A case series comparison of semantically focused vs. phonologically focused cued naming treatment in aphasia

There is a lack of clarity in the field regarding how to best predict which naming treatment will be most beneficial for a particular individual with aphasia. The purpose of this study was to elucidate whether or not semantic or phonological therapy differentially impacts on outcomes for people with a range of different aphasic profiles when given both therapies. A single-participant design, with multiple repeated baselines for naming, replicated across four participants, was used. Participants were provided with a counterbalanced order of Semantic Feature Analysis (SFA) and Phonological Components Analysis (PCA) treatment. Findings demonstrated differential effects across participants. This seemed to be influenced by factors such as severity of anomia, order of treatment presentation, and capacity limits. Clinical implications of these findings highlight the importance of expanding our picture of a participant’s behaviours to consider what other important factors can inform intervention decisions.     

基于数据挖掘的脑白质疏松症相关因素分析

目的 利用数据挖掘技术分析脑白质疏松症相关因素。      

Radiomic Feature Robustness and Reproducibility in Quantitative Bone Radiography: A Study on Radiologic Parameter Changes

The purpose of this study was to investigate the robustness of different radiography radiomic features over different radiologic parameters including kV, mAs, filtration, tube angles, and source skin distance (SSD). A tibia bone phantom was prepared and all imaging studies was conducted on this phantom. Different radiologic parameters including kV, mAs, filtration, tube angles, and SSD were studied. A region of interest was drawn on the images and many features from different feature sets including histogram, gradient, run-length matrix, co-occurrence matrix, autoregressive model, and wavelet derived parameters were extracted. All radiomic features were categorized based on coefficient of variation (COV). Bland-Altman analysis also was used to evaluate the mean, standard deviation, and upper/lower reproducibility limits for radiomic features in response to variation in each testing parameters. Results on COV in all features showed that 22%, 34%, and 45% of features were most robust (COV ≤ 5%) against kV, mAs, and SSD respectively and there was no robust features against filtration and tube angle. Also, all features (100%) and 76% of which showed large variations (COV > 20%) against filtrations and tube angle respectively. Autoregressive model feature set has no robust features against all radiologic parameters. Features including sum-average, sum-entropy, correlation, mean, and percentile (50, 90, and 99) belong to co-occurrence matrix and histogram feature sets were found as most robust features. Bland-Altman analysis showed the high reproducibity of some feature sets against radiologic parameter changes. The results presented here indicated that radiologic parameters have great impacts on radiomic feature values and caution should be taken into account when work with these features. In quantitative bone studies, robust features with low COV can be selected for clinical or research applications. Reproducible features also can be obtained using Bland-Altman analysis.     

Machine-based classification of ADHD and nonADHD participants using time/frequency features of event-related neuroelectric activity

Objective

Attention-deficit/hyperactivity disorder (ADHD) is the most frequent diagnosis among children who are referred to psychiatry departments. Although ADHD was discovered at the beginning of the 20th century, its diagnosis is still confronted with many problems.

临沂市2004～2009年流行性腮腺炎发病特征分析

目的分析临沂市2004～2009年以来流行性腮腺炎发病特征及趋势,为科学制定适合本地特点的防控措施提供依据。方法采用描述流行病学方法对全市历年流行性腮腺炎疫情监测资料进行分析,探索其流行特征和趋势。结果 2004～2009年全市共报告腮腺炎4460例,6年的发病率依次为5.37/10万、2.53/10万、4.18/10万、7.64/10万、28.03/10万和7.61/10万;平均发病率为9.29/10万,低于全国平均发病水平(19.88/10万),但高于本地2001～2003年平均发病水平(6.65/10万)。其流行特征为3～5月为发病高峰,占总数的36.6%,5～15岁为高发年龄段,占72.58%,发病职业以学生(占72.85%)为主,其次是幼托儿童(11.70%)和散居儿童(9.64%)。暴发疫情主要发生在小学,占暴发总次数的81.48%。结论流行性腮腺炎的易感人群仍为学龄儿童,应提高适龄儿童的预防接种率并加强对5～15岁儿童的腮腺炎疫苗的查漏补种工作,防止疫情暴发。     

Automatic evaluation of pressure sore status by combining information obtained from high-frequency ultrasound and digital photography

In this study, the different phases of pressure sore generation and healing are investigated through a combined analysis of high-frequency ultrasound (20 MHz) images and digital color photographs. Pressure sores were artificially induced in guinea pigs, and the injured regions were monitored for 21 days (data were obtained on days 3, 7, 14, and 21). Several statistical features of the images were extracted, relating to both the altering pattern of tissue and its superficial appearance. The features were grouped into five independent categories, and each category was used to train a neural network whose outputs were the four days. The outputs of the five classifiers were then fused using a fuzzy integral to provide the final decision. We demonstrate that the suggested method provides a better decision regarding tissue status than using either imaging technique separately. This new approach may be a viable tool for detecting the phases of pressure sore generation and healing in clinical settings.     

A new dataset evaluation method based on category overlap

The quality of dataset has a profound effect on classification accuracy, and there is a clear need for some method to evaluate this quality. In this paper, we propose a new dataset evaluation method using the R-value measure. This proposed method is based on the ratio of overlapping areas among categories in a dataset. A high R-value for a dataset indicates that the dataset contains wide overlapping areas among its categories, and classification accuracy on the dataset may become low. We can use the R-value measure to understand the characteristics of a dataset, the feature selection process, and the proper design of new classifiers.     

A machine learning approach for distinguishing age of infants using auditory evoked potentials

Objective

To develop a high performance machine learning (ML) approach for predicting the age and consequently the state of brain development of infants, based on their event related potentials (ERPs) in response to an auditory stimulus.

Methods

The ERP responses of twenty-nine 6-month-olds, nineteen 12-month-olds and 10 adults to an auditory stimulus were derived from electroencephalogram (EEG) recordings. The most relevant wavelet coefficients corresponding to the first- and second-order moment sequences of the ERP signals were then identified using a feature selection scheme that made no a priori assumptions about the features of interest. These features are then fed into a classifier for determination of age group.

Results

We verified that ERP data could yield features that discriminate the age group of individual subjects with high reliability. A low dimensional representation of the selected feature vectors show significant clustering behavior corresponding to the subject age group. The performance of the proposed age group prediction scheme was evaluated using the leave-one-out cross validation method and found to exceed 90% accuracy.

Conclusions

This study indicates that ERP responses to an acoustic stimulus can be used to predict the age and consequently the state of brain development of infants.

Significance

This study is of fundamental scientific significance in demonstrating that a machine classification algorithm with no a priori assumptions can classify ERP responses according to age and with further work, potentially provide useful clues in the understanding of the development of the human brain. A potential clinical use for the proposed methodology is the identification of developmental delay: an abnormal condition may be suspected if the age estimated by the proposed technique is significantly less than the chronological age of the subject.     

A model study on the circuit mechanism underlying decision-making in Drosophila

Previous elegant experiments in a flight simulator showed that conditioned Drosophila is able to make a clear-cut decision to avoid potential danger. When confronted with conflicting visual cues, the relative saliency of two competing cues is found to be a sensory ruler for flies to judge which cue should be used for decision-making. Further genetic manipulations and immunohistological analysis revealed that the dopamine system and mushroom bodies are indispensable for such a clear-cut or nonlinear decision. The neural circuit mechanism, however, is far from being clear. In this paper, we adopt a computational modeling approach to investigate how different brain areas and the dopamine system work together to drive a fly to make a decision. By developing a systems-level neural network, a two-pathway circuit is proposed. Besides a direct pathway from a feature binding area to the motor center, another connects two areas via the mushroom body, a target of dopamine release. A raised dopamine level is hypothesized to be induced by complex choice tasks and to enhance lateral inhibition and steepen the units’ response gain in the mushroom body. Simulations show that training helps to assign values to formerly neutral features. For a circuit model with a blocked mushroom body, the direct pathway passes all alternatives to the motor center without changing original values, giving rise to a simple choice characterized by a linear choice curve. With respect to an intact circuit, enhanced lateral inhibition dependent on dopamine critically promotes competition between alternatives, turning the linear- into nonlinear choice behavior. Results account well for experimental data, supporting the reasonableness of model working hypotheses. Several testable predictions are made for future studies.     

从《脾胃论》的学术思想看李东垣论治脾胃病的理法方药特点

金元四大医家之一李东垣代表作《脾胃论》,开创了疾病的内伤学说,奠定了脾胃学说的理论基础,其"内伤脾胃,百病由生"的著名论点,理论内涵精辟丰富,受到后世医家的推崇和重视,"甘温除热"理论和所创的补中益气汤等方剂开辟了内伤发热治疗之蹊径。因此通过对《脾胃论》学术思想的深入研究,进而从中借鉴李东垣论治脾胃病的理法方药特点,从而指导临床实践。