Stress evolution in GaAsN alloy films

We have investigated stress evolution in dilute nitride GaAs1−xNxGaAs1−xNx alloy films grown by plasma-assisted molecular-beam epitaxy. For coherently strained films (x2.5%x>2.5%, in situ wafer curvature measurements reveal a signature for stress relaxation. Atomic force microscopy and transmission electron microscopy measurements indicate that stress relaxation occurs by a combination of elastic relaxation via island formation and plastic relaxation associated with the formation of stacking faults.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/87566/2/103523_1.pd

Association of systemic inflammation with shock severity, 30-day mortality, and therapy response in patients with cardiogenic shock

Background: Mortality in cardiogenic shock (CS) remains high even when mechanical circulatory support (MCS) restores adequate circulation. To detect a potential contribution of systemic inflammation to shock severity, this study determined associations between C-reactive protein (CRP) concentrations and outcomes in patients with CS. Methods: Unselected, consecutive patients with CS and CRP measurements treated at a single large cardiovascular center between 2009 and 2019 were analyzed. Adjusted regression models were fitted to evaluate the association of CRP with shock severity, 30-day in-hospital mortality and treatment response to MCS. Results: The analysis included 1116 patients [median age: 70 (IQR 58–79) years, 795 (71.3%) male, lactate 4.6 (IQR 2.2–9.5) mmol/l, CRP 17 (IQR 5–71) mg/l]. The cause of CS was acute myocardial infarction in 530 (48%) patients, 648 (58%) patients presented with cardiac arrest. Plasma CRP concentrations were equally distributed across shock severities (SCAI stage B–E). Higher CRP concentrations were associated with 30-day in-hospital mortality (8% relative risk increase per 50 mg/l increase in CRP, range 3–13%; p < 0.001), even after adjustment for CS severity and other potential confounders. Higher CRP concentrations were only associated with higher mortality in patients not treated with MCS [hazard ratio (HR) for CRP > median 1.50; 95%-CI 1.21–1.86; p < 0.001], but not in those treated with MCS (HR for CRP > median 0.92; 95%-CI 0.67–1.26; p = 0.59; p-interaction = 0.01). Conclusion: Elevated CRP concentrations are associated with increased 30-day in-hospital mortality in unselected patients with cardiogenic shock. The use of mechanical circulatory support attenuates this association

The long-term impact of infant rearing background on the affective state of adult common marmosets (Callithrix jacchus)

Early life environment, including temporary family separation, can have a major influence on affective state. Using a battery of tests, the current study compared the performance of adult common marmosets (Callithrix jacchus), reared as infants under 3 different conditions: family-reared twins, family-reared animals from triplet litters where only 2 remain (2stays) and supplementary fed triplets. No significant differences were found in latency to approach and obtain food from a human or a novel object between rearing conditions, suggesting no effect on neophobia. There were no differences in cognitive bias task acquisition time, or proportion of responses to each ambiguous probe. Very minor differences were found in response to the probes, with only supplementary fed marmosets making fewer responses to the middle probe, compared to the probe nearest the rewarded stimuli. Similarly, in a test for anhedonia, no difference was found between rearing conditions in consumption of milkshake at different concentrations. There was just one very small difference in reward motivation, with only supplementary fed triplets demonstrating a lack of preference for milkshake over water at the lowest concentration. This consistent pattern of results suggest that the supplementary feeding of large litters of marmosets at this facility did not have a major effect on welfare, and is unlikely to influence performance in reward-related scientific tasks. Therefore, while family separation is not recommended, this particular practice should be used if it is necessary, such as to reduce infant mortality. Regular positive interactions with humans are also encouraged, to reduce fear and improve welfare of marmosets kept in captivity

Phosphorus derivatives of mesoionic carbenes: synthesis and characterization of triazaphosphole-5-ylidene → BF3 adducts

Trimethylsilyl-substituted triazaphospholes were synthesized by a [3+2] cycloaddition reaction between organic azides and (CH3)3Si–C[triple bond, length as m-dash]P. In an attempt to isolate their N-alkylated products, the formation of BF3 adducts of unprecedented triazaphosphol-5-ylidenes was found. The nature of the carboncarbene–boron bond was investigated within the DFT framework, revealing a strong donation of electrons from the carbene carbon atom to the boron atom combined with weak back-bonding

Gene selection for classification of microarray data based on the Bayes error

<p>Abstract</p> <p>Background</p> <p>With DNA microarray data, selecting a compact subset of discriminative genes from thousands of genes is a critical step for accurate classification of phenotypes for, e.g., disease diagnosis. Several widely used gene selection methods often select top-ranked genes according to their individual discriminative power in classifying samples into distinct categories, without considering correlations among genes. A limitation of these gene selection methods is that they may result in gene sets with some redundancy and yield an unnecessary large number of candidate genes for classification analyses. Some latest studies show that incorporating gene to gene correlations into gene selection can remove redundant genes and improve classification accuracy.</p> <p>Results</p> <p>In this study, we propose a new method, Based Bayes error Filter (BBF), to select relevant genes and remove redundant genes in classification analyses of microarray data. The effectiveness and accuracy of this method is demonstrated through analyses of five publicly available microarray datasets. The results show that our gene selection method is capable of achieving better accuracies than previous studies, while being able to effectively select relevant genes, remove redundant genes and obtain efficient and small gene sets for sample classification purposes.</p> <p>Conclusion</p> <p>The proposed method can effectively identify a compact set of genes with high classification accuracy. This study also indicates that application of the Bayes error is a feasible and effective wayfor removing redundant genes in gene selection.</p

Gene selection with multiple ordering criteria

BACKGROUND: A microarray study may select different differentially expressed gene sets because of different selection criteria. For example, the fold-change and p-value are two commonly known criteria to select differentially expressed genes under two experimental conditions. These two selection criteria often result in incompatible selected gene sets. Also, in a two-factor, say, treatment by time experiment, the investigator may be interested in one gene list that responds to both treatment and time effects. RESULTS: We propose three layer ranking algorithms, point-admissible, line-admissible (convex), and Pareto, to provide a preference gene list from multiple gene lists generated by different ranking criteria. Using the public colon data as an example, the layer ranking algorithms are applied to the three univariate ranking criteria, fold-change, p-value, and frequency of selections by the SVM-RFE classifier. A simulation experiment shows that for experiments with small or moderate sample sizes (less than 20 per group) and detecting a 4-fold change or less, the two-dimensional (p-value and fold-change) convex layer ranking selects differentially expressed genes with generally lower FDR and higher power than the standard p-value ranking. Three applications are presented. The first application illustrates a use of the layer rankings to potentially improve predictive accuracy. The second application illustrates an application to a two-factor experiment involving two dose levels and two time points. The layer rankings are applied to selecting differentially expressed genes relating to the dose and time effects. In the third application, the layer rankings are applied to a benchmark data set consisting of three dilution concentrations to provide a ranking system from a long list of differentially expressed genes generated from the three dilution concentrations. CONCLUSION: The layer ranking algorithms are useful to help investigators in selecting the most promising genes from multiple gene lists generated by different filter, normalization, or analysis methods for various objectives

ANMM4CBR: a case-based reasoning method for gene expression data classification

<p>Abstract</p> <p>Background</p> <p>Accurate classification of microarray data is critical for successful clinical diagnosis and treatment. The "curse of dimensionality" problem and noise in the data, however, undermines the performance of many algorithms.</p> <p>Method</p> <p>In order to obtain a robust classifier, a novel Additive Nonparametric Margin Maximum for Case-Based Reasoning (ANMM4CBR) method is proposed in this article. ANMM4CBR employs a case-based reasoning (CBR) method for classification. CBR is a suitable paradigm for microarray analysis, where the rules that define the domain knowledge are difficult to obtain because usually only a small number of training samples are available. Moreover, in order to select the most informative genes, we propose to perform feature selection via additively optimizing a nonparametric margin maximum criterion, which is defined based on gene pre-selection and sample clustering. Our feature selection method is very robust to noise in the data.</p> <p>Results</p> <p>The effectiveness of our method is demonstrated on both simulated and real data sets. We show that the ANMM4CBR method performs better than some state-of-the-art methods such as support vector machine (SVM) and <it>k </it>nearest neighbor (<it>k</it>NN), especially when the data contains a high level of noise.</p> <p>Availability</p> <p>The source code is attached as an additional file of this paper.</p

Identifying differential correlation in gene/pathway combinations

<p>Abstract</p> <p>Background</p> <p>An important emerging trend in the analysis of microarray data is to incorporate known pathway information a priori. Expression level "summaries" for pathways, obtained from the expression data for the genes constituting the pathway, permit the inclusion of pathway information, reduce the high dimensionality of microarray data, and have the power to elucidate gene-interaction dependencies which are not already accounted for through known pathway identification.</p> <p>Results</p> <p>We present a novel method for the analysis of microarray data that identifies joint differential expression in gene-pathway pairs. This method takes advantage of known gene pathway memberships to compute a summary expression level for each pathway as a whole. Correlations between the pathway expression summary and the expression levels of genes not already known to be associated with the pathway provide clues to gene interaction dependencies that are not already accounted for through known pathway identification, and statistically significant differences between gene-pathway correlations in phenotypically different cells (e.g., where the expression level of a single gene and a given pathway summary correlate strongly in normal cells but weakly in tumor cells) may indicate biologically relevant gene-pathway interactions. Here, we detail the methodology and present the results of this method applied to two gene-expression datasets, identifying gene-pathway pairs which exhibit differential joint expression by phenotype.</p> <p>Conclusion</p> <p>The method described herein provides a means by which interactions between large numbers of genes may be identified by incorporating known pathway information to reduce the dimensionality of gene interactions. The method is efficient and easily applied to data sets of ~10²arrays. Application of this method to two publicly-available cancer data sets yields suggestive and promising results. This method has the potential to complement gene-at-a-time analysis techniques for microarray analysis by indicating relationships between pathways and genes that have not previously been identified and which may play a role in disease.</p