Gly-LysPred: Identification of Lysine Glycation Sites in Protein Using Position Relative Features and Statistical Moments via Chou’s 5 Step Rule

Glycation is a non-enzymatic post-translational modification which assigns sugar molecule and residues to a peptide.It is a clinically important attribute to numerous age-related,metabolic,and chronic diseases such as diabetes,Alzheimer’s,renal failure,etc.Identification of a non-enzymatic reaction are quite challenging in research.Manual identification in labs is a very costly and timeconsuming process.In this research,we developed an accurate,valid,and a robust model named as Gly-LysPred to differentiate the glycated sites from non-glycated sites.Comprehensive techniques using position relative features are used for feature extraction.An algorithm named as a random forest with some preprocessing techniques and feature engineering techniques was developed to train a computational model.Various types of testing techniques such as self-consistency testing,jackknife testing,and cross-validation testing are used to evaluate the model.The overall model’s accuracy was accomplished through self-consistency,jackknife,and cross-validation testing 100%,99.92%,and 99.88%with MCC 1.00,0.99,and 0.997 respectively.In this regard,a user-friendly webserver is also urbanized to accumulate the whole procedure.These features vectorization methods suggest that they can play a critical role in other web servers which are developed to classify lysine glycation.

Feature Relationship and Process Planning

Concurrent engineering(CE)involves the consideration during the design phase of the various factors associated with the life cycle of the product.Using the principle of CE,a feature-based CAPP system is proposed.On the basis of feature modeling,the system is able to reason feature relationships,produce feature digraph of a part,and decide the machining sequence of features.

A CHINESE ECONOMIC CIRCLE IN REALITY——On Five Basic Features of the Economic Relations Between the China's Mainland and Hongkong/Macao

Generally speaking, "an economic circle" refers to a group of countriesand regions whose economic relations override the universally accepted in-ternational practice or norms and they have formulated new economic ruleswhich are applicable only to countries and regions inside the circle.

An experimental study of the relations between bispectra and nonlinear apparent features of wind waves

To investigate the nonlinear properties of wind waves, experiments are carried out in a wind-wave flume with slope bottom at different wind speeds and fetches. Both the internal structure and apparent features of the nonlin-earity of wind waves are studied by using bispectral and statistical analysis of surface elevations. The relations between bispectra and nonlinear apparent characteristics of wind waves are established and confirmed.

A substructure-aware graph neural network incorporating relation features for drug-drug interaction prediction

Identifying drug–drug interactions(DDIs)is an important aspect of drug design research,and predicting DDIs serves as a crucial guarantee for avoiding potential adverse effects.Current substructure-based prediction methods still have some limitations:(i)The process of substructure extraction does not fully exploit the graph structure information of drugs,as it only evaluates the importance of different radius substructures from a single perspective.(ii)The process of constructing drug representations has overlooked the significant impact of relation embedding on optimizing drug representations.In this work,we propose a substructure-aware graph neural network incorporating relation features(RFSA-DDI)for DDI prediction,which introduces a directed message passing neural network with substructure attention mechanism based on graph self-adaptive pooling(GSP-DMPNN)and a substructure-aware interaction module incorporating relation features(RSAM).GSP-DMPNN utilizes graph self-adaptive pooling to comprehensively consider node features and local drug information for adaptive extraction of substructures.RSAM interacts drug features with relation representations to enhance their respective features individually,highlighting substructures that significantly impact predictions.RFSA-DDI is evaluated on two real-world datasets.Compared to existing methods,RFSA-DDI demonstrates certain advantages in both transductive and inductive settings,effectively handling the task of predicting DDIs for unseen drugs and exhibiting good generalization capability.The experimental results show that RFSA-DDI can effectively capture valuable structural information of drugs more accurately for DDI prediction,and provide more reliable assistance for potential DDIs detection in drug development and treatment stages.

Mapping of Freshwater Lake Wetlands Using Object-Relations and Rule-based Inference

Inland freshwater lake wetlands play an important role in regional ecological balance. Hongze Lake is the fourth biggest freshwater lake in China. In the past three decades, there has been significant loss of freshwater wet- lands within the lake and at the mouths of neighboring rivers, due to disturbance, primarily from human activities. The main purpose of this paper was to explore a practical technology for differentiating wetlands effectively from upland types in close proximity to them. In the paper, an integrated method, which combined per-pixel and per-field classifi- cation, was used for mapping wetlands of Hongze Lake and their neighboring upland types. Firstly, Landsat ETM＋ imagery was segmented and classified by using spectral and textural features. Secondly, ETM＋ spectral bands, textural features derived from ETM＋ Pan imagery, relative relations between neighboring classes, shape fea^xes, and elevation were used in a decision tree classification. Thirdly, per-pixel classification results from the decision tree classifier were improved by using classification results from object-oriented classification as a context. The results show that the technology has not only overcome the salt-and-pepper effect commonly observed in the past studies, but also has im- proved the accuracy of identification by nearly 5%.

In Silico Investigation of Agonist Activity of a Structurally Diverse Set of Drugs to hPXR Using HM-BSM and HM-PNN

The human pregnane X receptor（hPXR） plays a critical role in the metabolism, transport and clearance of xenobiotics in the liver and intestine. The hPXR can be activated by a structurally diverse of drugs to initiate clinically relevant drug-drug interactions. In this article, in silico investigation was performed on a structurally diverse set of drugs to identify critical structural features greatly related to their agonist activity towards h PXR. Heuristic method（HM）-Best Subset Modeling（BSM） and HM-Polynomial Neural Networks（PNN） were utilized to develop the linear and non-linear quantitative structure-activity relationship models. The applicability domain（AD） of the models was assessed by Williams plot. Statistically reliable models with good predictive power and explain were achieved（for HM-BSM, r^2=0.881, q^2_（LOO）=0.797, q^2_（EXT）=0.674; for HM-PNN, r^2=0.882, q^2_（LOO）=0.856, q^2_（EXT）=0.655）. The developed models indicated that molecular aromatic and electric property, molecular weight and complexity may govern agonist activity of a structurally diverse set of drugs to h PXR.

Estimating posterior inference quality of the relational infinite latent feature model for overlapping community detection

Overlapping community detection has become a very hot research topic in recent decades,and a plethora of methods have been proposed.But,a common challenge in many existing overlapping community detection approaches is that the number of communities K must be predefined manually.We propose a flexible nonparametric Bayesian generative model for count-value networks,which can allow K to increase as more and more data are encountered instead of to be fixed in advance.The Indian buffet process was used to model the community assignment matrix Z,and an uncol-lapsed Gibbs sampler has been derived.However,as the community assignment matrix Zis a structured multi-variable parameter,how to summarize the posterior inference results andestimate the inference quality about Z,is still a considerable challenge in the literature.In this paper,a graph convolutional neural network based graph classifier was utilized to help tosummarize the results and to estimate the inference qualityabout Z.We conduct extensive experiments on synthetic data and real data,and find that empirically,the traditional posterior summarization strategy is reliable.

EVOLUTIONAL FEATURES OF INTERANNUAL LOW-FREQUENCY OSCILLATIONS AND THEIR RELATION TO THE OCCURRENCE OF EL NINO

A simplified data set with 8°×8° grid system in a region of 32°S--32°N from 1951 to 1979 for the elements of sea surface temperature (SST), zonal wind at sea level (U), sea level pressure (SLP) and total cloud amount (CA) is made from the COADS. The oscillation components with periods of 2 years (QBO), 3.5 years (SO) and 5.5 years (FYO) in interannual low-frequency oscillations have been studied by using the methods of extended EOF (EEOF) and lag correlation analysis with the oscillational components of SST in the equator of eastern Pacific as the reference element. In our paper, the relationship between oscilla- tion components and occurrence of El Nino is also investigated.