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Featured Application: The Middle European ecotype of Cd hyperaccumulator Solanum nigrum L. ssp. nigrum was found to show extraordinarily strong tolerance to high contents of Cd in soil (over 50 mg kg−1 Cd) and high Cd accumulation capacity at this concentration range. Its adapted A50 variety obtained from the seeds of first-generation plants grown in soil with 50 mg kg−1 Cd appeared to display further considerable enhancement of resistance to Cd stress, accumulation capacity, and healthy state. This makes the Middle European ecotype and its adapted variety A50 particularly useful to sustainable decontamination of heavily polluted "hot spots" in degraded post-industrial areas. The Cd hyperaccumulator Solanum nigrum L. exhibits a cosmopolitan character and proven high and differentiated efficiency. This suggests the possibility of optimizing its Cd phytoremediation capacity and applicability through searching among remote ecotypes/genotypes. However, the extensive studies on this hyperaccumulator have been limited to Far East (Asian) regions. Pioneer pot experiments on the Middle European ecotype of S. nigrum within a concentration range of 0–50 mg kg−1 Cd in soil revealed its Cd phytoremediation capacity to be comparable to Asian ecotypes but with a fundamentally different Cd tolerance threshold. While biomass of the Asian ecotypes declined sharply at Csoil ≈ 10 mg kg−1 Cd, in the Middle European ecotype, a gradual mild biomass decrease occurred within the whole Csoil ≈ 0–50 mg kg−1 Cd range with no toxic symptoms. Its adapted A50 variety was obtained from the seeds of first-generation plants grown in soil with Csoil ≈ 50 mg kg−1 Cd. In this variety, Cd tolerance, accumulation performance, and all physiological parameters (chlorophyll, carotenoids, RuBisCO, and first- and second-line defense anti-oxidant activity) were significantly enhanced, while cell damage by ROS was considerably lesser. This makes the Middle European ecotype and its adapted variety A50 particularly useful to sustainable decontamination of heavily polluted "hot spots" in degraded post-industrial areas. [ABSTRACT FROM AUTHOR]

Drought is an increasingly critical global challenge, significantly impacting agricultural productivity, food security, and ecosystem stability. As climate change intensifies the frequency and severity of drought events, innovative strategies are essential to enhance plant resilience and sustain agricultural systems. This review explores the vital role of beneficial microbes in conferring drought tolerance, focusing on Plant Growth-Promoting Rhizobacteria (PGPR), mycorrhizal fungi, endophytes, actinomycetes, and cyanobacteria. These microorganisms mitigate drought stress through diverse mechanisms, including osmotic adjustment, enhancement of root architecture, modulation of phytohormones, induction of antioxidant defenses, and regulation of stress-responsive gene expression. Ecological and agricultural innovations leveraging these beneficial microbes have demonstrated significant potential in bolstering drought resilience. Strategies such as soil microbiome engineering, bioaugmentation, and the integration of microbial synergies within pest management frameworks enhance ecosystem resilience and agricultural sustainability. Additionally, advancements in agricultural practices, including seed coating, soil amendments, the development of microbial consortia, and precision agriculture technologies, have validated the effectiveness and scalability of microbial interventions in diverse farming systems. Despite promising advancements, several challenges hinder the widespread adoption of microbial solutions. Environmental variability can affect microbial performance, necessitating the development of robust and adaptable strains. Scale-up and commercialization hurdles, economic constraints, and regulatory and safety considerations also pose significant barriers. Furthermore, the complex interactions between microbes, plants, and their environments require a deeper understanding to optimize microbial benefits consistently. Future research should focus on integrating cutting-edge technologies such as genomics, synthetic biology, and precision agriculture to refine and enhance microbial interventions. Collaborative efforts among academia, industry, and government are essential to bridge the gap between research and practical implementation. By addressing these challenges and harnessing microbial innovations, it is possible to develop resilient and sustainable agricultural systems capable of thriving in an increasingly water-scarce world. [ABSTRACT FROM AUTHOR]

In the rapidly evolving landscape of electronics, Artificial Intelligence Circuits and Systems (AICAS) stand out as a groundbreaking frontier. This review provides an exhaustive examination of the advancements in AICAS, tracing its development from inception to its modern-day applications. Beginning with the foundational principles that underpin AICAS, we delve into the state-of-the-art architectures and design paradigms that are propelling the field forward. This review also sheds light on the multifaceted applications of AICAS, from optimizing energy efficiency in electronic devices to empowering next-generation cognitive computing systems. Key challenges, such as scalability and robustness, are discussed in depth, along with potential solutions and emerging trends that promise to shape the future of AICAS. By offering a comprehensive overview of the current state and potential trajectory of AICAS, this review serves as a valuable resource for researchers, engineers, and industry professionals looking to harness the power of AI in electronics. [ABSTRACT FROM AUTHOR]

Water-quality monitoring in urban lakes is of paramount importance due to the direct implications for ecosystem health and human well-being. This study presents a novel approach to predicting the Water Quality Index (WQI) in an urban lake over a span of two decades. Leveraging the power of Machine Learning (ML) algorithms, we developed models that not only predict, but also provide insights into, the intricate relationships between various water-quality parameters. Our findings indicate a significant potential in using ML techniques, especially when dealing with complex environmental datasets. The ML methods employed in this study are grounded in both statistical and computational principles, ensuring robustness and reliability in their predictions. The significance of our research lies in its ability to provide timely and accurate forecasts, aiding in proactive water-management strategies. Furthermore, we delve into the potential explanations behind the success of our ML models, emphasizing their capability to capture non-linear relationships and intricate patterns in the data, which traditional models might overlook. [ABSTRACT FROM AUTHOR]

Simple Summary: The future of plant biology, particularly rapidly advancing precision horticulture and predictive breeding, will require the transformation of huge volumes of multi-omics data into structured information and valuable knowledge, representing a key challenge. This review aims to delve into the transformative potential of integrating multi-omics data and artificial intelligence (AI) for a more comprehensive, high-throughput approach to plant phenotyping in horticultural research. We argue that the union of these advanced techniques can provide a robust analytical framework that can handle the complexity of plant biology, thus surmounting the limitations of traditional phenotyping methods. Our discussion also acknowledges the technical and non-technical challenges associated with this integration, critically evaluating their advantages and limitations, proposing potential solutions, and outlining promising future prospects. This review discusses the transformative potential of integrating multi-omics data and artificial intelligence (AI) in advancing horticultural research, specifically plant phenotyping. The traditional methods of plant phenotyping, while valuable, are limited in their ability to capture the complexity of plant biology. The advent of (meta-)genomics, (meta-)transcriptomics, proteomics, and metabolomics has provided an opportunity for a more comprehensive analysis. AI and machine learning (ML) techniques can effectively handle the complexity and volume of multi-omics data, providing meaningful interpretations and predictions. Reflecting the multidisciplinary nature of this area of research, in this review, readers will find a collection of state-of-the-art solutions that are key to the integration of multi-omics data and AI for phenotyping experiments in horticulture, including experimental design considerations with several technical and non-technical challenges, which are discussed along with potential solutions. The future prospects of this integration include precision horticulture, predictive breeding, improved disease and stress response management, sustainable crop management, and exploration of plant biodiversity. The integration of multi-omics and AI holds immense promise for revolutionizing horticultural research and applications, heralding a new era in plant phenotyping. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is significantly transforming the maritime industry, enabling the generation of vast amounts of data that can drive operational efficiency, safety, and sustainability. This review explores the role and potential of data analysis in maritime IoT applications. Through a series of case studies, it demonstrates the real-world impact of data analysis, from predictive maintenance to efficient port operations, improved navigation safety, and environmental compliance. The review also discusses the benefits and limitations of data analysis and highlights emerging trends and future directions in the field, including the growing application of AI and Machine Learning techniques. Despite the promising opportunities, several challenges, including data quality, complexity, security, cost, and interoperability, need to be addressed to fully harness the potential of data analysis in maritime IoT. As the industry continues to embrace IoT and data analysis, it becomes critical to focus on overcoming these challenges and capitalizing on the opportunities to improve maritime operations. [ABSTRACT FROM AUTHOR]

Drought conditions pose significant challenges to sustainable agriculture and food security. Identifying microbial strains that can mitigate drought effects is crucial to enhance crop resilience and productivity. This study presents a comprehensive comparison of several machine learning models, including Random Forest, Decision Tree, XGBoost, Support Vector Machine (SVM), and Artificial Neural Network (ANN), to predict optimal microbial strains for this purpose. Models were assessed on multiple metrics, such as accuracy, standard deviation of results, gains, total computation time, and training time per 1000 rows of data. Notably, the Gradient Boosted Trees model outperformed others in accuracy but required extensive computational resources. This underscores the balance between accuracy and computational efficiency in machine learning applications. Leveraging machine learning for selecting microbial strains signifies a leap beyond traditional methods, offering improved efficiency and efficacy. These insights hold profound implications for agriculture, especially concerning drought mitigation, thus furthering the cause of sustainable agriculture and ensuring food security. [ABSTRACT FROM AUTHOR]

The study of plant metabolism is one of the most complex tasks, mainly due to the huge amount and structural diversity of metabolites, as well as the fact that they react to changes in the environment and ultimately influence each other. Metabolic profiling is most often carried out using tools that include mass spectrometry (MS), which is one of the most powerful analytical methods. All this means that even when analyzing a single sample, we can obtain thousands of data. Data science has the potential to revolutionize our understanding of plant metabolism. This review demonstrates that machine learning, network analysis, and statistical modeling are some techniques being used to analyze large quantities of complex data that provide insights into plant development, growth, and how they interact with their environment. These findings could be key to improving crop yields, developing new forms of plant biotechnology, and understanding the relationship between plants and microbes. It is also necessary to consider the constraints that come with data science such as quality and availability of data, model complexity, and the need for deep knowledge of the subject in order to achieve reliable outcomes. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has become widespread. Mainly used in industry, it already penetrates into every sphere of private life. It is often associated with complex sensors and very complicated technology. IoT in life sciences has gained a lot of importance because it allows one to minimize the costs associated with field research, expeditions, and the transport of the many sensors necessary for physical and chemical measurements. In the literature, we can find many sensational ideas regarding the use of remote collection of environmental research. However, can we fully say that IoT is well established in the natural sciences? [ABSTRACT FROM AUTHOR]

Abiotic stresses are the most important environmental factors affecting seed germination, and negatively affect crop production worldwide. Water availability is essential for proper seed imbibition and germination. The mechanism by which seeds can germinate in areas with high soil salinity is, however, still unclear. The present study aims to investigate the protective roles of AgNPs in alleviating stress symptoms caused by salinity exposure in barley seeds. For this purpose, different treatment combinations of seed priming with PVP-AgNPs in salinity stress conditions were used. Salt stress (150 and 200 mM) was found to reduce seed germination by 100% when compared to the control. Under NaCl concentrations, seed priming with PVP-AgNPs (40 mg L−1) only for 2 h, reduced salinity effects. Salinity resulted in increased reactive oxygen species (ROS) generation compared to the control. However, increased antioxidants in the NPs treatments, such as SOD, CAT, GR, GPX (expression at both genes, such as HvSOD, HvCAT, HvGR or HvGPX, and protein levels) and glutathione content, scavenged these ROS. Considering all of the parameters under study, priming alleviated salt stress. To summarize, seed priming with AgNPs has the potential to alleviate salinity stress via reduced ROS generation and activation of the antioxidant enzymatic system during germination. [ABSTRACT FROM AUTHOR]

Acute, strenuous physical exertion requiring high levels of energy production induces the production of reactive oxygen species and metabolic disturbances that can damage the mitochondria. Thus, selective autophagic elimination of defective mitochondria may improve resistance to oxidative stress and potentially to inflammation. The main goal of this study was to evaluate the impacts of intense effort on changes in the expression of select genes related to post-effort inflammation and autophagy. Thirty-five men aged 16–21 years were recruited to the study. The impacts of both aerobic (endurance) and anaerobic (speed) efforts on selected genes encoding chemokines (CXCL5, 8–12) were analyzed. Significant increases in the expression of all studied genes excluding CXCL12 were observed. Moreover, both types of effort induced an increase in the expression of genes encoding IL-2, -4, -6, -10, IFN-γ and TNF-α, excluding IL-17A. Generally, these efforts caused a significant increase in the relative expression of apoptosis- (BCL2 and BAX) and autophagy- (BNIP3, BECN1, MAP1LC3B, ATG5, ATG7, ATG12, ATG16L1 and SQSTM1) related genes. It seems that the duration of physical activity and its bioenergetic cost has an important impact on the degree of increase in expression of this panel of autophagy-related genes. Anaerobic effort is more strenuous than aerobic effort and requires a higher bioenergetic investment. This may explain the stronger impact of anaerobic effort on the expression of the studied genes. This observation seems to support the protective role of autophagy proposed in prior studies. [ABSTRACT FROM AUTHOR]

The intracellular homeostasis of reactive oxygen species (ROS) and especially of superoxide anion and hydrogen peroxide participate in signaling cascades which dictate developmental processes and reactions to stresses. ROS are also biological molecules that play important roles in seed dormancy and germination. Because of their rapid reactivity, short half-life and low concentration, ROS are difficult to measure directly with high accuracy and precision. In presented work tissue printing method with image analysis and dual excitation flow cytometry (FCM) were developed for rapid detection and localization of O2•− and H2O2 in different part of seed. Tissue printing and FCM detection of ROS showed that germination of wild oat seeds was associated with the accumulation of O2•− and H2O2 in embryo (coleorhiza, radicle and scutellum), aleurone layer and coat. To verify if printing and FCM signals were specified, the detection of O2•− and H2O2 in seeds incubated in presence of O2•− generation inhibitor (DPI) or H2O2 scavenger (CAT) were examined. All results were a high level of agreement among the level of ROS derived from presented procedures with the ones created from spectrophotometric measured data. In view of the data obtained, tissue printing with image analysis and FCM are recommended as a simple and fast methods, which could help researchers to detection and level determination of ROS in the external and inner parts of the seeds. [ABSTRACT FROM AUTHOR]