Effects of Gamification Elements on Crowdsourcing Participation: The Mediating Role of Justice Perceptions

Justice perceptions have been regarded as an important influencing factor for solvers’ (i.e., users who solve tasks on the crowdsourcing platforms) continued participation in crowdsourcing. However, researchers and practitioners still lack of sufficient understanding on the design of crowdsourcing platform that can effectively foster solvers’ justice perceptions. By synthesizing theory of organizational justice and the literature on gamification, we examine the effects of solvers’ gamification element perceptions on their crowdsourcing participation through justice perceptions. Specifically, we propose a research model to explain the effects of three gamification element perceptions (i.e., point, feedback, social network) on solvers’ distributive, interactional, and informational justice perceptions which, in turn, foster their crowdsourcing participation. By collecting survey data from 295 solvers and analyzing the data with the partial least squares-structural equation modeling (PLS-SEM) approach, our study finds that point fosters crowdsourcing participation through distributive and interactional justice. Feedback enhances participation through distributive, interactional and informational justice. While social network strengthens participation via interactional and informational justice. Our study offers significant theoretical contributions and practical implications for the gamified crowdsourcing and organizational justice literatures

Enhancing Large Language Models for Clinical Decision Support by Incorporating Clinical Practice Guidelines

Background Large Language Models (LLMs), enhanced with Clinical Practice Guidelines (CPGs), can significantly improve Clinical Decision Support (CDS). However, methods for incorporating CPGs into LLMs are not well studied. Methods We develop three distinct methods for incorporating CPGs into LLMs: Binary Decision Tree (BDT), Program-Aided Graph Construction (PAGC), and Chain-of-Thought-Few-Shot Prompting (CoT-FSP). To evaluate the effectiveness of the proposed methods, we create a set of synthetic patient descriptions and conduct both automatic and human evaluation of the responses generated by four LLMs: GPT-4, GPT-3.5 Turbo, LLaMA, and PaLM 2. Zero-Shot Prompting (ZSP) was used as the baseline method. We focus on CDS for COVID-19 outpatient treatment as the case study. Results All four LLMs exhibit improved performance when enhanced with CPGs compared to the baseline ZSP. BDT outperformed both CoT-FSP and PAGC in automatic evaluation. All of the proposed methods demonstrated high performance in human evaluation. Conclusion LLMs enhanced with CPGs demonstrate superior performance, as compared to plain LLMs with ZSP, in providing accurate recommendations for COVID-19 outpatient treatment, which also highlights the potential for broader applications beyond the case study

Dynamic functional connectivity analysis with temporal convolutional network for attention deficit/hyperactivity disorder identification

IntroductionDynamic functional connectivity (dFC), which can capture the abnormality of brain activity over time in resting-state functional magnetic resonance imaging (rs-fMRI) data, has a natural advantage in revealing the abnormal mechanism of brain activity in patients with Attention Deficit/Hyperactivity Disorder (ADHD). Several deep learning methods have been proposed to learn dynamic changes from rs-fMRI for FC analysis, and achieved superior performance than those using static FC. However, most existing methods only consider dependencies of two adjacent timestamps, which is limited when the change is related to the course of many timestamps.MethodsIn this paper, we propose a novel Temporal Dependence neural Network (TDNet) for FC representation learning and temporal-dependence relationship tracking from rs-fMRI time series for automated ADHD identification. Specifically, we first partition rs-fMRI time series into a sequence of consecutive and non-overlapping segments. For each segment, we design an FC generation module to learn more discriminative representations to construct dynamic FCs. Then, we employ the Temporal Convolutional Network (TCN) to efficiently capture long-range temporal patterns with dilated convolutions, followed by three fully connected layers for disease prediction.ResultsAs the results, we found that considering the dynamic characteristics of rs-fMRI time series data is beneficial to obtain better diagnostic performance. In addition, dynamic FC networks generated in a data-driven manner are more informative than those constructed by Pearson correlation coefficients.DiscussionWe validate the effectiveness of the proposed approach through extensive experiments on the public ADHD-200 database, and the results demonstrate the superiority of the proposed model over state-of-the-art methods in ADHD identification

Adaptive spatial-temporal neural network for ADHD identification using functional fMRI

Computer aided diagnosis methods play an important role in Attention Deficit Hyperactivity Disorder (ADHD) identification. Dynamic functional connectivity (dFC) analysis has been widely used for ADHD diagnosis based on resting-state functional magnetic resonance imaging (rs-fMRI), which can help capture abnormalities of brain activity. However, most existing dFC-based methods only focus on dependencies between two adjacent timestamps, ignoring global dynamic evolution patterns. Furthermore, the majority of these methods fail to adaptively learn dFCs. In this paper, we propose an adaptive spatial-temporal neural network (ASTNet) comprising three modules for ADHD identification based on rs-fMRI time series. Specifically, we first partition rs-fMRI time series into multiple segments using non-overlapping sliding windows. Then, adaptive functional connectivity generation (AFCG) is used to model spatial relationships among regions-of-interest (ROIs) with adaptive dFCs as input. Finally, we employ a temporal dependency mining (TDM) module which combines local and global branches to capture global temporal dependencies from the spatially-dependent pattern sequences. Experimental results on the ADHD-200 dataset demonstrate the superiority of the proposed ASTNet over competing approaches in automated ADHD classification

Combinational Treatment of Bioscaffolds and Extracellular Vesicles in Spinal Cord Injury

Spinal cord injury (SCI) can result in an irreversible disability due to loss of sensorimotor function below the lesion. Presently, clinical treatments for SCI mainly include surgery, drugs and postoperative rehabilitation. The prospective roles of bioscaffolds and exosomes in several neurological diseases have been reported. Bioscaffolds can reconnect lesion gaps as well as transport cells and bioactive factors, which in turn can improve axonal and functional regeneration. Herein, we explicate the respective roles of bioscaffolds and exosomes in SCI, and elucidate on the usage of combinational therapy involving bioscaffolds and extracellular vesicles (EVs) in improving SCI

LDL receptor related protein 1 is an adverse prognostic biomarker that correlates with stromal remodeling and macrophages infiltration in bladder cancer

IntroductionBladder cancer (BLCA) is a highly heterogeneous disease influenced by the tumor microenvironment, which may affect patients' response to immune checkpoint blockade therapy. Therefore, identifying molecular markers and therapeutic targets to improve treatment is essential. In this study, we aimed to investigate the prognostic significance of LRP1 in BLCA.MethodsWe analyzed TCGA and IMvigor210 cohorts to investigate the relationship of LRP1 with BLCA prognosis. We utilized gene mutation analysis and enrichment to identify LRP1-associated mutated genes and biological processes. Deconvolution algorithms and single-cell analysis were used to understand the tumor-infiltrated cells and biological pathways associated with LRP1 expression. Immunohistochemistry was conducted to validate the bioinformatics analysis.ResultsOur study revealed that LRP1 was an independent risk factor for overall survival in BLCA patients and was associated with clinicopathological features and FGFR3 mutation frequency. Enrichment analysis demonstrated that LRP1 was involved in extracellular matrix remodeling and tumor metabolic processes. Furthermore, the ssGSEA algorithm revealed that LRP1 was positively correlated with the activities of tumor-associated pathways. Our study also found that high LRP1 expression impaired patients' responsiveness to ICB therapy in BLCA, which was predicted by TIDE prediction and validated by IMvigor210 cohort. Immunohistochemistry confirmed the expression of LRP1 in Cancer-Associated Fibroblasts (CAFs) and macrophages in the tumor microenvironment of BLCA.DiscussionOur study suggests that LRP1 may be a potential prognostic biomarker and therapeutic target in BLCA. Further research on LRP1 may improve BLCA precision medicine and enhance the efficacy of immune checkpoint blockade therapy

Spatiotemporal variations and driving factors of reference evapotranspiration in the Yiluo river basin

The variations in the reference evapotranspiration (ET0) are closely related to meteorological factors. The purpose of this study is to explore the relationships between the meteorological factors and the ET0. Based on meteorological data from 26 meteorological stations in the Yiluo River Basin (YLRB) and its surrounding areas from 1958 to 2020, in this study, the temporal and spatial variations and driving factors of the ET0 in the YLRB are investigated. The results are as follows. Spatially, the annual ET0 decreases from the northeast to the southwest in the YLRB. Temporally, the annual ET0 exhibits a fluctuating decreasing trend rather than a monotonic decreasing trend during the entire period. The trend of the ET0 contains two mutation points, in 1972 and 1994. Thus, the research period can be divided into three periods. It is concluded that the variations in the ET0 are the most sensitive to the relative humidity, but the driving factor that contributes the most to the variations in the ET0 is the wind speed. The driving factors are closely related to the rates of relative change of the meteorological factors

Electrical 180o switching of N\'eel vector in spin-splitting antiferromagnet

Antiferromagnetic spintronics have attracted wide attention due to its great potential in constructing ultra-dense and ultra-fast antiferromagnetic memory that suits modern high-performance information technology. The electrical 180o switching of N\'eel vector is a long-term goal for developing electrical-controllable antiferromagnetic memory with opposite N\'eel vectors as binary "0" and "1". However, the state-of-art antiferromagnetic switching mechanisms have long been limited for 90o or 120o switching of N\'eel vector, which unavoidably require multiple writing channels that contradicts ultra-dense integration. Here, we propose a deterministic switching mechanism based on spin-orbit torque with asymmetric energy barrier, and experimentally achieve electrical 180o switching of spin-splitting antiferromagnet Mn5Si3. Such a 180o switching is read out by the N\'eel vector-induced anomalous Hall effect. Based on our writing and readout methods, we fabricate an antiferromagnet device with electrical-controllable high and low resistance states that accomplishes robust write and read cycles. Besides fundamental advance, our work promotes practical spin-splitting antiferromagnetic devices based on spin-splitting antiferromagnet.Comment: 19 pages, 4 figure

Defense Responses to Short-term Hypoxia and Seawater Acidification in the Thick Shell Mussel Mytilus coruscus

The rising anthropogenic atmospheric CO2 results in the reduction of seawater pH, namely ocean acidification (OA). In East China Sea, the largest coastal hypoxic zone was observed in the world. This region is also strongly impacted by ocean acidification as receiving much nutrient from Changjiang and Qiantangjiang, and organisms can experience great short-term natural variability of DO and pH in this area. In order to evaluate the defense responses of marine mussels under this scenario, the thick shell mussel Mytilus coruscus were exposed to three pH/pCO2 levels (7.3/2800 μatm, 7.7/1020 μatm, 8.1/376 μatm) at two dissolved oxygen concentrations (DO, 2.0, 6.0 mg L−1) for 72 h. Results showed that byssus thread parameters, such as the number, diameter, attachment strength and plaque area were reduced by low DO, and shell-closing strength was significantly weaker under both hypoxia and low pH conditions. Expression patterns of genes related to mussel byssus protein (MBP) were affected by hypoxia. Generally, hypoxia reduced MBP1 and MBP7 expressions, but increased MBP13 expression. In conclusion, both hypoxia and low pH induced negative effects on mussel defense responses, with hypoxia being the main driver of change. In addition, significant interactive effects between pH and DO were observed on shell-closing strength. Therefore, the adverse effects induced by hypoxia on the defense of mussels may be aggravated by low pH in the natural environments