Miracle-dz/UniFAR-FTUs huggingface dataset:
Genomic Analysis of Metastatic Solid Tumors in Veterans: Findings From the VHA National Precision Oncology Program.

PURPOSE The Veterans Health Administration (VHA) is the largest cancer care provider in the United States, with the added challenge of serving more than twice the percentage of patients with cancer in rural areas than the national average. The VHA established the National Precision Oncology Program in 2016 to implement and standardize the practice of precision oncology across the diverse VHA system. METHODS Tumor or peripheral blood specimens from veterans with advanced solid tumors who were eligible for treatment were submitted for next-generation sequencing (NGS) at two commercial laboratories. Annotated results were generated by the laboratories and independently using IBM Watson for Genomics. Levels-of-evidence treatment recommendations were based on OncoKB criteria. RESULTS From July 2016 to June 2018, 3,698 samples from 72 VHA facilities were submitted for NGS testing, of which 3,182 samples (86%) were successfully sequenced. Most samples came from men with lung, prostate, and colorectal cancers. Thirty-four percent of samples were from patients who lived in a rural area. TP53, ATM, and KRAS were among the most commonly mutated genes. Approximately 70% of samples had at least one actionable mutation, with clinical trials identified as the recommended option in more than 50%. Mutations in genes associated with a neuroendocrine prostate cancer phenotype were expressed at increased frequency among veterans than in the general population. The most frequent therapies prescribed in response to NGS testing were immune checkpoint inhibitors, EGFR kinase inhibitors, and PARP inhibitors. CONCLUSION Clinical implementation of precision oncology is feasible across the VHA health care system, including rural sites. Veterans have unique occupational exposures that might inform the nature of the mutational signatures identified here. Importantly, these results underscore the importance of increasing clinical trial availability to veterans.
TRIM25 and its emerging RNA‐binding roles in antiviral defense

The innate immune system is the body's first line of defense against viruses, with pattern recognition receptors (PRRs) recognizing molecules unique to viruses and triggering the expression of interferons and other anti‐viral cytokines, leading to the formation of an anti‐viral state. The tripartite motif containing 25 (TRIM25) is an E3 ubiquitin ligase thought to be a key component in the activation of signaling by the PRR retinoic acid‐inducible gene I protein (RIG‐I). TRIM25 has recently been identified as an RNA‐binding protein, raising the question of whether its RNA‐binding activity is important for its role in innate immunity. Here, we review TRIM25's mechanisms and pathways in noninfected and infected cells. We also introduce models that explain how TRIM25 binding to RNA could modulate its functions and play part in the antiviral response. These findings have opened new lines of investigations into functional and molecular roles of TRIM25 and other E3 ubiquitin ligases in cell biology and control of pathogenic infections.
Cost‐effectiveness of basic vision rehabilitation (The basic VRS‐effect study): study protocol for a randomised controlled trial

To investigate the cost‐effectiveness of a basic vision rehabilitation service (basic‐VRS) in Portugal. We designed a parallel group, randomised controlled trial whose aim is to compare the effects and costs of ‘usual low vision care’ with a ‘basic‐VRS intervention’ on self‐reported visual ability and other psychosocial and health‐related quality‐of‐life outcomes.
Validity of Six Month L-Thyroxine Dose for Differentiation of Transient or Permanent Congenital Hypothyroidism

Objective: The tendency to reduce thyroid stimulating hormone (TSH) referral cut-off values in congenital hypothyroidism (CH) neonatal screening programs has resulted in an increase in the incidence of CH, but also the referral of infants with mild transient elevation of TSH. Therefore, there is a need to develop markers for differentiation of transient elevated TSH and permanent CH as early as safely possible to avoid unnecessary treatment. The aim was to evaluate sixth-month L-thyroxine (LT4) dose as a predictive marker for differentiation of transient elevated TSH and permanent CH. Methods: Data of patients who had been followed after referral from the neonatal screening programme between the year 2010 and 2019 in a tertiary pediatric endocrine centre were examined retrospectively. Results: There were 226 cases referred, of whom 186 (82.3%) had eutopic thyroid gland, and 40 (17.7%) had dysgenetic gland. In patients with a dysgentic gland there was a non-significant tendency to have lower diagnostic free thyroxine concentration but significantly higher TSH compared with those with eutopic gland (p=0.44 and p=0.023, respectively). Patients with thyroid dysgenesis required higher initial and six month LT4 doses compared with those with eutopic glands (p=0.001). Receiver operator curve analysis showed the optimum cut-off value for LT4 at six months for transient vs. permanent CH was 2 μg/kg/day (sensitivity 77% and specificity 55%), regardless of etiology. Similarly, in patients with eutopic glands the optimum cut-off value for LT4 dose at six months for permanent vs. transient patients was 2 μg/kg/day (sensitivity 72% and specificity 54%). Conclusion: Results suggest that LT4 requirement at six months of therapy may be a good marker for predicting transient TSH elevation in patients with eutopic thyroid gland, thus facilitating the decision to halt LT4 therapy.
A mixed methods exploration of how university students with physical disabilities perceive physical activity and the influence of perceptions on physical activity levels

Abstract Purpose: The purpose of this study was to investigate university students with physical disabilities’ (SWD) perceptions of physical activity (PA) and how these perceptions may influence activity levels. Methods: Using reliable and validated surveys with in-depth interviews, researchers examined the perspectives of 13 undergraduate SWD regarding PA in the context of their university environment. Four quantitative surveys were used to understand participants’ health practices (SRAHP), exercise self-efficacy (EXSE), barriers to PA (BARSE), and outcome expectations for PA (MOEES). To gather richer, exploratory data, interviews were focused on answering how do SWD perceive and define PA, and what factors influence their PA participation? Correlational analysis and independent t-tests were used to examine survey outcomes. Interviews were analyzed using thematic analysis and line-by-line coding strategies. Results: Quantitative analysis revealed correlations between all MOEES surveys, as well as the EXSE, BARSE, and SRAHP. Analysis of the surveys and interviews resulted in two primary themes and seven subthemes. The first primary theme, personal perceptions of PA, had two subthemes: Personal definitions and personal practices. The second theme, influences on PA participation, consisted of five subthemes: University assistive resources, aspects of personal well-being, symptoms and physical limitations, time and scheduling, and social inclusion. Conclusions: Results from this study helped to understand how SWD identified with PA and identified both motivations and barriers to PA unique to SWD. Data from this study can be used to improve accessibility and disability programming for SWD to promote PA.
Data-driven Metareasoning for Collaborative Autonomous Systems

When coordinating their actions to accomplish a mission, the agents in a multi-agent system may use a collaboration algorithm to determine which agent performs which task. This paper describes a novel data-driven metareasoning approach that generates a metareasoning policy that the agents can use whenever they must collaborate to assign tasks. This metareasoning approach collects data about the performance of the algorithms at many decision points and uses this data to train a set of surrogate models that can estimate the expected performance of different algorithms. This yields a metareasoning policy that, based on the current state of the system, estimated the algorithms’ expected performance and chose the best one. For a ship protection scenario, computational results show that one version of the metareasoning policy performed as well as the best component algorithm but required less computational effort. The proposed data-driven metareasoning approach could be a promising tool for developing policies to control multi-agent autonomous systems.
Demand Stimulus, Inflation and Marginal Costs: Empirical Evidence∗

The relationship between government spending, marginal costs and inflation is at the core of New Keynesian theories of price setting. In particular, the impact of a demand stimulus on marginal costs and inflation determines the size of the fiscal multiplier. Yet, despite the fundamental importance of this relationship to theory, documenting it in the data has proved elusive. In this paper we estimate the effect of a fiscal expansion on inflation using the Nielsen Retail Scanner Data. To identify this response of inflation to a demand stimulus (an increase in the generosity of unemployment insurance) we exploit a policy discontinuity at U.S. state borders. We find that inflation (and also spending) responds with a delay in a hump-shaped manner. To assess the impact on marginal costs we explore whether this response is consistent with two leading models of price setting, the sticky price model and the sticky information model, which we show to have different implications for the size of the fiscal multiplier. We find that the sticky information model is consistent with our empirical findings whereas the sticky price model is not.
Dependency-Based Relative Positional Encoding for Transformer NMT

This paper proposes a new Transformer neural machine translation model that incorporates syntactic distances between two source words into the relative position representations of the self-attention mechanism. In particular, the proposed model encodes pair-wise relative depths on a source dependency tree, which are differences between the depths of the two source words, in the encoder’s self-attention. The experiments show that our proposed model achieves 0.5 point gain in BLEU on the Asian Scientific Paper Excerpt Corpus Japanese-to-English translation task.
A Survey of the Perceived Text Adaptation Needs of Adults with Autism

NLP approaches to automatic text adaptation often rely on user-need guidelines which are generic and do not account for the differences between various types of target groups. One such group are adults with high-functioning autism, who are usually able to read long sentences and comprehend difficult words but whose comprehension may be impeded by other linguistic constructions. This is especially challenging for real-world user-generated texts such as product reviews, which cannot be controlled editorially and are thus a particularly good applcation for automatic text adaptation systems. In this paper we present a mixed-methods survey conducted with 24 adult web-users diagnosed with autism and an age-matched control group of 33 neurotypical participants. The aim of the survey was to identify whether the group with autism experienced any barriers when reading online reviews, what these potential barriers were, and what NLP methods would be best suited to improve the accessibility of online reviews for people with autism. The group with autism consistently reported significantly greater difficulties with understanding online product reviews compared to the control group and identified issues related to text length, poor topic organisation, and the use of irony and sarcasm.
Cross-Lingual Word Embeddings for Morphologically Rich Languages

Cross-lingual word embedding models learn a shared vector space for two or more languages so that words with similar meaning are represented by similar vectors regardless of their language. Although the existing models achieve high performance on pairs of morphologically simple languages, they perform very poorly on morphologically rich languages such as Turkish and Finnish. In this paper, we propose a morpheme-based model in order to increase the performance of cross-lingual word embeddings on morphologically rich languages. Our model includes a simple extension which enables us to exploit morphemes for cross-lingual mapping. We applied our model for the Turkish-Finnish language pair on the bilingual word translation task. Results show that our model outperforms the baseline models by 2% in the nearest neighbour ranking.
From Image to Text in Sentiment Analysis via Regression and Deep Learning

Images and text represent types of content which are used together for conveying user emotions in online social networks. These contents are usually associated with a sentiment category. In this paper, we investigate an approach for mapping images to text for three types of sentiment categories: positive, neutral and negative. The mapping from images to text is performed using a Kernel Ridge Regression model. We considered two types of image features: i) RGB pixel-values features, and ii) features extracted with a deep learning approach. The experimental evaluation was performed on a Twitter data set containing both text and images and the sentiment associated with these. The experimental results show a difference in performance for different sentiment categories, in particular the mapping that we propose performs better for the positive sentiment category in comparison with the neutral and negative ones. Furthermore, the experimental results show that the more complex deep learning features perform better than the RGB pixel-value features for all sentiment categories and for larger training sets.
A simple classification framework for predicting Alzheimer's disease from region-based grey matter volume and APOE genotype status

Alzheimer’s Disease (AD) is a prevalent neurodegenerative disease currently affecting more than 47 million people in the world. There are now many complex classifiers that can accurately distinguish AD patients from healthy controls, based on the subject’s structural magnetic resonance imaging (MRI) brain scan. Most such automated diagnostic systems are blackboxes: While their predictions are accurate, it is difficult for clinicians to interpret those predictions, due to the large number of features used by the classifier, and/or by the complexity of that classifier. This work demonstrates that an automated learning algorithm can produce a simple classifier that can correctly distinguish AD patients from healthy controls (HC) similar to its more-complex counterparts. Here we buildthis classifier from the data in the Alzheimer’s Disease Neuroimaging Initiative database, using a fairly small set of features, including grey matter volumes of 33 regions of interest derived from structural MRI, as well as the APOE genotype. We first considered three simple base-learners that each produce a classifier that is simple and interpretable. Running our overall learner, involving standard feature selection processes and these simple base-learners, on these features, produced a 7-feature elastic net model, EN7, that achieved accuracy of 89.28% on the test set. Next, we ran the same overall learner using two more-complex base-learners over the same initial dataset. The accuracy of the best model here was 90.47%, which was not statistically different from the performance of our much simpler EN7 model.
Generating T1DM Virtual Patients for In Silico Clinical Trials via AI-Guided Statistical Model Checking

The availability of a representative population of virtual patients, i.e., a population large enough to represent all relevant human patient behaviours, is a key enabler for the design of In Silico Clinical Trials (ISCTs), that is trials following simulation-based approaches for the safety and efficacy assessment of pharmacological treatments and biomedical devices. This involves the development of Virtual Physiological Human (VPH) models able to represent the whole phenotypes spectrum of the human physiology of interest. Usually, such models are open-loop models, i.e., their behaviour depends also on exogenous inputs (such as, e.g., pharmacological drugs). In this paper, we propose a methodology to convert an open-loop VPH model into a closed-loop model. As a case study, we apply our methodology to a state-of-the-art VPH model defining the human glucose regulation system of individuals with Type 1 Diabetes Mellitus (T1DM), and show how we generate a representative population of T1DM virtual patients.
Contribution of Knowledge Management to the Optimization of Healthcare Settings' Performance in Cameroon

Public health issues are priority issues for any government. According to WHO, health is a complete state of mental, physical and social well-being and not just the absence of disease or infirmity. In other words, a person with a balanced combination of mental, physical and social conditions is considered healthy. One of the core challenges of the current modern-day environment is the quality or the shortage of human resources. More than ever, it has stood at the centre of concerns in all countries, albeit with some acuteness in developing countries. However, while healthcare centres and hospitals around the world have striving to have performant human resources, they are expected to do the same in terms of knowledge management and sharing. The sharing of experiences plays a key role in optimizing the performance of health practitioners. And this inevitably entails a structured management of knowledge generated by the daily practice of the various health structures.
Affective Reactions to Real-Life Immoral Events

Fransson, N. , Biel, A. , & Dahlstrand, U. Affective reactions to real-life immoral events. Göteborg Psychological Reports, 1997, 27, No. 2. The assumption that affective reactions to immoral events correspond to appraisals that differ in specificity was tested. A general appraisal is invariant to different kinds of immoral events, while more specific appraisals vary systematically with prominent components of the event. In a first study, 58 psychology students used adjectives to describe the emotional reactions of the original story-teller in 16 real-life events. In a previous study these events had been categorized by means of a factor analysis into 8 groups (Biel, Fransson, Dahlstrand, in press) where each group was linked to one moral value. Support for the hypothesis that some affective reactions are elicited by a general appraisal was obtained by the fact that certain emotions were frequently used across events. Based on frequency of the emotion words used, correlations were significantly higher across events within the group for five of the eight groups, than with the mean correlation to events not belonging to the group, lending some support for more specific appraisals. In addition to replicating the result of Study 1 regarding anger being a frequent reaction across events, Study 2 provided additional support for specific appraisals in that different components of events elicited different emotional reaction. The perspective of the reacting individual, whether, observer or victim, was found to be important for which components of an event were conceived as prominent.
Susceptibility of Singapore Chinese schoolgirls to anorexia nervosa--part II. (family factors).

: It was found that of a group of 656 GCE 'O' level Chinese Singapore schoolgirls in schools of good academic standing, where pressure was expected to be high, 15 (6%) had high drive for thinness (DT) scores. The families of these latter girls were not perceived to be significantly more enmeshed or rigid than those with low DT scores, and other family functioning was also not significantly different.
Explanations of Effects of Prior Outcomes on Intertemporal Choices

Karlsson, N. , Gärling, T. , & Selart, M. Explanations of effects of prior outcomes on intertemporal choices. Göteborg Psychological Report, 1997, 27, No. 4. In two experiments with undergraduates as subjects, explanations were tested of how a prior outcome in the form of a temporary income change influences choices between immediate and deferred consumption. In Experiment 1, predictions from the behavioral life-cycle hypothesis (H. M. Shefrin & R. H. Thaler, 1988), the renewable resources model (P. W. Linville & G. W. Fischer, 1991), and the loss-sensitivity principle (T. Gärling & J. Romanus, 1996) were contrasted. The results were inconsistent with the renewable resources model and the loss-sensitivity principle since the framing of consumption as positive (buying something wanted) or negative (replacing something broken) did not interact with the income change. Congruent with the notion of mental accounts implied by the behavioral life-cycle hypothesis, the propensity to consume was greater when subjects received an income increase than when they received an income decrease whith saved money available. This was further supported in Experiment 2, in which four income-change conditions and two types of goods (durables and nondurables) were introduced. However, there was also a tendency that the consumption of durable and nondurable goods were best accounted for by different specifications of how consumption is constrained through the use of mental accounts. For the durable goods, the propensity to consume seemed to follow the amount of current income, while for the nondurable goods the propensity to consume seemed to decrease only when current income was not perceived as sufficient to cover the cost of consumption.
A General Theory of International Policy Cooperation

In an interconnected world, national economic policies regularly lead to large international spillover effects, which frequently trigger calls for international policy cooperation. However, the premise of successful cooperation is that there is a Pareto inefficiency, i.e. that there is scope to make some nations better off without hurting others. This paper presents a first welfare theorem for open economies that defines an efficient benchmark and spells out the conditions that need to be violated to generate inefficiency and scope for cooperation. These are: (i) policymakers act competitively in the international market, (ii) policymakers have sufficient external policy instruments and (iii) international markets are free of imperfections. Our theorem holds even if each economy suffers from a wide range of domestic market imperfections and targeting problems. We provide examples of current account intervention, monetary policy, fiscal policy, macroprudential policy/capital controls, and exchange rate management and show that the resulting spillovers are Pareto efficient, as long as the three conditions are satisfied. Furthermore, we develop general guidelines for how policy cooperation can improve welfare when the conditions are violated. JEL Codes: F42, F13, F33, D61
The Effectiveness of Model Eliciting Activities with Ethnomathematics on Students' Mathematical Communication Capabilities

The purpose of this study was to analyze the effectiveness of Model Eliciting Activities (MEAs) with Ethnomathematics on students' mathematical communication skills. This research was a quasi-experimental study. The population of this study was seventh grade students of SMP Negeri 1 Delanggu in the academic year of 2018/2019. The sample in this study were students of VII D, VII E, and VII F class. Methods of data collection in this study were observation, test, documentation, and literature studies. Data analysis used proportion test, one sample t-test and independent t-test. The results of the test indicate that students who receive MEAs learning with ethnomathematic were completed both individually and classically. The average and proportion of students who completed the ethnomathematic MEAs learning were more than the class that receives MEAs and control class. Model Eliciting Activities with Ethnomathematics based on Klaten District are effectively applied in classroom learning.