Experience of undergraduate nursing students participating in artificial intelligence + project task driven learning at different stages: a qualitative study.

BACKGROUND: Artificial intelligence is a growing phenomenon that will soon facilitate wide-scale changes in many professions, and is expected to play an important role in the field of medical education. This study explored the realistic feelings and experiences of nursing undergraduates participating in different stages of artificial intelligence + project task driven learning, and provide a basis for artificial intelligence participation in nursing teaching. METHODS: We conducted face-to-face semi-structured interviews with nursing undergraduates participating in Nursing Research Course which adopts artificial intelligence + project task driven learning from a medical university in Ningxia from September to November 2023, to understand their experience of using artificial intelligence for learning and the emotional changes at different stages. The interview guide included items about their personal experience and feelings of completing project tasks through dialogue with artificial intelligence, and suggestions for course content. Thematic analysis was used to analyze interview data. This study followed the COREQ checklist. RESULTS: According to the interview data, three themes were summarized. Undergraduate nursing students have different experiences in participating in artificial intelligence + project task driven learning at different stages, mainly manifested as diverse emotional experiences under initial knowledge deficiency, the individual growth supported by external forces during the adaptation period, and the expectations and suggestions after the birth of the results in the end period. CONCLUSIONS: Nursing undergraduates can actively adapt to the integration of artificial intelligence into nursing teaching, dynamically observe students' learning experience, strengthen positive guidance, and provide support for personalized teaching models, better leveraging the advantages of artificial intelligence participation in teaching.

Effects of Oat ß-Glucan on the Textural and Sensory Properties of Low-Fat Set Type Pea Protein Yogurt.

This study investigated the effect of oat ß-glucan as a fat substitute on the structure formation, texture, and sensory properties of pea protein yogurt. The results showed that the incorporation of 0.5% ß-glucan significantly accelerated the lactic acid bacteria-induced fermentation, with the time for reaching the target pH of 4.6 shortened from 3.5 h to 3 h (p < 0.05); increased the plastic module (G') from 693 Pa to 764 Pa when fermenting 3 h (p < 0.05); and enhanced the water-holding capacity from 77.29% to 82.15% (p < 0.05). The identification of volatile organic compounds (VOCs) in low-fat pea protein yogurt by GC-IMS revealed a significant decrease in aldehydes and a significant increase in alcohols, ketones and acids in the pea yogurt after fermentation (p < 0.05). Among them, the levels of acetic acid, acetone, 2,3-butanedione, 3-hydroxy-2-butanone, and ethyl acetate all significantly increased with the addition of oat ß-glucan (p < 0.05), thereby providing prominent fruity, sweet, and creamy flavors, respectively. Combined with the results of sensory analysis, the quality characteristics of pea protein yogurt with 1% oil by adding 1% oat ß-glucan were comparable to the control sample with 3% oil. Therefore, oat ß-glucan has a good potential for fat replacement in pea protein yogurt.

Characterization of the complete mitochondrial genomes of two species of Penaeidae (Decapoda: Dendrobranchiata) and the phylogenetic implications for Penaeoidea.

In the present study, mitogenomes of the species Trachypenaeus curvirostris and Parapenaeus fissuroides (Decapoda: Dendrobranchiata: Penaeidae) were sequenced. The total lengths of the two species were 15,956 bp and 15,937 bp in length with A + T biases of 67.08% and 67.69%, respectively. Both two species showed positive AT skews (0.016, 0.058) and negative GC skews (-0.254, -0.310). Both mitogenomes contained 13 protein-coding genes, 22 transfer RNA genes, and 2 ribosomal RNA genes. Results of phylogenetic analyses support close relationships among Aristeidae, Benthesicymidae and Solenoceridae. The family Sicyoniidae was observed to be deeply nested within Penaeidae. Within Penaeidae, T. curvirostris and P. fissuroides were most closely related to the genus Parapenaeopsis and Metapenaeopsis, respectively, indicated that these two species belong to Penaeidae. These results will help to better understand the evolutionary position of Penaeidae and provide reference for further phylogenetic research on Penaeoidea species.

Dietary diversity, diet quality, and oxidative stress in older adults.

This cross-sectional study aimed to explore the relationship between diet quality, dietary diversity, and oxidative stress levels in older adults in parts of the Ningxia Hui Autonomous Region in northwestern China. A total of 335 participants voluntarily participated in the study from April to July 2021. Laboratory tests and questionnaires were used to obtain general characteristics, dietary conditions, and indicators of oxidative stress. The dietary diversity scores of the participants were 5.20±1.39, the diet quality indices were 56.91±11.14, and most had poor diet quality. The levels of the oxidative stress markers malondialdehyde, superoxide dismutase, 8-iso-prostaglandin F2α, and total antioxidant capacity were 4.77±1.77, 40.33±9.85, 763.10±245.41, and 1.02±0.14, respectively. The results showed that the diet quality scores of participants were related to dietary diversity and whether the scores passed or not was significantly related to total antioxidant capacity.

Can Swimming Microalgal Cells be Vehicles for ZnO Nanoparticle Transportation and Thus Lead to Zn Diffusion?

The concentration of eco-toxic zinc oxide nanoparticles (nZnO) in aquatic ecosystems is increasing, and an effective method for their removal is needed. We hypothesize that microalgal cells may act as nZnO vehicles-if the nZnO concentration does not affect their swimming ability-enabling Zn diffusion and sedimentation. We conducted experiments using flasks connected via a U-type vessel; the first flask contained nZnO suspensions and second flask contained artificial seawater, respectively. We added microalgae to the first flask and illuminated the second. The microalgae appeared to promote sedimentation. However, only a few microalgal cells passed via phototaxis into the second flask, so the detection of nZnO or Zn ions in the second flask was not possible. Therefore, to confirm whether the microalgae affect Zn transportation, a more accurate method to detect nZnO or Zn ions at very low concentrations is needed.

Sequence comparison of the mitochondrial genomes in two species of the genus Nerita (Gastropoda: Neritimorpha: Neritidae): phylogenetic implications and divergence time estimation for Neritimorpha.

Many Nerita species live in warm-water environments, and they are some of the few organisms from the intertidal zone that can live in both freshwater and seawater. Previous comparative studies of the mitogenomes of Nerita species suggest that the genome rearrangements are very conservative. Generally, the species possess a set of similar mitochondrial gene arrangements, but nucleotide sequences can be used to elucidate phylogenetic relationships at various levels of divergence. Here, the mitogenomes of Nerita undata and Nerita balteata were sequenced and found to be 15,583 bp and 15,571 bp, respectively. The mitogenomes of both species contain 13 protein-coding genes, 22 tRNA genes, and two rRNA genes. The nucleotides of the two mitogenomes are highly similar, with the same gene composition and genomic organization as those present in other Nerita species. The tRNA secondary structures were different from those of other gastropods: trnS2 is not folded into typical secondary structures, and the dihydrouridine (DHU) arm simply forms a loop. The phylogenetic analysis showed that Neritimorpha is a sister group of Vetigastropoda and Caenogastropoda. Nerita balteata is a sister group of Nerita versicolor and Nerita undata, and all three species belong to Neritimorpha. This study contributes towards the comparative mitogenomic analysis of Neritidae and phylogenetic considerations among Neritimorpha species. The estimation of divergence time revealed that the two Nerita species were differentiated in the late Paleogene of the Cenozoic Era, and their evolution may be related to environmental changes.

Strong genetic differentiation of the clam Meretrix lamarckii in the China Sea revealed by mitochondrial DNA marker.

The hard clam Meretrix lamarckii is ecologically and economically important in the coastal regions of China. In this study, we evaluated the genetic diversity and population structure of six M. lamarckii populations in the East China Sea and the South China Sea using mitochondrial cytochrome c oxidase subunit 1 (COI) and cytochrome b (Cytb) genes. We obtained 582 bp of partly sequences comprising 28 novel haplotypes of COI gene from 138 specimens and 1168 bp of partly sequences comprising 22 novel haplotypes of Cytb gene from 125 specimens. The haplotype diversity of COI and Cytb genes ranged from 0.606 to 0.862 and 0.562 to 0.863, respectively. The nucleotide diversity ranged from 0.0015 to 0.0038 in COI gene and ranged from 0.0007 to 0.0032 in Cytb gene. Thus, there is moderate-level genetic diversity in M. lamarckii in the China Sea. The F-statistics showed that the Zhoushan (ZS) and Xiangshan (XS) populations were significantly (P < 0.01) differed from the populations of Wenzhou (WZ), Zhangpu (ZP), Shantou (ST), and Zhanjiang (ZJ) in both COI and Cytb genes. Both haplotypes network and plot of STRUCTURE analysis suggested obviously genetic divergence between East China Sea and South China Sea regions. Knowledge on genetic variation and population structure of M. lamarckii populations along the Southeast China Sea obtained from this study will support the aquaculture management and conservation of M. lamarckii in China.

CNNDLP: A Method Based on Convolutional Autoencoder and Convolutional Neural Network with Adjacent Edge Attention for Predicting lncRNA-Disease Associations.

It is well known that the unusual expression of long non-coding RNAs (lncRNAs) is closely related to the physiological and pathological processes of diseases. Therefore, inferring the potential lncRNA-disease associations are helpful for understanding the molecular pathogenesis of diseases. Most previous methods have concentrated on the construction of shallow learning models in order to predict lncRNA-disease associations, while they have failed to deeply integrate heterogeneous multi-source data and to learn the low-dimensional feature representations from these data. We propose a method based on the convolutional neural network with the attention mechanism and convolutional autoencoder for predicting candidate disease-related lncRNAs, and refer to it as CNNDLP. CNNDLP integrates multiple kinds of data from heterogeneous sources, including the associations, interactions, and similarities related to the lncRNAs, diseases, and miRNAs. Two different embedding layers are established by combining the diverse biological premises about the cases that the lncRNAs are likely to associate with the diseases. We construct a novel prediction model based on the convolutional neural network with attention mechanism and convolutional autoencoder to learn the attention and the low-dimensional network representations of the lncRNA-disease pairs from the embedding layers. The different adjacent edges among the lncRNA, miRNA, and disease nodes have different contributions for association prediction. Hence, an attention mechanism at the adjacent edge level is established, and the left side of the model learns the attention representation of a pair of lncRNA and disease. A new type of lncRNA similarity and a new type of disease similarity are calculated by incorporating the topological structures of multiple bipartite networks. The low-dimensional network representation of the lncRNA-disease pairs is further learned by the autoencoder based convolutional neutral network on the right side of the model. The cross-validation experimental results confirm that CNNDLP has superior prediction performance compared to the state-of-the-art methods. Case studies on stomach cancer, breast cancer, and prostate cancer further show the ability of CNNDLP for discovering the potential disease lncRNAs.

Dual Convolutional Neural Network Based Method for Predicting Disease-Related miRNAs.

Identification of disease-related microRNAs (disease miRNAs) is helpful for understanding and exploring the etiology and pathogenesis of diseases. Most of recent methods predict disease miRNAs by integrating the similarities and associations of miRNAs and diseases. However, these methods fail to learn the deep features of the miRNA similarities, the disease similarities, and the miRNA⁻disease associations. We propose a dual convolutional neural network-based method for predicting candidate disease miRNAs and refer to it as CNNDMP. CNNDMP not only exploits the similarities and associations of miRNAs and diseases, but also captures the topology structures of the miRNA and disease networks. An embedding layer is constructed by combining the biological premises about the miRNA⁻disease associations. A new framework based on the dual convolutional neural network is presented for extracting the deep feature representation of associations. The left part of the framework focuses on integrating the original similarities and associations of miRNAs and diseases. The novel miRNA and disease similarities which contain the topology structures are obtained by random walks on the miRNA and disease networks, and their deep features are learned by the right part of the framework. CNNDMP achieves the superior prediction performance than several state-of-the-art methods during the cross-validation process. Case studies on breast cancer, colorectal cancer and lung cancer further demonstrate CNNDMP's powerful ability of discovering potential disease miRNAs.

Prediction of potential disease-associated microRNAs based on random walk.

MOTIVATION: Identifying microRNAs associated with diseases (disease miRNAs) is helpful for exploring the pathogenesis of diseases. Because miRNAs fulfill function via the regulation of their target genes and because the current number of experimentally validated targets is insufficient, some existing methods have inferred potential disease miRNAs based on the predicted targets. It is difficult for these methods to achieve excellent performance due to the high false-positive and false-negative rates for the target prediction results. Alternatively, several methods have constructed a network composed of miRNAs based on their associated diseases and have exploited the information within the network to predict the disease miRNAs. However, these methods have failed to take into account the prior information regarding the network nodes and the respective local topological structures of the different categories of nodes. Therefore, it is essential to develop a method that exploits the more useful information to predict reliable disease miRNA candidates. RESULTS: miRNAs with similar functions are normally associated with similar diseases and vice versa. Therefore, the functional similarity between a pair of miRNAs is calculated based on their associated diseases to construct a miRNA network. We present a new prediction method based on random walk on the network. For the diseases with some known related miRNAs, the network nodes are divided into labeled nodes and unlabeled nodes, and the transition matrices are established for the two categories of nodes. Furthermore, different categories of nodes have different transition weights. In this way, the prior information of nodes can be completely exploited. Simultaneously, the various ranges of topologies around the different categories of nodes are integrated. In addition, how far the walker can go away from the labeled nodes is controlled by restarting the walking. This is helpful for relieving the negative effect of noisy data. For the diseases without any known related miRNAs, we extend the walking on a miRNA-disease bilayer network. During the prediction process, the similarity between diseases, the similarity between miRNAs, the known miRNA-disease associations and the topology information of the bilayer network are exploited. Moreover, the importance of information from different layers of network is considered. Our method achieves superior performance for 18 human diseases with AUC values ranging from 0.786 to 0.945. Moreover, case studies on breast neoplasms, lung neoplasms, prostatic neoplasms and 32 diseases further confirm the ability of our method to discover potential disease miRNAs. AVAILABILITY AND IMPLEMENTATION: A web service for the prediction and analysis of disease miRNAs is available at http://bioinfolab.stx.hk/midp/.

Crystal structure of human Intersectin-2L C2 domain.

Intersectin-2L (ITSN-2L) is a long isoform of ITSN family, which is a multimodule scaffolding protein functioning in membrane-associated molecular trafficking and signal transduction pathways. ITSN-2L possesses a carboxy-terminal extension encoding a Dbl homology domain (DH), a pleckstrin homology domain (PH) and a C2 domain, suggesting that it could act as a guanine nucleotide exchange factor for Rho-like GTPases. But the role of C2 domain is obscure in this process. Here we report the crystal structure of human ITSN-2L C2 domain at 1.56Å resolution. The sequence and structural alignment of ITSN-2L C2 domain with other members of C2 domain protein family indicate its vital cellular roles in membrane trafficking, the generation of lipid-second messengers and activation of GTPases. Moreover, our data show the possible roles of ITSN-2L C2 domain in regulating the activity of Cdc42.

Tudor domains of the PRC2 components PHF1 and PHF19 selectively bind to histone H3K36me3.

PRC2 is the major H3K27 methyltransferase and is responsible for maintaining repressed gene expression patterns throughout development. It contains four core components: EZH2, EED, SUZ12 and RbAp46/48 and some cell-type specific components. In this study, we focused on characterizing the histone binding domains of PHF1 and PHF19, and found that the Tudor domains of PHF1 and PHF19 selectively bind to histone H3K36me3. Structural analysis of these Tudor domains also shed light on how these Tudor domains selectively bind to histone H3K36me3. The histone H3K36me3 binding by the Tudor domains of PHF1, PHF19 and likely MTF2 provide another recruitment and regulatory mechanism for the PRC2 complex. In addition, we found that the first PHD domains of PHF1 and PHF19 do not exhibit histone H3K4 binding ability, nor do they affect the Tudor domain binding to histones.

Structural basis for recognition of arginine methylated Piwi proteins by the extended Tudor domain.

Arginine methylation modulates diverse cellular processes and represents a molecular signature of germ-line-specific Piwi family proteins. A subset of Tudor domains recognize arginine methylation modifications, but the binding mechanism has been lacking. Here we establish that, like other germ-line Tudor proteins, the ancestral staphylococcal nuclease domain-containing 1 (SND1) polypeptide is expressed and associates with PIWIL1/Miwi in germ cells. We find that human SND1 binds PIWIL1 in an arginine methylation-dependent manner with a preference for symmetrically dimethylated arginine. The entire Tudor domain and a bifurcated SN domain are required for this binding activity, whereas the canonical Tudor domain alone is insufficient for methylarginine ligand binding. Crystal structures show that the intact SND1 extended Tudor domain forms a wide and negatively charged binding groove, which can accommodate distinct symmetrically dimethylated arginine peptides from PIWIL1 in different orientations. This analysis explains how SND1 preferentially recognizes symmetrical dimethylarginine via an aromatic cage and conserved hydrogen bonds, and provides a general paradigm for the binding mechanisms of methylarginine-containing peptides by extended Tudor domains.

Supplement and Suppression: Both Boundary and Nonboundary Are Helpful for Salient Object Detection.

Current methods aggregate multilevel features from the backbone and introduce edge information to get more refined saliency maps. However, little attention is paid to how to suppress the regions with similar saliency appearances in the background. These regions usually exist in the vicinity of salient objects and have high contrast with the background, which is easy to be misclassified as foreground. To solve this problem, we propose a gated feature interaction network (GFINet) to integrate multiple saliency features, which can utilize nonboundary features with background information to suppress pseudosalient objects and simultaneously apply boundary features to supplement edge details. Different from previous methods that only consider the complementarity between saliency and boundary, the proposed network introduces nonboundary features into the decoder to filter the pseudosalient objects. Specifically, GFINet consists of global features aggregation branch (GFAB), boundary and nonboundary features' perception branch (B&NFPB), and gated feature interaction module (GFIM). According to the global features generated by GFAB, boundary and nonboundary features produced by B&NFPB and GFIM employ a gate structure to adaptively optimize the saliency information interchange between abovementioned features and, thus, predict the final saliency maps. Besides, due to the imbalanced distribution between the boundary pixels and nonboundary ones, the binary cross-entropy (BCE) loss is difficult to predict the pixels near the boundary. Therefore, we design a border region aware (BRA) loss to further boost the quality of boundary and nonboundary, which can guide the network to focus more on the hard pixels near the boundary by assigning different weights to different positions. Compared with 12 counterparts, experimental results on five benchmark datasets show that our method has better generalization and improves the state-of-the-art approach by 4.85% averagely in terms of the regional and boundary evaluation measures. In addition, our model is more efficient with an inference speed of 50.3 FPS when processing a 320 ×320 image. Code has been made available at https://github.com/lesonly/GFINet.

BTCRSleep: a boundary temporal context refinement-based fully convolutional network for sleep staging with single-channel EEG.

Objective. Sleep staging studies on single-channel EEG mainly exploit deep learning methods that combine convolutional neural networks (CNNs) and recurrent neural networks. However, when typical brain waves (such as K-complexes or sleep spindles) that identify sleep stages span two epochs, the abstract process of a CNN extracting features from each sleep stage may cause the loss of boundary context information. This study attempts to capture the boundary context, which contains the characteristics of brain waves during sleep stage transition, to improve the performance of sleep staging.Approach. In this paper we propose a fully convolutional network with boundary temporal context refinement, called BTCRSleep (Boundary Temporal Context Refinement Sleep). The boundary temporal context refinement module refines the boundary information on sleep stages on the basis of extracting multi-scale temporal dependences between epochs and enhances the abstract capability of the boundary temporal context. In addition, we design a class-aware data augmentation method to effectively learn the boundary temporal context between the minority class and other sleep stages.Main results. We evaluate the performance of our proposed network using four public datasets: the 2013 version of Sleep-EDF Expanded (SEDF), the 2018 version of Sleep-EDF Expanded (SEDFX), the Sleep Heart Health Study (SHHS) and CAP Sleep Database (CAP). The evaluation results on the four datasets showed that our model obtains the best total accuracy and kappa score compared with state-of-the-art methods. On average, accuracies of 84.9% in SEDF, 82.9% in SEDFX, 85.2% in SHHS and 76.9% in CAP are obtained under subject-independent cross-validation. We demonstrate that the boundary temporal context contributes to the improvement in capturing the temporal dependences across different epochs.

PRC2 complexes with JARID2, MTF2, and esPRC2p48 in ES cells to modulate ES cell pluripotency and somatic cell reprogramming.

Polycomb repressive complex two (PRC2) has been implicated in embryonic stem (ES) cell pluripotency; however, the mechanistic roles of this complex are unclear. It was assumed that ES cells contain PRC2 with the same subunit composition as that identified in HeLa cells and Drosophila embryos. Here, we report that PRC2 in mouse ES cells contains at least three additional subunits: JARID2, MTF2, and a novel protein denoted esPRC2p48. JARID2, MTF2, and esPRC2p48 are highly expressed in mouse ES cells compared to differentiated cells. Importantly, knockdowns of JARID2, MTF2, or esPRC2p48 alter the level of PRC2-mediated H3K27 methylation and result in the expression of differentiation-associated genes in ES cells. Interestingly, expression of JARID2, MTF2, and esPRC2p48 together, but not individually, enhances Oct4/Sox2/Klf4-mediated reprogramming of mouse embryonic fibroblasts (MEFs) into induced pluripotent stem cells, whereas knockdown or knockout of JARID2, MTF2, or esPRC2p48 significantly inhibits reprogramming. JARID2, MTF2, and esPRC2p48 modulate H3K27 methylation and facilitate repression of lineage-associated gene expression when transduced into MEFs, and synergistically stimulate the histone methyltransferase activity of PRC2 in vitro. Therefore, these studies identify JARID2, MTF2, and esPRC2p48 as important regulatory subunits of PRC2 in ES cells and reveal critical functions of these subunits in modulating PRC2's activity and gene expression both in ES cells and during somatic cell reprogramming.

Mouse Piwi interactome identifies binding mechanism of Tdrkh Tudor domain to arginine methylated Miwi.

Tudor domains are protein modules that mediate protein-protein interactions, potentially by binding to methylated ligands. A group of germline specific single and multiTudor domain containing proteins (TDRDs) represented by drosophila Tudor and its mammalian orthologs Tdrd1, Tdrd4/RNF17, and Tdrd6 play evolutionarily conserved roles in germinal granule/nuage formation and germ cell specification and differentiation. However, their physiological ligands, and the biochemical and structural basis for ligand recognition, are largely unclear. Here, by immunoprecipitation of endogenous murine Piwi proteins (Miwi and Mili) and proteomic analysis of complexes related to the piRNA pathway, we show that the TDRD group of Tudor proteins are physiological binding partners of Piwi family proteins. In addition, mass spectrometry indicates that arginine residues in RG repeats at the N-termini of Miwi and Mili are methylated in vivo. Notably, we found that Tdrkh/Tdrd2, a novel single Tudor domain containing protein identified in the Miwi complex, is expressed in the cytoplasm of male germ cells and directly associates with Miwi. Mutagenesis studies mapped the Miwi-Tdrkh interaction to the very N-terminal RG/RA repeats of Miwi and showed that the Tdrkh Tudor domain is critical for binding. Furthermore, we have solved the crystal structure of the Tdrkh Tudor domain, which revealed an aromatic binding pocket and negatively charged binding surface appropriate for accommodating methylated arginine. Our findings identify a methylation-directed protein interaction mechanism in germ cells mediated by germline Tudor domains and methylated Piwi family proteins, and suggest a complex mode of regulating the organization and function of Piwi proteins in piRNA silencing pathways.

SleepContextNet: A temporal context network for automatic sleep staging based single-channel EEG.

BACKGROUND AND OBJECTIVE: Single-channel EEG is the most popular choice of sensing modality in sleep staging studies, because it widely conforms to the sleep staging guidelines. The current deep learning method using single-channel EEG signals for sleep staging mainly extracts the features of its surrounding epochs to obtain the short-term temporal context information of EEG epochs, and ignore the influence of the long-term temporal context information on sleep staging. However, the long-term context information includes sleep stage transition rules in a sleep cycle, which can further improve the performance of sleep staging. The aim of this research is to develop a temporal context network to capture the long-term context between EEG sleep stages. METHODS: In this paper, we design a sleep staging network named SleepContextNet for sleep stage sequence. SleepContextNet can extract and utilize the long-term temporal context between consecutive EEG epochs, and combine it with the short-term context. we utilize Convolutional Neural Network(CNN) layers for learning representative features from each sleep stage and the representation features sequence learned are fed into a Recurrent Neural Network(RNN) layer for learning long-term and short-term context information among sleep stage in chronological order. In addition, we design a data augmentation algorithm for EEG to retain the long-term context information without changing the number of samples. RESULTS: We evaluate the performance of our proposed network using four public datasets, the 2013 version of Sleep-EDF (SEDF), the 2018 version of Sleep-EDF Expanded (SEDFX), Sleep Heart Health Study (SHHS) and the CAP Sleep Database. The experimental results demonstrate that SleepContextNet outperforms state-of-the-art techniques in terms of different evaluation metrics by capturing long-term and short-term temporal context information. On average, accuracy of 84.8% in SEDF, 82.7% in SEDFX, 86.4% in SHHS and 78.8% in CAP are obtained under subject-independent cross validation. CONCLUSIONS: The network extracts the long-term and short-term temporal context information of sleep stages from the sequence features, which utilizes the temporal dependencies among the EEG epochs effectively and improves the accuracy of sleep stages. The sleep staging method based on forward temporal context information is suitable for real-time family sleep monitoring system.

Methylation-state-specific recognition of histones by the MBT repeat protein L3MBTL2.

The MBT repeat has been recently identified as a key domain capable of methyl-lysine histone recognition. Functional work has pointed to a role for MBT domain-containing proteins in transcriptional repression of developmental control genes such as Hox genes. In this study, L3MBTL2, a human homolog of Drosophila Sfmbt critical for Hox gene silencing, is demonstrated to preferentially recognize lower methylation states of several histone-derived peptides through its fourth MBT repeat. High-resolution crystallographic analysis of the four MBT repeats of this protein reveals its unique asymmetric rhomboid architecture, as well as binding mechanism, which preclude the interaction of the first three MBT repeats with methylated peptides. Structural elucidation of an L3MBTL2-H4K20me1 complex and comparison with other MBT-histone peptide complexes also suggests that an absence of distinct surface contours surrounding the methyl-lysine-binding pocket may underlie the lack of sequence specificity observed for members of this protein family.

The first mitochondrial genome of the genus Exhippolysmata (Decapoda: Caridea: Lysmatidae), with gene rearrangements and phylogenetic associations in Caridea.

The complete mitochondrial genome (mitogenome) of animals can provide useful information for evolutionary and phylogenetic analyses. The mitogenome of the genus Exhippolysmata (i.e., Exhippolysmata ensirostris) was sequenced and annotated for the first time, its phylogenetic relationship with selected members from the infraorder Caridea was investigated. The 16,350 bp mitogenome contains the entire set of 37 common genes. The mitogenome composition was highly A + T biased at 64.43% with positive AT skew (0.009) and negative GC skew (- 0.199). All tRNA genes in the E. ensirostris mitogenome had a typical cloverleaf secondary structure, except for trnS1 (AGN), which appeared to lack the dihydrouridine arm. The gene order in the E. ensirostris mitogenome was rearranged compared with those of ancestral decapod taxa, the gene order of trnL2-cox2 changed to cox2-trnL2. The tandem duplication-random loss model is the most likely mechanism for the observed gene rearrangement of E. ensirostris. The ML and BI phylogenetic analyses place all Caridea species into one group with strong bootstrap support. The family Lysmatidae is most closely related to Alpheidae and Palaemonidae. These results will help to better understand the gene rearrangements and evolutionary position of E. ensirostris and lay a foundation for further phylogenetic studies of Caridea.