Miracle-dz/UniFAR-FTUs huggingface dataset:
Misinformation Exposure and Acceptance: The Role of Information Seeking and Processing

ABSTRACT The present study tests and extends the RISP model (a) by applying the model in the context of COVID-19 in South Korea and (b) by examining the impacts of information seeking and processing on misinformation exposure and acceptance. Based on a survey of 346 Korean adults, this study showed that information avoidance, but not information seeking, was a positive predictor of misinformation exposure. In addition, heuristic processing, but not systematic processing, moderated the relationship between misinformation exposure and misinformation acceptance, such that the relationship between misinformation exposure and misinformation acceptance was stronger among those who showed greater tendency for heuristic processing. In addition, information insufficiency was a negative predictor of both information avoidance and heuristic processing. Theoretical and practical implications are discussed.
A nanotechnological approach in the current therapy of COVID-19: model drug oseltamivir-phosphate loaded PLGA nanoparticles targeted with spike protein binder peptide of SARS-CoV-2

Coronavirus disease 2019 (COVID-19) is today’s most serious epidemic disease threatening the human race. The initial therapeutic approach of SARS-CoV-2 disease is based upon the binding the receptor-binding site of the spike protein to the host cell’s ACE-2 receptor on the plasma membrane. In this study, it is aimed to develop a biocompatible and biodegradable polymeric drug delivery system that is targeted to the relevant receptor binding site and provides controlled drug release. Oseltamivir phosphate (OP) is an orally administered antiviral prodrug for primary therapy of the disease in biochemically activated carboxylate form (oseltamivir carboxylate OC). In the presented study, model drug OP loaded poly(lactic-co-glycolic acid) (PLGA) nanoparticles (NPs) targeted with spike-binding peptide 1 (SBP1) of SARS-CoV-2 were designed to be used as an efficient and prolonged released antiviral drug delivery system. RY, EE, and DL values of the OP-loaded NPs produced by the solvent evaporation method were calculated to be 59.3%, 61.4%, and 26.9%, respectively. The particle size of OP-loaded NPs and OP-loaded NPs targeted with SBP1 peptide were 162.0 ± 11.0 and 226.9 ± 21.4 nm, respectively. While the zeta potential of the produced OP-loaded NPs was achieved negatively −23.9 ± 1.21 mV), the result of the modification with SBP1 peptide this value approached zero as −4.59 ± 0.728 mV. Morphological features of the OP-loaded NPs were evaluated using FEG-SEM. The further characterization and surface modification of the NPs were analyzed by FT-IR. In-vitro release studies of NPs showed that sustained release of OP occurred for two months that fitting the Higuchi kinetic model. By evaluating these outputs, it was reported that surface modification of OP-loaded NPs was significantly effective on characteristics such as size, zeta potential values, surface functionality, and release behavior. The therapeutic model drug-loaded polymeric formulation targeted with a specific peptide may serve as an alternative to more effective and controlled release pharmaceuticals in the treatment of COVID-19 upon an extensive investigation.
MONARCH 2: Subgroup Analysis of Patients Receiving Abemaciclib Plus Fulvestrant as First-Line and Second-Line Therapy for HR+, HER2−-Advanced Breast Cancer

Purpose: In MONARCH 2, abemaciclib plus fulvestrant significantly prolonged progression-free survival (PFS) and overall survival (OS) versus placebo plus fulvestrant in patients with hormone receptor positive (HR+), HER2− advanced breast cancer. This exploratory analysis assessed the efficacy of abemaciclib plus fulvestrant across subgroups of patients receiving study therapy as first- or second-line treatment for metastatic disease. Patients and Methods: Improvements were estimated using Cox models, and a test of interactions of subgroups with treatment was performed. Results: The benefit in PFS [first-line, HR, 0.57; 95% confidence interval (CI), 0.45–0.73; second-line, HR, 0.48; 95% CI, 0.36–0.64] and OS (first-line, HR, 0.85; 95% CI, 0.64–1.14; second-line, HR, 0.66; 95% CI, 0.46–0.94) was observed across both subgroups, consistent with the intent-to-treat (ITT) population. In first-line patients (abemaciclib arm, n = 265; placebo arm, n = 133), the numerically largest effect on PFS and OS was observed in patients with primary resistance to endocrine therapy (ET; PFS, HR, 0.40; 95% CI, 0.26–0.63; OS, HR, 0.58; 95% CI, 0.35–0.97) and visceral disease (PFS, HR, 0.54; 95% CI, 0.39–0.73; OS, HR, 0.82; 95% CI, 0.58–1.20). In second-line patients (abemaciclib arm, n = 170; placebo arm, n = 86), a numerical benefit in PFS and OS was observed across primary and secondary ET resistance, with numerically more pronounced effects observed in patients with visceral disease (PFS, HR, 0.39; 95% CI, 0.27–0.57; OS, HR, 0.51; 95% CI, 0.33–0.81). Prolongation of time to second disease progression, time to chemotherapy, and chemotherapy-free survival was observed in both subgroups. Conclusions: Consistent with the ITT population, a benefit in PFS and OS was observed across the first- and second-line subgroups in MONARCH 2.
Effect of multidimensional comprehensive intervention on medication compliance, social function and incidence of MACE in patients undergoing PCI.

OBJECTIVE To analyze the effect of multidimensional comprehensive intervention on medication compliance, social function and incidence of major adverse cardiovascular events (MACE) in patients undergoing percutaneous coronary intervention (PCI). METHODS Ninety-eight patients with coronary heart disease (CHD) who underwent PCI in our hospital were selected and divided into the regular group (n=46, receiving regular nursing intervention) and the comprehensive group (n=52, receiving multidimensional comprehensive nursing intervention) according to the different nursing intervention methods. The medication compliance, social function, quality of life, and incidence of MACE were compared between the two groups. RESULTS The comprehensive group showed significantly higher rates of taking medication on time, taking medication according to the proper amount, taking medication at the recomended times, no increase or decrease in the amount of medication, and taking medication without interruption than the regular group (P < 0.05). The comprehensive group exhibited significantly higher scores of medication compliance than the regular group (P < 0.05). The Social Disability Screening Schedule (SDSS) scores of both groups during intervention for 8 weeks were lower than those before intervention and after intervention for 2 and 4 weeks (P < 0.05). The SDSS scores of intervention for 2, 4, and 8 weeks in the comprehensive group were significantly lower than that in the regular group (P < 0.05). After intervention, the comprehensive group showed significantly higher scores of physiological function, psychological function, cognitive function, emotional function, role function, and total quality of life than the regular group (P < 0.05). The incidence of MACE in the comprehensive group was significantly lower than that in the regular group (P < 0.05). CONCLUSION The use of multidimensional comprehensive intervention for patients undergoing PCI can effectively improve patients' medication compliance, social function and quality of life, and reduce the incidence of MACE.
Health‐related quality of life, uncertainty and coping strategies in solid organ transplant recipients during shielding for the COVID‐19 pandemic

Strict isolation of vulnerable individuals has been a strategy implemented by authorities to protect people from COVID‐19. Our objective was to investigate health‐related quality of life (HRQoL), uncertainty and coping behaviours in solid organ transplant (SOT) recipients during the COVID‐19 pandemic. A cross‐sectional survey of adult SOT recipients undergoing follow‐up at our institution was performed. Perceived health status, uncertainty and coping strategies were assessed using the EQ‐5D‐5L, Short‐form Mishel Uncertainty in Illness Scale (SF‐MUIS) and Brief Cope, respectively. Interactions with COVID‐19 risk perception, access to health care, demographic and clinical variables were assessed. The survey was completed by 826 of 3839 (21.5%) invited participants. Overall, low levels of uncertainty in illness were reported, and acceptance was the major coping strategy (92%). Coping by acceptance, feeling protected, self‐perceived susceptibility to COVID‐19 were associated with lower levels of uncertainty. Health status index scores were significantly lower for those with mental health illness, compromised access to health care, a perceived high risk of severe COVID‐19 infection and higher levels of uncertainty. A history of mental health illness, risk perceptions, restricted healthcare access, uncertainty and coping strategies was associated with poorer HRQoL in SOT recipients during strict isolation. These findings may allow identification of strategies to improve HRQoL in SOT recipients during the pandemic.
‘A Garuda in my Heart, a Tiger in my Stomach’: Brokering an Informal Economic Border Enclave in the Borneo Highlands

This article studies the life histories of two broker figures from the Borneo highlands to explore the changing borderland dynamics of Krayan, an Indonesian region bordering on Malaysia. Rather than a lagging or rebellious periphery, the region can best be understood as a site of experimentation. Krayan’s history is strongly shaped by shifting patterns of infrastructural connectivity and mobile networks. This, I posit, has resulted in an indigenous version of an informal special economic zone which is economically integrated with Malaysia but leverages political clout in Indonesia. The regular cross-border caravan of pick-up trucks has started to receive government attention. The highland elite now seeks to formalise Krayan’s constellation into a special administrative status by showcasing its links to Malaysia, thus leveraging the governmental pride and anxiety vested in the border. Rather than evading the state, I argue that this manoeuvring centres on improving the terms of opting in.
Stress-Generated Free Radicals Detected by Electron Paramagnetic Resonance Spectroscopy with Nitrone Spin Trap in Vicia faba Root

Electron paramagnetic resonance (EPR) spectroscopy is a promising technique for detection and quantification of short-lived free radicals in biological systems. In the present investigation, root of Viciafaba seedlings were subjected to 0.5 mM (T1) and 1 mM (T2) sodium azide (NaN3) in hydroponic medium. The stress-generated free radicals (superoxide and hydroxyl free radicals) in the root tips of the seedlings were analyzed by EPR spectroscopy using 5,5-dimethyl-1-pyrroline-N-oxide (DMPO) nitrone spin. Single cell gel electrophoresis (comet assay) and mitotic index along with the spectrophotometric measurement of the two major antioxidant enzymes, namely, superoxide dismutase (SOD) and catalase (CAT), in combination with analysis of lipid peroxidation product, malondialdehyde (MDA), were done to validate the EPR results. Data obtained from comet assay of root tip cells revealed significant increase in the olive moment in T1 and T2 as compared to control. Decreased mitotic index in T2 (60.63 ± 7.74%) as compared to control (88.23 ± 1.01%) in these cells as a consequence of increased reactive oxygen species (ROS) was depicted. Furthermore, the decrease in the activity of major antioxidant enzymes, SOD and CAT justified the intensified DMPO-OH specific peaks in T2 as compared to control obtained in the EPR spectra. The results establish that EPR spectroscopy can be used successfully at room temperature to monitor changes in the production of free radicals (combined superoxide and hydroxyl free radicals) in plant tissues using DMPO as a spin trap.
Rethinking Decision Support Under Conditions of Irreducible Uncertainty: Co-Designing a Serious Game to Navigate Baltic Sea Nutrient Enrichment

Abstract Science-informed, reductionist policy has systematically failed to address wicked situations. Such situations are highly interconnected and unpredictable. As a consequence, the implementation of so-called desirable interventions can lead to the export of vulnerabilities within and across different societal domains, sectors, intersections and scales. Systemic practice is an emerging field, and highlights the need to enrich scientific inquiry and policy actions through action learning with an “extended peer community'' as a means to navigate wicked situations. In this paper, we report on the potential of game co-design as a systemic practice to improve the situation of Baltic Sea nutrient enrichment. Findings from water catchments in Finland, Sweden and Poland suggest that the co-design of serious games can both enhance the comprehension of wicked situations, and foster self-organized concerted action without imposing a convergence of perspectives amongst diverse stakeholders.
On the correction of temperatures derived from meteor wind radars due to geomagnetic activity

Abstract Radars used to observe meteor trails in the mesosphere deliver information on winds and temperature. Use of these radars is becoming a standard method for determining mesospheric dynamics and temperatures worldwide due to relatively low costs and ease of deployment. However, recent studies have revealed that temperatures may be overestimated in conditions such as high geomagnetic activity. The effect is thought to be most prevalent at high latitude, although this is not yet proven. Here, we demonstrate how temperatures might be corrected for geomagnetic effects; the demonstration is for a particular geographic location (Svalbard, 78°N, 16°E) because it is local geomagnetic disturbances that affects local temperature measurements, therefore requiring co-located instruments. We see that summer temperatures require a correction (reduction) of a few Kelvin, but winter estimates are more accurate.
Quantifying and Addressing Ranking Disparity in Human-Powered Data Acquisition

Algorithmic bias has been identified as a key challenge in many AI applications. One major source of bias is the data used to build these applications. For instance, many AI applications rely on human users to generate training data. The generated data might be biased if the data acquisition process is skewed towards certain groups of people based on say gender, ethnicity or location. This typically happens as a result of a hidden association between the people's qualifications for data acquisition and the people's protected attributes. In this paper, we study how to unveil and address disparity in data acquisition. We focus on the case where the data acquisition process involves ranking of people and we define disparity as the unbalanced targeting of people by the data acquisition process. To quantify disparity, we formulate an optimization problem that partitions people on their protected attributes, computes the qualifications of people in each partition, and finds the partitioning that exhibits the highest disparity in qualifications. Due to the combinatorial nature of our problem, we devise heuristics to navigate the space of partitions. We also discuss how to address disparity between partitions. We conduct a series of experiments on real and simulated datasets that demonstrate that our proposed approach is successful in quantifying and addressing ranking disparity in human-powered data acquisition.
Just Let Me Do My Job!

This study seeks to broaden our understanding of the popular, yet under-researched, concept of micromanagement in the IT workforce by exploring IT professionals' trust in the competence of their supervisor as an antecedent to their perceptions of being micromanaged. The study also explores whether felt responsibility is the mechanism via which micromanagement negatively affects IT professionals' job satisfaction and organizational commitment, both proximal factors of turnover. These relationships are explored under the aegis of the Management Control Systems models, leader-member exchange theory, and the job characteristics model. Results indicate that trust in supervisor competence is a significant antecedent to IT professionals' perceptions of being micromanaged and that felt responsibility fully mediates the relationship between micromanagement and organizational commitment. The findings underscore the importance of building IT professionals' trust in their supervisor's competence and suggest that organizations proactively provide early intervention to negate the potential adverse impact on organizational outcomes.
MPCSL - A Modular Pipeline for Causal Structure Learning

The examination of causal structures is crucial for data scientists in a variety of machine learning application scenarios. In recent years, the corresponding interest in methods of causal structure learning has led to a wide spectrum of independent implementations, each having specific accuracy characteristics and introducing implementation-specific overhead in the runtime. Hence, considering a selection of algorithms or different implementations in different programming languages utilizing different hardware setups becomes a tedious manual task with high setup costs. Consequently, a tool that enables to plug in existing methods from different libraries into a single system to compare and evaluate the results is substantial support for data scientists in their research efforts. In this work, we propose an architectural blueprint of a pipeline for causal structure learning and outline our reference implementation MPCSL that addresses the requirements towards platform independence and modularity while ensuring the comparability and reproducibility of experiments. Moreover, we demonstrate the capabilities of MPCSL within a case study, where we evaluate existing implementations of the well-known PC-Algorithm concerning their runtime performance characteristics.
DeGNN: Improving Graph Neural Networks with Graph Decomposition

Mining from graph-structured data is an integral component of graph data management. A recent trending technique, graph convolutional network (GCN), has gained momentum in the graph mining field, and plays an essential part in numerous graph-related tasks. Although the emerging GCN optimization techniques bring improvements to specific scenarios, they perform diversely in different applications and introduce many trial-and-error costs for practitioners. Moreover, existing GCN models often suffer from oversmoothing problem. Besides, the entanglement of various graph patterns could lead to non-robustness and harm the final performance of GCNs. In this work, we propose a simple yet efficient graph decomposition approach to improve the performance of general graph neural networks. We first empirically study existing graph decomposition methods and propose an automatic connectivity-ware graph decomposition algorithm, DeGNN. To provide a theoretical explanation, we then characterize GCN from the information-theoretic perspective and show that under certain conditions, the mutual information between the output after l layers and the input of GCN converges to 0 exponentially with respect to l. On the other hand, we show that graph decomposition can potentially weaken the condition of such convergence rate, alleviating the information loss when GCN becomes deeper. Extensive experiments on various academic benchmarks and real-world production datasets demonstrate that graph decomposition generally boosts the performance of GNN models. Moreover, our proposed solution DeGNN achieves state-of-the-art performances on almost all these tasks.
SSML: Self-Supervised Meta-Learner for En Route Travel Time Estimation at Baidu Maps

Travel time estimation (TTE) is one of the most critical modules at Baidu Maps, which plays a vital role in intelligent transportation services such as route planning and navigation. During the driving en route, the navigation system of Baidu Maps can provide real-time estimations on when a user will arrive at the destination. It automatically recalculates and updates the remaining travel time from the driver's current position to the destination (hereafter referred to as remaining route) every few minutes. The previously deployed TTE model at Baidu Maps, i.e., ConSTGAT, takes the remaining route as well as the current time as input and provides the corresponding estimated time of arrival. However, it ignores the route that has been already traveled from the origin to the driver's current position (hereafter referred to as traveled route), which could contribute to improving the accuracy of time estimation. In this work, we believe that the traveled route conveys valuable evidence that could facilitate the modeling of driving preference and take that into consideration for the task of en route travel time estimation (ER-TTE). This task is non-trivial because it requires adapting fast to a user's driving preference using a few observed behaviors in the traveled route. To this end, we frame ER-TTE as a few-shot learning problem and consider the observed behaviors in the traveled route as training examples while the future behaviors in the remaining route as test examples. To tackle the few-shot learning problem, we propose a novel model-based meta-learning approach, called SSML, to learn the meta-knowledge so as to fast adapt to a user's driving preference and improve the time estimation of the remaining route. SSML leverages the technique of self-supervised learning, which is equivalent to generating a significant number of synthetic learning tasks, to further improve the performance. Extensive offline tests conducted on large-scale real-world datasets collected from Baidu Maps demonstrate the superiority of SSML. The online tests before deploying in production were successfully performed, which confirms the practical applicability of SSML.
Removing Disparate Impact on Model Accuracy in Differentially Private Stochastic Gradient Descent

In differentially private stochastic gradient descent (DPSGD), gradient clipping and random noise addition disproportionately affect underrepresented and complex classes and subgroups. As a consequence, DPSGD has disparate impact: the accuracy of a model trained using DPSGD tends to decrease more on these classes and subgroups vs. the original, non-private model. If the original model is unfair in the sense that its accuracy is not the same across all subgroups, DPSGD exacerbates this unfairness. In this work, we study the inequality in utility loss due to differential privacy, which compares the changes in prediction accuracy w.r. t. each group between the private model and the non-private model. We analyze the cost of privacy w.r. t. each group and explain how the group sample size along with other factors is related to the privacy impact on group accuracy. Furthermore, we propose a modified DPSGD algorithm, called DPSGD-F, to achieve differential privacy, equal costs of differential privacy, and good utility. DPSGD-F adaptively adjusts the contribution of samples in a group depending on the group clipping bias such that differential privacy has no disparate impact on group accuracy. Our experimental evaluation shows the effectiveness of our removal algorithm on achieving equal costs of differential privacy with satisfactory utility.