Explore the vast range of titles available.

Carotenoid cleavage dioxygenases (CCDs) form hormones and signaling molecules. Here we show that a member of an overlooked plant CCD subfamily from rice, that we name Zaxinone Synthase (ZAS), can produce zaxinone, a novel apocarotenoid metabolite in vitro. Loss-of-function mutants ( zas ) contain less zaxinone, exhibit retarded growth and showed elevated levels of strigolactones (SLs), a hormone that determines plant architecture, mediates mycorrhization and facilitates infestation by root parasitic weeds, such as Striga spp. Application of zaxinone can rescue zas phenotypes, decrease SL content and release and promote root growth in wild-type seedlings. In conclusion, we show that zaxinone is a key regulator of rice development and biotic interactions and has potential for increasing crop growth and combating Striga , a severe threat to global food security. Strigolactone and abscisic acid are carotenoid-derived plant hormones. Here the authors describe the identification of zaxinone, a further apocarotenoid metabolite, which down-regulates strigolactone content and is required for normal growth and development in rice.

Abiotic stresses, including drought, salinity, extreme temperatures and nutrient deficiencies, pose significant challenges to crop production and global food security. To combat these challenges, the integration of bioinformatics educational tools and AI applications provide a synergistic approach to identify and analyze stress-responsive genes, regulatory networks and molecular markers associated with stress tolerance. Bioinformatics educational tools offer a robust framework for data collection, storage and initial analysis, while AI applications enhance pattern recognition, predictive modeling and real-time data processing capabilities. This review uniquely integrates bioinformatics educational tools and AI applications, highlighting their combined role in managing abiotic stress in plants and crops. The novelty is demonstrated by the integration of multiomics data with AI algorithms, providing deeper insights into stress response pathways, biomarker discovery and pattern recognition. Key AI applications include predictive modeling of stress resistance genes, gene regulatory network inference, omics data integration and real-time plant monitoring through the fusion of remote sensing and AI-assisted phenomics. Challenges such as handling big omics data, model interpretability, overfitting and experimental validation remain there, but future prospects involve developing user-friendly bioinformatics educational platforms, establishing common data standards, interdisciplinary collaboration and harnessing AI for real-time stress mitigation strategies in plants and crops. Educational initiatives, interdisciplinary collaborations and trainings are essential to equip the next generation of researchers with the required skills to utilize these advanced tools effectively. The convergence of bioinformatics and AI holds vast prospects for accelerating the development of stress-resilient plants and crops, optimizing agricultural practices and ensuring global food security under increasing environmental pressures. Moreover, this integrated approach is crucial for advancing sustainable agriculture and ensuring global food security amidst growing environmental challenges.

This paper examines the influence of adopting resource efficiency actions, saving water, saving energy, using renewable energy, saving materials, minimizing waste, selling scrap, recycling, using durable products, promoting environmental responsibility, and offering green marketing products and services on the performance of small and medium-sized enterprises (SMEs). More specifically, we investigate specific resource efficiency actions and their impact on production costs, investment, the available support for product expansion, and the effect of encountered barriers on SME performance. We develop a theoretical framework based on stakeholder- and resource-based theories to serve as the foundation for this analysis. We use these theories to explain the link between eco-efficiency actions, firm performance, and ecological behavior, along with public policy and innovation. This study uses Flash Eurobarometer survey datasets FL342, FL381, FL426, and FL456, which cover SMEs across time and sectors in 28 EU countries. The data are analyzed through descriptive and ordered logit regression analysis, using the Statistical Package for the Social Sciences (SPSS) to test the relationship between the above variables and the parameters. In terms of practical implications, these findings are crucial in helping SMEs pursue sustainable development. According to the findings, SMEs lack information on how implementing eco-efficiency action affects their financial health and sustainable innovation. This study can provide valuable insights into how implementing eco-efficiency practices can positively impact a company’s bottom line, good health, and employees’ well-being and how SMEs can use this information to make more informed decisions. Additionally, the findings can help inform policy makers about how to better support SMEs in pursuing sustainable development.

Background High throughput experiments have generated a significantly large amount of protein interaction data, which is being used to study protein networks. Studying complete protein networks can reveal more insight about healthy/disease states than studying proteins in isolation. Similarly, a comparative study of protein–protein interaction (PPI) networks of different species reveals important insights which may help in disease analysis and drug design. The study of PPI network alignment can also helps in understanding the different biological systems of different species. It can also be used in transfer of knowledge across different species. Different aligners have been introduced in the last decade but developing an accurate and scalable global alignment algorithm that can ensures the biological significance alignment is still challenging. Results This paper presents a novel global pairwise network alignment algorithm, SAlign, which uses topological and biological information in the alignment process. The proposed algorithm incorporates sequence and structural information for computing biological scores, whereas previous algorithms only use sequence information. The alignment based on the proposed technique shows that the combined effect of structure and sequence results in significantly better pairwise alignments. We have compared SAlign with state-of-art algorithms on the basis of semantic similarity of alignment and the number of aligned nodes on multiple PPI network pairs. The results of SAlign on the network pairs which have high percentage of proteins with available structure are 3–63% semantically better than all existing techniques. Furthermore, it also aligns 5–14% more nodes of these network pairs as compared to existing aligners. The results of SAlign on other PPI network pairs are comparable or better than all existing techniques. We also introduce SAlign mc , a Monte Carlo based alignment algorithm, that produces multiple network alignments with similar semantic similarity. This helps the user to pick biologically meaningful alignments. Conclusion The proposed algorithm has the ability to find the alignments that are more biologically significant/relevant as compared to the alignments of existing aligners. Furthermore, the proposed method is able to generate alternate alignments that help in studying different genes/proteins of the specie.

Drought stress is a major limitation in wheat production around the globe. Organic amendments could be the possible option in semi-arid climatic conditions to mitigate the adverse effects of drought at critical growth stages. Wheat straw biochar (BC 0 = Control, BC 1 = 3% biochar and BC 2 = 5% biochar) was used to alleviate the drought stress at tillering (DTS), flowering (DFS), and grain filling (DGFS) stages. Drought stress significantly reduced the growth and yield of wheat at critical growth stages, with DGFS being the most susceptible stage, resulting in significant yield loss. Biochar application substantially reduced the detrimental effects of drought by improving plant height (15.74%), fertile tiller count (17.14%), spike length (16.61%), grains per spike (13.89%), thousand grain weight (10.4%), and biological yield (13.1%) when compared with the control treatment. Furthermore, physiological parameters such as water use efficiency (38.41%), stomatal conductance (42.76%), chlorophyll a (19.3%), chlorophyll b (22.24%), transpiration rate (39.17%), photosynthetic rate (24.86%), electrolyte leakage (-42.5%) hydrogen peroxide (-18.03%) superoxide dismutase (24.66%), catalase (24.11%) and peroxidase (-13.14%) were also improved by biochar application. The use of principal component analysis linked disparate scales of our findings to explain the changes occurred in wheat growth and yield in response to biochar application under drought circumstances. In essence, using biochar at 5% rate could be a successful strategy to promote wheat grain production by reducing the hazardous impacts of drought stress.

MAIN CONCLUSION : Strigolactone changes and cross talk with ABA unveil a picture of root-specific hormonal dynamics under stress. Strigolactones (SLs) are carotenoid-derived hormones influencing diverse aspects of development and communication with (micro)organisms, and proposed as mediators of environmental stimuli in resource allocation processes; to contribute to adaptive adjustments, therefore, their pathway must be responsive to environmental cues. To investigate the relationship between SLs and abiotic stress in Lotus japonicus, we compared wild-type and SL-depleted plants, and studied SL metabolism in roots stressed osmotically and/or phosphate starved. SL-depleted plants showed increased stomatal conductance, both under normal and stress conditions, and impaired resistance to drought associated with slower stomatal closure in response to abscisic acid (ABA). This confirms that SLs contribute to drought resistance in species other than Arabidopsis. However, we also observed that osmotic stress rapidly and strongly decreased SL concentration in tissues and exudates of wild-type Lotus roots, by acting on the transcription of biosynthetic and transporter-encoding genes and independently of phosphate abundance. Pre-treatment with exogenous SLs inhibited the osmotic stress-induced ABA increase in wild-type roots and down-regulated the transcription of the ABA biosynthetic gene LjNCED2. We propose that a transcriptionally regulated, early SL decrease under osmotic stress is needed (but not sufficient) to allow the physiological increase of ABA in roots. This work shows that SL metabolism and effects on ABA are seemingly opposite in roots and shoots under stress.

SUMMARY The outbreak of the Novel Coronavirus (COVID-19) in December 2019 has progressed to the status of a global pandemic, with countries across the seven continents adversely affected and the number of human cases exceeding two million. With no available vaccine, the treatment is primarily symptomatic for those affected and preventative for those at risk. Most countries have taken action to curtail the spread of COVID-19 through measures such as lockdowns, social distancing and voluntary self-isolation. Whilst necessary, such measures and the disease itself, may have an adverse impact on mental health. In view of research from previous pandemic crises, it is known that such situations are likely to increase stress levels and have negative psychiatric effects. The impact is likely to be felt by the general public, sufferers of COVID-19, their families and friends, persons with pre-existing mental health conditions and healthcare workers.

Tobacco (Nicotiana tabacum), is a world’s major non-food agricultural crop widely cultivated for its economic value. Among several color change associated biological processes, plastid pigment metabolism is of trivial importance in postharvest plant organs during curing and storage. However, the molecular mechanisms involved in carotenoid and chlorophyll metabolism, as well as color change in tobacco leaves during curing, need further elaboration. Here, proteomic analysis at different curing stages (0 h, 48 h, 72 h) was performed in tobacco cv. Bi’na1 with an aim to investigate the molecular mechanisms of pigment metabolism in tobacco leaves as revealed by the iTRAQ proteomic approach. Our results displayed significant differences in leaf color parameters and ultrastructural fingerprints that indicate an acceleration of chloroplast disintegration and promotion of pigment degradation in tobacco leaves due to curing. In total, 5931 proteins were identified, of which 923 (450 up-regulated, 452 down-regulated, and 21 common) differentially expressed proteins (DEPs) were obtained from tobacco leaves. To elucidate the molecular mechanisms of pigment metabolism and color change, 19 DEPs involved in carotenoid metabolism and 12 DEPs related to chlorophyll metabolism were screened. The results exhibited the complex regulation of DEPs in carotenoid metabolism, a negative regulation in chlorophyll biosynthesis, and a positive regulation in chlorophyll breakdown, which delayed the degradation of xanthophylls and accelerated the breakdown of chlorophylls, promoting the formation of yellow color during curing. Particularly, the up-regulation of the chlorophyllase-1-like isoform X2 was the key protein regulatory mechanism responsible for chlorophyll metabolism and color change. The expression pattern of 8 genes was consistent with the iTRAQ data. These results not only provide new insights into pigment metabolism and color change underlying the postharvest physiological regulatory networks in plants, but also a broader perspective, which prompts us to pay attention to further screen key proteins in tobacco leaves during curing.

Early detection of the crop disease using agricultural remote sensing is crucial as a precaution against its spread. However, the traditional method, relying on the disease symptoms, is lagging. Here, an early detection model using machine learning with hyperspectral images is presented. This study first extracted the normalized difference texture indices (NDTIs) and vegetation indices (VIs) to enhance the difference between healthy and powdery mildew wheat. Then, a partial least-squares linear discrimination analysis was applied to detect powdery mildew with the combined optimal features (i.e., VIs & NDTIs). Further, a regression model on the partial least-squares regression was developed to estimate disease severity (DS). The results show that the discriminant model with the combined VIs & NDTIs improved the ability for early identification of the infected leaves, with an overall accuracy value and Kappa coefficient over 82.35% and 0.56 respectively, and with inconspicuous symptoms which were difficult to identify as symptoms of the disease using the traditional method. Furthermore, the calibrated and validated DS estimation model reached good performance as the coefficient of determination (R2) was over 0.748 and 0.722, respectively. Therefore, this methodology for detection, as well as the quantification model, is promising for early disease detection in crops.

The use of recycled aggregate (RA) as a partial or full replacement of natural aggregate (NA) is a suitable method of concrete production that has positive impacts on the environment. However, recycled aggregate concrete (RAC) has relatively lower strength and durability than that of normal concrete. To improve concrete performance, silica-fume (SF) was added with 2.5% increment up to 7.5% and metakaolin (MK) is added with a 2.5% decrement from 15 to 7.5%. The concrete with 50% RA, 10% MK and 5% SF showed notable advancement in performance after 28 days of curing. At 28 days of curing, the concrete samples had 31.5 MPa compressive strength, 5.7 MPa splitting tensile strength, and 10.6 MPa flexural strength, a strength improvement of 5.19%, 16.47%, and 8.52% over control concrete. Ultrasonic pulse velocity (UPV) indicated a 16.13% increase alongside a 20.87% reduction in water absorption which confirmed stronger bond performance and better durability of modified concrete. RCA content influences acid resistance negatively when reaching 75% RCA shows maximum deterioration. In addition, the fire resistance of such concrete resulted in higher performance at different temperature conditions for the concrete. This is due to the small particles of silica fume and metakaolin which acted as major factors and led to performance enhancements by filling in the concrete matrix gaps. The combination provides affordable, sustainable construction alternatives. Experiments show that SCMs can produce high-performance recycled concrete for modern building construction.