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PurposeDigital labor platforms (DLPs) are transforming the nature of the work for an increasing number of workers, especially through extensively employing automated algorithms for performing managerial functions. In this novel working setting – characterized by algorithmic governance, and automatic matching, rewarding and punishing mechanisms – gig-workers play an essential role in providing on-demand services for final customers. Since gig-workers’ continued participation is crucial for sustainable service delivery in platform contexts, this study aims to identify and examine the antecedents of their working outcomes, including burnout and engagement.Design/methodology/approachWe suggested a theoretical framework, grounded in the job demands-resources heuristic model to investigate how the interplay of job demands and resources, resulting from working in DLPs, explains gig-workers’ engagement and burnout. We further empirically tested the proposed model to understand how DLPs' working conditions, in particular their algorithmic management, impact gig-working outcomes.FindingsOur findings indicate that job resources – algorithmic compensation, work autonomy and information sharing– have significant positive effects on gig-workers’ engagement. Furthermore, our results demonstrate that job insecurity, unsupportive algorithmic interaction (UAI) and algorithmic injustice significantly contribute to gig-workers’ burnout. Notably, we found that job resources substantially, but differently, moderate the relationship between job demands and gig-workers’ burnout.Originality/valueThis study contributes a theoretically accurate and empirically grounded understanding of two clusters of conditions – job demands and resources– as a result of algorithmic management practice in DLPs. We developed nuanced insights into how such conditions are evaluated by gig-workers and shape their engagement or burnout in DLP emerging work settings. We further uncovered that in gig-working context, resources do not similarly buffer against the negative effects of job demands.

Genetically identical cell populations exhibit considerable intercellular variation in the level of a given protein or mRNA. Both intrinsic and extrinsic sources of noise drive this variability in gene expression. More specifically, extrinsic noise is the expression variability that arises from cell-to-cell differences in cell-specific factors such as enzyme levels, cell size and cell cycle stage. In contrast, intrinsic noise is the expression variability that is not accounted for by extrinsic noise, and typically arises from the inherent stochastic nature of biochemical processes. Two-color reporter experiments are employed to decompose expression variability into its intrinsic and extrinsic noise components. Analytical formulas for intrinsic and extrinsic noise are derived for a class of stochastic gene expression models, where variations in cell-specific factors cause fluctuations in model parameters, in particular, transcription and/or translation rate fluctuations. Assuming mRNA production occurs in random bursts, transcription rate is represented by either the burst frequency (how often the bursts occur) or the burst size (number of mRNAs produced in each burst). Our analysis shows that fluctuations in the transcription burst frequency enhance extrinsic noise but do not affect the intrinsic noise. On the contrary, fluctuations in the transcription burst size or mRNA translation rate dramatically increase both intrinsic and extrinsic noise components. Interestingly, simultaneous fluctuations in transcription and translation rates arising from randomness in ATP abundance can decrease intrinsic noise measured in a two-color reporter assay. Finally, we discuss how these formulas can be combined with single-cell gene expression data from two-color reporter experiments for estimating model parameters.

The delegation of managerial functions such as job allocations, performance appraisals, and disciplining work behaviors to automated, intelligent algorithms has transformed various aspects of workplace dynamics. Despite the increasing prevalence of algorithmic management in today’s workplaces, its implications for work outcomes remain underspecified. Given the contextual novelty of this research, we adopted a mixed methods approach to theorize an algorithmic management resource model and investigate its configural relationships with crowdworkers’ engagement and burnout. This was achieved by analyzing online crowdworker community narratives and subsequently developing nuanced insights into the resources that algorithmic management offers or impedes. In Phase 1, drawing on conservation of resources (COR) theory tenets, we utilized computational text analysis to explore resource gains and losses associated with algorithmic management. Then, using configurational analysis over two studies (N = 322), we identified and empirically examined the interrelationships among resource passageways and work outcomes, specifically engagement and burnout. Our results support a theoretical understanding of the algorithmic management resource model and shed greater light on several configurations of algorithmic resource passageways, sufficiently explaining crowdworkers’ engagement and burnout in distributed, dispatched work settings such as online labor platforms.

Sustainable competitiveness (SC) encourages nations not only to meet the needs of the current generation but also to sustain or even expand national wealth in the future without depleting natural and social capital. Drawing on complexity theory, we used a configurational approach to identify under what necessary and sufficient conditions, digitalisation contributes to achieve higher SC. Shifting attention from net effects to configuration analysis improves our understanding of cross-national differences in sustainability by exploring how the digitalisation factors combine to strengthen SC power across countries. To address the complexity of this configuration, we have incorporated fsQCA and NCA techniques in the modelling of high and low levels of sustainable competitiveness recipes. Analysis of data from 127 countries advanced our perception of how access to ICT infrastructures and capabilities, combined with the adoption and usage of ICT could result in different degrees of sustainable competitiveness. Theoretically, this study contributes to the literature on digitalisation and national sustainability; and it can practically act as a guideline for policymakers to understand the complex interactions and causal configurations of digitalisation factors on sustainability.

The dimeric transporter, EmrE, effluxes polyaromatic cationic drugs in a proton-coupled manner to confer multidrug resistance in bacteria. Although the protein is known to adopt an antiparallel asymmetric topology, its high-resolution drug-bound structure is so far unknown, limiting our understanding of the molecular basis of promiscuous transport. Here we report an experimental structure of drug-bound EmrE in phospholipid bilayers, determined using 19 F and 1 H solid-state NMR and a fluorinated substrate, tetra(4-fluorophenyl) phosphonium (F 4 -TPP + ). The drug-binding site, constrained by 214 protein-substrate distances, is dominated by aromatic residues such as W63 and Y60, but is sufficiently spacious for the tetrahedral drug to reorient at physiological temperature. F 4 -TPP + lies closer to the proton-binding residue E14 in subunit A than in subunit B, explaining the asymmetric protonation of the protein. The structure gives insight into the molecular mechanism of multidrug recognition by EmrE and establishes the basis for future design of substrate inhibitors to combat antibiotic resistance. The small proton-coupled transporter EmrE confers multidrug resistance in bacteria. The structure of drug-bound EmrE in phospholipid bilayers is now determined using solid-state NMR. The structure provides detailed insights into the molecular mechanism of substrate recognition by this transporter.

PurposeBig data analytics (BDA) is recognized as a recent breakthrough technology with potential business impact, however, the roadmap for its successful implementation and the path to exploiting its essential value remains unclear. This study aims to provide a deeper understanding of the enablers facilitating BDA implementation in the banking and financial service sector from the perspective of interdependencies and interrelations.Design/methodology/approachWe use an integrated approach that incorporates Delphi study, interpretive structural modelling (ISM) and fuzzy MICMAC methodology to identify the interactions among enablers that determine the success of BDA implementation. Our integrated approach utilizes experts' domain knowledge and gains a novel insight into the underlying causal relations associated with enablers, linguistic evaluation of the mutual impacts among variables and incorporating two innovative ways for visualizing the results.FindingsOur findings highlight the key role of enabling factors, including technical and skilled workforce, financial support, infrastructure readiness and selecting appropriate big data technologies, that have significant driving impacts on other enablers in a hierarchical model. The results provide reliable, robust and easy to understand insights about the dynamics of BDA implementation in banking and financial service as a whole system while demonstrating potential influences of all interconnected influential factors.Originality/valueThis study explores the key enablers leading to successful BDA implementation in the banking and financial service sector. More importantly, it reveals the interrelationships of factors by calculating driving and dependence degrees. This exploration provides managers with a clear strategic path towards effective BDA implementation.

In this work, graphene and graphene oxide were synthesized by the modified Hummers method. In order to use graphene in dye-sensitized solar cell (DSSC), TiO2–graphene was prepared by a simple chemical method and used in the DSSC photoanode at different concentrations of graphene to investigate DSSC performance. Utilizing the FE-SEM images, it was observed that accumulation of TiO2 nanoparticles disappeared and a different distribution of nanoparticles was formed on the graphene sheet. Moreover, the UV-vis spectra showed that TiO2–graphene nanocomposites can absorb a wide range of light in comparison with pure TiO2. Structural characterization of TiO2–graphene nanocomposites is confirmed by the FT-IR and Raman analysis. The results have shown that in the presence of graphene, the DSSC performance significantly improved by reducing the recombination. In addition, it has been shown that excess graphene concentration is not proper for DSSC performance. The best result for TiO2–graphene nanocomposite was obtained when the concentration of 1.5% graphene was applied.

Background and objectives This systematic review and meta-analysis compares glutamate, glutamine, and GABA levels in cerebrospinal fluid (CSF), blood, and brain tissue between individuals with Alzheimer’s disease (AD) and cognitively unimpaired (CU) controls. Methods We systematically searched PubMed and Web of Science up to February 20, 2025, for studies reporting GABA, glutamate, or glutamine levels in AD and CU controls. Effect sizes were calculated using Hedges’ g, with heterogeneity assessed via I² statistics and publication bias evaluated using funnel plots and Egger’s and Begg’s tests. Results From 14,857 records, 53 studies were included. Glutamate levels were significantly lower in AD brains, including the cortex (SMD = − 0.42; 95% CI [–0.79, − 0.05]; I² = 67.26%; p  = 0.03), hippocampus (SMD = − 0.56; 95% CI [–0.91, − 0.20]; I² = 37.29%; p  < 0.05), and temporal cortex (SMD = − 0.87; 95% CI [–1.52, − 0.23]; I² = 77.60%; p  = 0.01), but not in CSF or blood. Glutamine showed no significant differences in brain regions, CSF, or blood. GABA levels were significantly lower in AD patients across the cortex (SMD = − 0.53; 95% CI [–0.81, − 0.25]; I² = 58.60%; p  < 0.05), CSF (SMD = − 0.38; 95% CI [–0.65, − 0.11]; I² = 0.00%; p  = 0.01), and blood (SMD = − 0.72; 95% CI [–1.08, − 0.37]; I² = 43.18%; p  < 0.05). Conclusion Our findings underscore the potential of targeting glutamatergic and GABAergic systems in AD clinical research. We recommend prioritizing future investigations in earlier disease stages, such as preclinical AD and mild cognitive impairment.

The genome contains several high-affinity non-functional binding sites for transcription factors (TFs) creating a hidden and unexplored layer of gene regulation. We investigate the role of such “decoy sites” in controlling noise (random fluctuations) in the level of a TF that is synthesized in stochastic bursts. Prior studies have assumed that decoy-bound TFs are protected from degradation, and in this case decoys function to buffer noise. Relaxing this assumption to consider arbitrary degradation rates for both bound/unbound TF states, we find rich noise behaviors. For low-affinity decoys, noise in the level of unbound TF always monotonically decreases to the Poisson limit with increasing decoy numbers. In contrast, for high-affinity decoys, noise levels first increase with increasing decoy numbers, before decreasing back to the Poisson limit. Interestingly, while protection of bound TFs from degradation slows the time-scale of fluctuations in the unbound TF levels, the decay of bound TFs leads to faster fluctuations and smaller noise propagation to downstream target proteins. In summary, our analysis reveals stochastic dynamics emerging from nonspecific binding of TFs and highlights the dual role of decoys as attenuators or amplifiers of gene expression noise depending on their binding affinity and stability of the bound TF.

Exosomes derived from solid tumour cells are involved in immune suppression, angiogenesis and metastasis; however, the role of leukaemia‐derived exosomes has less been investigated. Hence, changes in immune response‐related genes and human T cells apoptosis co‐incubated with exosomes isolated from patients' pre‐B cell acute lymphoblastic leukaemia were evaluated in this in vitro study. Vein blood sample was obtained from each newly diagnosed acute lymphoblastic leukaemia (ALL) patient prior any therapy. ALL serum exosomes were isolated by ultrafiltration and characterized using Western blotting and transmission electron microscopy. Exosomes were then co‐incubated with T lymphocytes and the gene expressions, as well as functions of human T cells were quantified by qRT‐PCR. Apoptosis and caspase‐3 and caspase‐9 protein expression were also evaluated by flowcytometry and Western blotting analysis, respectively. Exosomes isolated from ALL patients affected T lymphocytes and elevated the apoptosis. Moreover, these exosomes altered the T cells profile into regulatory type by increasing the expression of FOXP3 and Tregs‐related cytokines, including TGF‐B and IL‐10. The expression level of Th17‐related transcription factors (RoRγt) and interleukins (IL‐17 and IL‐23) decreased after this treatment. According to our findings, exosomes derived from ALL patients' sera carry immunosuppressive molecules, indicating the possible effect of exosomes as liquid biomarkers for cancer staging.