Nonimmunological alterations of glomerular filtration by s-PAF in the rat kidney

Nonimmunological alterations of glomerular filtration by s-PAF in the rat kidney. Rat kidneys were isolated and perfused with a cell-free perfusion buffer containing 4% albumin. Infusion of platelet activating factor (s-PAF) into the isolated perfused kidney caused a dose-dependent fall in renal vascular resistance (RVR): 12 ± 6% at 10nM s-PAF, 18 ± 3% at 100nM s-PAF and 20 ± 7% at 1 µM. s-PAF. Glomerular filtration rate fell by 32 ± 5% at 10nM, 38 ± 6% at 100nM, and 52 ± 10% at 1 µM. s-PAF (50nM) increased urinary protein excretion after 20 minutes. Because GFR fell to a greater extent than RVR, possible changes in glomerular permeability after s-PAF treatment were assessed morphologically using native ferritin. After s-PAF treatment (100nM), the number of ferritin particles/µm2 increased from 1.2 ± 0.9 (control) to 795 ± 69 in the glomerular basement membrane (GBM) and from 0.2 ± 0.06 (control) to 98 ± 29 in lamina rara externa (LRE). To quantitate changes in fixed anionic charges, polyethylenimine (PEI) was quantitated morphologically in GBM. No significant change between s-PAF treated and untreated kidneys was seen. s-PAF did not alter the sialoglycoprotein pattern in the perfused kidney as assessed by lysozyme staining. These results are in contrast to findings with s-PAF in vivo where in addition to increased glomerular permeability, a reduction of fixed anionic charges is seen. Thus, these results help to differentiate a dual mechanism of s-PAF: 1) a direct action of s-PAF on glomerular epithelial and vascular cells and, 2) an indirect action of s-PAF on glomerular structures via stimulation of release of inflammatory mediators from circulatory cells

Bioinformatics tools in predictive ecology: Applications to fisheries

This article is made available throught the Brunel Open Access Publishing Fund - Copygith @ 2012 Tucker et al.There has been a huge effort in the advancement of analytical techniques for molecular biological data over the past decade. This has led to many novel algorithms that are specialized to deal with data associated with biological phenomena, such as gene expression and protein interactions. In contrast, ecological data analysis has remained focused to some degree on off-the-shelf statistical techniques though this is starting to change with the adoption of state-of-the-art methods, where few assumptions can be made about the data and a more explorative approach is required, for example, through the use of Bayesian networks. In this paper, some novel bioinformatics tools for microarray data are discussed along with their ‘crossover potential’ with an application to fisheries data. In particular, a focus is made on the development of models that identify functionally equivalent species in different fish communities with the aim of predicting functional collapse

Impact of the Siena College Tech Valley Scholars Program on Student Outcomes

The experimental group for this study included 38 students who entered the Tech Valley Scholars (TVS) program over the course of three academic years, from 2009-10 through 2011-12. Two groups of controls were used: a randomly selected sample of STEM students who matriculated in the same time frame; and a matched sample. The TVS students and controls were compared on two primary outcome variables: graduation (or retention to senior year), and final cumulative GPA. The major findings of this study are that (1) the TVS students had better outcomes than both the randomly selected comparison group and the matched control group, (2) unmet financial need is an important risk factor for non-retention, (3) students with moderately high unmet need can be academically successful if retained, and (4) the TVS program is having a positive impact on at-risk students. Recommendations for effective and efficient allocation of scholarship funds are given and future statistical studies are recommended

The identification of informative genes from multiple datasets with increasing complexity

Background In microarray data analysis, factors such as data quality, biological variation, and the increasingly multi-layered nature of more complex biological systems complicates the modelling of regulatory networks that can represent and capture the interactions among genes. We believe that the use of multiple datasets derived from related biological systems leads to more robust models. Therefore, we developed a novel framework for modelling regulatory networks that involves training and evaluation on independent datasets. Our approach includes the following steps: (1) ordering the datasets based on their level of noise and informativeness; (2) selection of a Bayesian classifier with an appropriate level of complexity by evaluation of predictive performance on independent data sets; (3) comparing the different gene selections and the influence of increasing the model complexity; (4) functional analysis of the informative genes. Results In this paper, we identify the most appropriate model complexity using cross-validation and independent test set validation for predicting gene expression in three published datasets related to myogenesis and muscle differentiation. Furthermore, we demonstrate that models trained on simpler datasets can be used to identify interactions among genes and select the most informative. We also show that these models can explain the myogenesis-related genes (genes of interest) significantly better than others (P < 0.004) since the improvement in their rankings is much more pronounced. Finally, after further evaluating our results on synthetic datasets, we show that our approach outperforms a concordance method by Lai et al. in identifying informative genes from multiple datasets with increasing complexity whilst additionally modelling the interaction between genes. Conclusions We show that Bayesian networks derived from simpler controlled systems have better performance than those trained on datasets from more complex biological systems. Further, we present that highly predictive and consistent genes, from the pool of differentially expressed genes, across independent datasets are more likely to be fundamentally involved in the biological process under study. We conclude that networks trained on simpler controlled systems, such as in vitro experiments, can be used to model and capture interactions among genes in more complex datasets, such as in vivo experiments, where these interactions would otherwise be concealed by a multitude of other ongoing events

Discovering study-specific gene regulatory networks

This article has been made available through the Brunel Open Access Publishing Fund.Microarrays are commonly used in biology because of their ability to simultaneously measure thousands of genes under different conditions. Due to their structure, typically containing a high amount of variables but far fewer samples, scalable network analysis techniques are often employed. In particular, consensus approaches have been recently used that combine multiple microarray studies in order to find networks that are more robust. The purpose of this paper, however, is to combine multiple microarray studies to automatically identify subnetworks that are distinctive to specific experimental conditions rather than common to them all. To better understand key regulatory mechanisms and how they change under different conditions, we derive unique networks from multiple independent networks built using glasso which goes beyond standard correlations. This involves calculating cluster prediction accuracies to detect the most predictive genes for a specific set of conditions. We differentiate between accuracies calculated using cross-validation within a selected cluster of studies (the intra prediction accuracy) and those calculated on a set of independent studies belonging to different study clusters (inter prediction accuracy). Finally, we compare our method's results to related state-of-the art techniques. We explore how the proposed pipeline performs on both synthetic data and real data (wheat and Fusarium). Our results show that subnetworks can be identified reliably that are specific to subsets of studies and that these networks reflect key mechanisms that are fundamental to the experimental conditions in each of those subsets

The Incidence and Propensity of Head Acceleration Events in a Season of Men’s and Women’s English Elite-Level Club Rugby Union Matches

Objectives To describe and compare the incidence and propensity of head acceleration events (HAEs) using instrumented mouthguards (iMG) by playing position in a season of English elite-level men’s and women’s rugby union matches. Methods iMG data were collected for 255 men and 133 women from 1,865 and 807 player-matches, respectively, and synchronised to video-coded match footage. Head peak resultant linear acceleration (PLA) and peak resultant angular acceleration (PAA) were extracted from each HAE. Mean incidence and propensity values were calculated across different recording thresholds for forwards and backs in addition to positional groups (front row, second row, back row, half backs, centres, back three) with 95% confidence intervals (CI) estimated. Significance was determined based on 95% CI not overlapping across recording thresholds. Results For both men and women, HAE incidence was twice as high for forwards than backs across the majority of recording thresholds. HAE incidence and propensity were significantly lower in the women’s game compared to the men’s game. Back-row and front-row players had the highest incidence across all HAE thresholds for men’s forwards, while women’s forward positional groups and men’s and women’s back positional groups were similar. Tackles and carries exhibited a greater propensity to result in HAE for forward positional groups and the back three in the men’s game, and back row in the women’s game. Conclusion These data offer valuable benchmark and comparative data for future research, HAE mitigation strategies, and management of HAE exposure in elite rugby players. Positional-specific differences in HAE incidence and propensity should be considered in future mitigation strategies

Multi-method genome- and epigenome-wide studies of inflammatory protein levels in healthy older adults

The molecular factors which control circulating levels of inflammatory proteins are not well understood. Furthermore, association studies between molecular probes and human traits are often performed by linear model-based methods which may fail to account for complex structure and interrelationships within molecular datasets.In this study, we perform genome- and epigenome-wide association studies (GWAS/EWAS) on the levels of 70 plasma-derived inflammatory protein biomarkers in healthy older adults (Lothian Birth Cohort 1936; n = 876; Olink® inflammation panel). We employ a Bayesian framework (BayesR+) which can account for issues pertaining to data structure and unknown confounding variables (with sensitivity analyses using ordinary least squares- (OLS) and mixed model-based approaches). We identified 13 SNPs associated with 13 proteins (n = 1 SNP each) concordant across OLS and Bayesian methods. We identified 3 CpG sites spread across 3 proteins (n = 1 CpG each) that were concordant across OLS, mixed-model and Bayesian analyses. Tagged genetic variants accounted for up to 45% of variance in protein levels (for MCP2, 36% of variance alone attributable to 1 polymorphism). Methylation data accounted for up to 46% of variation in protein levels (for CXCL10). Up to 66% of variation in protein levels (for VEGFA) was explained using genetic and epigenetic data combined. We demonstrated putative causal relationships between CD6 and IL18R1 with inflammatory bowel disease and between IL12B and Crohn’s disease. Our data may aid understanding of the molecular regulation of the circulating inflammatory proteome as well as causal relationships between inflammatory mediators and disease

Player and match characteristics associated with head acceleration events in elite-level men’s and women’s rugby union matches

Objective To examine the likelihood of head acceleration events (HAEs) as a function of previously identified risk factors: match time, player status (starter or substitute) and pitch location in elite-level men’s and women’s rugby union matches. Methods Instrumented mouthguard data were collected from 179 and 107 players in the men’s and women’s games and synchronised to video-coded match footage. Head peak resultant linear acceleration (PLA) and peak resultant angular acceleration were extracted from each HAE. Field location was determined for HAEs linked to a tackle, carry or ruck. HAE incidence was calculated per player hour across PLA recording thresholds with 95% CIs estimated. Propensity was calculated as the percentage of contact events that caused HAEs across PLA recording thresholds, with a 95% CI estimated. Significance was assessed by non-overlapping 95% CIs. Results 29 099 and 6277 HAEs were collected from 1214 and 577 player-matches in the men’s and women’s games. No significant differences in match quarter HAE incidence or propensity were found. Substitutes had higher HAE incidence than starters at lower PLA recording thresholds for men but similar HAE propensity. HAEs were more likely to occur in field locations with high contact event occurrence. Conclusion Strategies to reduce HAE incidence need not consider match time or status as a substitute or starter as HAE rates are similar throughout matches, without differences in propensity between starters and substitutes. HAE incidence is proportional to contact frequency, and strategies that reduce either frequency or propensity for contact to cause head contact may be explored

The application of match‐event and instrumented mouthguard data to inform match limits: An example using rugby union Premiership and rugby league Super League data from England

The study aimed to illustrate how contact (from match‐event data) and head acceleration event (HAE) (from instrumented mouthguard [iMG]) data can be combined to inform match limits within rugby. Match‐event data from one rugby union and rugby league season, including all competitive matches involving players from the English Premiership and Super League, were used. Playing exposure was summarised as full game equivalents (FGE; total minutes played/80). Expected contact and HAE exposures at arbitrary thresholds were estimated using match‐event and iMG data. Generalised linear models were used to identify differences in contact and HAE exposure per FGE. For 30 FGEs, forwards had greater contact than backs in rugby union (n = 1272 vs. 618) and league (n = 1569 vs. 706). As HAE magnitude increased, the differences between positional groups decreased (e.g., rugby union; n = 34 and 22 HAE &gt;40 g for forwards and backs playing 30 FGEs). Currently, only a relatively small proportion of rugby union (2.5%) and league (7.3%) players exceeded 25 FGEs. Estimating contact and HAEs per FGE allows policymakers to prospectively plan and model estimated overall and position‐specific loads over a season and longer term. Reducing FGE limits by a small amount would currently only affect contact and HAE exposure for a small proportion of players who complete the most minutes. This may be beneficial for this cohort but is not an effective HAE and contact exposure reduction strategy at a population level, which requires individual player management. Given the positional differences, FGE limits should exist to manage appropriate HAE and contact exposure