Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessment of the Impacts From the World's Largest Floating Macroalgae Blooms on the Water Clarity at the West Yellow Sea Using MODIS Data (2002–2016)

Water clarity (Secchi disk depth, SDD) is a very important factor for marine ecological environment. The world's largest “green tide” caused by the macroalgal blooms (MABs) of green macroalgae has occurred every summer in the Yellow Sea since 2008. In this study, we first present the effects of MABs on the water clarity in the west Yellow Sea. A regional empirical retrieval algorithm of SDD on the basis of moderate resolution imaging spectroradiometer (MODIS) remote sensing reflectance is evaluated with the field data and satellite reflectance data: the spectral simulation with the end-member reflectance of sea water and macroalgae, and the MODIS Level-2 standard products of the remote sensing reflectance. The results show that the mixture of sea water and macroalgae will lead to decreased water clarity when the SDD is larger than 1.2 m and increased chlorophyll-a, i.e., false values in the standard products for pure sea water which therefore should be used with caution for the regions with large scale of floating macroalgae blooms. The long-term SDD in June and July (2002–2016) over the Yellow Sea is investigated and analyzed with the presence of “green tide. ” The significant decrease in the SDD by 2.6 m and with 12 544 km2 of sea surface in total in July while no pronouncing changes in June suggests that the water clarity in the west Yellow Sea has been strongly affected from the period of 2002–2007 (the pre-MAB phase) to the period of 2008–2016 (the MAB phase).
Cross-Site Reliability of Human Induced Pluripotent stem cell-derived Cardiomyocyte Based Safety Assays Using Microelectrode Arrays: Results from a Blinded CiPA Pilot Study

Abstract Recent in vitro cardiac safety studies demonstrate the ability of human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) to detect electrophysiologic effects of drugs. However, variability contributed by unique approaches, procedures, cell lines, and reagents across laboratories makes comparisons of results difficult, leading to uncertainty about the role of hiPSC-CMs in defining proarrhythmic risk in drug discovery and regulatory submissions. A blinded pilot study was conducted to evaluate the electrophysiologic effects of 8 well-characterized drugs on 4 cardiomyocyte lines using a standardized protocol across 3 microelectrode array platforms (18 individual studies). Drugs were selected to define assay sensitivity of prominent repolarizing currents (E-4031 for IKr, JNJ303 for IKs) and depolarizing currents (nifedipine for ICaL, mexiletine for INa) as well as drugs affecting multichannel block (flecainide, moxifloxacin, quinidine, and ranolazine). Inclusion criteria for final analysis was based on demonstrated sensitivity to IKr block (20% prolongation with E-4031) and L-type calcium current block (20% shortening with nifedipine). Despite differences in baseline characteristics across cardiomyocyte lines, multiple sites, and instrument platforms, 10 of 18 studies demonstrated adequate sensitivity to IKr block with E-4031 and ICaL block with nifedipine for inclusion in the final analysis. Concentration-dependent effects on repolarization were observed with this qualified data set consistent with known ionic mechanisms of single and multichannel blocking drugs. hiPSC-CMs can detect repolarization effects elicited by single and multichannel blocking drugs after defining pharmacologic sensitivity to IKr and ICaL block, supporting further validation efforts using hiPSC-CMs for cardiac safety studies.
LSDSem 2017 Shared Task: The Story Cloze Test

The LSDSem’17 shared task is the Story Cloze Test, a new evaluation for story understanding and script learning. This test provides a system with a four-sentence story and two possible endings, and the system must choose the correct ending to the story. Successful narrative understanding (getting closer to human performance of 100%) requires systems to link various levels of semantics to commonsense knowledge. A total of eight systems participated in the shared task, with a variety of approaches including.
Memory bias for emotional facial expressions in major depression

Sixteen clinically depressed patients and sixteen healthy controls were presented with a set of emotional facial expressions and were asked to identify the emotion portrayed by each face. They, were subsequently given a recognition memory test for these faces. There was no difference between the groups in terms of their ability to identify emotion between from faces. All participants identified emotional expressions more accurately than neutral expressions, with happy expressions being identified most accurately. During the recognition memory phase the depressed patients demonstrated superior memory for sad expressions, and inferior memory for happy expressions, relative to neutral expressions. Conversely, the controls demonstrated superior memory for happy expressions, and inferior memory for sad expressions, relative to neutral expressions. These results are discussed in terms of the cognitive model of depression proposed by Williams, Watts, MacLeod, and Mathews (1997).
Investor Platform Choice: Herding, Platform Attributes, and Regulations

Abstract Online peer-to-peer (P2P) lending, one of the most successful technology-enabled initiatives in the fintech revolution, has drastically changed the way individual investors and borrowers meet and transact. While prior research has found herding among investors at the listing level, such social behavior has been underexplored at a macro, platform level. In this study, we attempt to fill this gap by examining whether subsequent investors follow their predecessors’ actions when choosing which platform to invest, and if so, how various platform attributes and regulations moderate herding behavior. We collected a novel data set from leading platforms in a large P2P lending market. Our baseline analysis reveals that herding exists at the platform level. Using a multilevel model, we further identify several interesting moderators: the investor’s herding behavior is accentuated by platforms’ market share and the cumulative amount funded, but attenuated by their time in operation. Finally, we find that government regulatory events dampen the magnitude of the herding effect, suggesting that more information disclosure and stricter operation standards reduce the value of observational learning. The results from our analysis provide implications for P2P lending investors, platform designers, and policymakers.
Low Dose of Bisphenol A Modulates Ovarian Cancer Gene Expression Profile and Promotes Epithelial to Mesenchymal Transition Via Canonical Wnt Pathway

The xenoestrogen bisphenol A (BPA) is a synthetic endocrine disrupting chemical, having the potential to increase the risk of hormone-dependent ovarian cancer. Thus, a deeper understanding of the molecular and cellular mechanisms is urgently required in the novel cell models of ovarian cancer which express estrogen receptors. To understand the possible mechanisms underlying the effects of BPA, human ovarian adenocarcinoma SKOV3 cells were exposed to BPA (10 or 100 nM) or 0.1% DMSO for 24 h, and then global gene expression profile was determined by high-throughput RNA sequencing. Also, enrichment analysis was carried out to find out relevant functions and pathways within which differentially expressed genes were significantly enriched. Transcriptomic analysis revealed 94 differential expression genes. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses indicated that these genes related to tumorigenesis and metastasis. Further studies were carried out to validate the results of functional annotation, which indicated that BPA (10 and 100 nM) increased migration and invasion as well as induced epithelial to mesenchymal transitions in SKOV3 and A2780 cells. Accordingly, environmentally relevant-dose BPA activated the canonical Wnt signaling pathway. Our study first comprehensively analyzed the possible mechanisms underlying the effects of BPA on ovarian cancer. Environmentally relevant doses of BPA modulated the gene expression profile, promoted epithelial to mesenchymal transition progress via canonical Wnt signaling pathway of ovarian cancer.
Strong Baselines for Neural Semi-Supervised Learning under Domain Shift

Novel neural models have been proposed in recent years for learning under domain shift. Most models, however, only evaluate on a single task, on proprietary datasets, or compare to weak baselines, which makes comparison of models difficult. In this paper, we re-evaluate classic general-purpose bootstrapping approaches in the context of neural networks under domain shifts vs. recent neural approaches and propose a novel multi-task tri-training method that reduces the time and space complexity of classic tri-training. Extensive experiments on two benchmarks for part-of-speech tagging and sentiment analysis are negative: while our novel method establishes a new state-of-the-art for sentiment analysis, it does not fare consistently the best. More importantly, we arrive at the somewhat surprising conclusion that classic tri-training, with some additions, outperforms the state-of-the-art for NLP. Hence classic approaches constitute an important and strong baseline.
A Nonlinear Spectral Method for Core-Periphery Detection in Networks

We derive and analyse a new iterative algorithm for detecting network core--periphery structure. Using techniques in nonlinear Perron-Frobenius theory, we prove global convergence to the unique solution of a relaxed version of a natural discrete optimization problem. On sparse networks, the cost of each iteration scales linearly with the number of nodes, making the algorithm feasible for large-scale problems. We give an alternative interpretation of the algorithm from the perspective of maximum likelihood reordering of a new logistic core--periphery random graph model. This viewpoint also gives a new basis for quantitatively judging a core--periphery detection algorithm. We illustrate the algorithm on a range of synthetic and real networks, and show that it offers advantages over the current state-of-the-art.
Effectiveness of a brief psycho-oncology training program for general nurses: a preliminary study

Many nurses are not confident in management of psychological distress in cancer patients. We developed a brief psycho-oncology training program for general nurses, and explored the usefulness of the program preliminary. Seventy-two nurses in 17 designated cancer hospitals received a 4-h program comprising an e-learning lecture on assessment and management of normal psychological responses to cancer and an onsite workshop, including a role-play exercise and group work. Primary outcomes were changes in self-reported confidence, knowledge and attitudes toward caring for patients with normal psychological response between pre-training and post-training. All outcomes, excepting several aspects of attitude, were significantly improved after training (P < 0.05). Intervention acceptability was good as no participants dropped out and all participants considered the program useful in clinical practice. Further consideration is required to clarify whether the cancer care provided by nurses who received psycho-oncology training is effective to ameliorate psychological distress in cancer patients.
Direct Experience With Nature and the Development of Biological Knowledge

ABSTRACT Research Findings: An emerging consensus is that casual, direct contact with nature influences the development of children’s biological knowledge. Here we review the existing literature on this topic, focusing on the effects of (a) rural versus urban rearing environments and (b) pet ownership and care on children’s biological concepts and reasoning. Although the research is limited, the evidence suggests that these factors positively influence children’s understanding of specific biological phenomena (e.g., internal organ function, biological inheritance) and reasoning patterns (e.g., anthropocentrism, ecological reasoning). Practice or Policy: In the modern context of diminishing outdoor playtime and limited experience with nature, this review highlights the importance of direct exposure to the natural world for children’s conceptual development in the domain of biology.
The effect of human resource management system on employees’ commitment: The mediating role of the AMO model

ABSTRACT:Although the field of Human Resource Management has been extensively studied in the previous decades, still remaining theoretical and methodological questions are yet to be answered. These questions were found to evolve around how to conceptualize and operationalize HRM as well as the mechanism through which HRM affects performance. Consequently, numerous models were proposed to address these questions and among which the AMO model is considered the one that grasp the attention of many scholars. The current study, therefore, aims to address these questions by investigating the mediating effect of employees’ Ability (A), Motivation (M), and Opportunity to participate (O) model on the relationship between HRM system and employees’ affective commitment. An abstract level of HRM system was operationalized and hypothesized to have an indirect effect on employees’ affective commitment via the AMO model. A cross-sectional data was gathered through a questionnaire survey distributed to 800 employees working in the telecommunications sector in Kuwait. Structural equation modelling (SEM) via AMOS22 was used to build and test the hypothesized model. The confirmatory factor analysis (CFA) has revealed that the abstract level HRM system is better conceptualized as having three categories namely, Skill enhancing HRM practices, Motivation enhancing HRM practices, and Empowerment enhancing HRM practices. Moreover, the results have indicated that HRM system significantly and positively affect employees’ ability, motivation, and opportunity to participate. More importantly, the results have supported that the relationship between HRM system and employees’ affective commitment is partially mediated by the AMO model. This study has contributed to the field of Human Resource Management by empirically justifying the critical role that employees’ ability, motivation, and opportunity to participate (AMO) have in increasing employees’ affective commitment. Accordingly, the current study is directing the attention of the telecommunications companies’ managers toward designing their HRM system in ways that increase the three dimensions of the AMO model, which inn turn could result in increased level of commitment.
Recommendations for Replication Research in Special Education

Special education researchers conduct studies that can be considered replications. However, they do not often refer to them as replication studies. The purpose of this article is to consider the potential benefits of conceptualizing special education intervention research within a framework of systematic, conceptual replication. Specifically, we advocate for the value and importance of replication research that includes both closely aligned and distal conceptual replications. We acknowledge the challenges associated with conducting replications in applied school-based research and also provide recommendations for how to design, conduct, and report replication studies in special education research with the goal of supporting the identification of effective practices for individuals with disabilities.
Neural Dynamics Associated with Semantic and Episodic Memory for Faces: Evidence from Multiple Frequency Bands

Prior semantic knowledge facilitates episodic recognition memory for faces. To examine the neural manifestation of the interplay between semantic and episodic memory, we investigated neuroelectric dynamics during the creation (study) and the retrieval (test) of episodic memories for famous and nonfamous faces. Episodic memory effects were evident in several EEG frequency bands: theta (4–8 Hz), alpha (9–13 Hz), and gamma (40–100 Hz). Activity in these bands was differentially modulated by preexisting semantic knowledge and by episodic memory, implicating their different functional roles in memory. More specifically, theta activity and alpha suppression were larger for old compared to new faces at test regardless of fame, but were both larger for famous faces during study. This pattern of selective semantic effects suggests that the theta and alpha responses, which are primarily associated with episodic memory, reflect utilization of semantic information only when it is beneficial for task performance. In contrast, gamma activity decreased between the first (study) and second (test) presentation of a face, but overall was larger for famous than nonfamous faces. Hence, the gamma rhythm seems to be primarily related to activation of preexisting neural representations that may contribute to the formation of new episodic traces. Taken together, these data provide new insights into the complex interaction between semantic and episodic memory for faces and the neural dynamics associated with mnemonic processes.
NBER WORKING PAPER SERIES THE MACROECONOMIC IMPACT OF FINANCIAL AND UNCERTAINTY SHOCKS

The extraordinary events surrounding the Great Recession have cast a considerable doubt on the traditional sources of macroeconomic instability. In their place, economists have singled out financial and uncertainty shocks as potentially important drivers of economic fluctuations. Empirically distinguishing between these two types of shocks, however, is difficult because increases in economic uncertainty are strongly associated with a widening of credit spreads, an indication of a tightening in financial conditions. This paper uses the penalty function approach within the SVAR framework to examine the interaction between financial conditions and economic uncertainty and to trace out the impact of these two types of shocks on the economy. The results indicate that (1) financial shocks have a significant adverse effect on economic outcomes and that such shocks were an important source of cyclical fluctuations since the mid-1980s; (2) uncertainty shocks, especially those implied by uncertainty proxies that do not rely on financial asset prices, are also an important source of macroeconomic disturbances; and (3) uncertainty shocks have an especially negative economic impact in situations where they elicit a concomitant tightening of financial conditions. Evidence suggests that the Great Recession was likely an acute manifestation of the toxic interaction between uncertainty and financial shocks. Dario Caldara Federal Reserve Board 20th Street and Constitution Avenue, NW, Washington, DC 20551 dario.caldara@frb.gov Cristina Fuentes-Albero Federal Reserve Board 20th Street and Constitution Avenue, NW, Washington, DC 20551 Cristina.Fuentes-Albero@frb.gov Simon Gilchrist Department of Economics Boston University 270 Bay State Road Boston, MA 02215 and NBER sgilchri@bu.edu Egon Zakrajšek Division of Monetary Affairs Federal Reserve Board 20th Street & Constitution Avenue, NW Washington, D.C. 20551 egon.zakrajsek@frb.gov
Computer Gaming and Test Scores: Cross-Country Gender Differences Among Teenagers

Using the PISA surveys (2000-2012), this paper explores the relationship between math test scores and everyday computer gaming by gender and for high income and middle income countries. We use two identification strategies in the spirit of an ideal experiment that would reduce computer gaming through limited internet access or through schools alternative demands. We find that everyday computer gaming has positive effects for boys, but negative effects for girls arising mostly in collaborative games suggesting a role for social effects. Computer gaming is becoming the new "swimming upstream" factor in the quest to close the gender gap in math.
Estimating Mixture of Gaussian Processes by Kernel Smoothing

When functional data are not homogenous, for example, when there are multiple classes of functional curves in the dataset, traditional estimation methods may fail. In this article, we propose a new estimation procedure for the mixture of Gaussian processes, to incorporate both functional and inhomogenous properties of the data. Our method can be viewed as a natural extension of high-dimensional normal mixtures. However, the key difference is that smoothed structures are imposed for both the mean and covariance functions. The model is shown to be identifiable, and can be estimated efficiently by a combination of the ideas from expectation-maximization (EM) algorithm, kernel regression, and functional principal component analysis. Our methodology is empirically justified by Monte Carlo simulations and illustrated by an analysis of a supermarket dataset.
Natural Disasters and Demand for Redistribution: Lessons from an Earthquake

Abstract The literature shows that when a society believes that wealth is determined by random “luck” rather than by merit, it demands more redistribution. Adverse shocks, like earthquakes, strengthen the belief that random “bad luck” can frustrate the outcomes achieved with merit. We theoretically illustrate that individuals react to such shocks by raising support for redistribution. We then present evidence of this behavior by exploiting a natural experiment provided by one of the strongest seismic events that occurred in Italy in the last three decades, the L’Aquila earthquake in 2009. We assemble a novel dataset by matching information on the ground acceleration registered throughout the National Strong Motion Network during the earthquake with survey data about individual opinions on redistribution collected a few months later. The empirical analysis illustrates that the intensity of the shakes is associated with subsequent stronger beliefs that, for a society to be fair, income inequalities should be levelled by redistribution.