Miracle-dz/UniFAR-FTUs huggingface dataset:
Transcriptome Analysis of Ophiocordyceps Sinensis Under Low-Temperature Treatment

This study aims to investigate the effect of low temperature on the gene expression in Ophiocordyceps Sinensis. To set up the experiments, two groups of Ophiocordyceps Sinensis were grown at 4°C and 18°C, respectively, for 60 days. Differential gene expression was analyzed by transcriptome sequencing and the morphology of mycelium was studied under scanning electron microscope for the two groups. The results showed that there were significant differences in hypha morphology between the two groups. Transcriptome analysis indicated that 2799 genes were differentially expressed in the low-temperature treatment group compared to the normal temperature group. Among these genes, 1489 were up-regulated, whereas 1310 were down-regulated. Further analysis revealed that 33 genes were only expressed at low temperature compared to 26 genes which were only expressed at normal temperature. GO enrichment analysis showed that the 33 genes were related to redox enzymes, which have functions in clearing reactive oxygen species and free radicals caused by low-temperature environment. KEGG enrichment analysis showed that the 33 genes were mainly involved in the cyanoamino acid metabolism, biosynthesis of amino acids, carbon metabolism, pantothenate and CoA biosynthesis, biotin metabolism, phenylpropanoid biosynthesis, apoptosis and other metabolic pathways. Our observations provide fundamental clues on the relationship between the low-temperature stimulation and the fruiting body development and further promote the research progress on Ophiocordyceps Sinensis.
Regional inequalities, economic crises and policies: an international panel analysis

ABSTRACT This paper examines the effects of economic downturns on regional inequalities. In a sample of 25 OECD countries for 1990–2014 period, we show that economic downturns are associated with a significant and long-lasting reduction in regional inequalities. Expansionary fiscal policy as well as higher share of the European development (cohesion) funds facilitate the response of lagging regions to negative nation-wide shocks, contributing to further stimulate the reduction in regional disparities. Additional evidence suggests that the effect of downturns tends to be larger in economies with a higher initial level of regional disparities in unemployment and human capital endowment.
Partisan Bias in Responses to Sexual Misconduct Allegations against Male Politicians

Across two studies, we tested whether evaluations of sexual misconduct allegations against male politicians are made in a partisan biased manner. First, we investigated the likelihood a sexual misconduct allegation made by a subordinate female staffer was perceived as legitimate by Democratic and Republican participants when the accused politician’s party affiliation was aligned (versus unaligned) with the participant’s own affiliation (Study 1). We also tested whether potential partisan bias was conditional on the strength of the participant’s expressive partisanship (Study 2). In Study 1, 182 Democratic and 159 Republican affiliates (Total N = 341) were recruited via Amazon Mechanical Turk and were randomly allocated to one of three conditions (Democratic, Republican, or unaffiliated accused politician). Findings indicated that participants were more likely to perceive a sexual misconduct allegation against an unaligned politician as more legitimate than against a politician of their own party (when the unaffiliated accused politician condition was dropped), although there was no evidence of differences in bias between Democrats and Republicans. Study 2 replicated this finding with 301 Democratic and 301 Republican affiliates (Total N = 602) via Amazon MTurk, showing that Republicans but not Democrats demonstrate partisan bias. Expressive partisanship did not moderate this partisan effect.
Chapitre 9

2. Démarche expérimentale : 2.1. On fait l’hypothèse que le calorimètre Dewar est une enceinte parfaitement adiabatique, ce qui n’est pas le cas. Il y a donc des pertes de chaleur lié à l’appareil lui-même, non pris en compte dans les calculs. 2.2. On va donc faire une erreur sur la valeur obtenue (on s’attend pour un bon manipulateur à une erreur de l’ordre de 15%). Ajouter à cela les imprécisions de mesures sur les températures et les masses. 2.3. Afin de limiter au maximum les erreurs sur la valeur obtenue, on peut déterminer l’équivalent en eau du calorimètre. <0 >0
Antioxidant, and Antimicrobial Activities of Phenolic and Flavonoid Rich Medicinal Plants (Fritillaria zagrica and Tulipa kurdica) Bulbs Collected in Kurdistan Region of Iraq

Fritillaria zagrica and Tulipa kurdica had been used as traditional herbal remedies since antiquity to treat human diseases in the Kurdistan region of Iraq. This is the first report and conceive to analyze these two medicinal plants based. Methanol, 80% ethanol and aqueous extracts of two medicinal plants ( Fritillaria zagrcia and Tulipa kurdica ) were evaluated for their antibacterial activity and antifungal activities. We have quantified the total phenolic (TPC) and flavonoid (TFC) contents and their relation to antioxidants (ABTS) [2,2’-azinobis-(3-ethylbenzothiazoline-6-sulfonate) and 2,2′-diphenyl-1 picryl hydrazyl (DPPH)scavenging free radicals in a dose dependent method. Results showed that F. zagrica and T. kurdica bulb extractions by using different solvents exhibited strong Antimicrobial activities against selected bacterial and fungal strains except C. guilliermondii ATCC 6260 . Aqueous bulb extract of F. zagrica and T. kurdica showed better antibacterial activity compared to other solvent extracts. Methanol F. zagrica bulb extracts contain significantly higher phenolic content than T. kurdica . The TPC and TFC contents were varied between the plant bulb extracts and the DPPH and ABTS scavenging activities were exhibited to be significantly correlated with the amount of TPC and TFC.
Seamless Parametrization of Spheres with Controlled Singularities

We present a method for constructing seamless parametrization for genus‐0 surfaces, which can handle any feasible cone configuration, thus allowing users to arbitrarily design and tailor a mapping as desired. The method directly constructs a self‐overlapping metapolygon of the domain boundary of the mapped cut mesh, which relieves the need of using an auxiliary surface. This simplifies the pipeline and allows for a necessary optimization of the boundary polygon before mapping the interior. Moreover, it enables handling larger meshes with more cones than previous methods can handle. Our construction is purely combinatorial, and it guarantees that the mapping is locally injective – a prerequisite to today's advanced optimization methods. This is achieved via careful construction of a simple domain boundary polygon, where existence of such a polygon is proven for all cases. We offer a numerically robust algorithm to automate the construction, which involves a solution of two linear problems. We offer a full pipeline, suggesting elegant solutions to sub‐problems, and demonstrate robustness through extensive experiments.
Amyotrophic lateral sclerosis

The term motor neuron disease refers to several diseases affecting the motor neurons and is sometimes used interchangeably to refer to amyotrophic lateral sclerosis (ALS), which is the most common motor neuron disease. This chapter will focus on ALS. A complex combination of molecular pathways and cell interactions cause ALS. About 10% of ALS cases are genetic, although it has been hypothesized that as more genes are discovered to contribute to the disease, a larger percentage of cases will be quoted. This chapter discusses in detail the most common genetic forms of ALS and current research on targeted treatments.
Dynamics of gene expression in single root cells ofA. thaliana

Single-cell RNA-seq can yield high-resolution cell-type-specific expression signatures that reveal new cell types and the developmental trajectories of cell lineages. Here, we apply this approach toA. thalianaroot cells to capture gene expression in 3,121 root cells. We analyze these data with Monocle 3, which orders single cell transcriptomes in an unsupervised manner and uses machine learning to reconstruct single-cell developmental trajectories along pseudotime. We identify hundreds of genes with cell-type-specific expression, with pseudotime analysis of several cell lineages revealing both known and novel genes that are expressed along a developmental trajectory. We identify transcription factor motifs that are enriched in early and late cells, together with the corresponding candidate transcription factors that likely drive the observed expression patterns. We assess and interpret changes in total RNA expression along developmental trajectories and show that trajectory branch points mark developmental decisions. Finally, by applying heat stress to whole seedlings, we address the longstanding question of possible heterogeneity among cell types in the response to an abiotic stress. Although the response of canonical heat shock genes dominates expression across cell types, subtle but significant differences in other genes can be detected among cell types. Taken together, our results demonstrate that single-cell transcriptomics holds promise for studying plant development and plant physiology with unprecedented resolution.
Wood Extractives of Silver Fir and Their Antioxidant and Antifungal Properties

The chemical composition of extractives in the sapwood (SW), heartwood (HW), knotwood (KW), and branchwood (BW of silver fir (Abies alba Mill.) was analyzed, and their antifungal and antioxidant properties were studied. In addition, the variability of extractives content in a centripetal direction, i.e., from the periphery of the stem towards the pith, was investigated. The extracts were analyzed chemically with gravimetry, spectrophotometry, and chromatography. The antifungal and antioxidative properties of the extracts were evaluated by the agar well diffusion method and the diphenyl picrylhydrazyl radical scavenging method. Average amounts of hydrophilic extractives were higher in KW (up to 210.4 mg/g) and BW (148.6 mg/g) than in HW (34.1 mg/g) and SW (14.8 mg/g). Extractives identified included lignans (isolariciresinol, lariciresinol, secoisolariciresinol, pinoresinol, matairesinol) phenolic acids (homovanillic acid, coumaric acid, ferulic acid), and flavonoids epicatechin, taxifolin, quercetin). Secoisolariciresinol was confirmed to be the predominant compound in the KW (29.8 mg/g) and BW (37.6 mg/g) extracts. The largest amount of phenolic compounds was extracted from parts of knots (281.7 mg/g) embedded in the sapwood and from parts of branches (258.9 mg/g) adjacent to the stem. HW contained more lignans in its older sections. Hydrophilic extracts from knots and branches inhibited the growth of wood-decaying fungi and molds. KW and BW extracts were better free radical scavengers than HW extracts. The results of the biological activity tests suggest that the protective function of phenolic extracts in silver fir wood can also be explained by their antioxidative properties. The results of this study describe BW as a potential source of phenolic extractives in silver fir.
MoReV2X - A New Radio Vehicular Communication Module for ns-3

The Third Generation Partnership Project (3GPP) recently released the 5G New Radio (NR) Vehicle-to-Everything (V2X) standard. NR-V2X is expected to support a large set of enhanced V2X applications, characterized by stringent requirements in terms of latency, reliability and data rate. The use of simulations plays a crucial role in the preliminary assessment of NR-V2X achievable performance, providing valuable insights on the strengths and limits of the new solution. This work introduces MoReV2X, an open-source ns-3 module for the simulation of NR-V2X communications. The development of MoReV2X adheres to 3GPP specifications, includes various traffic models, features an accurate implementation of the NR propagation channel between vehicles and allows to determine a thorough set of Key Performance Indicators (KPIs). A first set of exemplary results is reported to testify the proper functioning of the module. Moreover, the results reveal the impact that different subcarrier spacings have on system performance, when periodic and aperiodic traffic sources are considered.
Batch Removal of Aqueous Nitrate Ions Using an Effective Nano-Biocomposite

The intent of this research is the investigation of the nitrate reduction from aqueous solution using biomass (pine cone (PC) and PC activated carbon (PCAC)) – supported zero valent iron nanoparticles (PC-Fe0 and PCAC-Fe0). The synthesis of these nano-bio-composites was based on the reduction of ferrous iron with borohydride, in which PC and PCAC acted as porous-based support materials. The structure of the adsorbents was characterized by SEM, XRD, FTIR and BET analysis. The SEM images revealed that nZVI immobilized on PC-Fe0 and PCAC-Fe0 were spherical and uniformly dispersed on the surface of the stabilizers. Batch experiments indicated that the nitrate removal efficiency decreased with the increase in the initial nitrate concentration and initial pH, but increased with the increase in the sorbent dosage and temperature. The maximum adsorption capacities of PC-Fe0 and PCAC-Fe0 for nitrate were 18.16 and 22.30(mg/g), respectively. The nitrogen mass balance calculations revealed that zero valent iron nanoparticles in both PC-Fe0 and PCAC-Fe0 beads could generally reduce nitrate into ammonium, and nitrite usually was occurred as intermediate. Subsequently, PCAC-Fe0 absorbs completely un-wanted ammonium, while PC-Fe0 releases little ammonium in the solution.
Forecasting the Romanian Unemployment Rate in Time of Health Crisis—A Univariate vs. Multivariate Time Series Approach

Economic crises cause significant shortages in disposable income and a sharp decline in the living conditions, affecting healthcare sector, hitting the profitability and sustainability of companies leading to raises in unemployment. At micro level, these sharp decreases in earnings associated with unemployment and furthermore with the lack of social protection will impact the quality of life and finally the health of individuals. In time of crisis, it becomes vital to support not only the critical sectors of the economy, the assets, technology, and infrastructure, but to protect jobs and workers. This health crisis has hit hard the jobs dynamics through unemployment and underemployment, the quality of work (through wages, or access to social protection), and through the effects on specific groups, with a higher degree of vulnerability to unfavorable labor market outcomes. In this context, providing forecasts as recent as possible for the unemployment rate, a core indicator of the Romanian labor market that could include the effects of the market shocks it becomes fundamental. Thus, the paper aims to offer valuable forecasts for the Romanian unemployment rate using univariate vs. multivariate time series models for the period 2021–2022, highlighting the main patterns of evolution. Based on the univariate time series models, the paper predict the future values of unemployment rate based on its own past using self-forecasting and implementing ARFIMA and SETAR models using monthly data for the period January 2000–April 2021. From the perspective of multivariate time series models, the paper uses VAR/VECM models, analyzing the temporal interdependencies between variables using quarterly data for the period 2000Q1–2020Q4. The empirical results pointed out that both SETAR and VECM provide very similar results in terms of accuracy replicating very well the pre-pandemic period, 2018Q2–2020Q1, reaching the value of 4.1% at the beginning of 2020, with a decreasing trend reaching the value of 3.9%, respectively, 3.6% at the end of 2022.
Language Models in Sociological Research: An Application to Classifying Large Administrative Data and Measuring Religiosity

Computational methods have become widespread in the social sciences, but probabilistic language models remain relatively underused. We introduce language models to a general social science readership. First, we offer an accessible explanation of language models, detailing how they estimate the probability of a piece of language, such as a word or sentence, on the basis of the linguistic context. Second, we apply language models in an illustrative analysis to demonstrate the mechanics of using these models in social science research. The example application uses language models to classify names in a large administrative database; the classifications are then used to measure a sociologically important phenomenon: the spatial variation of religiosity. This application highlights several advantages of language models, including their effectiveness in classifying text that contains variation around the base structures, as is often the case with localized naming conventions and dialects. We conclude by discussing language models’ potential to contribute to sociological research beyond classification through their ability to generate language.
Deep-Learning-Based Intrusion Detection for Autonomous Vehicle-Following Systems

Autonomous vehicle-following systems, including Adaptive Cruise Control (ACC) and Cooperative Adaptive Cruise Control (CACC), improve safety, efficiency, and string stability for a vehicle (the ego vehicle) following its leading vehicle. The ego vehicle senses or receives information, such as the position, velocity, acceleration, or even intention, of the leading vehicle and controls its own behavior. However, it has been shown that sensors and wireless channels are vulnerable to security attacks, and attackers can modify data sensed from sensors or received from other vehicles. To address this problem, in this paper, we design three types of stealthy attacks on ACC or CACC inputs, where the stealthy attacks can deceive a rule-based detection approach and impede system properties (collision-freeness and vehicle-following distance). We then develop two deep-learning models, a predictor-based model and an encoder-decoder- based model to detect the attacks, where the two models do not need attacker models for training. The experimental results demonstrate the respective strengths of different models and lead to a methodology for the design of learning-based intrusion detection approaches.
Sistem Monitoring dan Deteksi Stres Pada Anak Berbasis Wearable Device

The need for monitoring of children today is very important, especially for children under five, whose physical and verbal abilities are still inadequate to be able to communicate effectively with their parents or caregivers about the conditions they are experiencing. Previous studies related to child safety that were studied generally focused on responses to events that could potentially harm children.This study aims to design a prototype child monitoring system consisting of a wearable device that is used on a child's wrist equipped with sensors and connected to a server via a wireless network. Monitoring software that runs on the server will collect all data parameters from wearable devices with built-in audio signal, temperature, and heart rate sensor then with machine learning algorithm implemented in software will allow the system to predict if stress conditions happen on children and then system can give warnings to child-caregiver through monitoring applications or SMS messages. Using the Decision Tree and Naive Bayes classification methods the system can effectively predict stress conditions in children with an accuracy of 82.8 percent using audio, temperature, and heart rate parameters. This shows that the system has the capability to contribute to increasing child safety in the supervision environment.
Simulation studies on corrosion of stone coated roofing sheets sold in Nigeria

ABSTRACT. Acid rain condition were simulated on three selected stone-coated roofing sheets sold in Nigeria to assess the rate of metallic dissolution using (0.25, 0.50, 0.75, and 1.00 M) concentration of sulfuric acid, simulated acid rain (0.5 M of HCl, HNO3 and H2SO4) for 30 days in a controlled environment. Six metals (Pb, Fe, Zn, Al, Si, and Cd) concentration were determined using same concentration of sulfuric acid and distilled water for three hours at elevated temperature of 55 ºC. The results showed that as concentration increases across the three samples, the corrosion rate of stone-coated roofing sheets increases. The analysis of variance (ANOVA) showed that the weight loss of the samples were significant by all input variables. The fourth order polynomial model conducted for weight loss and corrosion showed best fit with regression (R2) which ranged from 0.95 and 0.99 across three samples except for 1.00 M of sulfuric acid at 0.8953, 0.8862, and 0.8933 for Sample 1, 2 and 3, respectively. Metal dissolution conducted for three hours showed that zinc had highest dissolution across different concentration, followed by iron respectively, aluminum dissolved at 0.75 M sulfuric acid; lead dissolved at 0.25 M, 1 M and distilled water while silicon and cadmium had relatively low dissolution accordingly. Acid rain has negative impact on corrosion stone-coated roofing sheets thus influencing its lifespan and durability. KEY WORDS: Simulation, Acid rain, Metal dissolution, Corrosion, Stone-coated roof, Nigeria Bull. Chem. Soc. Ethiop. 2021, 35(2), 461-470. DOI: https://dx.doi. org/10.4314/bcse.v35i2. 18
Resolving the Tetrastigma loheri s. l. Species Complex (Vitaceae) in the Philippines: No Evidence for Recognizing More than One Species

Tetrastigma loheri (Vitaceae) is a vine species native to Borneo and the Philippines. Because it is a commonly encountered forest species in the Philippines, T. loheri is potentially suitable for studying patterns of genetic diversity and connectivity among fragmented forestecosystems in various parts of this country. However, previous research suggests that T. loheri is part of a species complex in the Philippines (i.e. the T. loheri s. l. complex) that potentially also contains Philippine plants identified as T. diepenhorstii, T. philippinense, T. stenophyllum, andT. trifoliolatum. This uncertainty about its taxonomic delimitation can make it challenging to draw conclusions that are relevant to conservation from genetic studies using this species. Here, we tested the hypothesis that T. loheri s. l. is composed of more than one species in the Philippines.For this, we used generalized mixed Yule coalescent (GMYC) and Poisson tree process (PTP) species delimitation models to identify clades within DNA sequence phylogenies of T. loheri s. l. that might constitute species within this complex. Although these methods identified several putative species, these are statistically poorly supported and subsequent random forest analyses using a geometric morphometric leafshape dataset and several other vegetative characters did not result in the identification of characters that can be used to discriminate these putative species morphologically. Furthermore, the results of principal component and principal coordinates analyses of these data suggest the absence of morphological discontinuities within the species complex. Under a unified species concept that uses phylogenetic and morphological distinction as operational criteria for species recognition, we therefore conclude that the currently available data do not support recognizing multiple species in the T. loheri s. l. complex. This implies that T. loheri is best considered as a single, morphologically variable specieswhen used for studying patterns of genetic diversity and connectivity in the Philippines.