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Examining Utilize and Twin Use of Cigarettes along with Pot among Mn Grownups.
We propose a new method for EEG source localization. An efficient solution to this problem requires choosing an appropriate regularization term in order to constraint the original problem. In our work, we adopt the Bayesian framework to place constraints; hence, the regularization term is closely connected to the prior distribution. More specifically, we propose a new sparse prior for the localization of EEG sources. The proposed prior distribution has sparse properties favoring focal EEG sources. In order to obtain an efficient algorithm, we use the variational Bayesian (VB) framework which provides us with a tractable iterative algorithm of closed-form equations. Additionally, we provide extensions of our method in cases where we observe group structures and spatially extended EEG sources. We have performed experiments using synthetic EEG data and real EEG data from three publicly available datasets. The real EEG data are produced due to the presentation of auditory and visual stimulus. We compare the proposed method with well-known approaches of EEG source localization and the results have shown that our method presents state-of-the-art performance, especially in cases where we expect few activated brain regions. The proposed method can effectively detect EEG sources in various circumstances. Overall, the proposed sparse prior for EEG source localization results in more accurate localization of EEG sources than state-of-the-art approaches.In this study, an essential application of remote sensing using deep learning functionality is presented. Gaofen-1 satellite mission, developed by the China National Space Administration (CNSA) for the civilian high-definition Earth observation satellite program, provides near-real-time observations for geographical mapping, environment surveying, and climate change monitoring. Cloud and cloud shadow segmentation are a crucial element to enable automatic near-real-time processing of Gaofen-1 images, and therefore, their performances must be accurately validated. In this paper, a robust multiscale segmentation method based on deep learning is proposed to improve the efficiency and effectiveness of cloud and cloud shadow segmentation from Gaofen-1 images. The proposed method first implements feature map based on the spectral-spatial features from residual convolutional layers and the cloud/cloud shadow footprints extraction based on a novel loss function to generate the final footprints. The experimental results using Gaofen-1 images demonstrate the more reasonable accuracy and efficient computational cost achievement of the proposed method compared to the cloud and cloud shadow segmentation performance of two existing state-of-the-art methods.
Breast invasive carcinoma (BRCA) is not a single disease as each subtype has a distinct morphology structure. Although several computational methods have been proposed to conduct breast cancer subtype identification, the specific interaction mechanisms of genes involved in the subtypes are still incomplete. To identify and explore the corresponding interaction mechanisms of genes for each subtype of breast cancer can impose an important impact on the personalized treatment for different patients.

We integrate the biological importance of genes from the gene regulatory networks to the differential expression analysis and then obtain the weighted differentially expressed genes (weighted DEGs). A gene with a high weight means it regulates more target genes and thus holds more biological importance. Besides, we constructed gene coexpression networks for control and experiment groups, and the significantly differentially interacting structures encouraged us to design the corresponding Gene Ontology (GO) enrich. The GOEGCN with weighted DEGs for control and experiment groups presented a novel GO enrichment analysis results and the novel enriched GO terms would further unveil the changes of specific biological functions among all the BRCA subtypes to some extent. The R code in this research is available at https//github.com/yxchspring/GOEGCN_BRCA_Subtypes.
The weighted DEGs contain biological importance derived from the gene regulatory network. Based on the weighted DEGs, five binary classifiers were learned and showed good performance concerning the "Sensitivity," "Specificity," "Accuracy," "F1," and "AUC" metrics. The GOEGCN with weighted DEGs for control and experiment groups presented a novel GO enrichment analysis results and the novel enriched GO terms would further unveil the changes of specific biological functions among all the BRCA subtypes to some extent. The R code in this research is available at https//github.com/yxchspring/GOEGCN_BRCA_Subtypes.In the study of pediatric automatic bone age assessment (BAA) in clinical practice, the extraction of the object area in hand radiographs is an important part, which directly affects the prediction accuracy of the BAA. But no perfect segmentation solution has been found yet. This work is to develop an automatic hand radiograph segmentation method with high precision and efficiency. We considered the hand segmentation task as a classification problem. The optimal segmentation threshold for each image was regarded as the prediction target. We utilized the normalized histogram, mean value, and variance of each image as input features to train the classification model, based on ensemble learning with multiple classifiers. 600 left-hand radiographs with the bone age ranging from 1 to 18 years old were included in the dataset. Compared with traditional segmentation methods and the state-of-the-art U-Net network, the proposed method performed better with a higher precision and less computational load, achieving an average PSNR of 52.43 dB, SSIM of 0.97, DSC of 0.97, and JSI of 0.91, which is more suitable in clinical application. Furthermore, the experimental results also verified that hand radiograph segmentation could bring an average improvement for BAA performance of at least 13%.Transcriptional coordination is a fundamental component of prokaryotic and eukaryotic cell biology, underpinning the cell cycle, physiological transitions, and facilitating holistic responses to environmental stress, but its overall dynamics in eukaryotic algae remain poorly understood. Better understanding of transcriptional partitioning may provide key insights into the primary metabolism pathways of eukaryotic algae, which frequently depend on intricate metabolic associations between the chloroplasts and mitochondria that are not found in plants. Here, we exploit 187 publically available RNAseq datasets generated under varying nitrogen, iron and phosphate growth conditions to understand the co-regulatory principles underpinning transcription in the model diatom Phaeodactylum tricornutum. Using WGCNA (Weighted Gene Correlation Network Analysis), we identify 28 merged modules of co-expressed genes in the P. tricornutum genome, which show high connectivity and correlate well with previous microarray-based surveys of gene co-regulation in this species. We use combined functional, subcellular localization and evolutionary annotations to reveal the fundamental principles underpinning the transcriptional co-regulation of genes implicated in P. tricornutum chloroplast and mitochondrial metabolism, as well as the functions of diverse transcription factors underpinning this co-regulation. The resource is publically available as PhaeoNet, an advanced tool to understand diatom gene co-regulation.Native and introduced plant populations vary in leaf physiology, biochemistry, and biotic interactions. These aboveground traits may help invasive plants in competition for resources with co-occurring native species. Root physiological traits may affect invasive plant performance because of the roles of roots in resource absorption. The aim of this study was to test this prediction, using invasive Chinese tallow tree (Triadica sebifera), as a model species. Here we examined carbohydrate (soluble sugar, sucrose, fructose, starch, and cellulose) concentrations and the mass of roots, stems, and leaves, along with root water potential and arbuscular mycorrhizal fungi (AMF) colonization of soil-cultured T. sebifera seedlings from 10 native (China) and 10 introduced (United States) populations in a common garden. Introduced populations had a significantly greater stem and leaf mass than native populations but their root masses did not differ, so they had lower RS. Introduced populations had higher soluble sugar concentrations but lower starch and cellulose concentrations in their leaves, stems, and roots. Introduced populations had more negative root water potentials and higher AMF colonization. Together, our results indicate that invasive plants shift their carbohydrate allocation, leading to faster growth and a greater aboveground allocation strategy. Higher AMF colonization and more negative water potential in invasive plants likely facilitate more efficient water absorption by the roots. Thus, such physiological variation in root characteristics could play a role in plant invasion success.Tomato chlorosis virus (genus Crinivirus, family Closteroviridae) (ToCV) is rapidly emerging, causing increased damage to tomato production worldwide. The virus is transmitted in a semipersistent manner by several whitefly (Hemiptera Aleyrodidae) species and is expanding its geographical and host ranges associated with the emergence of whiteflies of the Bemisia tabaci complex. Control is based essentially on intensive insecticide applications against the insect vector but is largely ineffective. No virus-resistant or tolerant commercial tomato cultivars are available. Recently, a B. tabaci-resistant tomato line based on the introgression of type IV leaf glandular trichomes and secretion of acylsucroses from the wild tomato Solanum pimpinellifolium was shown to effectively control the spread of tomato yellow leaf curl virus, a begomovirus (genus Begomovirus, family Geminiviridae) persistently transmitted by B. tabaci. 4Octyl As short acquisition and transmission periods are associated to the semipersistent transmission of ToCV, its possible control by means of the B. tabaci-resistant tomato could be compromised. Moreover, if the antixenosis effect of the resistance trait present in those tomato plants results in increased B. tabaci mobility, an increased ToCV spread might even occur. We demonstrated, however, that the use of acylsugar-producing B. tabaci-resistant tomatoes effectively controls ToCV spread compared to a near-isogenic line without type IV trichomes and acylsugar secretion. No increase in the primary ToCV spread is observed, and secondary spread could be reduced significantly decreasing the incidence of this virus. The possible use of host plant resistance to whiteflies to limit spread of ToCV opens up new alternatives for a more effective control of this virus to reduce the damage caused in tomato crops.Rice is the model plant system for monocots and the sequencing of its genome has led to the identification of a vast array of genes for characterization. The tedious and time-consuming effort of raising rice transgenics has significantly delayed the pace of rice research. The lack of highly efficient transient assay protocol for rice has only added to the woes which could have otherwise helped in rapid deciphering of the functions of genes. Here, we describe a technique for efficient transient gene expression in rice seedlings. It makes use of co-cultivation of 6-day-old rice seedlings with Agrobacterium in the presence of a medium containing Silwet® L-77, acetosyringone and glucose. Seedlings can be visualized 9 days after co-cultivation for transient expression. The use of young seedlings helps in significantly reducing the duration of the experiment and facilitates the visualization of rice cells under the microscope as young leaves are thinner than mature rice leaves. Further, growth of seedlings at low temperature, and the use of surfactant along with wounding and vacuum infiltration steps significantly increases the efficiency of this protocol and helps in bypassing the natural barriers in rice leaves, which hinders Agrobacterium-based transformation in this plant.
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