Regulator of G protein signaling 6 mediates exercise-induced recovery of hippocampal neurogenesis,learning,and memory in a mouse model of Alzheimer’s disease

Hippocampal neuronal loss causes cognitive dysfunction in Alzheimer’s disease.Adult hippocampal neurogenesis is reduced in patients with Alzheimer’s disease.Exercise stimulates adult hippocampal neurogenesis in rodents and improves memory and slows cognitive decline in patients with Alzheimer’s disease.However,the molecular pathways for exercise-induced adult hippocampal neurogenesis and improved cognition in Alzheimer’s disease are poorly understood.Recently,regulator of G protein signaling 6(RGS6)was identified as the mediator of voluntary running-induced adult hippocampal neurogenesis in mice.Here,we generated novel RGS6fl/fl;APP_(SWE) mice and used retroviral approaches to examine the impact of RGS6 deletion from dentate gyrus neuronal progenitor cells on voluntary running-induced adult hippocampal neurogenesis and cognition in an amyloid-based Alzheimer’s disease mouse model.We found that voluntary running in APP_(SWE) mice restored their hippocampal cognitive impairments to that of control mice.This cognitive rescue was abolished by RGS6 deletion in dentate gyrus neuronal progenitor cells,which also abolished running-mediated increases in adult hippocampal neurogenesis.Adult hippocampal neurogenesis was reduced in sedentary APP_(SWE) mice versus control mice,with basal adult hippocampal neurogenesis reduced by RGS6 deletion in dentate gyrus neural precursor cells.RGS6 was expressed in neurons within the dentate gyrus of patients with Alzheimer’s disease with significant loss of these RGS6-expressing neurons.Thus,RGS6 mediated voluntary running-induced rescue of impaired cognition and adult hippocampal neurogenesis in APP_(SWE) mice,identifying RGS6 in dentate gyrus neural precursor cells as a possible therapeutic target in Alzheimer’s disease.

Differential diagnosis of Crohn’s disease and intestinal tuberculosis based on ATR-FTIR spectroscopy combined with machine learning

BACKGROUND Crohn’s disease(CD)is often misdiagnosed as intestinal tuberculosis(ITB).However,the treatment and prognosis of these two diseases are dramatically different.Therefore,it is important to develop a method to identify CD and ITB with high accuracy,specificity,and speed.AIM To develop a method to identify CD and ITB with high accuracy,specificity,and speed.METHODS A total of 72 paraffin wax-embedded tissue sections were pathologically and clinically diagnosed as CD or ITB.Paraffin wax-embedded tissue sections were attached to a metal coating and measured using attenuated total reflectance fourier transform infrared spectroscopy at mid-infrared wavelengths combined with XGBoost for differential diagnosis.RESULTS The results showed that the paraffin wax-embedded specimens of CD and ITB were significantly different in their spectral signals at 1074 cm^(-1) and 1234 cm^(-1) bands,and the differential diagnosis model based on spectral characteristics combined with machine learning showed accuracy,specificity,and sensitivity of 91.84%,92.59%,and 90.90%,respectively,for the differential diagnosis of CD and ITB.CONCLUSION Information on the mid-infrared region can reveal the different histological components of CD and ITB at the molecular level,and spectral analysis combined with machine learning to establish a diagnostic model is expected to become a new method for the differential diagnosis of CD and ITB.

A gated recurrent unit model to predict Poisson’s ratio using deep learning

Static Poisson’s ratio(vs)is crucial for determining geomechanical properties in petroleum applications,namely sand production.Some models have been used to predict vs;however,the published models were limited to specific data ranges with an average absolute percentage relative error(AAPRE)of more than 10%.The published gated recurrent unit(GRU)models do not consider trend analysis to show physical behaviors.In this study,we aim to develop a GRU model using trend analysis and three inputs for predicting n s based on a broad range of data,n s(value of 0.1627-0.4492),bulk formation density(RHOB)(0.315-2.994 g/mL),compressional time(DTc)(44.43-186.9 μs/ft),and shear time(DTs)(72.9-341.2μ s/ft).The GRU model was evaluated using different approaches,including statistical error an-alyses.The GRU model showed the proper trends,and the model data ranges were wider than previous ones.The GRU model has the largest correlation coefficient(R)of 0.967 and the lowest AAPRE,average percent relative error(APRE),root mean square error(RMSE),and standard deviation(SD)of 3.228%,1.054%,4.389,and 0.013,respectively,compared to other models.The GRU model has a high accuracy for the different datasets:training,validation,testing,and the whole datasets with R and AAPRE values were 0.981 and 2.601%,0.966 and 3.274%,0.967 and 3.228%,and 0.977 and 2.861%,respectively.The group error analyses of all inputs show that the GRU model has less than 5% AAPRE for all input ranges,which is superior to other models that have different AAPRE values of more than 10% at various ranges of inputs.

A Comparison of the lexical processing in Children's Word Acquisition and in Adults' Word Learning

Children can acquire knowledge of their mother tongue easily in a relatively short time,whereas adults are too inferior to bear the comparison in learning a second language.This paper sets out to study the background and process of children's and adults' language learning,make comparison and contrast,and find out an effective way to promote adults' second language learning.

Can serious postoperative complications in patients with Crohn’s disease be predicted using machine learning?

The routine introduction of novel anti-inflammatory therapies into the mana-gement algorithms of patients with Crohn’s disease over the last 2 decades has not substantially changed the likelihood of ultimate surgery.Rather it has delayed the operative need and altered the presentation phenotype.The prospect of complic-ations continues to remain high in this modern era but depending upon the cohort assessed,it remains difficult to make strict comparisons between individual spe-cialist centres.Those patients who present rather late after their diagnosis with a septic complication like an intra-abdominal abscess and a penetrating/fistulizing pattern of disease are more likely to have a complicated course particularly if they have clinical features such as difficult percutaneous access to the collection or multilocularity both of which can make preoperative drainage unsuccessful.Eq-ually,those cases with extensive adhesions where an initial laparoscopic approach needs open conversion and where there is an extended operative time,unsur-prisingly will suffer more significant complications that impact their length of hospital stay.The need for a protective stoma also introduces its own derivative costs,utilizing a range of health resources as well as resulting in important alte-rations in quality of life outcomes.Having established the parameters of the pro-blem can the statistical analysis of the available data identify high-risk cases,promote the notion of centralization of specialist services or improve the allo-cation of disease-specific health expenditure?

Machine learning in predicting postoperative complications in Crohn’s disease

Crohn's disease(CD)is a chronic inflammatory bowel disease of unknown origin that can cause significant disability and morbidity with its progression.Due to the unique nature of CD,surgery is often necessary for many patients during their lifetime,and the incidence of postoperative complications is high,which can affect the prognosis of patients.Therefore,it is essential to identify and manage post-operative complications.Machine learning(ML)has become increasingly im-portant in the medical field,and ML-based models can be used to predict post-operative complications of intestinal resection for CD.Recently,a valuable article titled“Predicting short-term major postoperative complications in intestinal resection for Crohn's disease:A machine learning-based study”was published by Wang et al.We appreciate the authors'creative work,and we are willing to share our views and discuss them with the authors.

Machine learning as a tool predicting short-term postoperative complications in Crohn’s disease patients undergoing intestinal resection: What frontiers?

The recent study,“Predicting short-term major postoperative complications in intestinal resection for Crohn’s disease:A machine learning-based study”invest-igated the predictive efficacy of a machine learning model for major postoperative complications within 30 days of surgery in Crohn’s disease(CD)patients.Em-ploying a random forest analysis and Shapley Additive Explanations,the study prioritizes factors such as preoperative nutritional status,operative time,and CD activity index.Despite the retrospective design’s limitations,the model’s robu-stness,with area under the curve values surpassing 0.8,highlights its clinical potential.The findings align with literature supporting preoperative nutritional therapy in inflammatory bowel diseases,emphasizing the importance of compre-hensive assessment and optimization.While a significant advancement,further research is crucial for refining preoperative strategies in CD patients.

Study on Self-consciousness of Children With Learning Disabilities and Related Factors

Objective To study the self-consciousness of children with learning disabilities (LD) and to identify related factors. Methods Five hundred and sixty pupils graded from 1 to 6 in an elementary school were investigated. According to the pupil rating scale revised screening for learning disabilities (PRS), combined Raven’s test (CRT) and achievement of main courses, 35 of 560 pupils were diagnosed as LD children. Thirty-five children were selected from the average children and 35 from advanced children in academic achievement equally matched in class, gender, and age with LD children as control groups. The three groups were tested by Piers-Harris children’s self-concept scale. Basic information of each subject was collected by self-made questionnaire. Results Compared with the average and advanced children, LD children got significantly lower scores in self-concept scale. Based on logistic regression analysis, 3 factors were identified, including family income per month, single child and delivery model. Conclusion The results suggest that self-consciousness of children with LD is lower than that of normal children.

Risk Factors of Learning Disabilities in Chinese Children in Wuhan

Objective To investigate prevalence rate of learning disabilities (LD) in Chinese children, and to explore related risk factors, and to provide theoretical basis for preventing such disabilities. Methods One thousand and one hundred fifty one children were randomly selected in primary schools. According to criteria set by ICD-10, 118 children diagnosed as LD were classified into the study group. Four hundred and ninety one children were classified into the normal control group. Five hundred and forty two children were classified into the excellent control group. The study instruments included PRS (The pupil rating scale revised screening for learning disabilities), Conners' children behavior check-list taken by parents and YG-WR character check-list. Results The prevalence rate of LD in Chinese children was 10.3%. Significant differences were observed between LD and normally learning children, and between the LD group and the excellent group, in terms of scores of Conners' behavior check-list (P<0.05). The study further showed that individual differences in character between the LD group and the control groups still existed even after controlling individual differences in age, IQ, and gender. Some possible causal explanations contributing to LD were improper teaching by parents, low educational level of the parents, and children's characteristics and social relationships. Conclusion These data underscore the fact that LD is a serious national public health problem in China. LD is resulted from a number of factors. Good studying and living environments should be created for LD children.

Prediction of Outcomes in Mini-Basketball Training Program for Preschool Children with Autism Using Machine Learning Models

In recent years evidence has emerged suggesting that Mini-basketball training program(MBTP)can be an effec-tive intervention method to improve social communication(SC)impairments and restricted and repetitive beha-viors(RRBs)in preschool children suffering from autism spectrum disorder(ASD).However,there is a considerable degree if interindividual variability concerning these social outcomes and thus not all preschool chil-dren with ASD profit from a MBTP intervention to the same extent.In order to make more accurate predictions which preschool children with ASD can benefit from an MBTP intervention or which preschool children with ASD need additional interventions to achieve behavioral improvements,further research is required.This study aimed to investigate which individual factors of preschool children with ASD can predict MBTP intervention out-comes concerning SC impairments and RRBs.Then,test the performance of machine learning models in predict-ing intervention outcomes based on these factors.Participants were 26 preschool children with ASD who enrolled in a quasi-experiment and received MBTP intervention.Baseline demographic variables(e.g.,age,body,mass index[BMI]),indicators of physicalfitness(e.g.,handgrip strength,balance performance),performance in execu-tive function,severity of ASD symptoms,level of SC impairments,and severity of RRBs were obtained to predict treatment outcomes after MBTP intervention.Machine learning models were established based on support vector machine algorithm were implemented.For comparison,we also employed multiple linear regression models in statistics.Ourfindings suggest that in preschool children with ASD symptomatic severity(r=0.712,p<0.001)and baseline SC impairments(r=0.713,p<0.001)are predictors for intervention outcomes of SC impair-ments.Furthermore,BMI(r=-0.430,p=0.028),symptomatic severity(r=0.656,p<0.001),baseline SC impair-ments(r=0.504,p=0.009)and baseline RRBs(r=0.647,p<0.001)can predict intervention outcomes of RRBs.Statistical models predicted 59.6%of variance in post-treatment SC impairments(MSE=0.455,RMSE=0.675,R2=0.596)and 58.9%of variance in post-treatment RRBs(MSE=0.464,RMSE=0.681,R2=0.589).Machine learning models predicted 83%of variance in post-treatment SC impairments(MSE=0.188,RMSE=0.434,R2=0.83)and 85.9%of variance in post-treatment RRBs(MSE=0.051,RMSE=0.226,R2=0.859),which were better than statistical models.Ourfindings suggest that baseline characteristics such as symptomatic severity of 144 IJMHP,2022,vol.24,no.2 ASD symptoms and SC impairments are important predictors determining MBTP intervention-induced improvements concerning SC impairments and RBBs.Furthermore,the current study revealed that machine learning models can successfully be applied to predict the MBTP intervention-related outcomes in preschool chil-dren with ASD,and performed better than statistical models.Ourfindings can help to inform which preschool children with ASD are most likely to benefit from an MBTP intervention,and they might provide a reference for the development of personalized intervention programs for preschool children with ASD.

A Study of English Learning Motivation of Left-behind Children from the Perspective of L2 Motivational Self System

A survey carried out among 286 left-behind children in a middle school through the lens of L2 Motivational Self System presents that although left-behind children possess low level of English learning motivation,they are favorably disposed towards studying English.Among the three dimensions of L2 Motivational Self System in this survey,L2 Learning Experience influences English learning motivation of left-behind children most and Ideal L2 Self with Ought-to L2 Self ranks the second in affecting their motivation.Practicalimplications are also provided.

An Empirical Study on Self-Efficacy of Online Learning of Overseas Chinese Children in Europe

In the post-Covid-19 pandemic era,it is more difficult for some Chinese schools in Europe to provide online extra classes for overseas Chinese children after school hours,as they did previously.To meet students'multifaceted learning needs,online extra classes teaching,including online Chinese language classes and some online art classes,is increasingly being offered as a supplement to the diversity of teaching activities in Chinese schools in Europe,with the ultimate goal of improving the learning abilities of overseas Chinese children while relieving pressure on teaching resources in schools.Children’s learning self-efficacy in online extracurricular courses has its own uniqueness,which can be considered from three dimensions,including learning confidence,learning ability,and self-assessment ability.This study aims to examine the factors influencing the self-efficacy of overseas Chinese children and to make optimization suggestions for better teaching methods.In search of that,an online questionnaire survey with 127 participants from overseas Chinese children aged􀀘to􀀔􀀘was collected.The findings indicate that the role of learning confidence in overseas Chinese children outweighs their learning ability and self-assessment ability.Gender and age have a negligible effect on self-efficacy but have an impact on learning confidence.Chinese schools in Europe do not need to show gender differences when conducting classroom activities in online teaching to improve the online self-efficacy of Chinese children,and efforts should also be made to keep the courage of older students to trial and error.Teachers are expected to investigate more aspects of their students'personalities in future classrooms rather than sticking to a consistent and unchanging teaching model.

ICARE Model:Learning Model Organization That Based on Work Skills Through Saori Weaving for Special Needs Children

Purpose:(1)Study the current state and needs of learning management,focusing on work skills through Saori weaving for special needs children;(2)Develop a learning management model focusing on work skills;(3)Develop a teacher-centered learning management approach through the use of Saori weaving for special needs children;(4)Study the effectiveness of the learning management model that focuses on working with special children.Research&Development Research Results:(1)The current state and needs of learning management focused on work skills through Saori weaving for special children;(2)The learning management model emphasizing for special children is four steps:a.prepare the actual context;b.set goals;c.plan and execute;d.monitoring;and Evaluatio Saori Weaving Class has five details:(1)Inspire:I(Explain:Inspired to work for success);(2)Choose:C(Explain:Choose what you like before you can see the future do it);(3)Aim&Act:A(Explain:Work with a happy goal that has set);(4)Reflect on learning:R(Explain:Reflect on learning);(5)Experiential-Based Learning Measure by concluding:ICARE Model.The results of teacher development on learning management focused on working skills:(1)Teachers with average scores after training,35.09%was 77.98%;(2)22 trainers were able to develop a skill management plan in the 21st century at the best evaluated in 73.33%and eight persons respectively in good or 26.67%.(3)The results of the study on the effectiveness of the model showed that the extra children had a higher grade of Saori weaving.After class,75.49%were higher than the standard set of 70%and the students were satisfied with Saori weaving as the most levels.

Lexical Tones and Word Learning in Mandarin-Speaking Children at Three Years of Age(Invited paper)

Unlike Indo-European languages,Mandarin relies heavily on lexical tones to distinguish word identity. Using the intermodal preferential looking paradigm, this study examined 3-year-old Mandarinspeakers＇ ability to use Mandarin lexical tones in learning new words. Results showed that when children were presented with Tone 2（rising） and Tone 4（falling）pairs, children successfully learned both words.However, when children were presented with Tone 2and Tone 3（dipping） pairs, they learned the Tone 2word but not the Tone 3 one. Children were then divided into two groups based on their learning performance on the Tone 3 word. Successful learning of Tone 3 words was observed in the high performers but not in the low performers, who consistently misused Tone 3 as Tone 2. This study showed that Mandarinspeaking 3-year-olds could use lexical tones to learn words under experimental conditions, and that the difficulty of Tone 3 acquisition may be related to its lower level of perceptual distinctiveness compared with other tones.

Family Support Situation and Educational Strategies for Primary School Children with Intellectual Disabilities Learning in Regular Class

This paper aims to verify the family support situation for primary school children with intellectual disabilities learning in regular class and to explore various educational strategies to promote their development.A self-made questionnaire was used in this survey,and the parents of 380 intellectual disabled students were the subjects of this survey.It turns out that the overall family support for intellectual disabled children learning in regular class in China is good,but it is affected by the degree of obstacles.Factors such as grade,gender,and parental education had no significant effect on family support.It is the shared responsibility of the government,schools,and parents to promote the level of family support.Governments at all levels must implement family support projects,schools must carry out family education guidance to impart scientific parenting knowledge,and parents must take note of their own responsibilities,so as to promote the physical and mental development of children with intellectual disabilities.

A Survey into Teachers＇ Roles in Web-based College English Autonomous Learning

The paper, with the backdrop of web-based autonomous learning put forward by the recent college English teaching reform, aims to explore teachers＇ roles in this learning process in students＇ perception through the means of questionnaires and interviews. It further analyzes the possible reasons why students perceive their teachers＇ roles in such a way, in the hope of providing some implications for web-based college English autonomous learning.

Is There an Optimum Age For Foreign Language Learning?

Age is one of the factors which influnce foreign language learning, but not the most important one. Comparision on the effect of foreign language learning between adults and children cannot rely merely on age. So the question of an optimum age for foreign language learning is not a simple one which is only related to age. There are different optimum ages for different aims and demands of learning foreign language.

Estimation of Gaussian overlapping nuclear pulse parameters based on a deep learning LSTM model

A long short-term memory(LSTM)neural network has excellent learning ability applicable to time series of nuclear pulse signals.It can accurately estimate parameters associated with amplitude,time,and so on,in digitally shaped nuclear pulse signals—especially signals from overlapping pulses.By learning the mapping relationship between Gaussian overlapping pulses after digital shaping and exponential pulses before shaping,the shaping parameters of the overlapping exponential nuclear pulses can be estimated using the LSTM model.Firstly,the Gaussian overlapping nuclear pulse(ONP)parameters which need to be estimated received Gaussian digital shaping treatment,after superposition by multiple exponential nuclear pulses.Secondly,a dataset containing multiple samples was produced,each containing a sequence of sample values from Gaussian ONP,after digital shaping,and a set of shaping parameters from exponential pulses before digital shaping.Thirdly,the Training Set in the dataset was used to train the LSTM model.From these datasets,the values sampled from the Gaussian ONP were used as the input data for the LSTM model,and the pulse parameters estimated by the current LSTM model were calculated by forward propagation.Next,the loss function was used to calculate the loss value between the network-estimated pulse parameters and the actual pulse parameters.Then,a gradient-based optimization algorithm was applied,to feedback the loss value and the gradient of the loss function to the neural network,to update the weight of the LSTM model,thereby achieving the purpose of training the network.Finally,the sampled value of the Gaussian ONP for which the shaping parameters needed to be estimated was used as the input data for the LSTM model.After this,the LSTM model produced the required nuclear pulse parameter set.In summary,experimental results showed that the proposed method overcame the defect of local convergence encountered in traditional methods and could accurately extract parameters from multiple,severely overlapping Gaussian pulses,to achieve optimal estimation of nuclear pulse parameters in the global sense.These results support the conclusion that this is a good method for estimating nuclear pulse parameters.

An Adaptive Strategy via Reinforcement Learning for the Prisoner＇s Dilemma Game

The iterated prisoner's dilemma(IPD) is an ideal model for analyzing interactions between agents in complex networks. It has attracted wide interest in the development of novel strategies since the success of tit-for-tat in Axelrod's tournament. This paper studies a new adaptive strategy of IPD in different complex networks, where agents can learn and adapt their strategies through reinforcement learning method. A temporal difference learning method is applied for designing the adaptive strategy to optimize the decision making process of the agents. Previous studies indicated that mutual cooperation is hard to emerge in the IPD. Therefore, three examples which based on square lattice network and scale-free network are provided to show two features of the adaptive strategy. First, the mutual cooperation can be achieved by the group with adaptive agents under scale-free network, and once evolution has converged mutual cooperation, it is unlikely to shift. Secondly, the adaptive strategy can earn a better payoff compared with other strategies in the square network. The analytical properties are discussed for verifying evolutionary stability of the adaptive strategy.