Miracle-dz/UniFAR-FTUs huggingface dataset:
Computational Study on the Structure, Stability, and Electronic Feature Analyses of Trapped Halocarbons inside a Novel Bispyrazole Organic Molecular Cage

Computational experiments on a novel crystal (Bharadwaj et al. Cryst. Growth Des.2019, 19, 369–375) having a series of seven host–guest complexes (HGCs) where the host species belong to the family of a novel bispyrazole organic cryptand (BPOC) and their structural, stability, and the electronic feature analyses have been reported using the quantum chemical calculation approach. This report systematically unravels an inclusive theory-based experiment on the well-known guest solvents (S) like halocarbon solvents [CCl4, CHCl3/CHCl3’ (two orientations), CH2Cl2, C2H4Cl2, C2H4Br2, and C2HCl3] and a few model chlorofluorocarbons (CFCs) (CClF3, CCl2F2, and CCl3F) trapped inside the host (BPOC) cryptand, which are the crux in forming the structures of biological and supramolecular systems. Using the implicitly dispersion-corrected DFT (M06-2X/6-31G*) approach, the BPOC molecular cage and its host–guest capabilities were evaluated for the encapsulation of the above said halocarbon solvents as well as the CFC models. The encapsulated C2H4Br2 solvent inside the BPOC cage is found to be the most stable among all the HGCs; however, common in the solid phase, similar binary complexes have not been formerly examined in any gas/solvent-phase studies of the BPOC host species. Moreover, very interestingly, the stability pattern of the host–guest complexes enhances for the CFC models when the number of Cl atoms is increased. As the halogenated solvents through halogen and H-bonding are very decisive in understanding and controlling chemical reactions, the NCI-plots support the presence of the halogen bonding (C–Cl/Br···π) and H-bonding (C–H···π) interactions playing an imperative role in stabilizing the guests (solvents) inside the hydrophobic cavity. To get more insights, the HOMO–LUMO and MESP plots as well as natural population analyses have also been highlighted. This theoretical study portrays an inclusive information about the structural, stability, and electronic feature analyses of the host–guest assemblies consisting of the halogen and H-bonding interactions at the atomic level where the influences of such halocarbon solvents play crucial roles in comprehending and managing chemical reactions.
Yeast Metabolism and Its Exploitation in Emerging Winemaking Trends: From Sulfite Tolerance to Sulfite Reduction

Sulfite is widely used as a preservative in foods and beverages for its antimicrobial and antioxidant activities, particularly in winemaking where SO2 is frequently added. Thus, sulfite resistance mechanisms have been extensively studied in the fermenting yeast Saccharomyces cerevisiae. Nevertheless, in recent years, a negative perception has developed towards sulfites in wine, because of human health and environmental concerns. Increasing consumer demand for wines with low SO2 content is pushing the winemaking sector to develop new practices in order to reduce sulfite content in wine, including the use of physical and chemical alternatives to SO2, and the exploitation of microbial resources to the same purpose. For this reason, the formation of sulfur-containing compounds by wine yeast has become a crucial point of research during the last decades. In this context, the aim of this review is to examine the main mechanisms weaponized by Saccharomyces cerevisiae for coping with sulfite, with a particular emphasis on the production of sulfite and glutathione, sulfite detoxification through membrane efflux (together with the genetic determinants thereof), and production of SO2-binding compounds.
Assessing the Effect of Training Sampling Design on the Performance of Machine Learning Classifiers for Land Cover Mapping Using Multi-Temporal Remote Sensing Data and Google Earth Engine

Machine learning classifiers are being increasingly used nowadays for Land Use and Land Cover (LULC) mapping from remote sensing images. However, arriving at the right choice of classifier requires understanding the main factors influencing their performance. The present study investigated firstly the effect of training sampling design on the classification results obtained by Random Forest (RF) classifier and, secondly, it compared its performance with other machine learning classifiers for LULC mapping using multi-temporal satellite remote sensing data and the Google Earth Engine (GEE) platform. We evaluated the impact of three sampling methods, namely Stratified Equal Random Sampling (SRS(Eq)), Stratified Proportional Random Sampling (SRS(Prop)), and Stratified Systematic Sampling (SSS) upon the classification results obtained by the RF trained LULC model. Our results showed that the SRS(Prop) method favors major classes while achieving good overall accuracy. The SRS(Eq) method provides good class-level accuracies, even for minority classes, whereas the SSS method performs well for areas with large intra-class variability. Toward evaluating the performance of machine learning classifiers, RF outperformed Classification and Regression Trees (CART), Support Vector Machine (SVM), and Relevance Vector Machine (RVM) with a >95% confidence level. The performance of CART and SVM classifiers were found to be similar. RVM achieved good classification results with a limited number of training samples.
Plant-Available Phosphorus after Application of Synthetic Chelating Agents

ABSTRACT Fertilizer phosphorus (P) can become immobilized in acidic soils through bonds with iron (Fe) and aluminum (Al). Two chelating agents, ethylenediamine tetraacetic acid disodium salt (EDTA) and hydroxyethyl ethylenediamine triacetic acid (HEEDTA), were tested in a greenhouse study for efficiency at increasing plant-available P to corn (Zea mays L.). Fertilizer P was added with or without chelate to the center of pots, simulating a starter band of P. Without the presence of chelates, biomass above and below ground increased linearly as P fertilizer rates increased at 0, 9.6, 19.3, 28.9, and 38.5 kg P ha−1. Applications of EDTA and HEEDTA did not significantly increase water-soluble P (WSP), Mehlich 1 P, and Mehlich 3 P compared to soils without chelates. Applications of EDTA increased P uptake in the belowground biomass. Despite previous research showing that chelates increased WSP in soils, a decrease in P sorption was not observed with the additions of chelating agents to soils.
Evaluation of Antifungal Activity of Lemon (Citrus lemon) in Marrakech and Kenitra cities Morocco

he main objective of this study is to evaluate antifungal activity by using fresh lemon peels in two cities: Marrakech and Kenitra, Morocco. This fungal activity was tested against seven pathogenic fungal strains which are saccharomyces cerivicae, candida albicans, candida spp1, candida spp2, candida spp3, candida spp4, and candida spp5. The study used, in this procedure, the extract of ethanol and methanol, besides the extraction of essential oils of lemon peels in the Marrakesh and Kenitra in order to obtain the yield, the minimum inhibitory concentrations(MIC), and the minimum concentrations of antifungals(MFC). As a result, the yield of essential oil, methanol, and ethanol extracts were 0.78%; 9.8%; 10.05%, and 0.64%, 8.3%, 8.9% in Marrakech, Kenitra, respectively. the minimum inhibitory concentrations(MIC) were tested at concentrations ranging from (0.1, 0.25, 0.5, 1.25 and 2.5 mg/ml) as wells as their the minimum concentrations of antifungals(MFC). Also, Zones of inhibition were recorded extend from 9 to 36 mm and from 8 to 18 mm in the concentrations of ethanolic extracts, the zones inhibition ranged from 10 to 26 mm and from 9 to 18 mm in the concentrations of methanolic extracts, and the zones inhibition ranged from 20 to 34 mm and 10 to 20in the concentrations of essential oil for Marrakech and Kenitra, successively. The minimum inhibitory concentrations (MIC) and minimum antifungal concentrations (MFC) to the lower concentration, as opposed to saccharomyces cerevisiae, is 0.1 mg/ml), minimum inhibitory concentrations (MIC) and minimum antifungal, concentrations (MFC) to higher concentrate against candida spp1 are 2.5 mg/ml). The results showed a difference in yields due to the difference in solvents and also in regions. The highest antifungal potentiality was exhibited by the ethanol followed by the methanol followed by the essential oil. Therefore, increased yield offset by high fungal activity, because of the difference in environmental conditions, climate, lack of water, distance, proximity to the sea and elevation, genes, extraction and season. The activities of the extracted oils depend on the availability of the active constituent based on the use of a solvent.
Amphoteric poly(vinyl alcohol) hydrogel as a material of artificial muscle.

人工筋素材としての観点から両性イオン型ポリビニルアルコール (PVA) ハイドロゲルの化学機械エネルギー変換性能の向上を図るため, 平衡膨潤特性, 受動的な力学的特性, 材料の異方性の収縮特性への影響, 収縮のダイナミクスについて, 素材合成条件との関連を明かにし, 性能向上のための指針をまとめた. それに従って作製した素材の出力密度は, 最大で骨格筋の出力密度に近い0. 1W/cm3を実現することができた. ゲルの内部構造をクライオSEMにより調べた結果はこの材料のいっそうの性能向上を約束するものであった.
Evaluate the Feasibility of the Implementation of E-Assessment in Objective Structured Clinical Examination (OSCE) in Pharmacy Education from the Examiner’s Perspectives

With the wide application of the OSCE in the field of pharmacy, the development of e-assessment in OSCE is a predictable trend. However, the feasibility of its practical application and the acceptance of examiners who are accustomed to using traditional paper-based methods are worthy of being discussed. The e-assessment system (EAS) was constructed in this study and used in the examination process. Examiner satisfaction and changes in acceptance of EAS were evaluated. Examiners showed high recognition for the advantages of EAS in data processing, but EAS obviously made the examiner more nervous than the paper-based method. After repeated use of the e-OSCE system, the examiner’s satisfaction and acceptance significantly improved, which showed that there was a certain correlation between examiner’s familiarity with the assessment method and their acceptance. Besides, EAS has great advantages over traditional paper-based methods, and it is feasible in clinical practice examination of pharmacy education.
Water Appropriation on the Agricultural Frontier in Western Bahia and Its Contribution to Streamflow Reduction: Revisiting the Debate in the Brazilian Cerrado

Over the last three decades, almost half of the Brazilian tropical savanna (Cerrado biome) has been converted into cropland and planted pastures. This study aims to understand the implications of the expansion of the agricultural frontier for water resources in western Bahia state. We use an interdisciplinary approach that combines quantitative and qualitative data (spatial and hydrological analysis, interviews) to tie together land use changes in the Corrente basin, the streamflow and precipitation time series in the Pratudão River sub-basin (part of the Corrente basin), and the perceptions of soybean farmers and smallholder communities about the transformations of the hydrological cycle over the last few years. We observed an almost 10-fold increase in agricultural surface area in the Corrente River basin over the last three decades (1986–2018), going on from 57,090 ha to 565,084 ha, while center-pivot irrigated areas increased from 240 ha to 43,631 ha. Over this period, the streamflow has reduced by 38% in the Pratudão River. Our hydrological analyses, based on the Mann-Kendall test, of seven fluviometric stations and 14 pluviometry stations showed a statistically significant streamflow trend in the Pratudão River sub-basin for both minimum and mean streamflow series (p ≤0.05). Surface runoff coefficient, which relates streamflow and precipitation annual data coefficient, decreased from around 0.4 in the late 1990s to less than 0.2 in 2015. In addition, most precipitation time series analysis (number of annual rainy days) showed no statistically significant trend (p > 0.05). Our results indicate that agricultural changes rather than climate change may be the main driver of downward streamflow trends in the Pratudão River sub-basin that is part of Corrente River basin.
Maternal high-fructose consumption provokes placental oxidative stress resulting in asymmetrical fetal growth restriction in rats

We aimed to determine the impact of high-fructose intake during pregnancy on the fetal-placental unit in rats, which may be the initial mechanism of the programming effect of fructose. Pregnant Sprague–Dawley rats were randomly assigned to three groups and respectively provided tap water (n = 10), 10% (w/v) fructose solution (n = 10), and 10% (w/v) glucose solution (n = 10) from embryonic day 0 to 20. Compared with the control and glucose groups, significantly lower fetal length, fetal weight, placental weight, and fetus/placenta ratio were found in the fructose group on embryonic day 20 (all p<0.05). In parallel with markedly increased uric acid concentrations in the dams, significantly decreased antioxidant enzymes activities and mRNA expression levels were observed in placentas in the fructose group (all p<0.05). In the fructose group, placental mRNA and protein expression of nuclear factor erythroid 2-related factor 2 was markedly downregulated and kelch-like ECH-associated protein 1 was significantly upregulated (all p<0.05). In conclusion, high-fructose consumption during pregnancy drives augmented oxidative stress in rats. Placental insufficiency under oxidative stress contributes to asymmetrical fetal growth restriction.
Mesoporous Nickel Ferrites with Spinel Structure Prepared by an Aerosol Spray Pyrolysis Method for Photocatalytic Hydrogen Evolution

Submicron-sized mesoporous nickel ferrite (NiFe2O4) spheres were prepared by an aerosol spray pyrolysis method using Pluronic F127 as a structure-directing agent, and their photocatalytic performance for hydrogen (H2) evolution was examined in an aqueous MeOH solution by visible light irradiation (λ > 420 nm). The structure of the spherical mesoporous nickel ferrites was studied by transmission electron microscopy, powder X-ray diffraction, and N2 adsorption–desorption isotherm measurements. Mesoporous NiFe2O4 spheres of high specific surface area (278 m2 g–1) with a highly crystalline framework were prepared by adjusting the amount of structure-directing agent and the calcining condition. High photocatalytic activity of mesoporous NiFe2O4 for H2 evolution from water with methanol was achieved due to the combination of high surface area and high crystallinity of the nickel ferrites.
Magnetically Agitated Nanoparticle-Based Batch Reactors for Biocatalysis with Immobilized Aspartate Ammonia-Lyase

In this study, we investigated the influence of different modes of magnetic mixing on effective enzyme activity of aspartate ammonia-lyase from Pseudomonas fluorescens immobilized onto epoxy-functionalized magnetic nanoparticles by covalent binding (AAL-MNP). The effective specific enzyme activity of AAL-MNPs in traditional shake vial method was compared to the specific activity of the MNP-based biocatalyst in two devices designed for magnetic agitation. The first device agitated the AAL-MNPs by moving two permanent magnets at two opposite sides of a vial in x-axis direction (being perpendicular to the y-axis of the vial); the second device unsettled the MNP biocatalyst by rotating the two permanent magnets around the y-axis of the vial. In a traditional shake vial, the substrate and biocatalyst move in the same direction with the same pattern. In magnetic agitation modes, the MNPs responded differently to the external magnetic field of two permanent magnets. In the axial agitation mode, MNPs formed a moving cloud inside the vial, whereas in the rotating agitation mode, they formed a ring. Especially, the rotating agitation of the MNPs generated small fluid flow inside the vial enabling the mixing of the reaction mixture, leading to enhanced effective activity of AAL-MNPs compared to shake vial agitation.
Two-Stage Joint Model for Multivariate Longitudinal and Multistate Processes, with Application to Renal Transplantation Data

In longitudinal studies, clinicians usually collect longitudinal biomarkers’ measurements over time until an event such as recovery, disease relapse, or death occurs. Joint modeling approaches are increasingly used to study the association between one longitudinal and one survival outcome. However, in practice, a patient may experience multiple disease progression events successively. So instead of modeling of a single event, progression of the disease as a multistate process should be modeled. On the other hand, in such studies, multivariate longitudinal outcomes may be collected and their association with the survival process is of interest. In the present study, we applied a joint model of various longitudinal biomarkers and transitions between different health statuses in patients who underwent renal transplantation. The full joint likelihood approaches are faced with the complexities in computation of the likelihood. So, here, we have proposed two-stage modeling of multivariate longitudinal outcomes and multistate conditions to avoid these complexities. The proposed model showed reliable results compared to the joint model in case of joint modeling of univariate longitudinal biomarker and the multistate process.
Predictive Maintenance: A Novel Framework for a Data-Driven, Semi-Supervised, and Partially Online Prognostic Health Management Application in Industries

Prognostic Health Management (PHM) is a predictive maintenance strategy, which is based on Condition Monitoring (CM) data and aims to predict the future states of machinery. The existing literature reports the PHM at two levels: methodological and applicative. From the methodological point of view, there are many publications and standards of a PHM system design. From the applicative point of view, many papers address the improvement of techniques adopted for realizing PHM tasks without covering the whole process. In these cases, most applications rely on a large amount of historical data to train models for diagnostic and prognostic purposes. Industries, very often, are not able to obtain these data. Thus, the most adopted approaches, based on batch and off-line analysis, cannot be adopted. In this paper, we present a novel framework and architecture that support the initial application of PHM from the machinery producers’ perspective. The proposed framework is based on an edge-cloud infrastructure that allows performing streaming analysis at the edge to reduce the quantity of the data to store in permanent memory, to know the health status of the machinery at any point in time, and to discover novel and anomalous behaviors. The collection of the data from multiple machines into a cloud server allows training more accurate diagnostic and prognostic models using a higher amount of data, whose results will serve to predict the health status in real-time at the edge. The so-built PHM system would allow industries to monitor and supervise a machinery network placed in different locations and can thus bring several benefits to both machinery producers and users. After a brief literature review of signal processing, feature extraction, diagnostics, and prognostics, including incremental and semi-supervised approaches for anomaly and novelty detection applied to data streams, a case study is presented. It was conducted on data collected from a test rig and shows the potential of the proposed framework in terms of the ability to detect changes in the operating conditions and abrupt faults and storage memory saving. The outcomes of our work, as well as its major novel aspect, is the design of a framework for a PHM system based on specific requirements that directly originate from the industrial field, together with indications on which techniques can be adopted to achieve such goals.
Tracking-DOSeqSLAM: A dynamic sequence-based visual place recognition paradigm

Konstantinos A. Tsintotas, Laboratory of Robotics and Automation, Department of Production and Management Engineering, School of Engineering, Democritus University of Thrace, Xanthi, Greece. Email: ktsintot@pme.duth. gr Abstract Simultaneous localization and mapping (SLAM) refers to a process that permits a mobile robot to build up a map of the environment and, at the same time, to use it to compute its location. One of its most important components is its ability to associate the most recently perceived visual measurement to the one derived from previsited locations, a technique widely known as loop closure detection. In this article, we evolve our previous approach, dubbed as ‘DOSeqSLAM’ by presenting a low complexity loop closure detection pipeline wherein the traversed trajectory (map) is represented by sequence‐ based locations (submaps). Each of these groups of images, referred to as place, is generated online through a point tracking repeatability check employed on the perceived visual sensory information. When querying the database, the proper candidate place is selected and, through an image‐to‐image search, the appropriate location is chosen. The method is subjected to an extensive evaluation on seven publicly available datasets, revealing a substantial improvement in computational complexity and performance over its predecessors, while performing favourably against other state‐of‐the art solutions. The system’s effectiveness is owed to the reduced number of places, which, compared to the original approach, is at least one order of magnitude less.
Identification of conserved peptide upstream open reading frames (CPuORFs) in oil palm (Elaeis guineensis) genome

Abstract. Subroto AP, Aditama R, Tanjung ZA, Utomo C, Liwang T. 2021. Identification of conserved peptide upstream open reading frames (CPuORFs) in oil palm (Elaeis guineensis) genome. Biodiversitas 22: 1829-1838. Upstream open reading frame (uORF) translation serves as one of several modulating mechanisms in gene expression. Conserved peptide uORF is a rare subset of uORF containing a peptide sequence that is conserved among eudicot families and negatively regulates the translation of its adjacent main ORF (mORF). This research aimed to identify oil palm CPuORFs by conducting a homology search between Arabidopsis (Arabidopsis thaliana) and rice (Oryza sativa) CPuORF databases against the existing oil palm (Elaeis guineensis) genome. Using alignment by Hidden Markov Model (HMM) approach, 48 oil palm CPuORFs were discovered which were later divided into 24 homology groups (HG) based on its gene ontology. The high degree of sequence homology indicates a high conservation CPuORFs among oil palm, Arabidopsis and rice. The CPuORFs peptide sequence conservation between oil palm progenies was later validated by sequencing of CPuORF22 and CPuORF29 as a reference, from several DxP Tenera crosses, individual Duras from different origins and American oil palm (Elaeis oleifera). This is the first study of oil palm CPuORF discovery which might provide a better understanding of gene expression regulation in oil palm.