Miracle-dz/UniFAR-FTUs huggingface dataset:
High-sensitivity troponins and mortality in the general population

Abstract Aims Cardiac troponin T and I can be measured using a number of high-sensitivity (hs) assays. This study aimed to characterize correlations between four such assays and test their comparative associations with mortality. Methods and results Among adults without cardiovascular disease in the 1999–2004 National Health and Nutrition Examination Survey, hs-troponin T was measured using one assay (Roche) and hs-troponin I using three assays (Abbott, Siemens, and Ortho). Cox regression was used to estimate associations with all-cause and cardiovascular mortality. Pearson’s correlation coefficients comparing concentrations from each assay ranged from 0.53 to 0.77. There were 2188 deaths (488 cardiovascular) among 9810 participants. Each hs-troponin assay [log-transformed, per 1 standard deviation (SD)] was independently associated with all-cause mortality: hazard ratio (HR) 1.20 [95% confidence interval (CI) 1.13–1.28] for Abbott hs-troponin I; HR 1.10 (95% CI 1.02–1.18) for Siemens hs-troponin I; HR 1.23 (95% CI 1.14–1.33) for Ortho hs-troponin I; and HR 1.31 (95% CI 1.21–1.42) for Roche hs-troponin T. Each hs-troponin assay was also independently associated with cardiovascular mortality (HR 1.44 to 1.65 per 1 SD). Associations of hs-troponin T and all-cause and cardiovascular mortality remained significant after adjusting for hs-troponin I. Furthermore, associations of hs-troponin I remained significant after mutually adjusting for hs-troponin I from the other individual assays: e.g. cardiovascular mortality HR 1.46 (95% CI 1.19–1.79) for Abbott after adjustment for the Siemens assay and HR 1.29 (95% CI 1.09–1.53) for Abbott after adjustment for the Ortho assay. Conclusion This study demonstrates only modest correlations between hs-troponin T and three hs-troponin I assays and that hs-troponin I assays can provide distinct risk information for mortality in the general population.
Word Familiarity Modulates the Interference Effects of Mind Wandering on Semantic and Reafferent Information Processing

Abstract It has been found that mind wandering interferes with the sensory and cognitive processing of widespread stimuli. However, it remains unclear what factors can modulate the magnitude of the interference effects of mind wandering. Here, we investigate whether and how word familiarity modulates the interference effects of mind wandering on semantic and reafferent information processing. High- and low-frequency words were used as stimuli to induce high- and low-familiarity contexts in a sustained attention to response task, in which participants were required to respond to Chinese nonanimal words (nontarget) and withhold responses to Chinese animal words (target) as well as to intermittently report whether their state was “on task” or “off task. ” Behavioral results revealed lower reaction stability for both high- and low-frequency nontarget words preceding “off-task” reports than those preceding “on-task” reports. However, ERP results revealed that low-frequency rather than high-frequency words elicited more negative N400, attenuated late positive complex, and attenuated reafferent potential for “off-task” reports than for “on-task” reports. The results suggest that mind wandering makes semantic extraction and integration more difficult for unfamiliar but not familiar two-character Chinese words and attenuates the reafferent feedback of the motor response. These findings are consistent with the decoupling hypothesis of mind wandering and provide the first neural evidence for how familiarity with external stimuli modulates the interference effects of mind wandering.
Discrimination of Models and Adaptive Bayes Estimator of the Parameters

In this paper, we study the various approaches such as Bayes factor, Akaike information criterion (AIC), Bayesian information criterion (BIC) and Deviance information criterion (DIC) for model selection in Bayesian paradigm. Probability of correct selections (PCS) between Gamma and Exponential distribution are obtained on the basis of Monte Carlo simulation for different sample sizes, shape parameter and hyper parameters. After selecting appropriate model, we propose Bayes adaptive estimators for both shape and scale parameter and performance of proposed estimators have been studied on the basis of their simulated risk. Keywords: Exponential model, Gamma model, Bayes factor, Bayesian criterion, adaptive estimation.
Black–White Differences in Offspring Educational Attainment and Older Parents’ Dementia

Emerging research documents the health benefits of having highly educated adult offspring. Yet less is known about whether those advantages vary across racial groups. This study examines how offspring education is tied to parents’ dementia risk for Black and White parents in the United States. Using data from the Health and Retirement Study, findings suggest that children’s education does not account for the Black–White gap in dementia risk. However, results confirm that parental race moderates the relationship between children’s education and dementia risk and that the association between children’s education and parents’ dementia risk is strongest among less-educated parents. Among less-educated parents, higher levels of children’s attainment prevent the risk of dementia onset for Black parents, but low levels of offspring schooling increase dementia risk among White parents. The study highlights how offspring education shapes the cognitive health of social groups differently and points to new avenues for future research.
Cost-effectiveness of psilocybin-assisted therapy for severe depression: exploratory findings from a decision analytic model

Abstract Background: There is growing evidence to support the use of the psychedelic drug psilocybin for difficult-to-treat depression. This paper compares the cost-effectiveness of psilocybin-assisted psychotherapy (PAP) with conventional medication, cognitive behavioural therapy (CBT), and the combination of conventional medication and CBT. Methods: A decision model simulated patient events (response, remission, and relapse) following treatment. Data on probabilities, costs and quality-adjusted life years (QALYs) were derived from previous studies or from best estimates. Expected healthcare and societal costs and QALYs over a 6-month time period were calculated. Sensitivity analyses were used to address uncertainty in parameter estimates. Results: The expected healthcare cost of PAP varied from £6132 to £7652 depending on the price of psilocybin. This compares to £3528 for conventional medication alone, £4250 for CBT alone, and £4197 for their combination. QALYs were highest for psilocybin (0.310), followed by CBT alone (0.283), conventional medication alone (0.278), and their combination (0.287). Psilocybin was shown to be cost-effective compared to the other therapies when the cost of therapist support was reduced by 50% and the psilocybin price was reduced from its initial value to £400 to £800 per person. From a societal perspective, psilocybin had improved cost-effectiveness compared to a healthcare perspective. Conclusions: Psilocybin has the potential to be a cost-effective therapy for severe depression. This depends on the level of psychological support that is given to patients receiving psilocybin and the price of the drug itself. Further data on long-term outcomes are required to improve the evidence base.
Monocyte-to-lymphocyte ratio as a determinant of survival in patients with gastric cancer undergoing gastrectomy: A cohort study

The monocyte-to-lymphocyte ratio (MLR) is an important prognostic determinant of various malignancies. However, the prognostic role of MLR in patients with gastric cancer undergoing gastrectomy remains unclear. Patients with stage I to III gastric cancer who underwent curative-intent gastric resection were enrolled in this study. Cox regression analysis was used to determine the independent variables for overall survival (OS) and disease-free survival (DFS). The established models were validated internally. Inter-model comparisons were performed using the integrated area under the receiver operating characteristic curve and the concordance index. Multivariate Cox regression analysis revealed that age, tumor–node–metastasis (TNM) stage, perineural invasion, serum albumin level, and MLR were prognostic factors for OS and DFS and constituted the full model. The full model was internally validated using calibration curves and decision curve analysis. The integrated area under the curve and concordance index of the full model outperformed those of TNM stage. The full model was a significant determinant of OS and DFS. Additionally, the full model was suggested to outperform TNM stage in predicting patient survival outcomes.
First order markov decision processes

Relational Markov Decision Processes (RMDP) are a useful abstraction for complex reinforcement learning problems and stochastic planning problems since one can develop abstract solutions for them that are independent of domain size or instantiation. This thesis develops compact representations for RMDPs and exact solution methods for RMDPs using such representations. One of the core contributions of the thesis is development of the First Order Decision Diagram (FODD), a representation that captures functions over relational structures, together with a set of operators to manipulate FODDs. FODDs offer a potentially compact representation for complex functions over relational structures and can therefore serve as underlying engine for efficient algorithms with relational structures. The second core contribution is developing exact solution methods for RMDPs based on FODD representations. In particular FODDs are used to represent value functions, transition probabilities, and domain dynamics of RMDPs. Special operations are developed to implement exact value iteration and a novel variant of policy iteration and the algorithms are shown to calculate optimal solutions for RMDPs. Finally we show how the algorithms for RMDPs using FODDs can be extended to handle relational Partially Observable MDPs.
Cardiopulmonary exercise testing predicts prognosis in amyloid cardiomyopathy: a systematic review and meta‐analysis

The clinical value of cardiopulmonary exercise testing (CPET) in cardiac amyloidosis (CA) is uncertain. Due to the growing prevalence of the disease and the current availability of disease‐modifying drugs, prognostic stratification is becoming fundamental to optimizing the cost‐effectiveness of treatment, patient phenotyping, follow‐up, and management. Peak VO2 and VE/VCO2 slope are currently the most studied CPET variables in clinical settings, and both demonstrate substantial, independent prognostic value in several cardiovascular diseases. We aim to study the association of peak VO2 and VE/VCO2 slope with prognosis in patients with CA.
Bounded Treewidth and the Infinite Core Chase: Complications and Workarounds toward Decidable Querying

The core chase, a popular algorithm for answering conjunctive queries (CQs) over existential rules, is guaranteed to terminate and compute a finite universal model whenever one exists, leading to the equivalence of the universal-model-based and the chase-based definitions of finite expansion sets (fes) -- a class of rulesets featuring decidable CQ entailment. In case of non-termination, however, it is non-trivial to define a ''result'' of the core chase, due to its non-monotonicity. This causes complications when dealing with advanced decidability criteria based on the existence of (universal) models of finite treewidth. For these, sufficient chase-based conditions have only been established for weaker, monotonic chase variants. This paper investigates the -- prima facie plausible -- hypothesis that the existence of a treewidth-bounded universal model and the existence of a treewidth-bounded core-chase sequence coincide -- which would conveniently entail decidable CQ entailment whenever the latter holds. Perhaps surprisingly, carefully crafted examples show that both directions of this hypothesized correspondence fail. On a positive note, we are still able to define an aggregation scheme for the infinite core chase that preserves treewidth bounds and produces a finitely universal model, i.e., one that satisfies exactly the entailed CQs. This allows us to prove that the existence of a treewidth-bounded core-chase sequence does warrant decidability of CQ entailment (yet, on other grounds than expected). Hence, for the first time, we are able to define a chase-based notion of bounded treewidth sets of rules that subsumes fes.
Conjunctive Queries With Self-Joins, Towards a Fine-Grained Enumeration Complexity Analysis

Even though query evaluation is a fundamental task in databases, known classifications of conjunctive queries by their fine-grained complexity only apply to queries without self-joins. We study how self-joins affect enumeration complexity, with the aim of building upon the known results to achieve general classifications. We do this by examining the extension of two known dichotomies: one with respect to linear delay, and one with respect to constant delay after linear preprocessing. As this turns out to be an intricate investigation, this paper is structured as an example-driven discussion that initiates this analysis. We show enumeration algorithms that rely on self-joins to efficiently evaluate queries that otherwise (i.e., if the relation names were replaced to eliminate self-joins) cannot be answered with the same guarantees. Due to these additional tractable cases, the hardness proofs are more complex than the self-join-free case. We show how to harness a known tagging technique to prove hardness of queries with self-joins. Our study offers sufficient conditions and necessary conditions for tractability and settles the cases of queries of low arity and queries with cyclic cores. Nevertheless, many cases remain open.
Making Sense of Citizens’ Input through Artificial Intelligence

Public sector institutions that consult citizens to inform decision-making face the challenge of evaluating the contributions made by citizens. This evaluation has important democratic implications but at the same time, consumes substantial human resources. However, until now the use of artificial intelligence such as computer-supported text analysis has remained an under-studied solution to this problem. We identify three generic tasks in the evaluation process that could benefit from natural language processing (NLP). Based on a systematic literature search in two databases on computational linguistics and digital government, we provide a detailed review of existing methods and their performance. While some promising approaches exist, for instance to group data thematically and to detect arguments and opinions, we show that there remain important challenges before these could offer any reliable support in practice. These include the quality of results, the applicability to non-English language corpuses and making algorithmic models available to practitioners through software. We discuss a number of avenues that future research should pursue that can ultimately lead to solutions for practice. The most promising of these bring in the expertise of human evaluators, for example through active learning approaches or interactive topic modelling.
Distributed Consistency Beyond Queries

Programming asynchronous distributed systems is a challenging task in which consistency is often achieved by use of expensive coordination protocols like Paxos and 2PC. The CALM theorem, first conjectured by Hellerstein, is one of the first results to challenge this practice by stating that a problem can have a consistent, coordination-free distributed implementation if (and only if) the problem is monotonic. This result was proven for queries and shown to extend beyond monotonic (yet monotonic-like) queries for data systems having specific knowledge about the partitioning of data over the network. In this article, we extend the latter results in several ways. We consider problems that can be modeled as mappings from distributed instances to distributed instances, enabling insights into a much broader range of problems than queries. Furthermore, our model can express arbitrary system configurations, allowing us to reason about the expressiveness of any particular distributed system and thereby revealing a nuanced gradient of problems with increasing coordination-needs. Finally, we apply our model to a recent question about the expressiveness of coordination-free queries, raised by Hellerstein and Alvaro.
XUV spectra of 2nd transition row elements: identification of 3d–4p and 3d–4f transition arrays

The use of laser produced plasmas (LPPs) in extreme ultraviolet/soft x-ray lithography and metrology at 13.5 nm has been widely reported and recent research efforts have focused on developing next generation sources for lithography, surface morphology, patterning and microscopy at shorter wavelengths. In this paper, the spectra emitted from LPPs of the 2nd transition row elements from yttrium (Z = 39) to palladium (Z = 46), with the exception of zirconium (Z = 40) and technetium (Z = 43), produced by two Nd:YAG lasers which delivered up to 600 mJ in 7 ns and 230 mJ in 170 ps, respectively, are reported. Intense emission was observed in the 2–8 nm spectral region resulting from unresolved transition arrays (UTAs) due to 3d–4p, 3d–4f and 3p–3d transitions. These transitions in a number of ion stages of yttrium, niobium, ruthenium and rhodium were identified by comparison with results from Cowan code calculations and previous studies. The theoretical data were parameterized using the UTA formalism and the mean wavelength and widths were calculated and compared with experimental results.
Analysis of quantitative management of online intelligent monitoring of tailing ponds based on the perspective of safety prevention and control

Abstract China’s tailing pond online monitoring technology started late, and the tailing pond is located in a harsh working environment, for the limitations of traditional manual monitoring of tailing pond, combined with the actual situation of Zhenhua Mining tailing pond. This paper constructs a risk monitoring index system and online monitoring early warning model based on (Language Model - Back Propagation, LM-BP) neural network to quantitatively assess tailing pond safety risks and analyze and judge safety risk trends. We extracted common indicators of regional tailing ponds, combined with meteorological data to establish a regional safety risk assessment model, integrated vulnerability of disaster-bearing bodies, environmental sensitivity and other influencing factors, realized regional risk coupling analysis, and dynamically built a risk cloud map. Based on the perspective of safety risk prevention and control, the integrity and accuracy of monitoring data are analyzed, the causes of early warning are inverted, alarm disposal mechanisms are established, and closed-loop management of early warning is realized to provide scientific auxiliary decision-making support for tailing pond safety supervisors.
A Hybrid Stochastic/Robust Planning Model for Integrated Energy System Considering Multiple Uncertainties

Abstract Complex energy flows exist in integrated energy systems (IESs), and the optimal assignment of different energy sources has become a primary challenge in the initial stage of its construction. In addition, multiple uncertainties, such as wind power fluctuations and multiple energy load fluctuations, are not well-respected in IES planning. In order to solve the above problems, the generation expansion planning model in IES is proposed. In the model the uncertainties of renewable energy resources (RESs) are described by scenarios, while the power price and fuel price are described by intervals, and a stochastic/robust (S/R) optimization is established to minimize the sum of annual investment cost, operation cost and reliability cost. Based on duality theory, the proposed multi-level S/R optimization model is transformed into a mixed integer linear programming (MILP) model, which can be solved using the CPLEX solver. The proposed model is tested on the IEEE 33-node distribution system with an 11-node cold-heat system. The validity and effectiveness of the proposed model are illustrated by case studies.
Development of a machine learning-based signature utilizing inflammatory response genes for predicting prognosis and immune microenvironment in ovarian cancer

Abstract Ovarian cancer (OC) represents a significant health challenge, characterized by a particularly unfavorable prognosis for affected women. Accumulating evidence supports the notion that inflammation-related factors impacting the normal ovarian epithelium may contribute to the development of OC. However, the precise role of inflammatory response-related genes (IRRGs) in OC remains largely unknown. To address this gap, we performed an integration of mRNA expression profiles from 7 cohorts and conducted univariate Cox regression analysis to screen 26 IRRGs. By utilizing these IRRGs, we categorized patients into subtypes exhibiting diverse inflammatory responses, with subtype B displaying the most prominent immune infiltration. Notably, the elevated abundance of Treg cells within subtype B contributed to immune suppression, resulting in an unfavorable prognosis for these patients. Furthermore, we validated the distribution ratios of stromal cells, inflammatory cells, and tumor cells using whole-slide digitized histological slides. We also elucidated differences in the activation of biological pathways among subtypes. In addition, machine learning algorithms were employed to predict the likelihood of survival in OC patients based on the expression of prognostic IRRGs. Through rigorous testing of over 100 combinations, we identified CXCL10 as a crucial IRRG. Single-cell analysis and vitro experiments further confirmed the potential secretion of CXCL10 by macrophages and its involvement in lymphangiogenesis within the tumor microenvironment. Overall, the study provides new insights into the role of IRRGs in OC and may have important implications for the development of novel therapeutic approaches.
Financial big data control and intelligent analysis method for investment decision of renewable energy projects

Abstract With the increasing scarcity of conventional energy and environmental degradation, countries around the world are increasing their investment in renewable energy development. In order to make a scientific investment evaluation of renewable energy projects, this paper examines the analysis and control of their financial data. The intelligent analysis system of financial data is constructed based on OLAP. Logistic regression model and decision tree algorithm model are selected as the operation algorithm of the system to complete the intelligent analysis of data. Combining random forest algorithm and autoregressive moving average model, under the guidance of Bagging idea, the financial status of renewable energy projects after investment is judged in order to achieve the purpose of dynamic control. According to the results of analysis and control of financial data of renewable energy projects, it is known that the correct probability of intelligent analysis of financial data reached 94.5%, 83.1%, and 92.7% for different sample sizes of data sets, respectively. There were significant improvements in the efficiency of capital usage and asset quality, with an increase in capital concentration of 30.42%, an increase in inventory turnover from 10.68% to 13.04%, and an increase in the recovery rate of overdue accounts receivable from 60.31% to 67.83%. It has been proven that the method can help investors to better utilize uncertainty to improve the investment value of project, providing investors with a new way of thinking about decision-making.