Question order effects are commonly observed in self-report measures of judgment and attitude. This article develops a quantum question order model (the QQ model) to account for four types of question order effects observed in literature. First, the postulates of the QQ model are presented. Second, an a priori, parameter-free, and precise prediction, called the QQ equality, is derived from these mathematical principles, and six empirical data sets are used to test the prediction. Third, a new index is derived from the model to measure similarity between questions. Fourth, we show that in contrast to the QQ model, Bayesian and Markov models do not generally satisfy the QQ equality and thus cannot account for the reported empirical data that support this equality. Finally, we describe the conditions under which order effects are predicted to occur, and we review a broader range of findings that are encompassed by these very same quantum principles. We conclude that quantum probability theory, initially invented to explain order effects on measurements in physics, appears to be a powerful natural explanation for order effects of selfreport measures in social and behavioral sciences, too.

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Protein contact map prediction is useful for protein folding rate prediction, model selection and 3D structure prediction. Here we describe NNcon, a fast and reliable contact map prediction server and software. NNcon was ranked among the most accurate residue contact predictors in the Eighth Critical Assessment of Techniques for Protein Structure Prediction (CASP8), 2008. Both NNcon server and software are available at http://casp.rnet.missouri.edu/nncon.html.

Highlights We proposed a novel framework, CHP-Net, to differentiate and localize COVID-19 from community acquired pneumonia. We used excessive data augmentation to extend the available dataset and optimize the CHP-Net generalization capability. Comparing to other ConvNet, CHP-Net works much more efficiently to extract feature information on chest X-Ray. All metrics, including categorical loss, accuracy, precision, recall and F1-score, proved CHP-Net fits good for the task. CHP-Net are better than the previous methods tested in detecting COVID-19 and exceeding to radiologist.

BackgroundIt is important to predict the quality of a protein structural model before its native structure is known. The method that can predict the absolute local quality of individual residues in a single protein model is rare, yet particularly needed for using, ranking and refining protein models.ResultsWe developed a machine learning tool (SMOQ) that can predict the distance deviation of each residue in a single protein model. SMOQ uses support vector machines (SVM) with protein sequence and structural features (i.e. basic feature set), including amino acid sequence, secondary structures, solvent accessibilities, and residue-residue contacts to make predictions. We also trained a SVM model with two new additional features (profiles and SOV scores) on 20 CASP8 targets and found that including them can only improve the performance when real deviations between native and model are higher than 5Å. The SMOQ tool finally released uses the basic feature set trained on 85 CASP8 targets. Moreover, SMOQ implemented a way to convert predicted local quality scores into a global quality score. SMOQ was tested on the 84 CASP9 single-domain targets. The average difference between the residue-specific distance deviation predicted by our method and the actual distance deviation on the test data is 2.637Å. The global quality prediction accuracy of the tool is comparable to other good tools on the same benchmark.ConclusionSMOQ is a useful tool for protein single model quality assessment. Its source code and executable are available at: http://sysbio.rnet.missouri.edu/multicom_toolbox/.

Fairness has emerged as a critical problem in federated learning (FL). In this work, we identify a cause of unfairness in FL -- conflicting gradients with large differences in the magnitudes. To address this issue, we propose the federated fair averaging (FedFV) algorithm to mitigate potential conflicts among clients before averaging their gradients. We first use the cosine similarity to detect gradient conflicts, and then iteratively eliminate such conflicts by modifying both the direction and the magnitude of the gradients. We further show the theoretical foundation of FedFV to mitigate the issue conflicting gradients and converge to Pareto stationary solutions. Extensive experiments on a suite of federated datasets confirm that FedFV compares favorably against state-of-the-art methods in terms of fairness, accuracy and efficiency. The source code is available at https://github.com/WwZzz/easyFL.

Methuosis is a novel nonapoptotic mode of cell death characterized by vacuole accumulation in the cytoplasm. In this article, we describe a series of azaindole-based compounds that cause vacuolization in MDA-MB-231 cells. The most potent vacuole inducer, compound 13 (compound 13), displayed differential cytotoxicities against a broad panel of cancer cell lines, such as MDA-MB-231, A375, HCT116, and MCF-7, but it did not inhibit the growth of the nontumorigenic epithelial cell line MCF-10A. A mechanism study confirmed that the cell death was caused by inducing methuosis. Furthermore, compound 13 exhibited substantial pharmacological efficacy in the suppression of tumor growth in a xenograft mouse model of MDA-MB-231 cells without apparent side effects, which makes this compound the first example of a methuosis inducer with potent in vivo efficacy. These results demonstrate that methuosis inducers might serve as novel therapeutics for the treatment of cancer.

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