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BACKGROUNDAn early sign of cognitive decline in older adults is often a disruption in social function, but our understanding of this association is limited.OBJECTIVEWe aimed to determine whether those screening positive for early stages of cognitive impairment have differences across multiple dimensions of social function and whether associations differ by gender.DESIGNUnited States nationally representative cohort (2010), the National Social life, Health, and Aging Project (NSHAP).PARTICIPANTSCommunity-dwelling adults aged 62–90 years (N = 3,310) with a response rate of 76.9 %.MAIN MEASURESCognition was measured using a survey adaptation of the Montreal Cognitive Assessment categorized into three groups: normal, mild cognitive impairment (MCI), and dementia. We measured three domains of social relationships, each comprised of two scales: network structure (size and density), social resources (social support and social strain), and social engagement (community involvement and socializing). We used multiple linear regression to characterize the relationship of each social relationship measure to cognition.KEY RESULTSIndividuals screened as at risk for MCI and early dementia had smaller network sizes by 0.3 and 0.6 individuals (p < 0.001), and a 10 % and 25 % increase in network density (p < 0.001), respectively. For social resources, individuals at risk for MCI and dementia had 4 % and 14 % less social strain (p = 0.01), but only women had 3 % and 6 % less perceived social support (p = 0.013), respectively. For social engagement, individuals screened positive for MCI and dementia had 8 % and 19 % less community involvement (p = 0.01), but only men had 8 % and 13 % increased social involvement with neighbors and family members (p < 0.001), respectively.CONCLUSIONChanges in social functioning provide an early indication to screen for cognitive loss. Recognition that early cognitive loss is associated with differences in social function can guide counseling efforts and help identify social vulnerabilities to ease the transition to overt dementia for both patients and caregivers.

The occurrence of accidents at container ports results in damages and economic losses in the terminal operation. Therefore, it is necessary to accurately predict accidents at container ports. Several machine learning models have been applied to predict accidents at a container port under various time intervals, and the optimal model was selected by comparing the results of different models in terms of their accuracy, precision, recall, and F1 score. The results show that a deep neural network model and gradient boosting model with an interval of 6 h exhibits the highest performance in terms of all the performance metrics. The applied methods can be used in the predicting of accidents at container ports in the future.

Cell-laden structures play a pivotal role in various tissue engineering applications, particularly in tissue restoration. Interactions between cells within bioprinted structures are crucial for successful tissue development and regulation of stem cell fate through intricate cell-to-cell signaling pathways. In this study, we developed a new technique that combines polyethylene glycol (PEG)-infused submerged bioprinting with a stretching procedure. This approach facilitated the generation of fully aligned collagen structures consisting of myoblasts and a low concentration (2 wt%) of collagen to efficiently encourage muscle tissue regeneration. By adjusting several processing parameters, we obtained biologically safe and mechanically stable cell-laden collagen filaments with uniaxial alignment. Notably, the cell filaments exhibited markedly elevated cellular activities compared to those exhibited by conventional bioprinted filaments, even at similar cell densities. Moreover, when we implanted structures containing adipose stem cells into mice, we observed a significantly increased level of myogenesis compared to that in normally bioprinted struts. Thus, this promising approach has the potential to revolutionize tissue engineering by fostering enhanced cellular interactions and promoting improved outcomes in regenerative medicine. [Display omitted] •A new hybrid bioprinting system was investigated for muscle regeneration.•High myogenesis of the cell-laden structure was achieved.•The structure significantly accelerated tibialis anterior muscle recovery on mouse model.

Multimodal emotion recognition (MER) aims to enhance the understanding of human emotions by integrating visual, auditory, and textual modalities. However, previous MER approaches often depend on a dominant modality rather than considering all modalities, leading to poor generalization. To address this, we propose Causal Inference in Multimodal Emotion Recognition (CausalMER), which leverages counterfactual reasoning and causal graphs to capture relationships between modalities and reduce direct modality effects contributing to bias. This allows CausalMER to make unbiased predictions while being easily applied to existing MER methods in a model-agnostic manner, without requiring any architectural modifications. We evaluate CausalMER on the IEMOCAP and CMU-MOSEI datasets, widely used benchmarks in MER, and compare it with existing methods. On the IEMOCAP dataset with the MulT backbone, CausalMER achieves an average accuracy of 83.4%. On the CMU-MOSEI dataset, the average accuracies with MulT, PMR, and DMD backbones are 50.1%, 48.8%, and 48.8%, respectively. Experimental results demonstrate that CausalMER is robust in missing modality scenarios, as shown by its low standard deviation in performance drop gaps. Additionally, we evaluate modality contributions and show that CausalMER achieves balanced contributions from each modality, effectively mitigating direct biases from individual modalities.

Background Many studies have been performed to identify various genomic loci and genes associated with the meat quality in pigs. However, the full genetic architecture of the trait still remains unclear in part because of the lack of accurate identification of related structural variations (SVs) which resulted from the shortage of target breeds, the limitations of sequencing data, and the incompleteness of genome assemblies. The recent generation of a new pig breed with superior meat quality, called Nanchukmacdon, and its chromosome-level genome assembly (the NCMD assembly) has provided new opportunities. Results By applying assembly-based SV calling approaches to various genome assemblies of pigs including Nanchukmacdon, the impact of SVs on meat quality was investigated. Especially, by checking the commonality of SVs with other pig breeds, a total of 13,819 Nanchukmacdon-specific SVs (NSVs) were identified, which have a potential effect on the unique meat quality of Nanchukmacdon. The regulatory potentials of NSVs for the expression of nearby genes were further examined using transcriptome- and epigenome-based analyses in different tissues. Conclusions Whole-genome comparisons based on chromosome-level genome assemblies have led to the discovery of SVs affecting meat quality in pigs, and their regulatory potentials were analyzed. The identified NSVs will provide new insights regarding genetic architectures underlying the meat quality in pigs. Finally, this study confirms the utility of chromosome-level genome assemblies and multi-omics analysis to enhance the understanding of unique phenotypes.

Blockchain network architecture is a promising technology for constructing highly secure Internet of Things (IoT) networks. IoT networks typically comprise various sensors and actuators. Blockchain network technology can be applied to secure control robots in smart factories or reliable drone deliveries in smart cities. The wide spread of transactions and shared smart contracts across blockchain networks guarantees ultimate network security. A typical wired blockchain network maintains sufficient redundancy within a stable configuration. However, IoT blockchain networks exhibit unavoidable instability. The dynamic configuration changes caused by flexible node membership make it impossible to achieve the same level of redundancy as a stable network. A trustworthy transaction spreading method provides practical transaction sharing for dynamic IoT networks. We propose a Q-learning framework and a graph convergence network (GCN) to search for the proper spreading path of each transaction. The proposed Q-learning framework determines the next spreading hop using node features. The GCN determines the reliable area based on the Q-learning results. The discovered reliable area guides the proper spreading path of transactions to the destination node. In addition, the proposed trustworthy transaction spreading was implemented over an InterPlanetary File system (IPFS). The IPFS-powered experiments confirmed the practicability of the proposed transaction spreading mechanism.

The bacterial effector proteins SseK and NleB glycosylate host proteins on arginine residues, leading to reduced NF-κB-dependent responses to infection. Salmonella SseK1 and SseK2 are E. coli NleB1 orthologs that behave as NleB1-like GTs, although they differ in protein substrate specificity. Here we report that these enzymes are retaining glycosyltransferases composed of a helix-loop-helix (HLH) domain, a lid domain, and a catalytic domain. A conserved HEN motif (His-Glu-Asn) in the active site is important for enzyme catalysis and bacterial virulence. We observe differences between SseK1 and SseK2 in interactions with substrates and identify substrate residues that are critical for enzyme recognition. Long Molecular Dynamics simulations suggest that the HLH domain determines substrate specificity and the lid-domain regulates the opening of the active site. Overall, our data suggest a front-face S N i mechanism, explain differences in activities among these effectors, and have implications for future drug development against enteric pathogens. The type III secretion system effectors NleB and SseK are glycosyltransferases (GT) that specifically glycosylate arginine residues. Here the authors provide insights into their mechanism by combining X-ray crystallography, NMR, enzyme kinetics measurements, molecular dynamics simulations and in vivo experiments and show that SseK/NleB enzymes are retaining GTs.

Background Advances in sequencing technologies have facilitated large-scale comparative genomics based on whole genome sequencing. Constructing and investigating conserved genomic regions among multiple species (called synteny blocks) are essential in the comparative genomics. However, they require significant amounts of computational resources and time in addition to bioinformatics skills. Many web interfaces have been developed to make such tasks easier. However, these web interfaces cannot be customized for users who want to use their own set of genome sequences or definition of synteny blocks. Results To resolve this limitation, we present mySyntenyPortal, a stand-alone application package to construct websites for synteny block analyses by using users’ own genome data. mySyntenyPortal provides both command line and web-based interfaces to build and manage websites for large-scale comparative genomic analyses. The websites can be also easily published and accessed by other users. To demonstrate the usability of mySyntenyPortal, we present an example study for building websites to compare genomes of three mammalian species (human, mouse, and cow) and show how they can be easily utilized to identify potential genes affected by genome rearrangements. Conclusions mySyntenyPortal will contribute for extended comparative genomic analyses based on large-scale whole genome sequences by providing unique functionality to support the easy creation of interactive websites for synteny block analyses from user’s own genome data.

Background: The fast-growing trend of urbanizations and the dwellers' stressful lifestyle in megacities has led to several drawbacks from the mental health perspective. Provided that there is a significant association between the green environment and mental health, we investigated different functions of a green roof from two perspectives of well-being and environment. Methods: After investigating on different functions of the green roof and classifying them into two sections, a self questionnaire survey was conducted in May 2020 on 100 citizens of the Seoul metropolitan area after, South Korea visiting the green roof. They were asked to clarify their perception of the different functions of green roof gardens. The statistical analysis was performed to show the differences in perception and how each variable can contribute to such differences. Results: The results illustrated a high satisfaction rate among the visitors of green roofs. The perception of the well-being functions was higher than those of environmental functions. However, age group can alter the perception significantly, as the senior citizens significantly perceived the well-being functions. In contrast, the awareness of the environmental functions was higher among the younger participants. Conclusion: Providing practical information about the ideal functions of green roofs, this study offered helpful insight for the planners who focus on society’s general health and work in two different sections: developing the city landscape and mental health improvement.

Genomic data have become major resources to understand complex mechanisms at fine-scale temporal and spatial resolution in functional and evolutionary genetic studies, including human diseases, such as cancers. Recently, a large number of whole genomes of evolving populations of yeast (Saccharomyces cerevisiae W303 strain) were sequenced in a time-dependent manner to identify temporal evolutionary patterns. For this type of study, a chromosome-level sequence assembly of the strain or population at time zero is required to compare with the genomes derived later. However, there is no fully automated computational approach in experimental evolution studies to establish the chromosome-level genome assembly using unique features of sequencing data. In this study, we developed a new software pipeline, the integrative meta-assembly pipeline (IMAP), to build chromosome-level genome sequence assemblies by generating and combining multiple initial assemblies using three de novo assemblers from short-read sequencing data. We significantly improved the continuity and accuracy of the genome assembly using a large collection of sequencing data and hybrid assembly approaches. We validated our pipeline by generating chromosome-level assemblies of yeast strains W303 and SK1, and compared our results with assemblies built using long-read sequencing and various assembly evaluation metrics. We also constructed chromosome-level sequence assemblies of S. cerevisiae strain Sigma1278b, and three commonly used fungal strains: Aspergillus nidulans A713, Neurospora crassa 73, and Thielavia terrestris CBS 492.74, for which long-read sequencing data are not yet available. Finally, we examined the effect of IMAP parameters, such as reference and resolution, on the quality of the final assembly of the yeast strains W303 and SK1. We developed a cost-effective pipeline to generate chromosome-level sequence assemblies using only short-read sequencing data. Our pipeline combines the strengths of reference-guided and meta-assembly approaches. Our pipeline is available online at http://github.com/jkimlab/IMAP including a Docker image, as well as a Perl script, to help users install the IMAP package, including several prerequisite programs. Users can use IMAP to easily build the chromosome-level assembly for the genome of their interest.