Lithium chloride ameliorates learning and memory ability and inhibits glycogen synthase kinase-3 beta activity in a mouse model of fragile X syndrome

In the present study,Fmr1 knockout mice （KO mice） were used as the model for fragile X syndrome.The results of step-through and step-down tests demonstrated that Fmr1 KO mice had shorter latencies and more error counts,indicating a learning and memory disorder.After treatment with 30,60,90,120,or 200 mg/kg lithium chloride,the learning and memory abilities of the Fmr1 KO mice were significantly ameliorated,in particular,the 200 mg/kg lithium chloride treatment had the most significant effect.Western blot analysis showed that lithium chloride significantly enhanced the expression of phosphorylated glycogen synthase kinase 3 beta,an inactive form of glycogen synthase kinase 3 beta,in the cerebral cortex and hippocampus of the Fmr1 KO mice.These results indicated that lithium chloride improved learning and memory in the Fmr1 KO mice,possibly by inhibiting glycogen synthase kinase 3 beta activity.

An improved Machine Learning Approach to Classify Sleep Stages and Apnea Events

Sleep apnea(SA)is a common sleep disorder.Identifying patients at risk by means of comprehensive monitoring that requires overnight stay at professional sleep clinics are costly and inconvenient and can lead to unreliable results in view of the unfamiliar sleep environment.Existing wearable devices for sleep monitoring,which can be used in a familiar home environment,do not provide the same comprehensive monitoring as through clinical monitoring.The larger objective of the present work is to develop a sleep monitoring system for home use,which can provide comprehensive monitoring.In the development in this paper,machine learning(ML)models are explored for the classification of SA and sleep stages using multisensory data,without neglecting any of the required signals.The data acquired through the sensors are normalized,their features are extracted using Composite Multiscale Sample Entropy(CMSE)and are standardized using a robust scaling algorithm.Processed features are classified using a Neural Network(NN)and the obtained results for the SA classification are compared with those obtained by using a Support Vector Machine(SVM)approach.The impact of neglecting signals when classifying sleep stages is analyzed as well.The results are presented in the paper and observations are made.The NN model trained with the Bayesian regularization algorithm has provided an overall average accuracy of 94.5%and performed slightly better than when trained using the scaled conjugate gradient backpropagation algorithm(93.2%).The SVMs have yielded lower accuracy levels compared to the NNs(<92%).It is observed that the use of all 14 signals for SS classification yields an overall test accuracy of 72.3%,which is higher than that when one or few signals are used.It is concluded that ML models are effective in classifying sleep data from multiple sensors.Accuracy levels are higher when fused multisensory data are used as inputs.Furthermore,NN models are found to be better suitable in practical application and can be incorporated into an inexpensive and convenient wearable device that can carry out comprehensive monitoring.

Reconstruction of Gene Regulatory Networks Based on Two-Stage Bayesian Network Structure Learning Algorithm

In the post-genomic biology era,the reconstruction of gene regulatory networks from microarray gene expression data is very important to understand the underlying biological system,and it has been a challenging task in bioinformatics.The Bayesian network model has been used in reconstructing the gene regulatory network for its advantages,but how to determine the network structure and parameters is still important to be explored.This paper proposes a two-stage structure learning algorithm which integrates immune evolution algorithm to build a Bayesian network.The new algorithm is evaluated with the use of both simulated and yeast cell cycle data.The experimental results indicate that the proposed algorithm can find many of the known real regulatory relationships from literature and predict the others unknown with high validity and accuracy.

THE MACHINE LEARNING SYSTEM OF TELEPHONE NETWORKS MANAGEMENT

The problem of fault information process in telephone networks manage ment system in AT & T in the US has been solved with stepanwise learning approach.This method makes the information decrease step by step by means of merge and sort, classifies the information to several typical classes and establishes the knowledge base (KB) eventually. If new fault information is inputted, we will call the knowl edge in KB and predict the related faults which will happen.

V Model of E-Learning Using Gagne Nine Steps of Education

This paper presents a V-model of e-learning using the well-known Gagne nine steps for quality education. Our suggested model is based on our experience at the computer engineering departments at AL-Hussein Bin Talal University, the University of Jordan and Albalqa Applied University. We applied the recommendations of the nine steps methodology to the e-learning environment. The V model suggested in this paper came up as a result of such application. Although this V model can be subject to some tuning and development in the future it proved to be highly efficient and easy to implement for the teacher and the student.

An Empirical Study of Chinese English Learners' Attitudes toward Errors and Error Correction in Question-asking

Based on the questionnaire, this study found that :1) Elementary learners were inclined to commit more global errors compared to their local errors, whilst advanced learners make more local errors; 2) Interlingual factors were more influential than intralingual factors in elementary learners' error making, but for advanced learners, intralingual factors played relatively a much more important role in error making; 3) Elementary learners preferred explicit correction whilst advanced learners favoured im?plicit correction in question-asking.

pLoc_Deep-mAnimal: A Novel Deep CNN-BLSTM Network to Predict Subcellular Localization of Animal Proteins

Current coronavirus pandemic has endangered mankind life. The reported cases are increasing exponentially. Information of animal protein subcellular localization can provide useful clues to develop antiviral drugs. To cope with such a catastrophe, a CNN based animal protein subcellular localization predictor called “pLoc_Deep-mAnimal” was developed. The predictor is particularly useful in dealing with the multi-sites systems in which some proteins may simultaneously occur in two or more different organelles that are the current focus of pharmaceutical industry. The global absolute true rate achieved by the new predictor is over 92% and its local accuracy is over 95%. Both have substantially exceeded the other existing state-of-the-art predictors. To maximize the convenience for most experimental scientists, a user-friendly web-server for the new predictor has been established at http://www.jci-bioinfo.cn/pLoc_Deep-mAnimal/, which will become a very useful tool for fighting pandemic coronavirus and save the mankind of this planet.

pLoc_Deep-mEuk: Predict Subcellular Localization of Eukaryotic Proteins by Deep Learning

Recently, the life of worldwide human beings has been endangering by the spreading of pneu- monia-causing virus, such as Coronavirus, COVID-19, and H1N1. To develop effective drugs against Coronavirus, knowledge of protein subcellular localization is prerequisite. In 2019, a predictor called “pLoc_bal-mEuk” was developed for identifying the subcellular localization of eukaryotic proteins. Its predicted results are significantly better than its counterparts, particularly for those proteins that may simultaneously occur or move between two or more subcellular location sites. However, more efforts are definitely needed to further improve its power since pLoc_bal-mEuk was still not trained by a “deep learning”, a very powerful technique developed recently. The present study was devoted to incorporating the “deep- learning” technique and developed a new predictor called “pLoc_Deep-mEuk”. The global absolute true rate achieved by the new predictor is over 81% and its local accuracy is over 90%. Both are overwhelmingly superior to its counterparts. Moreover, a user-friendly web- server for the new predictor has been well established at http://www.jci-bioinfo.cn/pLoc_Deep-mEuk/, by which the majority of experimental scientists can easily get their desired data.

pLoc_Deep-mHum: Predict Subcellular Localization of Human Proteins by Deep Learning

Recently, the life of human beings around the entire world has been endangering by the spreading of pneumonia-causing virus, such as Coronavirus, COVID-19, and H1N1. To develop effective drugs against Coronavirus, knowledge of protein subcellular localization is indispensable. In 2019, a predictor called “pLoc_bal-mHum” was developed for identifying the subcellular localization of human proteins. Its predicted results are significantly better than its counterparts, particularly for those proteins that may simultaneously occur or move between two or more subcellular location sites. However, more efforts are definitely needed to further improve its power since pLoc_bal-mHum was still not trained by a “deep learning”, a very powerful technique developed recently. The present study was devoted to incorporate the “deep-learning” technique and develop a new predictor called “pLoc_Deep-mHum”. The global absolute true rate achieved by the new predictor is over 81% and its local accuracy is over 90%. Both are overwhelmingly superior to its counterparts. Moreover, a user-friendly web-server for the new predictor has been well established at http://www.jci-bioinfo.cn/pLoc_Deep-mHum/, which will become a very useful tool for fighting pandemic coronavirus and save the mankind of this planet.

pLoc_Deep-mVirus: A CNN Model for Predicting Subcellular Localization of Virus Proteins by Deep Learning

The recent worldwide spreading of pneumonia-causing virus, such as Coronavirus, COVID-19, and H1N1, has been endangering the life of human beings all around the world. In order to really understand the biological process within a cell level and provide useful clues to develop antiviral drugs, information of virus protein subcellular localization is vitally important. In view of this, a CNN based virus protein subcellular localization predictor called “pLoc_Deep-mVirus” was developed. The predictor is particularly useful in dealing with the multi-sites systems in which some proteins may simultaneously occur in two or more different organelles that are the current focus of pharmaceutical industry. The global absolute true rate achieved by the new predictor is over 97% and its local accuracy is over 98%. Both are transcending other existing state-of-the-art predictors significantly. It has not escaped our notice that the deep-learning treatment can be used to deal with many other biological systems as well. To maximize the convenience for most experimental scientists, a user-friendly web-server for the new predictor has been established at http://www.jci-bioinfo.cn/pLoc_Deep-mVirus/.

pLoc_Deep-mPlant: Predict Subcellular Localization of Plant Proteins by Deep Learning

Current coronavirus pandemic has endangered mankind life. The reported cases are increasing exponentially. Information of plant protein subcellular localization can provide useful clues to develop antiviral drugs. To cope with such a catastrophe, a CNN based plant protein subcellular localization predictor called “pLoc_Deep-mPlant” was developed. The predictor is particularly useful in dealing with the multi-sites systems in which some proteins may simultaneously occur in two or more different organelles that are the current focus of pharmaceutical industry. The global absolute true rate achieved by the new predictor is over 95% and its local accuracy is about 90%?-?100%. Both have substantially exceeded the?other existing state-of-the-art predictors. To maximize the convenience for most?experimental scientists, a user-friendly web-server for the new predictor has been established?at?http://www.jci-bioinfo.cn/pLoc_Deep-mPlant/, by which the majority of experimental?scientists can easily obtain their desired data without the need to go through the?mathematical details.

pLoc_Deep-mGneg: Predict Subcellular Localization of Gram Negative Bacterial Proteins by Deep Learning

The recent worldwide spreading of pneumonia-causing virus, such as Coronavirus, COVID-19, and H1N1, has been endangering the life of human beings all around the world. In order to really understand the biological process within a cell level and provide useful clues to develop antiviral drugs, information of Gram negative bacterial protein subcellular localization is vitally important. In view of this, a CNN based protein subcellular localization predictor called “pLoc_Deep-mGnet” was developed. The predictor is particularly useful in dealing with the multi-sites systems in which some proteins may simultaneously occur in two or more different organelles that are the current focus of pharmaceutical industry. The global absolute true rate achieved by the new predictor is over 98% and its local accuracy is around 94% - 100%. Both are transcending other existing state-of-the-art predictors significantly. To maximize the convenience for most experimental scientists, a user-friendly web-server for the new predictor has been established at http://www.jci-bioinfo.cn/pLoc_Deep-mGneg/, which will become a very useful tool for fighting pandemic coronavirus and save the mankind of this planet.

iATC_Deep-mISF: A Multi-Label Classifier for Predicting the Classes of Anatomical Therapeutic Chemicals by Deep Learning

The recent worldwide spreading of pneumonia-causing virus, such as Coronavirus, COVID-19, and H1N1, has been endangering the life of human beings all around the world. To provide useful clues for developing antiviral drugs, information of anatomical therapeutic chemicals is vitally important. In view of this, a CNN based predictor called “iATC_Deep-mISF” has been developed. The predictor is particularly useful in dealing with the multi-label systems in which some chemicals may occur in two or more different classes. To maximize the convenience for most experimental scientists, a user-friendly web-server for the new predictor has been established at http://www.jci-bioinfo.cn/iATC_Deep-mISF/, which will become a very powerful tool for developing effective drugs to fight pandemic coronavirus and save the mankind of this planet.

pLoc_Deep-mGpos: Predict Subcellular Localization of Gram Positive Bacteria Proteins by Deep Learning

The recent worldwide spreading of pneumonia-causing virus, such as Coronavirus, COVID-19, and H1N1, has been endangering the life of human beings all around the world. In order to really understand the biological process within a cell level and provide useful clues to develop antiviral drugs, information of Gram positive bacteria protein subcellular localization is vitally important. In view of this, a CNN based protein subcellular localization predictor called “pLoc_Deep-mGpos” was developed. The predictor is particularly useful in dealing with the multi-sites systems in which some proteins may simultaneously occur in two or more different organelles that are the current focus of pharmaceutical industry. The global absolute true rate achieved by the new predictor is over 99% and its local accuracy is around 92% - 99%. Both are transcending other existing state-of-the-art predictors significantly. To maximize the convenience for most experimental scientists, a user-friendly web-server for the new predictor has been established at http://www.jci-bioinfo.cn/pLoc_Deep-mGpos/, which will become a very powerful tool for developing effective drugs to fight pandemic coronavirus and save the mankind of this planet.

Competitive Multi-Verse Optimization with Deep Learning Based Sleep Stage Classification

Sleep plays a vital role in optimum working of the brain and the body.Numerous people suffer from sleep-oriented illnesses like apnea,insomnia,etc.Sleep stage classification is a primary process in the quantitative examination of polysomnographic recording.Sleep stage scoring is mainly based on experts’knowledge which is laborious and time consuming.Hence,it can be essential to design automated sleep stage classification model using machine learning(ML)and deep learning(DL)approaches.In this view,this study focuses on the design of Competitive Multi-verse Optimization with Deep Learning Based Sleep Stage Classification(CMVODL-SSC)model using Electroencephalogram(EEG)signals.The proposed CMVODL-SSC model intends to effectively categorize different sleep stages on EEG signals.Primarily,data pre-processing is performed to convert the actual data into useful format.Besides,a cascaded long short term memory(CLSTM)model is employed to perform classification process.At last,the CMVO algorithm is utilized for optimally tuning the hyperparameters involved in the CLSTM model.In order to report the enhancements of the CMVODL-SSC model,a wide range of simulations was carried out and the results ensured the better performance of the CMVODL-SSC model with average accuracy of 96.90%.

Basic fibroblast growth factor improves learning and memory functions in chronic stress mice

Four weeks of uncertain stress was used to establish an animal model of chronic stress. Basic fibroblast growth factor was injected daily for 15 days following stress induction. Cell morphology in the hippocampal CA3 region of chronic stress mice revealed cell damage. Nitric oxide content and calcium concentration were significantly increased in the hippocampus, and learning and memory functions were significantly decreased. After basic fibroblast growth factor intervention, Ca2~ overload was decreased and neuronal damage was relieved in hippocampal neurons, which improved learning and memory functions in chronic stress mice. Latency was prolonged and the number of errors was decreased in a passive avoidance test.

多场景下基于传感器的行为识别

针对基于传感器的行为识别任务中识别场景单一且固定的问题,提出一种多场景下基于传感器的行为识别迁移模型,由基于传感器的动态感知算法(dynamic perception algorithm,DPA)和自适应场景的行为识别迁移方法(adaptive scene human recognition,AHR)两部分组成,解决在固定场景下对传感器的依赖性以及在场景转换时识别模型失效的问题。DPA提出两阶段迁移模式,将行为识别阶段和模型迁移阶段同步推进,保证模型在传感器异动发生后仍能持续拥有识别能力。进一步提出AHR场景迁移方法,实现模型在多场景下的行为识别能力。实验验证该模型具有更优的适应性和可扩展性。

基于深度学习算法的胰腺癌CT自动分期系统的构建与应用

目的:构建基于深度学习算法的胰腺癌计算机断层扫描(CT)自动分期系统,并探讨其应用价值。方法:回顾性分析我院2014年01月至2021年12月收治的286例胰腺癌患者的临床资料,均经CT检查且明确TNM分期,利用CT检查信息基于深度学习算法的胰腺癌CT自动分期系统。另选取2022年01月至2023年02月胰腺癌患者92例,均经CT检查,并利用上述系统进行TNM分期,分析该系统的准确性。结果:基于深度学习算法的胰腺癌CT自动分期系统共包括7个模块,可以实现胰腺癌TNM自动分期;92例患者中共有Ⅰ期12例、Ⅱ期31例、Ⅲ期36例、Ⅳ期13例,经基于深度学习算法的胰腺癌CT自动分期系统诊断共有Ⅰ期10例、Ⅱ期31例、Ⅲ期38例、Ⅳ期13例;该系统诊断胰腺癌TNM分期的灵敏度、特异度和准确度高,且与金标准高度一致(Kappa值=0.912,P<0.001)。结论:本研究构建了基于深度学习算法的胰腺癌CT自动分期系统,诊断价值高。

多元化教学模式在缺血性脑血管病介入进修医师培训中的应用探索

目的 探讨多元化教学模式在提高缺血性脑血管病介入诊疗进修医师培训质量和效率方面的应用。方法 选择2018年1月-2022年12月在南京大学医学院附属鼓楼医院神经内科接受脑血管介入培训的进修医师为研究对象,应用多元化教学模式,即将多种教学方法融合交叉的培训模式进行教学。采用问卷调查的形式对进修医师满意度及其在培训前后介入诊疗理论知识和实践操作能力进行评估。结果 共纳入55名进修医师,年龄32~50岁,其中男性51名(92.73%)。89.09%的进修医师对多元化教学模式非常满意;分别有83.64%和85.45%的进修医师认为多元化教学模式激发了学习兴趣和自主学习能力。经过多元化教学模式培训后,能够独立完成颅外支架置入治疗的进修医师显著增加(41.82%vs. 12.73%,P=0.002)。结论 多元化教学模式是提高缺血性脑血管病介入进修医师培训质量和效率的有效手段。