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ke, lymph node involvement, and prolonged period of hypothyroidism.Focal brain lesions, such as stroke and tumors, can lead to remote structural alterations across the whole-brain networks. Brain arteriovenous malformations (AVMs), usually presumed to be congenital, often result in tissue degeneration and functional displacement of the perifocal areas, but it remains unclear whether AVMs may produce long-range effects upon the whole-brain white matter organization. In this study, we used diffusion tensor imaging and graph theory methods to investigate the alterations of brain structural networks in 14 patients with AVMs in the presumed Broca's area, compared to 27 normal controls. Weighted brain structural networks were constructed based on deterministic tractography. We compared the topological properties and network connectivity between patients and normal controls. Functional magnetic resonance imaging revealed contralateral reorganization of Broca's area in five (35.7%) patients. Compared to normal controls, the patients exhibited preserved small-worldness of brain structural networks. However, AVM patients exhibited significantly decreased global efficiency (p = 0.004) and clustering coefficient (p = 0.014), along with decreased corresponding nodal properties in some remote brain regions (p less then 0.05, family-wise error corrected). Furthermore, structural connectivity was reduced in the right perisylvian regions but enhanced in the perifocal areas (p less then 0.05). The vulnerability of the left supramarginal gyrus was significantly increased (p = 0.039, corrected), and the bilateral putamina were added as hubs in the AVM patients. These alterations provide evidence for the long-range effects of AVMs on brain white matter networks. Our preliminary findings contribute extra insights into the understanding of brain plasticity and pathological state in patients with AVMs.Sign language translation (SLT) is an important application to bridge the communication gap between deaf and hearing people. In recent years, the research on the SLT based on neural translation frameworks has attracted wide attention. Despite the progress, current SLT research is still in the initial stage. In fact, current systems perform poorly in processing long sign sentences, which often involve long-distance dependencies and require large resource consumption. To tackle this problem, we propose two explainable adaptations to the traditional neural SLT models using optimized tokenization-related modules. First, we introduce a frame stream density compression (FSDC) algorithm for detecting and reducing the redundant similar frames, which effectively shortens the long sign sentences without losing information. Then, we replace the traditional encoder in a neural machine translation (NMT) module with an improved architecture, which incorporates a temporal convolution (T-Conv) unit and a dynamic hierarchical bidirectional GRU (DH-BiGRU) unit sequentially. The improved component takes the temporal tokenization information into consideration to extract deeper information with reasonable resource consumption. Our experiments on the RWTH-PHOENIX-Weather 2014T dataset show that the proposed model outperforms the state-of-the-art baseline up to about 1.5+ BLEU-4 score gains.As a representation of discriminative features, the time series shapelet has recently received considerable research interest. However, most shapelet-based classification models evaluate the differential ability of the shapelet on the whole training dataset, neglecting characteristic information contained in each instance to be classified and the classwise feature frequency information. Hence, the computational complexity of feature extraction is high, and the interpretability is inadequate. To this end, the efficiency of shapelet discovery is improved through a lazy strategy fusing global and local similarities. In the prediction process, the strategy learns a specific evaluation dataset for each instance, and then the captured characteristics are directly used to progressively reduce the uncertainty of the predicted class label. Moreover, a shapelet coverage score is defined to calculate the discriminability of each time stamp for different classes. The experimental results show that the proposed method is competitive with the benchmark methods and provides insight into the discriminative features of each time series and each type in the data.
In Namibia, the burden of mental illnesses is estimated at 25.6% and is expected to double by 2025. Few studies in sub-Saharan Africa estimate the consumption rates of psychotropic medicines as a proxy of irrational use.

The consumption rate of psychotropic medicines at a referral hospital was determined.

A hospital-based retrospective medicine utilization analysis of Facility Electronic Stock Card (FESC) psychotropic medication was conducted at Intermediate Hospital Katutura over a 7 year period, 2011-2017. Data on consumption and expenditure on psychotropic medicines were abstracted from FESC and analysed using descriptive statistics in SPSS v22. The main outcomes were consumption rates, daily Defined Dose, (DDD) and/or expenditure.

Of the 580 351,4 DDD of psychotropic medicines consumed, 84% were anti-psychotics, 9.2% anti-depressants and 6.8% anxiolytics. Anti-psychotics (48.8%) and anxiolytics (47.9%) had the highest consumption by cost relative to anti-depressants (3.3%). The most consumed antidepressants were imipramine (62%) by DDD and fluoxetine (55.8%) by cost. The most consumed anti-psychotics were chlorpromazine (74.6%) by DDD and haloperidol (68.4%) by cost respectively. Diazepam (79.4%) and hydroxyzine (94.2%) were most consumed sedative-hypnotics by DDD and cost respectively.

The consumption of new psychotropics contributes to higher costs. see more There is need for cost-effectiveness analysis of new versus conventional psychotropics to optimize treatment, outcomes and costs.
The consumption of new psychotropics contributes to higher costs. There is need for cost-effectiveness analysis of new versus conventional psychotropics to optimize treatment, outcomes and costs.
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