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Identifying hyperactive kinases in cancer is crucial for individualized treatment with specific inhibitors. Kinase activity can be discerned from global protein phosphorylation profiles obtained with mass spectrometry‐based phosphoproteomics. A major challenge is to relate such profiles to specific hyperactive kinases fueling growth/progression of individual tumors. Hitherto, the focus has been on phosphorylation of either kinases or their substrates. Here, we combined label‐free kinase‐centric and substrate‐centric information in an Integrative Inferred Kinase Activity (INKA) analysis. This multipronged, stringent analysis enables ranking of kinase activity and visualization of kinase–substrate networks in a single biological sample. To demonstrate utility, we analyzed (i) cancer cell lines with known oncogenes, (ii) cell lines in a differential setting (wild‐type versus mutant, +/− drug), (iii) pre‐ and on‐treatment tumor needle biopsies, (iv) cancer cell panel with available drug sensitivity data, and (v) patient‐derived tumor xenografts with INKA‐guided drug selection and testing. These analyses show superior performance of INKA over its components and substrate‐based single‐sample tool KARP, and underscore target potential of high‐ranking kinases, encouraging further exploration of INKA's functional and clinical value. Synopsis INKA (Integrative Inferred Kinase Activity) is an integrative data analysis approach ranking kinase activities in mass spectrometry‐based phosphoproteome data derived from single samples. INKA reveals oncogenes, differential kinase activity and drug targets. INKA combines kinase‐centric and substrate‐centric information and enables ranking kinase activities and visualizing kinase‐substrate networks in a single biological sample. INKA shows superior performance over its four components. INKA can be applied to both label‐free count and intensity data and was modified to accommodate labeling data. INKA can be used both for single‐sample and differential analysis and provides a versatile tool that can condense complex phosphoproteome data to actionable results. Graphical Abstract INKA (Integrative Inferred Kinase Activity) is an integrative data analysis approach ranking kinase activities in mass spectrometry‐based phosphoproteome data derived from single samples. INKA reveals oncogenes, differential kinase activity and drug targets.

We propose a high dimensional dynamic model for tennis match results with time varying player-specific abilities for different court surface types. Our statistical model can be treated in a likelihood-based analysis and can handle high dimensional data sets while the number of parameters remains small. In particular, we analyse 17 years of tennis matches for a panel of over 500 players, which leads to more than 2000 dynamic strength levels. We find that time varying player-specific abilities for different court surfaces are of key importance for analysing tennis matches. We further consider several other extensions including player-specific explanatory variables and the match configurations for Grand Slam tournaments. The estimation results can be used to construct rankings of players for different court surface types. We finally show that our proposed model produces accurate forecasts. We provide evidence that our model significantly outperforms existing models in the forecasting of tennis match results.

Spatial transcriptomic studies are reaching single-cell spatial resolution, with data often collected from multiple tissue sections. Here, we present a computational method, BASS, that enables multi-scale and multi-sample analysis for single-cell resolution spatial transcriptomics. BASS performs cell type clustering at the single-cell scale and spatial domain detection at the tissue regional scale, with the two tasks carried out simultaneously within a Bayesian hierarchical modeling framework. We illustrate the benefits of BASS through comprehensive simulations and applications to three datasets. The substantial power gain brought by BASS allows us to reveal accurate transcriptomic and cellular landscape in both cortex and hypothalamus.

Summary Biological assay has been based on analysis of all individuals collected from sample populations. Bulked sample analysis (BSA), which works with selected and pooled individuals, has been extensively used in gene mapping through bulked segregant analysis with biparental populations, mapping by sequencing with major gene mutants and pooled genomewide association study using extreme variants. Compared to conventional entire population analysis, BSA significantly reduces the scale and cost by simplifying the procedure. The bulks can be built by selection of extremes or representative samples from any populations and all types of segregants and variants that represent wide ranges of phenotypic variation for the target trait. Methods and procedures for sampling, bulking and multiplexing are described. The samples can be analysed using individual markers, microarrays and high‐throughput sequencing at all levels of DNA, RNA and protein. The power of BSA is affected by population size, selection of extreme individuals, sequencing strategies, genetic architecture of the trait and marker density. BSA will facilitate plant breeding through development of diagnostic and constitutive markers, agronomic genomics, marker‐assisted selection and selective phenotyping. Applications of BSA in genetics, genomics and crop improvement are discussed with their future perspectives.

The study proposes to include the financial analysis (FA) in optimal portfolio selection. The role of FA in investment decisions is well recognized. While comparing two stocks on FA of their companies it is important to have both drawn from the same sector of economy. This reason motivated us to propose a sectoral portfolio optimization (SPO) which, instead of looking to optimize among all stocks together, focuses on optimizing stocks within each sector on the basis of FA. These stocks are then pooled together and an optimal portfolio is formed from them with their FA weights and mean returns. In context of FA, the four financial ratios included in present study are return on asset (profitable ratio), debt-assets ratio (solvency ratio), current ratio (liquidity ratio), and price-to-earning ratio (valuation ratio). The risk in a portfolio is quantified using the second order stochastic dominance and to this effect constraints are added in the selection process to generate optimal portfolios for rational risk averse investors. The performance of the optimal portfolios from the proposed model is tested against the portfolios from the traditional second order stochastic dominance model [named (SSDP) in this work], the benchmark index and four 5-star rated mutual funds of India from diversified equity. The out-of-sample analysis is carried on mean returns, Sharpe ratio, Sortino ratio, and also their ability to dominate the benchmark index in almost second order stochastic dominance sense over the tolerable violation regions. The stock price data for the period April 2004 to November 2014 of S&P BSE 500 index is used for testing the models. The optimal portfolios generated from the SPO perform better than the portfolios generated from the (SSDP), the benchmark index and the MFs, indicating effectiveness of FA in SPO framework.

The current study explores the seasonal dynamics of microplastic (MP) pollution in the Ganga River of Varanasi City, Uttar Pradesh, India, focusing on water and sediment samples collected during pre-monsoon and post-monsoon periods. The analysis shows significant variations in MP occurrence, shape dynamics, color distribution, and size composition across diverse sampling sites. During the pre-monsoon season, MP concentrations ranged from 17 to 36 particles/L in water samples and 160 to 312 particles/kg in sediment, indicating a moderate to high level of contamination. Post-monsoon sampling showed higher MP concentrations at most sites, indicating the influence of seasonal hydrological changes on MP distribution. Shifts in MP shape dynamics were observed between seasons, with films, foams, fragments, and filaments showing variable distributions. Similarly, color variations in MPs exhibited site-specific patterns, with white, brown, blue, and other colors being predominant. These findings highlight the diverse sources and compositions of MPs in the river ecosystem, highlighting the complexity of MP pollution dynamics. Polymer-type distributions further elucidated the composition of MPs, with notable contributions from polyethylene terephthalate, rayon, polyester, and polyvinyl chloride. PCA analysis revealed significant shifts in particle size and shape distribution between pre-monsoon and post-monsoon periods in both water and sediment samples, with post-monsoon samples showing an increase in larger particles and filaments. These changes highlighted key factors driving the variance in microplastic contamination across different sites. The prevalence of these polymers features diverse sources of MP pollution, including textiles, packaging materials, and industrial waste. Ongoing monitoring and research are crucial to understanding its sources, distribution, and impact on river ecosystems, essential for protecting aquatic biodiversity and human health.

A better understanding of recirculating aquaculture system (RAS) biosecurity is crucial for the sustainable and ethical production of Atlantic salmon smolt and post-smolt in these systems. This study described and evaluated the performance of a RAS facility for fish infection research with Atlantic salmon as the main animal model. Fish body weight, length, water quality, and system metrics from five independent experimental trials conducted between September 2020 and July 2021 were used to analyze the variation within and between treatments. Statistical power analysis was performed to determine the minimum number of fish required. The fish parameters variability showed that the inter-class correlation coefficient was on average low (0.1) and that the variation within tanks was larger than the variation between the tanks. The power analysis showed that 15 fish were required to be sampled per tank under these study conditions. Variation of water quality and system management metrics among the five experimental trials was higher compared to the variation within the five experimental trials. Moreover, the variation of the water quality parameters controlled by sensors was relatively low, whereas the parameters depending on biofilter maturation level and performance presented a very high variation. Water exchange rate-dependent quality parameters showed a similar variation value, i.e., nitrate and water turbidity. The established baseline for variability and performance presents an important reference for the design and realization of future experiments in RAS facilities. It is foreseen that the current research facility will develop new knowledge to improve the RAS biosecurity in the Atlantic salmon aquaculture industry.

The present study reports on the development, adaptation, and optimization of a novel monoenzyme conductometric biosensor based on a recombinant arginine deiminase (ADI) for the determination of arginine in dietary supplements with a high accuracy of results. Aiming for the highly sensitive determination of arginine in real samples, we studied the effect of parameters of the working buffer solution (its pH, buffer capacity, ionic strength, temperature, and protein concentration) on the sensitivity of the biosensor to arginine. Thus, it was determined that the optimal buffer is a 5 mM phosphate buffer solution with pH 6.2, and the optimal temperature is 39.5 °C. The linear functioning range is 2.5–750 µM of L-arginine with a minimal limit of detection of 2 µM. The concentration of arginine in food additive samples was determined using the developed ADI-based biosensor. Based on the obtained results, the most effective method of biosensor analysis using the method of standard additions was chosen. It was also checked how the reproducibility of the biosensor changes during the analysis of pharmaceutical samples. The results of the determination of arginine in real samples using a conductometric biosensor based on ADI clearly correlated with the data obtained using the method of ion-exchange chromatography and enzymatic spectrophotometric analysis. We concluded that the developed biosensor would be effective for the accurate and selective determination of arginine in dietary supplements intended for the prevention and/or elimination of arginine deficiency.