Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models

BACKGROUND: The binding between peptide epitopes and major histocompatibility complex proteins (MHCs) is an important event in the cellular immune response. Accurate prediction of the binding between short peptides and the MHC molecules has long been a principal challenge for immunoinformatics. Recently, the modeling of MHC-peptide binding has come to emphasize quantitative predictions: instead of categorizing peptides as "binders" or "non-binders" or as "strong binders" and "weak binders", recent methods seek to make predictions about precise binding affinities. RESULTS: We developed a quantitative support vector machine regression (SVR) approach, called SVRMHC, to model peptide-MHC binding affinities. As a non-linear method, SVRMHC was able to generate models that out-performed existing linear models, such as the "additive method". By adopting a new "11-factor encoding" scheme, SVRMHC takes into account similarities in the physicochemical properties of the amino acids constituting the input peptides. When applied to MHC-peptide binding data for three mouse class I MHC alleles, the SVRMHC models produced more accurate predictions than those produced previously. Furthermore, comparisons based on Receiver Operating Characteristic (ROC) analysis indicated that SVRMHC was able to out-perform several prominent methods in identifying strongly binding peptides. CONCLUSION: As a method with demonstrated performance in the quantitative modeling of MHC-peptide binding and in identifying strong binders, SVRMHC is a promising immunoinformatics tool with not inconsiderable future potential

Comparative analysis of phosphoproteomic in the intestine of Sepia lycidas under different salinity environments

Cuttlefish are sensitive to the breeding environment, and the low-salinity environment significantly impacts their growth and immunity. So far, it is difficult to breed this species artificially. This study was conducted in Sepia lycidas. And the aim was to investigate the differences in protein phosphorylation in the intestine of S. lycidas under different salinity conditions. Firstly, 999 phosphoproteins (specific peptide ≥ 1), 1928 phosphopeptides, and 2727 phosphorylation sites were identified. Among them were 284 down-regulated expression phosphorylation sites (corresponding to 115 phosphoproteins) and 674 up-regulated expression phosphorylation sites (corresponding to 408 phosphoproteins) in the intestine under a low salinity environment compared with that under a natural salinity environment. Next, GO analysis found that more phosphoproteins corresponding to differentially expressed phosphorylation sites were related to anatomical structure development, multicellular organism development, regulation of the cellular process, etc. The molecular functions of these proteins mainly contain protein binding, transferase activity, catalytic activity, and heterocyclic compound binding. And they are mainly involved in the cellular components of intracellular anatomical structure, organelle, and cytoplasm. KEGG enrichment analysis of the differential phosphoproteins suggested that many significantly enriched pathways were related to the phosphatidylinositol signaling system, cell junction (adherens junction and tight junction), and inositol phosphate metabolism. Finally, changes in environmental salinity can affect the intestinal structure, metabolism, and immune homeostasis of S. lycidas

SVRMHC prediction server for MHC-binding peptides

BACKGROUND: The binding between antigenic peptides (epitopes) and the MHC molecule is a key step in the cellular immune response. Accurate in silico prediction of epitope-MHC binding affinity can greatly expedite epitope screening by reducing costs and experimental effort. RESULTS: Recently, we demonstrated the appealing performance of SVRMHC, an SVR-based quantitative modeling method for peptide-MHC interactions, when applied to three mouse class I MHC molecules. Subsequently, we have greatly extended the construction of SVRMHC models and have established such models for more than 40 class I and class II MHC molecules. Here we present the SVRMHC web server for predicting peptide-MHC binding affinities using these models. Benchmarked percentile scores are provided for all predictions. The larger number of SVRMHC models available allowed for an updated evaluation of the performance of the SVRMHC method compared to other well- known linear modeling methods. CONCLUSION: SVRMHC is an accurate and easy-to-use prediction server for epitope-MHC binding with significant coverage of MHC molecules. We believe it will prove to be a valuable resource for T cell epitope researchers

Integrated siRNA design based on surveying of features associated with high RNAi effectiveness

BACKGROUND: Short interfering RNAs have allowed the development of clean and easily regulated methods for disruption of gene expression. However, while these methods continue to grow in popularity, designing effective siRNA experiments can be challenging. The various existing siRNA design guidelines suffer from two problems: they differ considerably from each other, and they produce high levels of false-positive predictions when tested on data of independent origins. RESULTS: Using a distinctly large set of siRNA efficacy data assembled from a vast diversity of origins (the siRecords data, containing records of 3,277 siRNA experiments targeting 1,518 genes, derived from 1,417 independent studies), we conducted extensive analyses of all known features that have been implicated in increasing RNAi effectiveness. A number of features having positive impacts on siRNA efficacy were identified. By performing quantitative analyses on cooperative effects among these features, then applying a disjunctive rule merging (DRM) algorithm, we developed a bundle of siRNA design rule sets with the false positive problem well curbed. A comparison with 15 online siRNA design tools indicated that some of the rule sets we developed surpassed all of these design tools commonly used in siRNA design practice in positive predictive values (PPVs). CONCLUSION: The availability of the large and diverse siRNA dataset from siRecords and the approach we describe in this report have allowed the development of highly effective and generally applicable siRNA design rule sets. Together with ever improving RNAi lab techniques, these design rule sets are expected to make siRNAs a more useful tool for molecular genetics, functional genomics, and drug discovery studies

Simulation of CSSTs astrometric capability

The China Space Station Telescope (CSST) will enter a low Earth orbit around 2024 and operate for 10 years, with seven of those years devoted to surveying the area of the median-to-high Galactic latitude and median-to-high Ecliptic latitude of the sky. To maximize the scientific output of CSST, it is important to optimize the survey schedule. We aim to evaluate the astrometric capability of CSST for a given survey schedule and to provide independent suggestions for the optimization of the survey strategy. For this purpose, we first construct the astrometric model and then conduct simulated observations based on the given survey schedule. The astrometric solution is obtained by analyzing the simulated observation data. And then we evaluate the astrometric capability of CSST by analyzing the properties of the astrometric solution. We find that the accuracy of parallax and proper motion of CSST is better than 1 mas( yr1) for the sources of 18-22 mag in g band, and about 1-10 mas( yr1) for the sources of 22-26 mag in g band, respectively. The results from real survey could be worse since the assumptions are optimistic and simple. We find that optimizing the survey schedule can improve the astrometric accuracy of CSST. In the future, we will improve the astrometric capability of CSST by continuously iterating and optimizing the survey schedule.Comment: 17 pages, 10 figure

Exploring the Antibacterial Potential and Underlying Mechanisms of Prunella vulgaris L on Methicillin-Resistant Staphylococcus aureus

Prunella vulgaris L. (PV) is a widely distributed plant species, known for its versatile applications in both traditional and contemporary medicine, as well as in functional food development. Despite its broad-spectrum antimicrobial utility, the specific mechanism of antibacterial action remains elusive. To fill this knowledge gap, the present study investigated the antibacterial properties of PV extracts against methicillin-resistant Staphylococcus aureus (MRSA) and assessed their mechanistic impact on bacterial cells and cellular functions. The aqueous extract of PV demonstrated greater anti-MRSA activity compared to the ethanolic and methanolic extracts. UPLC-ESI-MS/MS tentatively identified 28 phytochemical components in the aqueous extract of PV. Exposure to an aqueous extract at ½ MIC and MIC for 5 h resulted in a significant release of intracellular nucleic acid (up to 6-fold) and protein (up to 10-fold) into the extracellular environment. Additionally, this treatment caused a notable decline in the activity of several crucial enzymes, including a 41.51% reduction in alkaline phosphatase (AKP), a 45.71% decrease in adenosine triphosphatase (ATPase), and a 48.99% drop in superoxide dismutase (SOD). Furthermore, there was a decrease of 24.17% at ½ MIC and 27.17% at MIC in tricarboxylic acid (TCA) cycle activity and energy transfer. Collectively, these findings indicate that the anti-MRSA properties of PV may stem from its ability to disrupt membrane and cell wall integrity, interfere with enzymatic activity, and impede bacterial cell metabolism and the transmission of information and energy that is essential for bacterial growth, ultimately resulting in bacterial apoptosis. The diverse range of characteristics exhibited by PV positions it as a promising antimicrobial agent with broad applications for enhancing health and improving food safety and quality

Checkpointing with Time Gaps for Unsteady Adjoint CFD

© Springer International Publishing AG 2019. Gradient-based optimisation using adjoints is an increasingly common approach for industrial flow applications. For cases where the flow is largely unsteady however, the adjoint method is still not widely used, in particular because of its prohibitive computational cost and memory footprint. Several methods have been proposed to reduce the peak memory usage, such as checkpointing schemes or checkpoint compression, at the price of increasing the computational cost even further. We investigate incomplete checkpointing as an alternative, which reduces memory usage at almost no extra computational cost, but instead offers a trade-off between memory footprint and the fidelity of the model. The method works by storing only selected physical time steps and using interpolation to reconstruct time steps that have not been stored. We show that this is enough to compute sufficiently accurate adjoint sensitivities for many relevant cases, and does not add significantly to the computational cost. The method works for general cases and does not require to identify periodic cycles in the flow

Effect of Gradually Decreasing Photoperiod on Immune Function in Siberian Hamsters

Animals usually use photoperiod as an important environmental cue to time the year. In terms of the winter immunocompetence enhancement hypothesis, animals in the non-tropical zone would actively enhance their immune function to decrease the negative influence of stressors such as low temperature and food shortage in winter. In the present study, we mimicked the transition from summer to winter by decreasing photoperiod gradually and examined the variations of immune repsonses in Siberian hamsters (Phodopus sungorus)  to test this hypothesis. Twenty two female adult hamsters were randomly divided into the control (12h light: 12h dark, Control, n=11) and the gradually decreasing photoperiod group (Experiment, n=11). In the experiment group, day length was decreased from 12 h: 12 h light-dark cycle to 8 h: 16 h light-dark cycle at the pace of half an hour per week. We found that gradually decreasing photoperiod had no effect on body composition (wet carcass mass, subcutaneous, retroperitoneal, mesenteric and total body fat mass) and the masses of the organs detected such as brain, heart, liver and so on in hamsters. Similarly, immunological parameters including immune organs (thymus and spleen), white blood cells and serum bacteria killing capacity indicative of innate immunity were also not influenced by gradually decreasing photoperiod, which did not support the winter immunocompetence enhancement hypothesis. However, gradually decreasing photoperiod increased phytohaemagglutinin response post-24h of PHA challenge, which supported this hypothesis. There was no correlation between cellular, innate immunity and body fat mass, suggesting that body fat was not the reasons of the changes of cellular immunity. In summary, distinct components of immune system respond to gradually decreasing photoperiod differently in Siberian hamsters

The relationship between stigma and quality of life in hospitalized middle-aged and elderly patients with chronic diseases: the mediating role of depression and the moderating role of psychological resilience

ObjectivePatients with chronic diseases may have some psychological problems due to their own or surrounding environmental factors, which can adversely affect the patient’s illness and life. Given that the number of chronically ill patients in China is currently increasing every year, more research is needed to determine the best ways to manage changes in psychological status and psychological stress responses in chronically ill patients. The researchers constructed a mediated moderation model to explore the impact of stigma on the quality of life of chronically ill patients, as well as the mediating role of depression and the moderating role of psychological resilience.MethodsA stratified sampling method was used to select 363 middle-aged and old-aged patients with chronic diseases aged 45 years and older from the Affiliated Hospital of Zhejiang University for the study. Data were collected from patients with chronic diseases such as cardiac, respiratory, renal, and other chronic diseases using the Cumulative Illness Rating Scale for Geriatrics (CIRS-G), the Stigma Scale for Patients with Chronic Diseases (SSCI), the Patient Health Questionaire-9 (PHQ-9), the Quality of Life Inventory (SF-12), and the Conner-Davidson Resilience Scale (CD-RISC) were collected from patients with cardiac, respiratory, renal, and other chronic diseases. A descriptive analysis was used to describe the sample. Linear regression was used to evaluate the relationship between the variables. Mediation and moderation analyses were used to explore the mediating role of depression and the moderating role of psychological resilience.ResultsThere was a moderate negative correlation between stigma and quality of life (r = -0.378, P < 0.01). There was a moderate negative correlation between depression and quality of life (r = -0.497, P < 0.01). There was a moderately positive correlation between psychological resilience and quality of life (r = 0.382, P < 0.01). There was a moderate negative correlation between psychological resilience and depression (r = -0.348, P < 0.01). There was a weak negative correlation between psychological resilience and stigma (r = -0.166, P < 0.01). There was a strong positive correlation between stigma and depression (r = 0.607, P < 0.01) The mediation study showed that stigma was a significant predictor of quality of life and that stigma and quality of life were mediated to some extent by depression, with the mediating effect accounting for 67.55% of the total effect. The direct path from stigma to depression is moderated by psychological resilience (β = -0.0018, P < 0.01).ConclusionsDepression mediates the relationship between stigma and quality of life, while psychological elasticity plays a moderating role between stigma and depression, and when the level of psychological elasticity increases, the more significant the role of stigma on depression. As a physiologically and psychologically vulnerable group, patients with chronic diseases’ overall quality of life and mental health should be taken more seriously, and clinical workers should pay timely attention to the psychological and mental conditions of patients with chronic diseases and provide timely and appropriate interventions and therapeutic measures. The relevant results of this study also provide a new perspective for clinical work on psychological intervention for patients with chronic diseases