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Micro RNAs (miRNA) are a type of non-coding RNA involved in gene regulation and can be associated with diseases such as cancer, cardiovascular, and neurological diseases. As such, identifying the entire genome of miRNA can be of great relevance. Since experimental methods for novel precursor miRNA (pre-miRNA) detection are complex and expensive, computational detection using Machine Learning (ML) could be useful. Existing ML methods are often complex black boxes that do not create an interpretable structural description of pre-miRNA. In this paper, we propose a novel framework that makes use of generative modeling through Variational Auto-Encoders to uncover the generative factors of pre-miRNA. After training the VAE, the pre-miRNA description is developed using a decision tree on the lower dimensional latent space. Applying the framework to miRNA classification, we obtain a high reconstruction and classification performance while also developing an accurate miRNA description.

Vegetables and their products have a low caloric value. Root vegetables are rich in bioactive compounds such flavonoids, polyphenolic acids, carotenoids. It also contains a some percentage of other functional components that can significantly affect human health.In order to achieve a high yield, excessive fertilization with nitrogen fertilizer is performed, which worsens the quality of vegetables. The aim of this paper is to analyze the impact cultivation system on the content of total phenolics and flavonoids, as well as a comparative assessment of their antioxidant activity in selected samples of vegetables. The antioxidant activity was studied using DPPH, and ABTS assays. The total phenolic content (TPC) and total flavonoid content (TFC) of the extracts were determined using Folin-Ciocalteuand and Aluminium chloride colorimetric methods, respectively. The highest content of total phenolics was determined for parsley from conventional production (427.38 mg GAE/100 g fresh sample), parsley from organic production the richest source of flavonoids (54.92 mg GAE/100 g fresh sample). Other samples of vegetables from conventional production had a lower flavonoid content compared to organic production. It was confirmed that the antioxidant activity of the analyzed vegetables in the maximum correlation with the content of total phenolics (R2 = 1). Organic samples of vegetables were characterized by a significantly higher content of flavonoids compared to vegetables grown conventionally. Organic production could be a good method to increase the concentration of bioactive compounds with antioxidant properties in vegetables.

This study focuses on predicting and optimizing the quality parameters of cookies enriched with dehydrated peach through the application of Support Vector Machine (SVM) and Artificial Neural Network (ANN) models. The purpose of the study is to employ advanced machine learning techniques to understand the intricate relationships between input parameters, such as the presence of dehydrated peach and treatment methods (lyophilization and lyophilization with osmotic pretreatment), and output variables representing various quality aspects of cookies. For each of the 32 outputs, including the parameters of the basic chemical compositions of the cookie samples, selected mineral contents, moisture contents, baking characteristics, color properties, sensorial attributes, and antioxidant properties, separate models were constructed using SVMs and ANNs. Results showcase the efficiency of ANN models in predicting a diverse set of quality parameters with r2 up to 1.000, with SVM models exhibiting slightly higher coefficients of determination for specific variables with r2 reaching 0.981. The sensitivity analysis underscores the pivotal role of dehydrated peach and the positive influence of osmotic pretreatment on specific compositional attributes. Utilizing established Artificial Neural Network models, multi-objective optimization was conducted, revealing optimal formulation and factor values in cookie quality optimization. The optimal quantity of lyophilized peach with osmotic pretreatment for the cookie formulation was identified as 15%.

This study aimed to evaluate the effect of convective and microwave drying on the bioactive-compounds content of blackberry (Rubus fruticosus) fruits, as well as drying parameters and energy consumption. The fruit was dehydrated in a convective dehydrator at a temperature of 50 °C and 70 °C and in a microwave oven at power levels of 90 W, 180 W and 240 W. The highest amount of anthocyanins, polyphenols and antioxidant capacity were obtained in blackberry fruits that were microwave dried at 90 W and 180 W (46.3–52.5 and 51.8–83.5 mg 100 g−1 dm of total anthocyanins, 296.3–255.8 and 418.4–502.2 mg 100 g−1 dm of total phenolics, and 1.20–1.51 and 1.45–2.35 mmol TE 100 g−1 dm of antioxidant capacity for 90 W and 180 W models, respectively). It turned out that microwave dehydration shortened the processing time and lowered the energy consumption compared to convective drying (a significantly reduced drying time of 92–99% with microwave dehydration). Blackberry fruits dehydrated at 240 W showed the shortest dehydration time (59–67 min), minimal energy consumption (0.23 kWh) and the most efficient diffusion (1.48–1.66 × 10−8 m2 s−1).

The aim of this study was to determine the changes in chemical composition of fresh red raspberry ‘Willamette’ fruits caused by the presence of raspberry leaf blotch emaravirus (RLBV). In three experimental orchards of ‘Willamette’ raspberry, fruits were harvested from RLBV-free and RLBV-infected plants in 2019 and 2020. Fruits were collected at appropriate maturity stages and further analyzed in terms of total phenolics, total anthocyanins, and selected individual phenolics. In all three experimental orchards, the phenolic profiles of the infected and uninfected fruit samples were considerably different during both studied years. Nonetheless, the intensity of the modifications varied greatly depending on the location and harvest year. Statistical analysis revealed that the influence of RLBV infection on the studied features was undeniable, although the influences of weather conditions and soil composition outweighed the influence of RLBV. Taking into consideration all the experimental and statistical data, it can be concluded that RLBV had an impact on the phenolic profile of raspberry ‘Willamette’ fruits, while sensitivity to environmental conditions and soil composition is emphasized.

Abstract The safe and reliable operation of a modern power system imposes the need to describe and estimate the system stability through impedance‐admittance measurement and identification. The integration and operation of medium voltage equipment in existing infrastructure requires impedance‐admittance measurement devices which are currently few in number and often limited in output voltage level and output bandwidth. The four‐quadrant Cascaded H‐Bridge (CHB) topology, featuring a three‐phase Active Front‐End (AFE) and a single‐phase H‐Bridge inverter presents itself as a potential solution to this problem. To support the development of the CHB topology the AFE hardware and control platforms require implementation and testing. The testing aspect of the process additionally includes the measurement of terminal characteristics such as the input admittance and the output impedance. The complexity of the CHB converter and its increased number of power electronics building blocks requires the use of industrial control platforms. This work defines the hardware implementation and control principle of the three‐phase AFE of a CHB converter as well as the system for measuring its terminal characteristics. The results obtained show that the proposed system can be successfully used to measure the input admittance and output impedance of the AFE upon which the development of the CHB is based.

Due to high water content, chokeberries (Aronia melanocarpa L.) are perishable. Therefore, energy-saving, combined drying technologies have been explored to improve the chokeberry drying. The combined microwave and the traditional convective drying method (MCD) have significantly enhanced the drying effectiveness, efficiency, and energy utilization rate and improved product quality. The MCD method, which implies the microwave power (MD) of 900 W for 9 s and the convective dehydration (CD) at 230 °C for 12 s, has the shortest dehydration time t (24 ± 2 min), has the maximum coefficient of diffusion (Deff = 6.0768 × 10−9 ± 5.9815 × 10−11 m2 s−1), and represents the most energy effective for dehydration process (Emin = 0.382 ± 0.036 kWh). A higher water-holding capacity (WHC) characterized the chokeberries obtained by the MCD method compared to the regular microwave method (MD). The mildest MCD (15 s of MD on 900 W, 7 s of CD on 180 °C) could dehydrate chokeberries with the highest WHC (685.71 ± 40.86 g H2O g−1 d.m.) and the greatest evaluations for sensory attributes in terms of all properties. The results of this study provide the drying behavior of chokeberries that can help develop efficient drying methods and improve existing ones.

In this research series, several sugar beet molasses of different osmolalities were inoculated with a mix of the following microorganisms, Escherichia coli, Salmonella spp. and Listeria monocytogenes, to develop mathematical models and correlations of the effect of different levels of osmolality and different exposure time to the viability of the selected microorganisms. The respective enumerations of Escherichia coli, Salmonella spp., Listeria monocytogenes, Enterobacteriaceae, and total plate count were conducted on inoculated molasses samples of different osmolalities (from 5500 to 7000 mmol/kg) and at different exposure times (from 0 to 5 h). The results showed that by increasing molasses osmolalities, all the selected microorganisms’ exposure time viability measures statistically decreased significantly. Salmonella spp. showed the highest viability of all the tested microorganisms in a high osmotic environment. In contrast, Listeria monocytogenes showed the least resilience to osmotic stress, with a reduction in the numbers below the detection limit. The developed mathematical models of microorganisms’ viability exposed to molasses’s high-osmolality environment were statistically significant, allowing for the good prediction of a number of microorganisms based on exposure time and osmolality levels. The obtained results describe molasses’s excellent microbial load-reducing capability and provide the potential for applications in the production of safe foods.

The aim of this study was to examine the effect of different fertilizers on the yield and antioxidant capacity of two lettuce genotypes "Santoro RZ" and "Kiribati RZ". Lettuce genotypes are fertilized with organic fertilizer (Slavol) and organic-inorganic NPK fertilizer (Fitofert hemisuper plus) during the vegetation. The analyzed parameters were root length and head weight of lettuce, total phenols, and flavonoids, as well as antioxidant capacity. Lettuce genotypes "Santoro RZ" and "Kiribati RZ" fertilized with organic fertilization showed the highest content of total phenols (358.13 ± 1.30 mg RU/100 g of fresh sample), the total content of flavonoids (114.22 ± 0.3 mg RE/100 g of fresh sample) and antioxidant capacity (neutralization of DPHH radicals 58.72 ± 1.88%). The results revealed that the yield and antioxidant capacity of lettuce can be improved by using organic fertilizers. [ABSTRACT FROM AUTHOR]

Spatial reasoning, a fundamental aspect of human intelligence, is essential for machine learning models to understand and interpret object relationships. It is crucial for numerous real-world applications, ranging from autonomous navigation to urban planning. The lack of comprehensive datasets limits the development and evaluation of models that can effectively handle spatial reasoning tasks. Existing datasets often contain complex spatial reasoning problems with overlapping spatial relationships, making it challenging to diagnose specific aspects that a model struggles with. We address this gap by introducing a new dataset of linear layouts. This dataset is systematically designed to exhibit a range of spatial relations and complexity levels. Analyzing spatial reasoning through linear layout generation offers a more structured and manageable approach to understanding how models learn and interpret spatial relationships. Linear layout generation has broad applicability and is of fundamental importance in design and optimization. To benchmark dataset, we develop LinLayCNN, a generic data-driven method that applies shallow, one-dimensional convolutional neural network (CNN), to generate linear layouts in an iterative process. Experimental results reveal that LinLayCNN can effectively solve fundamental spatial challenges even with the relatively small size of the training set. It is capable of precise object placement, making it a robust tool for linear layout generation. Current layout generation methods focus on domain-specific solutions and often fail to maintain the precision needed for technical domains, such as accurate sizing, and object counting. They also require a substantial amount of data to function effectively. LinLayCNN overcame these issues. This study further clarifies CNNs’ capabilities in spatial reasoning, highlight their potential to advance the field of layout generation. As a result, our approach establishes a clear benchmark for evaluating spatial reasoning and aids in development of models that can more effectively understand and reason about space.

Aim of study: To investigate the changes in chemical composition of fresh plum fruits cv. ‘Čačanska Lepotica’ prompted by the presence of various strains of plum pox virus (PPV). Area of study: Serbia Material and methods: In an experimental orchard of ‘Čačanska Lepotica’ plum cultivar, fruits were picked from virus-free and PPV-infected trees (PPV-D and PPV-Rec strains) in four harvest stages in 2017 and 2018. Fruits were further analyzed on total phenolics, flavonoids, anthocyanins, antioxidant capacity and selected phenolics. Main results: The results indicate that virus infection causes chemical changes to a certain extent, but mostly in initial harvest stages, while the values are equal in later stages. In the last harvest stage, as the most utilizable in commercial purposes, only chlorogenic acid content was affected in 2017, while in 2018 contents of neochlorogenic acid and chrysanthemin were altered by the PPV infection. Total contents of flavonoids and phenolics revealed no influence of virus infection during both 2017 and 2018, while PPV-Rec infected samples were richer in anthocyanins under heavy rainfall during summer months of 2018. Given the number of identified compounds (10) and the vast experimental data, it might be concluded that influence of PPV infection on chemical composition of ‘Čačanska Lepotica’ plum fruits was quite limited. Research highlights: Plum cultivar ‘Čačanska Lepotica’ should be considered as highly tolerant cultivar to PPV, and can be grown in heavily infected environment with no risk. Therefore, it might be a great replacement for sensitive plum cultivars, such as ‘Požegača’ and ‘Čačanska Rodna’.