Crowdsourcing for Sustainable Urban Logistics: Exploring the Factors Influencing Crowd Workers’ Participative Behavior

With crowd logistics becoming a crucial part of the last-mile delivery challenge in many cities, continued participation of crowd workers has become an essential issue affecting the growth of the crowd logistics platform. Understanding how people are motivated to continue their participation in crowd logistics can provide some clarity as to what policies and measures should be undertaken by the industry to support its further growth. Using the Push-Pull-Mooring (PPM) theory, we developed a research model to explain the factors influencing crowd workers' participative behavior. Survey data from 455 crowd workers were analyzed using SmartPLS3.0 software. The results show monetary rewards and trust have a significant positive impact on the willingness of crowd workers to continue participating in crowd logistics, while work enjoyment from previous work and entry barriers for work have a significant negative impact. Trust plays an intermediary role between monetary incentives and crowd workers' willingness to continue participating. Based on the findings of this study, we recommend that crowd logistics platforms should offer reasonable monetary incentives and keep these under constant review, build a high degree of trust and cooperation with their crowd workers, and initiate activities geared towards promoting satisfaction at work

Continual Learning on Dynamic Graphs via Parameter Isolation

Many real-world graph learning tasks require handling dynamic graphs where new nodes and edges emerge. Dynamic graph learning methods commonly suffer from the catastrophic forgetting problem, where knowledge learned for previous graphs is overwritten by updates for new graphs. To alleviate the problem, continual graph learning methods are proposed. However, existing continual graph learning methods aim to learn new patterns and maintain old ones with the same set of parameters of fixed size, and thus face a fundamental tradeoff between both goals. In this paper, we propose Parameter Isolation GNN (PI-GNN) for continual learning on dynamic graphs that circumvents the tradeoff via parameter isolation and expansion. Our motivation lies in that different parameters contribute to learning different graph patterns. Based on the idea, we expand model parameters to continually learn emerging graph patterns. Meanwhile, to effectively preserve knowledge for unaffected patterns, we find parameters that correspond to them via optimization and freeze them to prevent them from being rewritten. Experiments on eight real-world datasets corroborate the effectiveness of PI-GNN compared to state-of-the-art baselines

Immunogenic cell death-associated biomarkers classification predicts prognosis and immunotherapy efficacy in pancreatic ductal adenocarcinoma

IntroductionImmunogenic cell death (ICD) is a sort of regulated cell death (RCD) sufficient to trigger an adaptive immunological response. According to the current findings, ICD has the capacity to alter the tumor immune microenvironment by generating danger signals or damage-associated molecular patterns (DAMPs), which may contribute in immunotherapy. It would be beneficial to develop ICD-related biomarkers that classify individuals depending on how well they respond to ICD immunotherapy.Methods and resultsWe used consensus clustering to identify two ICD-related groupings. The ICD-high subtype was associated with favorable clinical outcomes, significant immune cell infiltration, and powerful immune response signaling activity. In addition, we developed and validated an ICD-related prognostic model for PDAC survival based on the tumor immune microenvironment. We also collected clinical and pathological data from 48 patients with PDAC, and patients with high EIF2A expression had a poor prognosis. Finally, based on ICD signatures, we developed a novel PDAC categorization method. This categorization had significant clinical implications for determining prognosis and immunotherapy.ConclusionOur work emphasizes the connections between ICD subtype variations and alterations in the immune tumor microenvironment in PDAC. These findings may help the immune therapy-based therapies for patients with PDAC. We also created and validated an ICD-related prognostic signature, which had a substantial impact on estimating patients' overall survival times (OS)

Reciprocal genomic evolution in the ant-fungus agricultural symbiosis

The attine ant–fungus agricultural symbiosis evolved over tens of millions of years, producing complex societies with industrial-scale farming analogous to that of humans. Here we document reciprocal shifts in the genomes and transcriptomes of seven fungus-farming ant species and their fungal cultivars. We show that ant subsistence farming probably originated in the early Tertiary (55–60 MYA), followed by further transitions to the farming of fully domesticated cultivars and leaf-cutting, both arising earlier than previously estimated. Evolutionary modifications in the ants include unprecedented rates of genome-wide structural rearrangement, early loss of arginine biosynthesis and positive selection on chitinase pathways. Modifications of fungal cultivars include loss of a key ligninase domain, changes in chitin synthesis and a reduction in carbohydrate-degrading enzymes as the ants gradually transitioned to functional herbivory. In contrast to human farming, increasing dependence on a single cultivar lineage appears to have been essential to the origin of industrial-scale ant agriculture

Reference genome of wild goat (<i>capra aegagrus</i>) and sequencing of goat breeds provide insight into genic basis of goat domestication

BACKGROUND: Domestic goats (Capra hircus) have been selected to play an essential role in agricultural production systems, since being domesticated from their wild progenitor, bezoar (Capra aegagrus). A detailed understanding of the genetic consequences imparted by the domestication process remains a key goal of evolutionary genomics. RESULTS: We constructed the reference genome of bezoar and sequenced representative breeds of domestic goats to search for genomic changes that likely have accompanied goat domestication and breed formation. Thirteen copy number variation genes associated with coat color were identified in domestic goats, among which ASIP gene duplication contributes to the generation of light coat-color phenotype in domestic goats. Analysis of rapidly evolving genes identified genic changes underlying behavior-related traits, immune response and production-related traits. CONCLUSION: Based on the comparison studies of copy number variation genes and rapidly evolving genes between wild and domestic goat, our findings and methodology shed light on the genetic mechanism of animal domestication and will facilitate future goat breeding. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12864-015-1606-1) contains supplementary material, which is available to authorized users

Accelerating Sequence Searching: Dimensionality Reduction Method

Similarity search over long sequence dataset becomes increasingly popular in many emerging applications, such as text retrieval, genetic sequences exploring, etc. In this paper, a novel index structure, namely Sequence Embedding Multiset tree (SEM - tree), has been proposed to speed up the searching process over long sequences. The SEM-tree is a multi-level structure where each level represents the sequence data with different compression level of multiset, and the length of multiset increases towards the leaf level which contains original sequences. The multisets, obtained using sequence embedding algorithms, have the desirable property that they do not need to keep the character order in the sequence, i.e. shorter representation, but can reserve the majority of distance information of sequences. Each level of the tree serves to prune the search space more efficiently as the multisets utilize the predicability to finish the searching process beforehand and reduce the computational cost greatly. A set of comprehensive experiments are conducted to evaluate the performance of the SEM-tree, and the experimental results show that the proposed method is much more efficient than existing representative methods.Computer Science, Artificial IntelligenceComputer Science, Information SystemsSCI(E)6ARTICLE3301-3222

A Young Drosophila Duplicate Gene Plays Essential Roles in Spermatogenesis by Regulating Several Y-Linked Male Fertility Genes

Gene duplication is supposed to be the major source for genetic innovations. However, how a new duplicate gene acquires functions by integrating into a pathway and results in adaptively important phenotypes has remained largely unknown. Here, we investigated the biological roles and the underlying molecular mechanism of the young kep1 gene family in the Drosophila melanogaster species subgroup to understand the origin and evolution of new genes with new functions. Sequence and expression analysis demonstrates that one of the new duplicates, nsr (novel spermatogenesis regulator), exhibits positive selection signals and novel subcellular localization pattern. Targeted mutagenesis and whole-transcriptome sequencing analysis provide evidence that nsr is required for male reproduction associated with sperm individualization, coiling, and structural integrity of the sperm axoneme via regulation of several Y chromosome fertility genes post-transcriptionally. The absence of nsr-like expression pattern and the presence of the corresponding cis-regulatory elements of the parental gene kep1 in the pre-duplication species Drosophila yakuba indicate that kep1 might not be ancestrally required for male functions and that nsr possibly has experienced the neofunctionalization process, facilitated by changes of trans-regulatory repertories. These findings not only present a comprehensive picture about the evolution of a new duplicate gene but also show that recently originated duplicate genes can acquire multiple biological roles and establish novel functional pathways by regulating essential genes