Activation of MAPK signaling pathway is essential for Id-1 induced serum independent prostate cancer cell growth

published_or_final_versio

Self-organised droplet flow patterns in microchannels

This paper was presented at the 2nd Micro and Nano Flows Conference (MNF2009), which was held at Brunel University, West London, UK. The conference was organised by Brunel University and supported by the Institution of Mechanical Engineers, IPEM, the Italian Union of Thermofluid dynamics, the Process Intensification Network, HEXAG - the Heat Exchange Action Group and the Institute of Mathematics and its Applications.In this work, we have investigated the generation and behaviour of self-organised droplet flow patterns in microchannels. The water droplets, which are generated at a T-junction where the carrier is oil, move into an expanded channel and are self reorganised into various flow patterns: single-profile, double-helix-profile, triple-helix-profile, and more. We find that increasing water/oil flow rate ratio and Capillary number lead to more densely packed droplet flow patterns. The channel geometry also plays an essential role where the 300-μm-deep expansion channel can form multiple layers of droplets while only single layer of droplets can be observed in the 200-μm-deep expansion channel

The Cardiac Timing Toolbox (CaTT): Testing for physiologically plausible effects of cardiac timing on behaviour

There is a long history of, and renewed interest in, cardiac timing effects on behaviour and cognition. Cardiac timing effects may be identified by expressing events as a function of their location in the cardiac cycle, and applying circular (i.e. directional) statistics to test cardiac time-behaviour associations. Typically this approach ‘stretches’ all points in the cardiac cycle equally, but this is not necessarily physiologically valid. Moreover, many tests impose distributional assumptions that are not met by such data. We present a set of statistical techniques robust to this, instantiated within our new Cardiac Timing Toolbox (CaTT) for MATLAB: A physiologically-motivated method of wrapping behaviour to the cardiac cycle; and a set of non-parametric statistical tests that control for common confounds and distributional characteristics of these data. Using a reanalysis of previously published data, we guide readers through analyses using CaTT, aiding researchers in identifying physiologically plausible associations between heart-timing and cognition

Effect of sensory stimuli on dynamic loading induced by people bouncing

Prediction of dynamic loads induced by groups and crowds of people bouncing is a hot topic among designers of grandstands and floors in entertaining venues. Using motion capture technology transferred and adapted from biomedical research, this study aims to investigate effect of visual, auditory and tactile cues on the ability of people to coordinate or synchronise their bouncing movements in groups of two. The numerical results showed a great significance of such stimuli on people's mutual interaction during bouncing, signifying that their effect should be considered in developing much-needed models of crowd dynamic loading of structures due to coordinated rhythmic activities. © The Society for Experimental Mechanics, Inc. 2013

Telemonitoring Parkinson's disease using machine learning by combining tremor and voice analysis

BACKGROUND: With the growing number of the aged population, the number of Parkinson's disease (PD) affected people is also mounting. Unfortunately, due to insufficient resources and awareness in underdeveloped countries, proper and timely PD detection is highly challenged. Besides, all PD patients' symptoms are neither the same nor they all become pronounced at the same stage of the illness. Therefore, this work aims to combine more than one symptom (rest tremor and voice degradation) by collecting data remotely using smartphones and detect PD with the help of a cloud-based machine learning system for telemonitoring the PD patients in the developing countries. METHOD: This proposed system receives rest tremor and vowel phonation data acquired by smartphones with built-in accelerometer and voice recorder sensors. The data are primarily collected from diagnosed PD patients and healthy people for building and optimizing machine learning models that exhibit higher performance. After that, data from newly suspected PD patients are collected, and the trained algorithms are evaluated to detect PD. Based on the majority-vote from those algorithms, PD-detected patients are connected with a nearby neurologist for consultation. Upon receiving patients' feedback after being diagnosed by the neurologist, the system may update the model by retraining using the latest data. Also, the system requests the detected patients periodically to upload new data to track their disease progress. RESULT: The highest accuracy in PD detection using offline data was [Formula: see text] from voice data and [Formula: see text] from tremor data when used separately. In both cases, k-nearest neighbors (kNN) gave the highest accuracy over support vector machine (SVM) and naive Bayes (NB). The application of maximum relevance minimum redundancy (MRMR) feature selection method showed that by selecting different feature sets based on the patient's gender, we could improve the detection accuracy. This study's novelty is the application of ensemble averaging on the combined decisions generated from the analysis of voice and tremor data. The average accuracy of PD detection becomes [Formula: see text] when ensemble averaging was performed on majority-vote from kNN, SVM, and NB. CONCLUSION: The proposed system can detect PD using a cloud-based system for computation, data preserving, and regular monitoring of voice and tremor samples captured by smartphones. Thus, this system can be a solution for healthcare authorities to ensure the older population's accessibility to a better medical diagnosis system in the developing countries, especially in the pandemic situation like COVID-19, when in-person monitoring is minimal

A genome scan for parent-of-origin linkage effects in alcoholism

BACKGROUND: Alcoholism is a complex disease in which genomic imprinting may play an important role in its susceptibility. OBJECTIVE: To conduct a genome-wide search for loci that may have strong parent-of-origin linkage effects in alcoholism; to compare the linkage results between the microsatellites and the two single-nucleotide polymorphism (SNP) platforms. METHODS: Nonparametric linkage analyses were performed using ALLEGRO with the three sets of markers provided by the Genetic Analysis Workshop 14 for the Collaborative Study on the Genetics of Alcoholism Problem 1 data. Both sex-averaged and sex-specific genetic maps were used. We also provided a valid statistical test to determine whether the parental allele sharing differed significantly. RESULTS: Significant maternal linkage effects (paternal imprinting) were observed on chromosome 12 using either the microsatellite markers or the two SNP panels. The two SNP sets did not improve the linkage signals compared to the results from the microsatellite markers on chromosome 12. Possible paternal linkage effects (maternal imprinting) on chromosome 7 and maternal linkage effects (paternal imprinting) on chromosome 10 were found using the two SNP panels. CONCLUSION: For diseases which may have parent-of-origin effects, linkage analysis looking at parental sharing separately may reduce locus heterogeneity and increase the ability to identify that which can not be identified with usual linkage analysis

A genome scan for parent-of-origin linkage effects in alcoholism

BACKGROUND: Alcoholism is a complex disease in which genomic imprinting may play an important role in its susceptibility. OBJECTIVE: To conduct a genome-wide search for loci that may have strong parent-of-origin linkage effects in alcoholism; to compare the linkage results between the microsatellites and the two single-nucleotide polymorphism (SNP) platforms. METHODS: Nonparametric linkage analyses were performed using ALLEGRO with the three sets of markers provided by the Genetic Analysis Workshop 14 for the Collaborative Study on the Genetics of Alcoholism Problem 1 data. Both sex-averaged and sex-specific genetic maps were used. We also provided a valid statistical test to determine whether the parental allele sharing differed significantly. RESULTS: Significant maternal linkage effects (paternal imprinting) were observed on chromosome 12 using either the microsatellite markers or the two SNP panels. The two SNP sets did not improve the linkage signals compared to the results from the microsatellite markers on chromosome 12. Possible paternal linkage effects (maternal imprinting) on chromosome 7 and maternal linkage effects (paternal imprinting) on chromosome 10 were found using the two SNP panels. CONCLUSION: For diseases which may have parent-of-origin effects, linkage analysis looking at parental sharing separately may reduce locus heterogeneity and increase the ability to identify that which can not be identified with usual linkage analysis

Classification between normal and tumor tissues based on the pair-wise gene expression ratio

BACKGROUND: Precise classification of cancer types is critically important for early cancer diagnosis and treatment. Numerous efforts have been made to use gene expression profiles to improve precision of tumor classification. However, reliable cancer-related signals are generally lacking. METHOD: Using recent datasets on colon and prostate cancer, a data transformation procedure from single gene expression to pair-wise gene expression ratio is proposed. Making use of the internal consistency of each expression profiling dataset this transformation improves the signal to noise ratio of the dataset and uncovers new relevant cancer-related signals (features). The efficiency in using the transformed dataset to perform normal/tumor classification was investigated using feature partitioning with informative features (gene annotation) as discriminating axes (single gene expression or pair-wise gene expression ratio). Classification results were compared to the original datasets for up to 10-feature model classifiers. RESULTS: 82 and 262 genes that have high correlation to tissue phenotype were selected from the colon and prostate datasets respectively. Remarkably, data transformation of the highly noisy expression data successfully led to lower the coefficient of variation (CV) for the within-class samples as well as improved the correlation with tissue phenotypes. The transformed dataset exhibited lower CV when compared to that of single gene expression. In the colon cancer set, the minimum CV decreased from 45.3% to 16.5%. In prostate cancer, comparable CV was achieved with and without transformation. This improvement in CV, coupled with the improved correlation between the pair-wise gene expression ratio and tissue phenotypes, yielded higher classification efficiency, especially with the colon dataset – from 87.1% to 93.5%. Over 90% of the top ten discriminating axes in both datasets showed significant improvement after data transformation. The high classification efficiency achieved suggested that there exist some cancer-related signals in the form of pair-wise gene expression ratio. CONCLUSION: The results from this study indicated that: 1) in the case when the pair-wise expression ratio transformation achieves lower CV and higher correlation to tissue phenotypes, a better classification of tissue type will follow. 2) the comparable classification accuracy achieved after data transformation suggested that pair-wise gene expression ratio between some pairs of genes can identify reliable markers for cancer