Miracle-dz/UniFAR-FTUs huggingface dataset:
Discovering Statistically Significant Periodic Gene Expression

One frequent application of microarray experiments is in the study of monitoring gene activities in a cell during cell cycle or cell division. High throughput gene expression time series data are produced from such microarray experiments. A new computational and statistical challenge for analyzing such gene expression time course data, resulting from cell cycle microarray experiments, is to discover genes that are statistically significantly periodically expressed during the cell cycle. Such a challenge occurs due to the large number of genes that are simultaneously measured, a moderate to small number of measurements per gene taken at different time points and high levels of non‐normal random noises inherited in the data. Computational and statistical approaches to discovery and validation of periodic patterns of gene expression are, however, very limited. A good method of analysis should be able to search for significant periodic genes with a controlled family‐wise error (FWE) rate or controlled false discovery rate (FDR) and any other variations of FDR, when all gene expression profiles are compared simultaneously. In this review paper, a brief summary of currently used methods in searching for periodic genes will be given. In particular, two methods will be surveyed in details. The first one is a novel statistical inference approach, the C & G Procedure that can be used to effectively detect statistically significantly periodically expressed genes when the gene expression is measured on evenly spaced time points. The second one is the Lomb–Scargle periodogram analysis, which can be used to discover periodic genes when the gene profiles are not measured on evenly spaced time points or when there are missing values in the profiles. The ultimate goal of this review paper is to give an expository of the two surveyed methods to researchers in related fields.
The role of Nrf2/SIRT1 pathway in the hepatoprotective effect of PEITC against HFD/STZ-induced diabetic liver disease. 

Short title: PEITC ameliorates liver injury via Nrf2/SIRT1 pathways

Aim: We intended to assess the hepatoprotective effects of phenethyl isothiocyanate (PEITC) against oxidative liver injury induced by a high-fat diet (HFD) and streptozotocin (STZ) diabetes through the Nuclear Factor E2-Related Factor 2 (Nrf2) and Sirtuin 1 (SIRT1) pathways in rats. Materials and Methods: Thirty male Wistar Albino rats were separated into three groups: the control group, the second group (HFD+STZ) fed HFD and injected with STZ (35mg/kg b.w.), and the third group (HFD+ STZ+PEITC) fed an HFD, injected with STZ (35mg/kg b.w.), and given PEITC (40mg/kg b.w. by oral gavage). Feeding with HFD and PEITC was given for two weeks and continued one more week following STZ. Serum ALT and lipids levels, antioxidant enzyme activities, MDA, GST, SIRT1, NF-ƙβ, and Nrf2 levels were measured. Liver histological changes were detected. Results: In comparison with the control group, in the HFD+STZ group, serum HDL levels, activities of hepatic antioxidant enzymes, Nrf2 activity, levels of GST, SIRT1, and NF-ƙβ reduced and besides, serum ALT, TG, TC, LDL / VLDL, and hepatic MDA levels increased (p <0.05). PEITC pre-administration led to improvement in these changes made by HFD-STZ (p <0.05). Conclusion: Our data presented that PEITC ameliorated hepatic injury and serum lipid profile induced by HFD and STZ via the activation of Nrf2 and SIRT1 pathways. We can suggest that PEITC could be a possible candidate agent against liver diseases.
Cirrus cloud retrieval using infrared sounding data : multilevel cloud errors

Abstract In this study we perform an error analysis for cloud-top pressure retrieval using the High-Resolution Infrared Radiometric Sounder (HIRS/2) 15-µm CO2 channels for the two-layer case of transmissive cirrus overlying an overcast, opaque stratiform cloud. This analysis includes standard deviation and bias error due to instrument noise and the presence of two cloud layers, the lower of which is opaque. Instantaneous cloud pressure retrieval errors are determined for a range of cloud amounts (0.11.0) and cloud-top pressures (850−250 mb). Large cloud-top pressure retrieval errors are found to occur when a lower opaque layer is present underneath an upper transmissive cloud layer in the satellite field of view (FOV). Errors tend to increase with decreasing upper-cloud elective cloud amount and with decreasing cloud height (increasing pressure). Errors in retrieved upper-cloud pressure result in corresponding errors in derived effective cloud amount. For the case in which a HIRS FOV has two distinct clo.
Superradiance dynamics caused by internal reflections

We consider a pencil-shaped active medium consisting of two- level initially excited atoms and described by 1D Maxwell- Bloch equations. With the help of these equations solution without slowly varying envelope approximation we shown, that internal field reflections from the ends of the sample transform the so-called 'lethargic' regime of super-radiance (SR) pulse amplification to the amplification regime similar to usual exponential law. iT can lead to the pulse development acceleration and counter-propagating waves synchronization. A simple formula for SR pulse delay time evaluation is presented. The criterion of the counter- propagating waves synchronization is discussed.
Interdependency and hierarchy of exact epidemic models on networks

Over the years numerous models of SIS (susceptible - infected - susceptible) disease dynamics unfolding on networks have been proposed. Here, we discuss the links between many of these models and how they can be viewed as more general motif-based models. We illustrate how the different models can be derived from one another and, where this is not possible, discuss extensions to established models that enables this derivation. We also derive a general result for the exact differential equations for the expected number of an arbitrary motif directly from the Kolmogorov/master equations and conclude with a comparison of the performance of the different closed systems of equations on networks of varying structure.
QuoteTarget: A sequence‐based transformer protein language model to identify potentially druggable protein targets

The development of efficient computational methods for drug target protein identification can compensate for the high cost of experiments and is therefore of great significance for drug development. However, existing structure‐based drug target protein‐identification algorithms are limited by the insufficient number of proteins with experimentally resolved structures. Moreover, sequence‐based algorithms cannot effectively extract information from protein sequences and thus display insufficient accuracy. Here, we combined the sequence‐based self‐supervised pretraining protein language model ESM1b with a graph convolutional neural network classifier to develop an improved, sequence‐based drug target protein identification method. This complete model, named QuoteTarget, efficiently encodes proteins based on sequence information alone and achieves an accuracy of 95% with the nonredundant drug target and nondrug target datasets constructed for this study. When applied to all proteins from Homo sapiens, QuoteTarget identified 1213 potential undeveloped drug target proteins. We further inferred residue‐binding weights from the well‐trained network using the gradient‐weighted class activation mapping (Grad–Cam) algorithm. Notably, we found that without any binding site information input, significant residues inferred by the model closely match the experimentally confirmed drug molecule‐binding sites. Thus, our work provides a highly effective sequence‐based identifier for drug target proteins, as well to yield new insights into recognizing drug molecule‐binding sites. The entire model is available at https://github.com/Chenjxjx/drug-target-prediction.
Benchmark Beating and Earnings Manipulation in Nigerian Firms

Earnings management among firms remains a central focus for academics, auditors and regulatory bodies. Benchmark-motivated earnings management occurs when managers engage in opportunistic activities, including flexible use of accounting standards to misrepresent the information in a firm’s financial reports. Academic research has focused on how firms manage earnings to beat benchmarks, but the evidence regarding firms in emerging African stock markets is scarce and none is available for Nigeria. We applied both accruals quality and discretionary accruals models to detect whether firms that beat earnings benchmarks report earnings differently from others. Using 161 firms listed on the Nigerian Stock Exchange from 2002 to 2019, the study verifies how benchmark beaters manage earnings under the framework of two earnings thresholds – earnings (level) and positive earnings changes. Earnings persistence tests were carried out to verify whether benchmark beaters are consistent manipulators relative to non-beaters. The findings indicate that positive earnings benchmarks differ among the dichotomized groups. The evidence is not sufficient to validate that the change in earnings benchmarks motivates earnings discretions. However, the evidence may improve for larger samples. The study offers insights for informed decisions on the expectation of investment returns for investors, creditors, and other market partakers that require earnings information.
Religiosity: Mediation of Resilience and Hopefulness; The Case of Students of Islamic Azad University, Qom Branch

Background and Objectives: Religiosity is one of the constructs that has been recently considered in the field of positive psychology as it can play a significant role as an effective factor in preventing disorders and maintaining mental health. The aim of this study is to investigate the mediating role of religiosity in the relationship between hope and resilience among the students. Methods: The research method is descriptive-correlational and the population is students of Qom Islamic Azad University selected according to Morgan Index from among 3750 persons through accessible simple random sampling. To collect data, connor and davidson resilience scale (CD-RIS-25), Snyder Hope Questionnaire and Gluck and Stark Religiosity Scale were used and data were analyzed using AMOS and SPSS software, v. 19 through Pearson Coefficient Correlation, Multiple Regression and Path analysis. Results: The coefficient correlation between the hope parameters (factorial and strategic thinking) and religiosity and resilience has been positive at the alpha level of 0.01 (P<0.01). Based on the results of Path analysis, religiosity parameters (factorial and strategic thinking) have direct and indirect positive effect on resilience. Too, the correlation between hope and religiosity has been positive and meaningful at the Alpha level of 0.01(P<0.01) The correlation between the hope and religiosity is also positive and significant at the alpha level of 0.01 (P<0.01). Conclusion: It is concluded that enhancement of hope affects resilience. Moreover, if the subjects experience higher level of religiosity their resilience and hope are, accordingly, enhanced.
Maximum likelihood estimation of models for residual covariance in spatial regression

We describe the maximum likelihood method for fitting the linear model when residuals are correlated and when the covariance among the residuals is determined by a parametric model containing unknown parameters. Observations are assumed to be Gaussian. We give conditions which ensure consistency and asymptotic normality of the estimators. Our main concern is with the analysis of spatial data and in this context we describe some simulation experiments to assess the small sample behaviour of estimators. We also discuss an application of the spectral approximation to the likelihood for processes on a lattice.
Fabrication of pyramidal corrugated quantum well infrared photodetector focal plane arrays by inductively coupled plasma etching with BCl3/Ar

Abstract. We developed an optimized inductively coupled plasma etching process to produce gallium arsenide (GaAs) pyramidal corrugated quantum well infrared photodetector focal plane arrays (C-QWIP FPAs). A statistically designed experiment was performed to optimize the etching parameters. The resulting parameters are discussed in terms of the effect on the etching rate and profile. This process uses a small amount of mask corrosion and the control of the etching mask gap to give a 45 deg to 50 deg V-groove etching profile, which is independent of the crystal orientation of GaAs. In the etching development, scanning electron microscope was used to observe the surface morphology and the pattern profile. In addition, x-ray photoelectron spectroscopy was used to obtain the elemental composition and contamination of the etching surface. It is found that extremely small stoichiometric change and surface damage of the etching surface can be achieved while keeping a relatively high etching rate and ∼45 deg V-groove etching profile. This etching process is applied to the fabrication of pyramidal C-QWIP FPAs successfully, which are expected to have better performance than the regular prism-shaped C-QWIPs according to electromagnetic modeling.
Pre- And Post-Pandemic Corporate Social Responsibility (CSR) and Organisational Risk Management Practices

Corporate Social Responsibility (CSR), which has been around for a while in the business world, is frequently seen as a group of efforts meant to improve an organization's reputation by enhancing its public relations techniques. Businesses can gain from globalisation, but it also introduces new risks and sources of instability. There is increased uncertainty in corporate decision-making as firms become more reliant and exposed. Inexperienced players have emerged because of this interconnection, necessitating innovative risk management techniques. This study examines the association between CSR and organisational risk management practises in Malaysia's publicly traded corporations before and after the epidemic. The 91 Malaysian listed PLCs' annual and sustainability reports from 2004 to 2021 were the subject of an exploratory content analysis for this study. From a macro perspective, this study's findings suggest that the COVID-19 pandemic has increased Malaysian companies' concern about actively engaging in CSR and organisational risk management practises. This paper demonstrates CSR practises and organisational risk management practises in Malaysia to show an increasing trend in comparisons between pre-and post-pandemic situations, which may suggest manifesting towards better CSR performance to lever any foreseen or unforeseen organisational risk signals.
An Investigation of the Power of the Likelihood Ratio Goodness-of-Fit Statistic in Detecting Differential Item Functioning.

The purpose of this study was to investigate the power and Type I error rate of the likelihood ratio goodness-of-fit (LR) statistic in detecting differential item functioning (DIF) under Samejima's (1969, 1972) graded response model. A multiple-replication Monte Carlo study was utilized in which DIF was modeled in simulated data sets which were then calibrated with MULTILOG (Thissen, 1991) using hierarchically nested item response models. In addition, the power and Type I error rate of the Mantel (1963) approach for detecting DIF in ordered response categories were investigated using the same simulated data, for comparative purposes. The power of both the Mantel and LR procedures was affected by sample size, as expected. The LR procedure lacked the power to consistently detect DIF when it existed in reference/focal groups with sample sizes as small as 500/500. The Mantel procedure maintained control of its Type I error rate and was more powerful than the LR procedure when the comparison group ability distributions were identical and there was a constant DIF pattern. On the other hand, the Mantel procedure lost control of its Type I error rate, whereas the LR procedure did not, when the comparison groups differed in mean ability; and the LR procedure demonstrated a profound power advantage over the Mantel procedure under conditions of balanced DIF in which the comparison group ability distributions were identical. The choice and subsequent use of any procedure requires a thorough understanding of the power and Type I error rates of the procedure under varying conditions of DIF pattern, comparison group ability distributions. –or as a surrogate, observed score distributions–and item characteristics.
Nonlinear rule reduction and robust control

We propose an approach for modeling and model reduction using a generalized form of Takagi-Sugeno fuzzy systems. First, we define a generalized form of Takagi-Sugeno fuzzy systems. The structure of these fuzzy systems has some advantages: they accord with dynamics of the original model and they are suitable for reducing the number of rules. Thus, these fuzzy systems are constructed by many if-then rules. Next, we derive the conditions to reduce the number of rules represented in terms of LMIs. The main idea is to find a structure of if-then rules of the reduced model that agrees well with the dynamics of the original model. Furthermore, we estimate the lower bound of the norm of model uncertainty of the Takagi-Sugeno fuzzy system that can cover the reduction error. Finally, we illustrate an example of model reduction and robust control for a nonlinear system.
Regional Variability in the Terrestrial Carbon-Cycle Response to Global Warming in the 21st Century: Simulation Analysis with AOGCM-Based Climate Projections

Regional variability in the carbon-cycle response of terrestrial ecosystems to global warming was simulated with a spatially explicit, process-based model (Sim-CYCLE), and analyzed for 22 regions worldwide. The terrestrial net flux of carbon dioxide (CO 2 ) during the period 2001-2099, was globally simulated on the basis of greenhouse gas emission scenarios (IPCC-SRES), and climate projections by coupled atmosphere-ocean general circulation models (AOGCMs). Most terrestrial ecosystems acted as net carbon sinks but with different magnitudes among the experiments (globally, 24 to 286 Pg C). Substantial inter-regional variability was found in terrestrial carbon-cycle response, because of differences in biome distributions and climate-change regimes. Moreover, among the experiments using the same CO 2 scenario, but different AOGCM climate projections, inconsistent results were obtained in the Amazon basin, Africa, Australia, and northern Asia, indicating the need for further research.
Satellite Remote Sensing of Global Land Surface Temperature: Definition, Methods, Products, and Applications

Land surface temperature (LST) is a crucial parameter that reflects land–atmosphere interaction and has thus attracted wide interest from geoscientists. Owing to the rapid development of Earth observation technologies, remotely sensed LST is playing an increasingly essential role in various fields. This review aims to summarize the progress in LST estimation algorithms and accelerate its further applications. Thus, we briefly review the most‐used thermal infrared (TIR) LST estimation algorithms. More importantly, this review provides a comprehensive collection of the widely used TIR‐based LST products and offers important insights into the uncertainties in these products with respect to different land cover conditions via a systematic intercomparison analysis of several representative products. In addition to the discussion on product accuracy, we address problems related to the spatial discontinuity, spatiotemporal incomparability, and short time span of current LST products by introducing the most effective methods. With the aim of overcoming these challenges in available LST products, much progress has been made in developing spatiotemporal seamless LST data, which significantly promotes the successful applications of these products in the field of surface evapotranspiration and soil moisture estimation, agriculture drought monitoring, thermal environment monitoring, thermal anomaly monitoring, and climate change. Overall, this review encompasses the most recent advances in TIR‐based LST and the state‐of‐the‐art of applications of LST products at various spatial and temporal scales, identifies critical further research needs and directions to advance and optimize retrieval methods, and promotes the application of LST to improve the understanding of surface thermal dynamics and exchanges.
Combined Modes of the Northern Stratosphere, Tropical Oceans, and East Asian Spring Rainfall: A Novel Method to Improve Seasonal Forecasts of Precipitation

Seasonal forecasting models have a harder time accurately predicting regional precipitation anomalies in East Asia than the large‐scale drivers of these precipitation anomalies. These large‐scale drivers include both sea surface temperature (SST) anomalies and conditions in the polar stratosphere. A maximum covariance analysis is used to isolate the SST and stratospheric patterns most associated with anomalous East Asian precipitation, which are employed to correct seasonal forecasts by projecting the forecasted stratospheric circulation and SST onto the observed combined modes. The nonuniform predictability of East Asian rainfall in seasonal forecast models is improved after error correction with the observed linkage between the stratosphere–tropical ocean modes and East Asian rainfall. This method should be helpful for improving rainfall forecasts in other regions as well.
Eigenproblem for monotone and toeplitz matrices in a Max-algebra

The eigenproblem for monotone and Toeplitz matrices in a max-algebra is shown to be solvable in O(n 2) time. Two algorithms are described which, for a given n × n real monotone and for a given n × n real Toeplitz matrix compute an eigenvalue λ and all eigenvectors of the form x = (x 1, x 2, … , x n ) such that These results improve standard O(n 3) algorithms used in the general case.