Quantitative standardization of pharmacologically active components from antiplasmodial plant Solanum nudum Dunal (wild and in vitro)

Solanum nudum Dunal (Solanaceae) is most commonly known and used by the population of the colombian Pacific coast as an antimalarial treatment. This article study into optimization and quantitative analysis of compounds steroidal over time of development of this species when grown in vitro and wild. A new steroidal compound named SN6 was elucidated by NMR and a new method of quantification of seven steroidal compounds (Diosgenone DONA and six steroids SNs) using HPLC-DAD-MS in extracts of cultures in vitro and wild was investigated. Biology activity of extracts was found to a range of antiplasmodial activity in FCB2 and NF-54 with inhibitory concentration (IC50) between (17.04 -100 μg/mL) and cytotoxicity in U-937 of CC50 (7.18 -104.7 μg/mL). This method creates the basis for the detection of seven sterols antiplasmodial present in extracts from S. nudum plant as a quality parameter in the control and expression of phytochemicals.

Survival Analysis to Predict How Color Influences the Shelf Life of Strawberry Leather

Color change of fruit-based products during storage is an important quality parameter to determine their shelf life. In this study, a combination of relative humidity (RH) and illumination was evaluated on the stability of strawberry leathers. Samples were conditioned at 25 °C, in chambers with RH of 22.5% and 52.3% and under two levels of illumination (no illumination and with a light-emitting diode (LED) illumination at 1010 lx). Samples were analyzed during storage by instrumental color measurements, total anthocyanin content, and consumers’ acceptance/rejection of the product color. Current-status survival analysis was performed to estimate the sensory-based shelf-life of the strawberry leather. The chromatic parameters (a* and ΔE* values) and anthocyanin content changed with increasing storage time and RH, fitting a first-order fractional conversion model. Samples conditioned at the higher RH showed a higher reduction of a* values and anthocyanins losses when stored under LED illumination than those without illumination. The increase of RH resulted in a faster increase of the consumer rejection probability and a shorter shelf life of the strawberry leather. For 50% of consumers’ rejection, the sensory shelf life of the strawberry leather equilibrated at 22.5% RH was estimated as at least 54 days, while it was reduced to approximately 2 days at 52.3% RH. The red chromatic parameter (a* value) strongly correlated to the percentage of consumer rejection in all storage conditions, suggesting that this analytical parameter can be useful as a predictor of strawberry leather’s shelf life. Therefore, the results of this study show the applicability of an approach that integrates instrumental and sensory data to acquire faster information on color changes during the storage of strawberry leather and product shelf-life prediction.

JAX-ReaxFF: A Gradient Based Framework for Extremely Fast Optimization of Reactive Force Fields

Molecular dynamics (MD) simulations facilitate the study of physical and chemical processes of interest. Traditional classical MD models lack reactivity to explore several important phenomena; while quantum mechanical (QM) models can be used for this purpose, they come with steep computational costs. The reactive force field (ReaxFF) model bridges the gap between these approaches by incorporating dynamic bonding and polarizability. To achieve realistic simulations using ReaxFF, model parameters must be optimized against high fidelity training data, typically with QM accuracy. Existing parameter optimization methods for ReaxFF consist of black-box techniques using genetic algorithms or Monte-Carlo methods. Due to the stochastic behavior of these methods, the optimization process can require millions of error evaluations for complex parameter fitting tasks, significantly hampering the rapid development of high quality parameter sets. In this work, we present JAX ReaxFF, a novel software tool that leverages modern machine learning infrastructure to enable extremely fast optimization of ReaxFF parameters. By calculating gradients of the loss function using the JAX library, we are able to utilize highly effective local optimization methods, such as the limited Broyden–Fletcher–Goldfarb–Shanno (LBFGS) and Sequential Least Squares Programming (SLSQP) methods. As a result of the performance portability of JAX, JAX-ReaxFF can execute efficiently on multi-core CPUs, GPUs (or even TPUs). By leveraging the gradient information and modern hardware accelerators, we are able to decrease parameter optimization time for ReaxFF from days to mere minutes. JAX-ReaxFF framework can also serve as a sandbox environment for domain scientists to explore customizing the ReaxFF functional form for more accurate modeling.

Effects of Chemical Fertilizer, Fym and Bio-Inoculants On Nutrient Content, Uptake and Quality of Wheat (Triticum Aestivum L.)

Effects of different levels of chemical fertilizer, farmyard manure (FYM) and bio-inoculants on nutrient content, uptake and quality parameter of wheat were studied. Results indicated that increasing levels of chemical fertilizer, FYM and bio-inoculants significantly enhanced nutrient content and uptake by wheat, while quality parameters of wheat showed significant results with bio-inoculants application. Maximum N, P and K content and their uptake in grain (80.3, 11.07 and 25.29%, respectively) and straw (32.18, 7.14 and 95.92%, respectively) were noticed with 100% NPK over control. Application of FYM @ 10 t/ha significantly increased nutrient content (NPK) in grain and straw and their uptake over the control. The total N, P and K uptake by wheat were found to be maximum 80.97, 12.68 and 86.10 kg/ha, respectively with the application of FYM over control. Combined use of fertilizer levels and FYM, and combined use of fertilizer levels and bio-inoculants significantly increased the nutrient uptake by wheat. Bangladesh J. Bot. 50(4): 1109-1117, 2021 (December)

Evaluating Service Characteristics of Working Surfaces of Car Parts by Microgeometric Quality Parameters

The correlation between the service characteristics of the working surfaces of car parts belonging to the rotary body class, and quality parameters—in particular, the height-related roughness parameter Ra—was estimated. Low values of Ra were found to be unable to guarantee an optimal microrelief geometry and, accordingly, high-performance characteristics of the working surface. The oil-accumulation power of the parts was investigated as a primary characteristic of sliding friction using the group of Rk parameters in the Abbott–Firestone diagram, based on the profilogram of the test specimen’s surfaces. The oil-absorption power of the surfaces formed by different technological operations was compared with different microgeometric quality parameter values.

Effects of Production Methods on Flavour Characteristics of Nonalcoholic Wine

The growing awareness on the negative effects of alcohol on health and other factors like religious beliefs, responsible driving, and strict alcohol regulatory laws have contributed to the overwhelming demand for nonalcoholic wines. Numerous methods are available for producing nonalcoholic wines which encompass both restrictive ethanol production processes (interrupted fermentation, cold fermentation, juice/wine blends, use of unripe fruit, enzyme, and special and immobilized yeasts) and alcohol removal methods (heat, membrane, and extraction techniques). Studies have shown that these methods significantly affect the flavour characteristics of the wine, which is a key quality parameter in wine purchasing and consumption. It is in view of this that this work seeks to review current articles on the effects of production methods on the flavour characteristics of nonalcoholic wine. This review will provide insight on nonalcoholic wine production methods, their merits and demerits, and contributions to flavour characteristics. It will also unfold research opportunities in the field of nonalcoholic wine production for continual improvement and development of the wine industry.

Utilization of Explainable Machine Learning Algorithms for Determination of Important Features in ‘Suncrest’ Peach Maturity Prediction

Peaches (Prunus persica (L.) Batsch) are a popular fruit in Europe and Croatia. Maturity at harvest has a crucial influence on peach fruit quality, storage life, and consequently consumer acceptance. The main goal of this study is to develop a machine learning model that will detect the most important features for predicting peach maturity by first training models and then using the importance ratings of these models to detect nonlinear (and linear) relationships. Thus, the most important peach features at a given stage of its ripening could be revealed. To date, this method has not been used for this purpose, and at the same time, it has the potential to be applied to other similar peach varieties. A total of 33 fruit features are measured on the harvested peaches, and three imbalanced datasets are created using firmness thresholds of 1.84, 3.57, and 4.59 kg·cm−2. These datasets are balanced using the SMOTE and ROSE techniques, and the Random Forest machine learning model is trained on them. Permutation Feature Importance (PFI), Variable Importance (VI), and LIME interpretability methods are used to detect variables that most influence predictions in the given machine learning models. PFI shows that the h° and a* ground color parameters, COL ground color index, SSC/TA, and TA inner quality parameters are among the top ten most contributing variables in all three models. Meanwhile, VI shows that this is the case for the a* ground color parameter, COL and CCL ground color indexes, and the SSC/TA inner quality parameter. The fruit flesh ratio is highly positioned (among the top three according to PFI) in two models, but it is not even among the top ten in the third.

Deep Learning in Water Resources Management: Τhe Case Study of Kastoria Lake in Greece

The effects of climate change on water resources management have drawn worldwide attention. Water quality predictions that are both reliable and precise are critical for an effective water resources management. Although nonlinear biological and chemical processes occurring in a lake make prediction complex, advanced techniques are needed to develop reliable models and effective management systems. Artificial intelligence (AI) is one of the most recent methods for modeling complex structures. The applications of machine learning (ML), as a part of AI, in hydrology and water resources management have been increasing in recent years. In this paper, the ability of deep neural networks (DNNs) to predict the quality parameter of dissolved oxygen (DO), in Lake Kastoria, Greece, is tested. The available dataset from 11 November 2015, to 15 March 2018, on an hourly basis, from four telemetric stations located in the study area consists of (1) Chl-a (μg/L), (2) pH, (3) temperature—Tw (°C), (4) conductivity (μS/cm), (5) turbidity (NTU), (6) ammonia (NH4, mg/L), (7) nitrate nitrogen (N–NO3, mg/L), and (8) dissolved oxygen (DO) (mg/L). Feed-forward deep neural networks (FF-DNNs) of DO, with different structures, are tested for all stations. All the well-trained DNNs give satisfactory results. The optimal selected FF-DNNs of DO for each station with a high efficiency (NSE > 0.89 for optimal selected structures/station) constitute a good choice for modeling dissolved oxygen. Moreover, they provide information in real time and comprise a powerful decision support system (DSS) for preventing accidental and emergency conditions that may arise from both natural and anthropogenic hazards.

Study on development of a device for assessment compost stability based on the determination of oxygen consumption using pressure measurement

Organic fertilizer or compost is an essential product in the current trend of high-tech agricultural development. Compost stability is not only an important quality parameter but can also be used to monitor the efficiency of the composting process. This study developed a device to evaluate the stability of compost based on the oxygen consumption method using the principle of pressure measurement. This homemade device has improved design, overcoming existing weaknesses in commercial equipment with the same operating principle. In which, the compost sample is put in the containers placed in the middle of the bottle while the produced CO2 is absorbed by the KOH solution at the bottom. The device is capable of working independently with the data recorded on the microSD card without connecting to a computer. The device is operationally tested in the laboratory to assess the oxygen consumption of two actual compost samples. The results showed that these samples both meet the EU’s regulations on compost stability with oxygen consumption in 4 days (AT4) less than 10.0 mg O2/g compost.

Plant Agronomic Features Can Predict Quality and Field Performance: A Bibliometric Analysis

Plant quality and survival prediction tools are useful when applied in the field in different agricultural sectors. The objectives of this study were to conduct a review and bibliometric analysis of the Dickson Quality Index (DQI) as a key plant quality indicator and with respect to its scientific applications. A third objective was to identify the main morphological and physiological parameters used in plant production research. The methodology and findings of 289 scientific articles were analysed based on the morphological, physiological, and mathematical parameters used as plant quality indicators in research on forest, medicinal, horticultural, aromatic, and ornamental species. During the last 10 years, the number of publications that have used the DQI as a plant quality parameter has increased by 150%, and Brazilian researchers stand out as the most frequent users. Forestry is the discipline where quality parameters and their biometric relationships are most often used to facilitate intensive plant production. Use of the DQI increases the certainty of prediction, selection, and productivity in the plant production chain. The DQI is a robust tool with scientific application and great potential for use in the preselection of plants with high quality standards among a wide range of plant species.