Linear magnetoresistance in commercial n-type silicon due to inhomogeneous doping

Free electron theory tells us that resistivity is independent of magnetic field. In fact, most observations match the semiclassical prediction of a magnetoresistance that is quadratic at low fields before saturating. However, a non-saturating linear magnetoresistance has been observed in exotic semiconductors such as silver chalcogenides, lightly-doped InSb, N-doped InAs, MnAs-GaAs composites, PrFeAsO, and epitaxial graphene. Here we report the observation of a large linear magnetoresistance in the ohmic regime in commonplace commercial n-type silicon wafer. It is well-described by a classical model of spatially fluctuating donor densities, and may be amplified by altering the aspect ratio of the sample to enhance current-jetting: increasing the width tenfold increased the magnetoresistance at 8 T from 445 % to 4707 % at 35 K. This physical picture may well offer insights into the large magnetoresistances recently observed in n-type and p-type Si in the non-ohmic regime.Comment: submitted to Nature Material

Comparison of the CBA-H and SF-36 for the screening of the psychological and behavioural variables in chronic dialysis patients

The aim of the study was to perform an analysis of the emotional reactions, perception of stressful life and behavioural changes related to Haemodialysis (HD) in order to identify those variables that can improve lifestyle and the adherence to treatment. Some psycho- metric assessment, such as the Cognitive Behavioural Assessment, Hospital Form, (CBA- H) and the Health Survey (SF-36), which provides two indexes: the Physical Component Score (PCS) and the Mental Component Score (MCS), are suitable to assess a patient’s psychological and behavioural style and their health-related quality of life. The study involved 37 Italian out-patients with end-stage renal disease under HD therapy. We calcu- lated the Spearman correlation between variables of CBA-H, SF-36, age and time on HD. We also performed a multivariate linear regression using the CBA-H variables as predictors and PCS and MCS as dependent variables. From the CBA-H, 95% of participants self- reported psychological characteristics comparable to Type A personality, which identifies an anxious, hyperactive and hostile subject. Physical limitations were found to be directly pro- portional to the time on dialysis (rs = -0.42). The condition of perceived stress worsens the state of mental health (rs = -0.68) and general health perception (rs = -0.44). The condition of vital exhaustion correlates both the PCS and the MCS (p<0.01) with possible outcomes of physical and mental illness. The psychological wellbeing of a dialyzed patient could be due to the combination of several factors, including life parameters, the positive perception of psychosocial outcomes, and the perceived quality of life. A multidisciplinary team (neurolo- gists, psychiatrists, psychologists, and nurses) is essential to plan effective psychological and psychotherapeutic interventions to improve a mind-body integration

Arterial oxygen content is precisely maintained by graded erythrocytotic responses in settings of high/normal serum iron levels, and predicts exercise capacity: an observational study of hypoxaemic patients with pulmonary arteriovenous malformations.

Oxygen, haemoglobin and cardiac output are integrated components of oxygen transport: each gram of haemoglobin transports 1.34 mls of oxygen in the blood. Low arterial partial pressure of oxygen (PaO2), and haemoglobin saturation (SaO2), are the indices used in clinical assessments, and usually result from low inspired oxygen concentrations, or alveolar/airways disease. Our objective was to examine low blood oxygen/haemoglobin relationships in chronically compensated states without concurrent hypoxic pulmonary vasoreactivity.165 consecutive unselected patients with pulmonary arteriovenous malformations were studied, in 98 cases, pre/post embolisation treatment. 159 (96%) had hereditary haemorrhagic telangiectasia. Arterial oxygen content was calculated by SaO2 x haemoglobin x 1.34/100.There was wide variation in SaO2 on air (78.5-99, median 95)% but due to secondary erythrocytosis and resultant polycythaemia, SaO2 explained only 0.1% of the variance in arterial oxygen content per unit blood volume. Secondary erythrocytosis was achievable with low iron stores, but only if serum iron was high-normal: Low serum iron levels were associated with reduced haemoglobin per erythrocyte, and overall arterial oxygen content was lower in iron deficient patients (median 16.0 [IQR 14.9, 17.4]mls/dL compared to 18.8 [IQR 17.4, 20.1]mls/dL, p<0.0001). Exercise tolerance appeared unrelated to SaO2 but was significantly worse in patients with lower oxygen content (p<0.0001). A pre-defined athletic group had higher Hb:SaO2 and serum iron:ferritin ratios than non-athletes with normal exercise capacity. PAVM embolisation increased SaO2, but arterial oxygen content was precisely restored by a subsequent fall in haemoglobin: 86 (87.8%) patients reported no change in exercise tolerance at post-embolisation follow-up.Haemoglobin and oxygen measurements in isolation do not indicate the more physiologically relevant oxygen content per unit blood volume. This can be maintained for SaO2 ≥78.5%, and resets to the same arterial oxygen content after correction of hypoxaemia. Serum iron concentrations, not ferritin, seem to predict more successful polycythaemic responses

Individualization as driving force of clustering phenomena in humans

One of the most intriguing dynamics in biological systems is the emergence of clustering, the self-organization into separated agglomerations of individuals. Several theories have been developed to explain clustering in, for instance, multi-cellular organisms, ant colonies, bee hives, flocks of birds, schools of fish, and animal herds. A persistent puzzle, however, is clustering of opinions in human populations. The puzzle is particularly pressing if opinions vary continuously, such as the degree to which citizens are in favor of or against a vaccination program. Existing opinion formation models suggest that "monoculture" is unavoidable in the long run, unless subsets of the population are perfectly separated from each other. Yet, social diversity is a robust empirical phenomenon, although perfect separation is hardly possible in an increasingly connected world. Considering randomness did not overcome the theoretical shortcomings so far. Small perturbations of individual opinions trigger social influence cascades that inevitably lead to monoculture, while larger noise disrupts opinion clusters and results in rampant individualism without any social structure. Our solution of the puzzle builds on recent empirical research, combining the integrative tendencies of social influence with the disintegrative effects of individualization. A key element of the new computational model is an adaptive kind of noise. We conduct simulation experiments to demonstrate that with this kind of noise, a third phase besides individualism and monoculture becomes possible, characterized by the formation of metastable clusters with diversity between and consensus within clusters. When clusters are small, individualization tendencies are too weak to prohibit a fusion of clusters. When clusters grow too large, however, individualization increases in strength, which promotes their splitting.Comment: 12 pages, 4 figure

Bias in random forest variable importance measures: Illustrations, sources and a solution

BACKGROUND: Variable importance measures for random forests have been receiving increased attention as a means of variable selection in many classification tasks in bioinformatics and related scientific fields, for instance to select a subset of genetic markers relevant for the prediction of a certain disease. We show that random forest variable importance measures are a sensible means for variable selection in many applications, but are not reliable in situations where potential predictor variables vary in their scale of measurement or their number of categories. This is particularly important in genomics and computational biology, where predictors often include variables of different types, for example when predictors include both sequence data and continuous variables such as folding energy, or when amino acid sequence data show different numbers of categories. RESULTS: Simulation studies are presented illustrating that, when random forest variable importance measures are used with data of varying types, the results are misleading because suboptimal predictor variables may be artificially preferred in variable selection. The two mechanisms underlying this deficiency are biased variable selection in the individual classification trees used to build the random forest on one hand, and effects induced by bootstrap sampling with replacement on the other hand. CONCLUSION: We propose to employ an alternative implementation of random forests, that provides unbiased variable selection in the individual classification trees. When this method is applied using subsampling without replacement, the resulting variable importance measures can be used reliably for variable selection even in situations where the potential predictor variables vary in their scale of measurement or their number of categories. The usage of both random forest algorithms and their variable importance measures in the R system for statistical computing is illustrated and documented thoroughly in an application re-analyzing data from a study on RNA editing. Therefore the suggested method can be applied straightforwardly by scientists in bioinformatics research

Expression of Regulatory Platelet MicroRNAs in Patients with Sickle Cell Disease

Background: Increased platelet activation in sickle cell disease (SCD) contributes to a state of hypercoagulability and confers a risk of thromboembolic complications. The role for post-transcriptional regulation of the platelet transcriptome by microRNAs (miRNAs) in SCD has not been previously explored. This is the first study to determine whether platelets from SCD exhibit an altered miRNA expression profile. Methods and Findings: We analyzed the expression of miRNAs isolated from platelets from a primary cohort (SCD = 19, controls = 10) and a validation cohort (SCD = 7, controls = 7) by hybridizing to the Agilent miRNA microarrays. A dramatic difference in miRNA expression profiles between patients and controls was noted in both cohorts separately. A total of 40 differentially expressed platelet miRNAs were identified as common in both cohorts (p-value 0.05, fold change>2) with 24 miRNAs downregulated. Interestingly, 14 of the 24 downregulated miRNAs were members of three families - miR-329, miR-376 and miR-154 - which localized to the epigenetically regulated, maternally imprinted chromosome 14q32 region. We validated the downregulated miRNAs, miR-376a and miR-409-3p, and an upregulated miR-1225-3p using qRT-PCR. Over-expression of the miR-1225-3p in the Meg01 cells was followed by mRNA expression profiling to identify mRNA targets. This resulted in significant transcriptional repression of 1605 transcripts. A combinatorial approach using Meg01 mRNA expression profiles following miR-1225-3p overexpression, a computational prediction analysis of miRNA target sequences and a previously published set of differentially expressed platelet transcripts from SCD patients, identified three novel platelet mRNA targets: PBXIP1, PLAGL2 and PHF20L1. Conclusions: We have identified significant differences in functionally active platelet miRNAs in patients with SCD as compared to controls. These data provide an important inventory of differentially expressed miRNAs in SCD patients and an experimental framework for future studies of miRNAs as regulators of biological pathways in platelets. © 2013 Jain et al

A Bayesian Network Driven Approach to Model the Transcriptional Response to Nitric Oxide in Saccharomyces cerevisiae

The transcriptional response to exogenously supplied nitric oxide in Saccharomyces cerevisiae was modeled using an integrated framework of Bayesian network learning and experimental feedback. A Bayesian network learning algorithm was used to generate network models of transcriptional output, followed by model verification and revision through experimentation. Using this framework, we generated a network model of the yeast transcriptional response to nitric oxide and a panel of other environmental signals. We discovered two environmental triggers, the diauxic shift and glucose repression, that affected the observed transcriptional profile. The computational method predicted the transcriptional control of yeast flavohemoglobin YHB1 by glucose repression, which was subsequently experimentally verified. A freely available software application, ExpressionNet, was developed to derive Bayesian network models from a combination of gene expression profile clusters, genetic information and experimental conditions

RegNetB: Predicting Relevant Regulator-Gene Relationships in Localized Prostate Tumor Samples

<p>Abstract</p> <p>Background</p> <p>A central question in cancer biology is what changes cause a healthy cell to form a tumor. Gene expression data could provide insight into this question, but it is difficult to distinguish between a gene that causes a change in gene expression from a gene that is affected by this change. Furthermore, the proteins that regulate gene expression are often themselves not regulated at the transcriptional level. Here we propose a Bayesian modeling framework we term RegNetB that uses mechanistic information about the gene regulatory network to distinguish between factors that cause a change in expression and genes that are affected by the change. We test this framework using human gene expression data describing localized prostate cancer progression.</p> <p>Results</p> <p>The top regulatory relationships identified by RegNetB include the regulation of RLN1, RLN2, by PAX4, the regulation of ACPP (PAP) by JUN, BACH1 and BACH2, and the co-regulation of PGC and GDF15 by MAZ and TAF8. These target genes are known to participate in tumor progression, but the suggested regulatory roles of PAX4, BACH1, BACH2, MAZ and TAF8 in the process is new.</p> <p>Conclusion</p> <p>Integrating gene expression data and regulatory topologies can aid in identifying potentially causal mechanisms for observed changes in gene expression.</p