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Studies have shown that as non-coding RNAs, miRNAs regulate all levels of life activities and most pathological processes. Therefore, identifying disease-related miRNAs is essential for disease diagnosis and treatment. However, traditional biological experiments are highly uncertain and time-consuming. Hence, advanced intelligent computational models are needed to address this problem. We propose a dual-channel transformer graph model, named DCTGM, to learn multi-scale representations for miRNA-disease association prediction. Specifically, DCTGM includes a transformer encoder (TE) and GraphSAGE encoder (GE). The TE intensely captures the important interaction information between miRNA-disease pairs, and the GE aggregates multi-hop neighbor information of miRNA-disease association heterograph to enrich node features. Then, an attention module is proposed to aggregate the dual-channel interactive representations, and we adopt a multi-layer perceptron (MLP) to predict the miRNA-disease association scores. The fivefold cross-validation experimental results demonstrate that our proposed DCTGM achieves the AP of 92.735%, F1 of 84.430%, accuracy of 85.255%, and ROC of 93.012%. In addition, we conduct case studies on brain neoplasms, kidney neoplasms, and breast neoplasms. The extensive experiments show that the dbDEMC database validates 100% of the top 20 predicted miRNAs associated with these diseases. This model can effectively predict the potential mirNA-disease association. Experiments have shown that miRNA associated with a new disease can also be predicted. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

The Chukchi Sea is an increasing CO2 sink driven by rapidclimate changes. Understanding the seasonal variation of air-sea CO 2 exchange and the underlying mechanisms of biogeochemical dynamics is important for predicting impacts of climate change on and feedbacks by the ocean. Here, we present a unique data set of underway sea surface partial pressure of CO2 (pCO2) and discrete samples of biogeochemical properties collected in five consecutivecruises in 2014 and examine the seasonal variations in air-sea CO 2 flux and net community production (NCP). We found that thermaland non-thermal effects have different impacts on sea surface pCO 2 and thus the air-sea CO2 flux in different water masses. The Bering summer water combined with meltwater has a significantly greater atmospheric CO2 uptake potential than that of the Alaskan Coastal Water in the southern Chukchi Sea in summer, due to stronger biological CO2 removal and a weaker thermal effect. By analyzing the seasonal drawdown of dissolved inorganic carbon (DIC) and nutrients, we found that DIC-based NCP was higher than nitrate-based NCP by 66%–84% and attributable to partially decoupled C and N uptake because of a variable phytoplankton stoichiometry. A box model with a non-Redfield C:N uptake ratio can adequately reproduce observed pCO2 and DIC, which reveals that, during the intensive growing season (late spring to early summer), 30%–46% CO2 uptake in the Chukchi Sea was supported by a flexible stoichiometry of phytoplankton. These findings have important ramification for forecasting the responses of CO2 uptake of the Chukchi ecosystem to climate change.

One of the outstanding analytical problems in X-ray single-particle imaging (SPI) is the classification of structural heterogeneity, which is especially difficult given the low signal-to-noise ratios of individual patterns and the fact that even identical objects can yield patterns that vary greatly when orientation is taken into consideration. Proposed here are two methods which explicitly account for this orientation-induced variation and can robustly determine the structural landscape of a sample ensemble. The first, termed common-line principal component analysis (PCA), provides a rough classification which is essentially parameter free and can be run automatically on any SPI dataset. The second method, utilizing variation auto-encoders (VAEs), can generate 3D structures of the objects at any point in the structural landscape. Both these methods are implemented in combination with the noise-tolerant expand-maximize-compress (EMC) algorithm and its utility is demonstrated by applying it to an experimental dataset from gold nanoparticles with only a few thousand photons per pattern. Both discrete structural classes and continuous deformations are recovered. These developments diverge from previous approaches of extracting reproducible subsets of patterns from a dataset and open up the possibility of moving beyond the study of homogeneous sample sets to addressing open questions on topics such as nanocrystal growth and dynamics, as well as phase transitions which have not been externally triggered.

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Ascorbate peroxidase (APX), an important antioxidant enzyme, plays a significant role in ROS scavenging by catalyzing the decrease of hydrogen peroxide under various environmental stresses. Nevertheless, information about the APX gene family and their evolutionary and functional attributes in peanut (Arachis hypogea L.) was not reported. Therefore, a comprehensive genome-wide study was performed to discover the APX genes in cultivated peanut genome. This study identified 166 AhAPX genes in the peanut genome, classified into 11 main groups. The gene duplication analysis showed that AhAPX genes had experienced segmental duplications and purifying selection pressure. Gene structure and motif investigation indicated that most of the AhAPX genes exhibited a comparatively well-preserved exon-intron pattern and motif configuration contained by the identical group. We discovered five phytohormones-, six abiotic stress-, and five growth and development-related cis-elements in the promoter regions of AhAPX. Fourteen putative ah-miRNAs from 12 families were identified, targeting 33 AhAPX genes. Furthermore, we identified 3,257 transcription factors from 38 families (including AP2, ARF, B3, bHLH, bZIP, ERF, MYB, NAC, WRKY, etc.) in 162 AhAPX genes. Gene ontology and KEGG enrichment analysis confirm the role of AhAPX genes in oxidoreductase activity, catalytic activity, cell junction, cellular response to stimulus and detoxification, biosynthesis of metabolites, and phenylpropanoid metabolism. Based on transcriptome datasets, some genes such as AhAPX4/7/17/77/82/86/130/133 and AhAPX160 showed significantly higher expression in diverse tissues/organs, i.e., flower, leaf, stem, roots, peg, testa, and cotyledon. Likewise, only a few genes, including AhAPX4/17/19/55/59/82/101/102/137 and AhAPX140, were significantly upregulated under abiotic (drought and cold), and phytohormones (ethylene, abscisic acid, paclobutrazol, brassinolide, and salicylic acid) treatments. qRT-PCR-based expr

Newborn screening (NBS) programs operate in many countries, processing millions of dried bloodspot (DBS) samples annually. In addition to early identification of various adverse health outcomes, these samples have considerable potential as a resource for population-based research that could address key questions related to child health. The feasibility of archival DBS samples for emerging targeted and untargeted multi-omics analysis has not been previously explored in the literature. This review aims to critically evaluate the latest advances to identify opportunities and challenges of applying omics analyses to NBS cards in a research setting. Medline, Embase and PubMed databases were searched to identify studies utilizing DBS for genomic, proteomic and metabolomic assays. A total of 800 records were identified after removing duplicates, of which 23 records were included in this review. These papers consisted of one combined genomic/metabolomic, four genomic, three epigenomic, four proteomic and 11 metabolomic studies. Together they demonstrate that the increasing sensitivity of multi-omic analytical techniques makes the broad use of NBS samples achievable for large cohort studies. Maintaining the pre-analytical integrity of the DBS sample through storage at temperatures below -20 °C will enable this important resource to be fully realized in a research capacity.

Understanding human emotions is a crucial ability for intelligent robots to provide better human-robot interactions. The existing works are limited to trimmed video-level emotion classification, failing to locate the temporal window corresponding to the emotion. In this paper, we introduce a new task, named Temporal Emotion Localization in videos (TEL), which aims to detect human emotions and localize their corresponding temporal boundaries in untrimmed videos with aligned subtitles. TEL presents three unique challenges compared to temporal action localization: 1) The emotions have extremely varied temporal dynamics; 2) The emotion cues are embedded in both appearances and complex plots; 3) The fine-grained temporal annotations are complicated and labor-intensive. To address the first two challenges, we propose a novel dilated context integrated network with a coarse-fine two-stream architecture. The coarse stream captures varied temporal dynamics by modeling multi-granularity temporal contexts. The fine stream achieves complex plots understanding by reasoning the dependency between the multi-granularity temporal contexts from the coarse stream and adaptively integrates them into fine-grained video segment features. To address the third challenge, we introduce a cross-modal consensus learning paradigm, which leverages the inherent semantic consensus between the aligned video and subtitle to achieve weakly-supervised learning. We contribute a new testing set with 3,000 manually-annotated temporal boundaries so that future research on the TEL problem can be quantitatively evaluated. Extensive experiments show the effectiveness of our approach on temporal emotion localization. The repository of this work is at https://github.com/YYJMJC/TemporalEmotion-Localization-in-Videos.

Background: There are no specific validated questionnaires assessing satisfaction of family-centered care experienced by parents of infants hospitalized in Chinese Neonatal Intensive Care Units (NICU). Aim: To adapt and test the reliability and validity of the Chinese version of the EMPATHIC-30 questionnaire in NICU settings. Methods: A prospective, cross-sectional design was adopted. The EMPATHIC-30 questionnaires were completed by parents of infants admitted to one of the four NICUs at Hunan Children's Hospital, China, between November 2018 and 2019. Inclusion criteria were parents whose infants were admitted to the NICU for at least 5 days. Exclusion criteria were parents whose children were discharged within 5 days after admission or whose infants died in the NICU. Reliability was tested with Cronbach's alpha. The congruent validity was tested using Spearman's Rank correlation analysis, and the non-differential validity was tested using Cohen's d. Results: Parents of 619 infants discharged from the NICUs completed and returned the questionnaire. Most infants were male (n = 337, 54.4%) and infants' length of stay was a median of 21 days (IQR = 14–37). Mostly, mothers completed the questionnaire (n = 523, 84.5%). The Cronbach's alpha values of the five individual domains were between 0.67 and 0.95, and the alpha of the total questionnaire was.90, providing an adequate internal consistency. Congruent validity was measured by correlating the five domains with four standard satisfaction scales, documenting a weak correlation (rs −0.025–0.327). Non-differential validity showed some significant effect size between four binary variables (mechanical ventilation, unplanned admission, admission after surgery, length of stay) four of the five domains. Conclusion: The Chinese version of EMPATHIC-30 questionnaire showed acceptable psychometric properties. This instrument might be considered a suitable instrument to measure parent satisfaction among Chinese parents whose infan

Video-and-Language Inference is a recently proposed task for joint video-and-language understanding. This new task requires a model to draw inference on whether a natural language statement entails or contradicts a given video clip. In this paper, we study how to address three critical challenges for this task: judging the global correctness of the statement involved multiple semantic meanings, joint reasoning over video and subtitles, and modeling long-range relationships and complex social interactions. First, we propose an adaptive hierarchical graph network that achieves in-depth understanding of the video over complex interactions. Specifically, it performs joint reasoning over video and subtitles in three hierarchies, where the graph structure is adaptively adjusted according to the semantic structures of the statement. Secondly, we introduce semantic coherence learning to explicitly encourage the semantic coherence of the adaptive hierarchical graph network from three hierarchies. The semantic coherence learning can further improve the alignment between vision and linguistics, and the coherence across a sequence of video segments. Experimental results show that our method significantly outperforms the baseline by a large margin.

Temporal grounding in videos aims to localize one target video segment that semantically corresponds to a given query sentence. Thanks to the semantic diversity of natural language descriptions, temporal grounding allows activity grounding beyond pre-defined classes and has received increasing attention in recent years. The semantic diversity is rooted in the principle of compositionality in linguistics, where novel semantics can be systematically described by combining known words in novel ways (compositional generalization). However, current temporal grounding datasets do not specifically test for the compositional generalizability. To systematically measure the compositional generalizability of temporal grounding models, we introduce a new Compositional Temporal Grounding task and construct two new dataset splits, i.e., Charades-CG and ActivityNet-CG. Evaluating the state-of-the-art methods on our new dataset splits, we empirically find that they fail to generalize to queries with novel combinations of seen words. To tackle this challenge, we propose a variational cross-graph reasoning framework that explicitly decomposes video and language into multiple structured hierarchies and learns fine-grained semantic correspondence among them. Experiments illustrate the superior compositional generalizability of our approach. The repository of this work is at ht tps: / / gi thub. com/YYJMJC/ Composi tional- Temporal-Grounding.

There has been continuously increasing attention attracted by Neural Architecture Search (NAS). Due to its computational efficiency, gradient-based NAS methods like DARTS have become the most popular framework for NAS tasks. Nevertheless, as the search iterates, the derived model in previous NAS frameworks becomes dominated by skip-connects, causing the performance downfall. In this work, we present a novel approach to alleviate this issue, named Neural Architecture search with Pruning (NAP). Unlike prior differentiable architecture search works, our approach draws the idea from network pruning. We first train an over-parameterized network, including all candidate operations. Then we propose a criterion to prune the network. Based on a newly designed relaxation of architecture representation, NAP can derive the most potent model by removing trivial and redundant edges from the whole network topology. Experiments show the effectiveness of our proposed approach. Specifically, the model searched by NAP achieves state-of-the-art performances (2.48% test error) on CIFAR-10. We transfer the model to ImageNet and obtains a 25.1% test error with only 5.0 M parameters, which is on par with modern NAS methods.