Miracle-dz/UniFAR-FTUs huggingface dataset:
Study of Carbohydrate Hydrolysis From Arracacha Roots (Arracacia Xanthorriza Bancroft) to Produce Fermentable Sugars

In Colombia, approximately 855 840 tons of arracacha are produced each year. The unsalable postharvest arracacha root (Arracacia xanthorriza Bancroft) is not commercialized, mainly due to mechanical damage or small and misshapen roots. In this work, dry samples were characterized and subjected to two treatments: one using thermal hydrolysis, applying saturated steam at pressures of 0,1034 MPa, 0,2068 MPa, and 0,4137 MPa; and another one using hydrolysis with sulfuric acid in concentrations between 0,252,00 M. Then, the cake resulting from the hydrolysis and filtration process was enzymatically hydrolyzed (Liquozyme SC DS, Novozymes) at 1,5, 5 and 10 KNU/g (pH 6, 80 _C, 2 h). Fermentation inhibitors (acetic acid and furfural) were evaluated in the best pretreatment. The results showed that the treatment with sulfuric acid at 1,00 M (2 h) has high yields in reducing sugars added to enzymatic hydrolysis. The maximum level of fermentable carbohydrates per gram of dry sample (1,04 g/g) was also reached. Regarding the fermentation inhibitors of the reducing sugar, a higher concentration of acetic acid was found with a lower furfural content. Therefore, arracacha discards are a promising raw material to increase the supply of bioethanol.
Looking In The Rear-View Mirror: A Thirty-Five Year Retrospective On The Russian Automotive Industry

Drawing on foundational arguments from the literature on emerging market institutional strategies, and employing a realist historical analysis, we present a retrospective on the Russian automotive industry. We begin by tracing the origins of the sector and synthesizing salient post World War II developments. We then recount the subsequent expansionary decade of the 1960s, followed by the stagnation of the late 1980s, culminating in the eventual collapse of socialism and the breakup of the USSR. Attracted by both the rapid growth in the local market, and the preferential tax rates that accompanied investments, we then document the swift expansion of production facilities in Russia by major global manufacturers in the 2000s. We show how eventually an ensuing sharp halt in local market growth, and an accompanying rise in spare production capacity, saw most manufacturers pursue institutional strategies aimed at obtaining new tax concessions and leveraging intra-industry cooperation, rather than divesting their recently established production facilities. Overall, our retrospective, in particular, calls attention to how global manufacturers were able not only to acquire and rejuvenate existing production facilities or install new greenfield ones, but also to exercise their agency in shaping the broader policy framework and in fashioning new sectoral institutions designed to buttress and sustain the industry.
Fifteen Years of the Australian Imaging, Biomarkers and Lifestyle (AIBL) Study: Progress and Observations from 2,359 Older Adults Spanning the Spectrum from Cognitive Normality to Alzheimer’s Disease

Background: The Australian Imaging, Biomarkers and Lifestyle (AIBL) Study commenced in 2006 as a prospective study of 1,112 individuals (768 cognitively normal (CN), 133 with mild cognitive impairment (MCI), and 211 with Alzheimer’s disease dementia (AD)) as an ‘Inception cohort’ who underwent detailed ssessments every 18 months. Over the past decade, an additional 1247 subjects have been added as an ‘Enrichment cohort’ (as of 10 April 2019). Objective: Here we provide an overview of these Inception and Enrichment cohorts of more than 8,500 person-years of investigation. Methods: Participants underwent reassessment every 18 months including comprehensive cognitive testing, neuroimaging (magnetic resonance imaging, MRI; positron emission tomography, PET), biofluid biomarkers and lifestyle evaluations. Results: AIBL has made major contributions to the understanding of the natural history of AD, with cognitive and biological definitions of its three major stages: preclinical, prodromal and clinical. Early deployment of Aβ-amyloid and tau molecular PET imaging and the development of more sensitive and specific blood tests have facilitated the assessment of genetic and environmental factors which affect age at onset and rates of progression. Conclusion: This fifteen-year study provides a large database of highly characterized individuals with longitudinal cognitive, imaging and lifestyle data and biofluid collections, to aid in the development of interventions to delay onset, prevent or treat AD. Harmonization with similar large longitudinal cohort studies is underway to further these aims.
Ho Chi Minh Stock Exchange Market: Operations and Efficiency

This paper presents an up-to-date account of market operations of the Ho Chi Minh Stock Exchange and examines its informational efficiency in recent years. The daily closing prices and rates of return of the Vietnam (VN) Index – the major market index of the Ho Chi Minh Stock Exchange (HOSE) – and ten stocks chosen from different sectors are employed, from January 2, 2018, to December 31, 2019, to investigate the random walk hypothesis of market efficiency using the Lo – MacKinlay variance ratio test and the Chow – Denning multiple variance ratio test. Our results show that the market index and individual sample stocks conform to the null hypothesis of a random walk type 3 model of a weak form market efficiency. The paper also presents the results of an event study to examine the semi-strong form market efficiency of the HOSE. The empirical results on this type indicate that there are significant abnormal returns and significant cumulative abnormal returns by trading the stocks around events. However, these results are inconsistent with the requirements of a semi-strong form market efficiency, and it thus appears that further improvements in the transmission of information and its speed within this market are needed to further improve the efficiency of this emerging market.
New species in Aciculosporium, Shimizuomyces and a new genus Morakotia associated with plants in Clavicipitaceae from Thailand

Three new fungal species in the Clavicipitaceae (Hypocreales, Ascomycota) associated with plants were collected in Thailand. Morphological characterisation and phylogenetic analyses based on multi-locus sequences of LSU, RPB1 and TEF1 showed that two species belong to Aciculosporium and Shimizuomyces. Morakotia occupies a unique clade and is proposed as a novel genus in Clavicipitaceae. Shimizuomyces cinereus and Morakotia fusca share the morphological characteristic of having cylindrical to clavate stromata arising from seeds. Aciculosporium siamense produces perithecial plates and occurs on a leaf sheath of an unknown panicoid grass.
Covariate Selection for Estimating Individual Treatment Effects in Psychotherapy Research: A Simulation Study and Empirical Example

Estimating individual treatment effects (ITEs) is crucial to personalized psychotherapy. It depends on identifying all covariates that interact with treatment, a challenging task considering the many patient characteristics hypothesized to influence treatment outcome. The goal of this study was to compare different covariate-selection strategies and their consequences on estimating ITEs. A Monte Carlo simulation was conducted to compare stepwise regression with and without cross-validation and shrinkage methods. The study was designed to mimic the setting of psychotherapy studies. No single covariate-selection strategy dominated all others across all factor-level combinations and on all performance measures. The least absolute shrinkage and selection operator showed the most accurate out-of-sample predictions, identified the highest number of true treatment-covariate interactions, and estimated ITEs with the highest precision across the most conditions. Domain backward stepwise regression and backward stepwise regression using Bayesian information criterion were least biased in estimating variance of ITEs across the most conditions.
Augmenting versatile peroxidase production from Lentinus squarrosulus and its role in enhancing ruminant feed

Lentinus squarrosulus, a white-rot Basidiomycete, produces versatile peroxidase under nutrient-limiting conditions. This study was undertaken to enhance the yield of versatile peroxidase from the fungus by optimizing the physiological factors of nutrients and their concentration, inducers, temperature, and pH. Optimal medium was devised using a central composite design – response surface methodology and experimental validation. The fungus produced high levels of versatile peroxidase at a C:N ratio of approximately 100, temperature of 35 °C, and initial pH of 7. Immobilization of Lentinus squarrosulus on inert polyurethane foam (PUF) with optimized medium for production enhanced the versatile peroxidase yield multifold. Maximal yield of versatile peroxidase achieved through optimization and immobilization strategies was 116 U/mL. Efficacy of crude versatile peroxidase on aromatics degradation was illustrated through Fourier transform infrared analysis. The results demonstrated the improvised yield of versatile peroxidase from Lentinus squarrosulus and its efficiency in delignification of crop residues, substantiating the role of this enzyme in biotechnological applications especially for enriching ruminant feed.
A Novel Classifier Ensemble Method Based on Subspace Enhancement for High-Dimensional Data Classification

High-dimensional small-size data seriously affects the performance of classifiers. By combining classifiers, ensemble learning obtains higher accuracy and more robust predictions. However, these classifier ensemble methods suffer from several limitations: 1) ensemble with sample space suffers from noise and redundant features; 2) constructing sample subspace on small-size data leads to an insufficient description of sample space; 3) ensemble with random feature subspace leads to information loss, which will degrade the performance of classifiers; 4) most ensemble methods implement directly on the original feature space, which is defective in high-dimensional data with redundant and noisy features. To overcome the above limitations, a new classifier ensemble method based on subspace enhancement (CESE) is proposed for high-dimensional data classification. First, a superior subspace enhancement scheme (SSE) is designed to effectively implement feature selection and transformation for high-dimensional data, followed by generating multiple superior feature subspaces with diversity and discrimination, which enhances the representative ability of features. Second, we develop a mixed space enhancement process (MSE) based on multiscale rotation reconstruction and various subspace enhanced features of SSE. By using MSE, an effective feature fusion is constructed to obtain more diverse features. Furthermore, to improve the capability of our method, we design various feature combination strategies for enhanced features from both SSE and MSE. Comparative results on 33 high-dimensional data sets indicate that our approach CESE outperforms different mainstream integrated systems.
One-pot scalable synthesis of rGO/AuNPs nanocomposite and its application in enzymatic glucose biosensor

Abstract A nanocomposite, rGO/AuNPs, was synthesized simultaneously using one-pot approach with gold nanoparticles (AuNPs) and reduced graphene oxide (rGO) within gelatin which used as a reducing and stabilizing agent. Then, the fabricated nanocomposites were characterized by UV-Vis, transmission electron microscope (TEM) and field emission scanning electron microscope (FE-SEM). The optimal nanocomposite was determined using electrochemical approaches. After facile and eco-friendly synthesis of rGO/AuNPs nanocomposites, it was used to fabricate the bioanode of enzymatic glucose biosensors. After drop-casting the nanocomposites on a screen-printed electrode (SPE), the glucose oxidase (GOx) was immobilized on the pre-treated SPE through a protein cross-linking approach using glutaraldehyde (GA) as a crosslinking reagent and 2,5-dihydroxybenzaldehyde (DHB) as a mediator to improve the electrochemical performance. Then, electrochemical performance of enzyme immobilized nanocomposites was studied using the potentiostat. The results demonstrate that the enzymatic biosensor made of rGO/AuNPs nanocomposites showed enhanced the sensitivity of selectivity for detection of glucose. Graphical Abstract
Emotion Recognition for Cognitive Edge Computing Using Deep Learning

The growing use of the Internet of Things (IoT) has increased the volume of data to be processed by manifolds. Edge computing can lessen the load of transmitting a massive volume of data to the cloud. It can also provide reduced latency and real-time experience to the users. This article proposes an emotion recognition system from facial images based on edge computing. A convolutional neural network (CNN) model is proposed to recognize emotion. The model is trained in a cloud during off time and downloaded to an edge server. During the testing, an end device such as a smartphone captures a face image and does some preprocessing, which includes face detection, face cropping, contrast enhancement, and image resizing. The preprocessed image is then sent to the edge server. The edge server runs the CNN model and infers a decision on emotion. The decision is then transmitted back to the smartphone. Two data sets, JAFFE and extended Cohn–Kanade (CK+), are used for the evaluation. Experimental results show that the proposed system is energy efficient, has less learnable parameters, and good recognition accuracy. The accuracies using the JAFFE and CK+ data sets are 93.5% and 96.6%, respectively.
Volatility and Slow Technology Diffusion

I study the effects of uncertainty on technology adoption and thereby on volatility and growth. I present an analytically-tractable model in which: (i) uncertainty about the returns to adoption delays technology diffusion; and (ii) the mean and volatility of output growth are jointly determined in equilibrium. I then test the key predictions of the model by studying the introduction of three major information and communication technologies (ICTs)—computers, internet, and cell phones. I find that countries with more volatile growth rates of real GDP per capita have higher time adoption lags and lower average growth, as predicted by the model.
Random effects regression trees for the analysis of INVALSI data

Mixed or multilevel models exploit random effects to deal with hierarchical data, where statistical units are clustered in groups and cannot be assumed as independent. Sometimes, the assumption of linear dependence of a response on a set of explanatory variables is not plausible, and model specification becomes a challenging task. Regression trees can be helpful to capture non-linear effects of the predictors. This method was extended to clustered data by modelling the fixed effects with a decision tree while accounting for the random effects with a linear mixed model in a separate step (Hajjem & Larocque, 2011; Sela & Simonoff, 2012). Random effect regression trees are shown to be less sensitive to parametric assumptions and provide improved predictive power compared to linear models with random effects and regression trees without random effects. We propose a new random effect model, called Tree embedded linear mixed model, where the regression function is piecewise-linear, consisting in the sum of a tree component and a linear component. This model can deal with both non-linear and interaction effects and cluster mean dependencies. The proposal is the mixed effect version of the semi-linear regression trees (Vannucci, 2019; Vannucci & Gottard, 2019). Model fitting is obtained by an iterative two-stage estimation procedure, where both the fixed and the random effects are jointly estimated. The proposed model allows a decomposition of the effect of a given predictor within and between clusters. We will show via a simulation study and an application to INVALSI data that these extensions improve the predictive performance of the model in the presence of quasi-linear relationships, avoiding overfitting, and facilitating interpretability.
PIAS1 Potentiates the Anti-viral Activity of SAMHD1 through SUMOylation

Background: Sterile alpha motif and HD domain 1 (SAMHD1) is a host restriction factor that suppresses the infection of a variety of RNA and DNA viruses, including herpesviruses. The anti-viral activity of SAMHD1 is finely tuned by post-translational modifications, including phosphorylation, acetylation and ubiquitination. Our recent studies also demonstrated that the E3 SUMO ligase PIAS1 functions as an Epstein-Barr virus (EBV) restriction factor. However, whether SAMHD1 is regulated by PIAS1 to restrict viral infection remains unknown.Results: In this study, we showed that PIAS1 interacts with SAMHD1 and promotes its SUMOylation. We identified three lysine residues (K469, K595 and K622) located on the surface of SAMHD1 as the major SUMOylation sites. We demonstrated that phosphorylation of SAMHD1 slightly promotes its SUMOylation by PIAS1 and SUMOylated SAMHD1 can still be phosphorylated by viral protein kinases. We further demonstrated that SUMOylation-deficient SAMHD1 loses its anti-EBV activity. Conclusion: Our study reveals that PIAS1-mediated SUMOylation of SAMHD1 enhances its anti-viral activity.
Modelling effects of pre-storage treatments, maturity stage, low-cost storage technology environment and storage period on the quality of tomato fruit

ABSTRACT The aim of this study was to develop a model for prediction of the quality of tomato during storage. Two storage conditions (evaporatively cooled and ambient), four disinfection treatments (tap, anolyte, hot and chlorinated water) and three maturity stages (green, pink and red) were employed in this experiment. Using multivariate analysis, the principal components (PC) (PC1, PC2) were analysed. The PC1 and PC2 contributed 47.9% and 26.0% of total variation, respectively. Maturity stages and the disinfection treatments had significant (p < 0.05) influence on PC1 and PC2. Non-linear fractional and polynomial models were fitted to the experimental data to screen the best models. Hue angle, firmness, titratable acidity (TA) and total soluble solid:TA demonstrated non-linear relationship with days of storage. TA, hue angle and firmness of tomatoes fitted well to the models developed. The models are recommended for use by tomato farmers to predict changes in the quality parameters.
Development of crystallographic texture in chemical solution deposited lead zirconate titanate seed layers

Direct seeding of {001} textured lead zirconate titanate (PZT) on platinized silicon substrates was achieved by chemical solution deposition. The processing space for {001} PZT texturing was explored, under fixed PZT pyrolysis and crystallization conditions, by varying the lead content in solution, dopant species, and PZT layer thicknesses for deposition on platinized Si substrates with different platinum grain sizes. Strong {001} texture was achieved on fine-grained (25 nm) platinum deposited at room temperature and dense, large-grained platinum (80 nm) deposited at elevated temperature. Increases in lead content of solutions (from lead excesses of 10 at. % to 16 at. %) reduced surface pyrochlore coverage, with no substantial influence on orientation or grain size. Seed layer texturing was found to be insensitive to doping (Mn and Nb) on room temperature platinum, although niobium doping increased pyrochlore coverage. Conversely, on platinum deposited at high temperature, manganese doping reduced the perovskite nucleation, producing a rosette microstructure. Undoped seed layers from 30-70 nm thick were found to be strongly {001} textured while thicker layers were {111} textured and thinner layers were poorly crystallized. This article is protected by copyright. All rights reserved.
Phosphatase Activity and Phosphorus Use Efficiency of Cowpea as Influenced by Genotypes, Phosphorus Levels and Liquid based PSB

Legumes are a vital component in Indian cropping systems owing to their ability to provide significant quantities of proteins to the vegetarian diet. Cowpea (Vigna unguiculata (L.) Walp) is a multipurpose grain legume grown commercially for its long green pods, seeds and fodder in arid and humid tropics. It is called ‘‘Vegetable meat’’ due to its high protein content with better biological value, bears green tender pods containing protein (4.3 g), carbohydrate (8.0 g), fats (0.6 g) and fiber (2 g) per 100g of edible portion. Amino acids particularly leucine, lysine and phenylalanine contents are comparatively high in cowpea. Tender fruits contain calcium (80 International Journal of Current Microbiology and Applied Sciences ISSN: 2319-7706 Volume 10 Number 02 (2021) Journal homepage: http://www.ijcmas. com
Altered postural stability in elderly women following a single session of head-mounted display virtual reality.

Purpouse: The aim of this study was to evaluate the effect of a single session of head-mounted display virtual reality on postural stability in elderly women. METHODS Forty-seven female subjects underwent a 20-minute virtual reality session. The mean age of the subjects was 70.12 years. As an immersive source, we used a relaxing virtual reality game with a head-mounted display device. The postural stability test was conducted using a Nintendo Wii force plate. Participants completed a set of three 30-s trials in which they took a quiet bipedal eyes-open stance while standing on a hard surface: before the virtual reality session, immediately after the virtual reality session, and 2 minutes after the virtual reality session. Centre of pressure parameters were analysed in the sagittal and frontal planes. RESULTS Analysing the results obtained immediately after the virtual reality session, significant differences were observed in almost all examined parameters. In the sagittal plane, centre of pressure path velocity increased by 10% ( p < 0.01) and path standard deviation by 15% ( p < 0.05). In the frontal plane, centre of pressure path velocity increased by 14% ( p < 0.01). After 2 minutes, all examined parameters showed no significant difference compared to before the virtual reality session. CONCLUSIONS Immediately after the virtual reality session, there was an increase in almost all examined parameters. However, after 2 minutes, all examined parameters had returned to baseline. Therefore, to reduce fall risk after a virtual reality session, it is recommended that the subject spend at least 2 minutes in a sitting position.