Miracle-dz/UniFAR-FTUs huggingface dataset:
Engineering Novel Aptameric Fluorescent Biosensors for Analysis of the Neurotoxic Environmental Contaminant Insecticide Diazinon from Real Vegetable and Fruit Samples.

BACKGROUND Diazinon is a widely used organophosphorus neurotoxic insecticide. It is a common environmental contaminant and a hazardous agri-waste. Its detection is critical to control entry into food systems and protect the environment. METHODS In this study, three single-stranded DNA aptamers specific for diazinon were discovered using the systematic evolution of ligands by the exponential enrichment (SELEX) process. Since aptamer-based sensors are quick and straightforward to analyze, they could potentially replace the time-consuming and labor-intensive traditional methods used for diazinon detection. RESULTS Here, we show the engineering of novel sensors for diazinon detection with a high affinity (Kd), specificity, and high sensitivity at the ppb level. Moreover, the aptamers were helpful in the simultaneous detection of two other structurally relevant insecticides, fenthion, and fenitrothion. Furthermore, the real vegetable and fruit samples confirmed the specific detection of diazinon using DIAZ-02. CONCLUSIONS We developed novel biosensors and optimized the assay conditions for the detection of diazinon from food samples, such as vegetables and fruit. The biosensor could be adopted to analyze toxicants and contaminants in food, water, and nature as point-of-care technology.
Taurine attenuates ER stress-associated apoptosis and catabolism in nucleus pulposus cells

Nucleus pulposus (NP) apoptosis and subsequent excessive degradation of the extracellular matrix (ECM) are key pathological characteristics of intervertebral disc degeneration (IDD). The present study aims to examine the signaling processes underlying the effects of taurine on IDD, with specific focus on endoplasmic reticulum (ER) stress-mediated apoptosis and ECM degradation, in NP cells. To clarify the role of taurine in IDD, NP cells were treated with various concentrations of taurine and IL-1β or thapsigargin (TG). Cell Counting Kit-8, western blotting, TUNEL, immunofluorescence assays and reverse transcription-quantitative PCR were applied to measure cell viability, the expression of ER stress-associated proteins (GRP78, CHOP and caspase-12), apoptosis and the levels of metabolic factors associated with ECM (MMP-1, 3, 9, ADAMTS-4, 5 and collagen II), respectively. Taurine was found to attenuate ER stress and prevent apoptosis in NP cells induced by IL-1β treatment. Additionally, taurine significantly decreased the expression of ER stress-activated glucose regulatory protein 78, C/EBP homologous protein and caspase-12. TUNEL results revealed that taurine decreased the number of apoptotic TG-treated NP cells. TG-treated NP cells also exhibited characteristics of increased ECM degradation, supported by observations of increased MMP-1, MMP-3, MMP-9 and A disintegrin and metalloproteinase with thrombospondin motifs (ADAMTS)-4 and ADAMTS-5 expression in addition to decreased collagen-II expression. However, taurine treatment significantly reversed all indicators of excessive ECM catabolism aforementioned. These data suggest that taurine can mediate protection against apoptosis and ECM degradation in NP cells by inhibiting ER stress, implicating therapeutic potential for the treatment of IDD.
Simulating violation of causality using a topological phase transition

We consider a topological Hamiltonian and establish a correspondence between its eigenstates and the resource for a causal order game introduced in Ref. [1], known as process matrix . We show that quantum correlations generated in the quantum many-body energy eigenstates of the model can mimic the statistics that can be obtained by exploiting different quantum measurements on the process matrix of the game. This provides an interpretation of the expectation values of the observables computed for the quantum many-body states in terms of the success probabilities of the game. As a result, we show that the ground state (GS) of the model can be related to the optimal strategy of the causal order game. Subsequently, we observe that at the point of maximum violation of the classical bound in the causal order game, corresponding quantum many-body model undergoes a second-order quantum phase transition (QPT). The correspondence equally holds even when we generalize the game for a higher number of parties.
Psychometric evaluation of the NORC diagnostic screen for gambling problems (NODS) for the assessment of DSM-5 gambling disorder.

The National Opinion Research Center (NORC) Diagnostic Screen for Gambling Problems (NODS) is one of the most used outcome measures in gambling intervention trials. However, a screen based on DSM-5 gambling disorder criteria has yet to be developed or validated since the DSM-5 release in 2013. This omission is possibly because the criteria for gambling disorder only underwent minor changes from DSM-IV to DSM-5: the diagnostic threshold was reduced from 5 to 4 criteria, and the illegal activity criterion was removed. Validation of a measure that captures these changes is still warranted. The current study examined the psychometric properties of an online self-report past-year adaptation of the NODS based on DSM-5 diagnostic criteria for gambling disorder (i.e., NODS-GD). A diverse sample of participants (N = 959) was crowdsourced via Amazon's TurkPrime. Internal consistency and one-week test-retest reliability were good. High correlations (r = 0.74-0.77) with other measures of gambling problem severity were observed in addition to moderate correlations (r = 0.21-0.36) with related but distinct constructs (e.g., gambling expenditures, time spent gambling, other addictive behaviors). All nine of the DSM-5 criteria loaded positively on one principal component, which accounted for 40% of the variance. Classification accuracy (i.e., sensitivity, specificity, predictive power) was generally very good with respect to the PGSI and ICD-10 diagnostic criteria. Future studies are encouraged to establish a gold standard self-report measure of gambling problems and develop agreed-upon recommendations for the use and interpretation of crowdsourced addiction data.
Linking Teacher Injustice, Conscientiousness, and Academic Dishonesty via Classroom Connectedness: A Moderated-Mediation Model

This study extends the general understanding of the antecedents of academic dishonesty by examining what can happen when students are treated unjustly by teachers, a phenomenon referred to as teacher injustice. Based on Conservation of Resources theory (CoRT), the study investigates the mediating role of classroom connectedness and the moderating role of trait conscientiousness in the teacher injustice–academic dishonesty relationship. Self-report data were collected in three waves from Pakistani secondary school students to test the moderated-mediation model. Findings demonstrate that teacher injustice is positively related to academic dishonesty through classroom connectedness and that conscientiousness buffers the negative effects of teacher injustice on classroom connectedness. Given the results, this study suggests certain potentially effective measures to educational institutions to control the incidence of teacher injustice and cultivate conscientiousness in students. It also hopes to inspire future research, and advance education management theory and practice.
Application of Machine Learning on Software Quality Assurance and Testing: A Chronological Survey

Ensuring the quality is essential for a successful Software System. Software systems need to be tested in every stage of the Software Development Life Cycle (SDLC) irrespective of the type of software being developed. If a software bug remains undetected in the early phase of the SDLC, it becomes harder to fix it at a later stage and becomes very costly. The application of machine learning in Software Quality Assurance and Testing can help testers in the testing process, including the early detection and prediction of a software bug. However, employing machine learning techniques brings new challenges to testing and quality assurance. Machine Learning (ML) uses Artificial Intelligence (AI) techniques that focus on a given dataset to find any trend present in the data. It has been observed that some software testing activities can, in fact, be represented as a learning problem. Thus, ML can be used as an efficient tool to automate software- testing activities, especially when the software system becomes very complex. This survey aims to study and summarize the application of machine learning on software quality assurance and testing in a chronological manner by selecting from articles published in the last twenty-six years or so.
Retinal OCT image report generation based on visual and semantic topic attention model

Optical coherence tomography (OCT) is widely used in the diagnosis of retinal diseases. Reading OCT images and summarizing its insights is a routine, yet nonetheless time-consuming task. Automatic report generation can alleviate this issue. There are two major challenges in this task: (1) An OCT image may contain several fundus abnormalities and it is difficult to detect them all simultaneously. (2) The diagnostic reports are complex, which need to describe multiple lesions. In this paper, we propose a deep learning-based model, named as VSTA model (Visual and Semantic Topic Attention model), which is able to generate report from the input OCT image. Our major contributions include: (1) Semantic attention and visual attention are jointly embedded to the model to generate diagnosis report with complex content. (2) Semantic tags based on image similarity is employed to initialize the semantic attention weights, which increases the prediction accuracy of the model. With the proposed VSTA model, the metric of BLEU-4, CIDEr and ROUGE-L reach 31.16, 264.22 and 52.58, which are better than some existing advanced methods.
Institutional Arrangements and Power Threat: Diversity, Democracy, and Punitive Attitudes

Abstract This research synthesizes studies on crime and punishment, work in political sociology, and race and ethnicity scholarship in order to theorize and empirically examine the democratic foundations of group threat theory. We argue that ethnic diversity is particularly threatening when coupled with robust democratic institutions that empower individuals to pose challenges to the extant political and social order. Making use of recent measurement advances in the study of democracy, this article uses multi-level modeling techniques across 39,926 survey respondents in 27 countries from the fifth wave of the European Social Survey to test the extent to which punitive attitudes toward criminals were associated with interaction effects of an index of ethnic diversity and democratic quality. Results strongly confirm our theoretical predictions that robust democratic institutions condition the effect of ethnic fractionalization on punitive attitudes in Europe.
Induction of accessory corpus luteum in goats and sheep: From physiological basis to reproductive effects

The identification of ovarian follicular waves and associated hormonal milieux has revealed that dominant follicles of the ovulatory wave (OWDF) grow in a hormonal environment where there is an increasing predominance of estradiol, unlike first-wave dominant follicles (FWDF) and intermediatewave dominant follicles (IWDF), which grow under increasing progesterone (P4) concentrations. The luteinizing hormone (LH) is considered the decisive gonadotropic hormone to direct whether a dominant follicle will (↑ LH) or will not (↓ LH) ovulate. Based on this, strategies have been developed to either increase endogenous LH (GnRH administration) or provide exogenous LH of porcine origin (pLH) or LH-like gonadotropins, such as human chorionic gonadotropin (hCG). Such strategies are able to provide LH for final maturation, ovulation, and/or luteinization of the FWDF or IWDF, forming accessory corpora lutea (aCL). As a consequence, P4 increases and favors the establishment of pregnancy, particularly when P4 is the limiting factor for the success of the conceptus implantation and maintenance. In sheep and goats, previous studies have administered hCG five to seven days after the onset of estrus and revealed that FWDF positively respond to hCG administration by either forming aCLs and/or Fonseca et al. Induction of accessory corpus luteum in goats and sheep: From physiological basis to reproductive effects. Rev Bras Reprod Anim, v.45, n.4, p.309-317, out. /dez. 2021 310 promoting hypertrofia of the original CL which, in turn, increases its luteal tissue area. Normally, P4 synthesis increases along with the increase in luteal tissue area. As a final and most desirable outcome, pregnancy and the birth of lambs/kids also increase. These concepts will be discussed in this review, focusing on aCL induction in sheep and goats.
Not Only Funding: How Healthcare Organizations Can Contribute to National Health Service Sustainability

ABSTRACT Healthcare organizations struggle to provide adequate assistance because of the scarcity of resources affecting National Health Systems (NHS) worldwide. It is necessary to find strategies to make the NHS sustainable. Funding cannot always be leveraged, and new variables need to be found. It is asked: What can and should be done in single health organizations to improve the efficacy of the NHS? To answer, several CEOs of Italian hospitals were interviewed. Results suggest that there is a widespread need for improvement of organizational aspects of professionals’ responsibilities, better coordination between health professionals, and in-service training. Several conclusions can be sketched by the analysis of interviews. Current possible NHS problems, as well as practical suggestions on how to improve it, are offered. Bureaucracy, slow decisional processes, and weak reward systems are seen as demons to fight. How? Reforming the regulation system, valorizing professional competencies, and placing stronger emphasis on organizations’ commitment.
Development of Welded Rotors for High-Temperature Steam Turbines

Steam turbine plants face strict market demand for high-temperature operation, streamlining, and shortened delivery times. The welded rotor enables streamlining through the use of material most appropriate for high to low-temperature parts. The use of materials will also enable a shift to larger capacity and shorter delivery times. The hetero-material welded rotor consisting of 2.25CrMoV, 9%Cr, and 3.5NiCrMoV steel was developed to meet these demands and verified at Takasago combined cycle plant. The plant has been conducting commercial and proving operations on the rotors. In this paper the authors introduce verification of work execution, strength, thermal stability, inspection for the welded rotor, and actual machine commercial operation. © 2005 ASME
Evaluation for Exit Time of Relay Protection Devices Based on Life Cycle Cost

In order to realize rational evaluation of the exit time of relay protection devices,an equipment failure rate model is developed first based on parameter estimation of the Marquardt method.Then,the equipment annual cost model related to the failure rate is built by analyzing the various factors of its life cycle cost.Meanwhile,an assessment strategy for equipment exit time based on the minimum annual cost is proposed.Finally,an example of a certain type of relay protection devices is given to seek the optimal equipment exit time.This method is useful for power production and planning departments to design rational equipment replacement policies.
Multidimensional frailty in older people in general practitioners’ clinical practice: the SELFY-MPI SIGOT project

The multidimensional prognostic index (MPI) is a comprehensive geriatric assessment (CGA) tool exploring the multiple domains of older subject. The knowledge and the diffusion of self-assessment tools for identifying frailty in general medicine is still limited. The aim of our study is to determine the prevalence of frailty using a multidimensional frailty screening tool (SELFY-MPI) in a cohort of older adults, belonging to the general practitioner’s (GPs) experience. In the frame of a national educational program organized by the Italian Geriatric Hospital and Community Society (SIGOT), expert geriatricians carried-out local courses addressed to GPs, focused on multidimensional approach in primary care. A cross-sectional study of the SELFY-MPI, based on eight different domains, in the general practitioners’ outpatient clinic was performed among 50 GPs. SELFY-MPI risk score was used for dividing the participants in robust, pre-frail, or frail. A total of 526 participants (mean age: 77.7 years; females=55.3%) fulfilled the SELFY-MPI. The participants were, on average, independent in the activities of daily living, had a good mobility, but they reported some cognitive difficulties, and they can be considered at risk of malnutrition. A high prevalence of comorbidities and polypharmacotherapy was also present. The 20.2% of the sample lived alone, suggesting a potential social frailty. The mean SELFY-MPI score was 0.26 0.17: therefore, 21.67% of the participants were categorized as pre-frail, and 3.99% as frail. Pre-frailty and frailty are common in GPs experience. SELFY-MPI is a feasible screening tool for multidimensional frailty in the GPs clinical practice.
The COVID-19 pandemic and ethical stock markets: further evidence of moral shock

ABSTRACT The aim of this study is twofold. First, we assess the evolution of stock returns for two classes of ethical investments—Islamic and sustainable funds—from September 2001–January 2021. This is the first study of the financial performance of ethical investments in a period covering technological (the dot-com bubble in 2000), financial (the 2008 financial crisis), and healthcare shocks (the COVID-19). Second, we analyze the dynamics of the financial returns of conventional and ethical ethnic markets in the COVID-19 context and model the impact of COVID-19 news. We perform different time-varying tests to apprehend market reactions to the COVID-19 pandemic. We present two interesting results. First, the COVID-19 pandemic has had a time-varying impact on the stock market. Basically, a close to zero effect at the early stage of the pandemic, followed by a negative and significant effect during the first wave from March 2020 to June 2020, was observed. However, this has since been attenuated owing to social restriction measures, teleworking, government stimulus policies, and massive vaccine rollouts. Second, among all markets, the Islamic stock market is the most resilient and least impacted by the pandemic, suggesting evidence of a new moral shock.
Model predictive control–based cooperative lane change strategy for improving traffic flow

Lane change has attracted more and more attention in recent years for its negative impact on traffic safety and efficiency. However, few researches addressed the multi-vehicle cooperation during lane change process. In this article, feasibility criteria of lane change are designed, which considers the acceptable acceleration/deceleration of neighboring vehicles; meanwhile, a cooperative lane change strategy based on model predictive control is proposed in order to attenuate the adverse impacts of lane change on traffic flow. The proposed strategy implements the centralized decision making and active cooperation among the subject vehicle performing lane change in the subject lane and the preceding vehicle and the following vehicle in the target lane during lane change. Using model predictive control, safety, comfort, and traffic efficiency are integrated as the objectives, and lane change process is optimized. Numerical simulation results of the cooperative lane change strategy suggest that the deceleration of following vehicle can be weakened and further the shock wave propagated in traffic flow can be alleviated to some degree compared with traditional lane change.
Bending and Collapse: Magnetic Recording Fidelity of Magnetofossils From Micromagnetic Simulation

Magnetosome chains produced by magnetotactic bacteria are important paleoenvironmental and paleomagnetic recorders. It has been shown that magnetic properties of magnetosome chains are closely related to their morphology and chain structures; however, the in situ structures of magnetosome chains in sediments (magnetofossils) are not known. Magnetosome chains are subject to various deformations after cell dissolution and are therefore unlikely to be fully intact, obscuring their original magnetic signals. Here, we use finite element micromagnetic simulations to quantify changes in magnetic signals in response to chain deformation, in particular, as a function of variable degrees of bending and collapse. Our results indicate that bending/collapse leads to a significant coercivity reduction and domain state transition of the chain. Therefore, hysteresis parameters can be used to assess the degree of chain bending/collapse in magnetofossil‐rich sediments. Calculations of the contributions of chain bending/collapse to the post‐depositional remanent magnetization (pDRM) of magnetofossils indicate that pDRM remains both faithful to the pre‐bending/collapse natural remanent magnetization, and that the remanence of some structurally deformed magnetofossil assemblages remains thermally stable over billion‐year timescales, suggesting that even strongly deformed magnetosome chains in ancient geological materials retain faithful paleomagnetic records and thus have potentials for tracing ancient geomagnetic field variations and microbial activities on early Earth.
SEMIPARAMETRIC REGRESSION MODELS AND APPLICABILITY IN AGRICULTURE

: Parametric regression models assume that the dependent variable is a linear relationship with the independent variables and the form of the relationship is known. Nonparametric regression methods are applied in cases where the relationship type is not known or assumptions cannot be provided. However, when there is more than one independent variable, some of the independent variables may be in a linear relationship with the dependent variable, while some may be in a nonlinear relationship. In order to model these variables, semiparametric regression models, which are a combination of parametric and nonparametric regression methods, are used. In this study parametric, nonparametric and semiparametric regression models, parametric estimates, fit statistical values of the models, confidence intervals and standard error values were calculated. As a result of the analysis, the parameters of the milking unit and the quarantine area among the parametric variables, the operation area, the ventilation area, the number of ventilation, the quarantine area, the infirmary area, the manure pit and the distance to the center among the non-parametric variables were found to be statistically very important (P<0.01). As a result, it was concluded that the correct definition of the variables (parametric and non-parametric) that are effective in determining the operating cost of agricultural enterprises and consequently the sales price, and the selection of the appropriate model are extremely important and that semiparametric models can be used easily in this field.