Background: Colorectal cancer (CRC) is one of the challenging types of cancers; thus, exploring effective biomarkers related to colorectal could lead to significant progresses toward the treatment of this disease. Methods: In the present study, CRC gene expression datasets have been reanalyzed. Mutual differentially expressed genes across 294 normal mucosa and adjacent tumoral samples were then utilized in order to build two independent transcriptional regulatory networks. By analyzing the networks topologically, genes with differential global connectivity related to cancer state were determined for which the potential transcriptional regulators including transcription factors were identified. Results: The majority of differentially connected genes (DCGs) were up-regulated in colorectal transcriptome experiments. Moreover, a number of these genes have been experimentally validated as cancer or CRC-associated genes. The DCGs, including GART, TGFB1, ITGA2, SLC16A5, SOX9, and MMP7, were investigated across 12 cancer types. Functional enrichment analysis followed by detailed data mining exhibited that these candidate genes could be related to CRC by mediating in metastatic cascade in addition to shared pathways with 12 cancer types by triggering the inflammatory events. Conclusion: Our study uncovered correlated alterations in gene expression related to CRC susceptibility and progression that the potent candidate biomarkers could provide a link to disease.

Recent developments in electronics and wireless communication play a leading role in manufacturing sensors with reduced power consumption that have wireless connectivity and limited processing capabilities. Due to the limitation of battery in sensor nodes, one of the main challenges in this type of network is energy consumption, which is directly related to the lifetime of the network. Another important issue is to keep nodes connected in the network during data transmission. For these purposes, a connectivity control system is required. By improving the tree growth algorithm in the network graph, an optimal graph using a suitable path for data transmission in the network is designed. connectivity control significantly improved system performance in terms of network power consumption and lifetime. In this paper, a new algorithm for connectivity and linkage control, based on sequential mode is presented, which has achieved a significant improvement compared to an ordinary algorithm. The outcomes of the proposed algorithm on the selected model show 56% improvement in the remaining battery charge. In addition, the end-to-end delay was reduced by 0. 5 m seconds in the network.

Background: Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder in children and adults and its early detection is effective in the successful treatment of children. Electroencephalography (EEG) has been widely used for classifying ADHD and normal children. In recent years, deep learning leads to more accurate classification. Objective: This study aims to adapt convolutional neural networks (CNNs) for classifying ADHD and normal children based on the connectivity measure of their EEG signals. Material and Methods: In this experimental study, the dataset consisted of 61 ADHD and 60 normal children from which 13021 epochs were extracted as input for model training and evaluation. Synchronization likelihood (SL) and wavelet coherence (WC) were considered connectivity measures. The neighborhood between EEG channels was arranged in a two-dimensional matrix for better representation. Four-dimensional (4D) and six-dimensional (6D) connectivity tensors were composed as model inputs. Two architectures were developed, one 4D and 6D CNN for SL and WC-based diagnosis of ADHD, respectively. Results: A 5-fold cross-validation was utilized to assess developed models. The average accuracy of 98. 56% for 4D CNN and 98. 85% for 6D CNN in epoch-based classification were obtained. In the case of subject-based classification, the accuracy was 99. 17% for both models. Conclusion: Based on the evaluation metrics of the proposed models, ADHD children can be diagnosed and ADHD and normal children can be successfully distinguished.