Stability analysis of a hyperbolic stochastic Galerkin formulation for the Aw-Rascle-Zhang model with relaxation

We investigate the propagation of uncertainties in the Aw-Rascle-Zhang model, which belongs to a class of second order traffic flow models described by a system of nonlinear hyperbolic equations. The stochastic quantities are expanded in terms of wavelet-based series expansions. Then, they are projected to obtain a deterministic system for the coefficients in the truncated series. Stochastic Galerkin formulations are presented in conservative form and for smooth solutions also in the corresponding non-conservative form. This allows to obtain stabilization results, when the system is relaxed to a first-order model. Computational tests illustrate the theoretical results

Modeling startle eyeblink electromyogram to assess fear learning

Pavlovian fear conditioning is widely used as a laboratory model of associative learning in human and nonhuman species. In this model, an organism is trained to predict an aversive unconditioned stimulus from initially neutral events (conditioned stimuli, CS). In humans, fear memory is typically measured via conditioned autonomic responses or fear-potentiated startle. For the latter, various analysis approaches have been developed, but a systematic comparison of competing methodologies is lacking. Here, we investigate the suitability of a model-based approach to startle eyeblink analysis for assessment of fear memory, and compare this to extant analysis strategies. First, we build a psychophysiological model (PsPM) on a generic startle response. Then, we optimize and validate this PsPM on three independent fear-conditioning data sets. We demonstrate that our model can robustly distinguish aversive (CS+) from nonaversive stimuli (CS-, i.e., has high predictive validity). Importantly, our model-based approach captures fear-potentiated startle during fear retention as well as fear acquisition. Our results establish a PsPM-based approach to assessment of fear-potentiated startle, and qualify previous peak-scoring methods. Our proposed model represents a generic startle response and can potentially be used beyond fear conditioning, for example, to quantify affective startle modulation or prepulse inhibition of the acoustic startle response

Tail and ear necrosis in piglets of sows with increased weight loss over the suckling period.

A farm belonging to a Swiss sow pool system reported increased cases of necrosis on the base of the tail or ears in their piglets. Therefore, herd examination was performed in February 2021, and it was found that about half of all examined litters included piglets with necrosis of different locations, and that the sows of these piglets were rather thin. Upon instruction, the farmer then documented the body condition score (BCS) and weight before farrowing and after weaning, and the number of liveborn piglets affected by necrosis of the tail or ear of the next four farrowing batches. In total, data of 97 sows with 1214 liveborn piglets were evaluated. Sows were retrospectively allocated into two groups: Those with piglets with ear and/or tail necrosis (NE), and those without (WN). Of the 97 litters, 40 included piglets with necrosis, with 28 of them having piglets only with tail necrosis, 8 only with ear necrosis, and 4 litters included piglets with both types of necrosis. The group NE lost significantly more weight and BCS points over the suckling period than the group WN, with a tendency of having a lower BCS after weaning (2,0 vs. 2,25/5,0). Blood samples of five sows were analyzed and tested positive for the Fusarium mycotoxin deoxynivalenol (DON). It could be possible that the sows previously consumed DON contaminated feed, which was then stored in their fat tissue, and released again into the blood stream during increased weight loss. Since DON can be transferred from the sow to her piglets during gestation or lactation, this release might have affected the piglets, leading to tail or ear necrosis. However, causative studies are needed to confirm this hypothesis

Establishing operant conflict tests for the translational study of anxiety in mice

Rationale In conflict-based anxiety tests, rodents decide between actions with simultaneous rewarding and aversive outcomes. In humans, computerised operant conflict tests have identified response choice, latency, and vigour as distinct behavioural components. Animal operant conflict tests for measurement of these components would facilitate translational study. Objectives In C57BL/6 mice, two operant conflict tests for measurement of response choice, latency, and vigour were established, and effects of chlordiazepoxide (CDZ) thereon investigated. Methods Mice were moderately diet-restricted to increase sucrose reward salience. A 1-lever test required responding under medium-effort reward/threat conditions of variable ratio 2–10 resulting in sucrose at p = 0.7 and footshock at p = 0.3. A 2-lever test mandated a choice between low-effort reward/threat with a fixed-ratio (FR) 2 lever yielding sucrose at p = 0.7 and footshock at p = 0.3 versus high-effort reward/no threat with a FR 20 lever yielding sucrose at p = 1. Results In the 1-lever test, CDZ (7.5 or 15 mg/kg i.p.) reduced post-trial pause (response latency) following either sucrose or footshock and reduced inter-response interval (increased response vigour) after footshock. In the 2-lever test, mice favoured the FR2 lever and particularly at post-reward trials. CDZ increased choice of FR2 and FR20 responding after footshock, reduced response latency overall, and increased response vigour at the FR2 lever and after footshock specifically. Conclusions Mouse operant conflict tests, especially 2-lever choice, allow for the translational study of distinct anxiety components. CDZ influences each component by ameliorating the impact of both previous punishment and potential future punishment

Cell Line Derived 5-FU and Irinotecan Drug-Sensitivity Profiles Evaluated in Adjuvant Colon Cancer Trial Data.

This study evaluates whether gene signatures for chemosensitivity for irinotecan and 5-fluorouracil (5-FU) derived from in vitro grown cancer cell lines can predict clinical sensitivity to these drugs. To test if an irinotecan signature and a SN-38 signature could identify patients who benefitted from the addition of irinotecan to 5-FU, we used gene expression profiles based on cell lines and clinical tumor material. These profiles were applied to expression data obtained from pretreatment formalin fixed paraffin embedded (FFPE) tumor tissue from 636 stage III colon cancer patients enrolled in the PETACC-3 prospective randomized clinical trial. A 5-FU profile developed similarly was assessed by comparing the PETACC-3 cohort with a cohort of 359 stage II colon cancer patients who underwent surgery but received no adjuvant therapy. There was no statistically significant association between the irinotecan or SN-38 profiles and benefit from irinotecan. The 5-FU sensitivity profile showed a statistically significant association with relapse free survival (RFS) (hazard ratio (HR) = 0.54 (0.41-0.71), p<1e-05) and overall survival (HR = 0.47 (0.34-0.63), p<1e-06) in the PETACC-3 subpopulation. The effect of the 5-FU profile remained significant in a multivariable Cox Proportional Hazards model, adjusting for several relevant clinicopathological parameters. No statistically significant effect of the 5-FU profile was observed in the untreated cohort of 359 patients (relapse free survival, p = 0.671). The irinotecan predictor had no predictive value. The 5-FU predictor was prognostic in stage III patients in PETACC-3 but not in stage II patients with no adjuvant therapy. This suggests a potential predictive ability of the 5-FU sensitivity profile to identify colon cancer patients who may benefit from 5-FU, however, any biomarker predicting benefit for adjuvant 5-FU must be rigorously evaluated in independent cohorts. Given differences between the two study cohorts, the present results should be further validated

Polo-like kinase 1 (PLK1) inhibition suppresses cell growth and enhances radiation sensitivity in medulloblastoma cells

<p>Abstract</p> <p>Background</p> <p>Medulloblastoma is the most common malignant brain tumor in children and remains a therapeutic challenge due to its significant therapy-related morbidity. Polo-like kinase 1 (<it>PLK1</it>) is highly expressed in many cancers and regulates critical steps in mitotic progression. Recent studies suggest that targeting PLK1 with small molecule inhibitors is a promising approach to tumor therapy.</p> <p>Methods</p> <p>We examined the expression of <it>PLK1 </it>mRNA in medulloblastoma tumor samples using microarray analysis. The impact of PLK1 on cell proliferation was evaluated by depleting expression with RNA interference (RNAi) or by inhibiting function with the small molecule inhibitor BI 2536. Colony formation studies were performed to examine the impact of BI 2536 on medulloblastoma cell radiosensitivity. In addition, the impact of depleting <it>PLK1 </it>mRNA on tumor-initiating cells was evaluated using tumor sphere assays.</p> <p>Results</p> <p>Analysis of gene expression in two independent cohorts revealed that <it>PLK1 </it>mRNA is overexpressed in some, but not all, medulloblastoma patient samples when compared to normal cerebellum. Inhibition of PLK1 by RNAi significantly decreased medulloblastoma cell proliferation and clonogenic potential and increased cell apoptosis. Similarly, a low nanomolar concentration of BI 2536, a small molecule inhibitor of PLK1, potently inhibited cell growth, strongly suppressed the colony-forming ability, and increased cellular apoptosis of medulloblastoma cells. Furthermore, BI 2536 pretreatment sensitized medulloblastoma cells to ionizing radiation. Inhibition of PLK1 impaired tumor sphere formation of medulloblastoma cells and decreased the expression of SRY (sex determining region Y)-box 2 (<it>SOX2</it>) mRNA in tumor spheres indicating a possible role in targeting tumor inititiating cells.</p> <p>Conclusions</p> <p>Our data suggest that targeting PLK1 with small molecule inhibitors, in combination with radiation therapy, is a novel strategy in the treatment of medulloblastoma that warrants further investigation.</p

Batch effect confounding leads to strong bias in performance estimates obtained by cross-validation.

BACKGROUND: With the large amount of biological data that is currently publicly available, many investigators combine multiple data sets to increase the sample size and potentially also the power of their analyses. However, technical differences ("batch effects") as well as differences in sample composition between the data sets may significantly affect the ability to draw generalizable conclusions from such studies. FOCUS: The current study focuses on the construction of classifiers, and the use of cross-validation to estimate their performance. In particular, we investigate the impact of batch effects and differences in sample composition between batches on the accuracy of the classification performance estimate obtained via cross-validation. The focus on estimation bias is a main difference compared to previous studies, which have mostly focused on the predictive performance and how it relates to the presence of batch effects. DATA: We work on simulated data sets. To have realistic intensity distributions, we use real gene expression data as the basis for our simulation. Random samples from this expression matrix are selected and assigned to group 1 (e.g., 'control') or group 2 (e.g., 'treated'). We introduce batch effects and select some features to be differentially expressed between the two groups. We consider several scenarios for our study, most importantly different levels of confounding between groups and batch effects. METHODS: We focus on well-known classifiers: logistic regression, Support Vector Machines (SVM), k-nearest neighbors (kNN) and Random Forests (RF). Feature selection is performed with the Wilcoxon test or the lasso. Parameter tuning and feature selection, as well as the estimation of the prediction performance of each classifier, is performed within a nested cross-validation scheme. The estimated classification performance is then compared to what is obtained when applying the classifier to independent data