Miracle-dz/UniFAR-FTUs huggingface dataset:
Influence of sulfite on ATPase activity of coupling factor CF(1) isolated from spinach chloroplasts

The present study of sulfite participation in the regulation of the chloroplast ATP-synthase complex can help to understand the mechanisms of acid rains toxic effects on the plant photosynthetic apparatus. The aim of the work was to study the effect of sulfite on Ca2+and Mg2+-dependent AtP hydrolysis catalysed by coupling factor CF1, exposed to preliminary short-term acid treatment. CF1 was isolated from spinach chloroplasts (Spinacia oleracea L.). The latent ATPase activity of CF1 was activated by sodium sulfite addition into incubation media. The rate of ATP hydrolysis was determined by the release of inorganic phosphate. In the presence of 25 mM exogenous sulfite the Mg2+-dependent hydrolytic activity of CF1 was increased 7-fold and ca2+-dependent activity 3-fold compared to the control. carbonic anhydrase inhibitors acetazolamide or ethoxyzolamide eliminated sulfite-induced stimulation of ATP hydrolysis. The sulfite stimulating effect on Ca2+and Mg2+-ATPase activity was increased dramatically after incubation (5 min) of isolated CF1 in a medium with pH 3.5 and its subsequent transition to the alkali medium (pH 8.0). In this case, 1 mM sulfite-induced a 10-fold acceleration of ATP hydrolysis. Carbonic anhydrase specific inhibitors (50 μM) removed the sulfite effect. These data suggest that sulfite was able to replace bicarbonate in the CF1 structure after the release of bound hcO3 during acid treatment. K e y w o r d s: spinach chloroplasts, CF1-ATPase, ATP hydrolysis, pH-dependence, sulfite, acetazolamide, ethoxyzolamide.
Alcoholism gender differences in brain responsivity to emotional stimuli

Men and women may use alcohol to regulate their emotions differently, with corresponding differences in neural responses. We examined how photographs of emotional stimuli impacted brain activity obtained through functional magnetic resonance imaging (fMRI) from 42 alcoholic (25 women) and 46 nonalcoholic (24 women) participants. Brain responsivity was blunted in alcoholic compared to nonalcoholic groups. Further analyses indicated significant gender differences in the impact of alcoholism. Brain activation of the alcoholic men (ALCM) was significantly lower than that of the alcoholic women (ALCW) and nonalcoholic men (NCM) in regions including the inferior parietal gyrus, anterior cingulate gyrus, and postcentral gyrus, whereas activation was higher in the ALCW than in the nonalcoholic women (NCW) in superior frontal and supramarginal cortical regions. The reduced brain reactivity of ALCM and increases for ALCW highlighted divergent brain regions and gender effects, suggesting possible differences in the underlying basis for development of alcohol use disorders.
Burning and mowing similarly increase prairie plant production in the spring, but not due to increased soil temperatures

Burning and mowing are two of the most common grassland disturbances across millions of hectares worldwide, but uncertainty remains about when and why these disturbances increase plant production. One of the main hypotheses for increased plant production is that disturbances increase soil temperature in the early growing season and thereby increase plant growth. I tested this hypothesis using a multi-decade study of the frequency (annual or quadrennial) and season (spring, summer, or autumn) of reconstructed tallgrass prairie burning and mowing. To determine plant production, I measured aboveground biomass during three periods of the 2015 growing season: (1) prior to mid-May; (2) mid-May to early July; and (3) early July to the end of the growing season in late September. I also measured soil temperatures from May 2014 to January 2016. This unique dataset allows a detailed picture of when burning and mowing are increasing plant production and whether these increases are likely caused by soil temperatures. I found that, compared to other treatments, autumn burning and mowing similarly increased plant production from the beginning of the growing season to mid-May (autumn disturbances increased production from 37 to 77 g/m) and, compared to other treatments, both autumn and spring burning and mowing similarly increased plant production from mid-May to early July (autumn and spring disturbances increased production from 363 to 439 g/m). Mowing had little effect on soil temperature but burning increased average daily maximum soil temperature at 2.5 cm depth by 6.4°C in the month after burning. Overall, these results suggest that burning did not increase early growing season plant production due to increased soil temperature, given that mowing similarly affected plant production but did not similarly affect soil temperature. I explore alternate explanations for changes in plant production, including increased light and nutrient availability, and decreased detritus.
Determination of Energy Consumption According to Wireless Network Topologies in Grid-Free Lighting Systems

While Wireless Sensor Networks (WSN) are used in various areas nowadays, they also come in front of us in the remote follow up and management of especially main street, road and city lighting systems and in autonomous applications relating with them. This study has been conducted with the aim to determine the energy consumed by Wireless Sensor Network (WSN) based monitoring and management systems as per topological sequence of lighting systems with renewable energy sources (RES) in a grid-free environment. In this way it was aimed to maximize the life time of WSN which are formed by minimum energy consumption of lighting elements that store energy with accumulator-battery in grid-free RES lighting systems and which use this energy later on. Physical installation of lighting systems having different topological distributions will show differences with respect to costs, labour force and time. Starting from here on, different topologies for grid-free lighting systems have been created in simulation environment and they have been analyzed and an optimal solution has been searched for. Energy consumptions of each lighting system having linear, random and tree lighting topology have been determined during data exchange. For each topology lighting systems with 25, 50, 100 and 200 armatures have been designed and their energy consumptions for data exchange have been found. It has been seen that data packages were influenced at first degree from node hopping numbers within topology and as being parallel to this, it has been seen that topology consuming most energy was linear lighting and that topology consuming minimum energy was tree lighting.
Analyzing Diabetic Data using Classification

In modern day, Diabetes has become one of prominent disease that affecting people all around the world. Over 425 million people worldwide with a majority of them are adults living in low and middle income countries had been affected by diabetes. According to the Diabetes Atlas from International Diabetes Federation in 2017, the number of people affected by diabetes disease are expected to increase to over 629 million by 2045. Along with the increasing of the computer science and information technology in researches, its has continued advent into many field including the medical and healthcare field which the cases of this disease and the symptoms are well recorded and documented. This paper aims to use several data mining techniques such as Naive Bayes, Sequential Minimal Optimization (SMO), RepTree and Simple Logistic Regression for classifying whether positive or negative result of diabetes diagnostic. Then, the results will be measured using confusion matrix in term of accuracy, precision and recall as evaluation metric for measuring the classification performance.
In-silico predictive identification of K-RasG12V inhibitors in natural compounds

As RAS protein is highly significant in signaling pathways, involving cell growth, differentiation and apoptosis; the Ras GTPase proteins play a significant as a master switch in the appearance of many diseases, including 20-30% of all cancers. So, the K-RasG12V mutant was selected as a drug target in present study. This mutant is involved in gastric cancer, lung and pancreatic carcinoma, and colon cancers. So, we employed the structure-based drug design methods and molecular dynamics simulations to undergo virtual screening on natural products small molecules and predicted some new potent therapeutic inhibitors. Finally, ZINC15671852, ZINC85592862, ZINC85567582 and ZINC03616630 final Hits were identified as potent inhibitors from among more than 79,000 bioactive compounds from natural resource. Molecular Mechanics Poisson-Boltzmann Surface Area (MM-P/GBSA) calculation results have also demonstrated that these molecules obtained higher binding free energy than co-crystalized reference ligand.
Amended Description and Notes on Campanula kamariana (C. sect. Quinqueloculares, Campanulaceae), an Endangered Endemic of Southern Peloponnesus, Greece

During our floristic studies in southern Peloponnesus (Greece), specimens of a chasmophytic Campanula with an uncommon pinnatifid foliage were observed on the walls of the Kelefa fortress on the Mani Peninsula. After detailed morphological comparisons with comprehensive material of C. sect. Quinqueloculares from southern Greece, we identified the species as C. kamariana, a recently described endemic of Peloponnesus. Our morphological analysis of specimens collected from the Kelefa fortress revealed some deviations from the original description of the taxon. Therefore, here we provide an amended description of C. kamariana based on newly collected specimens and information on two additional localities where this local endemic occurs.
Performance Evaluation of English Part-of-Speech Tagging Based on Typical Parameter Smoothing Algorithm

Part-of-speech (POS) tagging for English is the basis for implementing English automatic correction. Although researchers have done a lot of useful studies on English POS tagging, most of them are aimed at users with English as the first language, while studies for users with English as the second language are few. For this purpose, manual tagging is performed based on the typical parameter smoothing algorithm. On this basis, a performance evaluation method for English part-of-speech tagging is proposed, which integrates the features of term clustering, non-tagged corpus statistics, word pronunciation, etc. The experimental results show that the algorithm can improve the performance of POS tagging effectively, with the tagging accuracy improved from 94.49% to 97.07%
Enhancing Brain Plasticity to Promote Stroke Recovery

Stroke disturbs both the structural and functional integrity of the brain. The understanding of stroke pathophysiology has improved greatly in the past several decades. However, effective therapy is still limited, especially for patients who are in the subacute or chronic phase. Multiple novel therapies have been developed to improve clinical outcomes by improving brain plasticity. These approaches either focus on improving brain remodeling and restoration or on constructing a neural bypass to avoid brain injury. This review describes emerging therapies, including modern rehabilitation, brain stimulation, cell therapy, brain-computer interfaces, and peripheral nervous transfer, and highlights treatment-induced plasticity. Key evidence from basic studies on the underlying mechanisms is also briefly discussed. These insights should lead to a deeper understanding of the overall neural circuit changes, the clinical relevance of these changes in stroke, and stroke treatment progress, which will assist in the development of future approaches to enhance brain function after stroke.
First report, along with nomenclature adjustments, of Ulva ohnoi, U. tepida and U. torta (Ulvaceae, Ulvales, Chlorophyta) from northwestern Mexico

Abstract Examining morphological and anatomical characters, eight bloom-forming Ulva species were identified from La Paz Bay, Gulf of California, Mexico. However, DNA sequencing, using a multi-gene approach (rbcL, ITS2 and tufA) revealed that, of the eight species, four species were genetically distinct, none of which belonged to the species they would have been ascribed based on morpho-anatomical observations. The Ulva lactuca and Ulva rigida morphotypes (blades two cells thick, unbranched, attached by a small discoid holdfast) corresponded to Ulva ohnoi; the Ulva intestinalis morphotype (tubular and smooth thallus, one cell thick) corresponded to Ulva tepida; the Ulva clathrata and Ulva flexuosa morphotypes (tubular to slightly compressed thallus) corresponded to Ulva torta. The Ulva acanthophora morphotype (more or less tubular, profusely branched) did not correspond molecularly to any currently described species. Attempts to sequence the remaining two species, morpho-anatomically identified as Ulva lobata and Ulva nematoidea, were unsuccessful. This study similarly highlights the high degree of cryptic diversity in the genus Ulva, provides a nomenclature adjustment for several species of the genus Ulva from northwestern Mexico and is the first to report U. ohnoi, U. tepida and U. torta for the region.
A Causality Perspective of Genomic Breed Composition for Composite Animals

Genomic breed composition (GBC) of an individual animal refers to the partition of its genome according to the inheritance from its ancestors or ancestral breeds. For crossbred or composite animals, knowing their GBC is useful to estimate heterosis, to characterize their actual inheritance from foundation breeds, and to make management decisions for crossbreeding programs. Various statistical approaches have been proposed to estimate GBC in animals, but the interpretations of estimates have varied with these methods. In the present study, we proposed a causality interpretation of GBC based on path analysis. We applied this method to estimating GBC in two composite breeds of beef cattle, namely Brangus and Beefmaster. Three SNP panels were used to estimate GBC: a 10K SNP panel consisting of 10,226 common SNPs across three GeneSeek Genomic Profiler (GGP) bovine SNP arrays (GGP 30K, GGP 40K, and GGP 50K), and two subsets (1K and 5K) of uniformly distributed SNPs. The path analysis decomposed the relationships between the ancestors and the composite animals into direct and indirect path effects, and GBC was measured by the relative ratio of the coefficients of direct (D-GBC) and combined (C-GBC) effects from each ancestral breed to the progeny, respectively. Estimated GBC varied only slightly between different genotyping platforms and between the three SNP panels. In the Brangus cattle, because the two ancestral breeds had a very distant relationship, the estimated D-GBC and C-GBC were comparable to each other in the path analysis, and they corresponded roughly to the estimated GBC from the linear regression and the admixture model. In the Beefmaster, however, the strong relationship in allelic frequencies between Hereford and Shorthorn imposed a challenge for the linear regression and the admixture model to estimated GBC reliably. Instead, D-GBC by the path analysis included only direct ancestral effects, and it was robust to bias due to high genomic correlations between reference (ancestral) breeds.
Supplementation of matoa (Pometia pinnata) leaf extract and alginate suppressed the growth of Staphylococcus aureus and Escherichia coli in pasteurized milk

The usage of natural additives in pasteurized milk processing allows enhancing the functionality of the product. In this matter, matoa (Pometia enhancing) and alginate become a precious ingredient that exerts antimicrobial properties against Staphylococcus aureus and Escherichia coli. This present work aimed to investigate the proper level of matoa leaf extract and alginate showing the best inhibitory effects on the growth of Escherichia coli and Staphylococcus aureus. Completely randomized design with factorial was arranged, consisting of 2 factors: matoa leaf extracts (0%, 0.1%, 0.15%, dan 0.20%) and alginate (0%, 0.1%, 0.2%, dan 0.3%). The experiment was performed by triplicate. As a result, the treatments showed a wide range of inhibition towards S. aureus and E. coli from 2.68 to 10.48 mm and from 3.15 to 12.34 mm, respectively. In this case, matoa leaf extract was believed capable of suppressing the growth of both bacteria, and the inhibition seemed to increase as more extract was added. Meanwhile, alginate showed the antibacterial effect on E. coli, but not S. aureus. The higher level of alginate also produced more suppressing effects on the growth of E. coli. However, no interaction effect was found between matoa leaf extract and alginate in reducing the population of S. aureus and E. coli. In short, the best inhibition was achieved in the use of the matoa leaf extract of 0.20% and alginate of 0.2-0.3%.
Characterization of novel β‐1,3‐glucan recognition proteins from a Tibetan Plateau ghost moth Thitarodes pui (Lepidoptera, Hepialidae)

β‐1,3‐glucan recognition proteins (βGRPs) function as pattern recognition receptors in the innate immune response against invading pathogens. In the present study, we obtain full‐length cDNA clones for two novel putative βGRPs: TpβGRPc and TpβGRPd from the ghost moth Thitarodes pui (Lepidoptera: Hepialidae). Phylogenetic analysis shows a small distinct lineage, βGRP clade 4, consisting of T. pui βGRPs including TpβGRPa and TpβGRPb that have been identified previously. TpβGRPc and TpβGRPd, comprising 488 and 229 amino acids, have calculated molecular masses of 52 596 and 24 589 Da, respectively. TpβGRPc is 85.52% identical in sequence to TpβGRPa. TpβGRPb and TpβGRPd share the same deletion start site located at the conserved residue Pro 43, although TpβGRPd exhibits a much larger deletion of up to approximately 270 residues covering both the N‐ and C‐terminal regions. Affinity purification, associated with subsequent peptide sequencing, confirms the constitutive occurrence of TpβGRPa and TpβGRPc of similar size (approximately 65 kDa) in sixth‐instar larval haemolymph. These two βGRPs show clear binding affinities to curdlan, an insoluble β‐1,3‐glucan. A quantitative real‐time polymerase chain reaction analysis reveals the high‐level constitutive expression of TpβGRPc and TpβGRPd in the fat body of mid‐instar larvae, which are found to be susceptible to fungal pathogens in field investigations. Remarkable induction of both TpβGRPs occurs in response to haemocoelic challenge with entomopathogenic fungus Beauveria bassiana. The results of the present study suggest that TpβGRPs may contribute to the detection and control of fungal infections.
Classification of liver cancer with microrna data using the deep neural network (DNN) method

Liver cancer is one of the highest causes of death in the world. According to IARC data in 2018, the number of cases of liver cancer reached 841,080 cases and 781,631 of them declared dead. One effort that can be done to reduce cases of death from liver cancer is early detection by doing a classification on MicroRNA data. MicroRNA can be used to identify whether a cell is a cancer cell or not at the earliest stages. MicroRNA data studied was obtained from the GDC Data Portal of the National Cancer Institute (NCI). The Deep Neural Network (DNN) method is one of the methods that can be used in classifying cancer. Data normalization in the DNN method brings input values with an unlimited range into a limited range of output values. Data normalization at data pre-processing work more optimally by adding activation functions and Batch Normalization in each hidden layer. Different types of normalization and use of different activation functions can be compared to get the best accuracy value for DNN. This study compares three types of data normalization, namely Min-Max, Sigmoid, and Softmax. Whereas the activation functions being compared are ReLU, Sigmoid, and TanH. The results showed the best accuracy for the classification of liver cancer with MicroRNA data obtained using Min-Max data normalization, ReLU activation function, batch normalization with parameters of 2 hidden layers, 200 epochs, and 0.004 learning rate with an accuracy value reaching 98.33%.
Effects of deforestation on headwater stream fish assemblages in the Upper Xingu River Basin, Southeastern Amazonia

ABSTRACT The expansion of the Amazonian agricultural frontier represents the most extensive land cover change in the world, detrimentally affecting stream ecosystems which collectively harbor the greatest diversity of freshwater fish on the planet. Our goal was to test the hypotheses that deforestation affects the abundance, richness, and taxonomic structure of headwater stream fish assemblages in the Upper Xingu River Basin, in Southeastern Amazonia. Standardized sampling surveys in replicated first order streams demonstrated that deforestation strongly influences fish assemblage structure. Deforested stream reaches had twice the fish abundance than reference stream reaches in primary forests. These differences in assemblage structure were largely driven by increases in the abundance of a handful of species, as no influence of deforestation on species richness was observed. Stream canopy cover was the strongest predictor of assemblage structure, possibly by a combination of direct and indirect effects on the provision of forest detritus, food resources, channel morphology, and micro-climate regulation. Given the dynamic nature of change in land cover and use in the region, this article is an important contribution to the understanding of the effects of deforestation on Amazonian stream fish, and their conservation.
Assessing lignocellulosic biomass production from crop residues in the European Union: Modelling, analysis of the current scenario and drivers of interannual variability

This study assesses crop residues in the EU from major crops using empirical models to predict crop residues from yield statistics; furthermore it analyses the inter‐annual variability of those estimates over the period 1998‐2015, identifying its main drivers across Europe. The models were constructed based on an exhaustive collection of experimental data from scientific papers for the crops: wheat, barley, rye, oats, triticale, rice, maize, sorghum, rapeseed, sunflower, soybean, potato and sugarbeet. We discuss the assumptions on the relationship between yield and the harvest index, adopted by previous studies, to interpret the experimental data, quantify the uncertainties of these models, and establish the premises to implement them at regional scale –i.e., NUTS level 3– within the EU. To cope this, we created a consolidated sub‐national statistical data along with an algorithm able to aggregate (figures are provided at country level) and disaggregate (production at 25 km grid is provided assupplementary material) estimates. The total lignocellulosic biomass production in the EU28 over the review period, according to our models, is 419 Mt, from which wheat is the major contributor (155 Mt). Our results show that maize and rapeseed are the two crops with the highest residue yield, respectively 8.9 and 8.6 t ha‐1. The spatial analysis revealed that these three crops, which, according to our results, are feedstocks highly suitable a priori for second generation biofuels in the EU and are unevenly distributed across Europe. Weather fluctuation was identified as the major driver in residue production from cereals, while, in the case of starch crops and oilseeds – which are predominant in northern Europe – corresponded to the marked production trend likely influenced by the agricultural policies and agro‐management over the review period. Our results, among others, could help to understand and quantify the ecological boundaries of the bioeconomy from agriculture.