Miracle-dz/UniFAR-FTUs huggingface dataset:
Structural features and functional residues important for the activity of an unusual membrane bound O-acyltransferase

The membrane bound O-acyltransferase (MBOAT) family contains multi-pass membrane proteins that add fatty acids to different compounds. Despite their importance in economic activity and human health, little is known about the localization of the active site and regions important for determining substrate specificity of MBOATs. Euonymus alatus diacylglycerol acetyltransferase (EaDAcT) is the only known MBOAT enzyme that exhibits a high preference for acetyl-CoA, the shortest possible acyl-CoA. EaDAcT catalyzes the transfer of the acetate group from acetyl-CoA to the sn-3 position of diacylglycerol to form 3-acetyl-1,2-diacyl-snglycerol. Our goal was to investigate the structural features and the amino acid residues that define substrate specificity of EaDAcT to provide insights into the mechanism by which MBOAT family controls substrate selection. By mapping the membrane topology of EaDAcT we obtained the first experimentally determined topology model for a plant MBOAT. The EaDAcT model contains four transmembrane domains with both the Nand Ctermini oriented toward the endoplasmic reticulum lumen. The MBOAT signature region including the putative active site His-257 of the protein is embedded in the third transmembrane domain close to the interface between the membrane and the cytoplasm. In order to identify amino acid residues important for acetyltransferase activity, we isolated and characterized orthologs of EaDAcT from other acetyl-TAG producing plants. Among them, the acetyltransferase from Euonymus fortunei possessed the highest activity in vivo and in vitro. Mutagenesis of conserved residues of DAcTs revealed that Ser-253, His-257 and Asp-258 are essential for enzyme activity of EaDAcT, suggesting their involvement in the enzyme catalysis. Alteration of residues unique to acetyltransferases did not alter the acyl donor specificity of EaDAcT, implying that multiple amino acids are important for substrate recognition. Together, this work identifies the structural features of EaDAcT and offers an initial view of the amino acids important for activity of the
Cover Crop and Postemergence Herbicide Integration for Palmer amaranth Control in Cotton

Field experiments were conducted to evaluate the integration of cover crops and POST herbicides to control glyphosate-resistant Palmer amaranth in cotton. The winter-annual grasses accumulated the greatest amount of biomass and provided the most Palmer amaranth control. The estimates for the logistic regression would indicate that 1540 kg ha-1 would delay Palmer amaranth emerging and growing to 10 cm by an estimated 16.5 days. The Palmer amaranth that emerged in the cereal rye and wheat cover crop treatments took a longer time to reach 10 cm compared to the hairy vetch and crimson clover treatments. POST herbicides were needed for adequate control of Palmer amaranth. The glufosinate-based weed control system provided greater control (75% vs 31%) of Palmer amaranth than did the glyphosate system. These results indicate that a POST only herbicide weed management system did not provide sufficient control of Palmer amaranth, even when used in conjunction with cover crops that produced a moderate level of biomass. Therefore, future recommendations for GR Palmer amaranth control will include integrating cover crops with PRE herbicides, overlaying residual herbicides in-season, timely POST herbicide applications, and hand weeding in order to achieve season-long control of this pest. Nomenclature: Glufosinate, glyphosate, Palmer amaranth, Amaranthus palmeri S. Wats. , cereal rye, Secale cereale L. , cotton, Gossypium hirsutum L. , crimson clover, Trifolium incarnatum L. , hairy vetch, Vicia villosa Roth. , winter wheat, Triticum aestivum L.
Genetic Improvement through Standard Heterosis for fodder yield in Single cut of forage Sorghum [Sorghum Bicolor (L.) Moench]

A study was made in Sorghum bicolor with line x taster (6 females x 4 males) to estimate the magnitude of heterosis among different cross combinations for yield and its component traits. For this purpose, 24 specific cross combinations were developed by using 10 diverse parents during kharif season in 2014-15. These hybrids along with 10 parents and two standard checks (SSG 59-3and MFSH 4) were evaluated at two locations with two dates of sowing (Early and late sowing) during kharif season in 2015-16. The analysis of variance indicated the presence of variability among hybrids and their parents. Among male parents, HJ 541 and G 46 and among female parents, 467A and 56A showed maximum green and dry fodder yield. Maximum heterosis for green fodder yield as comparison with all the four environments over both the checks (SSG 59-3 and MFSH 4) was showed by 9A × IS 2389 (81.1 % and 75.3 % respectively). Alongwith green fodder yield this hybrid is also better for dry fodder yield (46.4 % and 41.4 % respectively) and leaf breadth (60.6 % and 41.9 % respectively). Hybrid 465A × HJ 513 demonstrate better heterosis for green fodder yield (80.7 %) as well as for dry fodder yield (57.7 %). Hybrid 467A × G 46 show better heterosis for green fodder yield (64.7 %) as well as for dry fodder yield (29.6 %). This is also better for plant height, leaf length, leaf breadth and stem diameter.
Enhancing wildfire spread modelling by building a gridded fuel moisture content product with machine learning

Wildland fire decision support systems require accurate predictions of wildland fire spread. Fuel moisture content (FMC) is one of the important parameters controlling the rate of spread of wildland fire. However, dead FMC measurements are provided by a relatively sparse network of remote automatic weather stations (RAWS), while live FMC is relatively infrequently measured manually. We developed a high resolution, gridded, real-time FMC data sets that did not previously exist for assimilation into operational wildland fire prediction systems based on ML. We used surface observations of live and dead FMC to train machine learning models to estimate FMC based on satellite observations. Moderate Resolution Imaging Spectrometer Terra and Aqua reflectances are used to predict the live and dead FMC measured by the Wildland Fire Assessment System and RAWS). We evaluate multiple machine learning methods including multiple linear regression, random forests (RFs), gradient boosted regression and artificial neural networks. The models are trained to learn the relationships between the satellite reflectances, surface weather and soil moisture observations and FMC. After training on data corresponding to the temporally and spatially nearest grid points to the irregularly spaced surface FMC observations, the machine learning models could be applied to all grid cells for a gridded product over the Conterminous United States (CONUS). The results show generally that the rule-based approaches have the lowest errors likely due to the sharp decision boundaries among the predictors, and the RF approach that utilizes bagging to avoid over-fitting has the lowest error on the test dataset. The errors are typically between 25%−33% the typical variability of the FMC data, which indicate the skill of the RF in estimating the FMC based on satellite data and surface characteristics. The FMC gridded product based on the RF runs operationally daily over CONUS and can be assimilated into WRF-Fire for more accurate wildland fire spread predictions.
Optimization of Biofertilizers Enriched in N by Diazotrophic Bacteria

Free-living diazotrophic bacteria can enrich organic biofertilizer in nutrients to application in sustainable agriculture. The objective of the study was to select diazotrophic bacteria isolated from different soils, evaluated by analyses of nitrogenase activity. The diazotrophic were submitted to gene rRNA 16S sequenced and the best isolates compared with strains used to produce the biofertilizer. The biofertilizers used earthworm compost and sugarcane mud cake mixed with PK rock biofertilizer in different proportions (3:2:1 and 2:3:1). The experiment conducted in a factorial 6x2x2, used six diazotrophic bacteria applied in two concentrations (100 and 200 mL) and organic matter in the selected proportions, with four replicates. Samples were collected in times 0; 10; 20; 30 and 40 days and analyzed: pH (H2O), total C, total N, available P and K. The rRNA 16S gene sequence identified the selected diazotrophic as Bacillus, Mesorhizobium, Paenibacillus and Beijerinckia. The strains increased nutrients in the biofertilizers, especially the isolate NFB 4 and strain NFB 1003, in incubation time of 25 and 30 days. The more effective diazotrophic increased nutrients, especially N in biofertilizer produced in proportion 3:2, and promoted significant interaction for all analyzed parameters, except to total C. Available P and K increased when applied in both proportions. The selected diazotrophic bacteria produce biofertilizers that may be used as alternative substrate to replacement of soluble fertilizer. Keywords: Biofertilizers; free-living diazotrophic; nutrient enrichment; organic matters; rock biofertilizers; sugarcane mud cake
Coverless Image Steganography Based on Generative Adversarial Network

Traditional image steganography needs to modify or be embedded into the cover image for transmitting secret messages. However, the distortion of the cover image can be easily detected by steganalysis tools which lead the leakage of the secret message. So coverless steganography has become a topic of research in recent years, which has the advantage of hiding secret messages without modification. But current coverless steganography still has problems such as low capacity and poor quality . To solve these problems, we use a generative adversarial network (GAN), an effective deep learning framework, to encode secret messages into the cover image and optimize the quality of the steganographic image by adversaring. Experiments show that our model not only achieves a payload of 2.36 bits per pixel, but also successfully escapes the detection of steganalysis tools.
The Role of Government Regulations in Enhancing Corporate Social Responsibility Disclosure and Firm Value

This study investigates, first, whether the extent of corporate social and environmental responsibility disclosure (CSERD) differs between 2010 and 2014; second, whether government regulation affects the extent of CSERD; and, third, whether the CSERD is valued by investors. Content analysis method was used to extract 466 companies’ annual reports to measure the extent of social and environmental responsibility disclosure based on the Global Reporting Initiative (GRI) checklist. Independent sample t-test and multivariate regression analysis were also conducted to test the differences of the extent of CSERD as well as determinants and consequence of CSERD. Our results show that the extent of CSERD in 2014 is 21.60 percent higher than in 2010 (13.39 percent). Government regulation has a significant effect on the extent of CSERD. This study also finds that market values positively CSER information disclosed by company. Given that government regulation has a positive impact, however, the findings of this study suggests that the extent of CSERD is still low. To enhance CSERD, government should continuously encourage companies to abide by the regulations as mandated. This study provides a more comprehensive insights of CSRED practices from an emerging country and the effect of government regulation in enhancing CSERD.
Analytic $I_{\text{sc}}$–$V_{\text{oc}}$ Method and Power Loss Modes From Outdoor Time-Series $I$–$V$ Curves

Utilizing large-scale outdoor $I$ – $V$ data streams, the authors demonstrated a feasible, repeatable method to construct outdoor time-series $I_{\text{sc}}$ – $V_{\text{oc}}$ curves. Resulting Analytic $I_{\text{sc}}$ – $V_{\text{oc}}$ curves are validated with laboratory $Suns$ - $V_{\text{oc}}$ measurements. Comparing $I_{\text{sc}}$ – $V_{\text{oc}}$ with real $I$ – $V$ curves, power loss modes including uniform current loss, recombination loss, series resistance loss, and current mismatch loss can be quantified in units of watts or percent nominal power, making them directly comparable. Above power loss modes are calculated for long-term $I$ – $V$ data from fielded modules in different climate zones, and dominant power loss modes were determined from the resulting time series. This method has been published as an open source package in R programming language.
Secondary prevention in minor ischemic stroke with antiplatelet treatment. Systematic review and meta-analysis of comparative studies with aspirin under non-inferiority criteria

Minor ischemic stroke is the most frequent presentation of cerebral vascular disease and treatment with antiplatelet drugs can be used for the prevention of its recurrence. This systematic review and meta-analysis was aimed to assess non-inferiority criteria about the effect in the comparison of different antiplatelet schemes using aspirin as active control. Twelve randomized studies with a total of 52204 patients were chosen. All met the inclusion criteria with minor recurrent ischemic stroke as end point and any extracranial bleeding as safety event. The results showed a significant risk reduction of 22% [RR (95% CI) = 0.78 (0.72-0.84), p<0.0001, NNT: 67] in the recurrence of ischemic events with any antiplatelet drug (combined or not with aspirin) versus aspirin alone and there were no differences in the bleeding risk [RR (95% CI) = 1.02 (0.74- 1.41), p= 0.899, NNH: 500]. Dual antiplatelet therapy (DAPT) and cilostazol were more effective compared with aspirin alone (22% and 32% risk reduction respectively) but only cilostazol showed a higher reduction (52%) of bleeding events. In conclusion, although in some instances equivalence was demonstrated, a clinical superiority in the risk reduction for recurrent ischemic stroke of any antiplatelet treatment versus aspirin alone was observed. With the sole exception of the cilostazol trials there was an increase of the bleeding risk when the antiplatelet drugs treatments were compared with aspirin alone.
Influence of arc constriction current (ACC) on microstructural evolution and tensile properties of tungsten inert gas welded thin sheets of aerospace alloy

ABSTRACT The electromagnetic arc constriction technique was implemented to mitigate the heat input related weldability issues in Tungsten Inert Gas (TIG) welding of Alloy 718 particularly solute segregation and substantial laves phase evolution in fusion zone which significantly deteriorates the strength and elongation of welded joints. This paper reports the direct effect of arc constriction current (ACC) on microstructural evolution and tensile properties of Constricted Arc TIG (CA-TIG) welded Alloy 718 joints. ACC is the important parameter which induces the electromagnetic filed and controls the arc constriction characteristics. The basic mechanism responsible for microstructural modification and corresponding influence on the tensile properties of joints is studied both in quantitative and qualitative manner related to the mechanics of electromagnetic arc constriction and correlated to the cooling rate. The joints made by applying ACC of 50 A yielded superior tensile properties. It is correlated to the grain refinement of fusion zone resulting in the evolution of laves phase with finer discrete morphology in interdendritic spacings. Increase in ACC above 50 A causes severe growth of grains in fusion zone and substantial decrement in tensile properties of joints. Beneficial effects of ACC were not perceived at maximum level owing to the high heat input.
Critical Factors Affecting Construction Price Index: An Integrated Fuzzy Logic and Analytical Hierarchy Process

Nowadays, many construction engineering and technology enterprises are evolving to find that prosperity is driven and inspired by an open economy with dynamic markets and fierce multifaceted competition. Besides brand and product uniqueness, the ability to quickly provide customers with quotes are matters of concern. Such a requirement for prompt cost estimation of construction investment projects with the use of a construction price index poses a significant challenge to contractors. This is because the nature of the construction industry is shaped by changes in domestic and foreign economic factors, socio-financial issues, and is under the influence of various micro and macro factors. This paper presents a fuzzy decision-making approach for calculating critical factors that affect the construction price index. A qualitative approach was implemented based on in-depth interviews of experts in the construction industry in Vietnam. A synthetic comparison matrix was calculated using Buckley approach. The CoA approach was applied to defuzzified the fuzzy weights of factors that affect the construction price index. The research results show that the top five critical factors affecting the construction price index in Vietnam are (1) consumer price index, (2) gross domestic product, (3) basic interest rate, (4) foreign exchange rate, and (5) total export and import.
ABOVEGROUND BIOMASS ESTIMATION IN A TROPICAL FOREST WITH SELECTIVE LOGGING USING RANDOM FOREST AND LIDAR DATA

The tropical forest is characterized by expressive biomass and stores high amounts of carbon, which is an important variable for climate monitoring. Thus, studies aiming to analyze suitable methods to predict biomass are crucial, especially in the tropics, where dense vegetation makes modeling difficult. Thus, the objective of the present study was to estimate aboveground biomass (AGB) in a tropical forest area with selective logging in the Amazon forest using the Random Forest (RF) machine learning algorithm and LiDAR data. For this, 85 sample units were used at Fazenda Cauaxi, in the municipality of Paragominas, Pará State. LiDAR data were collected in 2014 and made available by the Sustainable Landscapes Project. The software R was used for data analysis. Among the LiDAR metrics, the average height was used as it had the greatest significance to compose the model. The model presented a pseudo R2 of 0.69 (value obtained by the RF), Spearman's Correlation Coefficient of 0.80, RMSE of 47.05 Mg.ha (19.84%), and Bias of 2.06 Mg.ha (0.87%). With the results, it was possible to infer that the average height metric was enough to estimate AGB in a tropical forest with selective logging, in addition, the RF algorithm the biomass to be estimated, which can be used to assist in monitoring and action management in areas of selective logging and serve as a basis for climate change mitigation policies.
Do Lockdowns Bring about Additional Mortality Benefits or Costs? Evidence based on Death Records from 300 Million Chinese People

After the COVID-19 outbreak, China immediately adopted stringent lockdown policies to contain the virus. Using comprehensive death records covering around 300 million Chinese people, we estimate the impacts of city and community lockdowns on non-COVID-19 mortality outside of Wuhan. Employing a difference-in-differences method, we find that lockdowns reduced the number of non-COVID-19 deaths by 4.9% (cardiovascular deaths by 6.2%, injuries by 9.2%, and non-COVID-19 pneumonia deaths by 14.3%). The health benefits are likely driven by significant reductions in air pollution, traffic, and human interactions. A back-of-the-envelope calculation shows that more than 32,000 lives could have been saved from non-COVID-19 diseases/causes during the 40 days of the lockdown on which we focus. The results suggest that the rapid and strict virus countermeasures not only effectively controlled the spread of COVID-19 but also brought about massive unintended public health benefits. These findings can help better inform policymakers around the world about the benefits and costs of city and community lockdowns policies in dealing with the COVID-19 pandemic.
New information on the postcranial skeleton of Gracilisuchus stipanicicorum (Archosauria: Suchia) and reappraisal of its phylogenetic position

Gracilisuchus stipanicicorum Romer, 1972a, the only species in its genus, is a small basal suchian, recovered from the Middle–Late Triassic Chañares Formation (La Rioja, Argentina). This species has been interpreted as related to various taxa within Archosauria, including Ornithosuchidae, Rauisuchidae and Crocodylomorpha. Recent phylogenetic analyses are concordant in recovering it in a basal position within Suchia, and the most recent analysis recovered it within the clade Gracilisuchidae. Six specimens of G. stipanicicorum are known and all of them were studied and included in a phylogenetic analysis of Archosauriformes. Re-examination of the specimens allowed us to improve the diagnosis, identify new diagnostic characters (e.g. first sacral rib with a slight distal expansion relative to the proximal portion) and, in particular, redescribe the postcranial skeleton. The phylogenetic data matrix is an updated version of a previous study with the inclusion of additional characters and taxa. The strict consensus supports the most recent phylogenetic results, recovering Gracilisuchus as a basal suchian within Gracilisuchidae. The results of this analysis resolve some important polytomies and yield higher support values than recent analyses. All previous alternative hypotheses on the affinities of Gracilisuchus were found to be highly suboptimal for the new data matrix.
Impact of the new Saudi energy conservation code on Saudi Arabia residential buildings

ABSTRACT Air conditioning systems are the most viable source to avoid heat waves. Unfortunately, this puts a massive burden on the total electrical load. To deal with this critical issue, the Saudi Building Code National Committee has initiated the Saudi energy conservation code (SBC-602). This paper investigated the role of SBC-602 on the reduction of energy consumption in residential buildings. Furthermore, it also proposes the most effective building element to be insulated. A case study of energy consumption was modelled using SketchUp and analysed using OpenStudio Software. Inconsequence, three scenarios of U-values as an input in this work were suggested which are; building without thermal insulation, building with thermal insulation using Saudi electricity company (SEC) requirement, and using SBC-602 requirement. The study was conducted in three different cities Riyadh, Hail, and Abha, representing three climate zones in Saudi Arabia. Design of experiment (DOE) method was utilised to analyse the significant effects of the input factors. The result indicates that the decrease of the walls U-value is the most effective way to reduce the total electrical energy consumption. Moreover, decreasing roof U-value and increasing setpoint temperature were the following effective factor.
Determinants of Rabies Prophylaxis Involvement based on Children’s Experiences and Perspectives in An Endemic Area of Indonesia

Rabies studies involving children are still rare despite their vulnerability. As the critical preventive measure,understanding factors associated with their involvement in rabies post-exposure prophylaxis (PEP) isneeded. A storytelling interview was conducted for 23 dog-bite and non-bite child-victims, aged 7-15 years,in an endemic area in Indonesia. Content analysis was carried with the help of Atlas ti.7. Unreported woundsand PEP delays were occurred. Children’s capabilities, motivations, and opportunities can be critical toprevent it. Knowing the source of rabies, its visible features and consequences, and the first aid techniqueare needed. Children’s willingness to report and follow PEP seem to be regulated by their attitude towardsvaccine, familiarity of dog ownership, perceptions of wound type, and emotions around rabies consequences,parental punishment, and injection. Mother, peers, neighbours and dog owners need to be empowered in PEPmeasure. In conclusion, rabies PEP program should attentive to factors influencing children’s behaviour tosupport PEP uptake.