情感性精神障碍与气象要素

目的探讨情感性精神障碍与气象要素的关系。方法对天津市安定医院1997-1999年情感性精神障碍月入院量和同期气象资料的相关性进行回顾性分析。结果情感性精神障碍月入院量与同期气压、日照有显著意义的相关。结论日照、气压和风速可能是情感性精神障碍发病的主要气象影响因素。     

Outlier detection in scatterometer data: neural network approaches.

Satellite-borne scatterometers are used to measure backscattered micro-wave radiation from the ocean surface. This data may be used to infer surface wind vectors where no direct measurements exist. Inherent in this data are outliers owing to aberrations on the water surface and measurement errors within the equipment. We present two techniques for identifying outliers using neural networks; the outliers may then be removed to improve models derived from the data. Firstly the generative topographic mapping (GTM) is used to create a probability density model; data with low probability under the model may be classed as outliers. In the second part of the paper, a sensor model with input-dependent noise is used and outliers are identified based on their probability under this model.GTM was successfully modified to incorporate prior knowledge of the shape of the observation manifold; however, GTM could not learn the double skinned nature of the observation manifold. To learn this double skinned manifold necessitated the use of a sensor model which imposes strong constraints on the mapping. The results using GTM with a fixed noise level suggested the noise level may vary as a function of wind speed. This was confirmed by experiments using a sensor model with input-dependent noise, where the variation in noise is most sensitive to the wind speed input. Both models successfully identified gross outliers with the largest differences between models occurring at low wind speeds.     

Power quality control of an autonomous wind–diesel power system based on hybrid intelligent controller

Wind power generation is gaining popularity as the power industry in the world is moving toward more liberalized trade of energy along with public concerns of more environmentally friendly mode of electricity generation. The weakness of wind power generation is its dependence on nature—the power output varies in quite a wide range due to the change of wind speed, which is difficult to model and predict. The excess fluctuation of power output and voltages can influence negatively the quality of electricity in the distribution system connected to the wind power generation plant. In this paper, the authors propose an intelligent adaptive system to control the output of a wind power generation plant to maintain the quality of electricity in the distribution system. The target wind generator is a cost-effective induction generator, while the plant is equipped with a small capacity energy storage based on conventional batteries, heater load for co-generation and braking, and a voltage smoothing device such as a static Var compensator (SVC). Fuzzy logic controller provides a flexible controller covering a wide range of energy/voltage compensation. A neural network inverse model is designed to provide compensating control amount for a system. The system can be optimized to cope with the fluctuating market-based electricity price conditions to lower the cost of electricity consumption or to maximize the power sales opportunities from the wind generation plant.     

面神经炎发病和气象条件的关系

目的分析中国北方面神经炎患者与气象因素之间的相关性,初步探讨气象因素对面神经炎发病的影响。方法回顾性研究采集北京友谊医院神经内科及北京博爱医院神经内科2007年门诊和急诊收治的298例面神经炎患者的临床资料,并采集北京市气象台2007年逐月逐日平均气温、相对湿度、气压、风速、月平均气温、月平均相对湿度、月平均气压、月平均风速、风冷指数(WCF)等气象要素。分析气象因素与面神经炎发病之间的相关性。结果月平均相对湿度与面神经炎发病呈正相关(r=0.629,P=0.028),而月平均气温、平均气压、平均风速、WCF等未发现与面神经炎发病存在明显相关性(r=0.558,P=0.059;r=-0.509,P=0.091;r=-0.219,P=0.494;r=-0.562,P=0.057)。面神经炎发病并不存在季节性差异。结论月平均相对湿度可能是面神经炎发病的影响因素。     

A Stratified Model for Psychosis Prediction in Clinical Practice

Objective: Impaired cognition is an important dimension in psychosis and its at-risk states. Research on the value of impaired cognition for psychosis prediction in at-risk samples, however, mainly relies on study-specific sample means of neurocognitive tests, which unlike widely available general test norms are difficult to translate into clinical practice. The aim of this study was to explore the combined predictive value of at-risk criteria and neurocognitive deficits according to test norms with a risk stratification approach. Method: Potential predictors of psychosis (neurocognitive deficits and at-risk criteria) over 24 months were investigated in 97 at-risk patients. Results: The final prediction model included (1) at-risk criteria (attenuated psychotic symptoms plus subjective cognitive disturbances) and (2) a processing speed deficit (digit symbol test). The model was stratified into 4 risk classes with hazard rates between 0.0 (both predictors absent) and 1.29 (both predictors present). Conclusions: The combination of a processing speed deficit and at-risk criteria provides an optimized stratified risk assessment. Based on neurocognitive test norms, the validity of our proposed 3 risk classes could easily be examined in independent at-risk samples and, pending positive validation results, our approach could easily be applied in clinical practice in the future.Key words: prediction, psychosis, neurocognition, processing speed, at-risk criteria, risk estimation     

Metalearning and neuromodulation.

This paper presents a computational theory on the roles of the ascending neuromodulatory systems from the viewpoint that they mediate the global signals that regulate the distributed learning mechanisms in the brain. Based on the review of experimental data and theoretical models, it is proposed that dopamine signals the error in reward prediction, serotonin controls the time scale of reward prediction, noradrenaline controls the randomness in action selection, and acetylcholine controls the speed of memory update. The possible interactions between those neuromodulators and the environment are predicted on the basis of computational theory of metalearning.     

Recommendations for the Use of Prolonged‐Release Fampridine in Patients with Multiple Sclerosis (MS)

Prolonged‐release fampridine (fampridine PR) is a potassium channel blocker that improves conductivity of signal on demyelinated axons in central nervous system. Fampridine PR has been approved to improve speed of walking in patients with multiple sclerosis. This statement provides a brief summary of data on fampridine PR and recommendations on practical use of the medication in clinical practice, prediction, and evaluation of response to treatment and patient management.     

Deformation Prediction of Landslide Based on Improved Back-propagation Neural Network

In this paper, a modified method for landslide prediction is presented. This method is based on the back-propagation neural network (BPNN), and we use the combination of genetic algorithm and simulated annealing algorithm to optimize the weights and biases of the network. The improved BPNN modeling can work out the complex nonlinear relation by learning model and using the present data. This paper demonstrates that the revised BPNN modeling can be used to predict and calculate landslide deformation, quicken the learning speed of network, and improve the predicting precision. Applying this thinking and method into research of some landslide in the Three Gorges reservoir, the validity and practical value of this model can be demonstrated. And it also shows that the dynamic prediction of landslide deformation is very crucial.     

A study into predictors for the speed of response to electroconvulsive therapy

The efficacy of electroconvulsive therapy (ECT) has been firmly established in the treatment of depression. However, prediction of the speed of response to ECT is an issue that needs to be further explored. This study aims to examine the presence of predictors for the speed of response. In a retrospective chart review using 57 patients suffering from major depression who received ECT, the relation of several patient and ECT variables with the speed of response was explored. Response was defined as a drop of at least 35% in Hamilton rating scale of depression (HRSD) score from baseline after 3 or 4 ECT sessions. Patients received ECT with an aged-based stimulus dosage in a clinical setting. Multiple regression analysis showed that high baseline HRSD score and high seizure energy index (SEI) were significantly and independently associated with a rapid response. In a regression model, baseline HRSD score and SEI can be used to predict the speed of response to ECT. Rapid responders to ECT achieved remission significantly more often than slow responders did. SEI can be modified by the clinician. This offers the possibility to optimize ECT treatment.     

Ground-based telescope pointing and tracking optimization using a neural controller.

Neural network models (NN) have emerged as important components for applications of adaptive control theories. Their basic generalization capability, based on acquired knowledge, together with execution rapidity and correlation ability between input stimula, are basic attributes to consider NN as an extremely powerful tool for on-line control of complex systems. By a control system point of view, not only accuracy and speed, but also, in some cases, a high level of adaptation capability is required in order to match all working phases of the whole system during its lifetime. This is particularly remarkable for a new generation ground-based telescope control system. Infact, strong changes in terms of system speed and instantaneous position error tolerance are necessary, especially in case of trajectory disturb induced by wind shake. The classical control scheme adopted in such a system is based on the proportional integral (PI) filter, already applied and implemented on a large amount of new generation telescopes, considered as a standard in this technological environment. In this paper we introduce the concept of a new approach, the neural variable structure proportional integral, (NVSPI), related to the implementation of a standard multi layer perceptron network in new generation ground-based Alt-Az telescope control systems. Its main purpose is to improve adaptive capability of the Variable structure proportional integral model, an already innovative control scheme recently introduced by authors [Proc SPIE (1997)], based on a modified version of classical PI control model, in terms of flexibility and accuracy of the dynamic response range also in presence of wind noise effects. The realization of a powerful well tested and validated telescope model simulation system allowed the possibility to directly compare performances of the two control schemes on simulated tracking trajectories, revealing extremely encouraging results in terms of NVSPI control robustness and reliability.     

Is processing speed predictive of functional outcome in psychosis?

OBJECTIVE: To investigate the contribution of processing speed in the prediction of various domains of outcome in psychosis. METHOD: Data were drawn from the UK700 Case Management Trial of 708 patients with chronic psychotic illness. Regression analyses were applied to investigate cross-sectional and longitudinal associations between processing speed at baseline and measures of service use, social outcome and subjective outcome, taking into account current psychopathology and adjusting for baseline values of the outcome measure. RESULTS: Cross-sectionally, processing speed was associated with all three domains of outcome, although only associations in the social and subjective outcome domain remained significant after controlling for psychopathology and the effects differed between and within domains of outcome. Prospectively, only the subjective outcome measure of number of met and unmet needs (CAN) was weakly associated with baseline neurocognitive performance after adjustment for baseline needs. Other associations disappeared after adjustment for the baseline measure of outcome and/or baseline psychopathology. CONCLUSION: The finding of weak cross-sectional associations in the absence of specific and unconfounded longitudinal associations suggests that processing speed is an independent dimension of disease severity rather than a causal factor impacting on social outcome. Nevertheless, longitudinal change in patient reported needs may be weakly sensitive to baseline cognitive impairment.     

Independent predictors of cognitive decline in healthy elderly persons

BACKGROUND: Several studies have shown that individually memory, hippocampal volume, and motor measures presage the onset of dementia. It is unclear if these independently contribute to the prediction of mild cognitive impairment. OBJECTIVE: To determine the ability of memory, hippocampal volume, and a gait speed to independently predict cognitive decline in healthy elderly persons. DESIGN: A prospective, longitudinal, observational cohort study with a mean follow-up of 6 years. PARTICIPANTS: One hundred eight optimally healthy elderly cognitively intact subjects. MAIN OUTCOME MEASURES: Any cognitive impairment noted on the Clinical Dementia Rating Scale (score = 0.5) or persistent or progressive cognitive impairment. Cox modeling determined if time to onset of cognitive impairment was associated with baseline logical memory II test score (a measure of delayed recall), hippocampal volume (magnetic resonance imaging), or gait speed (time to walk 30 ft [9 m]) independent of age, sex, depression, or the allele producing the epsilon4 type of apolipoprotein E (APOE epsilon4). RESULTS: Questionable dementia occurred in 48 participants in a mean (SD) of 3.7 (2.4) years. This progressed to persistent cognitive impairment in 38 of these participants in a mean (SD) of 4.4 (2.4) years. Logical memory II test performance and hippocampal volume each predicted onset of questionable dementia, independent of age and sex. Time to walk 30 ft additionally contributed independently to the prediction of time to onset of persistent cognitive impairment. Possessing the APOE epsilon4 allele and depression did not enter either model significantly. CONCLUSIONS: Models combining multiple risk factors should refine the prediction of questionable dementia and persistent cognitive impairment, harbingers of dementia. Individuals at risk for cognitive impairment may represent a high-risk group for intervention.     

Lesions of the mammillary body region alter hippocampal movement signals and theta frequency: implications for path integration models

Cells throughout the hippocampal formation are involved in processing spatial information. These same cells also show an influence of locomotor activity, and these movement signals are thought to be critical for the path integration abilities of these cells. Nuclei in the mammillary region provide ascending influences to the hippocampal formation and have been implicated in influencing both hippocampal spatial and theta signals. Here, we report the effects of mammillary lesions on movement-related signals in several hippocampal subregions. We find first, as predicted by earlier work, these lesions cause an approximately 1 Hz reduction in the frequency of theta modulation of cell firing. According to recent theoretical work, this might, in turn, be expected to influence the size of hippocampal place fields. Our data do not confirm this prediction for any of the hippocampal regions examined. Second, we report lesion effects on the relationship between firing rate and running speed for the hippocampal cells. These lesions caused a reduction in both the slope and intercept of rate-by-speed functions for cells in the hippocampus and postsubiculum. Surprisingly, cells in subiculum showed an opposite effect, so that the excitatory influence of locomotion was enhanced. Path integration theories predict that the speed at which path integration occurs is related to the strength of this movement signal. In remarkable accordance with this prediction, we report that the timing of the place cell signals is slowed following mammillary lesions for hippocampal and postsubicular cells, but, in contrast, is speeded up for subicular cells. In fact, the timing for place signals across lesion condition and brain region is predicted by a single linear function which relates timing to the strength of the running speed signal. Thus, these data provide remarkable support for some aspects of current path integration theory, while posing a challenge for other aspects of these same theories.     

The influence of visual training on predicting complex action sequences

Linking observed and executable actions appears to be achieved by an action observation network (AON), comprising parietal, premotor, and occipitotemporal cortical regions of the human brain. AON engagement during action observation is thought to aid in effortless, efficient prediction of ongoing movements to support action understanding. Here, we investigate how the AON responds when observing and predicting actions we cannot readily reproduce before and after visual training. During pre‐ and posttraining neuroimaging sessions, participants watched gymnasts and wind‐up toys moving behind an occluder and pressed a button when they expected each agent to reappear. Between scanning sessions, participants visually trained to predict when a subset of stimuli would reappear. Posttraining scanning revealed activation of inferior parietal, superior temporal, and cerebellar cortices when predicting occluded actions compared to perceiving them. Greater activity emerged when predicting untrained compared to trained sequences in occipitotemporal cortices and to a lesser degree, premotor cortices. The occipitotemporal responses when predicting untrained agents showed further specialization, with greater responses within body‐processing regions when predicting gymnasts' movements and in object‐selective cortex when predicting toys' movements. The results suggest that (1) select portions of the AON are recruited to predict the complex movements not easily mapped onto the observer's body and (2) greater recruitment of these AON regions supports prediction of less familiar sequences. We suggest that the findings inform both the premotor model of action prediction and the predictive coding account of AON function. Hum Brain Mapp, 2013. © 2011 Wiley Periodicals, Inc.     

Predict MiRNA-Disease Association with Collaborative Filtering

The era of human brain science research is dawning. Researchers utilize the various multi-disciplinary knowledge to explore the human brain,such as physiology and bioinformatics. The emerging disease association prediction technology can speed up the study of diseases, so as to better understanding the structure and function of human body. There are increasing evidences that miRNA plays a significant role in nervous system development, adult function, plasticity, and vulnerability to neurological disease states. In this paper ,we proposed the novel improved collaborative filtering-based miRNA-disease association prediction (ICFMDA) approach. Known miRNA-disease associations can be viewed as a bipartite network between diseases and miRNAs. ICFMDA defined significance SIG between pairs of diseases or miRNAs to model the preference on the choices of other entities. The collaborative filtering algorithm is further improved by incorporating similarity matrices to enable the prediction for new miRNA or disease without known associations. Potential miRNA-disease associations are scored with the addition of bidirectional recommendation results with low computational cost. ICFMDA achieved a 0.9076 AUC of ROC curve in global leave-one-out cross validation, which outperformed the state-of-the-art models. ICFMDA is a compact and accurate tool for potential miRNA-disease association prediction. We hope that ICFMDA would be useful in future miRNA and brain researches,and achieve better understanding of the nervous system in molecular level, cellular level, cell change process, and thus can support the research of human brain.     

Time Patterns of Mental Health Outcomes

This study investigates whether children and adolescents in treatment systematically vary in their speed of improvement and if speed of improvement is predictable from baseline information. Two studies were conducted using the Fort Bragg Evaluation sample of 984 treated children. In the first study, a cluster analysis of cases sorted the Child Behavior Checklist timelines over 18 months into four patterns of outcome. Three categories showed that youths improved at different rates while one group showed no improvement. This finding was replicated using an independent sample from another study. In a second study, none of 43 child and family characteristics known at intake distinguished early improvers from later improvers of equal severity. Claims to identify how much treatment a child will need should be viewed skeptically until valid prediction has been demonstrated; for now, concurrent monitoring offers an alternative for determining when further treatment yields diminishing returns.     

Social participation and cognitive functioning in older adults

The association between aspects of social participation and components of cognitive functioning and the ability of these aspects to predict cognitive performance was studied in an age and gender stratified sample of 116 individuals aged 55-89 years in the Netherlands. Measures of social participation included questionnaires on societal participation, sociocultural activities and media use. Cognitive functioning was measured by tests of fluid intelligence, processing speed, word learning and recall, and everyday memory. All aspects of social participation showed significant bivariate correlations with all components of cognitive functioning. Independent of age, gender, education and functional limitations, societal participation and sociocultural activities contributed significantly to the prediction of processing speed and of delayed recall. Tendencies of independent association were apparent between both societal participation and sociocultural activities and word learning. No aspect of social participation independently predicted fluid intelligence or everyday memory. The evidence suggests independent associations between social participation characterized by an organizational setting and high level of activity, and basic components of cognitive functioning, such as information-processing speed and measures of learning and delayed recall. Further research needs to clarify the process underlying the causal relation between cognitive functioning and forms of participation.     

Neuronal Activities in the Mouse Visual Cortex Predict Patterns of Sensory Stimuli

Visual cortex forms the basis of visual processing and plays important roles in visual encoding. By using the recently published Allen Brain Observatory dataset consisting of large-scale calcium imaging of mouse V1 activities under visual stimuli, we were able to obtain high-quality data capturing simultaneous neuronal activities at multiple sub-areas and cortical depths of V1. Using prediction models, we analyzed the activity profiles related to static and drifting grating stimuli. We conducted a comprehensive survey of the coding ability of multiple cortical locations toward different stimulus attributes. Specifically, we focused on orientations and spatial frequencies (for static stimuli), as well as moving directions and speed (for drifting stimuli). By using results produced from a prediction model, we quantified the decoding performance profile at different sub-areas and layers of V1. In addition, we analyzed the interactions and interference between different stimulus attributes. The insights obtained from these discoveries would contribute to more precise and quantitative understanding of V1 coding mechanisms.     

Early Trajectory Prediction in Elite Athletes

Cerebellar plasticity is a critical mechanism for optimal feedback control. While Purkinje cell activity of the oculomotor vermis predicts eye movement speed and direction, more lateral areas of the cerebellum may play a role in more complex tasks, including decision-making. It is still under question how this motor-cognitive functional dichotomy between medial and lateral areas of the cerebellum plays a role in optimal feedback control. Here we show that elite athletes subjected to a trajectory prediction, go/no-go task manifest superior subsecond trajectory prediction accompanied by optimal eye movements and changes in cognitive load dynamics. Moreover, while interacting with the cerebral cortex, both the medial and lateral cerebellar networks are prominently activated during the fast feedback stage of the task, regardless of whether or not a motor response was required for the correct response. Our results show that cortico-cerebellar interactions are widespread during dynamic feedback and that experience can result in superior task-specific decision skills.