Miracle-dz/UniFAR-FTUs huggingface dataset:
NK cells acquire epigenetic memory of LPS-induced systemic inflammation

Natural killer cells are unique mediators of innate immunity, and as such, an attractive target for immunotherapy. Following viral infection, NK cells display immune memory properties, defined by heightened responses to re-stimulation, an expansion of specific NK cell sup-populations and a protective role against re-infection. However, similar memory to bacterial infection or systemic inflammation, and the molecular mechanisms behind NK cell memory remain elusive. Here we show that following LPS-induced endotoxemia in mice, NK cells acquire cell-intrinsic memory properties as displayed by an amplified production of IFNγ upon secondary stimulation. NK cell memory is acquired even under the post-endotoxemic suppressive environment and is detectable for at least 9 weeks. Furthermore, we define an epigenetic mechanism essential for NK cell memory, where an H3K4me1-marked latent enhancer is uncovered at the ifng locus. Chemical inhibition of histone methyltransferase activity erased the enhancer and prevented NK cells from acquiring memory. Thus, NK cells develop memory to LPS during endotoxemia, in a histone methylation-dependent manner, which ensures a heightened response to secondary stimulation and confers protection against bacterial infection.
Predicting HIV Incidence in the SEARCH Trial: A Mathematical Modelling Study

Introduction SEARCH is one of four randomized-controlled trials (RCTs) investigating the strategy of community-based treatment-as-prevention (TasP) for the reduction of HIV incidence in sub-Saharan Africa. SEARCH takes place among 32 pair-matched rural communities in three regions of East Africa and exceeded the UNAIDS 90-90-90 targets for HIV testing, linkage to care, and viral suppression in the intervention arm. We used mathematical modeling to estimate expected 3-year cumulative HIV incidence in both arms of the trial, using different assumptions about two main sources of uncertainty: scale-up of antiretroviral therapy (ART) in the control arm, and the degree of mixing between SEARCH residents and non-residents. Methods We used the HIV modelling software EMOD-HIV to configure and calibrate a new model of the SEARCH communities. The 32 trial communities were clustered into six nodes (three for the control arm and three for the intervention arm) using k-means clustering based on community HIV prevalence, male circumcision rates, mobility, and geographic region. The model was parameterized using data on demographics, HIV prevalence, male circumcision rates, and viral suppression data collected at trial baseline in 2013, and calibrated to nodespecific and age-specific HIV prevalence, ART coverage, and population size. Using data on ART scale-up in subsequent follow-up years in the trial, we varied linkage to ART in the control arm and the degree of external mixing between SEARCH residents and non-residents. Results If no external mixing and no additional control arm ART linkage occurred, we estimate the trial would report a relative risk (RR) of 0.60 (95% CI 0.54-0.67, p<0.001), with all simulations showing a significant result. However, if SEARCH residents mixed equivalently with non-residents and ART linkage in the control arm also increased such that the control arm also exceeded the 73% viral suppression target, the RR is estimated to be 0.96 (95% CI 0.87-1.06, p=0.458) and 72% of simulations are non-significant. Given our “best guess” assumptions about external mixing and year 3 data on ART linkage in the control arm, the RR is estimated to be 0.90 (95% CI 0.81-1.00, p=0.05), with 49% non-significant simulations. Conclusion The SEARCH trial is predicted to show a 4-40% reduction in cumulative 3-year incidence, but between 18-72% of simulations were non-significant if either or both ART linkage in the control arm and external mixing are substantial. Despite achieving the 90-90-90 targets, our “best guess” is that the SEARCH trial has an equal probability of reporting a non-significant reduction in HIV incidence as it does a significant reduction.
Pesticides affect pollinator abundance and productivity of sunflower (Helianthus annuus L.)

Pollination is considered one of the prime ecosystem services on the planet and is performed mainly by bees. The indiscriminate use of agricultural pesticides for pest control was evinced as one of the foremost causes of these pollinator declines. Sunflower crops demand an intense use of pesticides and, at the same time, are highly dependent on cross-pollination, essentially mediated by bees, for an increase in seed production. This study aimed to evaluate the impact of pesticides on richness and abundance of pollinators and productivity enhancement in sunflower. The study was carried out at the Água Limpa Experimental Farm located in the municipality of Uberlandia, Minas Gerais, Brazil, from May to August 2015. We conducted the study in two experimental crops of sunflower (Helianthus annuus L.). In one of them, we used the agricultural defensive bis(dithiocarbamate) for pest control, while in the other we only used a mechanical method. In both experimental crops, we quantified floral visitors during eight days, from 8 am to 4 pm. After maturation of inflorescences, we determined the mass of achenes and percentage of seed oil extracted. The abundance of floral visitors, the mass of fruits, and quantity of oil in the seeds were significantly lower after treatment with the pesticide, thus evidencing its negative effect on the sunflower productivity.
Deep Unfolding With Weighted ℓ₂ Minimization for Compressive Sensing

Compressive sensing (CS) aims to accurately reconstruct high-dimensional signals from a small number of measurements by exploiting signal sparsity and structural priors. However, signal priors utilized in existing CS reconstruction algorithms rely mainly on hand-crafted design, which often cannot offer the best sparsity-undersampling tradeoff because high-order structural priors of signals are hard to be captured in this manner. In this article, a new recovery guarantee of the unified CS reconstruction model-weighted $\ell _{1}$ minimization (WL1M) is derived, which indicates universal priors could hardly lead to the optimal selection of the weights. Motivated by the analysis, we propose a deep unfolding network for the general WL1M model. The proposed deep unfolding-based WL1M (D-WL1M) integrates universal priors with learning capability so that all of the parameters, including the crucial weights, can be learned from training data. We demonstrate the proposed D-WL1M outperforms several state-of-the-art CS-based methods and deep learning-based methods by a large margin via the experiments on the Caltech-256 image data set.
Impacts of climate change on high priority fruit fly species in Australia

Tephritid fruit flies are among the most destructive horticultural pests and pose risks to Australia’s multi-billion-dollar horticulture industry. Currently, there are 11 pest fruit fly species of economic concern present in various regions of Australia. Of these, nine are native to this continent (Bactrocera aquilonis, B. bryoniae, B. halfordiae, B. jarvisi, B. kraussi, B. musae, B. neohumeralis, B. tryoni and Zeugodacus cucumis), while B. frauenfeldi and Ceratitis capitata are introduced. To varying degrees these species are costly to Australia’s horticulture through in-farm management, monitoring to demonstrate pest freedom, quarantine and trade restrictions, and crop losses. Here, we used a common species distribution modelling approach, Maxent, to assess habitat suitability for these 11 species under current and future climate scenarios. These projections indicate that the Wet Tropics is likely to be vulnerable to all 11 species. The east coast of Australia will likely remain vulnerable to multiple species until at least 2070. Both the Cape York Peninsula and Northern Territory are also likely to be vulnerable, however, extrapolation to novel climates in these areas decrease confidence in model projections. The climate suitability of current major horticulture regions in north-western Australia, the Northern Territory, southern-central regions of New South Wales and southern Victoria to these pests is projected to increase as climate changes. Our study highlights areas at risk of pest range expansion in the future, to guide Australia’s horticulture industry in developing effective monitoring and management strategies.
Factors Influencing the Intention to Use Technology Services to Implement Self-Service Technology Case Study: Situation Pandemic Covid-19

This study aims to analyze a person's intention in using self-service technology (SST) services during the pandemic COVID-19 Where this time, raising problems one of the social distancing that affects a service provided, especially on services that use technology that applies SST This study develops from previous research where the customer has satisfaction in using SST services Still, this use does not know a person intention to use SST services, so it is necessary to develop with several factors the adoption of technology, namely influence that can make a person intention to use The research framework of this research consists of several factors and indicators, namely efficiency (4 indicators), Reliability (4 indicators), security (3 indicators), Convenience (3 indicators), Ease of use (4 indicators) This study uses an online survey with google form for users of self-service technology services in 5 major cities in Indonesia Dissemination of the survey for three months, and the obtained were 100 respondents valid This research conducts testing using Smarpls v3 0 The results of this study found two factors that influence perceived usefulness, namely: efficiency and reliability, and found three factors that did not have an impact, namely Security factors, Convenience Factors, Perceived Ease of use, and Service Trust do not have an impact on perceived usefulness This result is a very interesting finding because during the period pandemic COVID-19 These factors are no longer the top priority in the use of SST services and do not affect who intention to use a technology service Thus, it can be concluded that whose intention at a certain level of the problem will prioritize the reliability and efficiency of the system for service This factor can be the focus of the company in developing systems that adopt Self-service technology © 2020 ASTES Publishers All rights reserved
Non-Language-Major Students' Autonomy in Learning English in Vietnam

The English language is one of the international languages used for communication and learning worldwide. In Vietnam, English is taught as a second language in schools from elementary to advanced levels. In learning a certain foreign language, the autonomy of learners has become a vital subject for the past 30 years. This research surveys the autonomy of non-language-major-students in learning English in Ho Chi Minh City University of Education. The quantitative research method uses descriptive statistics and T-test. A group of 258 students (61 males and 197 females) took part in answering two questionnaires on learners' autonomy in learning English and the roles of lecturers and students in developing learner-autonomy in learning English. The results showed that the students were not familiar with the term learning autonomy. However, students were aware that they played a fundamental role in improving this ability. They also believed that the lecturers determined their English learning process. Thence, we propose solutions for the non-language-majors to improve their autonomy in learning English.
Variables Affecting the Retention Intentions of Students in Higher Education Institutions

The purpose of this study was to investigate the differences between domestic and international students with regards to the variables affecting students’ retention intentions. Altogether, 15 variables related to retention intentions were examined and significant differences were found in six of these variables. Variables related to personal issues (e.g., medical or family difficulties) were of equal importance to both groups, while the importance of institution and performance-centric variables differed between the groups. Social integration, ineffective study skills, difficulty adjusting to college life, poor extracurricular activities, and poor housing arrangements were perceived to be significantly more important by international students, while poor quality of instruction was perceived to be significantly more important by domestic students. Thus, international and domestic students require different retention strategies on the part of the institution.
Participatory forest management and smallholder farmers’ livelihoods improvement nexus in Northwest Ethiopia

ABSTRACT Participatory forest management (PFM) focuses on local communities as key stakeholders for sustainability. It entails access to some of or all of forest resources products that ensure a sustainable flow of goods and services. The objective of the study is to assess the contribution of PFM practices to smallholder farmers’ livelihood improvement. A cross-sectional survey design was employed to collect information from randomly selected household heads in two rural peasant associations. Quantitative data were analyzed using descriptive statistics and inferential test. The binary logistic result revealed that most of the economic, social, biophysical, and institutional factors are significant (P < 0.01 or P < 0.05) to influence self-reported PFM contribution to livelihood improvement condition of the communities either directly or indirectly. Apparently, forest resource management is impossible without the proper participation of the local communities.
Cradle-to-gate life cycle assessment of energy and carbon of a residential building in Sri Lanka

The growing concerns over the environment have led to increasing demand for environmentally-friendly buildings. So far, only a few studies on environmental impacts of buildings have been conducted in the context of Sri Lanka. Reliable data sources that match the specific conditions of the country are limited. Using the life cycle assessment (LCA) methodology, this paper presents a cradle-to-gate energy and carbon emission study of a multi-storey residential building in a Sri Lankan university. The total embodied energy and carbon of the building are 3.84 GJm-2 and 229.34 kgCO2m-2, respectively, which are comparable with results of similar studies found in literature. Reinforced concrete, the main structural material, contributed to 61 % of total embodied energy and 71 % of total embodied carbon of the building. Despite the relatively low material quantity used, aluminium, ceramic tiles and paint shared 18.67 % of total embodied energy. In order to achieve low-energy and low-carbon buildings in Sri Lanka, several strategies were identified; suitable construction practices and building designs to reduce quantities of mass materials, use of alternative materials with low energy and carbon intensities, material recycling and reuse, use of clean and renewable energy for production processes and popularising the concept of eco-labels for building materials. The reduction of embodied energy and carbon is expected to lighten the environmental footprint of buildings.
Position-Transitional Particle Swarm Optimization-Incorporated Latent Factor Analysis

High-dimensional and sparse (HiDS) matrices are frequently found in various industrial applications. A latent factor analysis (LFA) model is commonly adopted to extract useful knowledge from an HiDS matrix, whose parameter training mostly relies on a stochastic gradient descent (SGD) algorithm. However, an SGD-based LFA model's learning rate is hard to tune in real applications, making it vital to implement its self-adaptation. To address this critical issue, this study firstly investigates the evolution process of a particle swarm optimization algorithm with care, and then proposes to incorporate more dynamic information into it for avoiding accuracy loss caused by premature convergence without extra computation burden, thereby innovatively achieving a novel position-transitional particle swarm optimization (P2SO) algorithm. It is subsequently adopted to implement a P2SO-based LFA (PLFA) model that builds a learning rate swarm applied to the same group of LFs. Thus, a PLFA model implements highly efficient learning rate adaptation as well as represents an HiDS matrix precisely. Experimental results on four HiDS matrices emerging from real applications demonstrate that compared with an SGD-based LFA model, a PLFA model no longer suffers from a tedious and expensive tuning process of its learning rate, and it can achieve even higher prediction accuracy for missing data of an HiDS matrix. On the other hand, compared with state-of-the-art adaptive LFA models, a PLFA model's prediction accuracy and computational efficiency are highly competitive. Hence, it has high potential in addressing real industrial issues.
THE EFFECTS OF BOTANICAL OILS ON THE RED PALM WEEVIL, RHYNCHOPHORUS FERRUGINEUS OLIVIER (COLEOPTERA: CURCULIONIDAE)

Date palm trees are liable to be attacked by many insect pests which cause serious damage to the different parts of tree. The most serious insect pest is the red palm weevil, Rhynchophorus ferrugineus (Oliver) (RPWs). To evaluate the impact of four botanical oils of Melissa officinalis, Borago officinalis, Laurus nobilis and Carapichea ipecacuanha on R. ferrugineus. 4 botanical oils were tested against newly emerged adults of R. ferrugineus for the purpose of investigating their insecticidal activity and effects on some biological aspects. Males of R. ferrugineus were more susceptible to the four botanical oils than females, followed by the immature larvae. The larvae showed different degrees of susceptibility to the four botanical oils. They showed the highest susceptibility to C. ipecacuanha and the lowest to B. officinalis oil at LC50 levels. Adult males and females were more susceptible to C. ipecacuanha and the least to L. nobilis oil at LC50 levels. The four tested oils disrupted some biological aspects of the insects. These oils remarkably reduced male and female longevity, female fecundity and hatchability of the laid eggs.
Topological methods in geometry and discrete mathematics

We present solutions to several problems originating from geometry and discrete mathematics: existence of equipartitions, maps without Tverberg multiple points, and inscribing quadrilaterals. Equivariant obstruction theory is the natural topological approach to these type of questions. However, for the specific problems we consider it had yielded only partial or no results. We get our results by complementing equivariant obstruction theory with other techniques from topology and geometry.
NanoR: A user-friendly R package to analyze and compare nanopore sequencing data

MinION and GridION X5 from Oxford Nanopore Technologies are devices for real-time DNA and RNA sequencing. On the one hand, MinION is the only real-time, low cost and portable sequencing device and, thanks to its unique properties, is becoming more and more popular among biologists; on the other, GridION X5, mainly for its costs, is less widespread but highly suitable for researchers with large sequencing projects. Despite the fact that Oxford Nanopore Technologies’ devices have been increasingly used in the last few years, there is a lack of high-performing and user-friendly tools to handle the data outputted by both MinION and GridION X5 platforms. Here we present NanoR, a cross-platform R package designed with the purpose to simplify and improve nanopore data visualization. Indeed, NanoR is built on few functions but overcomes the capabilities of existing tools to extract meaningful informations from MinION sequencing data; in addition, as exclusive features, NanoR can deal with GridION X5 sequencing outputs and allows comparison of both MinION and GridION X5 sequencing data in one command. NanoR is released as free package for R at https://github.com/davidebolo1993/NanoR.
The New Hazardous Jobs and Worker Reallocation

This paper analyses several dimensions of workers’ safety that are relevant in the context of a pandemic. We provide a classification of occupations according to the risk of contagion: by considering a wider range of job characteristics and a more nuanced assessment of infection risk, we expand on the previous literature that almost exclusively looked at feasibility of working from home. We apply our classification to the United States and to European countries and we find that roughly 50% of jobs in our sample can be considered safe, although a large cross-country variation exists, notably in the potential incidence of remote working. We find that the most economically vulnerable workers (low-educated, low-wage workers, immigrants, workers on temporary contracts, and part-timers) are over-represented in unsafe jobs, notably in non-essential activities. We assess the nature of the reallocation of workers from unsafe to safe jobs that is likely to take place in the years to come, and the policies that could mitigate the social cost of this reallocation.
Genetic variability, correlation and path analysis for quantitative traits of seed yield, and yield components in chickpea (Cicer arietinum L.) at Maichew, Northern Ethiopia

Twelve chickpea genotypes were tested to assess variability, heritability, correlations and direct and indirect effects between yield and yield components. Maximum phenotypic and genotypic coefficient of variation was recorded for number of seeds per plant (33.8, 32.4), number of secondary branches per plant (30.3, 29.6), number of pods per plant (25.6, 24.7) and 100 seed weight (23.0, 22.7) respectively . High heritability coupled with high expected genetic advance as percent of mean were estimated for number of secondary branches per plant, number of pods per plant and 100 seed. Path coefficient analysis (seed yield as a dependent variable) revealed that seeds per plant followed by biomass yield, days to maturity and 100 seed weight had exerted positive direct effect on seed yield. To conclude, number of seeds per plant, biomass yield, 100 seed weight and days to maturity are important parameters for selecting maximum yielding genotypes in chickpea. Key words: Chickpea, genetic variability, path coefficient, heritability, correlation, genetic advance.
Sex ratio of offspring in domestic animals: Swine (6)

旧子安農園のヨークシャー(Y),バークシャー(B)の2品種合計17,363頭の産子性比を分析して次の結果を得た。 両品種合計の総性比は51. 8%で雄へかたよっている。 品種別にみるとY,Bそれぞれ51. 7,52.1%を示し,雄へかたよっている(Table 1)。 子の性比がかたよる父,母個体の率はそれぞれ9. 4,6.5%であったが(Tables2,3),それら個体の家系は調査できなかった。 死産率は両品種合計で7. 3%の高率で,その性比は55. 5%(Y54.1,B59.3%)で雄へのかたよりを示している。
Sparse Bayesian Learning of Delay-Doppler Channel for OTFS System

Orthogonal time frequency space (OTFS) modulation has superior performance than traditional orthogonal frequency division multiplexing (OFDM) in fast time-varying scenarios. However, due to the effect of Doppler shift, higher pilot overhead and pilot power are required to estimate the channel state information. According to the sparseness of the channel in the delay-Doppler domain, this letter proposes a new pilot pattern and a sparse Bayesian learning (SBL)-based channel estimation algorithm. There is no guard pilot in the pilot pattern, and the pilot has the same power as the data. Based on the new pilot pattern, we first convert the channel estimation problem to a sparse signal recovery problem. Then, we introduce a sparse Bayesian learning framework and construct a sparse signal prior model as a hierarchical Laplace prior. Finally, the expected maximum (EM) algorithm is used to update the parameters in the prior model. Numerical simulation highlights the superiority of the proposed algorithm in terms of pilot overhead, pilot power consumption, and anti-noise interference.