bluuebunny/crossref_metadata_2025 huggingface dataset:
Identification of infectious agents in early marine Chinook and Coho salmon associated with cohort survival
Authors: Bass Arthur L. , Bateman Andrew W. , Connors Brendan M. , Staton Benjamin A. , Rondeau Eric B. , Mordecai Gideon J. , Teffer Amy K. , Kaukinen Karia H. , Li Shaorong , Tabata Amy M. , Patterson David A. , Hinch Scott G. , Miller Kristina M.
Published: May 2022

Recent decades have seen an increased appreciation for the role infectious diseases can play in mass mortality events across a diversity of marine taxa. At the same time many Pacific salmon populations have declined in abundance as a result of reduced marine survival. However, few studies have explicitly considered the potential role pathogens could play in these declines. Using a multi-year dataset spanning 59 pathogen taxa in Chinook and Coho salmon sampled along the British Columbia coast, we carried out an exploratory analysis to quantify evidence for associations between pathogen prevalence and cohort survival and between pathogen load and body condition. While a variety of pathogens had moderate to strong negative correlations with body condition or survival for one host species in one season, we found that Tenacibaculum maritimum and Piscine orthoreovirus had consistently negative associations with body condition in both host species and seasons and were negatively associated with survival for Chinook salmon collected in the fall and winter. Our analyses, which offer the most comprehensive examination of associations between pathogen prevalence and Pacific salmon survival to date, suggest that pathogens in Pacific salmon warrant further attention, especially those whose distribution and abundance may be influenced by anthropogenic stressors.
The third law of thermodynamics, non-extensivity and energy definition in black hole physics
Authors: Moradpour H. , Ziaie A. H. , Lobo Iarley P. , Graça J. P. Morais , Sharma Umesh Kumar , Jahromi A. Sayahian
Published: May 2022

Working in the framework of generalized statistics, the problem of establishing third law of thermodynamics in the black hole physics is studied by focusing on Schwarzschild black hole which easily and clearly exposes the violation of this law in the common approach based on Bekenstein entropy. Additionally, it is addressed that some inconsistencies between the predictions of quantum field theory and thermodynamics about the black hole temperature may be reconciled by using the thermodynamics laws in order to broaden energy definition. It claims that thermodynamics should be employed as a powerful tool in looking for more comprehensive energy definitions in high-energy physics, still mysterious.
An Improved Cat Swarm Search-Based Deep Ensemble Learning Model for Group Recommender Systems
Authors: Roy Deepjyoti , Dutta Mala
Published: May 2022

Recommender systems are often employed in different fields such as music, travel, and movies. The recommender systems are broadly utilised nowadays due to the emergence of social activities, in which particular recommendations are offered by group recommender systems. It is a system for recommending the items to a set of users together based on their preferences. The user preferences are used from the behavioural and social aspects of group members to enhance the quality of products recommended in various groups for generating the group recommendations. These group recommender systems solve the cold start problem, which is raised in an individual recommendation system. The ultimate aim of this paper is to design and develop a new Improved Deep Ensemble Learning Model (ID-ELM) for the group recommender systems concerning different application-oriented datasets. Initially, the datasets from different applications like healthcare, e-commerce, and e-learning are gathered from benchmark sources and split the data into various groups. These data are given to the pre-processing for making it fit for further processing. The pre-processing steps like stop word removal, stemming, and punctuation removal are performed here. Then the features are extracted using the Continuous Bag of Words Model (CBOW), and Principal Component Analysis (PCA) is used for dimension reduction. These features are fed to the ID-ELM, in which the optimised Convolutional Neural Network (CNN) extracts the significant features from the pooling layer, and the fully connected layer is replaced by a set of classifiers termed Neural Networks (NN), AdaBoost, and Logistic Regression (LR). Finally, the ranking of the ensemble learning model based on the group reviews extends the recommendation outcome. The optimised CNN is proposed by the Adaptive Seeking Range-based Cat Swarm Optimisation (ASR-CSO) for attaining better results. This model is validated on the benchmark datasets to show the efficiency of the designed model with different meta-heuristic-based algorithms and classification algorithms.
A Digital Platform to Facilitate the Resilience of Rural Territories
Authors: Martinez-Gil Jorge , Pichler Mario , Lentini Gianluca , Mazzeschi Valerio , Doukhan Guillaume , Belet Claire
Published: May 2022

This work shows how we have designed and implemented a digital platform for Smart Villages, which can serve as a knowledge management and decision support system for rural stakeholders, including planners, administrative staff, and decision-makers. Our platform is intended to help pilot a smooth transition into a sustainable administration, help to boost rural services through digital innovation, and, in general, reduce the gap concerning the city’s world. Although the platform has arisen around a community belonging to the European Alpine Space, the lessons learned can be easily transferred to other scenarios in which determining and improving the degree of smartness maturity of the rural communities is a critical challenge that can facilitate undertaking public or private investments. Our goal is to provide the necessary tools for collaboration and sharing experiences and data collection and analysis to enable the best possible informed decision-making concerning the promotion of smarter villages.
The Paradox of Knowledge Networks: Why More Knowledge Does Not Always Make You More Successful
Authors: Smatt Cindi T. , Pratt Renée M. E. , Wasko Molly
Published: May 2022

The purpose of this research was to further the understanding of knowledge exchange within organisations by examining how the dyadic relationships between individuals, in terms of the channels of communication used (structural capital), knowledge awareness (cognitive capital), and the quality of their relationships (relational capital), influence opportunities for knowledge exchange (access to advice), and ultimately individual performance. data were analysed using social network analysis to determine individual network centralities, and structural equation modelling was used to test the hypotheses at the individual level. The findings suggest (1) face-to-face channels with trusted sources are the most preferred method for exchanging sensitive knowledge, (2) knowing where expertise resides and source availability is key to research knowledge exchange, and (3) centrality in knowledge network does not result in uniform increases in individual performance. While technology has the potential to increase the efficiency of knowledge exchange by removing the barriers to same-time, same-place interactions, computer-mediated communication may actually inhibit the exchange of tacit knowledge and advice because of the lean medium of the exchange, negatively impacting performance. Using a network perspective, this study adds to the literature on intra-organisational learning networks by examining how an individual’s use of different communication channels to share knowledge is related to centrality in knowledge networks, and how this impacts individual performance.
A Hybrid NDN-IP Architecture for Live Video Streaming: From Host-Based to Content-Based Delivery to Improve QoE
Authors: Dasgupta Ishita , Shannigrahi Susmit , Zink Michael
Published: May 2022

With live video streaming becoming accessible in various applications on all client platforms, it is imperative to create a seamless and efficient distribution system that is flexible enough to choose from multiple Internet architectures best suited for video streaming (live, on-demand, AR). In this paper, we highlight the benefits of such a hybrid system for live video streaming as well as present a detailed analysis with the goal to provide a high quality of experience (QoE) for the viewer. For our hybrid architecture, video streaming is supported simultaneously over TCP/IP and Named Data Networking (NDN)-based architecture via operating system and networking virtualization techniques to design a flexible system that utilizes the benefits of these varying Internet architectures. Also, to relieve users from the burden of installing a new protocol stack (in the case of NDN) on their devices, we developed a lightweight solution in the form of a container that includes the network stack as well as the streaming application. At the client, the required Internet architecture (TCP/IP versus NDN) can be selected in a transparent and adaptive manner. Based on a prototype, we have designed and implemented maintaining efficient use of network resources, we demonstrate that in the case of live streaming, NDN achieves better QoE per client than IP and can also utilize higher than allocated bandwidth through in-network caching. Even without caching, as opposed to IP-only, our hybrid setup achieves better average bitrate and better perceived visual quality (computed via VMAF metric) over live video streaming services. Furthermore, we present detailed analysis on ways adaptive video streaming with NDN can be further improved with respect to QoE.
Ranking Micro-Influencers: A Multimedia Framework with Multi-Task and Interpretable Architectures
Authors: Elwood Adam , Gasparin Alberto , Rozza Alessandro
Published: May 2022

With the rise in use of social media to promote branded products, the demand for effective influencer marketing has increased. Brands are looking for improved ways to identify valuable influencers among a vast catalogue; this is even more challenging with micro-influencers, which are more affordable than mainstream ones but difficult to discover. In this paper, we propose a novel multi-task learning framework to improve the state of the art in micro-influencer ranking based on multimedia content. Moreover, since the visual congruence between a brand and influencer has been shown to be a good measure of compatibility, we provide an effective visual method for interpreting our model’s decisions, which can also be used to inform brands’ media strategies. We compare with the current state of the art on a recently constructed public dataset and we show significant improvement both in terms of accuracy and model complexity. We also introduce a methodology for tuning the image and text contribution to the final ranking score. The techniques for ranking and interpretation presented in this work can be generalized to arbitrary multimedia ranking tasks that have datasets with a similar structure.
A moving Kriging meshfree approach for free vibration and buckling analyses of porous metal foam plates
Authors: Hung P. T. , Thai Chien H. , Phung-Van P.
Published: May 2022

In this paper, the free vibration and mechanical buckling analyses of the metal foam plates with porosities via a moving Kriging meshfree method based on the higher-order shear deformation theory are studied. The displacement fields are approximated by the moving Kriging shape functions, which satisfy Kronecker’s delta property. Thanks to that property, the essential boundary conditions can be directly imposed the same with finite element method. In this study, the porosities are distributed by uniform, symmetric, and asymmetric distributions along the thickness direction. The natural frequency and critical buckling load of the metal foam plate are determined by solving the explicit governing equations, which are derived through the variational formulation. The influence of the porosity distribution, porous coefficient, width-to-thickness ratio and boundary condition on the natural frequency and critical buckling load of the porous metal foam plate is investigated and discussed in detail. Numerical examples indicate that the present approach is stable and well accurate predictions for investigating vibration and buckling behaviors of the porous metal foam plate. Moreover, an increase of the porous coefficient makes a decline of the natural frequencies and critical buckling loads of the plate.
Quantum Entanglement Effects in Biomolecules
Authors: Sharma Preet
Published: May 2022

Biomolecules exhibit very complex dynamical properties and are constantly exchanging energy. These systems also behave very much like non-equilibrium systems. For example, there exist systems like proteins and their dynamics, cancer tumor progression, biophotonics and many more. These principles can also be used to understand the information processing in the DNA. There have been various studies which clearly indicate that classical physics is not enough to explain these systems. The two fundamental aspects of physics which can be applied to all systems are Quantum Mechanics and Electrodynamics. Every process or interaction has inbuilt in it these two fundamental properties.

In general, the field of biomolecules is studied as a macroscopic phenomenon and the focus is mostly on the results which we detect or measure in laboratory experiments.

Quantum physics concepts have been an extremely interesting tool to see the deeper aspects of such complex systems. There has always been an interest in complex systems from the Quantum Mechanics point of view. Early researchers such as Erwin Schrödinger was also said to have an interest in the quantum aspects of life. Quantum Mechanics deals with microscopic systems at the fundamental level and gives an insight into the phenomena from the lens of the dynamics of the process. Since the early days of the formulation of Quantum Mechanics, it has expressed itself as a robust theory which can describe systems such as molecular physics and chemistry, atomic physics and even systems such as proteins. This conjunction of biomolecules and quantum physics has gained immense interest recently.

In this research, we make an attempt to describe the dynamics of biomolecules from two aspects, namely:

(1) Fermi’s Golden Rule; (2) Quantum Entanglement in Biomolecules.

In this study, we will present a new idea based on an extension in Fermi’s Golden Rule and how Quantum Entanglement plays a part in explaining Fermi’s Golden Rule further. Further, we will explain how these concepts can be applied to specific complex systems in biology.
Entropic Analysis of Protein Aggregation using Langevin Equations and Fokker–Planck Equations
Authors: Cook Leslie , Sharma Preet
Published: May 2022

Protein aggregation is a sophisticated biological mechanism that can have detrimental consequences. It is recognized as the hallmark of neurodegenerative diseases, suffered by millions of people each year reported by World Health Organization, [Formula: see text]. Abnormal deposits of amyloid fibrils and/or oligomers accumulate in and around neurons causing irreparable damage that leads to severe deterioration of the surrounding brain tissue and cognitive function. As of now, early detection, therapeutic intervention and treatment options are extremely limited. Protein aggregation is known to be highly dynamic, irreversible process which is source of its difficulty to fully understand and remedy the problem. The design of our study is to interpret the mechanics of intrinsically disordered proteins that self-assemble into highly structured fibrils. The aim is to gain a deeper understanding of protein–protein interactions, environmental conditions and chaperone failure that attribute to the aggregation process. The complexity of the aggregation process cannot be modeled using statistical physics and statistical thermodynamics of equilibrium processes. There are numerous studies that suggest protein aggregation which is a non-equilibrium process. Based on non-equilibrium physics, one of the best ways to understand it is through the Langevin and Fokker–Planck equations. Langevin equations describe stochastic dynamics of non-equilibrium processes. The Fokker–Planck equation is used to calculate the probability distribution and explain the trend in entropy of a model independent protein aggregation process.
Studying the Logistic Model
Authors: Baxley Jacob , Lambert David , Grigolini Paolo
Published: May 2022

Several studies have used the logistic equation to model the growth of cancer cell populations 1 as seen in Eq. ( 1 ). This has included correlated multiplicative, [Formula: see text] and additive, [Formula: see text], noise terms. These noise terms can affect the growth rate, [Formula: see text], and death rate, [Formula: see text], of tumor cells and can be induced from factors such as radiotherapy or other cancer treatments. Depending on the intensity of the noise the terms, the fluctuations can induce a phase transition. Noise-induced transitions of nonlinear stochastic systems have applications in the fields of physics, chemistry and biology. [Formula: see text] We study the logistic differential equation with a multiplicative noise term before and at phase transition. Computational methods used to investigate this cancer cell model include a Diffusion Entropy Analysis method and a waiting time distribution method. 2 , 3 , 4 DEA will establish the scaling of a simulated series without altering the data through detrending. We hypothesize the treatment that causes a phase transition in the logistic model will induce tumor extinction and management. Understanding how to better evaluate and study cancer cell growth models will assist in assessing the efficacy of cancer treatments. Future work will include running simulations with a modified DEA method that includes the use of stripes. 2 For better statistics, the code will be adopted to run ensembles of simulated data instead of a single series. Generating and analyzing these large datasets can be computationally expensive. Through multiprocessing and the use of a supercomputer, we believe these computational limitations can be overcome.
From Social to Epidemic Criticality and Back
Authors: Lambert David , Mahmoodi Korosh , Piccinini Nicola , Grigolini Paolo
Published: May 2022

In this paper, we study the spreading of epidemic in a network of individuals who may either contract a disease through contact with infected nearest neighbors or be vaccinated under the influence of neighbors who are already vaccinated. We show that both interaction between susceptible S and infected individuals I and the imitation of vaccination, a form of sociological interaction between susceptible S and vaccinated V individuals, may lead to a phase transition. If the spreading of epidemic is in the supercritical condition, corresponding to an unlimited growth of infection, the interaction between S and V must reach the supercritical condition to generate control of the spreading of infection, and bring the system to criticality. By adopting a theoretical perspective like that of multilayer complex networks, we study the case where the epidemiological network is under the influence of a sociological debate on whether to be vaccinated. We show that at criticality this debate generates clusters of individuals in favor of vaccination and clusters of individuals opposing it. 3 , 4 We study the influence of this debate on the spread of infection. We show that because of this debate in the epidemic network, a pattern emerges mirroring the structures of the sociological network. Finally, we introduce feedback of the epidemic network on the sociological network, and we prove that because of this feedback the sociological system undergoes a process of self-organization maintaining it at criticality. This system exhibits temporal complexity and critical slowing down. 1 We hope that these results may have an important effect of giving interesting suggestions to behavioral psychologists and information scientists actively involved in the analysis of the social debate on the moral issues connected to sexual activities.
Accessibility in Software Practice: A Practitioner’s Perspective
Authors: Bi Tingting , Xia Xin , Lo David , Grundy John , Zimmermann Thomas , Ford Denae
Published: May 2022

Being able to access software in daily life is vital for everyone, and thus accessibility is a fundamental challenge for software development. However, given the number of accessibility issues reported by many users, e.g., in app reviews, it is not clear if accessibility is widely integrated into current software projects and how software projects address accessibility issues. In this article, we report a study of the critical challenges and benefits of incorporating accessibility into software development and design. We applied a mixed qualitative and quantitative approach for gathering data from 15 interviews and 365 survey respondents from 26 countries across five continents to understand how practitioners perceive accessibility development and design in practice. We got 44 statements grouped into eight topics on accessibility from practitioners’ viewpoints and different software development stages. Our statistical analysis reveals substantial gaps between groups, e.g., practitioners have Direct vs. Indirect accessibility relevant work experience when they reviewed the summarized statements. These gaps might hinder the quality of accessibility development and design, and we use our findings to establish a set of guidelines to help practitioners be aware of accessibility challenges and benefit factors. We suggest development teams put accessibility as a first-class consideration throughout the software development process, and we also propose some remedies to resolve the gaps between groups and to highlight key future research directions to incorporate accessibility into software design and development.
PyMatching: A Python Package for Decoding Quantum Codes with Minimum-Weight Perfect Matching
Authors: Higgott Oscar
Published: May 2022

This article introduces PyMatching, a fast open-source Python package for decoding quantum error-correcting codes with the minimum-weight perfect matching (MWPM) algorithm. PyMatching includes the standard MWPM decoder as well as a variant, which we call
local matching
, that restricts each syndrome defect to be matched to another defect within a local neighborhood. The decoding performance of local matching is almost identical to that of the standard MWPM decoder in practice, while reducing the computational complexity. We benchmark the performance of PyMatching, showing that local matching is several orders of magnitude faster than implementations of the full MWPM algorithm using NetworkX or Blossom V for problem sizes typically considered in error correction simulations. PyMatching and its dependencies are open-source, and it can be used to decode any quantum code for which syndrome defects come in pairs using a simple Python interface. PyMatching supports the use of weighted edges, hook errors, boundaries and measurement errors, enabling fast decoding, and simulation of fault-tolerant quantum computing.
Privacy-Preserving Decision Trees Training and Prediction
Authors: Akavia Adi , Leibovich Max , Resheff Yehezkel S. , Ron Roey , Shahar Moni , Vald Margarita
Published: May 2022

In the era of cloud computing and machine learning, data has become a highly valuable resource. Recent history has shown that the benefits brought forth by this data driven culture come at a cost of potential data leakage. Such breaches have a devastating impact on individuals and industry, and lead the community to seek privacy preserving solutions. A promising approach is to utilize

Fully Homomorphic Encryption (

\( \mathsf {FHE } \)

)

to enable machine learning over encrypted data, thus providing resiliency against information leakage. However, computing over encrypted data incurs a high computational overhead, thus requiring the redesign of algorithms, in an “

\( \mathsf {FHE } \)

-friendly” manner, to maintain their practicality.

In this work we focus on the ever-popular tree based methods, and propose a new privacy-preserving solution to training and prediction for trees over data encrypted with homomorphic encryption. Our solution employs a low-degree approximation for the step-function together with a lightweight interactive protocol, to replace components of the vanilla algorithm that are costly over encrypted data. Our protocols for decision trees achieve practical usability demonstrated on standard UCI datasets encrypted with fully homomorphic encryption. In addition, the communication complexity of our protocols is independent of the tree size and dataset size in prediction and training, respectively, which significantly improves on prior works.

1
Generating Quality Threat Intelligence Leveraging OSINT and a Cyber Threat Unified Taxonomy
Authors: Martins Cláudio , Medeiros Ibéria
Published: May 2022

Today’s threats use multiple means of propagation, such as social engineering, email, and application vulnerabilities, and often operate in different phases, such as single device compromise, lateral network movement, and data exfiltration. These complex threats rely on advanced persistent threats supported by well-advanced tactics for appearing unknown to traditional security defenses. As organizations realize that attacks are increasing in size and complexity, cyber threat intelligence (TI) is growing in popularity and use. This trend followed the evolution of advanced persistent threats, as they require a different level of response that is more specific to the organization. TI can be obtained via many formats, with open-source intelligence one of the most common, and using threat intelligence platforms (TIPs) that aid organizations to consume, produce, and share TI. TIPs have multiple advantages that enable organizations to quickly bootstrap the core processes of collecting, analyzing, and sharing threat-related information. However, current TIPs have some limitations that prevent their mass adoption. This article proposes AECCP, a platform that addresses some of the TIPs limitations. AECCP improves quality TI by classifying it accordingly a
single unified taxonomy
, removing the information with low value, enriching it with valuable information from open-source intelligence sources, and aggregating it for complementing information associated with the same threat. AECCP was validated and evaluated with three datasets of events and compared with two other platforms, showing that it can generate quality TI automatically and help security analysts analyze security incidents in less time.
Deep Learning for Time Series Forecasting: Tutorial and Literature Survey
Authors: Benidis Konstantinos , Rangapuram Syama Sundar , Flunkert Valentin , Wang Yuyang , Maddix Danielle , Turkmen Caner , Gasthaus Jan , Bohlke-Schneider Michael , Salinas David , Stella Lorenzo , Aubet François-Xavier , Callot Laurent , Januschowski Tim
Published: May 2022

Deep learning based forecasting methods have become the methods of choice in many applications of time series prediction or
forecasting
often outperforming other approaches. Consequently, over the last years, these methods are now ubiquitous in large-scale industrial forecasting applications and have consistently ranked among the best entries in forecasting competitions (e.g., M4 and M5). This practical success has further increased the academic interest to understand and improve deep forecasting methods. In this article we provide an introduction and overview of the field: We present important building blocks for deep forecasting in some depth; using these building blocks, we then survey the breadth of the recent deep forecasting literature.
A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part I: Models and Data Transformations
Authors: Kleyko Denis , Rachkovskij Dmitri A. , Osipov Evgeny , Rahimi Abbas
Published: May 2022

This two-part comprehensive survey is devoted to a computing framework most commonly known under the names Hyperdimensional Computing and Vector Symbolic Architectures (HDC/VSA). Both names refer to a family of computational models that use high-dimensional distributed representations and rely on the algebraic properties of their key operations to incorporate the advantages of structured symbolic representations and distributed vector representations. Notable models in the HDC/VSA family are Tensor Product Representations, Holographic Reduced Representations, Multiply-Add-Permute, Binary Spatter Codes, and Sparse Binary Distributed Representations but there are other models too. HDC/VSA is a highly interdisciplinary field with connections to computer science, electrical engineering, artificial intelligence, mathematics, and cognitive science. This fact makes it challenging to create a thorough overview of the field. However, due to a surge of new researchers joining the field in recent years, the necessity for a comprehensive survey of the field has become extremely important. Therefore, amongst other aspects of the field, this Part I surveys important aspects such as: known computational models of HDC/VSA and transformations of various input data types to high-dimensional distributed representations. Part II of this survey [
84
] is devoted to applications, cognitive computing and architectures, as well as directions for future work. The survey is written to be useful for both newcomers and practitioners.
Reprogramming of Hepatic Metabolism and Microenvironment in Nonalcoholic Steatohepatitis
Authors: Rui Liangyou , Lin Jiandie D.
Published: May 2022

Nonalcoholic fatty liver disease (NAFLD), a spectrum of metabolic liver disease associated with obesity, ranges from relatively benign hepatic steatosis to nonalcoholic steatohepatitis (NASH). The latter is characterized by persistent liver injury, inflammation, and liver fibrosis, which collectively increase the risk for end-stage liver diseases such as cirrhosis and hepatocellular carcinoma. Recent work has shed new light on the pathophysiology of NAFLD/NASH, particularly the role of genetic, epigenetic, and dietary factors and metabolic dysfunctions in other tissues in driving excess hepatic fat accumulation and liver injury. In parallel, single-cell RNA sequencing studies have revealed unprecedented details of the molecular nature of liver cell heterogeneity, intrahepatic cross talk, and disease-associated reprogramming of the liver immune and stromal vascular microenvironment. This review covers the recent advances in these areas, the emerging concepts of NASH pathogenesis, and potential new therapeutic opportunities.
The Life Cycle of the Vaccinia Virus Genome
Authors: Greseth Matthew D. , Traktman Paula
Published: May 2022

Poxviruses, of which vaccinia virus is the prototype, are a large family of double-stranded DNA viruses that replicate exclusively in the cytoplasm of infected cells. This physical and genetic autonomy from the host cell nucleus necessitates that these viruses encode most, if not all, of the proteins required for replication in the cytoplasm. In this review, we follow the life of the viral genome through space and time to address some of the unique challenges that arise from replicating a 195-kb DNA genome in the cytoplasm. We focus on how the genome is released from the incoming virion and deposited into the cytoplasm; how the endoplasmic reticulum is reorganized to form a replication factory, thereby compartmentalizing and helping to protect the replicating genome from immune sensors; how the cellular milieu is tailored to support high-fidelity replication of the genome; and finally, how newly synthesized genomes are faithfully and specifically encapsidated into new virions.
The effect of medium-term recovery status after COVID-19 illness on cardiopulmonary exercise capacity in a physically active adult population
Authors: Ladlow Peter , O’Sullivan Oliver , Bennett Alexander N. , Barker-Davies Robert , Houston Andrew , Chamley Rebecca , May Samantha , Mills Daniel , Dewson Dominic , Rogers-Smith Kasha , Ward Christopher , Taylor John , Mulae Joseph , Naylor Jon , Nicol Edward D. , Holdsworth David A.
Published: May 2022

At 5 mo postinfection, community-treated individuals who feel recovered have comparable cardiopulmonary exercise profiles to the physically trained and active controls, suggesting a readiness to return to higher intensity/volumes of exercise. However, both symptomatic groups and the hospital-recovered group have persistent functional limitations when compared with active controls, supporting the requirement for ongoing monitoring, rehabilitation, and recovery.
Blood flow restriction and stimulated muscle contractions do not improve metabolic or vascular outcomes following glucose ingestion in young, active individuals
Authors: Cohen Jeremy N. , Kuikman Megan A. , Politis-Barber Valerie , Stairs Brienne E. , Coates Alexandra M. , Millar Philip J. , Burr Jamie F.
Published: May 2022

Glucose ingestion challenges glycemic and vascular function. Exercise effectively counteracts these impairments, but is not always feasible. Blood flow restriction (BFR) and electric muscle stimulation (EMS) passively generate muscle hypoxia and contractions mimicking aspects of exercise. We tested BFR, EMS, and BFR + EMS in young, active participants post-glucose. No significant primary glycemic or vascular outcomes are observed. Cyclic BFR increased leg blood flow while BFR + EMS activated greater carbohydrate oxidation and lowered muscle oxygenation warranting future consideration.
Xanthine oxidoreductase activity in marathon runners: potential implications for marathon-induced acute kidney injury
Authors: Kosaki Keisei , Kumamoto Shota , Tokinoya Katsuyuki , Yoshida Yasuko , Sugaya Takeshi , Murase Takayo , Akari Seigo , Nakamura Takashi , Nabekura Yoshiharu , Takekoshi Kazuhiro , Maeda Seiji
Published: May 2022

This study is the first to show marathon running transiently increases plasma XOR activity and levels of purine degradation products (hypoxanthine, xanthine, and uric acid), and further to demonstrate that activated plasma XOR may contribute to marathon-induced elevations in biomarkers of AKI. These findings significantly extend our prior knowledge of the purine metabolic pathway and several AKI biomarkers under strenuous exercise conditions.
Correlation between Intestinal Microflora in Irritable Bowel Syndrome and Severity
Authors: Ji Min , Huang Hua , Lan Xianming
Published: May 2022

Background. Irritable bowel syndrome (IBS) is a common chronic functional gastrointestinal disease accompanied by changes in intestinal microecology. This study investigated the relationship between gut microbiota and disease severity in patients with irritable bowel syndrome (IBS). Methods. An observational study was performed on 60 IBS patients (study group) and 20 healthy controls admitted to our hospital from January 2013 to December 2014. Fecal samples were taken after admission to measure intestinal flora including Bifidobacterium, Lactobacillus, Enterobacter, and Enterococcus, and patient blood was collected to determine serum D-lactate and diamine oxidase (DAO) levels. The gut microbiota and serum markers of the two groups were analyzed. The correlation of gut microbiota index levels and serum markers with disease severity, as well as the correlation between gut microbiota index levels and serum markers, were analyzed. Results. The levels of intestinal Lactobacillus and Bifidobacterium were lower, while the levels of Enterococcus and Enterobacter and serum D-lactate were higher in the study group than those in the control group. The levels of intestinal Lactobacillus and Bifidobacterium were lower, while the levels of Enterococcus and Enterobacter, serum D-lactate, and DAO were higher in patients with moderate IBS than those in patients with mild IBS. The levels of intestinal Lactobacillus and Bifidobacterium were lower in patients with severe IBS than those with moderate IBS, while the levels of Enterococcus and Enterobacter, serum D-lactate, and DAO were higher in patients with severe IBS. There was a significant negative correlation between the levels of Lactobacillus and Bifidobacterium and disease severity and a significant positive correlation between the levels of Enterococcus and Enterobacter, D-lactate, and DAO and disease severity. There was a significant negative correlation between the levels of Lactobacillus and Bifidobacterium and serum D-lactate and DAO, while there was a significant positive correlation between the levels of Enterococcus and Enterobacter and serum D-lactate and DAO (

P
<
0.05

). Conclusion. Intestinal flora, D-lactate, and DAO were abnormal in IBS patients, and intestinal flora was closely correlated with disease severity, D-lactate, and DAO levels.
Clinical Comparative Study of Intravitreal Injection of Triamcinolone Acetonide and Aflibercept in the Treatment of Diabetic Retinopathy Cystoid Macular Edema
Authors: Zhu Yanxia , Li Jun , Yu Songping , Mao Bangxun , Ying Jia
Published: May 2022

Objective To compare the curative effect of intravitreal injection of triamcinolone acetonide and aflibercept on diabetic retinopathy (DR) cystoid macular edema. Methods A total of 102 patients with DR cystoid macular edema admitted to the hospital were enrolled between July 2018 and July 2021. According to random number table method, they were divided into the control group (intravitreal injection of triamcinolone acetonide) and the observation group (intravitreal injection of aflibercept), 51 cases in each group. All were followed up for half a year. The clinical curative effect, visual acuity, central subfield macular thickness (CSMT), macular volume, scores of quality of life, and levels of cytokines in aqueous humor (vascular endothelial growth factor (VEGF), monocyte chemoattractant protein-1 (MCP-1), human angiopoietin-like protein 4 (ANGPTL4)] at different time points (before and at 6 months after surgery) were compared between the two groups. The times of drugs injection and occurrence of adverse reactions in both groups were statistically analyzed. Results The total effective rate in observation group was higher than that in the control group (96.08% vs 82.35%) (

P
<
0.05

). After 6 months of treatment, visual acuity was improved, and CSMT and macular volume were decreased in both groups. Also, the above changes were more significant in the observation group than those in the control group (

P
<
0.05

). After 6 months of treatment, levels of cytokines in aqueous humor were decreased in both groups. The levels of VEGF, MCP-1, and ANGPTL4 in observation group were lower than those in the control group (

P
<
0.05

). After 6 months of treatment, quality of life scores in observation group were higher than those in the control group (

P
<
0.05

). In the follow-up period, average times of drugs injection in the observation group were more than those in the control group, and the incidence of adverse reactions was lower than that in control group (5.88% vs 21.57%) (

P
<
0.05

). Conclusion The curative effect of intravitreal injection of both triamcinolone acetonide and aflibercept is good on DR cystoid macular edema. The curative effect of aflibercept is better, which can improve visual acuity and quality of life, and regulate cytokines in aqueous humor, with high safety. However, aflibercept has a high price, and further research is needed to determine whether its price can be matched with clinical benefits. In clinic, medication plan should be selected according to the actual situation.
Performance Analysis of Soluble Bridge Plug Materials for Fracturing in Unconventional Oilfields
Authors: Wang Jianping , Jia Hongjuan , You Jiachun
Published: May 2022

With the continuous in-depth development of oil and gas resources, unconventional oil and gas resources show great potential. In recent years, 70% of the proven oil and gas resources in China are unconventional oil and gas resources. Therefore, the development of unconventional oil and gas resources has a huge impact on the increase in the production capacity of the oil and gas resources in China. Staged fracturing of horizontal wells is the main technical measure to improve unconventional oil and gas production stimulation. In the fracturing process, the soluble bridge plug technology can not only effectively solve the problems of large investment in drilling and grinding of composite bridge plugs, high risks, self-locking of oil, casing deformation, etc., causing the bridge plug to be unable to be drilled and remove and affect subsequent production but also can successfully make up for the problems of subsequent production. The large-diameter bridge plug cannot achieve the full diameter of the wellbore and cannot carry out operations such as postproduction logging and refracturing, which can ensure the integrity of the wellbore to the greatest extent. In this paper, HNBR was used to modify MPU to prepare HNBR/MPU soluble rubber, and the effect of blending ratio on the vulcanization characteristics, mechanical properties, compatibility, thermal stability, and degradation behavior of composites was studied. The results showed that with the increase in the amount of HNBR, the degree of cross-linking of the composite material decreased gradually, and the mechanical properties decreased slightly. With the increase in the amount of HNBR, the thermal stability of the material decreases. The use of HNBR greatly reduced the degradation rate of MPU. When the amount of HNBR was changed from 20 phr to 40 phr, the tensile strength retention rate of the composite after soaking increased from 14.73% to 39.72%.
Karst Collapse Monitoring and Early Warning Evaluation Method Based on Multisensor Internet of Things
Authors: Zhang Xin , Deng Longfu , Li Na
Published: May 2022

The international community has paid extensive attention to the numerous engineering problems faced by karst areas caused by the increasingly frequent human activities. China has a wide variety of karst forms. Among them, carbonate karst is the most widely distributed, and the development of carbonate karst is relatively strong in many areas. Countless property losses are caused by karst disasters every year. This article aims to study the real-time monitoring and timely early warning of karst collapse through the use of multisensor Internet of Things technology. To this end, this article proposes an improved method for multisensor data fusion. It optimizes and improves the transmission and delivery efficiency of its data. This makes the improved multisensor more in line with the research content of this article in terms of monitoring efficiency. At the same time, related experiments and analyses are designed to compare and analyze the karst collapse and the monitoring efficiency of the sensor. The experimental results of this article show that after the improvement, the anti-interference ability of the monitoring system is increased by 34%. The frequency of early warning has also been improved by 24%, which has high practical application value.
Balance Analysis of Peripheral Neuropathy in Type 2 Diabetes Mellitus Based on Logistic Regression Equation
Authors: Zhang Lixin , Du Qianqian , Yao Manman , Wang Mai , Ge Bing
Published: May 2022

This paper analyzes the factors of peripheral neuropathy in type 2 diabetes mellitus and puts forward a balanced analysis of peripheral neuropathy in type 2 diabetes mellitus based on logistic regression equation. A total of 1192 eligible patients were selected as the study subjects. All selected patients underwent 75 g oral glucose tolerance test to measure fasting blood glucose and insulin and 2-hour postprandial blood glucose and 2-hour postprandial insulin, as well as neuroelectrophysiological examination. The results showed that the OR values of age, course of disease, fingertip blood glucose immediately after admission, and 2-hour blood glucose were greater than 1, and the

P

values were all less than 0.05, which were the risk factors of diabetic peripheral neuropathy. OR value of β cell function index (HBCI) is less than 1.

P

is less than 0.05, and it is a protective factor of diabetic peripheral neuropathy. Laboratory indicators are as follows: 75 g OGTT: 0-hour blood glucose, 2-hour blood glucose, and glycosylated hemoglobin; serum creatinine; glutamate transaminase; fibrinogen; ten items of hemoglobin; and indexes reflecting islet function: islet β is thin, and there are significant differences in cell function index, insulin resistance index, and insulin secretion index between the non-DPN group and DPN group. Age, course of disease, fingertip blood glucose immediately after admission, and blood glucose within 2 hours after admission were the risk factors for diabetic peripheral neuropathy. Islet β cell function index (HBCI) is a protective factor of diabetic peripheral neuropathy.
[Retracted] Clinical Effect of Nicorandil Combined with Aspirin in the Treatment of Myocardial Ischemia
Authors: Li Yue , Zhao Chen , Xiong Chengxin , Gao Yuping
Published: May 2022

Objective. To investigate the clinical effect of nicorandil combined with aspirin in the treatment of myocardial ischemia. Methods. A total of 104 patients with myocardial ischemia were admitted to our hospital from June 2019 to August 2020. These patients were selected as the research objects and randomly divided into two groups: the control group and the observation group. The control group was given asilin, and the observation group was given nicorandil tablets based on the control group. Both groups were given continuous treatment for 3 months. The curative effect, cardiac function indexes, dynamic electrocardiogram, and the occurrence of adverse reactions were observed in the two groups. Results. The total effective rate of the observation group was 96.15% (50/52), which was higher than that of the control group (61.54%, 32/52), and the difference was statistically significant (P < 0.05). After treatment, left ventricular ejection fraction (LVEF) and peak early/late diastolic flow velocity (E/A) were increased (P < 0.05), while peak early diastolic flow velocity to peak mitral annular root movement velocity (E/Ea) was decreased (P < 0.05). After treatment, LVEF and E/A in the observation group were higher than those in the control group, while E/Ea was lower than that in the control group (P < 0.05). The frequency, duration of ST segment, and a total load of myocardial ischemia in the ST segment within 24 h after treatment were decreased compared with those before treatment (P < 0.05). The frequency and duration of ST segment decreased, and the total load of myocardial ischemia in the observation group was lower than those in the control group within 24 h after treatment (P < 0.05). After treatment, the total occurrence of adverse reactions in the observation group was lower than that in the control group (P < 0.05). Conclusion. Nicorandil combined with aspirin in the treatment of patients with myocardial ischemia has a significant effect, which can effectively improve the electrocardiogram and cardiac function indicators of patients and reduce the incidence of adverse reactions and is worthy of clinical application.