Miracle-dz/UniFAR-FTUs huggingface dataset:
Disentangling the origins of confidence in speeded perceptual judgments through multimodal imaging

Significance Our sense of confidence stems from the evidence leading to those decisions. This has made the study of confidence in isolation from decisional processes difficult. We devised a task in which participants rate their confidence in their own decisions or in observed decisions that are thus unrelated to decisional processes. We propose a computational account of the mechanisms underlying confidence in both conditions and reproduce participants’ behavior. Furthermore, we show that activity in the inferior frontal gyrus relates to confidence, even when confidence is unrelated to decisional processes. Activity in the frontal pole and insula was more related to confidence when related to participants’ own decisions. Our study provides evidence on the mechanisms and neural implementation of confidence. The human capacity to compute the likelihood that a decision is correct—known as metacognition—has proven difficult to study in isolation as it usually cooccurs with decision making. Here, we isolated postdecisional from decisional contributions to metacognition by analyzing neural correlates of confidence with multimodal imaging. Healthy volunteers reported their confidence in the accuracy of decisions they made or decisions they observed. We found better metacognitive performance for committed vs. observed decisions, indicating that committing to a decision may improve confidence. Relying on concurrent electroencephalography and hemodynamic recordings, we found a common correlate of confidence following committed and observed decisions in the inferior frontal gyrus and a dissociation in the anterior prefrontal cortex and anterior insula. We discuss these results in light of decisional and postdecisional accounts of confidence and propose a computational model of confidence in which metacognitive performance naturally improves when evidence accumulation is constrained upon committing a decision.
Differentiations of determinants for the community compositions of bacteria, fungi, and nitrogen fixers in various steppes

Abstract Different types of steppes could provide heterogeneous habitat environments for underground microorganisms, but much less is known about how soil microbes fit the distinct habitats and what are the underlying mechanisms in shaping their community patterns. We simultaneously examined the community compositions and structures of soil bacteria, fungi, and diazotrophs across desert, typical, and meadow steppes in Inner Mongolia using high‐throughput sequencing. The results showed that soil bacteria, fungi, and diazotrophs exhibited different distribution patterns across steppe types. Although different steppes displayed obvious differences in climate conditions, plant traits, and soil properties, most of bacterial species were shared by all the steppes while only a few species were unique, indicating that the soil bacterial compositions were hardly influenced by the steppe types. Nevertheless, the habitat heterogeneity could cause shifts in the relative abundance of some bacterial groups, which resulted in significant changes in the community structure of soil bacteria across steppes. However, the fungal community compositions and structures were similar in typical and meadow steppes but that in desert steppe were significantly different. Whereas, the community compositions and structures of diazotrophs were strongly related to the steppe types. In this study, the similar parent material backgrounds of the steppe soils might be the important factor in shaping the homologous bacterial compositions. However, the variations in soil fertility, soil water repellency, and plant species across steppes would be the major driving forces in regulating the compositions and structures of fungal communities, while the diazotrophic communities would be more closely related to the changes in plant traits and soil fertility among steppes. Our results provided evidence of habitat specificity for different microbial groups and their underlying drivers.
The Effect of Blended Learning on Language Proficiency of an EFL Class: An Empirical Study

The Effect of Blended Learning on Language Proficiency of an EFL Class: An Empirical Study Jamile Sulam Tango Rojas The combination of online learning and face-to-face lessons have become a relevant learning model in English Language Teaching in the last years. The goal of this research study was to investigate the integration of such a blended learning approach in an EFL class in Bolivia. The study was conducted at a binational language institution, and it focused on the effect of blended learning on the students’ overall language proficiency and each language skill individually. Data was collected via a proficiency pre-test at the beginning of the experiment, two quizzes during the module, and a final exam, all of them to assess the students’ learning. This study also investigated the students and teacher's attitudes towards the blended learning approach, for which individual interviews were conducted. The participants were 31 Bolivian students of parallel intermediate level classes of English. They were divided into a comparison group (n=18) and a treatment group (n=13). The results showed that, overall, the comparison group performed better than the treatment group. However, the treatment group performed equally or better than the comparison group in some language skills. Regarding the students’ and teacher's attitudes towards the online lessons, they were positive regarding the interactivity and the feedback, but negative regarding the opportunities of communication in the online lessons.
Investigating exercise intensity in virtual reality among healthy volunteers

Purpose. Currently, few studies have presented comprehensive reports on exercise intensity in virtual reality among healthy subjects. The aim of the paper was to evaluate the energy expenditure of young individuals during an active game in virtual reality. Methods. A total of 32 healthy adults, 12 males and 20 females, were examined. Their mean age was 20.6 (± 1.4) years. The average bMI equalled 23.29 (± 2.3) kg/m 2 . The subjects participated in 15-minute training sessions, with the frequency of 4 sessions weekly, for 2 weeks. Xbox 360 ® and Kinect ® Adventures software were used for the virtual reality training. Energy expenditure expressed in kcal, metabolic equivalent of task (MET), and the number of steps were the primary outcomes. The SenseWear Armband was used to assess energy expenditure. The study applied the Xbox 360 console along with a Kinect motion sensor. Results. During one day of training, the average energy value was 3.7 MET and 46 kcal. During one session, 318 steps were noted. The analysis of all predictors between trainings of each single day did not show any statistically significant difference. Conclusions. Training in virtual reality with the application of the Kinect system provides health benefits and can be an alternative to traditional physical activity. However, 15 minutes of daily training cannot be the only basis for physical activity.
A Covalent Modification Technique for Protein-Ligand Binding Analysis Using Mass Spectrometry-Based Proteomics Platforms

A Covalent Modification Technique for Protein-Ligand Binding Analysis Using Mass Spectrometry-Based Proteomics Platforms by Graham Meldahl West Department of Chemistry Duke University Date:_______________________ Approved: ___________________________ Michael C. Fitzgerald, Supervisor ___________________________ Katherine J. Franz ___________________________ Jie Liu ___________________________ Ross A. Widenhoefer An abstract of a dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Chemistry in the Graduate School of Duke University 2009 Copyright by Graham Meldahl West 2009
Exit, Voice, and Loyalty in the Family

Abstract This paper investigates how a basic income could transform families and gender power relations within them. We draw on Hirschman’s exit, voice, and loyalty framework to argue that a basic income can offer a structural foundation for a radical shift towards more equitable family relations. This is because a basic income can support couples through economic uncertainty and reduce women’s structural vulnerability to economic dependency within marriages that strips them of exit and voice. We build our case on novel data from an understudied social experiment from the late 1970s called the Manitoba Basic Income Experiment, or Mincome. Using difference-in-difference regression with individual fixed-effects, we analyze three types of family outcomes: separation, bargaining power, and marital conflict. We find that during Mincome unhappy couples became more likely to consider separation, but that separation overall did not increase. We also find that Mincome reduced marital conflict associated with financial stressors and that some measures of wives’ bargaining power increased. Taken together, our results speak in favor of the view that a basic income has the potential to foster more equitable family lives. Pilar Gonalons-Pons is an Assistant Professor the Department of Sociology the Pennsylvania. Her research interests include gender, family, social stratification, and social policy. She studies how work, families, and public policies structure economic inequalities. David Calnitsky Sociology of Western Ontario. His research interests include political economy, economic sociology, poverty, social policy, and social theory. This paper investigates how a basic income could transform families and gender power relations within them. We draw on Hirschman’s exit, voice, and loyalty framework to argue that a basic income can offer a structural foundation for a radical shift towards more equitable family relations. This is because a basic income can support couples through economic uncertainty and reduce women’s structural vulnerability to economic dependency within marriages that strips them of exit and voice . We build our case on novel data from an understudied social experiment from the late 1970s called the Manitoba Basic Income Experiment, or Mincome. Using difference-in-difference regression with individual fixed-effects, we analyze three types of family outcomes: separation, bargaining power, and marital conflict. We find that during Mincome unhappy couples became more likely to consider separation, but that separation overall did not increase. We also find that Mincome reduced marital conflict associated with financial stressors and that some measures of wives’ bargaining power increased. Taken together, our results speak in favor of the view that a basic income has the potential to foster more equitable family lives.
Primary care community engagement – delivery of an enhanced and brief homogeneous group TF-CBT intervention for trauma from a single-incident road traffic accident: a case study

Abstract The main current intervention for post-traumatic stress disorder (PTSD) in adult primary care is individual trauma-focused cognitive behavioural therapy (TF-CBT). Group TF-CBT for PTSD has been advocated in order to improve access and cost. Barriers to the development of group TF-CBT include the need for a large number of sessions and therapist input in order to manage high levels of affect, possible dissociation and secondary traumatisation. This case study was prompted via our community engagement project when local women who had been involved in a single road traffic accident requested group therapy. The aim was to develop a NICE guideline-compliant brief 8-session group TF-CBT intervention that circumvented the above-mentioned barriers and is described in detail. In order to improve access, the group was delivered in the community. Standard and PTSD-specific measures were administered pre-therapy and post-therapy. Eight clients were offered treatment: two dropped out and six completed treatment. At the end of treatment, 3/6 clients attained reliable improvement in their PTSD symptoms. Two of these three clients also reached recovery. This change was maintained at 3-month follow-up where 4/6 clients attained reliable improvement, with three maintaining recovery. The remaining two clients showed minimal change in their PTSD symptoms. Overall, clients reported high-level satisfaction with the treatment protocol. This case study demonstrates a potentially clinically effective and cost-effective group TF-CBT intervention for non-complex single-incident PTSD. Key learning aims It is hoped that the reader of this case study will increase their understanding of the following:(1) Use of a brief group TF-CBT protocol to treat homogeneous single incident trauma in adults. (2) Adaptations to overcome barriers to group TF-CBT in adults. (3) Implementation of individualised reliving based on written-narrative rather than spoken-narrative. (4) Focus on the processes of PTSD, whilst using content as a theme to contextualise the symptoms. (5) Emphasis on the use of homework in order to enhance group affect-modulation and individual learning.
The Dosimetric Comparisons of CRT, IMRT, ARC, CRT+IMRT, and CRT+ARC of Postoperative Radiotherapy in IIIA-N2 Stage Non-Small-Cell Lung Cancer Patients

Currently, studies about PORT in stage IIIA-N2 NSCLC patients in recent years have mostly adopted the conformal radiation therapy (CRT) technique, while other modern techniques such as intensity modulated radiation therapy (IMRT), volumetric modulated arc therapy (VMAT, hereinafter referred to as ARC), helical tomotherapy (HT), and so forth are also developing quickly. In this paper, we intended to compare the dosimetric characteristics of CRT, IMRT, ARC, CRT+IMRT, and CRT+ARC of PORT in stage IIIA-N2 NSCLC patients. Ten patients with stage IIIA-N2 completely resected NSCLC, whom were treated by PORT in the radiotherapy department of our hospital from January 1, 2017, to January 1, 2018, were randomly selected in this study. For each patient, the CRT plan, IMRT plan, ARC plan, CRT+IMRT plan, and CRT+ARC plan were designed separately on the same set of CT images. The isodose distribution and dose-volume histogram (DVH) of the five plans were compared to determine the dosimetric parameters of the targets, OAR (organs at risk), and the normal tissue (defined as body subtracted to PTV (planning target volume), B-P). No plan had absolute dosimetry advantages than any other plans. In clinical practice, the plans could be chosen according to their dosimetry characteristics.
A network supporting social influences in human decision-making

Social Decision-making is driven by normative influence (leading to public compliance) and informational influence (overwriting private beliefs), but how the brain encodes these modulating forces in probabilistic environments remains unanswered. Using a novel goal-directed learning paradigm in 185 participants, we observed opposite effects of group consensus on choice and confidence: people succumbed to the group when confronted with dissenting information, but increased their confidence when observing confirming information. Leveraging computational modeling and functional neuroimaging we captured the nuanced distinction between normative and informational influence, and identified their unique but interacting neural representations in the right temporoparietal junction (processing social information) and in prefrontal cortices (representing value computations), whose functional coupling instantiates a reward prediction error and a novel social prediction error that modulate behavioral adjustment. These results suggest that a closed-loop network between the brain’s reward hub and social hub supports social influence in human decision-making.
Expression profiling of muscle invasive and non-invasive bladder tumors for biomarkers identification related to drug resistance, sensitivity and tumor progression

Abstract Many bladder cancer (BC) patients with early disease are asymptomatic and diagnosed at advanced stage when the therapeutic options are limited. This necessitates the development of reliable predictive molecular biomarkers that will ensure a positive therapeutic response in every patient. The aim of this study was to screen for alterations in gene expression levels related to drug sensitivity and resistance that may be further explored as potential predictive therapeutic biomarkers. Gene expression analysis of the 168 genes from two panels for Cancer drug resistance and metabolism (PAHS004) and Cancer Drug Targets (PAHS507z) was performed. A total of 47 transitorial cell bladder cancer samples of stage pTa, pT1, pT2 were investigated using the pooling method, which allows reducing the effect of biological variation and detecting only significant expression changes. Differential gene expression was calculated using the ΔΔCt method with GPDH as a housekeeping gene. The 4.0-fold change in gene expression was used as the cut-off threshold to determine upregulation or downregulation compared to normal bladder tissue (negative control). Significance of the differences in the expression profiles was assessed by nonparametric one-way analysis of variance (ANOVA) with Dunn’s multiple comparison tests and Mann-Witney test. We demonstrated a correlation of tumor invasion and several up-regulated genes related to chemotherapy resistance. For the first time, this study demonstrated overexpression of CDK8, CDK9, FIGF, HDAC11, IGF1 and PDGFRA genes in muscle-invasive bladder carcinomas. These genes and their proteins could be used as potential biomarkers for bladder cancer progression or prospective therapeutic targets.
Effect of slaughter age and muscle type on selected meat quality traits and fatty acid composition of Lindovskaya geese

Demanding healthier, higher quality, and better tasting animal products is gradually increasing. Goose meat is one such product, whereby its quality characteristics and fatty acid content are quite different from other meats of poultry. In Turkey, most geese are native breeds and as of 2017, there are 978.834 geese in Turkey (30). Geese from different breeds have been recently reared in Turkey as well, but their number is not known exactly. These breeds include primarily Emden, Toulouse, Chinese and Linda geese. Linda are the result of the breeding of Russian native geese reared in the Nizhny Novgorod with Chinese, Adler, Solnechnogorsk, and Gorky geese. Linda goose corresponds to more than 60% of Russia’s goose population and has been acknowledged as a registered breed since 1994. The Linda geese have heavy bodies and white feathers (25). Many factors affect both meat quality and fatty acid composition (FAC) of poultry (34). Factors such as genetics, origin, sex, slaughter age, method of slaughter, slaughtering conditions, muscle type, breeding, and feeding as well as different methods used all affect the meat quality and FAC of geese (8, 12). In Turkey, several studies have been conducted in order to determine the meat quality and FAC of Turkish native geese species (26, 34). However, no study investigating the meat quality and FAC of Linda geese has been found to date. This study was conducted in order to determine the effect of slaughter age (SA) and muscle type (MT) MT on selected meat quality traits and fatty acid composiEffect of slaughter age and muscle type on selected meat quality traits and fatty acid composition of Lindovskaya geese
Global signal regression strengthens association between resting-state functional connectivity and behavior

Global signal regression (GSR) is one of the most debated preprocessing strategies for resting-state functional MRI. GSR effectively removes global artifacts driven by motion and respiration, but also discards globally distributed neural information and introduces negative correlations between certain brain regions. The vast majority of previous studies have focused on the effectiveness of GSR in removing imaging artifacts, as well as its potential biases. Given the growing interest in functional connectivity fingerprinting, here we considered the utilitarian question of whether GSR strengthens or weakens associations between resting-state functional connectivity (RSFC) and multiple behavioral measures across cognition, personality and emotion. By applying the variance component model to the Brain Genomics Superstruct Project (GSP), we found that behavioral variance explained by whole-brain RSFC increased by an average of 47% across 23 behavioral measures after GSR. In the Human Connectome Project (HCP), we found that behavioral variance explained by whole-brain RSFC increased by an average of 40% across 58 behavioral measures, when GSR was applied after ICA-FIX de-noising. To ensure generalizability, we repeated our analyses using kernel regression. GSR improved behavioral prediction accuracies by an average of 64% and 12% in the GSP and HCP datasets respectively. Importantly, the results were consistent across methods. A behavioral measure with greater RSFC-explained variance (using the variance component model) also exhibited greater prediction accuracy (using kernel regression). A behavioral measure with greater improvement in behavioral variance explained after GSR (using the variance component model) also enjoyed greater improvement in prediction accuracy after GSR (using kernel regression). Furthermore, GSR appeared to benefit task performance measures more than self-reported measures. Since GSR was more effective at removing motion-related and respiratory-related artifacts, GSR-related increases in variance explained and prediction accuracies were unlikely the result of motion-related or respiratory-related artifacts. However, it is worth emphasizing that the current study focused on whole-brain RSFC, so it remains unclear whether GSR improves RSFC-behavioral associations for specific connections or networks. Overall, our results suggest that at least in the case for young healthy adults, GSR strengthens the associations between RSFC and most (although not all) behavioral measures. Code for the variance component model and ridge regression can be found here: https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/preprocessing/Li2019_GSR. Highlights Global signal regression improves RSFC-behavior associations Global signal regression improves RSFC-based behavioral prediction accuracies Improvements replicated across two large-scale datasets and methods Task-performance measures enjoyed greater improvements than self-reported ones GSR beneficial even after ICA-FIX