Experimental Samples Research Articles

MULTIFRACTAL ANALYSIS OF POLARIZATION MAPS OF ELLIPTICITY OF MICROSCOPIC IMAGES OF BLOOD SMEARS AND DIFFERENTIAL DIAGNOSIS OF THYROID GLAND PATHOLOGY

Among the numerous biomedical diagnostic applications of the soft matter structure,Mueller- matrix polarimetry of the optical anisotropy of biological tissues occupies animportant place. Various Mueller- matrix polarimetry systems have been developed toimplement the diagnostic tasks.The aim of the study – to conduct a comparative analysis of the effectiveness of themultifractal processing method of polarization ellipticity maps of digital microscopicimages of supramolecular protein networks of polycrystalline blood smears in orderto determine new objective criteria (markers) of digital laser histological differentialdiagnosis of the thyroid gland pathology.Material and methods. Four observation groups were formed: 1. Control group – healthydonors (51 people). 2. Patients with nodular goiter (51 patients). 3. Patients with autoimmunethyroiditis (51 patients). 4. Patients with papillary cancer (51 patients). A comparative analysisof the effectiveness of the statistical and multifractal processing methods of polarizationellipticity maps of digital microscopic images of supramolecular protein networks ofpolycrystalline blood smears was carried out in order to determine new objective criteria(markers) of digital laser histological differential diagnosis of the thyroid pathology. Results. A comprehensive statistical and multifractal analysis of polarization ellipticitymaps of experimental blood samples of donors and patients with nodular goiter,autoimmune thyroiditis, and papillary cancer was performed. Objective statistical andmultifractal markers for differential diagnosis of the thyroid gland pathology weredetermined. An excellent level (~96 %-98 %) of the balanced accuracy of the multifractalanalysis method of polarization maps of blood for minimally invasive differentiation ofpathological conditions has been demonstrated.Conclusions. 1. The most optimal statistical markers of the method of mapping ellipticitymaps of polarization of digital microscopic images of blood smears were found to bethe most optimal, and a good diagnostic capacity of diagnosis (~87.3 %-88.3 %) anda satisfactory level (~78.4 %-81.4 %) of differentiation of thyroid pathology wereestablished. 2. Multifractal markers of the mapping ellipticity maps method of polarizationof digital microscopic images of blood smears were found to be the most optimal and anexcellent level (~96 %-98 %) of the balanced accuracy of differential diagnosis of thethyroid gland pathologies was established.

Data-driven performance analysis of an active chilled beam air conditioning system: A machine learning approach for energy efficiency and predictive maintenance

This study aims to enhance energy performance estimation and maintenance planning for office buildings in Tehran. Machine learning and data mining techniques were employed, utilizing Support Vector Machines (SVMs), Artificial Neural Networks (ANNs), Narrow Neural Networks, and optimizable SVMs. A comprehensive dataset of 2500 experimental samples was utilized with five input parameters (building size, occupancy patterns, environmental conditions, and ACB system parameters) and one output parameter (electricity consumption). GPR models performed better in estimating electricity consumption, achieving a Root Mean Square Error (RMSE) of 297.01 and an impressive R-squared value of 0.99. To optimize energy performance and design more energy-efficient buildings, this model accurately predicts energy consumption. Additionally, this study addressed maintenance planning for ACB systems, simulating maintenance issues with 2500 synthetic samples. The dataset included building age, size, number of floors, usage intensity, previous maintenance history, location, and environmental factors. Maintenance time was estimated using these features, and XGBoost was employed to predict ACB system maintenance time. Low MSE, RMSE, and R-squared values in the XGBoost model allow for accurate predictions. Using this model, facility managers can proactively plan maintenance activities, allocate resources, and minimize downtime. Through proactive measures, ACB performance and maintenance costs are optimized. The study accurately predicts ACB system energy consumption and maintenance time using the rational quadratic GPR model, applicable in Tehran and beyond. Additionally, MATLAB version R2022b was used for prediction, while DesignBuilder and EnergyPlus were utilized for technical analysis.

Experimental Investigation of High-performance Fiber-reinforced Cementitious Composite and its Effect on RC Beams by Numerical Method

In the present study, experimental investigations were initially conducted on high-performance fiber-reinforced cementitious composite (HPFRCC). A total of 9 samples were examined and subjected to a 4-point bending test. The sample lengths were standardized at 500 and 1700 mm. The variables under scrutiny included the impact of Micro Steel, Macro Steel, and polyvinyl alcohol fibers. Furthermore, the models were scrutinized under two conditions: with and without glass fiber reinforced polymer (GFRP) bars. The number of samples was 9. In the second part of the article, subsequent to verifying the experimental samples from the current study alongside a concrete beam, numerical analyses were carried out to assess the influence of HPFRCC on the behavior of RC beams. Similarly, the impact of GFRP diameter, as well as the height of HPFRCCs, on the seismic performance of RC beams, was investigated by conducting 36 numerical analyses. The analyses were carried out using nonlinear static methods, with monotonic loading. The model outputs encompass elastic stiffness, ultimate strength, relative stiffness, and energy dissipation. The experimental results showed that the use of macro steel fibers in models without GFRP rebars has better results on the flexural behavior of HPFRCC. Moreover, by reinforcing RC beams with HPFRCC, a 70% increase in energy dissipation was observed. The elastic stiffness and ultimate strength of the strengthened beam are directly proportional to the ratio of the HPFRCC's elastic flexural stiffness to that of the original beam. These results increase proportionally as this ratio rises.

Rapid identification of the coefficient of moisture expansion of polymer materials by the employment of plates with asymmetric concentration fields

The paper pursues the development of a novel methodology for the rapid identification of the diffuso-mechanical properties of polymer materials based on the employment of plates subject to asymmetric moisture concentration fields. The study is carried out on epoxy plate samples equipped with a thin aluminium foil on a surface exposed to the environment to promote asymmetric moisture absorption. The asymmetric moisture fields promote deformations of the plate. Mass gain and plate curvatures are measured as a function of time during conditioning. By using a weakly coupled diffuso-mechanical model: 1D Fick’s diffusion model and 2D plane stress hygroelastic model the diffuso-mechanical properties of the material can be then identified. Due to the chosen size of the experimental samples the present study allows the identification of the coefficient of moisture expansion of the epoxy material. For the material under study, the following values can be identified for saturation mass gain, water diffusivity and coefficient of moisture expansion respectively: 1.67%, 0.025 mm2.h−1, 0.1628.

Novel physics-informed optimization framework for complex multi-physics problems: Implementation for a sweeping gas membrane distillation module

The application of advanced machine learning algorithms such as deep learning is limited for the surrogate-based process optimization of complex multi-physics problems because high-quality experimental and numerical training samples required for deep-learning model training are expensive and scarce. To address this issue, in this study, a novel physics-informed process optimization (PIPO) framework was introduced. In the first step, surrogate models based on conventional neural networks (NN) were trained using a few available high-quality training samples. In the second step, the trained NN models were coupled to the process optimization solver in which the loss of physical laws was added to the optimizer’s objective function to find optimal design points that satisfy the laws of physics. As a result, the generalization performance of the framework was greatly improved for design targets outside the training range of NN models. PIPO is substantially different from the physics-informed neural networks where the loss of physics is added to the loss function used during NN model training. The PIPO framework was used to optimize a sweeping gas membrane distillation (SGMD) module. Eight input design variables, including process and geometrical parameters, were optimized for different challenging targets to achieve the best SGMD performance in terms of ammonia recovery ratio and concentration. It was shown that for noticeably few training samples of 68 experiments, the proposed framework was able to achieve the optimization targets within a reasonable computational cost. The optimum designs were verified and analyzed in detail by high-resolution computational fluid dynamics models.

Dynamic fractionation of methane carbon isotope during mass transport in coal with bidisperse pore structures: Experiments, numerical modeling, and applications

Gas-in-place (GIP) content and the in-situ free/adsorbed gas ratio are crucial factors for evaluating resource potential and optimizing production strategies of deep coalbed methane (CBM). While the prevailing notion that adsorbed gas predominates is widely accepted in shallow CBM, this idea has been contested in deep CBM. Various numerical models for calculating GIP content based on gas volume data from canister degassing experiments commonly suffer from low accuracy, strong multiplicity, and inability to evaluate critical parameters of free/adsorbed gas ratio. Isotope fractionation is an important tool for studying the occurrence state and transport mechanism of natural gas, and numerical modeling that considers both cumulative degassing content and carbon isotope data has proven effective in determining the GIP content and adsorbed gas ratio in shale. However, the applicability of the existing isotope fractionation models in deep CBM has yet to be demonstrated experimentally and theoretically. In this study, four coal samples from the Shanxi and Taiyuan formations in Ordos Basin were subjected to canister degassing, gas metering, and methane isotopic composition testing. The results revealed significant multi-stage carbon isotope fractionation characteristics throughout deep CBM degassing, reflecting the strong heterogeneous pore structure characteristics within the coal. Existing carbon isotope fractionation (CIF) models are unable to effectively characterize this complex phenomenon. Based on these findings, we innovatively developed a CIF model that considers bidisperse pore structures and multiple transport mechanisms. The model can better characterize the complex isotope fractionation behavior and reveal the mechanisms of carbon isotope transport in series structures of multi-scale pores. Using the model, we calculated the GIP content of four experimental samples from the Shanxi and Taiyuan formations on the southeastern edge of the Ordos Basin, ranging from 27.78 m3/t to 39.13 m3/t, with an average of 31.68 m3/t, and the in-suit free gas ratio ranges from 13.30 % to 32.89 %, with an average of 19.45 %. These research findings provided new insights for assessing the resource potential of deep CBM, screening sweet spots, and predicting gas well performance.

Results of research work on forming the readiness of future bachelors of physical culture and sports to provide pre-medical care

The article is devoted to the problem of forming the readiness of future bachelor’s of physical culture and sports to provide pre-medical care in the conditions of the educational process of a higher education institution. Readiness to provide pre-medical care is presented as a professional quality that ensures the compliance of future bachelor’s of physical culture and sports with the requirements of professional activity in situations of injuries and emergencies in athletes and consumers of physical culture and sports services. Based on the analysis of scientific sources, it has been proven that the issue of preparing future bachelor’s of physical culture and sports to provide pre-medical care is insufficiently developed. On this basis, a conclusion was made about the expediency of conducting a scientific study aimed at checking the effectiveness of innovative approaches and means of teaching students of the specialty 017 "Physical culture and sports" actions in situations of injuries and emergencies in the course of professional activity. The research work on checking the effectiveness of innovative methodological developments in training students to provide pre-medical assistance in professional activities is described. In particular, this characteristic is: dependent and independent variables; experimental and control samples; research design; methods of data collection, including a knowledge test, situational tasks, simulation tasks for providing pre-medical assistance in typical situations of sports activities). The results of research work in experimental and control institutions of higher education are highlighted. Cross-tabulation of data and histograms of their distribution, results of data comparison according to the Mann- Whitney test are presented. On the basis of the interpretation of the results of the research work, a conclusion was formulated about the effectiveness of the author's methodical studies in the context of the formation of the readiness of future bachelors of physical culture and sports to provide pre-medical care.

Discrete linear canonical transform on graphs: Uncertainty principle and sampling

With an increasing influx of classical signal processing methodologies into the field of graph signal processing, approaches grounded in discrete linear canonical transform have found application in graph signals. In this paper, we initially propose the uncertainty principle of the graph linear canonical transform (GLCT), which is based on a class of graph signals maximally concentrated in both vertex and graph spectral domains. Subsequently, leveraging the uncertainty principle, we establish conditions for recovering bandlimited signals of the GLCT from a subset of samples, thereby formulating the sampling theory for the GLCT. We elucidate interesting connections between the uncertainty principle and sampling. Further, by employing sampling set selection and experimental design sampling strategies, we introduce optimal sampling operators in the GLCT domain. Finally, we evaluate the performance of our methods through simulations and numerical experiments across applications.

Lacticaseibacillus casei as Anti-blowing Agents: Impact on the Evolution of Ripening and Sensory Profile of Montasio Cheese

AbstractRecently, the Lacticaseibacillus casei group strains have been gaining growing interest due to their potential to be used as secondary adjunct cultures in cheese. This work aimed to test autochthonous Lb. casei strains as anti-blowing agents and to evaluate their impact on the evolution of the ripening and the sensory profile of Montasio cheese. The cheesemaking trial included a control production without lysozyme (C1), a control with lysozyme (C2), and four experimental productions, each containing a different pool of autochthonous Lb. casei strains (EX1-4). Samples were taken during ripening, and physicochemical, microbiological, and sensory analyses were carried out. Lb. casei counts indicate that the selected strains survived the cheesemaking and maintained their viability of about 9 log cfu g−1 at the end of the ripening. Only EX3 showed a significant slowdown of the proteolytic index compared to controls over time. Furthermore, from the principal component analysis, it emerged that at the end of the 120-day-long ripening, C2 and the experimental samples were characterized by similar profiles of volatile compounds. The late-blowing defect (LBD) was observed exclusively in C1, whereas it was not detected in the control sample with lysozyme or in any experimental samples. These findings not only confirmed the efficacy of lysozyme in preventing LBD but also supported the effectiveness of the selected Lb. casei strains as anti-blowing agents with the ability to contribute to the final volatile profile without compromising the typicality of the product.

The influence of natural antioxidants on the quality and storage capacity of semi-finished horse meat products

The article is devoted to the influence of natural antioxidants on the quality parameters of meat semi-finished products from horse meat and the microbiological stability of the product during its storage. In solving the set tasks, the standard, generally accepted methods of research were used. Statistical processing of the obtained results and assessment of data reliability were carried out by statistical methods using Microsoft Excel and Statistica (version 12.0). A new antioxidant of natural origin, dihydroquercetin, has been proposed and tested. Effective doses of its application have been established: 0.05 - 0.075% for raw horse fat and 0.075% for semi-finished meat products from horse meat produced with its addition. It has been shown that the administration of dihydroquercetin (Dx) together with ascorbic acid (AA) and tocopherol (Tp) is 10-30% more effective. The effect of dihydroquercetin on the indicators of oxidative spoilage in the technological process, from the production of horse meat to finished products using the latter, has been studied. The organoleptic parameters (appearance, odour, colour) of the control sample and the sample with 0.025% Dx by fat weight corresponded in their characteristics to the experimental samples with 0.05%, 0.075% and 0.1% by 45 days of storage. The introduction of dihydroquercetin, ascorbic acid, and tocopherol also makes it possible to slow down the microbiological spoilage of the semi-finished products that have been obtained during storage.

Replication of soil analogues at the original scale by 3D printing: Quantitative assessment of accuracy and repeatability of the pore structural heterogeneity

The present study investigates the quality of four three-dimensional (3D) printing technologies to accurately reproduce the complex pore structure of a real undisturbed soil sample for laboratory experiments of transport in porous media at a 1:1 scale. Four state-of-the-art 3D printing technologies were evaluated (digital light synthesis, PolyJet with gel support material, low-force stereolithography, and PolyJet with water-soluble support material) using a combination of 3D image analysis from microtomopraphy and flow simulations of the pore structure produced with each 3D printing technique. Accuracy, as determined by matching solid and void volumes, permeability, connected porosity, specific surface area, and pore size distribution of the print against the original digital soil structure, was found to be substantially better for digital light synthesis, as compared to the other tested technologies. Repeatability, as determined by the same metrics but compared between identical prints, was found to be comparable across all printing technologies and did not significantly improve for prints at greater magnification (1.5×). Wettability of the samples was improved by plasma treatment of the prints. The thorough analysis herein presented demonstrates that advanced, yet relatively inexpensive 3D printing approaches can be used to generate real-scale high quality analogs of soils/rocks that are much needed for experimental laboratory work. Such a method can open countless opportunities for studying the coupling of pore-structure and hydrodynamics on reactive mass transport in environmental science and engineering, soil science, and other subsurface related fields.

Remote Sensing Inversion of Soil Organic Matter Content in Straw-Returned Fields in China’s Black Soil Region

China’s black earth region is the country’s corn golden belt, and returning corn straw to the field not only helps improve the Soil Organic Matter (SOM) content and soil fertility, but also resolves environmental pollution caused by straw burning. To study the effects of different years and amounts of straw returned to the field on SOM content, this study used soil sampling data from a conservation tillage experimental base in Gaojia Village, Lishu County, combined with indoor measurements of imaging spectral data, to establish a prediction model of SOM content by applying partial least squares regression, and inverting the SOM content in the study area. The results showed that the PLSR model accuracy using indoor measured soil imaging spectral data as the independent variable was high. The accuracy coefficients of samples with different field return and different field return amounts, R2, were 0.9176 and 0.8901, respectively, which better predicted SOM content. In the 0–50 cm tillage layer, the highest average SOM content of 39.73 g/kg was found under the NT-1 treatment with different no-tillage straw return year treatments. The depth of the tillage layer in the typical black soil region of Northeast China is around 0–20 cm, and the most significant increase in SOM content was observed in the experimental samples under the NT-1 treatment. SOM content in NT-1 treatment increased by 31.83% compared with CK-1, 68.24% compared with CK-2, 72.18% compared with NT-0, 699.48% compared with NT-2, and 311.44% compared with NT-3, respectively. The highest SOM content of 31.9 g/kg was found in NT-100 under the different treatments of different years of field return. At the 0–20 cm soil layer, the SOM content increases most significantly under NT-100 treatment, which is the most suitable treatment method for straw return to the field. And NT-100 is 22.09% higher than CK-1, 55.36% higher than CK-2, 58.99% higher than NC-0, 115.95% higher than NT-33, and 48.72% higher than NT-67, respectively. This study provides data that can support the conservation of soil ecosystem diversity and sustainable soil use, and it also enriches the application of the PLSR model application.

Physiological Responses of Crotalaria spp. to the Presence of High Aluminum Availability in the Soil.

Brazilian soils are predominantly rich in aluminum, which becomes mobile at pH < 5, affecting sensitive plants; however, some species have developed aluminum tolerance mechanisms. The purpose of this study was to compare the physiological responses of Crotalaria genus species, family Fabaceae, which have the ability to associate with nitrogen-fixing bacteria under the influence of Al3+ in the soil. The soil used was Oxisol; the experimental design was in randomized blocks in a factorial scheme (2 × 3): soil factor (available toxic aluminum content; correction of dolomitic limestone-MgCO3) and species factor (C. juncea; C. spectabilis; C. ochroleuca); cultivated within 43, 53, and 53 days, respectively, with five replications; 30 experimental samples. Mass and length, pigments, gas exchange, and changes in nitrogen metabolism were evaluated. C. juncea showed a higher concentration of amino acids in the leaves, internal carbon, and stomatal conductance in soil with Al3+, as well as higher production of ureides, allantoinic acid, allantoic acid, proteins, and amino acids in the nodules, with 78% of the Al3+ accumulation occurring in the roots. C. ochroleuca demonstrated greater shoot length and nodule number production in limed soil; in soil with Al3+, it showed a 91% increase in chlorophyll a content and 93% in carotenoids. C. spectabilis showed a 93% increase in ureide production in the leaves in soil with Al3+.

A Functional 67-bp Duplication Locating at the Core Promoter Region within the Bovine ADIPOQ Gene Is Associated with Ovarian Traits and mRNA Expression.

ADIPOQ plays a crucial role in regulating the reproductive system, but there are few reports on the effects of ADIPOQ on ovarian in dairy cows. Previous studies have verified the presence of a 67-bp mutation in the promoter region of the ADIPOQ gene. Hence, we employed ovarian tissues (n = 2111) and blood samples (n = 108) from Chinese Holstein cows as experimental samples to examine the association between ADIPOQ promoter variants and ovarian traits. We extracted DNA from these samples and conducted genetic typing identification on each sample using advanced techniques like PCR and agarose gel electrophoresis. Consequently, the DD, ID, and II genotypes were discovered. and it has been observed that the mutation frequency of this locus is low in the Chinese Holstein cow. Importantly, the correlational analysis unveiled a significant relationship (p < 0.05) between the weight of ovaries in late estrus and the width of ovaries during the estrus interval with the mutation. Result of the RT-PCR revealed that the ID genotype partially diminished the expression of the ADIPOQ gene. The results of this study suggest that the identified variable duplication could serve as a potential genetic marker for enhancing the ovarian traits of Chinese Holstein cows.

Evaluating Osteogenic Cell Differentiation Efficacy in the Presence of Polylactide Samples With Varied Compositions for Bone Grafting: In Vitro Study.

In oral implantology, surgeons often confront the need to improve alveolar bone quality and volume before implantation in patients with bone defects. Whereas guided bone regeneration with titanium meshes is a clinical gold standard for bone augmentation, mesh removal pre-implantation presents a drawback. This study explores biodegradable scaffolds as an alternative. The research investigates the impact of various compositions of customized bone-grafting scaffolds on proliferation and osteogenic differentiation processes in vitro. Plates (10 × 10 × 0.5 mm) were fabricated from polylactide (PLA), PLA with 15% hydroxyapatite nanoparticles (PLA/HA), and polylactide with glycolic acid copolymers (PLGA 60:40 and 85:15). Gingival fibroblasts assessed the influence of experimental samples on proliferation and osteogenic differentiation in a low-glucose medium. Osteogenic differentiation was induced, and alizarin red staining measured extracellular matrix calcification via spectrophotometry. Active proliferation of gingival fibroblasts occurred along scaffold edges during cultivation. Although cells proliferated with experimental samples, rates were lower than control cells. PLA/HA showed higher alizarin red staining intensity, indicating enhanced matrix calcification. Experimental samples (PLA, PLA/HA, PLGA 85:15, PLGA 60:40) supported cell proliferation at lower rates than control. PLA/HA demonstrated increased matrix calcification. Biodegradable membranes were nontoxic, suggesting potential for bone augmentation.

Toward cross-subject and cross-session generalization in EEG-based emotion recognition: Systematic review, taxonomy, and methods

A systematic review on machine-learning strategies for improving generalization in electroencephalography-based emotion classification was realized. In particular, cross-subject and cross-session generalization was focused. In this context, the non-stationarity of electroencephalographic (EEG) signals is a critical issue and can lead to the Dataset Shift problem. Several architectures and methods have been proposed to address this issue, mainly based on transfer learning methods. In this review, 449 papers were retrieved from the Scopus, IEEE Xplore and PubMed databases through a search query focusing on modern machine learning techniques for generalization in EEG-based emotion assessment. Among these papers, 79 were found eligible based on their relevance to the problem. Studies lacking a specific cross-subject or cross-session validation strategy, or making use of other biosignals as support were excluded. On the basis of the selected papers’ analysis, a taxonomy of the studies employing Machine Learning (ML) methods was proposed, together with a brief discussion of the different ML approaches involved. The studies reporting the best results in terms of average classification accuracy were identified, supporting that transfer learning methods seem to perform better than other approaches. A discussion is proposed on the impact of (i) the emotion theoretical models and (ii) psychological screening of the experimental sample on the classifier performances.

Enhancing marl soil stability: nanosilica’s role in mitigating ettringite formation

Marl soil is highly prone to erosion when exposed to water flow, posing a potential threat to structural stability. The common practice of stabilizing soil involves the addition of cement and lime. However, persistent reports of severe ruptures in many stabilized soils, even after extended periods, have raised concerns. In stabilized marls, unexpected ruptures primarily result from the formation of ettringite, which gradually damages the soil structure. This article aims to assess the impact of nanosilica on the formation of ettringite and the nanostructure of calcium silicate hydrate (C-S-H) during the marl soil stabilization process with lime. To achieve this, marl soil was stabilized with varying percentages of lime and nanosilica. X-ray diffraction (XRD) patterns and scanning electron microscopy (SEM) images were collected to observe changes in mineralogy and microstructural properties. Various geotechnical parameters, including granularity, Atterberg limits, compressive strength, and pH, were measured. The results indicate that the uniform distribution of nanosilica in marl-lime soils enhances pozzolanic activities, calcium aluminate hydrate growth (C-A-H), and the nanostructure of calcium silicate hydrate (C-S-H). According to XRD and SEM experiments, the presence of nanosilica reduces the formation of ettringite. Moreover, the compressive strength of modified samples exhibited an upward trend. In the experimental sample manipulated with 1% nanosilica combined with 6% lime, the compressive strength increased by 1.84 MPa during the initial 7 days, representing an approximately 18-fold improvement compared to the control sample.

Unveiling the toxicological effects and risks of prometryn on red swamp crayfish (Procambarus clarkii): Health assessments, ecological, and molecular insights

Prometryn is commonly used in agricultural and non-agricultural settings. However, possible harm to aquatic organisms remains a persistent concern. Prometryn was also the only one of the 26 triazine herbicides detected in this study. Numerous studies have assessed the harmful effects of prometryn in teleost fish and shrimp. There is a lack of information regarding the ecological and human health risks, as well as the toxic mechanisms affecting crayfish. In this study, human health risk assessment (THQ) and ecological risk assessment (RQ) were conducted on P. clarkii in the rice-crayfish co-culture (IRCC) farming model. The 96 h of exposure to 0.286 mg/L and 1.43 mg/L prometryn was conducted to investigate the potential effects and molecular mechanisms of hepatopancreatic resistance to prometryn in P. clarkii. The original sample analysis revealed that the THQ calculated from the prometryn levels in the muscle and hepatopancreas was below 0.1, suggesting no threat to human health. However, the calculated RQ values were >0.1, indicating a risk to P. clarkii. Histological analysis and biochemical index detection of the experimental samples revealed that the hepatopancreatic injury and oxidative damage in P. clarkii were caused by prometryn. Moreover, transcriptome analysis identified 2512 differentially expressed genes (DEGs) after 96 h of prometryn exposure. Prometryn exposure caused significant changes in metabolic pathways, including oxoacid metabolic processes and cytochrome P450-associated drug metabolism. Further hub gene analysis via PPI indicated that exposure to prometryn may inhibit lipid synthesis, storage, and amino acid transport and affect glucose metabolic pathways and hormone synthesis. Additionally, we hypothesized that prometryn-triggered cell death could be linked to the PI3K-Akt signaling cascade. This study's findings have significant meaning for the efficient and logical application of herbicides in IRCC, ultimately aiding in advancing a highly productive agricultural system.

Guided wave-driven machine learning for damage classification with limited dataset in aluminum panel

The goal of this research study is to classify damages in an aluminum panel using ultrasonic-guided waves (UGWs). The study is carried out on the premise that only a limited dataset, consisting of only two receivers, is available. In the experimental phase, three distinct parts, that is, stainless steel masses, were located on the surface of the panel, assumed to be mapped into four triangular regions, to experimentally simulate the effects induced by circular artificial damage of varied sizes. UGW-driven damage classification was thus performed via machine learning. A data synthesis technique called conditional generative adversarial network (cGAN) was used to generate 2000 samples (signals) for each of the 12 different malfunction scenarios (classes), based on the combination of damage region and size. Damage features were extracted using the wavelet time scattering technique, while the classification task was performed via a long short-term memory network with tuned hyperparameters. A validation procedure was executed to ensure that the classification network was not overfitted. Furthermore, two testing stages were performed to evaluate the designed framework. The designed framework was thus tested on the observations excluded from the training and validation phases, as well as on 10 additional novel experimental samples. The high accuracy and F1-score values of the validation and the two testing stages—97.7%, 98.5%, and 96.6%, respectively—attest to the generality of this methodology, which is neither underfitted nor overfitted. To further assess the robustness of the methodology, the cGAN was tested on a new sensor placement, demonstrating a consistent performance (95.3%) and confirming the framework applicability under different sensor configurations, showcasing potential extension to other classification problems featuring a limited dataset.

On the issue of experimental modeling of invasions and prospects for the use of immunostimulants in nematodes

Currently, the search for ways to overcome drug resistance to anthelmintics is relevant. This problem is solved by limiting the duration of use of drugs with the same mechanism of action and combination of anthelmintics. The studies presented in the article have shown that the use of immunotropic drugs is also very promising. Forty white mice and 20 white rats were used for experimental modeling of Trichinella spiralis and Trichinella nativa invasions. Changes in lymphoid tissue associated in the intestine, in the spleen, as well as in the peripheral blood of animals and birds during inoculation of the above species were studied. Further, the effect of drugs with immunotropic activity was studied on proven models of Trichinella spiralis invasions, for which 30 white mice (20.5±2.5 g) were used, which were randomized and divided into groups. The evaluation of the protective properties of immunotropic drugs in trichinosis was carried out on laboratory models to determine the survival rate in studies after euthanasia of all mice and counting the number of Trichinella larvae. Both experiments were conducted in accordance with the principles of the Helsinki Declaration. After removing laboratory animals from the experiment (in accordance with the principles of the above declaration), macro- and microanatomic studies and sampling of histological material (sets of intestines and spleens) were carried out for qualitative and quantitative assessment. The above is confirmed by the fact that parasitological studies have established the high effectiveness of polyoxidonium in relation to the survival of Trichinella larvae, which was accompanied by an increase in the area of lymphoid tissue associated with the intestine and the white pulp of the spleen, in which lymphoid follicles grew. Thus, polyoxidonium is an effective drug that can be used in the complex treatment of nematodes as an immunotropic agent.