In the last few years, deep learning has led to very good performance on a variety of problems, such as visual recognition, speech recognition and natural language processing. Among different types of deep neural networks, convolutional neural networks have been most extensively studied. Leveraging on the rapid growth in the amount of the annotated data and the great improvements in the strengths of graphics processor units, the research on convolutional neural networks has been emerged swiftly and achieved stateof-the-art results on various tasks. In this paper, we provide a broad survey of the recent advances in convolutional neural networks. We detailize the improvements of CNN on different aspects, including layer design, activation function, loss function, regularization, optimization and fast computation. Besides, we also introduce various applications of convolutional neural networks in computer vision, speech and natural language processing.

No abstract

Axon regeneration in the adult CNS is limited by the presence of inhibitory proteins. An interaction of Nogo on the oligodendrocyte surface with Nogo-66 Receptor (NgR) on axons has been suggested to play an important role in limiting axonal growth. Here, we compare the localization of these two proteins immunohistochemically as a test of this hypothesis. Throughout much of the adult CNS, Nogo-A is detected on oligodendrocyte processes surrounding myelinated axons, including areas of axon-oligodendrocyte contact. The NgR protein is detected selectively in neurons and is present throughout axons, indicating that Nogo-A and its receptor are juxtaposed along the course of myelinated fibers. NgR protein expression is restricted to postnatal neurons and their axons. In contrast, Nogo-A is observed in myelinating oligodendrocytes, embryonic muscle, and neurons, suggesting that Nogo-A has additional physiologic roles unrelated to NgR binding. After spinal cord injury, Nogo-A is upregulated to a moderate degree, whereas NgR levels are maintained at constant levels. Taken together, these data confirm the apposition of Nogo ligand and NgR receptor in situations of limited axonal regeneration and support the hypothesis that this system regulates CNS axonal plasticity and recovery from injury.

BackgroundLate embryogenesis abundant (LEA) proteins are large groups of hydrophilic proteins with major role in drought and other abiotic stresses tolerance in plants. In-depth study and characterization of LEA protein families have been carried out in other plants, but not in upland cotton. The main aim of this research work was to characterize the late embryogenesis abundant (LEA) protein families and to carry out gene expression analysis to determine their potential role in drought stress tolerance in upland cotton. Increased cotton production in the face of declining precipitation and availability of fresh water for agriculture use is the focus for breeders, cotton being the backbone of textile industries and a cash crop for many countries globally.ResultsIn this work, a total of 242, 136 and 142 LEA genes were identified in G. hirsutum, G. arboreum and G. raimondii respectively. The identified genes were classified into eight groups based on their conserved domain and phylogenetic tree analysis. LEA 2 were the most abundant, this could be attributed to their hydrophobic character. Upland cotton LEA genes have fewer introns and are distributed in all chromosomes. Majority of the duplicated LEA genes were segmental. Syntenic analysis showed that greater percentages of LEA genes are conserved. Segmental gene duplication played a key role in the expansion of LEA genes. Sixty three miRNAs were found to target 89 genes, such as miR164, ghr-miR394 among others. Gene ontology analysis revealed that LEA genes are involved in desiccation and defense responses. Almost all the LEA genes in their promoters contained ABRE, MBS, W-Box and TAC-elements, functionally known to be involved in drought stress and other stress responses. Majority of the LEA genes were involved in secretory pathways. Expression profile analysis indicated that most of the LEA genes were highly expressed in drought tolerant cultivars Gossypium tomentosum as opposed to drought susceptible, G. hirsutum. The tolerant genotypes have a greater ability to modulate genes under drought stress than the more susceptible upland cotton cultivars.ConclusionThe finding provides comprehensive information on LEA genes in upland cotton, G. hirsutum and possible function in plants under drought stress.Electronic supplementary materialThe online version of this article (10.1186/s12863-017-0596-1) contains supplementary material, which is available to authorized users.

With expanding applications of next-generation sequencing in medical genetics, increasing computational methods are being developed to predict the pathogenicity of missense variants. Selecting optimal methods can accelerate the identification of candidate genes. However, the performances of different computational methods under various conditions have not been completely evaluated. Here, we compared 12 performance measures of 23 methods based on three independent benchmark datasets: (i) clinical variants from the ClinVar database related to genetic diseases, (ii) somatic variants from the IARC TP53 and ICGC databases related to human cancers and (iii) experimentally evaluated PPARG variants. Some methods showed different performances under different conditions, suggesting that they were not always applicable for different conditions. Furthermore, the specificities were lower than the sensitivities for most methods (especially, for the experimentally evaluated benchmark datasets), suggesting that more rigorous cutoff values are necessary to distinguish pathogenic variants. Furthermore, REVEL, VEST3 and the combination of both methods (i.e. ReVe) showed the best overall performances with all the benchmark data. Finally, we evaluated the performances of these methods with de novo mutations, finding that ReVe consistently showed the best performance. We have summarized the performances of different methods under various conditions, providing tentative guidance for optimal tool selection.