evaluate the impact of a case of public planning, and more specifically, the effects on public art reception when the planning project is developed by elites, without the involvement and participation of residents. Public art planning involves substantial symbolic and emotional components, and therefore constitutes a suitable case study. This research examines urban sculpture planning in Shanghai. The primary research method is a questionnaire survey, completed by 244 respondents. We argue that public participation is not the sole determinant of public art reception; other factors, particularly locality, also matter: an authoritarian-style urban sculpture planning creates a unanimous reverence and appreciation for flagship art projects on prominent public venues in central cities. However, people's feelings towards sculptures vary in neighborhoods; people are more likely to resist imposed artworks in the environment of their everyday life. Finally, we conclude that a lack of public participation does not always result in a negative reception to cultural projects on the part of the public; however, this lack of public participation is, nevertheless, culturally unsustainable.
A parallel distributed computing framework for Newton-Raphson load flow analysis of large interconnected power systems

This paper proposes a simple parallel and distributed computing framework for the conventional Newton-Raphson load flow (NRLF) solution of large interconnected power systems. The proposed approach is based on message-passing distributed-memory architecture with separate workstations, and involves the piecewise analysis of power systems utilizing the network tearing procedure. The NRLF solution method, applied to each torn system at the selected buses, employs the matrix inversion lemma consisting of the factorization, forward elimination and back substitution procedures. The computational requirements of the state-of-the art parallel algorithm to obtain the correction vector involved in the back substitution procedure is reduced with the proposed approach in which the back substitution is carried out in parallel taking into account the split buses, rather than the order in which the forward elimination is performed. The investigations are carried out on the IEEE 118 bus standard test system in a Redhat Linux based 100 Mbps Ethernet LAN environment. The investigations reveal that the proposed method is significantly faster than the conventional NRLF and also the NRLF based on the state-of-the-art parallel algorithm, and thus finds potential applications for the real-time load flow solution of both regulated and deregulated power systems distributed over large geographical areas. (C) 2015 Elsevier Ltd. All rights reserved.
Briefing: The Breakwaters and Coastal Structures conference: what progress in 30 years?

The Institution of Civil Engineers 'Breakwaters' conference has been run for more than 30 years. The proceedings for each conference have presented both the papers and their discussions. This short briefing reviews changes in the 'state of art' in designing and constructing breakwaters and coastal structures as revealed by this series of ten conferences. The briefing highlights a number of key papers or reports part have made me most contribution to knowledge.
Dynamic Online Trajectory Planning for a UAV-Enabled Data Collection System

Due to the maneuverability and flexibility of unmanned aerial vehicles (UAVs), the UAV-enabled data collection systems for wireless sensor networks (WSN) have received widespread attention. However, the state-of-the-art UAV trajectory designs mainly focus on static environments, which are not applicable in the practical scenarios considered in this work, e.g., mobile nodes, decommissioning of existing nodes, and new emergency nodes. This article proposes a two-level deep reinforcement learning (DRL) framework to solve this challenge. The first-level deep neural network (DNN) is applied to model the dynamic changing environment. In the second level, we employ a deep Q-learning network to plan a trajectory online according to the environment features from the first level DNN. Besides, online trajectory planning is performed by a low-power UAV edge computing platform. To enable online planning on the power-constraint UAV edge-computing platform, all networks adopt a lightweight low-complexity optimization design. According to simulation results, the proposed system achieves higher data acquisition success rates when compared to existing state-of-the-art methods. We also perform field tests on the proposed UAV edge computing platform, which also demonstrates high data acquisition performance.
RESEARCH ON THE INTEGRATION OF ECOLOGICAL CULTURE INTO COLLEGE MUSIC TEACHING

With the acceleration of the modernisation of human society, the natural ecology is constantly destroyed, which inevitably brings about the crisis of human spirit. Similarly, music art is losing its function of expressing nature and ecology. The sustainable development of ecological environment is of great significance to the sustainable development of music. The development of music art is inseparable from the objective existence of music function and people's deepening aesthetic understanding of it. Therefore, music aesthetic education must be based on the original concept, guided by ecological philosophy, and learn from the concept of ecological aesthetics to reconstruct the overall function of music aesthetic education. This paper focuses on how to innovate and develop music, how to spread the value of ecological culture, and how to lead the value of ecological culture. From the perspective of music workers, this paper explores the essential requirements of music aesthetic education and the construction and development of ecological culture, so as to explore the important significance of the integration of traditional ecological culture and music aesthetic education.
Deterministic programming of human pluripotent stem cells into microglia facilitates studying their role in health and disease

Microglia, the resident immune cells of the central nervous system (CNS), are derived from yolk-sac macrophages that populate the developing CNS during early embryonic development. Once established, the microglia population is self-maintained throughout life by local proliferation. As a scalable source of microglia-like cells (MGLs), we here present a forward programming protocol for their generation from human pluripotent stem cells (hPSCs). The transient overexpression of PU.1 and C/EBPβ in hPSCs led to a homogenous population of mature microglia within 16 d. MGLs met microglia characteristics on a morphological, transcriptional, and functional level. MGLs facilitated the investigation of a human tauopathy model in cortical neuron-microglia cocultures, revealing a secondary dystrophic microglia phenotype. Single-cell RNA sequencing of microglia integrated into hPSC-derived cortical brain organoids demonstrated a shift of microglia signatures toward a more-developmental in vivo-like phenotype, inducing intercellular interactions promoting neurogenesis and arborization. Taken together, our microglia forward programming platform represents a tool for both reductionist studies in monocultures and complex coculture systems, including 3D brain organoids for the study of cellular interactions in healthy or diseased environments.
Large-scale investigation of zoonotic viruses in the era of high-throughput sequencing

Zoonotic diseases considerably impact public health and socioeconomics. RNA viruses reportedly caused approximately 94% of zoonotic diseases documented from 1990 to 2010, emphasizing the importance of investigating RNA viruses in animals. Furthermore, it has been estimated that hundreds of thousands of animal viruses capable of infecting humans are yet to be discovered, warning against the inadequacy of our understanding of viral diversity. High-throughput sequencing (HTS) has enabled the identification of viral infections with relatively little bias. Viral searches using both symptomatic and asymptomatic animal samples by HTS have revealed hidden viral infections. This review introduces the history of viral searches using HTS, current analytical limitations, and future potentials. We primarily summarize recent research on large-scale investigations on viral infections reusing HTS data from public databases. Furthermore, considering the accumulation of uncultivated viruses, we discuss current studies and challenges for connecting viral sequences to their phenotypes using various approaches: performing data analysis, developing predictive modeling, or implementing high-throughput platforms of virological experiments. We believe that this article provides a future direction in large-scale investigations of potential zoonotic viruses using the HTS technology.