Lesion Distribution and Early Changes of Right Hemisphere in Chinese Patients With Post-stroke Aphasia

The role of the right hemisphere (RH) in post-stroke aphasia (PSA) has not been completely understood. In general, the language alterations in PSA are normally evaluated from the perspective of the language processing models developed from Western languages such as English. However, the successful application of the models for assessing Chinese-language functions in patients with PSA has not been reported. In this study, the features of specific language-related lesion distribution and early variations of structure in RH in Chinese patients with PSA were investigated. Forty-two aphasic patients (female: 13, male: 29, mean age: 58 ± 12 years) with left hemisphere (LH) injury between 1 and 6 months after stroke were included. The morphological characteristics, both at the levels of gray matter (GM) and white matter (WM), were quantified by 3T multiparametric brain MRI. The Fridriksson et al.’s dual-stream model was used to compare language-related lesion regions. Voxel-based lesion-symptom mapping (VLSM) analysis has been performed. Our results showed that lesions in the precentral, superior frontal, middle frontal, and postcentral gyri were responsible for both the production and comprehension dysfunction of Chinese patients with PSA and were quite different from the lesions described by using the dual-stream model of Fridriksson et al. Furthermore, gray matter volume (GMV) was found significantly decreased in RH, and WM integrity was disturbed in RH after LH injury in Chinese patients with PSA. The different lesion patterns between Chinese patients with PSA and English-speaking patients with PSA may indicate that the dual-stream model of Fridriksson et al. is not suitable for the assessment of Chinese-language functions in Chinese patients with PSA in subacute phase of recovery. Moreover, decreased structural integrity in RH was found in Chinese patients with PSA.

Intelligent Strategies to Improve Food Safety Supervision Model

Intelligent supervision effectively deals with food safety problems from four aspects: concept, subject, activity and object. This paper makes a qualitative analysis on the current situation of intelligent supervision of food safety in Beijing, Tianjin and Hebei, compares and studies the intelligent supervision modes of food safety in three coastal areas in eastern China, constructs the analysis framework of intelligent supervision of food safety, and improves the intelligent supervision mode of food safety in Beijing, Tianjin and Hebei. By studying the policy path of intelligent supervision of food safety, extract the three-stage three source stream model of supervision mode from standard cultivation, informatization to standard unification and intelligence, to better promote the intelligent supervision mode in the country. For the challenges still faced by food safety supervision, it is proposed to improve the top-level design and strengthen the intelligent supervision mechanism of cross regional coordination; Promote the cooperation and sharing of data resources and optimize the cross regional risk early warning mechanism; Consolidate the rural digital foundation and realize the integration mechanism of urban and rural food safety supervision.

Aphasia and a Dual-Stream Language Model in a 4-Year-Old Female with Landau–Kleffner Syndrome

Landau–Kleffner syndrome (LKS) is a rare neurological disorder characterized by acquired aphasia. LKS presents with distinctive electroencephalography (EEG) findings, including diffuse continuous spike and wave complexes (CSW), particularly during sleep. There has been little research on the mechanisms of aphasia and its origin within the brain and how it recovers. We diagnosed LKS in a 4-year-old female with an epileptogenic zone located primarily in the right superior temporal gyrus or STG (nondominant side). In the course of her illness, she had early signs of motor aphasia recovery but was slow to regain language comprehension and recover from hearing loss. We suggest that the findings from our patient's brain imaging and the disparity between her recovery from expressive and receptive aphasias are consistent with the dual-stream model of speech processing in which the nondominant hemisphere also plays a significant role in language comprehension. Unlike aphasia in adults, the right-hemisphere disorder has been reported to cause delays in language comprehension and gestures in early childhood. In the period of language acquisition, it requires a process of understanding what the words mean by integrating and understanding the visual, auditory, and contextual information. It is thought that the right hemisphere works predominantly with respect to its integrating role.

Environmental Sound Classification Method Based on Two-Stream Lightweight Convolutional Neural Network

Neural networks have achieved success in the task of environmental sound classification. However, the traditional neural network model has too many parameters and high computational cost. The lightweight networks solve these problems by compressing parameters, but reduce the classification accuracy. To solve the problems in existing research, we propose a two-stream model based on two lightweight convolutional neural networks, called TSLCNN-DS, which saves memory and improves the classification performance of environmental sounds. Specifically, we first used data patching and data balancing to slightly expand the amount of experimental data. Then we designed two lightweight and efficient classification networks based on the attention mechanism and residual learning. Finally, the Dempster-Shafer evidence theory is used to fuse the output of the two networks, and the two-stream model is integrated. Experiments have shown that the model has achieved a classification accuracy of 97.44% on the UrbanSound8k dataset, using only 0.12 M parameters.

Interactive Graph Stream Analytics in Arkouda

Data from emerging applications, such as cybersecurity and social networking, can be abstracted as graphs whose edges are updated sequentially in the form of a stream. The challenging problem of interactive graph stream analytics is the quick response of the queries on terabyte and beyond graph stream data from end users. In this paper, a succinct and efficient double index data structure is designed to build the sketch of a graph stream to meet general queries. A single pass stream model, which includes general sketch building, distributed sketch based analysis algorithms and regression based approximation solution generation, is developed, and a typical graph algorithm—triangle counting—is implemented to evaluate the proposed method. Experimental results on power law and normal distribution graph streams show that our method can generate accurate results (mean relative error less than 4%) with a high performance. All our methods and code have been implemented in an open source framework, Arkouda, and are available from our GitHub repository, Bader-Research. This work provides the large and rapidly growing Python community with a powerful way to handle terabyte and beyond graph stream data using their laptops.

The missing pathway in current visuospatial processing models

Spatial configuration learning depends on the parahippocampal place area (PPA), a functionally heterogenous area which current visuo-spatial processing models place downstream from parietal cortex and area V4 of early visual cortex (EVC). Here, we present evidence for the medial occipital longitudinal tract (MOLT), a novel white matter pathway connecting the PPA with EVC earlier than V4. By using multimodal imaging and neuropsychological assessments in the unique case of Patient 1, we demonstrate that an occipital stroke sparing the PPA but disconnecting the MOLT can lead to chronic deficits in configuration learning. Further, through an advanced, data-driven clustering analysis of diffusion MRI structural connectivity in a large control cohort, we demonstrate that the PPA sits at the confluence of the MOLT and the parieto-medial-temporal branch of the dual-stream model. The MOLT may therefore support multi-stage learning of object configuration by allowing direct reciprocal exchange between the PPA and EVC.