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Recently, Hyperspectral Image (HSI) classification has gradually been getting attention from more and more researchers. HSI has abundant spectral and spatial information; thus, how to fuse these two types of information is still a problem worth studying. In this paper, to extract spectral and spatial feature, we propose a Double-Branch Multi-Attention mechanism network (DBMA) for HSI classification. This network has two branches to extract spectral and spatial feature respectively which can reduce the interference between the two types of feature. Furthermore, with respect to the different characteristics of these two branches, two types of attention mechanism are applied in the two branches respectively, which ensures to extract more discriminative spectral and spatial feature. The extracted features are then fused for classification. A lot of experiment results on three hyperspectral datasets shows that the proposed method performs better than the state-of-the-art method.

We conducted a cross-trait meta-analysis of genome-wide association study on schizophrenia (SCZ) (n = 65,967), bipolar disorder (BD) (n = 41,653), autism spectrum disorder (ASD) (n = 46,350), attention deficit hyperactivity disorder (ADHD) (n = 55,374), and depression (DEP) (n = 688,809). After the meta-analysis, the number of genomic loci

Spectral clustering (SC) has been used with success in the field of computer vision for data clustering. In this paper, a new algorithm named SC ensemble (SCE) is proposed for the segmentation of synthetic aperture radar (SAR) images. The gray-level cooccurrence matrix-based statistic features and the energy features from the undecimated wavelet decomposition extracted for each pixel being the input, our algorithm performs segmentation by combining multiple SC results as opposed to using outcomes of a single clustering process in the existing literature. The random subspace, random scaling parameter, and Nyström approximation for component SC are applied to construct the SCE. This technique provides necessary diversity as well as high quality of component learners for an efficient ensemble. It also overcomes the shortcomings faced by the SC, such as the selection of scaling parameter, and the instability resulted from the Nyström approximation method in image segmentation. Experimental results show that the proposed method is effective for SAR image segmentation and insensitive to the scaling parameter.

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Background Studies suggest that a functional polymorphism of the brain-derived neurotrophic factor gene (BDNF Val66Met) may mediate hippocampal-dependent cognitive functions. A few studies have reported its role in cognitive deficits in schizophrenia including its association with peripheral BDNF levels as a mediator of these cognitive deficits. Methods We assessed 657 schizophrenic inpatients and 445 healthy controls on the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), the presence of the BDNF Val66Met polymorphism and serum BDNF levels. We assessed patient psychopathology using the Positive and Negative Syndrome Scale (PANSS). Results We showed that visuospatial/constructional abilities significantly differed by genotype but not genotype×diagnosis, and the Val allele was associated with better visuospatial/constructional performance in both schizophrenic patients and healthy controls. Attention performance showed significant a genotype by diagnosis effect. Met allele-associated attention impairment was specific to schizophrenic patients and not shown in healthy controls. In the patient group, partial correlation analysis showed a significant positive correlation between serum BDNF and the RBANS total score. Furthermore, the RBANS total score showed a statistically significant BDNF level × genotype interaction. Conclusions We demonstrated an association between the BDNF Met variant and poor visuospatial/constructional performance. Furthermore, the BDNF Met variant may be specific to attentional decrements in schizophrenic patients. The association between decreased BDNF serum levels and cognitive impairment in schizophrenia is dependent on the BDNF Val66Met polymorphism.

The improvement of cognition with tropisetron appeared to be associated with normalization in P50 deficits. Thus, α7 nAChR agonists appear to be a promising therapeutic approach for the treatment of cognitive deficits that are related to abnormal P50 suppression in schizophrenia.

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