Effects of Hemodynamic Response Function Selection on Rat fMRI Statistical Analyses

The selection of the appropriate hemodynamic response function (HRF) for signal modeling in functional magnetic resonance imaging (fMRI) is important. Although the use of the boxcar-shaped hemodynamic response function (BHRF) and canonical hemodynamic response (CHRF) has gained increasing popularity in rodent fMRI studies, whether the selected HRF affects the results of rodent fMRI has not been fully elucidated. Here we investigated the signal change and t-statistic sensitivities of BHRF, CHRF, and impulse response function (IRF). The effect of HRF selection on different tasks was analyzed by using data collected from two groups of rats receiving either 3 mA whisker pad or 3 mA forepaw electrical stimulations (n = 10 for each group). Under whisker pad stimulation with large blood-oxygen-level dependent (BOLD) signal change (4.31 ± 0.42%), BHRF significantly underestimated signal changes (P < 0.001) and t-statistics (P < 0.001) compared with CHRF or IRF. CHRF and IRF did not provide significantly different t-statistics (P > 0.05). Under forepaw stimulation with small BOLD signal change (1.71 ± 0.34%), different HRFs provided insignificantly different t-statistics (P > 0.05). Therefore, the selected HRF can influence data analysis in rodent fMRI experiments with large BOLD responses but not in those with small BOLD responses

Functional characterization of cellulases identified from the cow rumen fungus Neocallimastix patriciarum W5 by transcriptomic and secretomic analyses

<p>Abstract</p> <p>Background</p> <p><it>Neocallimastix patriciarum</it> is one of the common anaerobic fungi in the digestive tracts of ruminants that can actively digest cellulosic materials, and its cellulases have great potential for hydrolyzing cellulosic feedstocks. Due to the difficulty in culture and lack of a genome database, it is not easy to gain a global understanding of the glycosyl hydrolases (<it>GHs</it>) produced by this anaerobic fungus.</p> <p>Results</p> <p>We have developed an efficient platform that uses a combination of transcriptomic and proteomic approaches to <it>N. patriciarum </it>to accelerate gene identification, enzyme classification and application in rice straw degradation. By conducting complementary studies of transcriptome (Roche 454 GS and Illumina GA IIx) and secretome (ESI-Trap LC-MS/MS), we identified 219 putative <it>GH </it>contigs and classified them into 25 <it>GH</it> families. The secretome analysis identified four major enzymes involved in rice straw degradation: β-glucosidase, endo-1,4-β-xylanase, xylanase B and Cel48A exoglucanase. From the sequences of assembled contigs, we cloned 19 putative cellulase genes, including the <it>GH1</it>, <it>GH3</it>, <it>GH5</it>, <it>GH6</it>, <it>GH9</it>, <it>GH18</it>, <it>GH43 </it>and <it>GH48 </it>gene families, which were highly expressed in <it>N. patriciarum </it>cultures grown on different feedstocks.</p> <p>Conclusions</p> <p>These <it>GH </it>genes were expressed in Pichia pastoris and/or Saccharomyces cerevisiae for functional characterization. At least five novel cellulases displayed cellulytic activity for glucose production. One β-glucosidase (W5-16143) and one exocellulase (W5-CAT26) showed strong activities and could potentially be developed into commercial enzymes.</p

Maternal diabetes and risk of attention-deficit/hyperactivity disorder in offspring in a multinational cohort of 3.6 million mother-child pairs

Previous studies report an association between maternal diabetes mellitus (MDM) and attention-deficit/hyperactivity disorder (ADHD), often overlooking unmeasured confounders such as shared genetics and environmental factors. We therefore conducted a multinational cohort study with linked mother-child pairs data in Hong Kong, New Zealand, Taiwan, Finland, Iceland, Norway and Sweden to evaluate associations between different MDM (any MDM, gestational diabetes mellitus (GDM) and pregestational diabetes mellitus (PGDM)) and ADHD using Cox proportional hazards regression. We included over 3.6 million mother-child pairs between 2001 and 2014 with follow-up until 2020. Children who were born to mothers with any type of diabetes during pregnancy had a higher risk of ADHD than unexposed children (pooled hazard ratio (HR) = 1.16, 95% confidence interval (CI) = 1.08-1.24). Higher risks of ADHD were also observed for both GDM (pooled HR = 1.10, 95% CI = 1.04-1.17) and PGDM (pooled HR = 1.39, 95% CI = 1.25-1.55). However, siblings with discordant exposure to GDM in pregnancy had similar risks of ADHD (pooled HR = 1.05, 95% CI = 0.94-1.17), suggesting potential confounding by unmeasured, shared familial factors. Our findings indicate that there is a small-to-moderate association between MDM and ADHD, whereas the association between GDM and ADHD is unlikely to be causal. This finding contrast with previous studies, which reported substantially higher risk estimates, and underscores the need to reevaluate the precise roles of hyperglycemia and genetic factors in the relationship between MDM and ADHD

Kicking against the PRCs - a domesticated transposase antagonises silencing mediated by polycomb group proteins and is an accessory component of polycomb repressive complex 2

The Polycomb group (PcG) and trithorax group (trxG) genes play crucial roles in development by regulating expression of homeotic and other genes controlling cell fate. Both groups catalyse modifications of chromatin, particularly histone methylation, leading to epigenetic changes that affect gene activity. The trxG antagonizes the function of PcG genes by activating PcG target genes, and consequently trxG mutants suppress PcG mutant phenotypes. We previously identified the ANTAGONIST OF LIKE HETEROCHROMATIN PROTEIN1 (ALP1) gene as a genetic suppressor of mutants in the Arabidopsis PcG gene LIKE HETEROCHROMATIN PROTEIN1 (LHP1). Here, we show that ALP1 interacts genetically with several other PcG and trxG components and that it antagonizes PcG silencing. Transcriptional profiling reveals that when PcG activity is compromised numerous target genes are hyper-activated in seedlings and that in most cases this requires ALP1. Furthermore, when PcG activity is present ALP1 is needed for full activation of several floral homeotic genes that are repressed by the PcG. Strikingly, ALP1 does not encode a known chromatin protein but rather a protein related to PIF/Harbinger class transposases. Phylogenetic analysis indicates that ALP1 is broadly conserved in land plants and likely lost transposase activity and acquired a novel function during angiosperm evolution. Consistent with this, immunoprecipitation and mass spectrometry (IP-MS) show that ALP1 associates, in vivo, with core components of POLYCOMB REPRESSIVE COMPLEX 2 (PRC2), a widely conserved PcG protein complex which functions as a H3K27me3 histone methyltransferase. Furthermore, in reciprocal pulldowns using the histone methyltransferase CURLY LEAF (CLF), we identify not only ALP1 and the core PRC2 components but also plant-specific accessory components including EMBRYONIC FLOWER 1 (EMF1), a transcriptional repressor previously associated with PRC1-like complexes. Taken together our data suggest that ALP1 inhibits PcG silencing by blocking the interaction of the core PRC2 with accessory components that promote its HMTase activity or its role in inhibiting transcription. ALP1 is the first example of a domesticated transposase acquiring a novel function as a PcG component. The antagonistic interaction of a modified transposase with the PcG machinery is novel and may have arisen as a means for the cognate transposon to evade host surveillance or for the host to exploit features of the transposition machinery beneficial for epigenetic regulation of gene activity.Fil: Liang, Shih Chieh. University of Edinburgh; Reino UnidoFil: Hartwig, Ben. Max Planck Institute for Plant Breeding Research; AlemaniaFil: Perera, Pumi. University of Edinburgh; Reino UnidoFil: Mora Garcia, Santiago. Fundación Instituto Leloir; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquimicas de Buenos Aires; ArgentinaFil: de Leau, Erica. University of Edinburgh; Reino UnidoFil: Thornton, Harry. University of Edinburgh; Reino UnidoFil: Lima de Alves, Flavia. University of Edinburgh; Reino UnidoFil: Rapsilber, Juri. University of Edinburgh; Reino UnidoFil: Yang, Suxin. University of Edinburgh; Reino UnidoFil: James, Geo Velikkakam. Max Planck Institute for Plant Breeding Research; AlemaniaFil: Schneeberger, Korbinian. Max Planck Institute for Plant Breeding Research; AlemaniaFil: Finnegan, E. Jean. University of Edinburgh; Reino UnidoFil: Turck, Franziska. Max Planck Institute for Plant Breeding Research; AlemaniaFil: Goodrich, Justin. Mc Gill University; Canad

Using a Time Delay Neural Network Approach to Diagnose the Out-of-Control Signals for a Multivariate Normal Process with Variance Shifts

With the rapid development of advanced sensor technologies, it has become popular to monitor multiple quality variables for a manufacturing process. Consequently, multivariate statistical process control (MSPC) charts have been commonly used for monitoring multivariate processes. The primary function of MSPC charts is to trigger an out-of-control signal when faults occur in a process. However, because two or more quality variables are involved in a multivariate process, it is very difficult to diagnose which one or which combination of quality variables is responsible for the MSPC signal. Though some statistical decomposition methods may provide possible solutions, the mathematical difficulty could confine the applications. This study presents a time delay neural network (TDNN) classifier to diagnose the quality variables that cause out-of-control signals for a multivariate normal process (MNP) with variance shifts. To demonstrate the effectiveness of our proposed approach, a series of simulated experiments were conducted. The results were compared with artificial neural network (ANN), support vector machine (SVM) and multivariate adaptive regression splines (MARS) classifiers. It was found that the proposed TDNN classifier was able to accurately recognize the contributors of out-of-control signal for MNPs

Using Grading of Recommendations Assessment, Development, and Evaluation (GRADE) to rate the certainty of evidence of study outcomes from systematic reviews: A quick tutorial

The Grading of Recommendations Assessment, Development, and Evaluation (GRADE) framework offers a structured approach to assess the certainty of evidence (CoE) in systematic reviews (SRs). The CoE for each outcome falls into one of the four categories: very low, low, moderate, or high. The judgment of CoE is based on five downgrading factors (including the risk of bias, indirectness, inconsistency, imprecision, and publication bias) and three upgrading factors (including large effect size, dose-response relationship, and opposing plausible residual bias and confounding). To improve the transparency of SRs, authors should indicate how they grade the CoE for each outcome and provide a rationale for downgrading or upgrading the CoE

Comparative Effectiveness and Safety of Standard-Dose and Low-Dose Pembrolizumab in Patients with Non-Small-Cell Lung Cancer: A Multi-Institutional Cohort Study in Taiwan

Fixed doses at 200 mg of pembrolizumab or 2 mg/kg every 3 weeks are the standard dosages for first- and second-line treatment of non-small-cell lung cancer (NSCLC); however, in clinical practice, patients with NSCLC may receive lower doses of pembrolizumab due to drug product availability or economic factors. To date, the comparative effectiveness and safety of the standard dose and lower doses of pembrolizumab in these patients still remains limited. We conducted a retrospective cohort study by analyzing electronic medical records data from the largest multi-institutional hospital system in Taiwan. Advanced NSCLC patients newly receiving pembrolizumab with or without chemotherapy were included. Patients were classified into: (1) the standard-dose group (&ge;2 mg/kg), and (2) the low-dose group (&lt;2 mg/kg). We applied inverse probability of treatment weighting (IPTW) to compare the overall survival (OS) and immune-related adverse events (irAEs) between the two treatment groups, and to evaluate the minimum clinically effective dose of pembrolizumab. We included a total of 147 NSCLC patients receiving standard-dose pembrolizumab (mean [range] age: 63.7 [58.0&ndash;73.0] years; male: 62.6%; mean [range] body weight: 60.5 [58.0&ndash;73.0] kg) and 95 patients receiving low-dose pembrolizumab (mean [range] age: 62.0 [50.0&ndash;68.8] years; male: 64.2%; mean [range] body weight: 63.9 [55.0&ndash;73.8] kg). After IPTW adjustments, the median OS was similar for both the standard-dose and low-dose pembrolizumab groups (19.3 vs. 14.3 months, log-rank p = 0.15). Also, the rate for all classes of irAEs was similar for both groups. We found that patients with a pembrolizumab dose &ge;1.8 mg/kg were associated with better OS than those receiving &lt;1.8 mg/kg. Our findings suggested no significant difference in OS and irAEs between patients receiving pembrolizumab &ge;2 mg/kg and &lt;2 mg/kg in clinical practice. A pembrolizumab dose &ge;1.8 mg/kg may be the clinically most efficient dose