Elastic Analysis of Steel-Concrete Composite Beams with Partial Interaction

The paper presents an exact analytical method for the elastic analysis of steel-concrete composite beams with partial interaction. Accepting the basic assumptions of the Newmark analytical model and adopting the axial force in the concrete slab as the main unknown, the second order nonhomogeneous differential equation of the steel-concrete composite element with partial interaction is derived. Further, the complete solutions for simply supported and fixed-ended composite beams subjected to concentrated and uniform loads respectively, are developed. The solution of the homogeneous equation is determined by imposing proper Dirichlet or Neumann boundary conditions depending on the static scheme of the element. The particular solutions are then derived for the considered loading conditions. It is shown that the internal axial force in concrete slab associated to composite beams with partial interaction can be expressed as a fraction of the axial force in concrete slab under full interaction through a non-dimensional function f(aL) which takes into account the connection’s stiffness, the mechanical properties and also the length of the element. Moreover, the solutions are included in a flexibility-based approach to derive the force-displacement relations of the beam element with partial interaction. For the resulted 2-noded beam-column element with 6DOF, the stiffness matrix is derived, showing that the partial composite action may be included at the element level by means of a series of correction factors applied to the standard full-interaction stiffness matrix coefficients. A numerical example is provided to demonstrate the accuracy and performance of the proposed method. Within the elastic range, the predicted load-midspan deflection curve is in very good agreement with both experimental and other numerical results retrieved from international literature. A parametric study was conducted to investigate the influence of the shear connection degree on the beam’s midspan deflection and the results were compared with those computed by using code provisions.

SNPxE: SNP-environment interaction pattern identifier

Background Interactions of single nucleotide polymorphisms (SNPs) and environmental factors play an important role in understanding complex diseases' pathogenesis. A growing number of SNP-environment studies have been conducted in the past decade; however, the statistical methods for evaluating SNP-environment interactions are still underdeveloped. The conventional statistical approach with a full interaction model with an additive SNP mode tests one specific interaction type, so the full interaction model approach tends to lead to false-negative findings. To increase detection accuracy, developing a statistical tool to effectively detect various SNP-environment interaction patterns is necessary. Results SNPxE, a SNP-environment interaction pattern identifier, tests multiple interaction patterns associated with a phenotype for each SNP-environment pair. SNPxE evaluates 27 interaction patterns for an ordinal environment factor and 18 patterns for a categorical environment factor. For detecting SNP-environment interactions, SNPxE considers three major components: (1) model structure, (2) SNP’s inheritance mode, and (3) risk direction. Among the multiple testing patterns, the best interaction pattern will be identified based on the Bayesian information criterion or the smallest p-value of the interaction. Furthermore, the risk sub-groups based on the SNPs and environmental factors can be identified. SNPxE can be applied to both numeric and binary phenotypes. For better results interpretation, a heat-table of the outcome proportions can be generated for the sub-groups of a SNP-environment pair. Conclusions SNPxE is a valuable tool for intensively evaluate SNP-environment interactions, and the SNPxE findings can provide insights for solving the missing heritability issue. The R function of SNPxE is freely available for download at GitHub (https://github.com/LinHuiyi/SIPI).

Air Pollutants’ Concentrations Are Associated with Increased Number of RSV Hospitalizations in Polish Children

Respiratory syncytial virus (RSV) contributes significantly to pediatric hospitalizations. An association between air pollution and an increased number of RSV cases has been suggested. We sought to evaluate the short-term impact of air pollutants on RSV hospitalizations in Polish children in the period 2010–2019. Daily concentrations of PM10 and PM2.5 (particulate matter with an aerodynamic diameter less than or equal to 10 μm and 2.5 μm, respectively) and nitrogen dioxide (NO2) were analyzed in general regression models (GRM) to establish their influence and full interaction scheme. Significant seasonal and annual periodicity among 53,221 hospitalizations was observed; finally, data from the 2012–2019 RSV high-risk seasons created models for seven agglomerations. The addition of PM2.5, PM10, and NO2 to the basic model for RSV seasonality explained 23% (4.9–31%, univariate model) to 31.4% (8.4–31%, multivariate model) of the variance in RSV hospitalizations. A 10 μg/m3 increase in PM2.5, PM10, and NO2 concentrations was associated with 0.134 (0.087–0.16), 0.097 (0.031–0.087), and 0.212 (0.04–0.29) average increases in hospitalizations, respectively. In the multivariate models, PM2.5, PM10, and NO2 alone, as well as PM2.5–NO2, PM2.5–PM10, and PM10–NO2 interactions, were associated with hospitalizations in some of the locations, while the metaregression showed statistically significant interactions between each of the pollutants, and between the pollutants and the year of the study. The inclusion of PM2.5, PM10, and NO2 in GRM explains a significant number of RSV hospitalizations. The pollutants act alone and interact together in a varied manner. Reducing air contamination might decrease the costs of hospital healthcare.

Human immunodeficiency virus and mortality from coronavirus disease 2019: A systematic review and meta-analysis

Background: Persons living with human immunodeficiency virus (PLWH) constitute a vulnerable population in view of their impaired immune status. At this time, the full interaction between HIV and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been incompletely described.Objective: The purpose of this study was to explore the impact of HIV and SARS-CoV-2 co-infection on mortality.Method: We systematically searched PubMed and the Europe PMC databases up to 19 January 2021, using specific keywords related to our aims. All published articles on coronavirus disease 2019 (COVID-19) and HIV were retrieved. The quality of the studies was evaluated using the Newcastle–Ottawa Scale for observational studies. Statistical analysis was performed with Review Manager version 5.4 and Comprehensive Meta-Analysis version 3 software.Results: A total of 28 studies including 18 255 040 COVID-19 patients were assessed in this meta-analysis. Overall, HIV was associated with a higher mortality from COVID-19 on random-effects modelling {odds ratio [OR] = 1.19 [95% confidence interval (CI) = 1.01–1.39], p = 0.03; I2 = 72%}. Meta-regression confirmed that this association was not influenced by age (p = 0.208), CD4 cell count (p = 0.353) or the presence of antiretroviral therapy (ART) (p = 0.647). Further subgroup analysis indicated that the association was only statistically significant in studies from Africa (OR = 1.13, p = 0.004) and the United States (OR = 1.30, p = 0.006).Conclusion: Whilst all persons ought to receive a SARS-CoV-2 vaccine, PLWH should be prioritised to minimise the risk of death because of COVID-19. The presence of HIV should be regarded as an important risk factor for future risk stratification of COVID-19.

Symmetry structure of the interactions in near-BPS corners of $$ \mathcal{N} $$ = 4 super-Yang-Mills

We consider limits of $$ \mathcal{N} $$ N = 4 super-Yang-Mills (SYM) theory that approach BPS bounds. These limits result in non-relativistic near-BPS theories that describe the effective dynamics near the BPS bounds and upon quantization are known as Spin Matrix theories. The near-BPS theories can be obtained by reducing $$ \mathcal{N} $$ N = 4 SYM on a three-sphere and integrating out the fields that become non-dynamical in the limits. We perform the sphere reduction for the near-BPS limit with SU(1, 2|2) symmetry, which has several new features compared to the previously considered cases with SU(1) symmetry, including a dynamical gauge field. We discover a new structure in the classical limit of the interaction term. We show that the interaction term is built from certain blocks that comprise an irreducible representation of the SU(1, 2|2) algebra. Moreover, the full interaction term can be interpreted as a norm in the linear space of this representation, explaining its features including the positive definiteness. This means one can think of the interaction term as a distance squared from saturating the BPS bound. The SU(1, 1|1) near-BPS theory, and its subcases, is seen to inherit these features. These observations point to a way to solve the strong coupling dynamics of these near-BPS theories.

Understanding Conformational Polymorphism in Ganciclovir: A Holistic Approach

We present a holistic crystallographic study of the antiviral ganciclovir, including insights into its solid-state behavior, which could prove useful during drug development, making the process more sustainable. A newly developed methodology was used incorporating a combination of statistical and thermodynamic approaches, which can be applied to various crystalline materials. We demonstrate how the chemical environment and orientation of a functional group can affect its accessibility for participation in hydrogen bonding. The difference in the nature and strength of intermolecular contacts between the two anhydrous forms, exposed through full interaction maps and Hirshfeld surfaces, leads to the manifestation of conformational polymorphism. Variations in the intramolecular geometry and intermolecular interactions of both forms of ganciclovir were identified as possible predictors for their relative thermodynamic stability. It was shown through energy frameworks how the extensive supramolecular network of contacts in form I causes a higher level of compactness and lower enthalpy relative to form II. The likelihood of the material to exhibit polymorphism was assessed through a hydrogen bond propensity model, which predicted a high probability associated with the formation of other relatively stable forms. However, this model failed to classify the stability of form I appropriately, suggesting that it might not have fully captured the collective impacts which govern polymorphic stability.

Interaction Between the SNARE SYP121 and the Plasma Membrane Aquaporin PIP2;7 Involves Different Protein Domains

Plasma membrane intrinsic proteins (PIPs) are channels facilitating the passive diffusion of water and small solutes. Arabidopsis PIP2;7 trafficking occurs through physical interaction with SNARE proteins including the syntaxin SYP121, a plasma membrane Qa-SNARE involved in membrane fusion. To better understand the interaction mechanism, we aimed at identifying the interaction motifs in SYP121 and PIP2;7 using ratiometric bimolecular fluorescence complementation assays in Nicotiana benthamiana. SYP121 consists of four regions, N, H, Q, and C, and sequential deletions revealed that the C region, containing the transmembrane domain, as well as the H and Q regions, containing the Habc and Qa-SNARE functional domains, interact with PIP2;7. Neither the linker between the Habc and the Qa-SNARE domains nor the H or Q regions alone could fully restore the interaction with PIP2;7, suggesting that the interacting motif depends on the conformation taken by the HQ region. When investigating the interacting motif(s) in PIP2;7, we observed that deletion of the cytosolic N- and/or C- terminus led to a significant decrease in the interaction with SYP121. Shorter deletions revealed that at the N-terminal amino acid residues 18–26 were involved in the interaction. Domain swapping experiments between PIP2;7 and PIP2;6, a PIP isoform that does not interact with SYP121, showed that PIP2;7 N-terminal part up to the loop C was required to restore the full interaction signal, suggesting that, as it is the case for SYP121, the interaction motif(s) in PIP2;7 depend on the protein conformation. Finally, we also showed that PIP2;7 physically interacted with other Arabidopsis SYP1s and SYP121 orthologs.

Simulation of Partial Interaction for Composite Deck of “Al-SABTEA Bridge” Taking The Influence of Static Vehicle loads

The composite bridge has consisted of different materials such as the girder to be steel or precast that connected with deck concrete slab using shear connectors for working as one. In the present study ALSABTEA bridge rehabilitation of the space of the bridge using the composite steel girder existing composite bridge constructed in Diyala-Iraq in 1981 that designed and constructed to behave as full interaction. Representation of composite steel bridge using finite element approach with different parameters to assess the doing of the composite bridge under the effects of static loading using actual dimensions and mechanical properties. The representation of channel shear connectors through elements of COMBIN39 provided simple and powerful modeling of the connectors in comparison with using elements of the 3D solid types. Examining the push-out test and comparing results with the model established by ANSYS proved the proposed numerical model could represent the shear connector's behavior. The difference is small (2.5% to 3.7%) between the model by using the representation shear connector as solid element and combined 39 also, the difference in the results of displacement is small (5%) between the experimental test and model established by ANSYS. The effect has been studied included. Partial and full interaction of Al-SABTEA Bridge under the effects of Static loadings applied at bridge based on Iraqi specification where the final assessment the results deflection within permissible limits according to all models.

Autoencoders for Drug-Target Interaction Prediction

Background: Because it is so laborious and expensive to experimentally identify Drug-Target Interactions (DTIs), only a few DTIs have been verified. Computational methods are useful for identifying DTIs in biological studies of drug discovery and development. Results: For drug-target interaction prediction, we propose a novel neural network architecture, DAEi, extended from Denoising AutoEncoder (DAE). We assume that a set of verified DTIs is a corrupted version of the full interaction set. We use DAEi to learn latent features from corrupted DTIs to reconstruct the full input. Also, to better predict DTIs, we add some similarities to DAEi and adopt a new nonlinear method for calculation. Similarity information is very effective at improving the prediction of DTIs. Conclusion: Results of the extensive experiments we conducted on four real data sets show that our proposed methods are superior to other baseline approaches.Availability: All codes in this paper are open-sourced, and our projects are available at: https://github.com/XiuzeZhou/DAEi.