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Methodological high quality with regard to methodical testimonials associated with negative occasions with medical interventions: a new cross-sectional study.
Object tracking is an important procedure in the computer vision field as it estimates the position, size, and state of an object along the video's timeline. Although many algorithms were proposed with high accuracy, object tracking in diverse contexts is still a challenging problem. The paper presents some methods to track the movement of two types of objects arbitrary objects and humans. Both problems estimate the state density function of an object using particle filters. PF-543 For the videos of a static or relatively static camera, we adjusted the state transition model by integrating the movement direction of the object. Also, we propose that partitioning the object needs tracking. To track the human, we partitioned the human into N parts and, then, tracked each part. During tracking, if a part deviated from the object, it was corrected by centering rotation, and the part was, then, combined with other parts.It is necessary to improve the performance of the object detection algorithm in resource-constrained embedded devices by lightweight improvement. In order to further improve the recognition accuracy of the algorithm for small target objects, this paper integrates 5 × 5 deep detachable convolution kernel on the basis of MobileNetV2-SSDLite model, extracts features of two special convolutional layers in addition to detecting the target, and designs a new lightweight object detection network-Lightweight Microscopic Detection Network (LMS-DN). The network can be implemented on embedded devices such as NVIDIA Jetson TX2. The experimental results show that LMS-DN only needs fewer parameters and calculation costs to obtain higher identification accuracy and stronger anti-interference than other popular object detection models.Background Pulsatile pituitary gonadotropin secretion governed by hypothalamic gonadotropin-releasing hormone (GnRH) is essential for the pubertal onset. The epigenetic mechanism underlying the activation of GnRH-dependent regulatory axis in hypothalamus remains elusive. This study aims to explore the potential correlation between the signature of DNA (hydroxyl)methylation and pubertal process. Methods Hypothalamic arcuate nucleus (ARC) of mouse at early (4-weeks) and late pubertal (8-weeks) stages underwent RNA-, RRBS-, and RRHP-seq to investigate the genome-wide profiles of transcriptome, differential DNA methylation and hydroxymethylation. Results A series of differential expressed genes (DEGs) involved in sexual development could be separated into three subgroups with the significant difference of DNA methylation or hydroxymethylation or both in promoter regions. Compared to DNA methylation, DNA hydroxymethylation partook in more signaling pathways including synapse morphology, channel activity and glial development, which could enhance transsynaptic change and glia-to-neuron communication to faciliate GnRH release. The correlation between transcription and these epigenetic modifications indicated that DNA hydroxymethylation impacted with gene transcription independently of DNA methylation spanning puberty. Conclusion Our results characterized the hydroxymethylation pattern and provided an insight into the novel epigenetic regulation on gene expression during pubertal process.As a data-driven dimensionality reduction and visualization tool, t-distributed stochastic neighborhood embedding (t-SNE) has been successfully applied to a variety of fields. In recent years, it has also received increasing attention for classification and regression analysis. This study presented a t-SNE based classification approach for compositional microbiome data, which enabled us to build classifiers and classify new samples in the reduced dimensional space produced by t-SNE. The Aitchison distance was employed to modify the conditional probabilities in t-SNE to account for the compositionality of microbiome data. To classify a new sample, its low-dimensional features were obtained as the weighted mean vector of its nearest neighbors in the training set. Using the low-dimensional features as input, three commonly used machine learning algorithms, logistic regression (LR), support vector machine (SVM), and decision tree (DT) were considered for classification tasks in this study. The proposed approach was applied to two disease-associated microbiome datasets, achieving better classification performance compared with the classifiers built in the original high-dimensional space. The analytic results also showed that t-SNE with Aitchison distance led to improvement of classification accuracy in both datasets. In conclusion, we have developed a t-SNE based classification approach that is suitable for compositional microbiome data and may also serve as a baseline for more complex classification models.Diabetes-related diseases (DRDs), especially cancers pose a big threat to public health. Although people have explored pathological pathways of a few common DRDs, there is a lack of systematic studies on important biological processes (BPs) connecting diabetes and its related diseases/cancers. We have proposed and compared 10 protein-protein interaction (PPI)-based computational methods to study the connections between diabetes and 254 diseases, among which a method called DIconnectivity_eDMN performs the best in the sense that it infers a disease rank (according to its relation with diabetes) most consistent with that by literature mining. DIconnectivity_eDMN takes diabetes-related genes, other disease-related genes, a PPI network, and genes in BPs as input. It first maps genes in a BP into the PPI network to construct a BP-related subnetwork, which is expanded (in the whole PPI network) by a random walk with restart (RWR) process to generate a so-called expanded modularized network (eMN). Since the numbers of known disease genes are not high, an RWR process is also performed to generate an expanded disease-related gene list. For each eMN and disease, the expanded diabetes-related genes and disease-related genes are mapped onto the eMN. The association between diabetes and the disease is measured by the reachability of their genes on all eMNs, in which the reachability is estimated by a method similar to the Kolmogorov-Smirnov (KS) test. DIconnectivity_eDMN achieves an area under receiver operating characteristic curve (AUC) of 0.71 for predicting both Type 1 DRDs and Type 2 DRDs. In addition, DIconnectivity_eDMN reveals important BPs connecting diabetes and DRDs. For example, "respiratory system development" and "regulation of mRNA metabolic process" are critical in associating Type 1 diabetes (T1D) and many Type 1 DRDs. It is also found that the average proportion of diabetes-related genes interacting with DRDs is higher than that of non-DRDs.
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