Reward motivation adaptation in people with negative schizotypal features: development of a novel behavioural paradigm and identifying its neural correlates using resting-state functional connectivity analysis.

Reward motivation in individuals with high levels of negative schizotypal traits (NS) has been found to be lower than that in their counterparts. But it is unclear that whether their reward motivation adaptively changes with external effort-reward ratio, and what resting-state functional connectivity (rsFC) is associated with this change. Thirty-five individuals with high levels of NS and 44 individuals with low levels of NS were recruited. A 3T resting-state functional brain scan and a novel reward motivation adaptation behavioural task were administrated in all participants. The behavioural task was manipulated with three conditions (effort > reward condition vs. effort < reward condition vs. effort = reward condition). Under each condition were rated 'wanting' and 'liking' for rewards. The seed-based voxel-wise rsFC analysis was conducted to explore the rsFCs associated with the 'wanting' and 'liking' ratings in individuals with high levels of NS. 'Wanting' and 'liking' ratings of individuals with high levels of NS significantly declined in the effort > reward condition but did not rebound as high as their counterparts in the effort < reward condition. The rsFCs in NS group associated with these ratings were altered. The altered rsFCs in NS group involved regions in the prefrontal lobe, dopaminergic brain regions (ventral tegmental area, substantia nigra), hippocampus, thalamus and cerebellum. Individuals with high levels of NS manifested their reward motivation adaptation impairment as a failure of adjustment adaptively during effort-reward imbalance condition and altered rsFCs in prefrontal, dopaminergic and other brain regions.

Relationship Between Interoception and Autistic Traits: A Resting-State Functional Connectivity Study.

Interoception, the sense of the physiological condition of our body, is impaired in individuals with autism spectrum disorders. Evidence suggests that subclinical autistic traits are mild manifestations of autistic symptoms, present in the general population. We examined the resting-state functional connectivity (rsFC) associating with interoception and autistic traits in 62 healthy young adults. Autistic traits correlated negatively with the rsFC between the lateral ventral anterior insula and anterior cingulate cortex. Interoceptive accuracy and sensibility correlated positively with the rsFC between interoceptive brain networks and the cerebellum, supplementary motor area, and visual regions. The results suggest that a negative relationship between interoception and autistic traits is largely accounted for by both self-report measures and decreased rsFC amongst the interoceptive brain network.

Negative schizotypal traits predict the reduction of reward motivation in effort-reward imbalance.

The schizotypy construct is useful for studying the effects of environmental stress on development of subclinical negative symptoms. The relationship among self-report motivation, effort-reward imbalance (ERI), and schizotypal features has seldom been studied. We aimed to examine the possible moderation effect of schizotypal traits on ERI and reward motivation. Eight-hundred-and-forty-three college students were recruited online to complete a set of self-reported measures capturing schizotypal traits, effort-reward imbalance and reward motivation, namely the Schizotypal Personality Questionnaire (SPQ), the Effort-Reward Imbalance-School Version Questionnaire (C-ERI-S) and the Motivation and Pleasure Scale-Self Report (MAP-SR). We conducted multiple linear regression to construct models to investigate the moderating effects of schizotypal traits on the relationship between ERI and reward motivation. Stressful ERI situation predicted the reduction of reward motivation. Negative schizotypal traits showed a significant negative moderating effect on the relationship between ERI and reward motivation, while positive and disorganized schizotypal traits had significant positive moderating effects. Schizotypal traits subtypes differently moderate the relationship between ERI and reward motivation. Only negative schizotypal traits and stressful ERI situation together have negative impact on reward motivation.

Neural correlates of audiovisual speech synchrony perception and its relationship with autistic traits.

The anterior insula (AI) has the central role in coordinating attention and integrating information from multiple sensory modalities. AI dysfunction may contribute to both sensory and social impairments in autism spectrum disorder (ASD). Little is known regarding the brain mechanisms that guide multisensory integration, and how such neural activity might be affected by autistic-like symptoms in the general population. In this study, 72 healthy young adults performed an audiovisual speech synchrony judgment (SJ) task during fMRI scanning. We aimed to investigate the SJ-related brain activations and connectivity, with a focus on the AI. Compared with synchronous speech, asynchrony perception triggered stronger activations in the bilateral AI, and other frontal-cingulate-parietal regions. In contrast, synchronous perception resulted in greater involvement of the primary auditory and visual areas, indicating multisensory validation and fusion. Moreover, the AI demonstrated a stronger connection with the anterior cingulate gyrus (ACC) in the audiovisual asynchronous (vs. synchronous) condition. To facilitate asynchrony detection, the AI may integrate auditory and visual speech stimuli, and generate a control signal to the ACC that further supports conflict-resolving and response selection. Correlation analysis, however, suggested that audiovisual synchrony perception and its related AI activation and connectivity did not significantly vary with different levels of autistic traits. These findings provide novel evidence for the neural mechanisms underlying multisensory temporal processing in healthy people. Future research should examine whether such findings would be extended to ASD patients.

A comparative study of antihypertensive drugs prediction models for the elderly based on machine learning algorithms.

Background: Globally, blood pressure management strategies were ineffective, and a low percentage of patients receiving hypertension treatment had their blood pressure controlled. In this study, we aimed to build a medication prediction model by correlating patient attributes with medications to help physicians quickly and rationally match appropriate medications. Methods: We collected clinical data from elderly hypertensive patients during hospitalization and combined statistical methods and machine learning (ML) algorithms to filter out typical indicators. We constructed five ML models to evaluate all datasets using 5-fold cross-validation. Include random forest (RF), support vector machine (SVM), light gradient boosting machine (LightGBM), artificial neural network (ANN), and naive Bayes (NB) models. And the performance of the models was evaluated using the micro-F1 score. Results: Our experiments showed that by statistical methods and ML algorithms for feature selection, we finally selected Age, SBP, DBP, Lymph, RBC, HCT, MCHC, PLT, AST, TBIL, Cr, UA, Urea, K, Na, Ga, TP, GLU, TC, TG, γ-GT, Gender, HTN CAD, and RI as feature metrics of the models. LightGBM had the best prediction performance with the micro-F1 of 78.45%, which was higher than the other four models. Conclusion: LightGBM model has good results in predicting antihypertensive medication regimens, and the model can be beneficial in improving the personalization of hypertension treatment.

The Association Between Air Pollutants and Daily Outpatient Visits for Allergic Rhinitis: A Time-Series Analysis Based on Distribution Lag Nonlinear Model in Chongqing, China.

Background: Allergic rhinitis (AR) is a severe and the most common chronic allergic disease, affecting 10-40% of the world population. The effect of air pollutants on AR has been confirmed in clinical experiments. Purpose: This study aimed to quantify the association between air pollutants and daily outpatient visits for AR in Chongqing, China. Methods: Based on the data of AR outpatients in the primary urban area of Chongqing from 2016 to 2017, along with the atmospheric pollutants and meteorological data in the same period, the distributed lag nonlinear model (DLNM) and generalized additive model (GAM) were used to analyze the time-series. We examined the effects of the single and double pollutant models with a maximum lag day of 30 days. Effect estimates were described as relative risk (RR) and 95% confidence intervals (CIs) in daily outpatient visits for AR per 10 µg/m3 increases in PM2.5, PM10, SO2, NO2, O3, and per 1 mg/m3 increase in CO. Results: A single pollutant's O3 level had an immediate positive effect on AR within two days, the relative risks (RR, 95% CI) were 1.066 (1.008-1.127), 1.057 (1.005-1.112) and 1.048 (1.002-1.097). PM2.5 had a lag effect within 11-18 days, the max relative risks (RR, 95% CI) were 1.083 (1.010-1.160). Moreover, O3, PM2.5, PM10, SO2 and NO2 had significant effects on AR in the two-pollutant model. The RR cumulative effect of PM2.5 became more pronounced as the concentration increased. The cumulative effect of NO2 was lesser than PM2.5. Conclusion: Air pollutants were associated with the daily outpatient visits for AR, which may have considerable implications for developing tailored health policies and services to prevent AR in Chongqing and even all over the world.

Entropy-Enhanced Attention Model for Explanation Recommendation.

Most of the existing recommendation systems using deep learning are based on the method of RNN (Recurrent Neural Network). However, due to some inherent defects of RNN, recommendation systems based on RNN are not only very time consuming but also unable to capture the long-range dependencies between user comments. Through the sentiment analysis of user comments, we can better capture the characteristics of user interest. Information entropy can reduce the adverse impact of noise words on the construction of user interests. Information entropy is used to analyze the user information content and filter out users with low information entropy to achieve the purpose of filtering noise data. A self-attention recommendation model based on entropy regularization is proposed to analyze the emotional polarity of the data set. Specifically, to model the mixed interactions from user comments, a multi-head self-attention network is introduced. The loss function of the model is used to realize the interpretability of recommendation systems. The experiment results show that our model outperforms the baseline methods in terms of MAP (Mean Average Precision) and NDCG (Normalized Discounted Cumulative Gain) on several datasets, and it achieves good interpretability.

The effect of effort-reward imbalance on brain structure and resting-state functional connectivity in individuals with high levels of schizotypal traits.

INTRODUCTION: Effort-reward imbalance (ERI) is a typical psychosocial stress. Schizotypal traits are attenuated features of schizophrenia in the general population. According to the diathesis-stress model, schizotypal traits and psychosocial stress contribute to the onset of schizophrenia. However, few studies examined the effects of these factors on brain alterations. This study aimed to examine relationships between ERI, schizotypal traits and brain structures and functions. METHODS: We recruited 37 (13 male, 24 female) participants with high levels of schizotypal traits and 36 (12 male, 24 female) participants with low levels of schizotypal traits by the Schizotypal Personality Questionnaire (SPQ). The Chinese school version of the effort-reward imbalance questionnaire (C-ERI-S) was used to measure ERI. We conducted the voxel-based morphometry (VBM) and whole brain resting-state functional connectivity (rsFC) analysis using reward or stress-related regions as seeds. RESULTS: Participants with high levels of schizotypal traits were more likely to perceive ERI. The severity of ERI was correlated with grey matter volume (GMV) reduction of the left pallidum and altered rsFC among the prefrontal, striatum and cerebellum in participants with high levels of schizotypal traits. CONCLUSION: ERI is associated with GMV reduction and altered rsFC in individuals with high levels of schizotypal traits.

Application of Machine Learning for the Prediction of Etiological Types of Classic Fever of Unknown Origin.

Background: The etiology of fever of unknown origin (FUO) is complex and remains a major challenge for clinicians. This study aims to investigate the distribution of the etiology of classic FUO and the differences in clinical indicators in patients with different etiologies of classic FUO and to establish a machine learning (ML) model based on clinical data. Methods: The clinical data and final diagnosis results of 527 patients with classic FUO admitted to 7 medical institutions in Chongqing from January 2012 to August 2021 and who met the classic FUO diagnostic criteria were collected. Three hundred seventy-three patients with final diagnosis were divided into 4 groups according to 4 different etiological types of classical FUO, and statistical analysis was carried out to screen out the indicators with statistical differences under different etiological types. On the basis of these indicators, five kinds of ML models, i.e., random forest (RF), support vector machine (SVM), Light Gradient Boosting Machine (LightGBM), artificial neural network (ANN), and naive Bayes (NB) models, were used to evaluate all datasets using 5-fold cross-validation, and the performance of the models were evaluated using micro-F1 scores. Results: The 373 patients were divided into the infectious disease group (n = 277), non-infectious inflammatory disease group (n = 51), neoplastic disease group (n = 31), and other diseases group (n = 14) according to 4 different etiological types. Another 154 patients were classified as undetermined group because the cause of fever was still unclear at discharge. There were significant differences in gender, age, and 18 other indicators among the four groups of patients with classic FUO with different etiological types (P < 0.05). The micro-F1 score for LightGBM was 75.8%, which was higher than that for the other four ML models, and the LightGBM prediction model had the best performance. Conclusions: Infectious diseases are still the main etiological type of classic FUO. Based on 18 statistically significant clinical indicators such as gender and age, we constructed and evaluated five ML models. LightGBM model has a good effect on predicting the etiological type of classic FUO, which will play a good auxiliary decision-making function.

Online Doctor Recommendation with Convolutional Neural Network and Sparse Inputs.

The recommendation system in the online medical consultation website is a system to assist patients to find appropriate doctors. Based on the analysis of the current situation of the development of an online medical community (Haodf.com) in China, this paper puts forward recommendation suggestions of finding the right hospital and doctor to promote the rapid integration of Internet technology and traditional medical services. A new recommendation model called Probabilistic Matrix Factorization integrated with Convolutional Neural Network (PMF-CNN) is proposed in the paper. Doctors' data in Haodf.com were used to evaluate the performance of our system. The model improves the performance of medical consultation recommendations by fusing review text and doctor information based on CNN (Convolutional Neural Network). Specifically, CNN is used to learn the feature representation of the review text and the doctors' information. Furthermore, the extended matrix factorization model is exploited to fuse the review information feature and the initial value of the doctors' information for recommendation. As is shown in the experimental results on Haodf.com datasets, the proposed PMF-CNN achieves better recommendation performances than the other state-of-the-art recommendation algorithms. And the recommendation system in an online medical website improves the utilization efficiency of doctors and the balance of public health resources allocation.

Fabrication of gradient vapor grown carbon fiber based polyurethane foam for shape memory driven microwave shielding.

Gradient vapor grown carbon fiber (VGCF) based shape memory polyurethane foam (VGCF@SMPUF) was fabricated by alternate dipping in a gradually diluted VGCF@SMPU/DMF solution and distilled water for shape memory driven microwave shielding. Shape memory performance for this VGCF@SMPUF was achieved by heat transfer of thermally conductive VGCF. Shielding effectiveness (SE) was adjusted through different degrees of angle recovery. A consistent shielding effect from either side indicated that electromagnetic reflection may take place at both the surface and inside of the non-homogeneous composite shield. For shape memory effect, hot compression made this VGCF@SMPUF achieve a faster recovery time and higher recovery ratio owing to improved thermal conductivity. Moreover, VGCF@SMPUF, which was bent to the positive side (PS) with a higher VGCF content, showed shorter recovery time and higher recovery ratio than that bent to the negative side (NS) with a lower VGCF content. We attribute this result to the relatively small mechanical compression strength of the negative side with the lower VGCF content at the bending point when expanding from the positive side. Furthermore, hot compression obviously improved the shielding effectiveness of the VGCF@SMPUF, mainly through a considerable increase of the electrical conductivity. The VGCF@SMPUF hot compressed to a thickness of 0.11 mm achieved a SE value of ∼30 dB, corresponding to a shielding efficiency of ∼99.9%.

Co-Dispersion Behavior of ZrB2-SiC-B4C-C Powders with Polyethyleneimine.

The aqueous dispersion behavior of ZrB2, SiC powders with B4C and C as sintering aids was investigated. Well co-dispersed suspension can be obtained in acidic solutions in presence of polyethyleneimine (PEI). The adsorption of PEI on the powder surface was measured by thermal gravimetric (TG) analysis. Rheological measurements displayed the effect of dispersant on the flow behavior of as-prepared slurries. An optimum condition was obtained with 1 wt % PEI. The viscosity of 40 vol % ZrB2-SiC-B4C-C (ZSBC) suspension at 100 s-1 was as low as 0.74 Pa·s, which was suitable for aqueous processing.

Upgrading of liquid fuel from the pyrolysis of biomass.

Pyrolysis of biomass was carried out in a fluidized bed unit (5 kg/h) with the objective of maximizing liquid yield. Liquid product formed in pyrolysis was separated into two phases: water phase and oil phase. The oil phase was upgraded by sulfided Co-Mo-P catalyst in an autoclave. Effects of reaction conditions on the product distribution were investigated, and optimal conditions were therefore concluded. Comparison was made by analysis between the raw oil phase and the upgraded liquid fuel. The significant difference between the raw pyrolytic oil and the upgraded oil was that the former was methanol-soluble while the latter was oil-soluble.