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Abstract Objective To explore the association between cardiovascular health (CVH) measured by Life’s Essential 8 (LE8) and the prevalence of urinary incontinence (UI). Method A cross-section study was conducted using data from the National Health and Nutrition Examination Survey 2007–2012. 22,609 people aged ≥ 20 years with complete information on LE8 metrics and UI questionnaires were enrolled. Participants were divided into three groups (low:

Major histocompatibility complex Class II (MHCII) proteins initiate and regulate immune responses by presentation of antigenic peptides to CD4+ T-cells and self-restriction. The interactions between MHCII and peptides determine the specificity of the immune response and are crucial in immunotherapy and cancer vaccine design. With the ever-increasing amount of MHCII-peptide binding data available, many computational approaches have been developed for MHCII-peptide interaction prediction over the last decade. There is thus an urgent need to provide an up-to-date overview and assessment of these newly developed computational methods. To benchmark the prediction performance of these methods, we constructed an independent dataset containing binding and non-binding peptides to 20 human MHCII protein allotypes from the Immune Epitope Database, covering DP, DR and DQ alleles. After collecting 11 known predictors up to January 2022, we evaluated those available through a webserver or standalone packages on this independent dataset. The benchmarking results show that MixMHC2pred and NetMHCIIpan-4.1 achieve the best performance among all predictors. In general, newly developed methods perform better than older ones due to the rapid expansion of data on which they are trained and the development of deep learning algorithms. Our manuscript not only draws a full picture of the state-of-art of MHCII-peptide binding prediction, but also guides researchers in the choice among the different predictors. More importantly, it will inspire biomedical researchers in both academia and industry for the future developments in this field.

ObjectiveThis study aims to examine the correlation between overactive bladder (OAB) and food insecurity.MethodsWe conducted a cross-sectional analysis utilizing extensive population data derived from the National Health and Nutrition Examination Survey 2007–2018. The status of Household food insecurity is evaluated by the US Food Security Survey Module. To explore the relationship between food insecurity and OAB, three multivariable logistic regression models were carried out. Additionally, interaction and stratified analyses were also performed to find whether some factors have the potential to alter the correlation.ResultsThere were 29,129 participants enrolled in the study. Compared to the other three groups, individuals with full food security exhibited a lower proportion of nocturia, urinary urgency incontinence, and OAB. In the fully-adjusted model, it was found that people experiencing food insecurity have a significantly higher prevalence of OAB compared to those with food security in the fully-adjusted model (OR = 1.540, 95%CI 1.359–1.745). Additionally, there was a significant association between the levels of food insecurity and an increased risk of OAB prevalence was also observed (marginal food security: OR = 1.312, low food security: OR = 1.559, and very low food security: OR = 1.759). No significant interaction was seen in the fully-adjusted model.ConclusionThere is a strong positive correlation between food insecurity and the prevalence of OAB. Similarly, the correlation between levels of food insecurity and OAB also indicates the same trend. Namely, the more insecure food, the higher risk of OAB prevalence in the population.

Intrinsically disordered proteins (IDPs) participate in many biological processes by interacting with other proteins, including the regulation of transcription, translation, and the cell cycle. With the increasing amount of disorder sequence data available, it is thus crucial to identify the IDP binding sites for functional annotation of these proteins. Over the decades, many computational approaches have been developed to predict protein-protein binding sites of IDP (IDP-PPIS) based on protein sequence information. Moreover, there are new IDP-PPIS predictors developed every year with the rapid development of artificial intelligence. It is thus necessary to provide an up-to-date overview of these methods in this field. In this paper, we collected 30 representative predictors published recently and summarized the databases, features and algorithms. We described the procedure how the features were generated based on public data and used for the prediction of IDP-PPIS, along with the methods to generate the feature representations. All the predictors were divided into three categories: scoring functions, machine learning-based prediction, and consensus approaches. For each category, we described the details of algorithms and their performances. Hopefully, our manuscript will not only provide a full picture of the status quo of IDP binding prediction, but also a guide for selecting different methods. More importantly, it will shed light on the inspirations for future development trends and principles.

Various substances that possess liquid states include drinking water, various types of fuel, pharmaceuticals, and chemicals, which are indispensable in our daily lives. There are numerous real-world applications for liquid content detection in transparent containers, for example, service robots, pouring robots, security checks, industrial observation systems, etc. However, the majority of the existing methods either concentrate on transparent container detection or liquid height estimation; the former provides very limited information for more advanced computer vision tasks, whereas the latter is too demanding to generalize to open-world applications. In this paper, we propose a dataset for detecting liquid content in transparent containers (LCDTC), which presents an innovative task involving transparent container detection and liquid content estimation. The primary objective of this task is to obtain more information beyond the location of the container by additionally providing certain liquid content information which is easy to achieve with computer vision methods in various open-world applications. This task has potential applications in service robots, waste classification, security checks, and so on. The presented LCDTC dataset comprises 5916 images that have been extensively annotated through axis-aligned bounding boxes. We develop two baseline detectors, termed LCD-YOLOF and LCD-YOLOX, for the proposed dataset, based on two identity-preserved human posture detectors, i.e., IPH-YOLOF and IPH-YOLOX. By releasing LCDTC, we intend to stimulate more future works into the detection of liquid content in transparent containers and bring more focus to this challenging task.

Theaflavin-3,3′-digallate (TF3) is a representative theaflavin of black tea and is remarkable for the anti-coronary heart disease effect. As an adaptive response to heart failure, pathological cardiac hypertrophy (PCH) has attracted great interest. In this study, the PCH cell model was established with H9c2 cells by angiotensin II, and the prevention effect and mechanisms of TF3 were investigated. The results showed that the cell size and fetal gene mRNA level were significantly reduced as pretreated with TF3 at the concentration range of 1–10 μM, also the balance of the redox system was recovered by TF3 at the concentration of 10 μM. The intracellular Ca2+ level decreased, Calcineurin (CaN) expression was down-regulated and the p-NFATc3 expression was up-regulated. These results indicated that TF3 could inhibit the activation of the CaN-NFAT signal pathway to prevent PCH, and TF3 may be a potentially effective natural compound for PCH and heart failure.

The gaseous plant hormone ethylene influences many physiological processes in plant growth and development. Plant ethylene responses are mediated by a family of ethylene receptors, in which the N-terminal transmembrane domains are responsible for ethylene binding and membrane localization. Until now, little structural information was available on the molecular mechanism of ethylene responses by the transmembrane binding domain of ethylene receptors. Here, we screened different constructs, fusion tags, detergents, and purification methods of the transmembrane sensor domain of ethylene receptors. However, due to their highly hydrophobic transmembrane domain (TMD), only a KSI-fused LeETR21–131 from tomato yielded a good-quality nuclear magnetic resonance (NMR) spectrum in the organic solvent. Interestingly, a dimer model of LeETR21–131 built by the AlphaFold2 algorithm showed greatly converged structures. The interaction analysis of ethylene and LeETR21–131 using molecular docking and molecular dynamics (MD) simulations demonstrated the potential binding sites of ethylene in LeETR21–131. Our exploration provides valuable knowledge for further understanding of the ethylene-perception process in ethylene receptors.

Necroptosis is a type of programmed cell death executed through the plasma membrane disruption by mixed lineage kinase domain-like protein (MLKL). Previous studies have revealed that an N-terminal four-helix bundle domain (NBD) of MLKL is the executioner domain for the membrane permeabilization, which is auto-inhibited by the first brace helix (H6). After necroptosis initiation, this inhibitory brace helix detaches and the NBD can integrate into the membrane, and hence leads to necroptotic cell death. However, how the NBD is released and induces membrane rupture is poorly understood. Here, we reconstituted MLKL2–154 into membrane mimetic bicelles and observed the structure disruption and membrane release of the first brace helix that is regulated by negatively charged phospholipids in a dose-dependent manner. Using molecular dynamics simulation we found that the brace region in an isolated, auto-inhibited MLKL2–154 becomes intrinsically disordered in solution after 7 ns dynamic motion. Further investigations demonstrated that a cluster of arginines in the C-terminus of MLKL2–154 is important for the molecular conformational switch. Functional mutagenesis showed that mutating these arginines to glutamates hindered the membrane disruption of full-length MLKL and thus inhibited the necroptotic cell death. These findings suggest that the brace helix also plays an active role in MLKL regulation, rather than an auto-inhibitory domain.

CdS with well-defined crystallinity is anchored on carbon nitride photoelectrodes by a successive chemical bath deposition. And the as-synthesized samples were characterized by X-ray diffraction, scanning electron microscopy, transmission electron microscopy, ultraviolet-visible diffuse reflection spectroscopy, X-ray photoelectron spectroscopy, and photoluminescence spectroscopy techniques. The effect of the amount of CdS on the catalytic activity for the degradation of acid Orange II is investigated under visible light irradiation. Results show that the photoelectrodes composed of CdS/CN exhibit much higher catalytic activity than pure CN photoelectrodes. A possible photocatalytic mechanism of the CdS/CN electrodes is proposed under visible light irradiation.

Major histocompatibility complex Class II (MHCII) proteins initiate and regulate immune responses by presentation of antigenic peptides to CD4+ T-cells and self-restriction. The interactions between MHCII and peptides determine the specificity of the immune response and are crucial in immunotherapy and cancer vaccine design. With the ever-increasing amount of MHCII-peptide binding data available, many computational approaches have been developed for MHCIIpeptide interaction prediction over the last decade. There is thus an urgent need to provide an up-to-date overview and assessment of these newly developed computational methods. To benchmark the prediction performance of these methods, we constructed an independent dataset containing binding and nonbinding peptides to 20 human MHCII protein allotypes from the Immune Epitope Database, covering DP, DR and DQ alleles. After collecting 11 known predictors up to January 2022, we evaluated those available through a webserver or standalone packages on this independent dataset. The benchmarking results show that MixMHC2pred and NetMHCIIpan-4.1 achieve the best performance among all predictors. In general, newly developed methods perform better than older ones due to the rapid expansion of data on which they are trained and the development of deep learning algorithms. Our manuscript not only draws a full picture of the state-of-art of MHCII-peptide binding prediction, but also guides researchers in the choice among the different predictors. More importantly, it will inspire biomedical researchers in both academia and industry for the future developments in this field. [ABSTRACT FROM AUTHOR]