Miracle-dz/UniFAR-FTUs huggingface dataset:
The Role of Expectancy Violation in Extinction Learning: A Two-Day Online Fear Conditioning Study

Background Exposure therapy is at the core of the treatment of pathological anxiety. While the inhibitory learning model proposes a framework for the mechanisms underlying exposure therapy, in particular expectancy violation, causal evidence for its assumptions remains elusive. Therefore, the aim of the current study was to provide evidence for the influence of expectancy violation on extinction retention by manipulating the magnitude of expectancy violation during extinction learning. Method In total, 101 individuals completed a web-based fear conditioning protocol, consisting of a fear acquisition and extinction phase, as well as a spontaneous recovery and fear reinstatement test 24h later. To experimentally manipulate expectancy violation, participants were presented only with states of the conditioned stimulus that either weakly or strongly predicted the aversive outcome. Consequently, the absence of any aversive outcomes in the extinction phase resulted in low or high expectancy violation, respectively. Results We found successful fear acquisition and manipulation of expectancy violation, which was associated with reduced threat ratings for the high compared to the low expectancy violation group directly after extinction learning. On Day 2, inhibitory CS-noUS associations could be retrieved for expectancy ratings, whereas there were no substantial group differences for threat ratings. Conclusion These findings indicate that the magnitude of expectancy violation is related to the retrieval of conscious threat expectancies, but it is unclear how these changes translate to affective components (i.e., threat ratings) of the fear response and to symptoms of pathological anxiety.
Estimating the effect of currency substitution on exchange rate volatility: Evidence from Ghana

Abstract This paper investigates the impact of currency substitution on exchange rate volatility using monthly data from January 1990 to May 2019. The paper applies the exponential generalized autoregressive conditional heteroscedastic in mean (EGARCH-M) model as the estimation technique. The results reveal that currency substitution has a significant positive impact on exchange rate volatility. The paper also confirms the existence of leverage effects in the exchange rate volatility. It is also revealed that negative shocks are found to have greater effect than positive shocks. Furthermore, the results indicate that inflation targeting framework has a significant positive impact on exchange rate volatility. Based on the findings and discussion, the paper concludes that currency substitution increases exchange rate volatility in Ghana. Given the findings, vital policy implications aimed at reducing or eliminating volatility in exchange rate have been provided for policy consideration.
Antecedents and consequences of interpersonal electronic surveillance in romantic relationships

ABSTRACT The proliferation of communication technologies and the growth of social networking sites provide new avenues for electronic surveillance of romantic relationship partners. This study evaluated attachment dimensions and romantic jealousy as complementary explanations for interpersonal electronic surveillance in romantic relationships. In addition, this study investigated the moderating effect of dyadic trust on the link between interpersonal electronic surveillance and relationship satisfaction. Three hundred and twenty-one individuals currently involved in romantic relationships with active social media accounts completed measures assessing attachment anxiety and avoidance; cognitive and emotional jealousy; interpersonal electronic surveillance; dyadic trust; and relationship satisfaction. Results indicated partial support for a statistical model in which jealousy mediated the associations between attachment and interpersonal electronic surveillance. In addition, findings supported the moderating role of dyadic trust on the association between interpersonal electronic surveillance and relationship satisfaction.
Spatial analysis of flash flood and Drought impact from Climate Change in Phongsaly District, Phogsaly Province, by using Geo-Informatics Technology and Modelling

A method for predicting the water resource in the region in the future to be used as a basis for mitigating the consequences is to study how climate change affects hydrology. The purpose of this study is to i). choose a global climate model that is suitable for the area, ii). rainfall run-off modelling, iii). drought and flood hazard index map. The SSP-126, SSP-245, and SSP-585 scenarios were chosen as the most appropriate global climate model among the four institutes, with efficiency criteria using the coefficient of Nash-Sutcliffe and Kling-Gupta and then calibrate the data with the Bias Correction Linear Scaling method which divides the analysis period into 2 periods for Near-Future and Far-Future from analyzing Rainfall Run-off Modeling from Rainfall Concentration 1-hours, 3-hours and 6-hours. It was found that the SSP-585 scenario in the Rainfall Concentration 1-hours model has the most dangerous area for very high risk until the end of the 21st century. For the analysis of drought indices SPI_1, SPI_3 and SPI_6 in Near-Future, it was found that the frequency of droughts is increasing according to the worst scenario, the scenario with the most drought is SSP-585 and in the Far-Future, the frequency of drought is decreasing according to the worst scenario, the scenario with the most drought is SSP-126.
A new efficient algorithm for determining all structurally different realizations of kinetic systems

In this paper we present a novel algorithm for computing all possible reaction graph structures representing linearly conjugate realizations of a polynomial kinetic system assuming a fixed set of complexes. The computation is based on the repeated application of linear programming steps. The correctness of the method is formally proved. The approach is compared to the only solution known from the literature using two examples, and it is shown that the number of optimization steps and the overall execution time are significantly lower in the case of the proposed new method.
Influence of cytostatics on relative gene expression in redox status, apoptosis and migration colorectal carcinoma model system

Colorectal cancer is one of the leading causes of mortality worldwide. It is the most common malignancy and there is a need for new approaches in therapies. Surgery, radiation and chemotherapy are the key components of colon cancer treatment. Besides common chemotherapy, alternative therapies are being studied to increase treatment effectiveness and reduce side effects. In this article, colorectal carcinoma cells were treated with chemotherapeutics and relative gene expression was investigated for the genes coding cytoskeleton proteins: CDH1, CTNNB1 and CDH2; for redox status genes: GPX1, GPX2, GPX3, GPX4, TXNRD1, GSTP1, NFE2L2, NFKB1, HIF1A; and for apoptosis genes: CASP3, CASP8, CASP9, FAS, BCL-2 and BAX. The results of our research showed that some concentrations of chemotherapeutics increased the expression of certain genes. Molecular alterations that lead to colorectal cancer can determine appropriate and effective treatment - chemotherapeutics, as well as the design of direct therapeutic targets.
Kinematic Contribution to Javelin Velocity at Different Run-Up Velocities in Male Athletes

In javelin training, many athletes improve their throwing technique by throwing from a slower run-up velocity than in competitions. However, whether the acquisition of javelin velocity in throwing from a slower run-up velocity is the same as in full run-up throwing is unclear. The purpose of this study was to clarify the differences in the contribution of each movement to the javelin velocity caused by changes in the run-up velocity within an individual. Twelve collegiate male javelin throwers were included in this study. Athletes performed two types of throws: one-cross throwing (Cross) and full run-up throwing (Run). The coordinates of reflective markers attached to the thrower’s body and the javelin were recorded using an optical motion capture system. The percentage contribution of each joint movement to the javelin velocity was calculated and compared between Cross and Run. Cross had a lower contribution of trunk forward lean to forward and upward javelin velocities compared to Run. On the other hand, Cross had a higher contribution of trunk counter-clockwise rotation to forward and upward javelin velocities than Run. These results suggest that as the velocity of run-up changes within an individual, the acquisition of javelin velocity also changes.
Intervention decision-making in MAV/UAV cooperative engagement based on human factors engineering

Aiming at the intervention decision-making problem in manned/unmanned aerial vehicle (MAV/UAV) cooperative engagement, this paper carries out a research on allocation strategy of emergency discretion based on human factors engineering (HFE). Firstly, based on the brief review of research status of HFE, it gives structural description to emergency in the process of cooperative engagement and analyzes intervention of commanders. After that, constraint conditions of intervention decision-making of commanders based on HFE (IDMCBHFE) are given, and the mathematical model, which takes the overall efficiency value of handling emergencies as the objective function, is established. Then, through combining K-best and variable neighborhood search (VNS) algorithm, a K-best optimization variable neighborhood search mixed algorithm (KBOVNSMA) is designed to solve the model. Finally, through three groups of simulation experiments, effectiveness and superiority of the proposed algorithm are verified.
Distributed Unknown Input Observer

In this article, a distributed observer is designed for the state reconstruction of continuous-time linear dynamical systems with unknown inputs. Specifically, a group of sensor nodes are employed to measure the output of such a system. Due to its limited measurement, a single node may fail to reconstruct the full state. By acting on a consensus cooperative strategy, however, each node can produce an accurate state estimate, regardless of the specific input signal. The attractive performance of distributed unknown input observer relies on nothing but common neighboring communication, as well as an adequate local measurement that overlaps the effect of the unknown input along system dynamics. Correspondingly, the required network connectivity and rank conditions are given, with their linkages to the classic result being discussed.
Connecting the science and practice of implementation – applying the lens of context to inform study design in implementation research

The saying “horses for courses” refers to the idea that different people and things possess different skills or qualities that are appropriate in different situations. In this paper, we apply the analogy of “horses for courses” to stimulate a debate about how and why we need to get better at selecting appropriate implementation research methods that take account of the context in which implementation occurs. To ensure that implementation research achieves its intended purpose of enhancing the uptake of research-informed evidence in policy and practice, we start from a position that implementation research should be explicitly connected to implementation practice. Building on our collective experience as implementation researchers, implementation practitioners (users of implementation research), implementation facilitators and implementation educators and subsequent deliberations with an international, inter-disciplinary group involved in practising and studying implementation, we present a discussion paper with practical suggestions that aim to inform more practice-relevant implementation research.
Bayesian Ensemble of Regression Trees for Multinomial Probit and Quantile Regression

This dissertation proposes multinomial probit Bayesian additive regression trees (MPBART), ordered multiclass Bayesian additive classification trees (O-MBACT) and Bayesian quantile additive regression trees (BayesQArt) as extensions of BART Bayesian additive regression trees for tackling multinomial choice, multiclass classification, ordinal regression and quantile regression problems. The proposed models exhibit very good predictive performances. In particular, ranking among the top performing procedures when non-linear relationships exist between the response and the predictors. The proposed procedures can readily be applied on data sets with the number of predictors larger than the number of observations. MPBART is sufficiently flexible to allow inclusion of predictors that describe the observed units as well as the available choice alternatives and it can also be used as a general multiclass classification procedure. Through two simulation studies and four real data examples, we show that MPBART exhibits very good out-ofsample predictive performance in comparison to other discrete choice and multiclass classification methods. To implement MPBART, the R package mpbart is freely available from CRAN repositories. When ordered gradation is exhibited by a multinomial response, ordinal regression is an appealing framework. Ensemble of trees models, while widely used for binary classification, multiclass classification and continuous response regression, have not been extensively applied to solve ordinal regression problems. This work fills this void with Bayesian sum of regression trees. The predictive performance of our ordered Bayesian ensemble of trees model is illustrated through simulation studies and real v data applications. Ensemble of regression trees have become popular statistical tools for the estimation of conditional mean given a set of predictors. However, quantile regression trees and their ensembles have not yet garnered much attention despite the increasing popularity of the linear quantile regression model. This work proposes a Bayesian quantile additive regression trees model that shows very good predictive performance illustrated using simulation studies and real data applications. Further extension to tackle binary classification problems is also considered.
Homocysteine and Glaucoma

Elevated levels of homocysteine (Hcy), a non-proteinogenic amino acid, may lead to a host of manifestations across the biological systems, particularly the nervous system. Defects in Hcy metabolism have been associated with many neurodegenerative diseases including glaucoma, i.e., the leading cause of blindness. However, the pathophysiology of elevated Hcy and its eligibility as a risk factor for glaucoma remain unclear. We aimed to provide a comprehensive review of the relationship between elevated Hcy levels and glaucoma. Through a systemic search of the PubMed and Google Scholar databases, we found that elevated Hcy might play an important role in the pathogenesis of glaucoma. Further research will be necessary to help clarify the specific contribution of elevated Hcy in the pathogenesis of glaucoma. A discovery and conceptual understanding of Hcy-associated glaucoma could be the keys to providing better therapeutic treatment, if not prophylactic treatment, for this disease.
Semi-parametric Bayesian Forecasting with an Application to Stochastic Volatility

We propose a new and highly exible Bayesian sampling algorithm for nonlinear state space models under nonparametric distributions. The estimation framework combines a particle filtering and smoothing algorithm for the latent process with a Dirichlet process mixture model for the error term of the observable variables. In particular, we overcome the problem of constraining the models by transformations or the need for conjugate distributions. We use the Chinese restaurant representation of the Dirichlet process mixture, which allows for a parsimonious and generally applicable sampling algorithm. Thus, our estimation algorithm combines a pseudo marginal Metropolis Hastings scheme with a marginalized hierarchical semiparametric model. We test our approach for several nested model specifications using simulated data and provide density forecasts. Furthermore, we carry out a real data example using S&P 500 returns.
When the gig isn’t up: The importance (and relevance) of trust on gig workers’ performance and commitment

Millions of employees are now classified as gig workers – a subset of contingent employees with alternative employment arrangements. This type of work arrangement can be beneficial for both managers (e.g. cost savings, specialised skillsets) and employees (e.g. work preferences such as flexibility). Yet little research has addressed how trust for a manager might factor into gig workers’ performance when compared to traditional employees, perhaps because research has implied that trust is irrelevant to gig workers. We test this prediction across four studies to show that low trust is a double-edged sword with unfavourable and favourable outcomes. On the one hand, we find that less trust in the manager leads to lower performance and commitment among gig workers. Yet, on the other, we find that lower levels of trust help to offset or mitigate the harmful outcomes of trust violations, or unexpected, negative workplace events. Our findings highlight the important role of trust in this context of gig versus traditional workers.
Characterization of a barley (Hordeum vulgare L.) mutant with multiple stem nodes and spikes and dwarf (msnsd) and fine-mapping of its causal gene

Introduction Multiple nodes and dwarf mutants in barley are a valuable resource for identifying genes that control shoot branching, vegetative growth and development. Methods In this study, physiological, microscopic and genetic analysis were conducted to characterize and fine-map the underling gene of a barley mutant with Multiple Stem Nodes and Spikes and Dwarf (msnsd), which was selected from EMS- and 60Co-treated barley cv. Edamai 934. Results and discussion The msnsd mutant had more stem nodes, lower plant height and a shorter plastochron than Edamai 934. Moreover, the mutant had two or more spikes on each tiller. Microscopic analysis showed that the dwarf phenotype of msnsd resulted from reduced cell lengths and cell numbers in the stem. Further physiological analysis showed that msnsd was GA3-deficient, with its plant height increasing after external GA3 application. Genetic analysis revealed that a single recessive nuclear gene, namely, HvMSNSD, controlled the msnsd phenotype. Using a segregating population derived from Harrington and the msnsd mutant, HvMSNSD was fine-mapped on chromosome 5H in a 200 kb interval using bulked segregant analysis (BSA) coupled with RNA-sequencing (BSR-seq), with a C-T substitution in the exon of HvTCP25 co-segregating with the msnsd phenotype. RNA-seq analysis showed that a gene encoding gibberellin 2-oxidase 8, a negative regulator of GA biosynthesis, was upregulated in the msnsd mutant. Several known genes related to inflorescence development that were also upregulated and enriched in the msnsd mutant. Collectively, we propose that HvMSNSD regulates the plastochron and morphology of reproductive organs, likely by coordinating GA homeostasis and changed expression of floral development related genes in barley. This study offers valuable insights into the molecular regulation of barley plant architecture and inflorescence development.
Rare variants confer shared susceptibility to gastrointestinal tract cancer risk

Background Cancers arising within the gastrointestinal tract are complex disorders involving genetic events that cause the conversion of normal tissue to premalignant lesions and malignancy. Shared genetic features are reported in epithelial-based gastrointestinal cancers which indicate common susceptibility among this group of malignancies. In addition, the contribution of rare variants may constitute parts of genetic susceptibility. Methods A cross-cancer analysis of 38,171 shared rare genetic variants from genome-wide association assays was conducted, which included data from 3,194 cases and 1,455 controls across three cancer sites (esophageal, gastric and colorectal). The SNP-level association was performed by multivariate logistic regression analyses for single cancer, followed by association analysis for SubSETs (ASSET) to adjust the bias of overlapping controls. Gene-level analyses were conducted by SKAT-O, with multiple comparison adjustments by false discovery rate (FDR). Based on the significant genes indicated by SKATO analysis, pathways analysis was conducted using Gene Ontology (GO), the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome databases. Results Meta-analysis in three gastrointestinal (GI) cancers identified 13 novel susceptibility loci that reached genome-wide significance (P ASSET< 5×10-8). SKAT-O analysis revealed EXOC6, LRP5L and MIR1263/LINC01324 to be significant genes shared by GI cancers (P adj<0.05, P FDR<0.05). Furthermore, GO pathway analysis identified significant enrichment of synaptic transmission and neuron development pathways shared by all three cancer types. Conclusion Rare variants and the corresponding genes potentially contribute to shared susceptibility in different GI cancer types. The discovery of these novel variants and genes offers new insights for the carcinogenic mechanisms and missing heritability of GI cancers.
Negative expressions are shared more on Twitter for public figures than for ordinary users

Abstract Social media users tend to produce content that contains more positive than negative emotional language. However, negative emotional language is more likely to be shared. To understand why, research has thus far focused on psychological processes associated with tweets' content. In the current study, we investigate if the content producer influences the extent to which their negative content is shared. More specifically, we focus on a group of users that are central to the diffusion of content on social media—public figures. We found that an increase in negativity was associated with a stronger increase in sharing for public figures compared to ordinary users. This effect was explained by two user characteristics, the number of followers and thus the strength of ties and the proportion of political tweets. The results shed light on whose negativity is most viral, allowing future research to develop interventions aimed at mitigating overexposure to negative content.