MHCherryPan, a novel model to predict the binding affinity of pan-specific class I HLA-peptide

The human leukocyte antigen (HLA) system or complex plays an essential role in regulating the immune system in humans. Accurate prediction of peptide binding with HLA can efficiently help to identify those neoantigens, which potentially make a big difference in immune drug development. HLA is one of the most polymorphic genetic systems in humans, and thousands of HLA allelic versions exist. Due to the high polymorphism of HLA complex, it is still pretty difficult to accurately predict the binding affinity. In this thesis, we presented a new algorithm to combine convolutional neural network and long short-term memory to solve this problem. Compared with other current popular algorithms, our model achieved the state-of-the-art results

On the Whitney disks in Heegaard Floer homology theory

The Whitney disks play a central role in defining Heegaard Floer homology of a $3$-dimensional manifold. We use Nielsen theory to a simple criterion to the existence of Whitney disks, connecting two given intersections

Persistence of sub-chain groups

In this work, we present a generalization of extended persistent homology to filtrations of graded sub-groups by defining relative homology in this setting. Our work provides a more comprehensive and flexible approach to get an algebraic invariant overcoming the limitations of the standard approach. The main contribution of our work is the development of a stability theorem for extended persistence modules using an extension of the definition of interleaving and the rectangle measure. This stability theorem is a crucial property for the application of mathematical tools in data analysis. We apply the stability theorem to extended persistence modules obtained from extended path homology of directed graphs and extended homology of hypergraphs, which are two important examples in topological data analysis

Resonant Frequency Modeling of Microwave Antennas Using Gaussian Process Based on Semisupervised Learning

For the optimal design of electromagnetic devices, it is the most time consuming to obtain the training samples from full wave electromagnetic simulation software, including HFSS, CST, and IE3D. Traditional machine learning methods usually use only labeled samples or unlabeled samples, but in practical problems, labeled samples and unlabeled samples coexist, and the acquisition cost of labeled samples is relatively high. This paper proposes a semisupervised learning Gaussian Process (GP), which combines unlabeled samples to improve the accuracy of the GP model and reduce the number of labeled training samples required. The proposed GP model consists two parts: initial training and self-training. In the process of initial training, a small number of labeled samples obtained by full wave electromagnetic simulation are used for training the initial GP model. Afterwards, the trained GP model is copied to another GP model in the process of self-training, and then the two GP models will update after crosstraining with different unlabeled samples. Using the same test samples for testing and updating, a model with a smaller error will replace another. Repeat the self-training process until a predefined stopping criterion is met. Four different benchmark functions and resonant frequency modeling problems of three different microstrip antennas are used to evaluate the effectiveness of the GP model. The results show that the proposed GP model has a good fitting effectiveness on benchmark functions. For microstrip antennas resonant frequency modeling problems, in the case of using the same labeled samples, its predictive ability is better than that of the traditional supervised GP model

Free cash flow productivity among Chinese listed companies: A comparative study of SOEs and non-SOEs

This paper investigates the free cash flow productivity of SOEs compared with non-SOEs and examines its possible determinants. We find that SOEs have slightly weak free cash flow productivity but significantly stronger than non-SOEs. Similar performance exists among commercial class I and II SOEs and public-benefit SOEs. Further analyses suggest that firm size, age, sales growth, ownership concentration, government subsidies, and industry monopoly factors cannot explain this phenomenon. The common driver for all types of SOEs to generate stronger free cash flows than non-SOEs is their stronger expense control capability

Automated Vessel Tracing for Cerebral Vascularity Study in Microscopy Images

Conference Name:1st International Conference on Environment Science and Biotechnology (ICESB). Conference Address: Male, MALDIVES. Time:NOV 25-27, 2011.Congenital hydrocephalus is a buildup of excess cerebrospinal fluid in the brain at birth. The micro-vascular specimens of these brains showed a deranged vascular pattern and poor vascular network in the brain mantle. As the first step of processing and analyzing the vascular network, we proposed a automatic computational approach to label all the vessel in the images. The method, based on advanced curvilinear structure detector, can extract the vessel skeletons and address the branching issue of the vascular network at the same time. (C) 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of the Asia-Pacific Chemical, Biological & Environmental Engineering Society (APCBEES