Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of Contractile Function of Permeabilized Human Hypertrophic Cardiomyopathy Multicellular Heart Tissue

Background: Many forms of hypertrophic cardiomyopathy (HCM) show an increased myofilament Ca2+ sensitivity. This observation has been mainly made in HCM mouse models, myofilament systems, and cardiomyocytes. Studies of multicellular tissues from patients with different HCM-associated gene mutations are scarce. We investigated Ca2+ sensitivity in multicellular cardiac muscle strips of HCM patients. We furthermore evaluated the use of epigallocatechin-3-gallate (EGCg), a Ca2+ desensitizer. Methods: After strip isolation from cardiac tissues with single (MYBPC3, MYH7) or double heterozygous mutations (MYBPC3/FLNC, MYH7/LAMP2, MYBPC3/MYH7) and permeabilization, we performed contractility measurements ±EGCg. We furthermore evaluated gene expression with a customized heart failure gene panel using the NanoString technology. Results: Fmax tended to be higher in HCM than in non-failing (NF) control strips and in single than in double heterozygous strips. Ca2+ sensitivity was higher by trend in most HCM vs. NF strips and by trend in tissues with double vs. single heterozygous mutations. EGCg desensitized myofilaments to Ca2+ in most of the strips and tended to induce a more pronounced shift in strips with truncating than missense or single than double heterozygous mutations. Gene expression analysis revealed lower ATP2A2, PPP1R1A, and FHL2 and higher NPPA, NPPB, COL1A1, CTGF, and POSTN marker levels in HCM than in NF tissues. NPPA, NPPB, ACTA1, CTGF, COL1A1, and POSTN levels were higher in tissues with missense than truncating mutations. Conclusion: We report an increased myofilament Ca2+ sensitivity in native multicellular cardiac HCM strips, which by trend was more pronounced in samples with double heterozygous mutations. EGCg could have differential effects depending on the underlying genetic status (single vs. double heterozygous) and type (missense vs. truncating).
Large sample properties of partitioning-based series estimators

We present large sample results for partitioning-based least squares nonparametric regression, a popular method for approximating conditional expectation functions in statistics, econometrics, and machine learning. First, we obtain a general characterization of their leading asymptotic bias. Second, we establish integrated mean squared error approximations for the point estimator and propose feasible tuning parameter selection. Third, we develop pointwise inference methods based on undersmoothing and robust bias correction. Fourth, employing different coupling approaches, we develop uniform distributional approximations for the undersmoothed and robust bias-corrected t-statistic processes and construct valid confidence bands. In the univariate case, our uniform distributional approximations require seemingly minimal rate restrictions and improve on approximation rates known in the literature. Finally, we apply our general results to three partitioning-based estimators: splines, wavelets, and piecewise polynomials. The supplemental appendix includes several other general and example-specific technical and methodological results. A companion R package is provided.
Impact of El Niño-Southern Oscillation 2015-2016 on the soluble proteomic profile and cytolytic activity of Millepora alcicornis (“fire coral”) from the Mexican Caribbean

Reef-forming cnidarians are extremely susceptible to the “bleaching” phenomenon caused by global warming. The effect of elevated seawater temperature has been extensively studied on Anthozoans; however, to date the impact of thermal stress on the expression of genes and proteins in Hydrozoan species has not been investigated. The present study aimed to determine the differential proteomic profile of Millepora alcicornis, which inhabits the Mexican Caribbean, in response to the El Niño-Southern Oscillation 2015–2016. Additionally, the cytolytic activity of the soluble proteomes obtained from normal and bleached M. alcicornis was assessed. Bleached specimens showed decreased symbiont’s density and chlorophyll a and c2 levels. After bleaching, we observed a differential expression of 17 key proteins, tentatively identified as related to exocytosis, calcium homeostasis, cytoskeletal organization, and potential toxins, including a metalloprotease, a phospholipase A2 (PLA2), and an actitoxin. Although, some of the differentially expressed proteins included potential toxins, the hemolytic, PLA2, and proteolytic activities elicited by the soluble proteomes from bleached and normal specimens were not significantly different. The present study provides heretofore-unknown evidence that thermal stress produces a differential expression of proteins involved in essential cellular processes of Hydrozoan species. Even though our results showed an over-expression of some potential toxin-related proteins, the cytolytic effect (as assessed by hemolytic, PLA2, and caseinolytic activities) was not increased in bleached M. alcicornis, which suggests that the cytolysis is mainly produced by toxins whose expression was not affected by temperature stress. These findings allow hypothesizing that this hydrocoral is able to prey heterotrophically when suffering from moderate bleaching, giving it a better chance to withstand the effects of high temperature.
Simultaneous Mean-Variance Regression

We propose simultaneous mean-variance regression for the linear estimation and approximation of conditional mean functions. In the presence of heteroskedasticity of unknown form, our method accounts for varying dispersion in the regression outcome across the support of conditioning variables by using weights that are jointly determined with the mean regression parameters. Simultaneity generates outcome predictions that are guaranteed to improve over ordinary least-squares prediction error, with corresponding parameter standard errors that are automatically valid. Under shape misspecification of the conditional mean and variance functions, we establish existence and uniqueness of the resulting approximations and characterize their formal interpretation and robustness properties. In particular, we show that the corresponding mean-variance regression location-scale model weakly dominates the ordinary least-squares location model under a Kullback-Leibler measure of divergence, with strict improvement in the presence of heteroskedasticity. The simultaneous mean-variance regression loss function is globally convex and the corresponding estimator is easy to implement. We establish its consistency and asymptotic normality under misspecification, provide robust inference methods, and present numerical simulations that show large improvements over ordinary and weighted least-squares in terms of estimation and inference in finite samples. We further illustrate our method with two empirical applications to the estimation of the relationship between economic prosperity in 1500 and today, and demand for gasoline in the United States.
Extrapolating Treatment Effects in Multi-Cutoff Regression Discontinuity Designs

Abstract–In nonexperimental settings, the regression discontinuity (RD) design is one of the most credible identification strategies for program evaluation and causal inference. However, RD treatment effect estimands are necessarily local, making statistical methods for the extrapolation of these effects a key area for development. We introduce a new method for extrapolation of RD effects that relies on the presence of multiple cutoffs, and is therefore design-based. Our approach employs an easy-to-interpret identifying assumption that mimics the idea of “common trends” in difference-in-differences designs. We illustrate our methods with data on a subsidized loan program on post-education attendance in Colombia, and offer new evidence on program effects for students with test scores away from the cutoff that determined program eligibility. Supplementary materials for this article are available online.
Sustainable composites from poly(3-hydroxybutyrate) (PHB) bioplastic and agave natural fibre

A sustainable green composite based on poly(3-hydroxybutyrate) (PHB) and agave fibre was produced. The synergy between PHB bioplastic and agave natural fibre was investigated in the presence of an organic peroxide through one step reactive extrusion processing. The work focuses on controlling the crystallinity of PHB to tailor the mechanical properties, especially for improving the impact properties of highly brittle PHB. Optical microscopy showed a decrease in spherulite formation, and hence controlled reduction of the crystalline region of the PHB matrix, with the addition of agave fibre and peroxide treated fibre. Scanning electron microscopy showed the role of organic peroxide in improving the interfacial interaction in fibre matrix adhesion, and hence resulting in improved mechanical properties. An expected reduction in tensile strength of PHB was found with the addition of 25 wt% of agave fibre due to the reduction in crystallinity induced by the treated agave fibre. Interestingly, optimal composition of composites having 25 wt% agave fibre with 0.1 phr peroxide improved the flexural strength by 46%, impact strength by 45% and heat deflection temperature (HDT) by 39% compared to the neat PHB. An effective reactive extrusion of agave fibre and PHB in the presence of peroxide can provide a sustainable and cost-effective alternative to petroleum-based plastics for food packaging applications.
SABCEMM-A Simulator for Agent-Based Computational Economic Market Models

We introduce the simulation tool SABCEMM (Simulator for Agent-Based Computational Economic Market Models) for agent-based computational economic market (ABCEM) models. Our simulation tool is implemented in C++ and we can easily run ABCEM models with several million agents. The object-oriented software design enables the isolated implementation of building blocks for ABCEM models, such as agent types and market mechanisms. The user can design and compare ABCEM models in a unified environment by recombining existing building blocks using the XML-based SABCEMM configuration file. We introduce an abstract ABCEM model class which our simulation tool is built upon. Furthermore, we present the software architecture as well as computational aspects of SABCEMM. Here, we focus on the efficiency of SABCEMM with respect to the run time of our simulations. We show the great impact of different random number generators on the run time of ABCEM models. The code and documentation is published on GitHub at this https URL, such that all results can be reproduced by the reader.
Asymptotic distribution of capital in a model of an investment market with competition

We consider a stochastic game-theoretic model of an investment market in continuous time where investors compete for dividend income from several assets. Asset prices are determined endogenously from the equality of supply and demand. The main results are related to the question in what proportions the total capital will be distributed among the investors, and what will be the prices of the assets asymptotically on the infinite time horizon depending on the strategies of the investors. We prove that there exists a strategy with the following properties: the proportion of its capital on the entire time horizon is separated from zero with probability 1 regardless of the strategies of competitors; the relative asset prices are asymptotically determined by it; the proportion of capital of investors who follow essentially different strategies tends to zero. We also show that investors who follow this strategy will accumulate capital faster than their competitors under several definitions of the speed of capital growth.
Towards population screening for Cerebral Visual Impairment: Validity of the Five Questions and the CVI Questionnaire

Introduction Cerebral Visual Impairment (CVI) is the most common cause of visual impairment in children in the developed world and appears to be more prevalent in children with additional support needs (ASN). There is an urgent need for routine screening for CVI, particularly in children with ASN, however, current screening questionnaires for CVI have limited validation. The aim of this study was to evaluate two screening tools: the Five Questions and the CVI Questionnaire. Additionally, the distribution of CVI across neurodevelopmental disorders is unknown. This too was investigated. Methods An online survey was completed by 535 parents. The survey was advertised via social media, CVI websites and parent email systems of four schools. The survey comprised of the Five Questions, the CVI Questionnaire and additional questions regarding the child’s diagnoses. Whether or not a child had a diagnosis of CVI and/or additional neurodevelopmental disorders was based on parental report. Results Based on parent reports, both the screening tools accurately screened for CVI diagnoses in children. The Five Questions and the CVI Questionnaire have construct validity (as determined through factor analysis), high internal consistency (as determined by Cronbach’s alpha) and convergent validity (as determined by correlation analysis of the raw scores of each questionnaire). This study also highlights that among children with neurodevelopmental disorders, a large proportion have parent-reported CVI (23%-39%) and potential CVI (6.59–22.53%; as identified by the questionnaires). Conclusion The current study demonstrates that the Five Questions and CVI Questionnaire have good convergent validity, internal consistency and a reliable factor structure and may therefore be suitable as screening tools. The study also highlights that reported or potential CVI is evident in a large proportion of children with neurodevelopmental disorders.
Robust Principal Component Analysis with Non-Sparse Errors

We show that when a high-dimensional data matrix is the sum of a low-rank matrix and a random error matrix with independent entries, the low-rank component can be consistently estimated by solving a convex minimization problem. We develop a new theoretical argument to establish consistency without assuming sparsity or the existence of any moments of the error matrix, so that fat-tailed continuous random errors such as Cauchy are allowed. The results are illustrated by simulations.
Profile of blood donors who presented adverse reactions to the donation.

OBJECTIVE identify the adverse reactions presented by blood donors and outline their sociodemographic profile. METHOD a quantitative, cross-sectional retrospective study of 780 records of blood donors from a public hemocenter in the southern region of Brazil, from December 2015 to January 2016. For the analysis the descriptive statistics was used. RESULTS it was identified that throughout 12 months, the total blood donors corresponded to 27,300 people, in which 780 developed at least one reaction. They were characterized by female and recurrent donors, single, with a complete average level of education, ranging from 16 to 30 years, who triggered between 1 and 3 reactions. Mild reactions were more frequent, followed by moderate and severe reactions. CONCLUSION There is a high rate of adverse reactions from donors emphasizing the need for changes in hemotherapy care practices.
Assessment of iron status and interplay between lipid peroxidation and antioxidant capacity in common hemoglobin variants in Osun State, southwestern Nigeria

Hemoglobin (Hb) and iron are prooxidants in nature and sources of free radicals in the biological system of all Hb phenotypes. Recent evidence linked abnormal hemoglobin S and C (HbSC) in sickle cell disease (SCD) to various complications in multiple oxidative processes. However, similar studies in relation to abnormal Hb traits are sparse. Besides, reports on activities of antioxidant enzymes and iron status in SCDs are still contradictory. This study assessed the interplay between lipid peroxidation and antioxidant defense capacity in various Hb variants. We enrolled 193 participants with different Hb phenotypes. They were consecutive patients with sickle cell anemia (HbSS, n = 32) and hemoglobin SC (HbSC) disease (n = 28) regularly followed up in a steady state. Other participants were subjects with abnormal Hb traits (HbAS, n = 50; HbAC, n = 33) and normal controls (HbAA, n = 50). The hematocrit (Hct) level, hemoglobin (Hb) concentration, iron status, and biochemical parameters including malondialdehyde (MDA), total antioxidant status (TAS), superoxide dismutase (SOD), and glutathione peroxidase (GPx) enzymes were investigated simultaneously. The MDA and SOD levels were significantly higher (P < 0.05) in Hb variants in order of HbSS>HbSC>HbAC>HbAS when compared with controls. Conversely, GPx and TAS levels showed significant reductions (P < 0.05). Similarly, Hct, Hb, and iron concentrations showed significant reductions (P < 0.05) sequentially following HbAC > HbAS > HbSC > HbSS compared with controls. The results suggest that both SCDs and the carriers were relatively more vulnerable to systemic oxidative stress against normal phenotype, and may be owing to ineffective antioxidant mechanisms needed for keeping spontaneous generations of free radicals in control without necessarily iron‐mediated.
Vitamin E is Necessary to Protect Neural Crest Cells in Developing Zebrafish Embryos

Vitamin E (VitE) deficiency causes vertebrate embryonic lethality. The alpha-tocopherol transfer protein (Ttpa) likely regulates VitE distribution in the early zebrafish embryo because Ttpa knockdown causes impaired nervous system development and embryonic death by 15–18 hours post-fertilization (hpf). We propose that VitE is necessary for normal brain and peripheral nervous system development. Zebrafish embryos are obtained from adults fed either VitE sufficient (E+) or deficient (E–) diets for at least 80 days. Embryos at 12 and 24 hpf are subjected to RNA whole mount in situ hybridization (WISH). RNA is also collected from embryos at 12, 18 and 24 hpf for RT-qPCR of specific targets. At 12 hpf, the midbrain-hindbrain boundary and otic placodes are malformed in E– embryos, as shown by Pax2a expression. Similarly, Sox10 expression shows that E– embryos lack clear neural plate borders. Nonetheless, in 12 hpf E + and E− embryos Ttpa is localized similarly throughout the nervous system. Pax2a expression initiates collagen formation in the developing notochord. Collagen genes, col2a1a and col9a2, expression patterns showed abnormal notochord structures in 24 hpf E– embryos. At 24 hpf in E + embryos, Sox10 expressing-neural crest cells are localized both in the central nervous system and dorsal root ganglia (DRG), while the Sox10 signal is diminished in E– embryos in both the DRG and early enteric nervous system. At 24 hpf, Ttpa expression outlines the brain ventricle borders; critically E– embryos show reduced Ttpa signal and impaired ventricle closing. Gene expression by qPCR will be used to confirm these results. This VitE deficient embryo model suggests that the carefully programmed development of the nervous system is distorted due to lack of adequate VitE. Thus, Ttpa and VitE are critical molecules for neural plate and neural tube formation, and neural crest cell migration. The authors received no specific funding for this work.
The Psychoanalytic Paris Square: On Decolonization and Psychoanalysis

The article is based on questions posed by Lucia Murat’s movie called “Paris Square” (2018) [1] and aims to situate psychoanalysis in the discussion around the issue of epistemic decolonization. The film, which is set at the State University of Rio de Janeiro, portrays the troubles of a Portuguese psychoanalyst in dealing with the clinical psychological care of a resident of the Providência favela. From that perspective and supported by the contribution of other fields’ mainly anthropology’s decolonial literature, we will question the apparent colonizing perspective of psychoanalysis. Thus, a possible path is sought through a certain notion of experience in psychoanalysis and a dialogue with the Amerindian’s perspectivism.
Dimensionality assessment of binary response test items: A non-parametric approach of bayesian item response theory measurement

This study investigated dimensionality of Binary Response Items through a non-parametric technique of Item Response Theory measurement framework. The study used causal comparative research type of non-experimental design. The sample consisted of 5,076 public senior secondary school examinees (SSS3) between the age of 14-16 years from 45 schools, which were drawn randomly from three senatorial districts of Osun State, Nigeria. Instrument used for this study was 2018 Osun State unified multiple-choice mathematics achievement test items with empirical reliability coefficient of 0.82. Data obtained were analysed using Non-linear factor analysis, Stout’s Test of Essential Unidimensionality (STEU), Factor Analysis (FA), Full Information Factor Analysis (FIFA) and Bootstrap Modified Parallel Analysis Test (BMPAT). Results showed that both the BMPAT and STEU ascertained violation of unidimensionality assumption of the test items (the observed difference in the second eigenvalue of the observed data and that of second eigenvalue of the simulated data was statistically significant, p = 0.0099; Stout’s test rejected the assumption of essential unidimensionality, T = 10.6260, p<0.05). Non-linear factor analysis and full information factor analysis revealed that four dimensions embedded in the test items and loadings of the items showed within-item multidimensionality respectively. The authors’ concluded that modeling examinees’ performance with unidimensional model when it was actually multidimensional in nature would affect performance of examinees adversely and could lead to blur judgment. Consequently, it is recommended that unidimensional scoring method of Osun State unified mathematics achievement test implicit in Classical Test Theory should be jettisoned and an appropriate scoring model (multidimensional) should be embraced.
Synthetic Control Methods and Big Data

Many macroeconomic policy questions may be assessed in a case study framework, where the time series of a treated unit is compared to a counterfactual constructed from a large pool of control units. I provide a general framework for this setting, tailored to predict the counterfactual by minimizing a tradeoff between underfitting (bias) and overfitting (variance). The framework nests recently proposed structural and reduced form machine learning approaches as special cases. Furthermore, difference-in-differences with matching and the original synthetic control are restrictive cases of the framework, in general not minimizing the bias-variance objective. Using simulation studies I find that machine learning methods outperform traditional methods when the number of potential controls is large or the treated unit is substantially different from the controls. Equipped with a toolbox of approaches, I revisit a study on the effect of economic liberalisation on economic growth. I find effects for several countries where no effect was found in the original study. Furthermore, I inspect how a systematically important bank respond to increasing capital requirements by using a large pool of banks to estimate the counterfactual. Finally, I assess the effect of a changing product price on product sales using a novel scanner dataset.