Miracle-dz/UniFAR-FTUs huggingface dataset:
Cooperation Between Systemic and Mucosal Antibodies Induced by Virosomal Vaccines Targeting HIV-1 Env: Protection of Indian Rhesus Macaques Against Low-Dose Intravaginal SHIV Challenges

A virosomal vaccine inducing systemic/mucosal anti-HIV-1 gp41 IgG/IgA had previously protected Chinese-origin rhesus macaques (RMs) against vaginal SHIVSF162P3 challenges. Here, we assessed its efficacy in Indian-origin RMs by intramuscular priming/intranasal boosting (n=12/group). Group K received virosome-P1-peptide alone (harboring the Membrane Proximal External Region), Group L combined virosome-rgp41 plus virosome-P1, and Group M placebo virosomes. Vaccination induced plasma binding but no neutralizing antibodies. Five weeks after boosting, all RMs were challenged intravaginally with low-dose SHIVSF162P3 until persistent systemic infection developed. After SHIV challenge #7, six controls were persistently infected versus only one Group L animal (vaccine efficacy 87%; P=0.0319); Group K was not protected. After a 50% SHIV dose increase starting with challenge #8, protection in Group L was lost. Plasmas/sera were analyzed for IgG phenotypes and effector functions; the former revealed that protection in Group L was significantly associated with increased binding to FcγR2/3(A/B) across several time-points, as were some IgG measurements. Vaginal washes contained low-level anti-gp41 IgGs and IgAs, representing a 1-to-5-fold excess over the SHIV inoculum’s gp41 content, possibly explaining loss of protection after the increase in challenge-virus dose. Virosomal gp41-vaccine efficacy was confirmed during the initial seven SHIV challenges in Indian-origin RMs when the SHIV inoculum had at least 100-fold more HIV RNA than acutely infected men’s semen. Vaccine protection by virosome-induced IgG and IgA parallels the cooperation between systemically administered IgG1 and mucosally applied dimeric IgA2 monoclonal antibodies that as single-agents provided no/low protection – but when combined, prevented mucosal SHIV transmission in all passively immunized RMs.
Changes in Soil Chemistry After 17 Years in Urban and Rural Forest Patches

Cycling of carbon (C), nitrogen (N), calcium (Ca), phosphorus (P), and sulfur (S) is an important ecosystem service that forest soils provide. Humans influence these biogeochemical processes through the deposition of atmospheric pollutants and site disturbances. One way to study these potential anthropogenic trajectories is through long-term monitoring in association with human-caused environmental gradients such as urban-rural gradients. The objective of this study was to characterize changes in surface soil chemistry of urban, suburban and rural forest patches in the Baltimore Metropolitan area. Soil composite samples (0–10 cm) were analyzed for macro- and micronutrients, pH, and C. A total of 12 sites in forest patches dominated by white oak (Quercus alba) and tulip poplar (Liriodendron tulipifera) were established in 2001, and resampled in 2018. We hypothesized that after almost two decades (1) concentrations of N, Ca, and P, as well as soil pH would be higher, especially in urban forest patches due to local deposition; (2) S levels would be lower due to decreased regional atmospheric deposition and; (3) total soil C would increase overall, but the rate of increase would be higher in the urban end of the gradient due to increased NPP. Overall, means of Ca concentration, pH, and C:N ratios significantly changed from 2001 to 2018. Calcium increased by 35% from 622 to 844 mg kg–1, pH increased from 4.1 to 4.5, and C:N ratios decreased from 17.8 to 16.7. Along the gradient, Ca, N, P, and S were statistically significant with Ca concentration higher in the urban sites; S and N higher in the suburban sites; and P lower in the urban sites. Confounding factors, such as different geologic parent material may have affected these results. However, despite the unique site conditions, patterns of surface soil chemistry in space and time implies that local and regional factors jointly affect soil development in these forest patches. The increase in pH and Ca is especially notable because other long-term studies demonstrated changes in the opposite direction.
Room Temperature Electrodeposition of Ready-to-Use TiOx for Uniform p-n Heterojunction Over Nanoarchitecture

The photocathodes are essential in photoelectrochemical systems for harvesting solar energy as green fuels. However, the light-absorbing p-type semiconductor in them usually suffers from carrier recombination issues. An effective strategy to address it is fabricating the p-n heterojunction to create an interfacial electric field. However, plenty of deposition process of the n-type layer for this purpose requires either sophisticated instruments or subsequent treatments, which may damage the vulnerable p-type structure. Herein, we report a mild approach for a ready-to-use n-type layer with full functionality. Structural analyses proved the successful coating of a uniform titania layer (up to 40 nm) over Cu2O without damaging its structure. Owing to the high Ti3+ content, the layer possesses excellent charge transport ability and requires no additional annealing. The heterojunction effectively facilitates the carrier separation and positively shifts the photocurrent onset potential for 0.2 V. The Mott–Schottky plot and the impedance study reveal an enhanced carrier collection with reduced charge transfer resistances. Such a nano-heterojunction can be further loaded with the hydrogen evolution catalyst, which almost doubles the photocurrent with an extended lifetime than that of the pristine Cu2O nanoarray. This approach puts forward a potentially scalable and efficient choice for fabricating photoelectrochemical devices.
The Gut Microbiota in Inflammatory Bowel Disease

Epidemiological surveys indicate that the incidence of inflammatory bowel disease (IBD) is increasing rapidly with the continuous growth of the economy. A large number of studies have investigated the relationship between the genetic factors related to the susceptibility to IBD and the gut microbiota of patients by using high-throughput sequencing. IBD is considered the outcome of the interaction between host and microorganisms, including intestinal microbial factors, abnormal immune response, and a damaged intestinal mucosal barrier. The imbalance of microbial homeostasis leads to the colonization and invasion of opportunistic pathogens in the gut, which increases the risk of the host immune response and promotes the development of IBD. It is critical to identify the specific pathogens related to the pathogenesis of IBD. An in-depth understanding of various pathogenic factors is of great significance for the early detection of IBD. This review highlights the role of gut microbiota in the pathogenesis of IBD and provides a theoretical basis for the personalized approaches that modulate the gut microbiota to treat IBD.
IEEE Std 1588-2008精确时间同步协议行业规范在电力系统的应用研究

This paper provides a summary of the IEEE C37.238-2011 standard. Firstly, it introduces the relationship between the IEEE C37.238 standard and the IEEE1588 standard. Then, it introduces the difference on the parameter and mechanism, which are defined by C37.238 standard and the IEEE1588 standard. And it introduces the characteristic parameters of C37.238. At last, it analyzes the problems and corresponding solutions may be faced by PTP precision time protocol in future applying. It specifies a subset of PTP parameters and options to provide global time availability, device interoperability, and failure management. This set of PTP parameters and options allows IEEE 1588-based time synchronization to be used in mission critical power system protection, control, automation, and data communication applications utilizing Ethernet communications architecture.
CatchmentView: visualização de anycast catchment

IP anycast is extensively used to distribute services in the Internet. It relies on BGP, which makes management complex. Furthermore, the availability of such services in IPv4 and IPv6 (dual stack) increases management complexity. In this paper we introduce CatchmentView, a visualization tool that builds graphics of routes using traceroute data from measurement sources to anycast sites. Visualization from CatchmentView helps operators identifying differences between routing and catchment of anycast dual stack services. We built CatchmentView based on the Root DNS servers that are dual stack and leverage anycast for distribution. Using traceroutedata from RIPE Atlas we validate CatchmentView. Resumo. IP anycast é amplamente utilizado para distribuir serviços na Internet. A dependência no roteamento BGP faz com que a implementação e gerenciamento de anycast seja complexo e, ainda, a disponibilidade de serviços anycast em IPv4 e IPv6 (dual stack) aumenta a complexidade do gerenciamento. Nesse artigo apresentamos a ferramenta CatchmentView que, utilizando dados de traceroute, constrói gráficos de rotas a partir de pontos de medição até réplicas de um serviço anycast. Os recursos visuais do CatchmentView auxiliam operadores na identificação de diferenças entre roteamento e catchment de um serviço anycast dual stack. A ferramenta foi desenvolvida tendo como base os servidores raiz do DNS, que são dual stack e extensivamente distribuı́dos via anycast. Usando dados de traceroute da plataforma RIPE Atlas validamos a ferramenta desenvolvida e mostramos sua utilidade.
Improving the Interoperability of gbXML Data Model through Redefining Data Mapping Rules of HVAC Systems

Green Building XML (gbXML) is one of the most prevalent data models used for Building Information Modeling (BIM). It enables interoperability between BIM and Building Energy Modeling (BEM). BEM is often used in building performance analysis software tools. However, through interviewing engineers and building energy modeling professionals in the industry, it turned out that most gbXML files are only used for importing and exporting building geometry information. Information such as Heating, Ventilation, and Ait-conditioning (HVAC) systems and internal loads are rarely handled due to the lack of functionality in current BIM software. Taking account of more than 15% of the total energy consumption in the US is used by HVAC systems (DOE 2011), it is crucial to enable seamless HVAC data exchange between BIM software and BEM software. This paper researched the definition rules of HVAC systems in the current gbXML schema 6.01 version. Firstly, through a detailed data mapping of ASHRAE baseline HVAC system from IDF data model (EnergyPlus version 9.0) to current gbXML schema (version 6.01), interoperability issues were discovered and were concluded into three catecgories: missing components, overlapping components, and complex data mapping rules. Secondly, through redefining data mapping rules of current gbXML schema in terms of HVAC systems, the ASHRAE baseline HVAC system is able to be coded as a gbXML file. Finally, the gbXML file will be validated through a translation case study, focusing on primary HVAC system and boiler data. Revit 2020.1 Architecture is used in this study as the BIM tool. OpenStudio 2.8. 1 and EnergyPlus 9.1. 0 are used as the BEM tools. They are opensource and cross-platform, being adopted by lots of mainstream building performance analysis software. Based on the result of this study, current gbXML schema version 6.01 is capable of defining HVAC systems data, but the data mapping rules need to be documented and presented. Missing components and overlapping components issue could be solved by updating the schema. Interoperability improvement will eliminate the duplicate generation of HVAC data and allows a bidirectional information update between BIM and BEM software tools, supporting a more accurate and efficient building performance analysis process.
Representation Learning using Graph Neural Nets: A case-study in HMMs

Hidden Markov models (HMMs) belong to the class of double embedded stochastic models which were originally leveraged for speech recognition and synthesis. HMMs subsequently became a generic sequence model across multiple domains like NLP, bio-informatics and thermodynamics to name a few. Literature has several heuristic metrics to compare two HMMs by factoring in their structure and emission probability distributions in HMM nodes. However, typical structure-based metrics overlook the similarity between HMMs having diﬀerent structures yet similar behavior and typical behavior-based metrics rely on the representativeness of the reference sequence used for assessing the similarity in behavior. Further, little exploration has taken place in leveraging the recent advancements in deep graph neural networks for learning eﬀective representations for HMMs. In this paper, we propose ﬁrst-of-their-kind deep neural network based approaches based on graph variational autoencoder and a diﬀpooling based graph convolutional network to learn embeddings for HMMs and evaluate the validity of the embeddings based on subsequent clustering and classiﬁcation tasks.
Stable Operation Limits in Dual Active Bridge for SuperCapacitor Applications

This paper presents an idea for limiting the phase-shift angle in Dual Active Bridge (DAB) which ensures the converter’s stability. The stability is the main criterion in a converter’s operation without which the converter will stop working. In an ideal DAB which comprises of ideal components, the phase-shift angle limit is 90. We developed a detailed model in Matlab/Simulink for DAB based on which the limits for the stable operation of converter are derived. In the developed model, attempts are made to employ as accurate as possible models for the components. In this way, the limits are expected to be very close to the practice. The DAB is a bi-directional converter; meaning that the power flow occurs in both forward and reverse directions. We derived the limits separately for forward and reverse modes. It is shown that the limits are the same for both directions confirming the fact that the DAB is symmetrical. We employed the DAB as the SuperCapacitor (SC) energy storage’s interface in this paper. doi: 10.5829/ije.2022. 35.05b. 04
Understanding the Research Landscape of Deep Learning in Biomedical Science: Scientometric Analysis

Background Advances in biomedical research using deep learning techniques have generated a large volume of related literature. However, there is a lack of scientometric studies that provide a bird’s-eye view of them. This absence has led to a partial and fragmented understanding of the field and its progress. Objective This study aimed to gain a quantitative and qualitative understanding of the scientific domain by analyzing diverse bibliographic entities that represent the research landscape from multiple perspectives and levels of granularity. Methods We searched and retrieved 978 deep learning studies in biomedicine from the PubMed database. A scientometric analysis was performed by analyzing the metadata, content of influential works, and cited references. Results In the process, we identified the current leading fields, major research topics and techniques, knowledge diffusion, and research collaboration. There was a predominant focus on applying deep learning, especially convolutional neural networks, to radiology and medical imaging, whereas a few studies focused on protein or genome analysis. Radiology and medical imaging also appeared to be the most significant knowledge sources and an important field in knowledge diffusion, followed by computer science and electrical engineering. A coauthorship analysis revealed various collaborations among engineering-oriented and biomedicine-oriented clusters of disciplines. Conclusions This study investigated the landscape of deep learning research in biomedicine and confirmed its interdisciplinary nature. Although it has been successful, we believe that there is a need for diverse applications in certain areas to further boost the contributions of deep learning in addressing biomedical research problems. We expect the results of this study to help researchers and communities better align their present and future work.
Compressive Sensing - Mathematical Principles and Practical Implications

The mathematical foundations of the compressive sensing which goes against the common wisdom of data acquisition (the Nyquist-Shannon theorem) is reviewed. The compressive sensing asserts that one can reconstruct images or signals of interest accurately from a number of samples far smaller than the desired resolution of the the image (e.g., the number of pixels in the image). The compressive sensing has far reaching implications. It suggests the new data acquisition protocols that translates analog information to digital form with fewer sensors considered necessary.
Sensitivity Analysis of a Bayesian Belief Network in a Tactical Intelligence Application

In this paper, a variety of targeted sensitivity analysis approaches are explored for a Bayesian Belief Network (BBN) constructed as an expert tool for enemy course of action (COA) assessment at the tactical level in a conventional mid-intensity scenario. Robustness analysis is used to measure the level to which the posterior probability of the states at the root node are affected by instantiation of individual nodes in the network. Likewise, value of information analysis and gain in belief updating are used to compare how nodes of interest affect posterior probabilities at the root node, the former measuring Shannon Entropy and the latter Kullback Distance. Finally, sensor effectiveness analysis is used to measure how the reliability of reconnaissance and surveillance (RandS) assets affects updating of belief at the root node. It was found that each of the sensitivity analysis approaches could be used to optimise allocation of RandS, to identify the commander's decision points, and to identify influential nodes for which the conditional probability tables (CPTs) should be refined. In terms of utility, it was concluded that, as in the case of the use of BBNs in the tactical COA assessment domain in general, the utility of sensitivity analysis of the BBN would be reduced in conditions of high operational tempo and myriad variables influencing tactical COA selection. Nevertheless, in a slower operational tempo environment, the benefits in refinement and utility of the BBN derived through sensitivity analysis would be significant.
HIGHWAY CONSTRUCTION NOISE MODELING

A model and interactive computer program for predicting highway construction noise levels have been developed and evaluated for the Federal Highway Administration (FHWA), as part of its on-going efforts to provide state-of-the-art tools for highway-noise analysis. The model addresses noise sources as points, lines, or areas and has a built-in data base for 53 different sources. Noise barrier attenuation may also be analyzed. The results of the calculations are the total 8-h equivalent sound levels (L sub eq (8h)) at noise receptor as well as the individual contributions from each source. Use of the model will not be required by FHWA; however, the model can serve as a useful tool for meeting the requirements of the FHWA noise standards for impacted areas and for evaluating abatement measures. It may also be used during construction as a diagnostic tool for investigating citizen complaints and for designing mitigation strategies, if necessary. (Authors)
Antibacterial and Cytotoxicity Analyses on Aloe vera Body Soap

Aloe vera is a well-known succulent plant and it is often used to moisturise and sooth the skin. In this study, Aloe vera is incorporated into natural soap to investigate the antibacterial properties and cytotoxicity of the samples. The antibacterial properties of the Aloe vera sample was determined by performing zone inhibition and bacterial colony test on Escherichia coli (E. coli). The cell viability test was conducted with human skin fibroblast cells through MTT assay. The Aloe vera samples showed antibacterial properties through the presence of inhibition zones and in the bacterial colony analysis, the Aloe vera samples have inhibitory effect against E. coli with a bacterial retardation percentage of 31.97±0.90%. The cell viability percentage of 95.30±11.16% was found on the Aloe vera sample with normal cell morphology. These results testified that the Aloe vera sample was biocompatible and possessed antibacterial properties which is suitable to be incorporated in skin products.
Antibakteriyel İlaçların Farmakokinetik/Farmakodinamik Entegrasyonu Üzerine Metodolojik Bir İnceleme

: Inappropriate application of antimicrobial agents can result in resistance by bacteria to drugs and changes in bacterial ecology. In particular, the emergence of multi-drug resistant bacteria seriously aff ects the antibacterial eﬃ cacy of drugs, which threatens the health and lives of humans and animals. Pharmacokinetic/Pharmacodynamic (PK/PD) models can be used to analyze the relationship between PK and PD data and the antibacterial eff ect. PK/PD models provide valuable guidance for optimization of dosage regimens, development of new drugs, setting of susceptibility breakpoints, and analyses of resistant mutants. Th e main models of PK/PD integration are in vitro PK/PD, ex vivo PK/PD, and in vivo PK/PD. Each of these models has its own advantages and disadvantages. Hence, knowing how to choose the appropriate PK/PD model has a huge infl uence on obtaining accurate PK/PD data. In this review, we describe the commonly used PK/PD methods. In this way, we provide a reference for optimizing drug regimens and preventing and controlling drug-resistant bacterial infections. drugs, PK/PD integration model, Multi-drug resistance, Dosage regimen optimization, In vivo PK/PD model
INFLUÊNCIA DOS FENÔMENOS CLIMÁTICOS DO EL NIÑO E DA LA NIÑA NA PREVISÃO DA MÉDIA DIÁRIA DE IRRADIAÇÃO GLOBAL NA CIDADE DE FORTALEZA

Neste trabalho, previsões da média diária de irradiação solar global foram obtidas pela aplicação de algoritmos de aprendizagem de máquina em dois conjuntos de dados formados por variáveis exógenas (insolação, temperatura do ar, precipitação, etc), variáveis endógenas (série temporal da média diária de irradiação solar global) e variáveis temporais (ano, mês e dia da medição). A diferença entre os conjuntos de dados está relacionada ao fato de que em um se considera as intensidades dos fenômenos climáticos do El Niño e da La Niña como preditores para os modelos de aprendizagem utilizados, enquanto no outro não se considera. Desta forma, foi possível avaliar se a adição do preditor relacionado ao El Niño/La Niña contribui para uma melhor acurácia de previsão por parte dos modelos aplicados: Máquina de Aprendizagem Mínima, Regressão por Vetor Suporte, Florestas Aleatórias, K-Vizinhos mais Próximos e uma árvore de regressão com o uso de Bootstrap. As métricas de erro Erro Médio Absoluto, Erro de Viés Médio, Raiz do Erro Quadrático Médio, Raiz do Erro Quadrático Médio Relativo e Habilidade de Previsão foram utilizadas para a análise do desempenho dos algoritmos. A média aritmética da Raiz do Erro Quadrático Médio e da Habilidade de Previsão para o caso em que se considerou o El Niño/La Niña como atibutos foram de 40.78 W/m² e 7,87% , respectivamente. Já para o caso em que não se considera tais preditores os valores obtidos foram de 40.86 W/m² e 7.69%. Indicando que o uso destes preditores aumenta a acurácia de previsão dos algoritmos em questão.