Even Faster Accelerated Coordinate Descent Using Non-Uniform Sampling

Accelerated coordinate descent is widely used in optimization due to its cheap per-iteration cost and scalability to large-scale problems. Up to a primal-dual transformation, it is also the same as accelerated stochastic gradient descent that is one of the central methods used in machine learning. In this paper, we improve the best known running time of accelerated coordinate descent by a factor up to $\sqrt{n}$. Our improvement is based on a clean, novel non-uniform sampling that selects each coordinate with a probability proportional to the square root of its smoothness parameter. Our proof technique also deviates from the classical estimation sequence technique used in prior work. Our speed-up applies to important problems such as empirical risk minimization and solving linear systems, both in theory and in practice.Comment: same result, but polished writin

Water pollutant fingerprinting tracks recent industrial transfer from coastal to inland China: a case study

In recent years, China’s developed regions have transferred industries to undeveloped regions. Large numbers of unlicensed or unregistered enterprises are widespread in these undeveloped regions and they are subject to minimal regulation. Current methods for tracing industrial transfers in these areas, based on enterprise registration information or economic surveys, do not work. The authors have developed an analytical framework combining water fingerprinting and evolutionary analysis to trace the pollution transfer features between water sources. We collected samples in Eastern China (industrial export) and Central China (industrial acceptance) separately from two water systems. Based on the water pollutant fingerprints and evolutionary trees, we traced the pollution transfer associated with industrial transfer between the two areas. The results are consistent with four episodes of industrial transfers over the past decade. The results also show likely types of the transferred industries - electronics, plastics, and biomedicines - that contribute to the water pollution transfer

Novel DNA Aptamers for Parkinson’s Disease Treatment Inhibit a-Synuclein Aggregation and Facilitate its Degradation

Parkinson\u27s disease (PD) is one of the most prevalent forms of synucleinopathies, and it is characterized neuropathologically by the presence of intracellular inclusions composed primarily of the protein α-synuclein (α-syn) in neurons. The previous immunotherapy targeting the α-syn in PD models with monoclonal antibodies has established α-syn protein as an effective target for neuronal cell death. However, due to the essential weaknesses of antibody and the unique features of aptamers, the aptamers could represent a promising alternative to the currently used antibodies in immunotherapy for PD. In this study, the purified human α-syn was used as the target for in vitro selection of aptamers using systematic evolution by exponential enrichment. This resulted in the identification of two 58-base DNA aptamers with a high binding affinity and good specificity to the α-syn, with KD values in the nanomolar range. Both aptamers could effectively reduce α-syn aggregation in vitro and in cells and target the α-syn to intracellular degradation through the lysosomal pathway. These effects consequently rescued the mitochondrial dysfunction and cellular defects caused by α-syn overexpression. To our knowledge, this is the first study to employ aptamers to block the aberrant cellular effects of the overexpressed α-syn in cells

Asymmetric bagging and feature selection for activities prediction of drug molecules

<p>Abstract</p> <p>Background</p> <p>Activities of drug molecules can be predicted by QSAR (quantitative structure activity relationship) models, which overcomes the disadvantages of high cost and long cycle by employing the traditional experimental method. With the fact that the number of drug molecules with positive activity is rather fewer than that of negatives, it is important to predict molecular activities considering such an unbalanced situation.</p> <p>Results</p> <p>Here, asymmetric bagging and feature selection are introduced into the problem and asymmetric bagging of support vector machines (asBagging) is proposed on predicting drug activities to treat the unbalanced problem. At the same time, the features extracted from the structures of drug molecules affect prediction accuracy of QSAR models. Therefore, a novel algorithm named PRIFEAB is proposed, which applies an embedded feature selection method to remove redundant and irrelevant features for asBagging. Numerical experimental results on a data set of molecular activities show that asBagging improve the AUC and sensitivity values of molecular activities and PRIFEAB with feature selection further helps to improve the prediction ability.</p> <p>Conclusion</p> <p>Asymmetric bagging can help to improve prediction accuracy of activities of drug molecules, which can be furthermore improved by performing feature selection to select relevant features from the drug molecules data sets.</p

Selecting subsets of newly extracted features from PCA and PLS in microarray data analysis

<p>Abstract</p> <p>Background</p> <p>Dimension reduction is a critical issue in the analysis of microarray data, because the high dimensionality of gene expression microarray data set hurts generalization performance of classifiers. It consists of two types of methods, i.e. feature selection and feature extraction. Principle component analysis (PCA) and partial least squares (PLS) are two frequently used feature extraction methods, and in the previous works, the top several components of PCA or PLS are selected for modeling according to the descending order of eigenvalues. While in this paper, we prove that not all the top features are useful, but features should be selected from all the components by feature selection methods.</p> <p>Results</p> <p>We demonstrate a framework for selecting feature subsets from all the newly extracted components, leading to reduced classification error rates on the gene expression microarray data. Here we have considered both an unsupervised method PCA and a supervised method PLS for extracting new components, genetic algorithms for feature selection, and support vector machines and <it>k </it>nearest neighbor for classification. Experimental results illustrate that our proposed framework is effective to select feature subsets and to reduce classification error rates.</p> <p>Conclusion</p> <p>Not only the top features newly extracted by PCA or PLS are important, therefore, feature selection should be performed to select subsets from new features to improve generalization performance of classifiers.</p

The Molecular Mechanism Of Alpha-Synuclein Dependent Regulation Of Protein Phosphatase 2A Activity

Background/Aims: Alpha-synuclein (α-Syn) is a neuronal protein that is highly implicated in Parkinson\u27s disease (PD), and protein phosphatase 2A (PP2A) is an important serine/threonine phosphatase that is associated with neurodegenerative diseases, such as PD. α-Syn can directly upregulate PP2A activity, but the underling mechanism remains unclear. Therefore, we investigated the molecular mechanism of α-Syn regulating PP2A activity. Methods: α-Syn and its truncations were expressed in E.coli, and purified by affinity chromatography. PP2A Cα and its mutants were expressed in recombinant baculovirus, and purified by affinity chromatography combined with gel filtration chromatography. The interaction between α-Syn and PP2A Cα was detected by GST pull-down assay. PP2A activity was investigated by the colorimetric assay. Results: The hydrophobic non-amyloid component (NAC) domain of α-Syn interacted with PP2A Cα and upregulated its activity. α-Syn aggregates reduced its ability to upregulate PP2A activity, since the hydrophobic domain of α-Syn was blocked during aggregation. Furthermore, in the hydrophobic center of PP2A Cα, the residue of I123 was responsible for PP2A to interact with α-Syn, and its hydrophilic mutation blocked its interaction with α-Syn as well as its activity upregulation by α-Syn. Conclusions: α-Syn bound to PP2A Cα by the hydrophobic interaction and upregulated its activity. Blocking the hydrophobic domain of α-Syn or hydrophilic mutation on the residue I123 in PP2A Cα all reduced PP2A activity upregulation by α-Syn. Overall, we explored the mechanism of α-Syn regulating PP2A activity, which might offer much insight into the basis underlying PD pathogenesis

The occurrence and potential health risk of microcystins in drinking water of rural areas in China.

Large-scale use of nitrogen and phosphorus fertilizers in agricultural production, environmental pollution and climate warming cause frequent algal blooms and the generation of algal toxins in water bodies in China. Algal pollution is increasing and microcystins (MCs) are detectable in both surface and ground water in China at sub- μg/L and μg/L levels. Toxicological studies show that microcystins have hepatic and  renal toxicity, genotoxicity, tumor-promoting effects, neurotoxicity, reproductive and developmental toxicity. Epidemiological evidence from China further reveals that chronic exposure to MCs through drinking water and liver cancer are positively correlated and demonstrate that MCs in drinking water are a main risk factor in liver cancer. Effectively controlled water pollution, reduced sewage discharge, and enhanced wastewater treatments are pivotal measures to control algal pollution and toxins in the drinking water of rural China

Mixup-Augmented Meta-Learning for Sample-Efficient Fine-Tuning of Protein Simulators

Molecular dynamics simulations have emerged as a fundamental instrument for studying biomolecules. At the same time, it is desirable to perform simulations of a collection of particles under various conditions in which the molecules can fluctuate. In this paper, we explore and adapt the soft prompt-based learning method to molecular dynamics tasks. Our model can remarkably generalize to unseen and out-of-distribution scenarios with limited training data. While our work focuses on temperature as a test case, the versatility of our approach allows for efficient simulation through any continuous dynamic conditions, such as pressure and volumes. Our framework has two stages: 1) Pre-trains with data mixing technique, augments molecular structure data and temperature prompts, then applies a curriculum learning method by increasing the ratio of them smoothly. 2) Meta-learning-based fine-tuning framework improves sample-efficiency of fine-tuning process and gives the soft prompt-tuning better initialization points. Comprehensive experiments reveal that our framework excels in accuracy for in-domain data and demonstrates strong generalization capabilities for unseen and out-of-distribution samples