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.

Investigation and Analysis on New Harmful Features Related to Frozen Ground in Permafrost Regions in Qinghai-Tibet Railway

At first, the forming conditions and developing characteristics of several kinds of typical harmful features related to frozen ground are discussed in the paper, such as flooding ice, icing mound, frost mound, thick ground ice, thaw slumping, thermokarst lake and swampland. Secondly, the investigating results of new harmful permafrost features in winter along Qinghai-Tibet Railway are analysed and summarized. Lastly, some data and suggestions will be provided to designing and construction departments.

Robust Radiometric Normalization of the near Equatorial Satellite Images Using Feature Extraction and Remote Sensing Analysis

Relative radiometric normalization (RRN) minimizes radiometric differences among images caused by inconsistencies of acquisition conditions rather than changes in surface. Scale invariant feature transform (SIFT) has the ability to automatically extract control points (CPs) and is commonly used for remote sensing images. However, its results are mostly inaccurate and sometimes contain incorrect matching caused by generating a small number of false CP pairs. These CP pairs have high false alarm matching. This paper presents a modified method to improve the performance of SIFT CPs matching by applying sum of absolute difference (SAD) in a different manner for the new optical satellite generation called near-equatorial orbit satellite and multi-sensor images. The proposed method, which has a significantly high rate of correct matches, improves CP matching. The data in this study were obtained from the RazakSAT satellite a new near equatorial satellite system. The proposed method involves six steps: 1) data reduction, 2) applying the SIFT to automatically extract CPs, 3) refining CPs matching by using SAD algorithm with empirical threshold, and 4) calculation of true CPs intensity values over all image’ bands, 5) preforming a linear regression model between the intensity values of CPs locate in reverence and sensed image’ bands, 6) Relative radiometric normalization conducting using regression transformation functions. Different thresholds have experimentally tested and used in conducting this study (50 and 70), by followed the proposed method, and it removed the false extracted SIFT CPs to be from 775, 1125, 883, 804, 883 and 681 false pairs to 342, 424, 547, 706, 547, and 469 corrected and matched pairs, respectively.

Optimizing feature vectors and removal unnecessary channels in BCI speller application

In this paper we will discuss novel algorithms to develop the brain-computer interface (BCI) system in speller application based on single-trial classification of electroencephalogram (EEG) signal. The idea is to employ proper methods for reducing the number of channels and optimizing feature vectors. Removal unnecessary channels and reducing feature dimension result in cost decrement, time saving and improve the BCI implementation eventually. Optimal channels will be gotten after two stages sifting. In the first stage, the channels reduced up to 30% based on channels of the important event related potential (ERP) components and in the next stage, optimal channels were extracted by backward forward selection (BFS) algorithm. Also we will show that suitable single-trial analysis requires applying proper feature vector that was constructed by recognizing important ERP components, so as to propose an algorithm to distinguish less important features in feature vectors. F-Score criteria used to recognize effective features which created more discrimination between different classes and feature vectors were reconstructed based on effective features. Our algorithm has tested on dataset II of BCI competition III. The results show that we achieve accuracy up to 31% in single-trial, which is better than the performance of winner who is in this competition (about 25.5%). Also we use simple classifier and few channels to compute output performances while more complicated classifier and all channels are used by them.

A Hybrid K-Means-GRA-SVR Model Based on Feature Selection for Day-Ahead Prediction of Photovoltaic Power Generation

In order to ensure that the large-scale application of photovoltaic power generation does not affect the stability of the grid, accurate photovoltaic (PV) power generation forecast is essential. A short-term PV power generation forecast method using the combination of K-means++, grey relational analysis (GRA) and support vector regression (SVR) based on feature selection (Hybrid Kmeans-GRA-SVR, HKGSVR) was proposed. The historical power data were clustered through the multi-index K-means++ algorithm and divided into ideal and non-ideal weather. The GRA algorithm was used to match the similar day and the nearest neighbor similar day of the prediction day. And selected appropriate input features for different weather types to train the SVR model. Under ideal weather, the average values of MAE, RMSE and R2 were 0.8101, 0.9608 kW and 99.66%, respectively. And this method reduced the average training time by 77.27% compared with the standard SVR model. Under non-ideal weather conditions, the average values of MAE, RMSE and R2 were 1.8337, 2.1379 kW and 98.47%, respectively. And this method reduced the average training time of the standard SVR model by 98.07%. The experimental results show that the prediction accuracy of the proposed model is significantly improved compared to the other five models, which verify the effectiveness of the method.

Semantic Features and Applications in Translation of English Words in Pairs

English words in pairs are a special form of English idioms, which have different kinds and are used widely. For English learners, words in pairs are one of the difficult points. This paper discusses their form patterns, semantic relations, grammatical functions, rhetoric features and their application in translation. Its purpose is to help learners understand and use them accurately and correctly so as to improve language expressing ability.

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.

Gate Feature Interaction Network for Relation Prediction in Knowledge Graph

Recently,many knowledge graph embedding models for knowledge graph completion have been proposed,ranging from the initial translation-based model such as TransE to recent CNN-based models such as ConvE.These models fill in the missing relations between entities by focusing on capturing the representation features to further complete the existing knowledge graph(KG).However,the above KG-based relation prediction research ignores the interaction information among entities in KG.To solve this problem,this work proposes a novel model called Gate Feature Interaction Network(GFINet)with a weighted loss function that takes the benefit of interaction information and deep expressive features together.Specifically,the proposed GFINet consists of a gate convolution block and an interaction attention module,corresponding to catching deep expressive features and interaction information based on these valid features respectively.Our method establishes state-of-the-art experimental results on the standard datasets for knowledge graph completion.In addition,we make ablation experiments to verify the effectiveness of the gate convolution block and the interaction attention module.

基于时频特征融合和关系网络的少样本轴承故障诊断方法研究

针对滚动轴承故障样本不足和特征信息获取不全面导致故障诊断准确率低的问题,提出了一种基于时频特征融合和关系网络的少样本故障诊断方法。该方法结合元学习的训练策略,首先设计了一个特征提取模块,用于获取滚动轴承振动信号的时频域信息并进行融合,以此加强获取特征的全面性;其次使用关系网络的度量模块计算支持样本和查询样本的相似得分,最终实现故障诊断。实验结果表明,在CWRU数据集的跨工况场景下,本方法展现出了优异的性能,故障诊断准确率最高可达99.82%,并有效验证了特征提取模块的有效性,显著提升了滚动轴承故障诊断的准确性和可靠性。

A Two-Phase Paradigm for Joint Entity-Relation Extraction

An exhaustive study has been conducted to investigate span-based models for the joint entity and relation extraction task.However,these models sample a large number of negative entities and negative relations during the model training,which are essential but result in grossly imbalanced data distributions and in turn cause suboptimal model performance.In order to address the above issues,we propose a two-phase paradigm for the span-based joint entity and relation extraction,which involves classifying the entities and relations in the first phase,and predicting the types of these entities and relations in the second phase.The two-phase paradigm enables our model to significantly reduce the data distribution gap,including the gap between negative entities and other entities,aswell as the gap between negative relations and other relations.In addition,we make the first attempt at combining entity type and entity distance as global features,which has proven effective,especially for the relation extraction.Experimental results on several datasets demonstrate that the span-based joint extraction model augmented with the two-phase paradigm and the global features consistently outperforms previous state-ofthe-art span-based models for the joint extraction task,establishing a new standard benchmark.Qualitative and quantitative analyses further validate the effectiveness the proposed paradigm and the global features.

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%.

AMFRel:一种中文电子病历实体关系联合抽取方法

中文电子病历实体关系抽取是构建医疗知识图谱,服务下游子任务的重要基础。目前,中文电子病例进行实体关系抽取仍存在因医疗文本关系复杂、实体密度大而造成医疗名词识别不准确的问题。针对这一问题,提出了基于对抗学习与多特征融合的中文电子病历实体关系联合抽取模型AMFRel(adversarial learning and multi-feature fusion for relation triple extraction),提取电子病历的文本和词性特征,得到融合词性信息的编码向量;利用编码向量联合对抗训练产生的扰动生成对抗样本,抽取句子主语;利用信息融合模块丰富文本结构特征,并根据特定的关系信息抽取出相应的宾语,得到医疗文本的三元组。采用CHIP2020关系抽取数据集和糖尿病数据集进行实验验证,结果显示:AMFRel在CHIP2020关系抽取数据集上的Precision为63.922%,Recall为57.279%,F1值为60.418%;在糖尿病数据集上的Precision、Recall和F1值分别为83.914%,67.021%和74.522%,证明了该模型的三元组抽取性能优于其他基线模型。

基于平行交互注意力网络的中文电子病历实体及关系联合抽取

基于电子病历构建医学知识图谱对医疗技术的发展具有重要意义,实体和关系抽取是构建知识图谱的关键技术。该文针对目前实体关系联合抽取中存在的特征交互不充分的问题,提出了一种平行交互注意力网络(PIAN)以充分挖掘实体与关系的相关性,在多个标准的医学和通用数据集上取得最优结果;当前中文医学实体及关系标注数据集较少,该文基于中文电子病历构建了实体和关系抽取数据集(CEMRIE),与医学专家共同制定了语料标注规范,并基于该文所提出的模型实验得出基准结果。

6例免疫检查点抑制剂相关性结肠炎的临床特征分析

目的收集并总结免疫检查点抑制剂(ICI)相关性结肠炎的临床特征。方法收集2021年1月至2022年2月上海中医药大学附属曙光医院收治的6例ICI相关性结肠炎患者的临床资料,分析并总结ICI相关性结肠炎的病程特点、临床表现、内镜及组织学特点、治疗方法以及患者预后转归情况。结果6例患者中男性5例,女性1例,ICI相关性结肠炎诊断年龄为48~70岁。6例患者在接受2~6次抗程序性死亡受体-1(Programmed death-1,PD-1)及其配体(Programmed death-ligand 1,PD-L1)疗程后,出现ICI相关性结肠炎,中位疗程为4.1个;首次抗PD-1/PD-L1治疗后,ICI相关性结肠炎起病时间为14~84 d,中位时间为53 d。6例患者中,4例为2级ICI相关性结肠炎,且病变在左半结肠,3级、4级各1例,病变范围均为全结肠。患者的症状主要为腹泻、腹痛、黏液血便。结肠镜下主要表现为弥漫而连续性的黏膜糜烂、充血和水肿,2例3~4级ICI相关性结肠炎患者结肠镜下可见多发不规则溃疡。黏膜活检病理均表现为黏膜活动性炎症伴隐窝炎、隐窝脓肿。6例患者完成结肠镜后,均接受了糖皮质激素治疗,其中2例3~4级ICI相关性结肠炎患者静脉使用甲基泼尼松龙3 d临床应答不佳,升级为英夫利昔单抗(IFX)治疗。6例患者均未重启ICI治疗。结论ICI相关性结肠炎有其相应的ICI治疗史以及临床、内镜、组织学特征,应根据患者症状予以分级治疗。糖皮质激素是最常用的治疗药物,应答不佳时,应尽早开启生物制剂治疗。

大连市伤害监测医院门急诊不同性别人群的动物伤害患者流行特征分析

目的了解大连市动物伤害流行病学特征,为动物伤害防控提供依据。方法收集大连市2018一2022年各监测医院动物伤害患者监测信息,对其人群分布、发生时间、地点、伤害部位、伤害严重程度等进行描述性流行病学分析。结果共收集动物伤害52864例,占总伤害病例的22.16%,位居各类伤害原因第2位。女性病例数高于男性,男女性别比为1:1.5。各年龄组人群中,15~29岁人群占比(29.64%)最高,60岁及以上人群占比(8.09%)最低。职业分布中以在校学生和商业/服务业人员为主,分别占27.65%和21.10%。各月份伤害发生时间集中于68月,分别占10.37%、10.74%和10.87%;一天24小时内,8时(9.32%)、10时(8.90%)和18时(7.66%)动物伤害发生最多。伤害发生地点以家中为主,占66.79%。伤害部位集中在上肢和下肢,分别占69.11%和26.29%;而0~14岁人群的头部受伤占比最高(6.42%),且其占比和年龄增长有关(P<0.01)。伤害严重程度以轻度为主,占91.14%;男性中度伤害占比(9.26%)高于女性(8.49%),有统计学差异(χ^(2)=9.93,P<0.01);中度伤害占比60岁及以上人群最高(13.96%),有统计学差异(χ^(2)=299.93,P<0.01)。结论动物伤害是大连市人群伤害的主要原因,15~29岁人群是防控重点人群,应加强宣传教育,提高居民防护意识,减少动物致伤事件的发生。

基于BERT的两次注意力机制远程监督关系抽取

针对词向量语义信息不完整以及文本特征抽取时的一词多义问题,提出基于BERT(Bidirectional Encoder Representation from Transformer)的两次注意力加权算法(TARE)。首先,在词向量编码阶段,通过构建Q、K、V矩阵使用自注意力机制动态编码算法,为当前词的词向量捕获文本前后词语义信息;其次,在模型输出句子级特征向量后,利用定位信息符提取全连接层对应参数,构建关系注意力矩阵;最后,运用句子级注意力机制算法为每个句子级特征向量添加不同的注意力分数,提高句子级特征的抗噪能力。实验结果表明:在NYT-10m数据集上,与基于对比学习框架的CIL(Contrastive Instance Learning)算法相比,TARE的F1值提升了4.0个百分点,按置信度降序排列后前100、200和300条数据精准率Precision@N的平均值(P@M)提升了11.3个百分点;在NYT-10d数据集上,与基于注意力机制的PCNN-ATT(Piecewise Convolutional Neural Network algorithm based on ATTention mechanism)算法相比,精准率与召回率曲线下的面积(AUC)提升了4.8个百分点,P@M值提升了2.1个百分点。在主流的远程监督关系抽取(DSER)任务中,TARE有效地提升了模型对数据特征的学习能力。