SUNY Canton

We examine internal factors influencing success in NCAA men's college soccer, analyzing data from 62 teams in the 2023 national tournament.Three hypotheses were tested through regression and cumulative probability analyses to evaluate how tactical variables affect NCAA tournament, conference, and nonconference games.Findings show these variables explained 99% of tournament wins, 88% of conference wins, and 77% of nonconference wins.Possession was critical in conference play, while transitions and restarts had a greater impact in nonconference matches.Counterattacks delivered quick results, whereas sustained possession led to gradual gains.These findings offer strategic guidance for coaches and athletic decision-makers.

Since its first observation in the 1970s, neutrino-induced neutral-current single positive pion production (NC1π^{+}) has remained an elusive and poorly understood interaction channel. This process is a significant background in neutrino oscillation experiments and studying it further is critical for the physics program of next-generation accelerator-based neutrino oscillation experiments. In this Letter, we present the first double-differential cross-section measurement of NC1π^{+} interactions using data from the ND280 detector of the T2K experiment collected in ν-beam mode. The measured flux-averaged integrated cross section is σ=(6.07±1.22)×10^{-41} cm^{2}/nucleon. We compare the results on a hydrocarbon target to the predictions of several neutrino interaction generators and final-state-interaction models. While model predictions agree with the differential results, the data show a weak preference for a cross-section normalization approximately 30% higher than predicted by most models studied in this Letter.

Evolution has optimized proteins to balance stability and function by reducing unfavorable energy states, leading to regions of flexibility and frustration on protein surfaces. These locally frustrated regions correspond to functionally important areas, such as active sites and regions for ligand binding and conformational plasticity. Typical strategies of structure-based drug discovery primarily concentrate on enhancing the binding affinity during compound screening and target identification. However, this often overlooks the binding specificity, which is critical for distinguishing specific binding partners from competing ones and avoiding off-target effects. According to the energy landscape theory, optimization of the intrinsic binding specificity involves globally minimizing the frustrations existing in the biomolecular interactions. Recent studies have demonstrated that identifying local frustrations provides a promising approach for screening more specific compounds binding with targets, and quantifying binding specificity complements typical strategies that focus on binding affinity only. This review explores the principles and strategies of computationally quantifying the binding specificity and local frustrations and discusses their applications in structure-based drug discovery. Moreover, given the advancements of artificial intelligence in protein science, this review aims to motivate the integration of AI and available approaches in quantifying the binding specificity and local frustration. We expect that an AI-powered prediction model will accelerate the drug discovery process and improve the success rate of hit compounds.

Lung cancer is one of the most common cancers in humans. However, there is still a need to understand the underlying mechanisms of a normal cell developing into a cancer cell. Here, we develop the chromosome dynamic structural model and quantify the important characteristics of the chromosome structural ensemble of the normal lung cell and the lung cancer A549 cell. Our results demonstrate the essential relationship among the chromosome ensemble, the epigenetic marks, and the gene expressions, which suggests the linkage between chromosome structure and function. The analysis reveals that the lung cancer cell may have a higher level of relative ensemble fluctuation (micro CFI) and a higher degree of phase separation between the two compartments than the normal lung cell. In addition, the significant conformational "switching off" events (from compartment A to B) are more than the significant conformational "switching on" events during the lung cancerization. We identify "nucleation seeds" or hot spots in chromosomes, which initiate the transitions and determine the mechanisms. The hot spots and interaction network results reveal that the lung cancerization process (from normal lung to A549) and the reversion process have different mechanisms. These investigations have revealed the cell fate determination mechanism of the lung cancer process, which will be helpful for the further prevention and control of cancers.

multivariate, cluster analysis, network analysis he Chinese experienced dynamic changes in the twentieth century and few groups were as transitional as Chinese intellectuals.