Miracle-dz/UniFAR-FTUs huggingface dataset:
Functional analysis of large MAF transcription factors and elucidation of their relationships with human diseases

The large MAF transcription factor group is a group of transcription factors with an acidic 2 region, a basic region, and a leucine zipper region. Four types of MAF, MAFA, MAFB, c-MAF, 3 and NRL, have been identified in humans and mice. In order to elucidate the functions of the large 4 MAF transcription factor group in vivo , our research group created genetically modified MAFA-, 5 MAFB-, and c-MAF-deficient mice and analyzed their phenotypes. MAFA is expressed in 6 pancreatic β cells and is essential for insulin transcription and secretion. MAFB is essential for 7 the development of pancreatic endocrine cells, formation of inner ears, podocyte function in the 8 kidneys, and functional differentiation of macrophages. c-MAF is essential for lens formation and 9 osteoblast differentiation. Furthermore, a single-base mutation in genes encoding the large MAF 10 transcription factor group causes congenital renal disease, eye disease, bone disease, diabetes, and 11 tumors in humans. This review describes the functions of large MAF transcription factors in vivo 12 and their relationships with human diseases.
Development of Blastocyst Complementation Technology without Contributions to Gametes and the Brain

: In Japan, it is possible to generate chimeric animals from specified embryos by combining animal blastocysts with human pluripotent stem (PS) cells (animal-human PS chimera). However, the production of animal-human PS chimeras has been restricted because of ethical concerns, such as the development of human-like intelligence and formation of humanized gametes in the animals, owing to the contributions of human PS cells to the brain and reproductive organs. To solve these problems, we established a novel blastocyst complementation technology that does not contribute to the gametes or the brain. First, we established GFP-expressing mouse embryonic stem cells (G-mESCs) in which the Prdm14 and Otx2 genes were knocked out and generated chimeric mice by injecting them into PDX-1-deficient blastocysts. The results showed that the G-mESCs did not contribute to the formation of gametes or the brain. Therefore, in the PDX-1-deficient mice complemented by G-mESCs without the Prdm14 and Otx2 genes, the germline was not transmitted to the next generations. This approach could address concerns regarding the development of both human gametes and a human-like brain upon mouse blastocyst complementation using human stem cells.
NPL REPORT IEA 17 NMS SUPPORT FOR INNOVATION AND BUSINESS OUTCOMES: A SYNTHESIS OF EVIDENCE FROM BELMANA’S ECONOMETRIC ANALYSIS

Belmana Ltd was commissioned by the National Physical Laboratory (NPL) and Innovate-UK (IUK) to conduct an impact assessment of the innovation support provided to a large sample of UK firms. Belmana’s analysis examined how this support affected the subsequent performance of these firms in terms of their employment, survival, and wage premiums. Additionally, Belmana provided exploratory analysis for firm productivity, patenting activity and knowledge spillovers. There was, however, a need to distil this analysis to extract key findings specific to NPL and other laboratories funded through the National Measurement System (NMS) programme. The headline findings for innovation support provided by the NMS laboratories are: • An average annual growth of 6.31 employees per regularly supported firm. • Only 4% of firms that receive regular support closed during a 7-year period as compared to 12% of firms in the matched control group. • An average annual wage premium of £4,083 for employees switching to supported firms. Moreover, this document develops a stylised economic model which helps with the interpretation of the estimates and provides further economic insights. The hope is that this will make it easier for non-specialist audiences to better grasp some its key findings. Hence, this document offers a simplified digest of the original report, spells out some of the implicit assumptions of empirical analysis, and presents a microeconomic model that compliments the econometric estimates.
DU GRAIN À LA MUSIQUE : MANIPULATIONS EN TEMPS RÉEL

RÉSUMÉ Dans cet article, nous présenterons une étude comparative de quelques techniques de manipulation du signal comme la Transformée de Fourier à Court Terme, la Synthèse Granulaire Asynchrone et PSOLA, notamment dans le cadre de leur utilisation en temps réel. Nous présenterons succinctement le fonctionnement de ces trois méthodes dans l’environnement Max / MSP avec l’utilisation de la librairie GABOR. La manipulation des paramètres propres à chaque méthode a des conséquences sur les variables musicales, et l’utilisation de ces méthodes implique des corrélations entre les paramètres techniques du traitement du signal et des attributs musicaux spécifiques. Ainsi, nous essayerons d’explorer les espaces des paramètres et de la perception musicales entre le temps, la période et le spectre (les rythmes, les fréquences et les timbres) en comparant les différentes méthodes en vue de leur croisement dans un système de traitement commun.
Modelling biogeochemical cycles in forest ecosystems: a Bayesian approach

Forest models are tools for explaining and predicting the dynamics of forest ecosystems. They simulate forest behavior by integrating information on the underlying processes in trees, soil and atmosphere. Bayesian calibration is the application of probability theory to parameter estimation. It is a method, applicable to all models, that quantifies output uncertainty and identifies key parameters and variables. This study aims at testing the Bayesian procedure for calibration to different types of forest models, to evaluate their performances and the uncertainties associated with them. In particular,we aimed at 1) applying a Bayesian framework to calibrate forest models and test their performances in different biomes and different environmental conditions, 2) identifying and solve structure-related issues in simple models, and 3) identifying the advantages of additional information made available when calibrating forest models with a Bayesian approach. We applied the Bayesian framework to calibrate the Prelued model on eight Italian eddy-covariance sites in Chapter 2. The ability of Prelued to reproduce the estimated Gross Primary Productivity (GPP) was tested over contrasting natural vegetation types that represented a wide range of climatic and environmental conditions. The issues related to Prelued's multiplicative structure were the main topic of Chapter 3: several different MCMC-based procedures were applied within a Bayesian framework to calibrate the model, and their performances were compared. A more complex model was applied in Chapter 4, focusing on the application of the physiology-based model HYDRALL to the forest ecosystem of Lavarone (IT) to evaluate the importance of additional information in the calibration procedure and their impact on model performances, model uncertainties, and parameter estimation. Overall, the Bayesian technique proved to be an excellent and versatile tool to successfully calibrate forest models of different structure and complexity, on different kind and number of variables and with a different number of parameters involved.
Happiness at Work: Organizational Values and Person-Organization Fit Impact1

Despite the large quantity of research on person-organization (PO) fit, there is a lack of empirical research using axiological theories applicable to the value structure at the individual and organizational levels. In this study, we incorporated the PO fit literature, Schwartz’ values theory and Warr’s vitamin analogy to evaluate the effect of values’ PO fit on happiness at work. Using a sample of 145 questionnaires collected in a military organization, data analyses were performed using hierarchical quadratic regressions and surface analysis of polynomial regressions, conducted at an individual level. Individual values moderated the curvilinear relationship between organizational values and happiness at work (R2 = .25; p < .01). Additionally, agreement between higher levels of values promoted higher levels of happiness at work (.07 ≥ R2 ≤ .18; p < .05). We concluded that axiological PO fit affects happiness at work and provide empirical support for Warr’s vitamin analogy.
The evolution of supergene enrichment in the Morenci porphyry copper deposit, Greenlee County, Arizona

Supergene enrichment in the Morenci porphyry copper deposit was formed as a result of the coupled processes of erosion and chemical weathering that accompanied five stages of landscape evolution in the Cenozoic Era. During Stage 1 (64 to 53 Ma), lowgrade primary chalcopyrite and pyrite mineralization was deposited as a result of Laramide magmatic and hydrothermal processes at about 55 Ma. During Stage 2 (53 to 30 Ma), initial unroofing and erosion removed approximately 1.8 km of rocks overlying the deposit and shed detritus to the north in the Eocene and to the south in the early Oligocene. During Stage 3 (30 to 18 Ma), the deposit was preserved under 640 to 950 meters of volcanic rocks as a result of mid-Tertiary extension and volcanism. During Stage 4 (18 to 2 Ma), most of the supergene copper enrichment at Morenci appears to have been formed as a result of Basin and Range deformation between ~13 and ~4 Ma. Sixteen new Ar/Ar ages from alunite, jarosite, and potassium-bearing manganese oxides in the district recorded three cycles of enrichment and leaching that peaked at about 7.3 Ma. Microbiological and geological studies revealed that acidophilic iron oxidizing bacteria and dissimilatory sulfate reducing bacteria contributed to leaching and enrichment of copper in the supergene environment, at least since the late Miocene. During Stage 5 (2 Ma to present), destruction of the current enriched blanket accompanied base-level drop and stream incision as a result of progressive drainage integration in southern Arizona in the late Pliocene and Pleistocene.
7th International Building Physics Conference IBPC 2018 Proceedings: Healthy, Intelligent and Resilient Buildings and Urban Environments

In recent years, probabilistic assessment of hygrothermal performance of building components has received increasing attention. Given the many uncertainties involved in the hygrothermal behaviour of building components, a probabilistic assessment enables to assess the damage risk more reliably. However, this typically involves thousands of simulations, which easily becomes computationally inhibitive. To overcome this time-efficiency issue, this paper proposes the use of much faster metamodels. This paper focusses on neural networks, as they have proven to be successful in other non-linear and non-stationary research applications. Two types of networks are considered: the traditional multilayer perceptron (with and without a time window) and memory neural networks (LSTM, GRU). Both are used for predicting the hygrothermal behaviour of a massive wall. The results showed that all networks are capable to predict the temperature profiles accurately, but only the LSTM and GRU networks could predict the slow responses of relative humidity and moisture content. Furthermore, the LSTM and GRU network were found to have almost equal predicting accuracy, though the GRU converged faster.
Kastamonu Eğitim Dergisi

The objective of this study was to examine the problems experienced and suggestions regarding effective children’s rights education (ECRE) in Turkey from the perspective of classroom teachers. In the study that was designed as a case study, the data were acquired by way of focus group interview and semi-structured interview methods. The study group was comprised of 15 classroom teachers selected via maximum variation sampling method which is among the purposive sampling methods. The study data were analyzed via content analysis. Problems related with ECRE were classified under three categories at the end of the study which are problems observed during application process, problems experienced during application process and problems observed in the country. Whereas the suggestions for ECRE were also classified in three different categories. These are; suggestions for the Ministry of Education, suggestions for teachers and school administration and other suggestions. The study is important since it puts forth problems and suggestions related with ECRE from the perspective of teachers who are part of the application process.
A pedometer-based physically active learning intervention : The importance of 1 using pre-intervention physical activity categories to assess effectiveness . 2

Northumbria University has developed Northumbria Research Link (NRL) to enable users to access the University’s research output. Copyright © and moral rights for items on NRL are retained by the individual author(s) and/or other copyright owners. Single copies of full items can be reproduced, displayed or performed, and given to third parties in any format or medium for personal research or study, educational, or not-for-profit purposes without prior permission or charge, provided the authors, title and full bibliographic details are given, as well as a hyperlink and/or URL to the original metadata page. The content must not be changed in any way. Full items must not be sold commercially in any format or medium without formal permission of the copyright holder. The full policy is available online: http://nrl.northumbria. ac.uk/policies.html
Time series analysis of non‐Gaussian observations based on state space models from both classical and Bayesian perspectives

The analysis of non‐Gaussian time series by using state space models is considered from both classical and Bayesian perspectives. The treatment in both cases is based on simulation using importance sampling and antithetic variables; Markov chain Monte Carlo methods are not employed. Non‐Gaussian disturbances for the state equation as well as for the observation equation are considered. Methods for estimating conditional and posterior means of functions of the state vector given the observations, and the mean‐square errors of their estimates, are developed. These methods are extended to cover the estimation of conditional and posterior densities and distribution functions. The choice of importance sampling densities and antithetic variables is discussed. The techniques work well in practice and are computationally efficient. Their use is illustrated by applying them to a univariate discrete time series, a series with outliers and a volatility series.
The Geographic Distribution of Excess Mortality Rate due to COVID-19 in Iranian Population: An Ecological Study

Background: COVID-19 has raised a worldwide trajectory since it emerged in Wuhan, China in December 2019. The direct and indirect mortalities in the world and as well as in Iran have increased significantly after the occurrence of this pandemic. Objectives: In this study, Excess Mortality Rate (EMR) was estimated by multilevel poison regression method and then this estimation was compared to the historical trends to obtain total deaths related to COVID-19. Additionally, the geographic distribution of EMR has also been presented for Iran. Methods: All-cause mortality rates of each province of Iran from March 21, 2013 to September 22, 2021 was downloaded from National Organization for Civil Registration (NOCR). The data of COVID-19 pandemic period (spring 1399 SH (Mar 20, 2020) to summer 1400 SH (Sep 22, 2021)) was removed from the data and then the multilevel poison model was applied to estimate all-cause mortality in this period. Then, EMR= (real deaths-expected death)/(real deaths) ratio was calculated. Results: The results of this study showed that Iran’s EMR in COVID-19 pandemic was 36% (Male=35%, Female=36%, P-value=0.798). Our findings also revealed four category of EMR including low (EMR≤30%, n=9), moderate (30 %< EMR≤35%, n=8), high (35 %< EMR≤40%, n=10) and very high (40 %< EMR, n=4) in different provinces. Conclusion: Due to the diverse EMR in different provinces of Iran, the type of disease management of provinces with low and moderate EMR can be used as an appropriate model to control EMR in provinces with high and very high EMR.
An Improved MSCNN and GRU Model for Rolling Bearing Fault Diagnosis

In this paper, a novel fault diagnosis method based on the fusion of squeeze and excitation-multiscale convolutional neural networks (SENet-MSCNN) and gate recurrent unit (GRU) is proposed to address the problem of low diagnosis rate caused by the fact that normal samples are much larger than fault samples in the vibration big data. The method takes the time-domain vibration signal as input and fuses the spatial features extracted by SENet-MSCNN. The temporal features extracted by GRU in order to bring them into the fully connected layer for identification so as to realize the intelligent diagnosis of rolling bearing adaptive feature extraction. Finally, the method is applied to the simulated signal and experimental data for testing and analysis. The results reveal that the model can reach 98.98 % and 76.44 % migration diagnostic accuracy in bearing and gearbox datasets. At the same time, it has strong noise immunity, adaptivity, and robustness, providing an effective way for intelligent diagnosis of rolling bearing vibration big data.
NUMBER NEEDED TO TREAT: TIME-RELATED ESTIMATION AND INTERPRETATION

The number needed to treat (NNT) is a simple measure of a treatment’s impact, increasingly reported in randomized trials and observational studies. Its calculation in studies involving varying follow-up times or recurrent outcomes has been criticized. We discuss the NNT in these contexts, illustrating using several published studies. The computation of the NNT is founded on the cumulative incidence of the outcome. Instead, several published studies use simple proportions that do not account for varying follow-up times, or use incidence rates per person-time. We show that these approaches can lead to erroneous values of the NNT and misleading interpretations. For example, after converting the incidence rate to a cumulative incidence, we show that a trial reporting a NNT of 4 “to prevent one exacerbation in 1 year” should have reported a NNT of 9. A survey of papers reporting NNT published in four major medical journals in 2009, found that 6 out of all 10 papers involving varying follow-up times did not correctly estimate the NNT. As the “number needed to treat” becomes increasingly used in complex studies and in the comparative effectiveness of therapies, its accurate estimation and interpretation become crucial to avoid erroneous clinical and public health decisions.
Secondary invasion by an invasive shrub limits native tree seedling growth in a size‐dependent manner following the eradication of a different invasive species in an island ecosystem

Following the control of an invasive tree species, native forest ecosystem restoration can be prevented by a secondary invasion by another invasive species that inhibits the reestablishment of native trees. In this study, we evaluated the effects of the invasive shrub Leucaena leucocephala on the initial growth of planted seedlings of four native tree species (Pandanus boninensis, Rhaphiolepis indica, Schima mertensiana, and Terminalia catappa) after eradication of the invasive tree Casuarina equisetifolia in the Ogasawara Islands in the northwestern Pacific Ocean. In a 24‐month field experiment, we compared the sizes of planted seedlings (diameter at ground level, height, and leaf number) among four treatments in a 2 × 2 design of initial seedling size (large vs. small) × L. leucocephala invasion (sowing L. leucocephala seeds vs. control). More than 75% of the seedlings (excluding S. mertensiana) survived regardless of the treatments. For all four species, initially large seedlings tended to become larger plants at the end of the experiment than initially small seedlings. We detected significant effects of L. leucocephala invasion on one or more plant size parameters in all four species. Seedlings grown in L. leucocephala invasion quadrats were smaller than those in control quadrats at the end of the experiment. We detected significant interactions between initial seedling sizes and L. leucocephala invasion for three species. Together, these results indicate that secondary invasion by L. leucocephala after controlling C. equisetifolia inhibits the initial growth of native tree seedlings, depending on their size at the time of invasion.
A Nonstandard Numerical Scheme for a Predator-Prey Model Involving Predator Cannibalism and Refuge

In this study, we implement a Nonstandard Finite Difference (NSFD) scheme for a predator-prey model involving cannibalism and refuge in predator. The scheme which is considered as a discrete dynamical system is analyzed. The performed analysis includes the determination of equilibrium point and its local stability. The system has four equilibrium points, namely the origin, the prey extinction point, the predator extinction point, and the coexistence point, which have exactly the same form and existence conditions as those in continuous system. The local stability of each first three equilibrium points is consistent with the one in continuous system. The stability of the coexistence point depends on the integration time step size. Nevertheless, the NSFD scheme allows us to choose the integration time step size for the solution to converge to a feasible point more flexible than the Euler and 4 order Runge-Kutta schemes. These are shown via numerical simulations.
An agent-based model of cardiac allograft vasculopathy: toward a better understanding of chronic rejection dynamics

Cardiac allograft vasculopathy (CAV) is a coronary artery disease affecting 50% of heart transplant (HTx) recipients, and it is the major cause of graft loss. CAV is driven by the interplay of immunological and non-immunological factors, setting off a cascade of events promoting endothelial damage and vascular dysfunction. The etiology and evolution of tissue pathology are largely unknown, making disease management challenging. So far, in vivo models, mostly mouse-based, have been widely used to study CAV, but they are resource-consuming, pose many ethical issues, and allow limited investigation of time points and important biomechanical measurements. Recently, agent-based models (ABMs) proved to be valid computational tools for deciphering mechanobiological mechanisms driving vascular adaptation processes at the cell/tissue level, augmenting cost-effective in vivo lab-based experiments, at the same time guaranteeing richness in observation time points and low consumption of resources. We hypothesize that integrating ABMs with lab-based experiments can aid in vivo research by overcoming those limitations. Accordingly, this work proposes a bidimensional ABM of CAV in a mouse coronary artery cross-section, simulating the arterial wall response to two distinct stimuli: inflammation and hemodynamic disturbances, the latter considered in terms of low wall shear stress (WSS). These stimuli trigger i) inflammatory cell activation and ii) exacerbated vascular cell activities. Moreover, an extensive analysis was performed to investigate the ABM sensitivity to the driving parameters and inputs and gain insights into the ABM working mechanisms. The ABM was able to effectively replicate a 4-week CAV initiation and progression, characterized by lumen area decrease due to progressive intimal thickening in regions exposed to high inflammation and low WSS. Moreover, the parameter and input sensitivity analysis highlighted that the inflammatory-related events rather than the WSS predominantly drive CAV, corroborating the inflammatory nature of the vasculopathy. The proof-of-concept model proposed herein demonstrated its potential in deepening the pathology knowledge and supporting the in vivo analysis of CAV.