jimnoneill/paper-to-field-training huggingface dataset:
Distributed State Estimation for Mixed Delays System Over Sensor Networks With Multichannel Random Attacks and Markov Switching Topology

Field: Computer Science (Physical Sciences)

This article deals with the distributed state estimation for mixed delays system under unknown attacks. A new multichannel random attack model is established for the first time, where network attacks are considered to exist in three channels: the target-to-sensor channel, the senor-to-sensor channel, and the sensor-to-estimator channel. In the above model, transmitted packets are allowed to be attacked multiple times simultaneously, and when they are successfully attacked, the transmitted information is modified. Besides, the topology of the sensor network is considered to change dynamically according to the Markov chain. Based on the newly established distributed estimation model, the estimation error system is proven to be asymptotically mean-square stable under a given H∞ antidisturbance index by using a Lyapunov theory and a stochastic analysis technique; then, the estimator parameter matrices are solved utilizing a linearization method. Finally, several simulation examples are listed to testify the effectiveness of the designed algorithm.
Uncovering BGP Action Communities and Community Squatters in the Wild

Field: Computer Science (Physical Sciences)

The Border Gateway Protocol (BGP) offers several "knobs" to control routing decisions, but they are coarse-grained and only affect routes received from neighboring Autonomous Systems (AS). To enhance policy expressiveness, BGP was extended with the communities attribute, allowing an AS to attach metadata to routes and influence the routing decisions of a remote AS. The metadata can carry information to ( e.g. , where a route was received) or request an action from a remote AS ( e.g. , not to export a route to one of its neighbors). Unfortunately, the semantics of BGP communities are not standardized, lack universal rules, and are poorly documented. In this work, we design and evaluate algorithms to automatically uncover BGP action communities and ASes that violate standard practices by consistently using the information communities of other ASes, revealing undocumented relationships between them (e.g., siblings). Our experimental evaluation with billions of route announcements from public BGP route collectors from 2018 to 2023 uncovers previously unknown AS relationships and shows that our algorithm for identifying action communities achieves average precision and recall of 92.5% and 86.5%, respectively.
Cross-Linked Forms of the Isolated N-Terminal Domain of the Lethal Factor Are Potent Inhibitors of Anthrax Toxin

Field: Biochemistry, Genetics and Molecular Biology (Life Sciences)

ABSTRACT The proteins that comprise anthrax toxin self-assemble at the mammalian cell surface into a series of toxic complexes, each containing a heptameric form of protective antigen (PA) plus up to a total of three molecules of the enzymatic moieties of the toxin (lethal factor [LF] and edema factor [EF]). These complexes are trafficked to the endosome, where the PA heptamer forms a pore in the membrane under the influence of low pH, and bound LF and EF unfold and translocate through the pore to the cytosol. To explore the hypothesis that the PA pore can translocate multiple, cross-linked polypeptides simultaneously, we cross-linked LF N , the N-terminal domain of LF, via an introduced cysteine at its N or C terminus and characterized the products. Both dimers and trimers of LF N retained the ability to bind to PA pores and block ion conductance, but they were unable to translocate across the membrane, even at high voltages or with a transmembrane pH gradient. The multimers were remarkably potent inhibitors of toxin action in mammalian cells (20- to 50-fold more potent than monomeric LF N ) and in a zebrafish model system. These findings show that the PA pore cannot translocate multimeric, cross-linked polypeptides and demonstrate a new approach to generating potent inhibitors of anthrax toxin.
Correlation between blood type and SARS-CoV-2 infection susceptibility: A literature review

Field: Medicine (Health Sciences)

The pandemic caused by SARS-CoV-2 is one outbreak that affects human health worldwide. Since the first confirmed case, several people have been infected across the globe and many have unfortunately lost their lives due to complications caused by Covid-19. Several studies have been published on the subject since the disease emerged, and among these, studies correlating blood type and levels of infection by SARS-Cov-2. The purpose of this work is to collect works published on the subject, during the period of 2020 and 2021, and to investigate whether it is possible to prove the existence or non-existence of this correlation. Articles were searched in 3 databases, and reaching a final number of 44 analyzed articles. Concluding a greater susceptibility to infection in individuals of type A and group O being considered protective against infection.
X-linked Charcot-Marie-Tooth Patient with a Novel Cys168Arg Missense Mutation in the Connexin32 Gene.

Field: Medicine (Health Sciences)

X-linked Charcot-Marie-Tooth (CMTX) disease is a clinically heterogeneous hereditary motor and sensory neuropathy. The X-linked inheritance showed an absence of male-to-male transmission and a more severe disease phenotype in affected males compared to that in affected female. A missense mutation, Cys168Arg, was found in connexin 32 gene (Cx32/GJB1) from a patient with CMTX neuropathy. The familial history of this patient also suggested that the disease is X-linked CMT. Thus, we report a CMTX family having the novel Cys168Arg mutation in the Cx32 gene.
HEDIS outcomes may not be the only ones you need to benchmark.

Field: Health Professions (Health Sciences)

The Foundation for Accountability (FACCT), a coalition of employers, is leading a trend away from process-oriented performance measures by generating patient-centered data sets for a variety of disease states, including asthma, diabetes, and depression. Many large employers, dissatisfied with the National Center for Quality Assurance's focus on cost, are pressuring managed care organizations to use the FACCT measures, which largely rely on patient self-reports about the care they receive. Some plans are using the data sets voluntarily, as a tool to better assess the quality of services they provide and to evaluate physician groups.
O uso de PRF e PRP em feridas resultantes do pé diabético / The use of PRF and PRP in wounds resulting from the diabetic foot

Field: Medicine (Health Sciences)

O Diabetes Mellitus (DM) é um problema de saúde que afeta as populações no geral, desde países desenvolvidos à países em desenvolvimento. Dentre as complicações dessa doença se destaca uma entidade que é frequente e limitadora: a neuropatia diabética. São variados os tratamentos utilizados para essa condição, entretanto é conhecido que muitas vezes são falhos, resultando em amputações desnecessárias. Nesse contexto, são utilizadas duas técnicas que auxiliam a eficácia do tratamento a longo prazo: a Fibrina Rica em Plaquetas (PRF) e o Plasma Rico em Plaquetas (PRP). Tendo como objetivo deste trabalho a explicação e comparação entre esses dois métodos. Foram utilizados artigos em Português, Inglês e Espanhol, tendo como critério de inclusão a língua e critério de exclusão algum viés de seleção. Os artigos destacam a forma de obtenção, a aplicação e os resultados em pacientes com o Pé Diabético. Concluindo que os métodos são baratos e práticos, porém que o PRF é preferível ao PRP por tempo de cura e resolução das feridas.
EVALUATION OF MASS TRANSIT FREQUENCIES AND PRICING POLICIES IN URBAN TRANSPORTATION SYSTEM

Field: Decision Sciences (Social Sciences)

本研究では, 都市交通体系を鉄道, バス, 乗用車ネットワークで構成される多手段・多経路ネットワークと考え, その上の交通現象を変動需要型交通均衡モデルとして表している. この交通体系において, 公共輸送機関の料金システムや輸送網整備計画が利用者行動に与える影響を分析し, 公共交通計画の評価主体ごとの評価について検討した後, 名古屋市における料金システムや輸送網整備のあり方について検討している.
Minimization of thermodynamic costs in cancer cell invasion

Field: Biochemistry, Genetics and Molecular Biology (Life Sciences)

Metastasis, the truly lethal aspect of cancer, occurs when metastatic cancer cells in a tumor break through the basement membrane and penetrate the extracellular matrix. We show that MDA-MB-231 metastatic breast cancer cells cooperatively invade a 3D collagen matrix while following a glucose gradient. The invasion front of the cells is a dynamic one, with different cells assuming the lead on a time scale of 70 h. The front cell leadership is dynamic presumably because of metabolic costs associated with a long-range strain field that precedes the invading cell front, which we have imaged using confocal imaging and marker beads imbedded in the collagen matrix. We suggest this could be a quantitative assay for an invasive phenotype tracking a glucose gradient and show that the invading cells act in a cooperative manner by exchanging leaders in the invading front.
Sensitivity of Various Serological (Lancefield) Groups of Streptococci to Penicillin.

Field: Pharmacology, Toxicology and Pharmaceutics (Life Sciences)

The tests reported here show that penicillin in amounts varying between 0.0039 and 0.0625 Oxford units sterilized an inoculum containing 1,000 to 10,000 cells of all the strains tested belonging to serological groups A through N with the exception of group D strains which required 2.0 to 5.0 units to inhibit completely the growth of a similar sized inoculum. Group M strains were the most sensitive; group A, C, G, H, and L strains were next; and group B, E, F, K, and N strains were the least sensitive of those found susceptible to the action of penicillin in small amounts.
Estrogen promotes chemotherapeutic drug resistance by a mechanism involving Bcl-2 proto-oncogene expression in human breast cancer cells.

Field: Medicine (Health Sciences)

Recent studies have shown that the Bcl-2 protein suppresses programmed cell death or apoptosis induced by a variety of stimuli including chemotherapeutic drugs. Because estrogen promotes the survival of estrogen-dependent breast cancer cells in vivo, we investigated whether estrogen might regulate levels of Bcl-2 gene expression in an estrogen-responsive human breast cancer cell line. Estrogen receptor-positive MCF-7 human breast cancer cells cultured in the presence of estrogen express the 8.5-kb Bcl-2 mRNA transcript. Depletion of estrogen from the medium results in loss of expression of the mRNA, whereas reexposure to estrogen markedly induces the Bcl-2 transcript. The changes in Bcl-2 mRNA are paralleled by changes in Bcl-2 protein levels. Estrogen-induced increases in Bcl-2 are significantly inhibited by inclusion of the pure antiestrogen ICI 164,384 in the medium. The Bax protein that heterodimerizes with Bcl-2 and promotes cell death is expressed in MCF-7 cells grown in the presence of estrogen and is unaffected by culture in estrogen-free medium. Estrogen depletion doubles the sensitivity of MCF-7 cells to the cytotoxic effects of Adriamycin compared with cells cultured in medium supplemented with estrogen, consistent with a decrease in the Bcl-2 levels. MCF-7 cells treated simultaneously with estrogen and ICI 164,384 exhibit markedly lower resistance to Adriamycin compared with cells treated with estrogen alone. In the absence of estrogen, MCF-7 cells transfected with Bcl-2 expression plasmids display a marked increase in resistance to Adriamycin. In the presence of estrogen, MCF-7 cells expressing Bcl-2 antisense transcripts are rendered twice as sensitive to acute Adriamycin cytotoxicity as a control clone. We conclude that estrogen can promote resistance of estrogen receptor bearing human breast cancer cells to chemotherapeutic drugs through a mechanism that involves regulation of the Bcl-2 proto-oncogene.
Symbiotic simulation for decision support in high-tech manufacturing and service networks

Field: Decision Sciences (Social Sciences)

With the increasing growth of manufacture networks as well as the global competitions in the lubricant industry, the management of supply chain is vital for large vertically-integrated petroleum companies. Operational decision-making should consider the entire supply chain which includes upstream raw material suppliers, downstream customers as well as the internal entities of the specialty chemicals company. A simulation model of the entire supply chain can serve as a valuable quantitative tool to aid offline analysis and optimization. However, solutions are still needed for on-line decision support and automate control. Symbiotic simulation system can be the solution. In this report, we present a symbiotic simulation control system (SSCS) which is based on a generic framework for symbiotic simulation being developed by Parallel and Distributed Computing Centre in Nanyang Technological University. An application-specific modification in scenario management, SCEM has been made to the SSCS. Together with the generic solution provided by the generic framework, an integrated SSCS is developed and integrated with a Jadex-based global lubricant supply chain simulation model. It utilizes proactive what-if analysis to improve the performance of inventory management and reactive what-if analysis to find solutions to low finished product fill rate. The experimental results demonstrate that this control system can achieve notable performance improvement over common practice and can be used to provide decision support and control in near real-time. In the current version of the SSCS, parameters such as threshold condition to trigger what-if analysis, the what-if simulation duration and physical system lock-up period are preset and fixed based on past experience or personal preference. However, the preset parameters may not be the optimal configurations for SSCS. Hence, SSCS should be able to dynamically find the optimal parameters. Moreover, based on current physical state, automate readjustment to the SSCS itself is required for the real-time implementation of SSCS. Since SSCS can extensively simulate and evaluate different scenarios, it can be the solution to its own problem as well. An application-specific two-level SSCS implementation is proposed.
$\textit{Ab initio}$ electron-phonon coupling theory of elastic helium atom scattering

Field: Physics and Astronomy (Physical Sciences)

We propose a fully $ \textit{ab initio} $ approach to predicting thermal attenuation in elastic helium atom scattering amplitudes, validated through strong agreement with experiments on Nb(100) and (3$\times$1)-O/Nb(100) surfaces. Our results reveal the relative contributions from bulk, resonant, and surface phonon modes, as well as from different surface mode polarizations, providing insights into differences between smooth and corrugated surfaces. These findings advance understanding of surface dynamics and electron-phonon coupling, laying groundwork for future studies on surface superconductivity.
Surfactant Imprinting Hyperactivated Immobilized Lipase as Efficient Biocatalyst for Biodiesel Production from Waste Cooking Oil

Field: Chemical Engineering (Physical Sciences)

Enzymatic production of biodiesel from waste cooking oil (WCO) could contribute to resolving the problems of energy demand and environment pollutions.In the present work, Burkholderia cepacia lipase (BCL) was activated by surfactant imprinting, and subsequently immobilized in magnetic cross-linked enzyme aggregates (mCLEAs) with hydroxyapatite coated magnetic nanoparticles (HAP-coated MNPs). The maximum hyperactivation of BCL mCLEAs was observed in the pretreatment of BCL with 0.1 mM Triton X-100. The optimized Triton-activated BCL mCLEAs was used as a highly active and robust biocatalyst for biodiesel production from WCO, exhibiting significant increase in biodiesel yield and tolerance to methanol. The results indicated that surfactant imprinting integrating mCLEAs could fix BCL in their active (open) form, experiencing a boost in activity and allowing biodiesel production performed in solvent without further addition of water. A maximal biodiesel yield of 98% was achieved under optimized conditions with molar ratio of methanol-to-WCO 7:1 in one-time addition in hexane at 40 °C. Therefore, the present study displays a versatile method for lipase immobilization and shows great practical latency in renewable biodiesel production.
The diversity of Cerambycidae in the protected Mediterranean landscape of the Natural Park of Carrascal de La Font Roja, Spain.

Field: Agricultural and Biological Sciences (Life Sciences)

The present work offers new information about the faunistic diversity of Cerambycidae (Coleoptera) in the Natural Park of Carrascal de La Font Roja, along with data on the family’s ecology. A total of 27 species were identified during the sampling period (2004-2009), the adult activity of which spans from early May to late August. Stenurella melanura (L.) and Chlorophorus trifasciatus (F.) were the most abundant species. Ecological studies were carried out in four different habitats (Quercus forest, Pinus forest, mixed forest and herbaceous), and identified Pinus forest as the most diverse habitat (Shannon Index = 2.467), and Quercus forest as the least diverse (Shannon Index = 1.068). Correspondence and neighbour joining analyses showed that certain Cerambycidae species are associated with certain habitats, although some species may be present in more than one habitat due to their lower plant specificity.
ACUTE ADMINISTRATION OF LACTOBACILLUS REUTERI IN NEURODEGENERATION AND FUNCTIONAL RECOVERY FOLLOWING JUVENILE TRAUMATIC BRAIN INJURY

Field: Neuroscience (Life Sciences)

Traumatic brain injury (TBI) is one of the leading causes of death and disability in children. The effects of TBI are not limited to the brain but also impacts the gut. Within the gut are residential microbes that can mediate brain physiology and resulting behavior. Chronic administration of the probiotic Lactobacillus reuteri (L. reuteri) provides neuroprotection and improves long-term behavioral outcomes after TBI. The primary goal of this study was to address how L. reuteri affects neurodegeneration and inflammation in the 7-day period after TBI. The secondary goal was to establish if short-term treatments with L. reuteri improved behavioral outcomes after TBI. For this series of studies, animals received a bilateral craniotomy with a single cortical contusion injury over the right parietal lobe. Following injury, animals were given daily doses of the probiotic L. reuteri or a broth vehicle control for up to 7 days. Animals were sacrificed at days 1, 3, 5, 7, and 30 following injury. These times represent the acute (1-7 days) and chronic (35 days) period after injury. The results of this study show that while injured animals demonstrated inflammation, neurodegeneration, and decreased cortical volume, L. reuteri treated animals had significantly attenuated changes compared to broth counterparts at days 1, 3, 5, and 7. By day 35, inflammation was not different between treatments but L. reuteri animals showed less cortical loss compared to broth treated animals. With respect to behavior, acute treatment of L. reuteri did not show improvements over broth treated animals. Taken together, it was observed that administration of the probiotic L. reuteri rapidly and robustly reduced pathophysiology after juvenile TBI (jTBI), however these effects did not translate into behavioral improvements. Thus, further studies are needed to examine whether animals need to be undergoing treatment at the time of testing in order to receive behavioral benefit.
Student Engagement And Attitude in Mathematics Achievement Using Single Valued Neutrosophic Set

Field: Mathematics (Physical Sciences)

Abstract Generalization of the Fuzzy set and Intuitionistic Fuzzy set concept is called as Neutrosophic set which is a powerful general formal framework. The components in Neutrosophic set has a degree of truth (T), indeterminacy (I) and falsity (F). The value of this components are between [0,1], respectively. A Neutrosphic set has a general formal framework for analysing uncertainty in data set or undetermined information. Not only uncertainty, Neutrosophic set can also analyse large information sets or big data sets as well. Single Valued Neutrosophioc sets (SVNs) was introduce to be used expediently to deal with real problems and it is appropriate in solving data mining problem and make a decision for the problem. In this paper, Single Valued Neutrosophic set (SVNs) was proposed to measuring factors impact on student engagement and attitude in mathematics achievement based on Trends in International Mathematics and Science Study TIMSS 2015 for ASEAN countries and use the data to illustrate the applicability of the proposed factors similarity measures in decision making. The result shows that the factor which is Confidence in Mathematics include in highly acceptable zone in students’ engagement and attitude in Mathematics Achievement for ASEAN countries.
Sentiment Analysis of Twitter Data Using Machine Learning

Field: Computer Science (Physical Sciences)

In the world of social media people are more responsive towards product or certain events \nthat are currently occurring. This response given by the user is in form of raw textual data \n(Semi Structured Data) in different languages and terms, which contains noise in data as \nwell as critical information that encourage the analyst to discover knowledge and pattern \nfrom the dataset available. This is useful for decision making and taking strategic decision \nfor the future market. To discover this unknown information from the linguistic data Natural Language \nProcessing (NLP) and Data Mining techniques are most focused research terms used for \nsentiment analysis. In the derived approach the analysis on Twitter data to detect sentiment \nof the people throughout the world using machine learning techniques. Here the data set \navailable for research is from Twitter for world cup Soccer 2014, held in Brazil. During \nthis period, many people had given their opinion, emotion and attitude about the game, \npromotion, players. By filtering and analyzing the data using natural language processing \ntechniques, and sentiment polarity has been calculated based on the emotion word detected \nin the user tweets. The data set is normalized to be used by machine learning algorithm and \nprepared using natural language processing techniques like Word Tokenization, Stemming \nand lemmatization, POS (Part of speech) Tagger, NER (Name Entity recognition) and \nparser to extract emotions for the textual data from each tweet. This approach is implemented using Python programming language and Natural Language Toolkit (NLTK), \nwhich is openly available for academic as well as for research purpose. Derived algorithm \nextracts emotional words using WordNet with its POS (Part-of-Speech) for the word in a \nsentence that has a meaning in current context, and is assigned sentiment polarity using \n‘SentWordNet’ Dictionary or using lexicon based method. The resultant polarity assigned \nis further analyzed using Naïve Bayes and SVM (support vector Machine) machine \nlearning algorithm and visualized data on WEKA platform. Finally, the goal is to compare \nboth the results of implementation and prove the best approach for sentiment analysis on \nsocial media for semi structured data.