MixBag: Bag-Level Data Augmentation for Learning from Label Proportions

Learning from label proportions (LLP) is a promising weakly supervised learning problem. In LLP, a set of instances (bag) has label proportions, but no instance-level labels are given. LLP aims to train an instance-level classifier by using the label proportions of the bag. In this paper, we propose a bag-level data augmentation method for LLP called MixBag, based on the key observation from our preliminary experiments; that the instance-level classification accuracy improves as the number of labeled bags increases even though the total number of instances is fixed. We also propose a confidence interval loss designed based on statistical theory to use the augmented bags effectively. To the best of our knowledge, this is the first attempt to propose bag-level data augmentation for LLP. The advantage of MixBag is that it can be applied to instance-level data augmentation techniques and any LLP method that uses the proportion loss. Experimental results demonstrate this advantage and the effectiveness of our method.Comment: Accepted at ICCV202

Design of Potent Inhibitors of Human RAD51 Recombinase Based on BRC Motifs of BRCA2 Protein: Modeling and Experimental Validation of a Chimera Peptide

We have previously shown that a 28-amino acid peptide derived from the BRC4 motif of BRCA2 tumor suppressor inhibits selectively human RAD51 recombinase (HsRad51). With the aim of designing better inhibitors for cancer treatment, we combined an in silico docking approach with in vitro biochemical testing to construct a highly efficient chimera peptide from eight existing human BRC motifs. We built a molecular model of all BRC motifs complexed with HsRad51 based on the crystal structure of the BRC4 motif-HsRad51 complex, computed the interaction energy of each residue in each BRC motif, and selected the best amino acid residue at each binding position. This analysis enabled us to propose four amino acid substitutions in the BRC4 motif. Three of these increased the inhibitory effect in vitro, and this effect was found to be additive. We thus obtained a peptide that is about 10 times more efficient in inhibiting HsRad51-ssDNA complex formation than the original peptide

Risk prediction models for dementia constructed by supervised principal component analysis using miRNA expression data

Alzheimer’s disease (AD) is the most common subtype of dementia, followed by Vascular Dementia (VaD), and Dementia with Lewy Bodies (DLB). Recently, microRNAs (miRNAs) have received a lot of attention as the novel biomarkers for dementia. Here, using serum miRNA expression of 1,601 Japanese individuals, we investigated potential miRNA biomarkers and constructed risk prediction models, based on a supervised principal component analysis (PCA) logistic regression method, according to the subtype of dementia. The final risk prediction model achieved a high accuracy of 0.873 on a validation cohort in AD, when using 78 miRNAs: Accuracy = 0.836 with 86 miRNAs in VaD; Accuracy = 0.825 with 110 miRNAs in DLB. To our knowledge, this is the first report applying miRNA-based risk prediction models to a dementia prospective cohort. Our study demonstrates our models to be effective in prospective disease risk prediction, and with further improvement may contribute to practical clinical use in dementia

Enzyme-Immobilized Microfluidic Process Reactors

Microreaction technology, which is an interdisciplinary science and engineering area, has been the focus of different fields of research in the past few years. Several microreactors have been developed. Enzymes are a type of catalyst, which are useful in the production of substance in an environmentally friendly way, and they also have high potential for analytical applications. However, not many enzymatic processes have been commercialized, because of problems in stability of the enzymes, cost, and efficiency of the reactions. Thus, there have been demands for innovation in process engineering, particularly for enzymatic reactions, and microreaction devices represent important tools for the development of enzyme processes. In this review, we summarize the recent advances of microchannel reaction technologies especially for enzyme immobilized microreactors. We discuss the manufacturing process of microreaction devices and the advantages of microreactors compared to conventional reaction devices. Fundamental techniques for enzyme immobilized microreactors and important applications of this multidisciplinary technology are also included in our topics

Enzyme-Immobilized Microfluidic Process Reactors

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Kinetic modeling of lipase-catalyzed esterification reaction between oleic acid and trimethylolpropane: A simplified model for multi-substrate multi-product ping-pong mechanisms.

Kinetic models are among the tools that can be used for optimization of biocatalytic reactions as well as for facilitating process design and upscaling in order to improve productivity and economy of these processes. Mechanism pathways for multi-substrate multi-product enzyme-catalyzed reactions can become very complex and lead to kinetic models comprising several tens of terms. Hence the models comprise too many parameters, which are in general highly correlated and their estimations are often prone to huge errors. In this study, Novozym® 435 catalyzed esterification reaction between oleic acid (OA) and trimethylolpropane (TMP) with continuous removal of side-product (water) was carried out as an example for reactions that follow multi-substrate multi-product ping-pong mechanisms. A kinetic model was developed based on a simplified ping-pong mechanism proposed for the reaction. The model considered both enzymatic and spontaneous reactions involved and also the effect of product removal during the reaction. The kinetic model parameters were estimated using nonlinear curve fitting through unconstrained optimization methodology and the model was verified by using empirical data from different experiments and showed good predictability of the reaction under different conditions. This approach can be applied to similar biocatalytic processes to facilitate their optimization and design