Modeling Evolution of Crosstalk in Noisy Signal Transduction Networks

Signal transduction networks can form highly interconnected systems within cells due to network crosstalk, the sharing of input signals between multiple downstream responses. To better understand the evolutionary design principles underlying such networks, we study the evolution of crosstalk and the emergence of specificity for two parallel signaling pathways that arise via gene duplication and are subsequently allowed to diverge. We focus on a sequence based evolutionary algorithm and evolve the network based on two physically motivated fitness functions related to information transmission. Surprisingly, we find that the two fitness functions lead to very different evolutionary outcomes, one with a high degree of crosstalk and the other without.Comment: 18 Pages, 16 Figure

An expanded clade of rodent Trim5 genes

AbstractTrim5α from primates (including humans), cows, and rabbits has been shown to be an active antiviral host gene that acts against a range of retroviruses. Although this suggests that Trim5α may be a common antiviral restriction factor among mammals, the status of Trim5 genes in rodents has been unclear. Using genomic and phylogenetic analyses, we describe an expanded paralogous cluster of at least eight Trim5-like genes in mice (including the previously described Trim12 and Trim30 genes), and three Trim5-like genes in rats. Our characterization of the rodent Trim5 locus, and comparison to the Trim5 locus in humans, cows, and rabbits, indicates that Trim5 has undergone independent evolutionary expansions within species. Evolutionary analysis shows that rodent Trim5 genes have evolved under positive selection, suggesting evolutionary conflicts consistent with important antiviral function. Sampling six rodent Trim5 genes failed to reveal antiviral activities against a set of eight retroviral challenges, although we predict that such activities exist

Prevalence of and factors associated with anxiety and depression among women in a lower middle class semi-urban community of Karachi, Pakistan

Objective: To study the prevalence of, and factors associated with anxiety and depression among women.Design: A cross sectional survey.SETTING: A lower middle class semi-urban community of Karachi, Pakistan.PARTICIPANTS: A total of 1218 women between the ages of 18-50 years.METHODOLOGY: Systematically every third household was identified from which a woman was randomly selected. The Aga Khan University Anxiety and Depression Scale and a socio-demographic questionnaire were administered verbally by trained interviewers for assessing the prevalence of, and associated factors for anxiety and depression.Results: A prevalence of 30% was found. Increasing age, lack of education and verbal abuse were the associated factors found to have an independent relationship.CONCLUSION: Providing education and reducing domestic abuse could lead to decrease in the prevalence of anxiety and depression in women

Evaluation of some biological activities of Abelia triflora R Br (Caprifoliaceae) constituents

Purpose: To investigate the antioxidant, anti-inflammatory, antidiabetic,  cardiovascular and cytotoxic activities of the leaf extract and major compounds isolated from Abelia triflora R. Br. (Caprifoliaceae)Methods: The chloroform soluble fraction of A. triflora leaves was subjected to several column chromatographic separations to isolate its constituents.  Anti-inflammatory and antioxidant activities were determined in terms of the ability to inhibit NF-kB, iNOS activity and lipoxygenase enzyme, and to decrease oxidative stress in HepG2 cells. Antidiabetic and cardiovascular activities were determined by screening for peroxisome proliferator-activated receptor alpha (PPARα) and PPARɣ agonistic activities. In vitro cytotoxic activity was determined against a set of four human cancer cell lines (SK-MEL, KB, BT-549, SK-OV-3) and two  non-cancerous kidney cell lines (LLC-PK1 and VERO). Cell viability was measured by neutral red assay.Results: Three triterpene acids were isolated from the chloroform fraction namely; ursolic acid (4), 2, 3-dihydroxy ursolic acid (5) and 2, 3, 21-trihydroxy ursolic acid (6). The results showed that ursolic acid exhibited potent inhibition of lipoxygenase enzyme and iNOS (inducible nitric oxide synthase) activity with IC50 (half-maximal inhibitory concentration) value of 13.0 μg/mL, compared to parthenolide positive standard (IC50, 0.3μg/mL); furthermore, it inhibited NF-kB (nuclear factor-kappa B) with IC50 of 25.0 μg/mL, compared to parthenolide (positive standard, (IC50, 0.5 μg/mL). Also, ursolic acid possessed the highest cytotoxic effect against the three cell lines, SK-MEL (IC50, 14.5 μg/mL), BT-549 (IC50, 16.0 μg/mL) and SK-OV-3 (IC50, 12.5 μg/mL). Only 2,3-dihydroxy ursolic acid activated PPARɣ (1.5-fold at 25 μM), compared to rosiglitazone (positive standard, 3.7 fold at 10 μM)Conclusion: Among the investigated compounds, ursolic acid exhibited the highest anti-inflammatory and cytotoxic activities, while 2,3-dihydroxy ursolic acid demonstrated antidiabetic activity via activation of PPARɣ.Keywords: Abelia triflora, Anti-inflammatory, Antidiabetic, Cardiovascular activity, Antioxidant, Cytotoxi

Uncertainty Aware Training to Improve Deep Learning Model Calibration for Classification of Cardiac MR Images

Quantifying uncertainty of predictions has been identified as one way to develop more trustworthy artificial intelligence (AI) models beyond conventional reporting of performance metrics. When considering their role in a clinical decision support setting, AI classification models should ideally avoid confident wrong predictions and maximise the confidence of correct predictions. Models that do this are said to be well-calibrated with regard to confidence. However, relatively little attention has been paid to how to improve calibration when training these models, i.e., to make the training strategy uncertainty-aware. In this work we evaluate three novel uncertainty-aware training strategies comparing against two state-of-the-art approaches. We analyse performance on two different clinical applications: cardiac resynchronisation therapy (CRT) response prediction and coronary artery disease (CAD) diagnosis from cardiac magnetic resonance (CMR) images. The best-performing model in terms of both classification accuracy and the most common calibration measure, expected calibration error (ECE) was the Confidence Weight method, a novel approach that weights the loss of samples to explicitly penalise confident incorrect predictions. The method reduced the ECE by 17% for CRT response prediction and by 22% for CAD diagnosis when compared to a baseline classifier in which no uncertainty-aware strategy was included. In both applications, as well as reducing the ECE there was a slight increase in accuracy from 69% to 70% and 70% to 72% for CRT response prediction and CAD diagnosis respectively. However, our analysis showed a lack of consistency in terms of optimal models when using different calibration measures. This indicates the need for careful consideration of performance metrics when training and selecting models for complex high-risk applications in healthcare