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Periodic frequency associated with Plasmodium falciparum an infection and use of insecticide-treated netting among young children inside three agroecosystems in Aboisso, Côte d'Ivoire.
In this study, research was carried out on the end-effector force estimation of two representative multi-muscle contraction tasks elbow flexion and palm-pressing. The aim was to ascertain whether an individual muscle or a combination of muscles is more suitable for the end-effector force estimation. High-density surface electromyography (HD-sEMG) signals were collected from four primary muscle areas of the upper arm and forearm the biceps brachii (BB), brachialis (BR), triceps brachii (TB), brachioradialis (BRD), and extensor digitorum communis (EDC). The wrist pulling and palm-pressing forces were measured in elbow flexion and palm-pressing tasks, respectively. The deep belief network (DBN) was adopted to establish the relation between HD-sEMG and the measured force. The representative signals of the four primary areas, which were considered as the input signal of the force estimation model, were extracted by HD-sEMG using the principle component analysis (PCA) algorithm, and fed separately or together into the DBN. An index termed mean impact value (MIV) was proposed to describe the priority of different muscle groups for estimating the end-effector force. The experimental results demonstrated that, in multi-muscle isometric contraction tasks, the dominant muscles with the highest activation degree could track variations in the end-effector force more effectively, and are more suitable than a combination of muscles. The main contributions of this research are as follows (1) To fuse the activation information from different muscles effectively, DBN was adopted to establish the relationship between HD-sEMG and the generated force, and achieved highly accurate force estimation. (2) Based on the well-trained DBN force estimation model, an index termed MIV was presented to evaluate the priority of muscles for estimating the generated force.Neurodegenerative diseases encompass a wide variety of pathological conditions caused by a loss of neurons in the central nervous system (CNS) and are severely debilitating. Exosome contains bio-signatures of great diagnostic and therapeutic value. There is proof that exosomal proteins can be biomarkers for Alzheimer's disease (AD) and Parkinson's disease (PD). MicroRNAs in exosome has potential to be an important source of biomarkers for neurodegenerative diseases. Here, we report exosomal microRNA performance of human plasma in neurodegenerative diseases by small RNA sequencing. A wide range of altered exo-miRNA expression levels were detected in both AD and PD patients. Down-regulated miRNAs in AD samples were enriched in ECM-receptor interaction pathway and both up-/down-regulated miRNAs in PD samples were enriched in fatty acid biosynthesis pathway. Compared to the control, 8 miRNAs were found to be significantly elevated/declined in AD and PD samples, of which 4 miRNAs were newly identified. Additionally, two exosome isolating methods were compared and the reproducibility of plasma exo-miRNA expression was confirmed, suggesting the feasibility of large-scale clinical application of this method. This study revealed exo-miRNA expression levels in neurodegenerative diseases, proposed new biomarkers and their potential functional pathway for AD and PD, confirmed the reproductivity of exo-miRNA profiles by using a different exosome isolating method, and compared the results with plasma miRNA expression. Therefore, this study also provides a precedent for identifying exosomal biomarkers of neurodegenerative diseases in plasma by high-throughput sequencing and it could extend the therapeutic repertoire of exosomal biomarkers.Astrocytes exhibit a region-dependent molecular and functional heterogeneity in the CNS. Although cortical astrocytes proliferate robustly during the first postnatal week and become proliferation quiescent, the temporal proliferation dynamics of astrocytes in subcortical regions during postnatal development remain essentially unknown. Whether subcortical astrocytes mature similarly to cortical astrocytes is also unexplored. SP-2577 In this current study, we examined proliferation of subcortical, especially hypothalamic, astrocytes during postnatal development using genetic labeling of astrocytes and pulse-chase EdU labeling of proliferating cells. While a lower number of proliferating astrocytes was found in the hypothalamus compared to cortex during the first postnatal week, astrocyte proliferation is much more active in hypothalamus than in cortex from P15 to P30 in both proliferating astrocyte density and percentage, indicating a persistent and distinct proliferation pattern of astrocytes in hypothalamus. This observation is further confirmed by Ki67 immunostaining with genetically or immunolabeled astrocytes in hypothalamus and cortex during P15-30. In addition, astrocytes in representative subcortical regions have a modest growth of their domain size and exhibit a significantly smaller domain size compared to cortical astrocytes at P30 when astrocytes have generally completed postnatal maturation. However, the expression of astrocyte-derived Sparc, an important synaptogenic inhibitor, is consistently higher in hypothalamic astrocytes than in cortical astrocytes throughout postnatal development. In summary, our study unveiled a distinct proliferation and maturation pattern of subcortical, especially hypothalamic, astrocytes during postnatal development.The primary claim of the Richiardi et al. (2015) Science article is that a measure of correlated gene expression, significant strength fraction (SSF), is related to resting state fMRI (rsfMRI) networks. However, there is still debate about this claim and whether spatial proximity, in the form of contiguous clusters, accounts entirely, or only partially, for SSF (Pantazatos and Li, 2017; Richiardi et al., 2017). Here, 13 distributed networks were simulated by combining 34 contiguous clusters randomly placed throughout cortex, with resulting edge distance distributions similar to rsfMRI networks. Cluster size was modulated (6-15 mm radius) to test its influence on SSF false positive rate (SSF-FPR) among the simulated "noise" networks. The contribution of rsfMRI networks on SSF-FPR was examined by comparing simulated networks whose clusters were sampled from (1) all 1,777 cortical tissue samples, (2) all samples, but with non-rsfMRI cluster centers, and (3) only 1,276 non-rsfMRI samples. Results show that SSF-FPR is influenced only by cluster size (r > 0.
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