Riding towards a sustainable future:an evaluation of bike sharing’s environmental benefits in Xiamen Island,China

In the pursuit of sustainable urbanization,Bike-Sharing Services(BSS)emerge as a pivotal instrument for promoting green,low-carbon transit.While BSS is often commended for its environmental benefits,we offer a more nuanced analysis that elucidates previously neglected aspects.Through the Dominant Travel Distance Model(DTDM),we evaluate the potential of BSS to replace other transportation modes for specific journey based on travel distance.Utilizing multiscale geographically weighted regression(MGWR),we illuminate the relationship between BSS’s environmental benefits and built-environment attributes.The life cycle analysis(LCA)quantifies greenhouse gas(GHG)emissions from production to operation,providing a deeper understanding of BSS’s environmental benefits.Notably,our study focuses on Xiamen Island,a Chinese“Type Ⅱ large-sized city”(1–3 million population),contrasting with the predominantly studied“super large-sized cities”(over 10 million population).Our findings highlight:(1)A single BSS trip in Xiamen Island reduces GHG emissions by an average of 19.97 g CO_(2)-eq,accumulating monthly savings of 144.477 t CO_(2)-eq.(2)Areas in the southwest,northeast,and southeast of Xiamen Island,characterized by high population densities,register significant BSS environmental benefits.(3)At a global level,the stepwise regression model identifies five key built environment factors influencing BSS’s GHG mitigation.(4)Regionally,MGWR enhances model precision,indicating that these five factors function at diverse spatial scales,affecting BSS’s environmental benefits variably.

Using multi-source data to assess livability in Hong Kong at the community-based level:A combined subjective-objective approach

With the emergence of new types of data(e.g.social media data)and cutting-edge computer technology(e.g.Natural Language Processing),the shortcomings of traditional methods(subjective and objective ways)for de-tecting urban livability can be overcome by an integrated approach.This study aims to develop a comprehensive approach to measure urban livability based on statistic data,geo-data(e.g.points of interest),questionnaires survey,and social media data(Instagram),from both objective and subjective angles.Hong Kong,as a city with a high level of urbanization and contrasting urban environments,is chosen as the study area in this research.Through this study,the question“which area of Hong Kong is more suitable for living”is answered by the visu-alization of GIS-based analysis.Also,the correlation between livability scores and individuals’sentiment scores are explored.Specifically,the results show that central areas of Hong Kong with a higher level of urbanization are relatively more livable than suburban regions.However,through sentiment analysis,individuals who post Instagram in suburban areas of Hong Kong usually express more positive content and happier emotion than those who post Instagram in central urban areas.The study could offer useful information for the policy action of authorities as well as the residential location choices of citizens.

An overview of detecting gene-trait associations by integrating GWAS summary statistics and eQTLs

Detecting genes that affect specific traits(such as human diseases and crop yields)is important for treating complex diseases and improving crop quality.A genome-wide association study(GWAS)provides new insights and directions for understanding complex traits by identifying important single nucleotide polymorphisms.Many GWAS summary statistics data related to various complex traits have been gathered recently.Studies have shown that GWAS risk loci and expression quantitative trait loci(e QTLs)often have a lot of overlaps,which makes gene expression gradually become an important intermediary to reveal the regulatory role of GWAS.In this review,we review three types of gene-trait association detection methods of integrating GWAS summary statistics and e QTLs data,namely colocalization methods,transcriptome-wide association study-oriented approaches,and Mendelian randomization-related methods.At the theoretical level,we discussed the differences,relationships,advantages,and disadvantages of various algorithms in the three kinds of gene-trait association detection methods.To further discuss the performance of various methods,we summarize the significant gene sets that influence highdensity lipoprotein,low-density lipoprotein,total cholesterol,and triglyceride reported in 16 studies.We discuss the performance of various algorithms using the datasets of the four lipid traits.The advantages and limitations of various algorithms are analyzed based on experimental results,and we suggest directions for follow-up studies on detecting gene-trait associations.

DeepCBA:A deep learning framework for gene expression prediction in maize based on DNA sequences and chromatin interactions

Chromatin interactions create spatial proximity between distal regulatory elements and target genes in the genome,which has an important impact on gene expression,transcriptional regulation,and phenotypic traits.To date,several methods have been developed for predicting gene expression.However,existing methods do not take into consideration the effect of chromatin interactions on target gene expression,thus potentially reducing the accuracy of gene expression prediction and mining of important regulatory elements.In this study,we developed a highly accurate deep learning-based gene expression prediction model(DeepCBA)based on maize chromatin interaction data.Compared with existing models,DeepCBA exhibits higher accuracy in expression classification and expression value prediction.The average Pearson correlation coefficients(PCCs)for predicting gene expression using gene promoter proximal interactions,proximaldistal interactions,and both proximal and distal interactions were 0.818,0.625,and 0.929,respectively,representing an increase of 0.357,0.16,and 0.469 over the PCCs obtained with traditional methods that use only gene proximal sequences.Some important motifs were identified through DeepCBA;they were enriched in open chromatin regions and expression quantitative trait loci and showed clear tissue specificity.Importantly,experimental results for the maize flowering-related gene ZmRap2.7 and the tillering-related gene ZmTb1 demonstrated the feasibility of DeepCBA for exploration of regulatory elements that affect gene expression.Moreover,promoter editing and verification of two reported genes(ZmCLE7 and ZmVTE4)demonstrated the utility of DeepCBA for the precise design of gene expression and even for future intelligent breeding.DeepCBA is available at http://www.deepcba.com/or http://124.220.197.196/.

Application of deep learning in genomics

In recent years, deep learning has been widely used in diverse fields of research, such as speech recognition, image classification,autonomous driving and natural language processing. Deep learning has showcased dramatically improved performance in complex classification and regression problems, where the intricate structure in the high-dimensional data is difficult to discover using conventional machine learning algorithms. In biology, applications of deep learning are gaining increasing popularity in predicting the structure and function of genomic elements, such as promoters, enhancers, or gene expression levels. In this review paper, we described the basic concepts in machine learning and artificial neural network, followed by elaboration on the workflow of using convolutional neural network in genomics. Then we provided a concise introduction of deep learning applications in genomics and synthetic biology at the levels of DNA, RNA and protein. Finally, we discussed the current challenges and future perspectives of deep learning in genomics.