Clustering by alcoholic Korsakoff patients

Twelve alcoholic Korsakoff patients and 12 alcoholic controls recalled two clusterable lists, and two nonclusterable lists. Korsakoff patients recalled more from the clusterable than the nonclusterable lists. Detailed analysis of the Korsakoff results indicate that while some forms of semantic organization are impaired, the ability to use associative structure remains intact.     

Clustering mild cognitive impairment by mini-mental state examination

We aimed to evaluate whether the performance of the mini-mental state examination (MMSE) could identify risky mild cognitive impairment (MCI). We recruited 122 amnestic MCI-single domain (ASM), 303 amnestic MCI-multiple domains (AMM), and 94 non-amnestic MCI (NAM). Two-step cluster and linear discriminant analyses were used for identifying the clusters of the MMSE with age and education, as well as establishing prediction models for each cluster. Conversion into dementia was compared among clusters. Cluster analyses revealed the following three: cluster 1 = 205 AMM (100 %); cluster 2 = 61 NAM (33.3 %) and 122 ASM (66.7 %); and cluster 3 = 33 NAM (25.2 %) and 98 AMM (74.8 %). Cluster 3 showed a significantly lower ability with regards to orientation to time and place, registration of three words, attention/calculation, language, and copying interlocking pentagons, than clusters 1 and 2. However, for delayed recall, cluster 1 was significantly more impaired than cluster 2. Patients in the cluster 1 showed the most common conversion into dementia [odds ratio (OR) = 2.940 vs. cluster 2, OR = 2.271 vs. cluster 3]. This study showed that clustering by performance in MMSE could help define groups at higher risk for conversion to dementia. Therefore, MMSE can be considered as a promising screening tool including subtyping for MCI when detailed neuropsychological tests are not feasible.     

巴曲酶促进小鼠腹腔巨噬细胞吞噬过氧化低密度脂蛋白

采用小鼠腹腔巨噬细胞在含有过氧化低密度脂蛋白地培养液中培养，并用巴曲酶进行干预，运用透射电子显微镜进行观察，旨在了解巴曲酶对巨噬细胞天噬ｐｏｘ－ＬＤＬ的影响。研究结果表明：小鼠腹腔巨噬细胞与纯培养剂ＤＭＥＭ正常低密度脂蛋白，以及ｎ－ＬＤＬ加巴曲酶培养４小时后，细胞的超微结构与培养前相似，而与Ｐｏｘ－ＬＤＬ一起培养４小时后细胞浆内则含大量的脂滴，加入巴曲酶及小鼠腹腔巨噬细胞内脂滴的生成明显加快。     

An Ensemble of Complementary Models for Deep Tracking

Cognitive Computation - Convolutional neural networks (CNNs) have shown favorable performance in recent tracking benchmark datasets. Some methods extract different levels of features based on...     

Clustering of Gaze During Dynamic Scene Viewing is Predicted by Motion

Where does one attend when viewing dynamic scenes? Research into the factors influencing gaze location during static scene viewing have reported that low-level visual features contribute very little to gaze location especially when opposed by high-level factors such as viewing task. However, the inclusion of transient features such as motion in dynamic scenes may result in a greater influence of visual features on gaze allocation and coordination of gaze across viewers. In the present study, we investigated the contribution of low- to mid-level visual features to gaze location during free-viewing of a large dataset of videos ranging in content and length. Signal detection analysis on visual features and Gaussian Mixture Models for clustering gaze was used to identify the contribution of visual features to gaze location. The results show that mid-level visual features including corners and orientations can distinguish between actual gaze locations and a randomly sampled baseline. However, temporal features such as flicker, motion, and their respective contrasts were the most predictive of gaze location. Additionally, moments in which all viewers’ gaze tightly clustered in the same location could be predicted by motion. Motion and mid-level visual features may influence gaze allocation in dynamic scenes, but it is currently unclear whether this influence is involuntary or due to correlations with higher order factors such as scene semantics.     

Reservoir Computing with an Ensemble of Time-Delay Reservoirs

Reservoir computing (RC) has attracted a lot of attention in the field of machine learning because of its promising performance in a broad range of applications. However, it is difficult to implement standard RC in hardware. Reservoir computers with a single nonlinear neuron subject to delayed feedback (delay-based RC) allow efficient hardware implementation with similar performance to standard RC. We propose and study two different ways to build ensembles of delay-based RC with several delayed neurons (time-delay reservoirs): one using decoupled neurons and the other using coupled neurons through the feedback lines. In both cases, the outputs of the different neurons are linearly combined to solve some benchmark tasks. Simulation results show that these schemes achieve better performance than the single-neuron case. Moreover, the proposed architectures boost the RC processing speed with respect to the single-neuron case. Both schemes are found to be robust against small mismatches between delayed neuron parameters.     

加快构建区域性卒中急救网络建设

正1我国卒中疾病负担:形势严峻卒中对人类生命和健康危害极大,具有发病率高、致死率高、致残率高、复发率高、经济负担重等特点,且有年轻化趋势,已经成为全球重大的公共卫生问题。最新流行病学数据显示,我国卒中年龄标准化的患病率、年发病率和死亡率分别为1114.8/10万、246.8/10万和     

Clustering of primary afferent fibers in peripheral nerve fascicles by sensory modality

The spatial organization of cutaneous afferent fibers in the cat saphenous nerve was studied by recording from functionally identified units in split filaments. It was found that within each fascicle, fibers tend to be clustered together with others of like modality; they are not randomly distributed. These results suggest that the sensory modality of primary afferent neurons is determined prior to their innervation of the skin.     

Clustering of GABAAReceptors by Rapsyn/43kD Proteinin Vitro

Rapsyn, a 43-kDa protein on the cytoplasmic face of the postsynaptic membrane, is essential for clustering acetylcholine receptors (AChR) at the neuromuscular junction. When transfected into nonmuscle cells (QT-6), rapsyn forms discrete membrane domains and can cluster AChR into these same domains. Here we examined whether rapsyn can cluster other ion channels as well. When expressed in QT-6 cells, the GABA_Areceptor (human α1, β1, and γ2 subunits) and the skeletal muscle sodium channel were each diffusely scattered across the cell surface. Rapsyn, when co-expressed, clustered the GABA_Areceptor as effectively as it clustered AChR in previous studies. Rapsyn did not cluster co-transfected sodium channel, confirming that it does not cluster ion channels indiscriminately. Rapsyn mRNA was detected at low levels in the brain by polymerase chain reaction amplification of reverse-transcribed RNA, raising the possibility of a broader role for rapsyn.     

Self-adaptive Extreme Learning Machine Optimized by Rough Set Theory and Affinity Propagation Clustering

Recently, a simple and efficient learning algorithm for single hidden layer feedforward networks (SLFNs) called extreme learning machine (ELM) has been developed by G.-B. Huang et al. One key strength of ELM algorithm is that there is only one parameter, the number of hidden nodes, to be determined while it has the significantly low computational time required for training new classifiers and good generalization performance. However, there is no effective method for finding the proper and universal number of hidden nodes. In order to address this problem, we propose a self-adaptive extreme learning machine (SELM) algorithm. SELM algorithm determines self-adaptively the number of hidden nodes and constructs Gaussian function as activation functions of hidden nodes. And in this algorithm, rough set theory acts as the pre-treatment cell to eliminate the redundant attributes of data sets. Then, affinity propagation clustering (AP Clustering) is used to self-adaptively determine the number of hidden nodes, while the centers and widths of AP clustering are utilized to construct a Gaussian function in the hidden layer of SLFNs. Empirical study of SELM algorithm on several commonly used classification benchmark problems shows that the proposed algorithm can find the proper number of hidden nodes and construct compact network classifiers, comparing with traditional ELM algorithm.     

Ensemble perception of emotions in autistic and typical children and adolescents

Ensemble perception, the ability to assess automatically the summary of large amounts of information presented in visual scenes, is available early in typical development. This ability might be compromised in autistic children, who are thought to present limitations in maintaining summary statistics representations for the recent history of sensory input. Here we examined ensemble perception of facial emotional expressions in 35 autistic children, 30 age- and ability-matched typical children and 25 typical adults. Participants received three tasks: a) an ‘ensemble’ emotion discrimination task; b) a baseline (single-face) emotion discrimination task; and c) a facial expression identification task. Children performed worse than adults on all three tasks. Unexpectedly, autistic and typical children were, on average, indistinguishable in their precision and accuracy on all three tasks. Computational modelling suggested that, on average, autistic and typical children used ensemble-encoding strategies to a similar extent; but ensemble perception was related to non-verbal reasoning abilities in autistic but not in typical children. Eye-movement data also showed no group differences in the way children attended to the stimuli. Our combined findings suggest that the abilities of autistic and typical children for ensemble perception of emotions are comparable on average.     

Multi-View Ensemble Classification of Brain Connectivity Images for Neurodegeneration Type Discrimination

Brain connectivity analyses using voxels as features are not robust enough for single-patient classification because of the inter-subject anatomical and functional variability. To construct more robust features, voxels can be aggregated into clusters that are maximally coherent across subjects. Moreover, combining multi-modal neuroimaging and multi-view data integration techniques allows generating multiple independent connectivity features for the same patient. Structural and functional connectivity features were extracted from multi-modal MRI images with a clustering technique, and used for the multi-view classification of different phenotypes of neurodegeneration by an ensemble learning method (random forest). Two different multi-view models (intermediate and late data integration) were trained on, and tested for the classification of, individual whole-brain default-mode network (DMN) and fractional anisotropy (FA) maps, from 41 amyotrophic lateral sclerosis (ALS) patients, 37 Parkinson’s disease (PD) patients and 43 healthy control (HC) subjects. Both multi-view data models exhibited ensemble classification accuracies significantly above chance. In ALS patients, multi-view models exhibited the best performances (intermediate: 82.9%, late: 80.5% correct classification) and were more discriminative than each single-view model. In PD patients and controls, multi-view models’ performances were lower (PD: 59.5%, 62.2%; HC: 56.8%, 59.1%) but higher than at least one single-view model. Training the models only on patients, produced more than 85% patients correctly discriminated as ALS or PD type and maximal performances for multi-view models. These results highlight the potentials of mining complementary information from the integration of multiple data views in the classification of connectivity patterns from multi-modal brain images in the study of neurodegenerative diseases.     

Temporal Dissociation of Global and Local Features by Hierarchy of Vision

Most objects in our environment are organized hierarchically with a global whole embedding its local parts, but the way we recognize these features remains unclear. Using a visual masking paradigm, we examined the temporal dissociation between global and local feature as proposed in Reverse Hierarchy Theory, RHT (), where global and local information are assumed to be processed, respectively, by feed-forward and feedback systems. We found that in a long Stimulus Onset Asynchrony (SOA) condition, both global and local information were recognized adequately. However, in a short SOA condition, global information was recognized correctly while local recognition was critically disrupted. Consistent with RHT, it is suggested that local information is processed in a feedback system; this processing is then interrupted by the mask stimulus presented later at the primary visual area. Global information, by contrast, is transferred via a feed-forward system, and is not disrupted by the mask.     

Clinical Features of Acute Subdural Hematomas Caused by Ruptured Intracranial Aneurysms

Objective

Spontaneous acute subdural hematomas (aSDH) secondary to ruptured intracranial aneurysms are rarely reported. This report reviews the clinical features, diagnostic modalities, treatments, and outcomes of this unusual and often fatal condition.

Methods

We performed a database search for all cases of intracranial aneurysms treated at our hospital between 2005 and 2010. Patients with ruptured intracranial aneurysms who presented with aSDH on initial computed tomography (CT) were selected for inclusion. The clinical conditions, radiologic findings, treatments, and outcomes were assessed.

Results

A total of 551 patients were treated for ruptured intracranial aneurysms during the review period. We selected 23 patients (4.2%) who presented with spontaneous aSDH on initial CT. Ruptured aneurysms were detected on initial 3D-CT angiography in all cases. All ruptured aneurysms were located in the anterior portion of the circle of Willis. The World Federation of Neurosurgical Societies grade on admission was V in 17 cases (73.9%). Immediate decompressive craniotomy was performed 22 cases (95.7%). Obliteration of the ruptured aneurysm was achieved in all cases. The Glasgow outcome scales for the cases were good recovery in 5 cases (21.7%), moderate disability to vegetative in 7 cases (30.4%), and death in 11 cases (47.8%).

Conclusion

Spontaneous aSDH caused by a ruptured intracranial aneurysm is rare pattern of aneurysmal subarachnoid hemorrhage. For early detection of aneurysm, 3D-CT angiography is useful. Early decompression with obliteration of the aneurysm is recommended. Outcomes were correlated with the clinical grade and CT findings on admission.     

Clustering of psychosocial symptoms in overweight children

OBJECTIVE: The aims of the present study were to (i) examine the relationship between children's degree of adiposity and psychosocial functioning; and (ii) compare patterns of clustering of psychosocial measures between healthy weight and overweight/obese children. METHOD: Cross-sectional data from a population-based cohort of 158 healthy weight, 77 overweight, and 27 obese children aged 8-13 years were analysed. Height, weight depression, quality of life, self-esteem, body dissatisfaction, eating disorder symptoms, peer relationships and behavioural and emotional problems were measured. RESULTS: Multi-level analysis showed significant linear associations between child body mass index z-scores and the psychosocial variables, with increasing adiposity associated with increasing levels of psychosocial distress. Principal components analyses indicated subtle differences between the healthy weight and overweight/obese groups with regards to the clustering of psychosocial measures. In particular, in overweight/obese, but not in healthy weight children, global self-worth clustered with body image and eating disorder symptoms. CONCLUSIONS: The psychosocial burden of excess weight is significant and broad reaching, with overweight/obese children showing multiple significant psychosocial problems.     

Clustering ensembles of neural network models.

We show that large ensembles of (neural network) models, obtained e.g. in bootstrapping or sampling from (Bayesian) probability distributions, can be effectively summarized by a relatively small number of representative models. In some cases this summary may even yield better function estimates. We present a method to find representative models through clustering based on the models' outputs on a data set. We apply the method on an ensemble of neural network models obtained from bootstrapping on the Boston housing data, and use the results to discuss bootstrapping in terms of bias and variance. A parallel application is the prediction of newspaper sales, where we learn a series of parallel tasks. The results indicate that it is not necessary to store all samples in the ensembles: a small number of representative models generally matches, or even surpasses, the performance of the full ensemble. The clustered representation of the ensemble obtained thus is much better suitable for qualitative analysis, and will be shown to yield new insights into the data.     

Coarse-Grained Clustering Dynamics of Heterogeneously Coupled Neurons

We study a population of spiking neurons which are subject to independent noise processes and a strong common time-dependent input. We show that the response of output spikes to independent noise shapes information transmission of such populations even when information transmission properties of single neurons are left unchanged. In particular, we consider two Poisson models in which independent noise either (i) adds and deletes spikes (AD model) or (ii) shifts spike times (STS model). We show that in both models suprathreshold stochastic resonance (SSR) can be observed, where the information transmitted by a neural population is increased with addition of independent noise. In the AD model, the presence of the SSR effect is robust and independent of the population size or the noise spectral statistics. In the STS model, the information transmission properties of the population are determined by the spectral statistics of the noise, leading to a strongly increased effect of SSR in some regimes, or an absence of SSR in others. Furthermore, we observe a high-pass filtering of information in the STS model that is absent in the AD model. We quantify information transmission by means of the lower bound on the mutual information rate and the spectral coherence function. To this end, we derive the signal–output cross-spectrum, the output power spectrum, and the cross-spectrum of two spike trains for both models analytically.