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  1. Regression ensembles consisting of a collection of base regression models are often used to improve the estimation/prediction performance of a single regression model. It has been shown that the individual accuracy of the base models and the ensemble diversity are the two key factors affecting the performance of an ensemble. In this paper, we derive a theory for regression ensembles that illustrates the subtle trade-off between individual accuracy and ensemble diversity from the perspective of statistical correlations. Then, inspired by our derived theory, we further propose a novel loss function and a training algorithm for deep learning regression ensembles. We then demonstrate the advantage of our training approach over standard regression ensemble methods including random forest and gradient boosting regressors with both benchmark regression problems and chemical sensor problems involving analysis of Raman spectroscopy. Our key contribution is that our loss function and training algorithm is able to manage diversity explicitly in an ensemble, rather than merely allowing diversity to occur by happenstance. 
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  2. Neoantigens are derived from tumor-specific somatic mutations. Neoantigen-based synthesized peptides have been under clinical investigation to boost cancer immunotherapy efficacy. The promising results prompt us to further elucidate the effect of neoantigen expression on patient survival in breast cancer. We applied Kaplan–Meier survival and multivariable Cox regression models to evaluate the effect of neoantigen expression and its interaction with T-cell activation on overall survival in a cohort of 729 breast cancer patients. Pearson’s chi-squared tests were used to assess the relationships between neoantigen expression and clinical pathological variables. Spearman correlation analysis was conducted to identify correlations between neoantigen expression, mutation load, and DNA repair gene expression. ERCC1, XPA, and XPC were negatively associated with neoantigen expression, while BLM, BRCA2, MSH2, XRCC2, RAD51, CHEK1, and CHEK2 were positively associated with neoantigen expression. Based on the multivariable Cox proportional hazard model, patients with a high level of neoantigen expression and activated T-cell status showed improved overall survival. Similarly, in the T-cell exhaustion and progesterone receptor (PR) positive subgroups, patients with a high level of neoantigen expression showed prolonged survival. In contrast, there was no significant difference in the T-cell activation and PR negative subgroups. In conclusion, neoantigens may serve as immunogenic agents for immunotherapy in breast cancer. 
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  3. Exploration of photovoltaic materials has received enormous interest for a wide range of both fundamental and applied research. Therefore, in this work, we identify a CsSi compound with a Zintl phase as a promising candidate for photovoltaic material by using a global structure prediction method. Electronic structure calculations indicate that this phase possesses a quasi-direct band gap of 1.45 eV, suggesting that its optical properties could be superior to those of diamond-Si for capturing sunlight from the visible to the ultraviolet range. In addition, a novel silicon allotrope is obtained by removing Cs atoms from this CsSi compound. The superconducting critical temperature ( T c ) of this phase was estimated to be of 9 K in terms of a substantial density of states at the Fermi level. Our findings represent a new promising CsSi material for photovoltaic applications, as well as a potential precursor of a superconducting silicon allotrope. 
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  4. Abstract

    Intracellular organelles are membrane‐bound and biochemically distinct compartments constructed to serve specialized functions in eukaryotic cells. Through extensive interactions, they form networks to coordinate and integrate their specialized functions for cell physiology. A fundamental property of these organelle networks is that they constantly undergo dynamic organization via membrane fusion and fission to remodel their internal connections and to mediate direct material exchange between compartments. The dynamic organization not only enables them to serve critical physiological functions adaptively but also differentiates them from many other biological networks such as gene regulatory networks and cell signaling networks. This review examines this fundamental property of the organelle networks from a systems point of view. The focus is exclusively on homotypic networks formed by mitochondria, lysosomes, endosomes, and the endoplasmic reticulum, respectively. First, key mechanisms that drive the dynamic organization of these networks are summarized. Then, several distinct organizational properties of these networks are highlighted. Next, spatial properties of the dynamic organization of these networks are emphasized, and their functional implications are examined. Finally, some representative molecular machineries that mediate the dynamic organization of these networks are surveyed. Overall, the dynamic organization of intracellular organelle networks is emerging as a fundamental and unifying paradigm in the internal organization of eukaryotic cells.

    This article is categorized under:

    Metabolic Diseases > Molecular and Cellular Physiology

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