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Recently, the analog matrix computing (AMC) concept has been proposed for fast, efficient matrix operations, by configuring global feedback loops with crosspoint resistive memory arrays and operational amplifiers (OAs). The implementation of a general real-valued matrix (containing both positive and negative elements) is enabled by using a set of analog inverters, which, however, is considered inefficient regarding circuit compactness, power consumption, and temporal response. Here, with the assistance of the conductance compensation (CC) strategy to take full advantage of the inherent differential inputs of OAs, new AMC circuits without analog inverters are designed. Such a design saves the area occupation and power dissipation of analog inverters, and thus turns to be smaller and lower-power. Simulation results reveal that the new circuit also shows a faster response towards the steady state, thanks to the reduction of poles in the circuit, which, again, is contributed by the elimination of analog inverters. Along with all of these benefits, extensive simulations demonstrate that the CC-AMC circuits do not compromise the computing performance in terms of relative error caused by various non-ideal factors in the circuit. [ABSTRACT FROM AUTHOR]

Comprehensive examinations of health literacy (HL) among students in Kazakhstan are lacking. The existing literature from adult populations in Kazakhstan suggests associations between higher HL and socioeconomic and demographic factors. The HLS19-Q12 tool was used in this study to assess the HL level of 3230 students with various backgrounds. A multivariate linear regression model was used to define determinants of HL. The mean HL score for the total sample was 85.86 ± 18.67 out of 100, which indicates "excellent" level of HL. The highest HL score was in students of Health Sciences field (88.22 ± 17.53), whereas mean HL score in students of Engineering field of study was 83.27 ± 20.07, and it was 86.13 ± 18.11 for the Humanities and Social sciences field of study. The factors negatively associated with HL were region of origin, health information searching, lack of basic life support skills, smoking, self-assessment of health as bad, and missing study days. Students who smoked and used tobacco for 6 days per week had a significantly lower HL. Interaction analysis showed positive three-way interaction for male students over 19 years studying in Engineering field. Socioeconomic factors, regional disparities, and health behaviors significantly influenced HL, with lower scores observed among students from the West region, rural areas, and those with unhealthy behaviors or low socioeconomic status. The following factors were positively associated with HL in this study: field of education, affordability of medical examination and treatment, social connections and support, age, and social status. This study will allow future research and youth health promotion programs to make decisions based on the field of study and the factors that negatively and positively influence HL. [ABSTRACT FROM AUTHOR]

Neuromorphic technologies using artificial neurons and synapses can offer a more efficient solution for executing artificial intelligence algorithms than traditional computing systems. Artificial neurons using memristors have recently been developed, but they have limited biological dynamics and cannot directly interact with artificial synapses in an integrated system. This paper aims to review the complexity levels and functions of neurons and synapses and to analyze the circuit implementation of individual types of neurons and neural networks. [ABSTRACT FROM AUTHOR]

Purpose: The hippocampus is a brain region susceptible to the deleterious effects of stressors. Therefore, it is crucial to comprehend the adverse consequences of various stressors on the hippocampus. Previous studies have proposed that hippocampal brain-derived neurotrophic factor (BDNF) and vascular endothelial growth factor (VEGF) are upregulated as protective mechanisms in response to stressors. The present study investigated whether the experience of exercising in an environment with a stressor could convert stress-free light intensity exercise to a stressor based on the biochemical responses in the hippocampus. Methods: In the experiment 1, C57BL/6J mice were subjected to 3 weeks of stress-free light intensity exercise (7.0 m/min, 30 min/day, 5 days/week) with or without a 110 dB noise stressor; then their plasma corticosterone levels were measured. Furthermore, in the experiment 2, the mice performed 3 weeks of light intensity exercise with or without a 110 dB noise stressor were subjected 30 min of acute light intensity exercise test (7.0 m/min) without noise. These mice were different from the mice used in Experiment 1. The hippocampi of mice were collected before or immediately after an acute exercise and were used to detect mRNA levels of Bdnf and Vegf. Results: The findings indicate that the 3-week intervention of light intensity exercise with or without a noise environment did not alter plasma corticosterone levels. However, the experience of exercising in a noisy environment disrupted the hippocampal Bdnf mRNA expression, which was further aggravated by subsequent acute exercise. A higher percentage of change in hippocampal Bdnf expression was shown in mice exposed to noise than those not. Moreover, hippocampal Vegf mRNA expression was upregulated in mice that performed regular exercise in a noisy environment but not in those that performed regular exercise without noise. Conclusion: Our findings suggest that performing regular exercise at light intensity with environmental noisy stressors transforms the exercise into a threat cue, implying the role of environmental factors in hippocampal health. [ABSTRACT FROM AUTHOR]

This study investigates whether sustainable and responsible investment (SRI) funds can serve as "social responsibility mentors" of focal firms in a Chinese context. On this basis, we posit that firms' greenwashing motives may lead to a bias between information disclosure and actual performance in corporate social responsibility (CSR). Using data from China's publicly listed companies, we find a U-shaped relationship between SRI fund ownership and CSR disclosure quality; intrinsic and extrinsic greenwashing motives can weaken this curvilinear relationship. Furthermore, we examine the influence mechanism and prove that SRI funds use social screening strategies as an indirect channel to affect CSR disclosure. This mechanism is also verified when we consider SRI funds' number and investment portfolio. We conclude that SRI funds have a multifaceted influence on the CSR disclosure of focal firms, enriching the understanding of SRI funds and corporate sustainability in the capital markets of emerging economies. [ABSTRACT FROM AUTHOR]

Networks can help implement the United Nations Sustainable Development Goals (SDGs) in a variety of ways. They can facilitate the sharing of best practices and resources among organisations, enabling them to more effectively implement the SDGs. They can also help spread awareness on the SDGs and encourage people to take action. Networks can also assist coordinate collaborations between different organisations to support the SDGs implementation, especially in the academic sector. This study aims to fill the literature gap in assessing the transformative power of sustainability networking and reports on a worldwide analysis of the international networks which are believed to have been playing a significant role in the implementation of the SDGs in higher education. A concise online questionnaire was designed and administered to experts via an expert database mailing list and social networks, and collected 153 responses from 37 countries. This was complemented by an assessment of a set of case studies, where 5 major sustainability networks—ESSSR, IUSDRP, SDSN, AASHE and Copernicus Alliance—were evaluated in respect of their scope, target groups, types of activities and contributions to the SDGs. The results have highlighted the fact that, whereas assessed networks are very active, there is still a perceived need for more concerted actions to allow these networks to become more engaged in the implementation of the SDGs. This study suggests some specific actions which may foster a greater collaboration among networks in order to maximise the impact of their sustainability initiatives. The process of networking contributes to the advancement of sustainability and is closely related with contributions to policy, economic development and society, as well as particularly to SDG 17—Partnerships for the Goals. This study supports the role of networking, while indicating the need for greater collaboration among networks in order to maximise the impact of their initiatives and their transformative power. [ABSTRACT FROM AUTHOR]

Agriculture is one of the most crucial sectors, meeting the fundamental food needs of humanity. Plant diseases increase food economic and food security concerns for countries and disrupt their agricultural planning. Traditional methods for detecting plant diseases require a lot of labor and time. Consequently, many researchers and institutions strive to address these issues using advanced technological methods. Deep learning-based plant disease detection offers considerable progress and hope compared to classical methods. When trained with large and high-quality datasets, these technologies robustly detect diseases on plant leaves in early stages. This study systematically reviews the application of deep learning techniques in plant disease detection by analyzing 160 research articles from 2020 to 2024. The studies are examined in three different areas: classification, detection, and segmentation of diseases on plant leaves, while also thoroughly reviewing publicly available datasets. This systematic review offers a comprehensive assessment of the current literature, detailing the most popular deep learning architectures, the most frequently studied plant diseases, datasets, encountered challenges, and various perspectives. It provides new insights for researchers working in the agricultural sector. Moreover, it addresses the major challenges in the field of disease detection in agriculture. Thus, this study offers valuable information and a suitable solution based on deep learning applications for agricultural sustainability. [ABSTRACT FROM AUTHOR]

This study aimed to examine the immuntoxic effects of arsenic in the nervous system. Our results showed that arsenic increased corticocerebral and hippocampal weights (p < 0.05). Morris water maze tests revealed that arsenic significantly increased the time spent in latency to platform on the fourth day in 50 mg/L arsenic exposure and the fifth day in 25 and 50 mg/L arsenic exposure, as well as reduced the path length in target quadrant, time spent in target quadrant, and crossing times of the platform (p < 0.05). Hematoxylin–eosin staining showed that the vacuolated degeneration and pyknosis was found in the cerebral cortex and hippocampus of arsenic-treated mice. The mRNA levels of corticocerebral and hippocampal brain-derived neurotrophic factor (BDNF) and vascular endothelial growth factor (VEGF) were decreased in the 50 mg/L arsenic-treated group (p < 0.05). In addition, immunofluorescence staining showed that 25 and 50 mg/L arsenic all increased the expression of CD11b and glial fibrillary acidic protein (GFAP) in the cerebral cortex and hippocampus (p < 0.05). Arsenic markedly raised antigen-presenting molecule MHCII and CD40 mRNA levels in the cerebral cortex and hippocampus and upregulated the cell chemokine receptor 5 (CCR5) and CCR7 mRNA levels in the cerebral cortex at the 50 mg/L arsenic group, and increased the CCR7 mRNA levels in the hippocampus at the 25 and 50 mg/L arsenic groups (p < 0.05). Arsenic activated the nucleotide-binding domain-like receptor protein-3 (NLRP3) inflammasome, and enhanced its upstream promoter NF-κB protein level and downstream regulators IL-18 mRNA levels. Collectively, these results provide new evidences for the neuro-immune toxicity of arsenic. [ABSTRACT FROM AUTHOR]

Background: Regular physical exercise is an essential part of a healthy lifestyle, maintaining and strengthening public health. Recently, a steady increase in the number of recurrent musculoskeletal injuries occurring during physical exercise is being diagnosed. A serious risk factor for such damage is initial connective tissue pathology. Pathognomonic dysplastic phenotypes, indicating a particular susceptibility to injuries, were established. The decisive role in increasing connective tissue disorders severity is attributed in part to magnesium deficiency that is one of the most common deficient human conditions. Objective: To present the magnesium role in connective tissue homeostasis for pathogenetic substantiation of magnesium correction in persons with predisposition to musculoskeletal injuries. Methods: An analytical review of recent data published between January 2004 and December 2023 in the electronic resources such as Global Health, ScienceDirect, Elsevier, Medline, Embase, PubMed-NCBI, RSCI Scopus, Cochrane Library, e-Library, Google Academy, and CyberLeninka was conducted. Data analysis was performed from January 2023 to December 2023. Results: The analysis of the obtained data allowed to identify the main pathogenetic mechanisms of magnesium deficiency effect on connective tissues, including RNA cell destabilization, inactivation of hyaluronan synthetases, modulation of hyaluronidase activity, stimulation of matrix metalloproteinases, deactivation of elastase, realization of autoimmune reaction, lysyl oxidase, and transglutaminase activation. These processes result in increased degradation of collagen, elastin, and hyaluronan polysaccharide chains; reduction of cross-linking of fibrous structure; and intensification of inflammatory processes. This leads to defective connective tissue formation and increases sensitivity to physical stress and the risk of injury. Active magnesium participates in almost all biochemical reactions of connective tissue metabolism, and the impossibility of its synthesis in the body requires sufficient intake of this element with food and water. In the case of moderate-to-severe magnesium deficiency, it is pathogenetically reasonable to prescribe magnesium supplementation, which should take into account comorbidities, severity of deficiency, and age. Conclusion: Regular therapy with magnesium preparations helps to improve the mechanical properties of connective tissues, prevent the progression of dysplastic disorders, reduce the risk of injury, and maintain health during physical exercise. [ABSTRACT FROM AUTHOR]

Plant diseases pose a significant problem for agricultural sustainability, notably reducing crop quality and yield. Addressing this challenge, this study introduces an AI-based smart agriculture 4.0 system specifically designed for diagnosing plant leaf diseases. Using a self-generated dataset collected from the Monastir region of Tunisia, the system employs various convolutional neural network (CNN) architectures, including AlexNet, VGG16, VGG19, ResNet50, ResNet152, and DenseNet121, to identify diseases in tomato and pepper plants. A comparative analysis of these CNN models was conducted, highlighting the efficacy of each in disease identification. Notably, the study reveals that a transfer learning-based ResNet152 model achieved the highest accuracy rate of 99%, outperforming other models. Hence, the proposed approach is promising and has the potential to make a significant impact on effective disease detection. [ABSTRACT FROM AUTHOR]

Impaired insulin and growth factor functions are thought to drive many alterations in neurodegenerative diseases like dementia and seem to contribute to oxidative stress and inflammatory responses. Recent studies revealed that nasal growth factor therapy could induce neuronal and oligodendroglia protection in rodent brain damage induction models. Impairment of several growth factors signaling was reported in neurodegenerative diseases. So, in the present study, we examined the effects of intranasal co-treatment of insulin and a pool of growth factor-rich serum (GFRS) which separated from activated platelets on memory, and behavioral defects induced by intracerebroventricular streptozotocin (icv-STZ) rat model also investigated changes in the hippocampal oxidative-nitrosative state and histology. We found that icv-STZ injection (3 mg/kg bilaterally) impairs spatial learning and memory in Morris Water Maze, leads to anxiogenic-like behavior in the open field arena, and induces oxidative-nitrosative stress, neuroinflammation, and neuronal/oligodendroglia death in the hippocampus. GFRS (1µl/kg, each other day, 9 doses) and regular insulin (4 U/40 µl, daily, 18 doses) treatments improved learning, memory, and anxiogenic behaviors. The present study showed that co-treatment (GFRS + insulin with respective dose) has more robust protection against hippocampal oxidative-nitrosative stress, neuroinflammation, and neuronal/oligodendroglia survival in comparison with the single therapy. Memory and behavioral improvements in the co-treatment of insulin and GFRS could be attributed to their effects on neuronal/oligodendroglia survival and reduction of neuroinflammation in the hippocampus. [ABSTRACT FROM AUTHOR]

Today, education is facing a new reality in which technology and new teaching methods are being quickly introduced into educational systems and institutions. Educational institutions are now dealing with the challenge of providing continuity to e-learning, turning it into a more flexible and up-to-date field, and considering assessment as a quality element in this transition. Therefore, with the aim of determining the current state of the research focused on assessment in digital environments (e-assessment), a mapping of the literature has been carried out. After examining 1,771 results extracted from Web of Science and Scopus and after the application of seven inclusion criteria, a total of 159 publications from the period of the past five years were read. The answer four research questions on the evolution of publications, the authors, the tools used, the contexts, the objects of study, and the future avenues of research, among others. The results show the increasing importance of e-assessment in this new context, moving toward a new reality in which technology plays a decisive and fundamental role in the teaching and learning processes. Thus, educational systems are heading towards a new context in which both teachers and students should rethink their roles and functions leading education to a more flexible, current, and digitally mediated context. [ABSTRACT FROM AUTHOR]

Ulcerative colitis (UC) is a refractory inflammatory bowel disease, which is known to cause psychiatric disorders such as anxiety and depression at a high rate in addition to peripheral inflammatory symptoms. However, the pathogenesis of these psychiatric disorders remains mostly unknown. While prior research revealed that the Enterococcus faecalis 2001 (EF-2001) suppressed UC-like symptoms and accompanying depressive-like behaviors, observed in a UC model using dextran sulfate sodium (DSS), whether it has an anxiolytic effect remains unclear. Therefore, we examined whether EF-2001 attenuates DSS-induced anxiety-like behaviors. Treatment with 2% DSS for seven days induced UC-like symptoms and anxiety-like behavior through the hole-board test, increased serum lipopolysaccharide (LPS) and corticosterone concentration, and p-glucocorticoid receptor (GR) in the prefrontal cortex (PFC), and decreased N-methyl-d-aspartate receptor subunit (NR) 2A and NR2B expression levels in the PFC. Interestingly, these changes were reversed by EF-2001 administration. Further, EF-2001 administration enhanced CAMKII/CREB/BDNF-Drebrin pathways in the PFC of DSS-treated mice, and labeling of p-GR, p-CAMKII, and p-CREB showed colocalization with neurons. EF-2001 attenuated anxiety-like behavior by reducing serum LPS and corticosterone levels linked to the improvement of UC symptoms and by facilitating the CAMKII/CREB/BDNF-Drebrin pathways in the PFC. Our findings suggest a close relationship between UC and anxiety. [ABSTRACT FROM AUTHOR]

Plants are acknowledged as being crucial because they are the main source of human energy generation due to their nutritional, therapeutic, and other benefits. Therefore, it is necessary to increase crop productivity. One of these significant factors contributing to reduced agricultural yields is the prevalence of bacterial, fungal, and viral illnesses. Applying techniques for plant disease identification can stop and treat these diseases. So, numerous machine learning (ML) and deep learning (DL) methods were created and tested by researchers to identify plant diseases. Therefore, this study gives a detailed discussion of the various research studies conducted in plant disease detection utilizing ML and DL-based techniques. This review offers research advancements in plant disease recognition from ML to DL techniques. Additionally, many datasets about plant diseases are thoroughly examined. It also addresses the difficulties and issues with the current systems. [ABSTRACT FROM AUTHOR]

Animal advocacy is a complex phenomenon. As a social movement encompassing diverse moral stances and lifestyle choices, veganism and vegetarianism (veg*) are at its core, and animal testing raises as a notably contentious issue within its members. This paper addresses this critical topic. Employing data from an international quantitative survey conducted between June and July 2021, our research explores how ethical vegans and vegetarians responded during the COVID-19 crisis. By comparing the experiences and choices between the two groups, we aimed to understand the variances in attitudes and behaviors in the face of an ethical dilemma, highlighting the interplay between personal beliefs and social pressures in times of a health crisis. Our findings reveal stark contrasts in how vegans and vegetarians navigated the pandemic; vegans displayed less conformity yet experienced a significant compromise of their ethical values, particularly in their overwhelming acceptance of vaccination. This study enhances the field of veg* research and social movement studies by exploring how a social crisis shapes members' behaviors and perspectives. Our findings also contribute to a better understanding of the challenges and prejudices that a minority group such as vegans may face and how they cope with the pressure to go against the mainstream at a time when society is polarized by a single discourse that goes against their moral values. [ABSTRACT FROM AUTHOR]

Leaf and fruit infections are the primary cause of the maximum harm to the crop, which decreases the quality and amount of the goods. To improve the productivity of plants, the timely identification of the infection is vital, which is a highly challenging task. Deep learning (DL) with image processing allows farmers to distinguish between healthy and infected crops. This work intends to identify healthy and diseased citrus leaf images using a convolutional neural network (CNN) on the Platform as a Service (PaaS) cloud. The dataset of five types of healthy and unhealthy citrus images was used, namely, black spot, melanose, canker, greening, and healthy. Furthermore, the four-transfer learning (TL) pre-trained deep CNN (DCNN) models, namely, ResNet152V2, InceptionResNetV2, DenseNet121, and DenseNet201, were used to classify the leaf type. The performance of the CNN and four DCNNs were assessed using the confusion matrix (accuracy, precision, recall, and F1-score) and receiver operating characteristic-area under the curve (ROC-AUC) curve. An augmentation technique was utilised to enhance the dataset images, which helped to improve the model's performance and achieved an accuracy of 98% precision and recall and an F1 score of 99% and an ROC-AUC score of 0.99. Moreover, the suggested CNN has only 15 layers, 427317 parameters, and 1.68MB size, while DCNN models have more layers, parameters, and large size. The small-size CNN was deployed to the Platform as a Service (PaaS) cloud. The deployed model link is available on a smartphone to upload a citrus leaf image to the cloud, and the result is instantly available on a mobile screen. [ABSTRACT FROM AUTHOR]

In agriculture around 22% of crop yield loss is due to living and non-living organisms such as biotic and abiotic stress/disease. The early-stage diagnosis of these stresses is an important issue for farmers through naked eyes. Using computer vision technologies can detect the pattern and clustering of diseases at an early stage. However, in recent times, deep learning technologies based on computer vision is helpful for the diagnosis of biotic stress (single biotic and multi biotic) in tomato plant leaves. In this work, the PlantVillage dataset is gathered for the segmentation of object detection. The labeled, enhanced and augmented data has been used for training the model. The proposed hybrid Deep Segmentation Convolutional Neural Network (Hybrid-DSCNN) model has been segmenting the diseased objects in the tomato plant. This Hybrid-DSCNN is assembled using U-Net and Seg-Net pre-trained models with instance segmentation for better detection of objects. The semantic segmented data has been recognized for the single and multiple leaf diseases for identification and classification in this work. A comparison of the predicted Hybrid-DSCNN model's output has been made with other modified U-Net, M-SegNet, and modified U-SegNet in terms of Accuracy, Precision, Recall, and Intersection over Union (IoU), and mean Intersection over Union (mIoU). The proposed model processed 1004 images in 30 ns,which is better than other compared models. The accuracy achieved using the proposed model is 98.24%, which is far better than other modified segmentation models. [ABSTRACT FROM AUTHOR]

Farmers are concerned about the automatic detection of lesions and pests that threaten tomato plants. Traditional computer vision and pattern recognition technologies have limits when it comes to tackling such difficult problems. However, deep learning had gained popularity in recent years, particularly for the detection and recognition of biotic stress in diseased leaf photos of plants with varying lighting conditions, complicated backdrops, and background noise. In this paper, a system was provided that uses several "Convolutional Neural Networks (CNN)" for the automatic recognition and identification of multi-biotic tomato leaf lesions collected from PlantVillage. For instance segmentation, a "Mask R-CNN" network is used in the first phase; for semantic segmentation a Hybrid Deep Segmentation Convolutional Neural Network Model (Hybrid-DSCNN) model is compared with U-Net and Seg-Net in the second phase, and a CNN model is used for classification in the third stage. The two backbone feature extractors were employed in the Mask R-CNN network, and the ResNet50 displays average precision of 73.00% in the instance segmentation test, which is superior to other models. For the segmentation and classification tasks, the Hybrid-DSCNN 2Layer-Convo-USN has achieved an accuracy of 98.25% which is better than the pre-trained models. The precision of the proposed Hybrid-DSCNN 2Layer-Convo-USN is 95.7% and of U-Net is 94.9%. The results are positive, indicating that the entire system may be implemented in any platform that can be used in the real world. [ABSTRACT FROM AUTHOR]

Convolutional Neural Networks (CNN) with different architectures have shown promising results in skin cancer diagnosis. However, CNN has a high computational cost, which makes the need for a light version of CNN a desirable step. This version can be used on small devices, such as mobile phones or tablets. A light version can be created using pruning techniques. In this study, iterative magnitude pruning (IMP) is utilized. This method depends on pruning the network iteratively. The IMP method is applied on AlexNet with transfer learning (TL) and data augmentation. The proposed IMP AlexNet with TL is applied on three different skin cancer datasets which are PAD-UFES-20, MED-NODE, and PH2 dataset. The datasets used are a combination of smartphone, dermoscopic, and non-dermoscopic images. Different CNN versions are applied on the same datasets for comparison with IMP AlexNet. The CNNs used are VGG-16, ShuffleNet, SqueezNet, DarkNet-19, DarkNet-53, and Inception-v3. The proposed IMP AlexNet achieved accuracies of 97.62%, 96.79%, and 96.75%, with accuracy losses of 1.53%, 2.3%, and 2.2%, respectively, compared to the original AlexNet. In addition, the proposed IMP AlexNet requires less running time and memory usage than the traditional AlexNet. The average running time for IMP AlexNet is 0.45 min, 0.28 min, and 0.3 min, for PAD-UFES-20, MED-NODE, and PH2 datasets, respectively. The average RAM usage with IMP AlexNet is 1.8 GB, 1.6 GB, and 1.7 GB, respectively. IMP AlexNet accelerates the average running time by approximately 15 times that of the traditional AlexNet and reduces the average RAM used by 40%. [ABSTRACT FROM AUTHOR]

Background: Heat stress is known to adversely affect testicular activity and manifest the pathogenesis of spermatogenesis. Morin hydrate is a plant-derived compound, which contains a wide range of biological activities. Thus, it is hypothesized that morin hydrate might have an ameliorative effect on heat-induced testicular impairment. There has not been any research on the impact of morin hydrate on heat-induced testicular damage. Methods: The experimental mice were divided into four groups, groups1 as the normal control group (CN), and the second which underwent heat stress (HS) by immersing the lower body for 15 min in a thermostatically controlled water bath kept at 43 °C (HS), and third and fourth heat-stressed followed by two different dosages of morin hydrate 10 mg/kg (HSM10) and 100 mg/kg (HSM100) for 14 days. Results: Morin hydrate treatment at 10 mg/kg improved, circulating testosterone levels (increases 3βHSD), and oxidative stress along with improvement in the testis and caput and corpus epididymis histoarchitecture, however, both doses of morin hydrate improved sperm parameters. Morin hydrate treatment significantly increases germ cell proliferation, (GCNA, BrdU staining), expression of Bcl2 and decreases expression of active caspase 3. Heat stress also decreased the expression of AR, ER- α, and ER-β, and Morin hydrate treatment increased the expression of these markers in the 10 mg/kg treatment group. Conclusion: Morin hydrate ameliorates heat-induced testicular impairment modulating testosterone synthesis, germ cell proliferation, and oxidative stress. These effects could be manifested by regulating androgen and estrogen receptors. However, the two doses showed differential effects of some parameters, which requires further investigations. [ABSTRACT FROM AUTHOR]

Background: Heat stress is known to adversely affect testicular activity and manifest the pathogenesis of spermatogenesis. Morin hydrate is a plant-derived compound, which contains a wide range of biological activities. Thus, it is hypothesized that morin hydrate might have an ameliorative effect on heat-induced testicular impairment. There has not been any research on the impact of morin hydrate on heat-induced testicular damage. Methods: The experimental mice were divided into four groups, groups1 as the normal control group (CN), and the second which underwent heat stress (HS) by immersing the lower body for 15 min in a thermostatically controlled water bath kept at 43 °C (HS), and third and fourth heat-stressed followed by two different dosages of morin hydrate 10 mg/kg (HSM10) and 100 mg/kg (HSM100) for 14 days. Results: Morin hydrate treatment at 10 mg/kg improved, circulating testosterone levels (increases 3βHSD), and oxidative stress along with improvement in the testis and caput and corpus epididymis histoarchitecture, however, both doses of morin hydrate improved sperm parameters. Morin hydrate treatment significantly increases germ cell proliferation, (GCNA, BrdU staining), expression of Bcl2 and decreases expression of active caspase 3. Heat stress also decreased the expression of AR, ER- α, and ER-β, and Morin hydrate treatment increased the expression of these markers in the 10 mg/kg treatment group. Conclusion: Morin hydrate ameliorates heat-induced testicular impairment modulating testosterone synthesis, germ cell proliferation, and oxidative stress. These effects could be manifested by regulating androgen and estrogen receptors. However, the two doses showed differential effects of some parameters, which requires further investigations. [ABSTRACT FROM AUTHOR]

Background and aim: Early-life stressful situations and binge drinking have been thus far acknowledged as two burdensome conditions that potentially give rise to negative outcomes and then synergistically affect brain development. In this context, the hippocampus, with the greatest number of glucocorticoid receptors (GCRs) in the brain, is responsible for regulating negative responses to stress. Prolonged glucocorticoid (GC) exposure can accordingly cause oxidative stress (OS), leading to cognitive and emotional dysfunction. Against this background, melatonin, as a powerful antioxidant and hypothalamus–pituitary–adrenal (HPA) axis regulator, was administered in this study to ameliorate cognitive impairments induced by perinatal ethanol and stress exposure in adolescent male rat progeny. Methods: Wistar rat dams were exposed to ethanol (4 g/kg) and melatonin (10 mg/kg) from gestational day (GD) 6 to postnatal day (PND) 14 and then limited nesting material (LNS) from PND0 to PND14 individually or in combination. Maternal behavior was then investigated in mothers. Afterward, the plasma corticosterone (CORT) concentration, the OS marker, the corticotropin-releasing hormone receptor type 1 (CRHR1) expression, and the GCR and brain-derived neurotrophic factor (BDNF) levels were measured in the male pups. Moreover, behavioral tasks, including the elevated plus maze (EPM), the Morris water maze (MWM), the novel object recognition (NORT), and the object-location memory (OLM) tests were completed and assessed. Results: The quantity and quality of maternal care significantly decreased in the mothers with dual exposure to ethanol and stress. The plasma CORT concentration in the progeny also dropped in the Ethanol + LNS group, but the risk-taking behavior elevated significantly. The ethanol and stress exposure further revealed a significant fall in the GCR and CRHR1 expression levels, compared with stress alone. The results of learning and memory tasks also indicated a significant reduction in spatial learning and memory among animals exposed to ethanol and stress. The BDNF mRNA levels correspondingly increased in the Ethanol + LNS group, compared with LNS alone. In the presence of ethanol and stress, the superoxide dismutase (SOD) and glutathione peroxidase (GPx) activities correspondingly declined. On the other hand, the malondialdehyde (MDA) levels augmented in the hippocampus of the animals with ethanol and LNS dual exposure, as compared with the control group. Melatonin treatment (MT) thus improved nursing behaviors in dams, prevented OS, enhanced the CRHR1 and GCR expression, and reduced the BDNF levels to the similar ones in the control group. The animals in the Ethanol + LNS + MT group ultimately showed an ameliorated performance at behavioral tasks, including the memory and risk-taking behavior. Conclusion: It was concluded that MT could prevent stress response and memory impairments arising from dual exposure to ethanol and stress by inhibiting OS. [ABSTRACT FROM AUTHOR]

Early diagnosis of plant diseases is one of the key elements determining plant productivity. The productivity and quality of plants are significantly reduced when plant diseases are not identified and prevented in a timely manner, which results in major financial losses for producers. Olive is a plant with high added value. While the fruit and oil of olive are consumed as food, its oil is used in cosmetics, medicine, etc. It is also used in industries. In addition, active substances such as oleuropein, triterpene, maslinic acid, and flavonoid found in olive leaves are also used in the pharmaceutical industry. Considering all these valuable uses of olive, the importance of productivity is understood. Plant diseases are one of the most significant factors affecting the yield of olives. Among these diseases, fungal disease called peacock eye can spread to the whole tree through the leaves. This disease causes reduced crop production, defoliation, and rot of tree branches. In this study, an efficient method was developed to detect peacock eye disease from olive leaves. In the first stage, an original dataset of healthy and diseased leaves was created. Then, by extracting deep features from this dataset with CNN models, diseased and healthy leaf classification was performed with the transfer learning approach. As a result of the experiments, very satisfactory results were obtained around 98.63%. [ABSTRACT FROM AUTHOR]

Freeze-drying and microwave drying (180, 360, 540, 720 and 900 W) were used to investigate drying behavior, and evaluate impacts of both drying methods on color, texture, rehydration, total phenolic content (TPC) and antioxidant activity of pine nuts. Microwave drying yielded higher effective moisture diffusivity coefficients (Deff) (5.60 × 10–9 - 9.78 × 10–9 m2 s−1) than the Deff value (1.35 × 10–10 m2 s−1) in freeze-drying. Microwave drying time was affected by microwave power level, leading to almost 50% reduction in the drying time when the microwave power level increased from 180 to 900 W. The total color difference (ΔE*) and browning index (BI) values increased with increasing microwave power levels while hardness, TPC and antioxidant activity decreased. The pine nut that was freeze-dried (FD) had the highest TPC and antioxidant activity whereas the pine nut that was microwave-dried (MWD) had a lower level of TPC and antioxidant activity. Antioxidant activity measurements correlated well (R2 = 0.954) with the phenolic content. The antioxidant activity assay revealed that phenolic compounds in pine nut had a good antioxidant activity. Higher rehydration temperatures caused higher rehydration ratio (RR) values for FD and MWD pine nuts, and the FD pine nut yielded the highest rehydration ratios. The quality characteristics of FD pine nuts were very close to those of raw pine nuts and were superior to those of MWD pine nuts. It should be stated that freeze-drying is a promising method of drying pine nuts. [ABSTRACT FROM AUTHOR]

Agriculture is the backbone of the country’s economy. Sugarcane is a globally important crop and a major source of sugar, ethanol and jaggery. One of the problems faced by the sugar cane industry is the diseases that attack the crops. If these diseases are not identified early, they may result in exterminating the whole crops surrounding them. Manually checking each and every corner of a large farm is physically impossible. Machine learning is the contemporary solution to the problem, which can be resolved using the Convolution Neural Network (CNN) techniques. The drone images of all corners of the farm can be fed into the trained model for distinguishing the health status. For training the model the secondary data is taken from Kaggle, which includes both healthy and unhealthy sugarcane plant images, with various diseases in the unhealthy class. Machine learning models effectively identify early-stage crop diseases. This helps the farmer to treat that part of the affected farm quickly in order to avoid the spread of the disease to the remaining parts of the farm. This study deals with sugarcane disease prediction using the CNN model. Two different layered CNN models (VGG-16 and VGG-19) that were tested. The models were trained based on the images of 2165 containing both healthy and unhealthy leaves. The whole dataset is divided into three parts, validating data, testing data, and training data. The selected model – VGG-19 performed better and was found to be analysing the image up to an accuracy of 92% with 90% precision. [ABSTRACT FROM AUTHOR]

Background: Surgical nurses face the risk of psychological problems while trying to cope with the challenges arising from the COVID-19 pandemic. Aim: This study aimed to determine levels of COVID-19-related fear and burnout and affecting factors in surgical nurses. Design: The study has a descriptive, cross-sectional design. Methods: The study sample included 321 nurses working in surgical units and operation rooms in Turkey. Data were gathered with a sociodemographic and occupational characteristics form, the Fear of COVID-19 Scale and the COVID-19 Burnout Scale through a Google form between 1 August and 15 October in 2021. Obtained data were analyzed with independent groups t-test, One-Way ANOVA and simple and multiple linear regression analyses. Results: The nurses had moderate levels of fear (20.00 ± 6.77; Min–Max: 7–35) and burnout (29.52 ± 10.03; Min–Max:10–50) due to COVID-19. The female gender and belief in health staff shortage were predictive of fear and burnout related to COVID-19. Age was not predictive of COVID-19 fear and receiving education about COVID-19, exposure to violence, having adequate supplies of goggles/face shields and having a limited number of aprons/work wear were not predictive of COVID-19-related burnout. Fear of COVID-19 was predictive of COVID-19 burnout. Conclusions: Female nurses and nurses believing in health staff shortage had higher levels of fear and burnout due to COVID-19. As COVID-19 fear increased, so did COVID-19 burnout. Nurses working in surgical units should be provided with education about coping strategies taking account of the factors affecting COVID-19-related fear and burnout. [ABSTRACT FROM AUTHOR]

Previous research has demonstrated a link between the administration of analgesic drugs and the reduction of empathy levels in humans. This apparent blunting effect of pain medication has been explained through shared neural mechanisms for the first-hand and the empathic experience of pain (simulation theory). Considering that analgesics are among the most consumed drugs in the world and the ability to empathize with others is fundamental to human social interactions, the aim of the present study was to investigate whether the typical day-to-day analgesic consumption rate in Austria and Germany is associated with a reduction in empathy and prosocial behavior. We therefore collected self-reports of analgesic consumption behavior as well as empathy for pain and prosocial behavior measures in an online survey (n = 940). Analyses revealed no significant association between the analgesic intake frequency and measures of empathy or prosocial behavior. However, liberal intake of analgesics (i.e. mind-set of "a pill is a quick solution") was linked to lower empathic concern and helping behavior, which may hint towards a negative effect in people who take pain medication for non-pain related issues or episodes of low pain. Nevertheless, further research is needed to investigate the effects of analgesic drugs in high frequency users. [ABSTRACT FROM AUTHOR]

Diabetic nephropathy (DN), a severe diabetes complication, causes kidney morphological and structural changes due to extracellular matrix accumulation. This accumulation is caused mainly by oxidative stress. Semi-essential amino acid derivative taurine has powerful antioxidant and antifibrotic effects. The aim of this study was to investigate the renoprotective effects of taurine through its possible roles in oxidative stress, extracellular matrix proteins, and the signaling pathways associated with the accumulation of extracellular matrix proteins in DN rats. 29 Wistar albino rats were randomly separated into control, taurine, diabetes, and diabetes + taurine groups. Diabetes animals were injected 45 mg/kg streptozosine. Taurine is given by adding to drinking water as 1% (w/v). Urine, serum, and kidney tissue were collected from rats for biochemical and histological analysis after 12 weeks. According to the studies, taurine significantly reduces the levels of malondialdehyde (MDA), total oxidant status (TOS), and protein expression of NADPH oxidase 4 (NOX4) that increase in diabetic kidney tissue. Also, decreased superoxide dismutase (SOD) activity levels significantly increased with taurine in diabetic rats. Moreover, increased mRNA and protein levels of fibronectin decreased with taurine. The matrix metalloproteinase (MMP)-2 and MMP-9 activities and their mRNA levels increased significantly, and this increase was significantly summed with taurine. There was a decrease in mRNA expression of Extracellular matrix metalloproteinase inducer (EMMPRIN). Taurine significantly increased this decrease. Diabetes increased mRNA expressions of transforming growth factor (TGF)-β and Smad2/3. Taurine significantly reduced this induction. TGF-β protein expression, p38, and Smad2/3 activations were also inhibited, but taurine was suppressed significantly. All these findings indicate that taurine may be an effective practical strategy to prevent renal diabetic injury. [ABSTRACT FROM AUTHOR]

Although studies suggest that cognitive functions in the elderly are impaired, elderly people tend to be more successful and wiser in solving emotional problems. In empathy-like behavior models, the observer rat rescues the distressed cage mate by displaying emotional and cognitive ability. The aim of the study was to investigate the changes in empathy-like behavior in older rats in comparison to adult rats. In addition, we wanted to determine the effects of alterations in neurochemicals (such as corticosterone, oxytocin, vasopressin, and their receptor levels) and emotional situations on this behavior. In our study, we initially completed empathy-like behavior tests and emotional tests (open field, elevated plus maze) and performed neurochemical examinations in the serum and brain tissues. In the second step of research, we applied a midazolam (benzodiazepine) treatment to examine the effect of anxiety on empathy-like behavior. In the old rats, we observed that empathy-like behavior deteriorated, and anxiety signs were more pronounced. We detected a positive correlation between the latency in empathy-like behavior and corticosterone levels and v1b receptor levels. The midazolam effect on empathy-like behavior was attenuated by flumazenil (a benzodiazepine receptor antagonist). The recordings of ultrasonic vocalization showed frequencies around 50 kHz emitted by the observer and this was associated with the expectation of social contact. Our results state that compared to adult rats, old rats were more concerned and failed during empathy-like behavior. Midazolam may improve this behavior by anxiolysis. [ABSTRACT FROM AUTHOR]

Previous research has demonstrated a link between the administration of analgesic drugs and the reduction of empathy levels in humans. This apparent blunting effect of pain medication has been explained through shared neural mechanisms for the first-hand and the empathic experience of pain (simulation theory). Considering that analgesics are among the most consumed drugs in the world and the ability to empathize with others is fundamental to human social interactions, the aim of the present study was to investigate whether the typical day-to-day analgesic consumption rate in Austria and Germany is associated with a reduction in empathy and prosocial behavior. We therefore collected self-reports of analgesic consumption behavior as well as empathy for pain and prosocial behavior measures in an online survey (n = 940). Analyses revealed no significant association between the analgesic intake frequency and measures of empathy or prosocial behavior. However, liberal intake of analgesics (i.e. mind-set of "a pill is a quick solution") was linked to lower empathic concern and helping behavior, which may hint towards a negative effect in people who take pain medication for non-pain related issues or episodes of low pain. Nevertheless, further research is needed to investigate the effects of analgesic drugs in high frequency users. [ABSTRACT FROM AUTHOR]

Methamphetamine (METH) abuse is known to cause executive dysfunction. However, the molecular mechanism underlying METH induced executive dysfunction remains unclear. Go/NoGo experiment was performed in mice to evaluate METH-induced executive dysfunction. Immunoblot analysis of Nuclear factor-E2-related factor 2 (Nrf2), phosphorylated Nrf2 (p-Nrf2), heme-oxygenase-1 (HO-1), Glucose Regulated Protein 78(GRP78), C/EBP homologous protein (CHOP), Bcl-2, Bax and Caspase3 was performed to evaluate the levels of oxidative stress, endoplasmic reticulum (ER) stress and apoptosis in the dorsal striatum (Dstr). Malondialdehyde (MDA) levels and glutathione peroxidase (GSH-Px) activity was conducted to evaluate the level of oxidative stress. TUNEL staining was conducted to detect apoptotic neurons. The animal Go/NoGo testing confirmed that METH abuse impaired the inhibitory control ability of executive function. Meanwhile, METH down-regulated the expression of p-Nrf2, HO-1 and GSH-Px and activated ER stress and apoptosis in the Dstr. Microinjection of Tert-butylhydroxyquinone (TBHQ), an Nrf2 agonist, into the Dstr increased the expression of p-Nrf2, HO-1, and GSH-Px, ameliorated ER stress, apoptosis and executive dysfunction caused by METH. Our results indicated that the p-Nrf2/HO-1 pathway was potentially involved in mediating methamphetamine-induced executive dysfunction by inducing endoplasmic reticulum stress and apoptosis in the dorsal striatum. [ABSTRACT FROM AUTHOR]

Leptin (LEP) can cross the blood–brain barrier and facilitate cross-talk between the adipose tissue and central nerve system (CNS). This study aimed to investigate the effect of 8-week high-intensity interval training (HIIT) on the LEP signaling in the hippocampus of rats with type 2 diabetes. 20 rats were randomly divided into four groups: (i) control (Con), (ii) type 2 diabetes (T2D), (iii) exercise (EX), and (iv) type 2 diabetes + exercise (T2D + EX). The rats in the T2D and T2D + EX were fed a high-fat diet for two months, then a single dose of STZ (35 mg/kg) was injected to induce diabetes. The EX and T2D + EX groups performed 4–10 intervals of treadmill running at 80–100% of Vmax. Serum and hippocampal levels of LEP as well as hippocampal levels of LEP receptors (LEP-R), Janus kinase 2 (JAK-2), signal transducer and activator of transcription 3 (STAT-3), activated protein kinase (AMP-K), proxy zoster receptor α (PGC-1α), beta-secretase 1 (BACE1), Beta-Amyloid (Aβ), Phosphoinositide 3-kinases (PI3K), protein kinase B (AKT), mammalian target of rapamycin (mTOR), Glycogen Synthase Kinase 3 Beta (GSK3β), and hyperphosphorylated tau proteins (TAU) were measured. One-way ONOVA and Tukey post-hoc tests were used to analyze the data. Serum and hippocampal levels of LEP as well as hippocampal levels of LEP-R, JAK-2, STAT-3, AMP-K, PGC1α, PI3K, AKT, and mTOR were increased while hippocampal levels of BACE1, GSK3B, TAU, and Aβ were decreased in T2D + EX compared with T2D group. Serum LEP and hippocampal levels of LEP, LEP-R, JAK-2, STAT-3, AMP-K, PGC1α, PI3K, AKT, and mTOR were decreased. Conversely hippocampal levels of BACE1, GSK3B, TAU, and Aβ were increased in T2D group compared with CON group. HIIT could improve LEP signaling in the hippocampus of rats with type 2 diabetes and decrease the accumulation of Tau and Aβ, which may reduce the risk of memory impairments. [ABSTRACT FROM AUTHOR]

Diabetes-related brain complications have been reported in clinical patients and experimental models. The objective of the present study was to investigate the neuroprotective mechanisms of Lactobacillus reuteri GMNL-263 in streptozotocin (STZ)-induced diabetic rats. In this study, three different groups, namely control group, STZ-induced (55 mg/kg streptozotocin intraperitoneally) diabetic rats (DM), and DM rats treated with Lactobacillus reuteri GMNL-263 (1 × 109 CFU/rat/day), were utilized to study the protective effect of GMNL-263 in the hippocampus of STZ-induced diabetic rats. The results demonstrated that GMNL-263 attenuated diabetes-induced hippocampal damage by enhancing the cell survival pathways and repressing both inflammatory and apoptotic pathways. Histopathological analysis revealed that GMNL-263 prevented structural changes in the hippocampus in the DM group and decreased the level of inflammation and apoptosis in the hippocampus of DM rats. The IGF1R cell survival signaling pathway also improved after GMNL-263 treatment. These results indicate that probiotic GMNL-263 exerts beneficial effects in the brain of diabetic rats and has potential ability for clinical application. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this scoping review is to appraise the published literature on taste disturbance in patients with advanced cancer, with the specific objectives being to determine its prevalence, clinical features and complications. Methods: This scoping review was conducted using the recommended methodological framework. A detailed search of databases (Medline, Embase, CINAHL and PsycInfo) was conducted to identify eligible studies: eligible studies needed to include patients with advanced cancer and needed to include details of clinical features and/or complications of taste disturbance. Standard bibliographic/systematic review software was used to store the records and manage the review process, respectively. Results: Twenty-five studies were identified from the database searches. The studies identified included eight physical and/or psychological symptom studies, six symptom cluster studies, five oral symptom studies and six taste and/or smell specific studies. Detailed data is presented on the clinical features and complications of taste disturbance and on the symptom clusters involving taste disturbance in this cohort of patients. Conclusion: This scoping review identified a relatively small number of relevant studies involving a relatively small number of participants. Nevertheless, it confirms that taste disturbance is a common problem in patients with advanced cancer and is associated with significant morbidity because of the primary condition and the associated complications. [ABSTRACT FROM AUTHOR]

In India, agricultural production is a crucial factor for economic growth. However, plants can be attacked by diseases that range from mild to severe, leading to their destruction. Therefore, it is essential to detect plant diseases at an early stage to prevent such damage. To address this issue, we propose a Hybrid Xception transfer learning with crossover optimized kernel extreme learning machine (HXTL-COKELM) method for identifying healthy and diseased plant leaves. The proposed approach preprocesses and extracts image features using transfer learning with the Xception model to improve computation accuracy before classification. Additionally, we developed a crossover-based tasmanian devil-optimized kernel extreme learning machine model to optimize the KELM's parameters using the TDO algorithm. We collected a dataset with 43,466 images of plant leaves from healthy and diseased plants to train, test, and validate the CNN model. Finally, we evaluated the system's efficacy using performance metrics such as accuracy, recall, specificity, F1-score, and kappa static value. Our experimental findings show that the proposed HXTL-COKELM method achieves a 98.9% accuracy rate, outperforming existing methods. The HXTL-COKELM model also exhibits superior overall performance. Thus, the proposed HXTL-COKELM model can effectively detect plant leaf diseases. [ABSTRACT FROM AUTHOR]

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Stress is a state that is known to impact an organism's physiological and psychological balance as well as the morphology and functionality of certain brain areas. In the present work, chronic restraint stress (CRS) model rats treated with treadmill exercise were used to examine anomalies associated to emotion and mood as well as molecular changes in the brain. Forty male Sprague-Dawley rats were divided into control, stress, exercise, and stress+exercise groups. CRS were exposed to stress group rats and exercise group underwent a chronic treadmill exercise. Depressive-like behavior was evaluated with the forced swim test (FST) and tail suspension test (TST). For assessing anxiety-like behavior, the light-dark test (LDT) and the open field test (OFT) were used. The Morris water maze test (MWMT) was used for testing memory and learning. Brain's monoamine level and the expression of genes related to stress were measured. It was discovered that CRS lengthens latency in the MWMT, increases immobility in the FST and TST, decreases time in the light compartment, and causes hypoactivity in the OFT. CRS reduced the dopamine levels in the nucleus accumbens(NAc). Brain-derived neurotrophic factor (BDNF), dopamine receptors, and serotonin receptor (HTR2A) gene expression in the prefrontal cortex, corpus striatum, and hypothalamus were decreased by CRS. Exercise on a treadmill leads to increase NAc's dopamine and noradrenaline levels and prevented behavioral alterations. Exercise increased the alterations of BDNF expressions in the brain in addition to improving behavior. As a result, CRS-induced behavioral impairments were effectively reversed by chronic treadmill exercise with molecular alterations in the brain. [ABSTRACT FROM AUTHOR]

This study evaluated the Turkish version of the Nurse Spiritual Therapeutic Scale for assessing oncology patients' and caregivers' needs. It involved 200 participants from July 2022 to May 2023. The scale demonstrated high validity (content validity index = 0.97) and good internal consistency (Cronbach's alpha = 0.879). Factor analysis revealed a 20-item single-factor structure with satisfactory representation (factor loadings: 0.29–0.89). Item-total score correlations indicated moderate to strong relationships (0.236–0.761). The model showed a good fit (goodness-of-fit indices > 0.90). The Turkish version of the scale is valid and reliable for assessing oncology patients' and caregivers' spiritual care needs. [ABSTRACT FROM AUTHOR]

Today performance and operational efficiency of computer systems on digital image processing are exacerbated owing to the increased complexity of image processing. It is also difficult for image processors based on complementary metal–oxide–semiconductor (CMOS) transistors to continuously increase the integration density, causing by their underlying physical restriction and economic costs. However, such obstacles can be eliminated by non-volatile resistive memory technologies (known as memristors), arising from their compacted area, speed, power consumption high efficiency, and in-memory computing capability. This review begins with presenting the image processing methods based on pure algorithm and conventional CMOS-based digital image processing strategies. Subsequently, current issues faced by digital image processing and the strategies adopted for overcoming these issues, are discussed. The state-of-the-art memristor technologies and their challenges in digital image processing applications are also introduced, such as memristor-based image compression, memristor-based edge and line detections, and voice and image recognition using memristors. This review finally envisages the prospects for successful implementation of memristor devices in digital image processing. [ABSTRACT FROM AUTHOR]

Purpose: Polypharmacy is a significant problem in patients with incurable cancer and a method to optimize pharmacotherapy in this patient group is lacking. Therefore, a drug optimization tool was developed and tested in a pilot test. Methods: A multidisciplinary team of health professionals developed a "Tool to Optimize Pharmacotherapy in Patients with Incurable Cancer" (TOP-PIC) for patients with a limited life expectancy. The tool consists of five sequential steps to optimize medications, including medication history, screening for medication appropriateness and drug interactions, a benefit–risk assessment using the TOP-PIC Disease-based list, and shared decision-making with the patient. For pilot testing of the tool, 8 patient cases with polypharmacy were analyzed by 11 oncologists before and after training with the TOP-PIC tool. Results: TOP-PIC was considered helpful by all oncologists during the pilot test. The median additional time required to administer the tool was 2 min per patient (P < 0.001). For 17.4% of all medications, different decisions were made by using TOP-PIC. Among possible treatment decisions (discontinuation, reduction, increase, replacement, or addition of a drug), discontinuation of medications was the most common. Without TOP-PIC, physicians were uncertain in 9.3% of medication changes, compared with only 4.8% after using TOP-PIC (P = 0.001). The TOP-PIC Disease-based list was considered helpful by 94.5% of oncologists. Conclusions: TOP-PIC provides a detailed, disease-based benefit–risk assessment with recommendations specific for cancer patients with limited life expectancy. Based on the results of the pilot study, the tool seems practicable for day-to-day clinical decision-making and provides evidence-based facts to optimize pharmacotherapy. [ABSTRACT FROM AUTHOR]

titania (titanium dioxide, TiO2) is known to induce neurotoxicity and CNS dysfunctions. Numerous studies have explored the neuroprotective effects of melatonin against neurotoxicity. This study evaluates the potential of melatonin to protect against titania-induced neurotoxicity and the role of the Keap1/Nrf2/ARE signaling pathway. One group of animals were treated with Titania (0.045 and 0.075 g/rat) alone while the other with added melatonin (1 mg/kg and 3 mg/kg) and behavioral alterations were assessed using OFT (open field test). Neurochemical and histopathological changes were also studied in the hippocampus by analyzing kelch ECH associating protein 1 (Keap1), nuclear factor erythroid 2-related factor 2 (Nrf2), and antioxidant response element (ARE). It was seen that the animals with added Melatonin had improved behavioral scores in the OFT, like anxiety and motor dysfunction triggered by TiO2. Melatonin also reduced lipid peroxidation, ROS, GSSG, IL1β, TNFα, Bax, and Keap1 levels, but boosted GSH, GPx, GR, SOD,IL10,IL4, Bcl2, Nrf2, and ARE levels and improved quadruple mitochondrial enzyme complex activity in titania-treated animals. Histopathological examination showed melatonin induced cytoprotection against vacuolization and necrosis in granular cells of DG and pyramidal cells of CA1 area of the hippocampus. In our study, pretreatment with melatonin reduced titania-induced neurotoxicity in the hippocampus through a mechanism potentially mediated by the Keap-1/Nrf2/ARE pathway. [ABSTRACT FROM AUTHOR]

Plant diseases are one of the major threats to the economy and food security of a country. Detection of such diseases timely and accurately on large scale is prone to human error. Techniques like machine learning (ML) and deep learning (DL) provide alternatives to build automated models that can detect such diseases efficiently. Several researchers have used deep learning techniques for plant disease detection. Fruit crops are major part of agricultural production. This paper proposes a lightweight and accurate deep learning model based on convolutional neural network (CNN) for the detection of diseased leaves in banana, guava and mango fruit crops. The model is proposed with the concept of feature reuse at three different levels. The model was trained using open database which consists of eight distinct classes of diseased and healthy leaves from three different fruit species. From the experiment, it was found that the model uses 101,000 numbers of parameters and achieves 99.14% success rate for disease leaves identification. Also, it outperforms 15 different state-of-the-art pre-trained models, namely VGG16, VGG19, ResNet50, ResNet50V2, ResNet152, ResNet152V2, InceptionV3, InceptionResNetV2, Xception, DenseNet121, DenseNet169, DenseNet201, MobileNetV2, ConvNeXtBase and ConvNeXtLarge, in terms of both accuracy and model complexity. [ABSTRACT FROM AUTHOR]

Resting metabolic rate (RMR) is the largest component of total energy expenditure and increasing it can be of great importance in reducing excess body fatness. Whole body vibration (WBV) can affect energy expenditure during single session of WBV, but the effects of repeated WBV on resting metabolic rate have not been reported. The purpose of this study was to investigate whether a series of WBV would increase resting metabolism in young men. Thirty-two healthy men aged of 21–23 were recruited and randomly assigned to two 16-member groups: a group participating in the WBV intervention and a group without the intervention. The intervention lasted 2 weeks and WBV was performed 5 times a week. Diet, physical activity, body composition and resting metabolic rate were analyzed in the participants. In WBV group significantly increased resting oxygen uptake (p = 0.049) and consequently RMR (p = 0.035) after the intervention. Similar changes were not observed in the control group. This indicates that applied type of vibration in this study can be an addition to obesity therapy, in which, WBV can, among other beneficial metabolic effects, increase RMR and thus total energy expenditure. [ABSTRACT FROM AUTHOR]

Use of the demanding techniques microdialysis or push-pull superfusion makes it possible to identify neurons in distinct brain areas involved in central control of peripheral functions, thus enabling brain mapping. Investigations with the push-pull superfusion technique have shown that mainly catecholaminergic neurons of the posterior and anterior hypothalamus, the locus coeruleus, and the nucleus of the solitary tract are of crucial importance for blood pressure regulation. Experimentally induced blood pressure changes also modify the release of histamine, glutamate, and taurine in the posterior hypothalamus and of serotonin in the locus coeruleus. Furthermore, histaminergic neurons of the nucleus accumbens are involved in memory, serotonergic neurons of the locus coeruleus in response to noxious stimuli, while nitric oxide of striatum has been implicated in neurotoxicity elicited by amphetamines. The involvement of several neurons in one brain function is discussed. [ABSTRACT FROM AUTHOR]

The advance and access to technology have allowed for the implementation of adaptive testing in international large-scale assessments (ILSAs). Multistage testing (MST) in ILSAs offers opportunities and advantages compared to linear testing. However, when dozens of highly heterogeneous systems participate in ILSAs, the sources of heterogeneity pose challenges to any cross-cultural measurement endeavor. With the recent implementation of adaptive designs in ILSAs, little is known about differential item functioning (DIF) effects, mainly when an MST design is used in an ILSA context. Through a simulation study grounded on the empirical basis of the Trends in International Mathematics and Science Study (TIMSS) data, this paper examines the impact of DIF in MST routing in ILSA under different routing strategies. Results showed that Merit routing is highly accurate even when the amount and magnitude of DIF is high, whereas suboptimal routing showed poor accuracy across DIF conditions. As expected, Merit routing has better proficiency recovery parameters than a suboptimal routing mechanism. Implications and recommendations for test developers are included in the discussion section. [ABSTRACT FROM AUTHOR]

The aim of the study is to investigate the measurement invariance of mathematics self-concept and self-efficacy across 40 countries that participated in the Programme for International Student Assessment (PISA) 2003 and 2012 cycles. The sample of the study consists of 271,760 students in PISA 2003 and 333,804 students in PISA 2012. Firstly, the traditional measurement invariance testing was applied in the multiple-group confirmatory factor analysis (MGCFA). Then, the alignment analyses were performed, allowing non-invariance to a minimum to estimate all of the parameters. Results from MGCFA indicate that mathematics self-concept and self-efficacy hold metric invariance across the 80 groups (cycle by country). The alignment method results suggest that a large proportion of non-invariance exists in both mathematics self-concept and self-efficacy factors, and the factor means cannot be compared across all participating countries. Results of the Monte Carlo simulation show that the alignment results are trustworthy. Implications and limitations are discussed, and some recommendations for future research are proposed. [ABSTRACT FROM AUTHOR]