Computational Strategies for Scalable Genomics Analysis.

The revolution in next-generation DNA sequencing technologies is leading to explosive data growth in genomics, posing a significant challenge to the computing infrastructure and software algorithms for genomics analysis. Various big data technologies have been explored to scale up/out current bioinformatics solutions to mine the big genomics data. In this review, we survey some of these exciting developments in the applications of parallel distributed computing and special hardware to genomics. We comment on the pros and cons of each strategy in the context of ease of development, robustness, scalability, and efficiency. Although this review is written for an audience from the genomics and bioinformatics fields, it may also be informative for the audience of computer science with interests in genomics applications

Deconvolute individual genomes from metagenome sequences through short read clustering.

Metagenome assembly from short next-generation sequencing data is a challenging process due to its large scale and computational complexity. Clustering short reads by species before assembly offers a unique opportunity for parallel downstream assembly of genomes with individualized optimization. However, current read clustering methods suffer either false negative (under-clustering) or false positive (over-clustering) problems. Here we extended our previous read clustering software, SpaRC, by exploiting statistics derived from multiple samples in a dataset to reduce the under-clustering problem. Using synthetic and real-world datasets we demonstrated that this method has the potential to cluster almost all of the short reads from genomes with sufficient sequencing coverage. The improved read clustering in turn leads to improved downstream genome assembly quality

Relationships Between Perceived Usefulness and Fitness Wearable Technology User Adoption Mediated by Culture, Gender, and COVID-19: A Meta-Analysis

With COVID-19 raging and individuals exercising at home, the adoption of fitness wearable technology (FWT) that help people monitor their physical state has become a pressing matter. Here, we try to bridge the gap that no researcher has examined the link between perceived usefulness (PU) and FWT adoption by using meta-analysis to comprehend the detailed numerical values and moderating factors affecting the relationship. A total of 24 articles with 7180 re-pendants were investigated from January 1, 2015 to March 1, 2022 A.D, producing significant results as follows: (1) There is publication bias in relevant research, and sensitivity analysis suggests that PU influences FWT adoption positively; (2) Subgroup test and regression analysis suggested that cultural background, gender, and COVID-19 can deepen this relationship to varying degrees. More precisely, female (r=0.669) have a greater impact on FWT adoption than men (r=0.658). In addition, users in other countries (r=0.670) are more concerned about PU than Chinese users (r=0.658). Regression analysis showed that after the COVID-19 outbreak, the coefficient (r=0.762) of PU on FWT adoption increased significantly, indicating that people need FWT-assisted exercise to understand and maintain their own physical fitness. The research opens up new ideas for different governments and scholars to promote mass fitness and public health during the COVID-19, providing a reference for the scientific research and development of PU and marketing promotion of the related enterprises’ FWT

microRNA-29a functions as a tumor suppressor in nasopharyngeal carcinoma 5-8F cells through targeting VEGF

Objective(s): microRNA-29 (miR-29) family miRNAs have been mentioned as tumor suppressive genes in several human cancers. The purpose of this study was to investigate the function of miR-29a in nasopharyngeal carcinoma (NPC) cells. Materials and Methods: Human NPC cell line 5-8F was transfected with mimic, inhibitor or scrambled controls specific for miR-29a. Subsequently, cell viability, migration, apoptosis and expression changes of VEGF were assessed by trypan blue staining, MTT assay, transwell assay, flow cytometry, Western blot and RT-qPCR. TargetScan online database was used to predict the targets of miR-29a, and luciferase reporter assay was carried out for testing the targeting relationship between VEGF and miR-29a. Western blot analysis was performed to determine the expression changes of core proteins in PI3K/AKT and JAK/STAT pathways. Results: Overexpression of miR-29a suppressed 5-8F cells viability and relative migration, but increased apoptotic cell rate. Consistently, Bcl-2 was downregulated, Bax was upregulated, and caspase-3 and -9 were cleaved by miR-29a overexpression. VEGF was a target gene of miR-29a. Besides, VEGF silence exerted similar effects like miR-29a, as the viability and migration were repressed and apoptosis was induced. Finally, we found that PI3K/AKT and JAK/STAT pathways were deactivated by miR-29a or VEGF silence. Conclusion: These findings highlighted the tumor suppressive effects of miR-29a on NPC cells, as its overexpression inhibited 5-8F cells viability, migration, and induced apoptosis. miR-29a exerted tumor suppressive functions might be via targeting VEGF and deactivating PI3K/AKT and JAK/STAT pathways

Towards Personalized Privacy: User-Governed Data Contribution for Federated Recommendation

Federated recommender systems (FedRecs) have gained significant attention for their potential to protect user's privacy by keeping user privacy data locally and only communicating model parameters/gradients to the server. Nevertheless, the currently existing architecture of FedRecs assumes that all users have the same 0-privacy budget, i.e., they do not upload any data to the server, thus overlooking those users who are less concerned about privacy and are willing to upload data to get a better recommendation service. To bridge this gap, this paper explores a user-governed data contribution federated recommendation architecture where users are free to take control of whether they share data and the proportion of data they share to the server. To this end, this paper presents a cloud-device collaborative graph neural network federated recommendation model, named CDCGNNFed. It trains user-centric ego graphs locally, and high-order graphs based on user-shared data in the server in a collaborative manner via contrastive learning. Furthermore, a graph mending strategy is utilized to predict missing links in the graph on the server, thus leveraging the capabilities of graph neural networks over high-order graphs. Extensive experiments were conducted on two public datasets, and the results demonstrate the effectiveness of the proposed method

DNABERT-S: Learning Species-Aware DNA Embedding with Genome Foundation Models

Effective DNA embedding remains crucial in genomic analysis, particularly in scenarios lacking labeled data for model fine-tuning, despite the significant advancements in genome foundation models. A prime example is metagenomics binning, a critical process in microbiome research that aims to group DNA sequences by their species from a complex mixture of DNA sequences derived from potentially thousands of distinct, often uncharacterized species. To fill the lack of effective DNA embedding models, we introduce DNABERT-S, a genome foundation model that specializes in creating species-aware DNA embeddings. To encourage effective embeddings to error-prone long-read DNA sequences, we introduce Manifold Instance Mixup (MI-Mix), a contrastive objective that mixes the hidden representations of DNA sequences at randomly selected layers and trains the model to recognize and differentiate these mixed proportions at the output layer. We further enhance it with the proposed Curriculum Contrastive Learning (C$^2$LR) strategy. Empirical results on 18 diverse datasets showed DNABERT-S's remarkable performance. It outperforms the top baseline's performance in 10-shot species classification with just a 2-shot training while doubling the Adjusted Rand Index (ARI) in species clustering and substantially increasing the number of correctly identified species in metagenomics binning. The code, data, and pre-trained model are publicly available at https://github.com/Zhihan1996/DNABERT_S

Adsorption equilibrium, isotherm, kinetics, and thermodynamic of modified bentonite for removing Rhodamine B

116-125Anionic and cationic surfactant modified sodium bentonite (Na-Bt) has been prepared by the cationic surfactant cetyltrimethyl ammonium bromide (CTAB) and the anionic surfactant sodium dodecyl benzene sulfonate (SDBS) to sodium bentonite, respectively. The properties of the modified samples are characterized by XRD, SEM, BET and FT-IR. The results of characterization shown that the cationic surfactant had changed the structure and properties of natural sodium bentonite, which proved that surfactants had been successfully implanted into sodium bentonite. But anionic surfactant had no change, this manifested SDBS didn’t insert the layers of bentonite. In addition, adsorption experiments of Rhodamine B (RhB) proved that the modified sodium bentonite adsorption performance is greatly improved. The adsorption experiments also indicated that CTAB-bentonite had the largest adsorption capacity compared with SDBS-bentonite due to the formation of a highly effective partition medium by cationic surfactant micelle. The adsorption data of RhB is analyzed with the isothermal model, thermodynamics and kinetics. Overall, this study provided high-efficiency method for the removal RhB by the surfactant modified bentonite

Insulin resistance and white adipose tissue inflammation are uncoupled in energetically challenged Fsp27-deficient mice.

Fsp27 is a lipid droplet-associated protein almost exclusively expressed in adipocytes where it facilitates unilocular lipid droplet formation. In mice, Fsp27 deficiency is associated with increased basal lipolysis, 'browning' of white fat and a healthy metabolic profile, whereas a patient with congenital CIDEC deficiency manifested an adverse lipodystrophic phenotype. Here we reconcile these data by showing that exposing Fsp27-null mice to a substantial energetic stress by crossing them with ob/ob mice or BATless mice, or feeding them a high-fat diet, results in hepatic steatosis and insulin resistance. We also observe a striking reduction in adipose inflammation and increase in adiponectin levels in all three models. This appears to reflect reduced activation of the inflammasome and less adipocyte death. These findings highlight the importance of Fsp27 in facilitating optimal energy storage in adipocytes and represent a rare example where adipose inflammation and hepatic insulin resistance are disassociated.This work was supported by grants from the National Basic Research Program (2013CB530602 and 2011CB910801 to P.L.), from the National Natural Science Foundation of China (31430040, 31321003 and 31030038), from the China Postdoctoral Science Foundation (2012M520249 and 2013T60103 to L.Z.) and from the Wellcome Trust (091551 to D.S.). This work was also supported by the Bio and Medical Technology Development Program of the National Research Foundation (NRF) funded by the Ministry of Science, ICT and Future Planning (NRF-2013M3A9D5072563 to C.C.) and Korea Healthcare Technology R&D Project, Ministry for Health, Welfare and Family Affairs, Korea (A102060 to C.C.).This is the final published version. It first appeared at http://www.nature.com/ncomms/2015/150107/ncomms6949/full/ncomms6949.html?WT.ec_id=NCOMMS-20150114