Compared to the available protein sequences of different organisms, the number of revealed protein–protein interactions (PPIs) is still very limited. So many computational methods have been developed to facilitate the identification of novel PPIs. However, the methods only using the information of protein sequences are more universal than those that depend on some additional information or predictions about the proteins. In this article, a sequence-based method is proposed by combining a new feature representation using auto covariance (AC) and support vector machine (SVM). AC accounts for the interactions between residues a certain distance apart in the sequence, so this method adequately takes the neighbouring effect into account. When performed on the PPI data of yeast Saccharomyces cerevisiae, the method achieved a very promising prediction result. An independent data set of 11 474 yeast PPIs was used to evaluate this prediction model and the prediction accuracy is 88.09%. The performance of this method is superior to those of the existing sequence-based methods, so it can be a useful supplementary tool for future proteomics studies. The prediction software and all data sets used in this article are freely available at http://www.scucic.cn/Predict_PPI/index.htm.

Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis.

Background: During the coronavirus disease (COVID-19) pandemic, harsh social distancing measures were taken in China to contain viral spread. We examined their impact on the lives of medical students. Methods: A nation-wide cross-sectional survey of college students was conducted from 4–12 February 2020. We enrolled medical students studying public health in Beijing and Wuhan to assess their COVID-19 awareness and to evaluate their mental health status/behaviors using a self-administered questionnaire. We used the Patient Generalized Anxiety Disorder-7 and Health Questionnaire-9 to measure anxiety disorders and depression. We used multivariable logistic regression and path analysis to assess the associations between covariates and anxiety disorder/depression. Results: Of 933 students, 898 (96.2%) reported wearing masks frequently when going out, 723 (77.5%) reported daily handwashing with soap, 676 (72.5%) washed hands immediately after arriving home, and 914 (98.0%) reported staying home as much as possible. Prevalence of anxiety disorder was 17.1% and depression was 25.3%. Multivariable logistic regression showed anxiety to be associated with graduate student status (odds ratio (aOR) = 2.0; 95% confidence interval (CI): 1.2–3.5), negative thoughts or actions (aOR = 1.6; 95% CI: 1.4–1.7), and feeling depressed (aOR = 6.8; 95% CI: 4.0–11.7). Beijing students were significantly less likely to have anxiety than those in the Wuhan epicenter (aOR = 0.9; 95% CI: 0.8–1.0), but depression did not differ. Depression was associated with female students (aOR = 2.0; 95% CI: 1.2–3.3), negative thoughts or actions (aOR = 1.7; 95% CI: 1.5–1.9), and anxiety disorder (aOR = 5.8; 95% CI: 3.4–9.9). Path analysis validated these same predictors. Conclusions: Despite medical students’ knowledge of disease control and prevention, their lives were greatly affected by social distancing, especially in the Wuhan epicenter. Even well-informed students needed psychological support during these extraordinarily stressful times.

Practical wearable e‐textiles must be durable and retain, as far as possible, the textile properties such as drape, feel, lightweight, breathability, and washability that make fabrics suitable for clothing. Early e‐textile garments were realized by inserting standard portable electronic devices into bespoke pockets and arranging interconnects and cabling across the garment. In these examples, the textile merely served as a vehicle to house the electronics and had no inherent electronic functionality. A reduction in electronic component size, the development of flexible circuits, and the ability to weave robust interconnects offer the potential for improved levels of electronic integration within the textile. The weaving of electronic circuit filaments less than 2 mm wide into fabrics such that the electronics are fully concealed in the textile and given extra protection by the surrounding textile fibers is introduced. The failure mechanisms for different filament circuit designs before and after integration into the textile are investigated with a 90° cyclical bending test. Results show that encapsulated filament circuits embedded within the textile survive 45 washing cycles and more than 1500 cycles of 90° bending around a bending radius of 10 mm, performing five times better than equivalent filament circuits before integration into the fabric.

A facile, economical and effective method to produce hierarchically porous nitrogen-rich carbon (HPNC) from wheat straw has been reported. Acid pretreatment is introduced before KOH activation, and plays the role of promoting the formation of thinner pore walls. Without any N-doping, the N content is as high as 5.13%. The HPNC when used as an anode for Li-ion batteries exhibits a superior specific capacity of 1470 mA h g À1 at 0.037 A g À1 , and possesses an ultrahigh rate capability of 344 mA h g À1 at 18.5 A g À1 . Even at an extremely high current density of 37 A g À1 , the reversible capacity is still as high as 198 mA h g À1 .