Activity

Movement screens are frequently used to identify differences in movement patterns such as pathological abnormalities or skill related differences in sport; however, abnormalities are often visually detected by a human assessor resulting in poor reliability. Therefore, our previous research has focused on the development of an objective movement assessment tool to classify elite and novice athletes’ kinematic data using machine learning algorithms. Classifying elite and novice athletes can be beneficial to objectively detect differences in movement patterns between the athletes, which can then be used to provide higher quality feedback to athletes and their coaches. Currently, the method requires optical motion capture, which is expensive and time-consuming to use, creating a barrier for adoption within industry. Therefore, the purpose of this study was to assess whether machine learning could classify athletes as elite or novice using data that can be collected easily and inexpensively in the field using inerily implemented in the field to potentially enhance screening, assessment, and rehabilitation in sport and clinical settings.The rate-limiting component of cellulase for efficient degradation of lignocellulosic biomass through the enzymatic route depends on glucosidase’s sensitivity to the end product (glucose). Therefore, there is still a keen interest in finding glucose-tolerant β-glucosidase (BGL) that is active at high glucose concentrations. The main objective of this study was to identify, isolate, and characterize novel highly glucose-tolerant and halotolerant β-glucosidase gene (PersiBGL1) from the mixed genome DNA of sheep rumen metagenome as a suitable environment for efficient cellulase by computationally guided experiments instead of costly functional screening. At first, an in silico screening approach was utilized to find primary candidate enzymes with superior properties. The structure-dependent mechanism of glucose tolerance was investigated for candidate enzymes. Among the computationally selected candidates, PersiBGL1 was cloned, isolated, and structurally characterized, which achieved very high activity in relatively high temperatures and alkaline pH and was successfully used for the hydrolysis of cellobiose. This enzyme exhibits a very high glucose tolerance, with the highest inhibition constant K i (8.8 M) among BGLs reported so far and retained 75% of its initial activity in the presence of 10 M glucose. Furthermore, a group of multivalent metal, including Mg2+, Mn2+, and Ca2+, as a cofactor, could improve the catalytic efficiency of PersiBGL1. Our results demonstrated the power of computational selected candidates to discover novel glucose tolerance BGL, effective for the bioconversion of lignocellulosic biomass.Gastric cancer (GC) is the second leading cause of cancer mortality and remains the fourth common cancer worldwide. The effective and feasible methods for predicting the possible outcomes for GC patients are still lacking. While genetic profiling might be suitable in some way, the application of gene expression signatures has been show to be a robust tool. Here, by performing a comprehensive search in PubMed, we provided an up-to-date summary of 39 prognostic gene signatures for GC patients, and described the processing procedure of the selection, calculation and construction of gene signature. We also reviewed current web tools including PROGgene and SurvExpress that can be used to analyze the prognostic value of multiple genes for GC. This review will aid in comprehensive understanding of the current prognostic gene signatures to accurately predict the outcome of GC patients, and may guide the future clinical management when the reliability of these signatures is validated in clinics.Over the past decade, the use of polymers as platform materials for biomedical applications including tissue engineering has been of rising interest. Recently, the use of naturally derived polysaccharides as 3-D scaffolds for tissue regeneration has shown promising material characteristics; however, due to complexities in composition, morphology, and optical properties, adequate spatial and temporal characterization of cellular behavior in these materials is lacking. Multiphoton microscopy has emerged as a viable tool for performing such quantification by permitting greater imaging depth while simultaneously minimizing un-favorable scattering and producing high-resolution optical cross sections for non-invasive analysis. Here we describe a method using endogenous contrast of cellulose nanofibers (CNF) using Second Harmonic Generation (SHG), combined with 2-photon fluorescence of Cell Tracker Orange for spatial and longitudinal imaging of cellular proliferation. Cell Tracker Orange is an ideal fluorophore to avoid the broad CNF autofluorescence allowing for segmentation of cells using a semi-automatic routine. Individual cells were identified using centroid locations for 3D cell proliferation. Overall, the methods presented are viable for investigation of cellular interactions with polysaccharide candidate biomaterials.In the last decade, a large number of genome-wide association studies have uncovered many single-nucleotide polymorphisms (SNPs) that are associated with complex traits and confer susceptibility to diseases, such as cancer. However, so far only a few heritable traits with medium-to-high penetrance have been identified. click here The vast majority of the discovered variants only leads to disease in combination with other still unknown factors. Furthermore, while many studies aimed to link the effect of SNPs to changes in molecular phenotypes, the analysis has been often focused on testing associations between a single SNP and a transcript, hence disregarding the dysregulation of gene regulatory networks that has been shown to play an essential role in disease onset, notably in cancer. Here we take a systems biology approach and develop GVITamIN (Genetic VarIaTIoN functional analysis tool), a new statistical and computational approach to characterize the effect of a SNP on both genes and transcriptional regulatory programs.