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Analyses of Arabidopsis thaliana defense response to the damping-off oomycete pathogen Pythium irregulare show that resistance to P. irregulare requires a multicomponent defense strategy. Penetration represents a first layer, as indicated by the susceptibility of pen2 mutants, followed by recognition, likely mediated by ERECTA receptor-like kinases. Subsequent signaling of inducible defenses is predominantly mediated by jasmonic acid (JA), with insensitive coi1 mutants showing extreme susceptibility. In contrast with the generally accepted roles of ethylene and salicylic acid cooperating with or antagonizing, respectively, JA in the activation of defenses against necrotrophs, both are required to prevent disease progression, although much less so than JA. Meta-analysis of transcriptome profiles confirmed the predominant role of JA in activation of P. irregulare-induced defenses and uncovered abscisic acid (ABA) as an important regulator of defense gene expression. Analysis of cis-regulatory sequences also revealed an unexpected overrepresentation of ABA response elements in promoters of P. irregulare-responsive genes. Subsequent infections of ABA-related and callose-deficient mutants confirmed the importance of ABA in defense, acting partly through an undescribed mechanism. The results support a model for ABA affecting JA biosynthesis in the activation of defenses against this oomycete.

A bstract We construct families of supersymmetric AdS 3 × Y 7 and AdS 3 × Y 8 solutions to type IIB string theory and M-theory, respectively. Here Y 7 is an S 5 fibration over Σ, while Y 8 is an S 4 fibration over Σ g × Σ, where Σ g is a Riemann surface of genus g > 1 and Σ is a two-dimensional orbifold known as a spindle. We interpret the solutions as near-horizon limits of N D3-branes wrapped on Σ and N M5-branes wrapped on Σ g × Σ, respectively. These are holographically dual to d = 2, (0 , 2) SCFTs, and we show that the central charge and superconformal R-symmetry of the gravity solutions agree with dual field theory calculations.

Adventitious root (AR) formation in excised plant parts is a bottleneck for survival of isolated plant fragments. AR formation plays an important ecological role and is a critical process in cuttings for the clonal propagation of horticultural and forestry crops. Therefore, understanding the regulation of excision-induced AR formation is essential for sustainable and efficient utilization of plant genetic resources. Recent studies of plant transcriptomes, proteomes and metabolomes, and the use of mutants and transgenic lines have significantly expanded our knowledge concerning excision-induced AR formation. Here, we integrate new findings regarding AR formation in the cuttings of diverse plant species. These findings support a new system-oriented concept that the phytohormone-controlled reprogramming and differentiation of particular responsive cells in the cutting base interacts with a co-ordinated reallocation of plant resources within the whole cutting to initiate and drive excision-induced AR formation. Master control by auxin involves diverse transcription factors and mechanically sensitive microtubules, and is further linked to ethylene, jasmonates, cytokinins and strigolactones. Hormone functions seem to involve epigenetic factors and cross-talk with metabolic signals, reflecting the nutrient status of the cutting. By affecting distinct physiological units in the cutting, environmental factors such as light, nitrogen and iron modify the implementation of the genetically controlled root developmental programme. Despite advanced research in the last decade, important questions remain open for future investigations on excision-induced AR formation. These concern the distinct roles and interactions of certain molecular, hormonal and metabolic factors, as well as the functional equilibrium of the whole cutting in a complex environment. Starting from model plants, cell type- and phase-specific monitoring of controlling processes and modification of gene expression are promising methodologies that, however, need to be integrated into a coherent model of the whole system, before research findings can be translated to other crops.

Adventitious root (AR) formation is a complex developmental process controlled by a plethora of endogenous and environmental factors. Based on fossil evidence and genomic phylogeny, AR formation might be considered the default state of plant roots, which likely evolved independently several times. The application of next-generation sequencing techniques and bioinformatics analyses to non-model plants provide novel approaches to identify genes putatively involved in AR formation in multiple species. Recent results uncovered that the regulation of shoot-borne AR formation in monocots is an adaptive response to nutrient and water deficiency that enhances topsoil foraging and improves plant performance. A hierarchy of transcription factors required for AR initiation has been identified from genetic studies, and recent results highlighted the key involvement of additional regulation through microRNAs. Here, we discuss our current understanding of AR formation in response to specific environmental stresses, such as nutrient deficiency, drought or waterlogging, aimed at providing evidence for the integration of the hormone crosstalk required for the activation of root competent cells within adult tissues from which the ARs develop.

Acoustic cavities play a role in many technological applications in civil, naval, and aerospace engineering. This study examines the vibroacoustic performance of a forced oscillating top membrane of a cylindrical container fully filled with a compressible and nonviscous fluid. For the case of harmonic motion and using Helmholtz’s equation, the velocity potential is deduced, and the acoustic pressure is obtained using Bernoulli’s linearized equation. Taking into account the dynamic equation for the membrane with the interacting fluid with the different terms expanded in a modal series and after an integration procedure over the membrane surface, a simple analytical quadratic equation is deduced, and the coupled natural frequencies of the membrane are obtained. For the case of forced vibrations, a transfer function is obtained for calculating the frequency spectrum response of the fluid–membrane interacting system. In particular, the membrane deformation spectrum and the acoustic cavity pressure spectrum are obtained for different location points. Moreover, the spectrum of the mean quadratic values of the membrane deflexion and acoustic pressure are deduced, along with its variation with different parameters such as drum height, membrane radius, fluid density, load position, sound speed, and membrane tension. The variation in sensitivity with frequency and other different parameters is also analysed. The results are contrasted with those obtained by other authors to validate the present work.

The use of gene-editing tools, such as zinc finger nucleases, TALEN, and CRISPR/Cas, allows for the modification of physiological, morphological, and other characteristics in a wide range of crops to mitigate the negative effects of stress caused by anthropogenic climate change or biotic stresses. Importantly, these tools have the potential to improve crop resilience and increase yields in response to challenging environmental conditions. This review provides an overview of gene-editing techniques used in plants, focusing on the cultivated tomatoes. Several dozen genes that have been successfully edited with the CRISPR/Cas system were selected for inclusion to illustrate the possibilities of this technology in improving fruit yield and quality, tolerance to pathogens, or responses to drought and soil salinity, among other factors. Examples are also given of how the domestication of wild species can be accelerated using CRISPR/Cas to generate new crops that are better adapted to the new climatic situation or suited to use in indoor agriculture.

Galicia (Spain) was one of the first regions worldwide to incorporate nirsevimab for universal respiratory syncytial virus (RSV) prophylaxis in infants into its immunisation programme. The NIRSE-GAL longitudinal population-based study aimed to assess nirsevimab effectiveness in preventing hospitalisations (ie, admittance to hospital). The 2023–24 immunisation campaign with nirsevimab in Galicia began on Sept 25, 2023, and concluded on March 31, 2024. The campaign targeted three groups: infants born during the campaign (seasonal group), infants younger than 6 months at the start of the campaign (catch-up group), and infants aged 6–24 months with high-risk factors at the start of the campaign (high-risk group). Infants in the seasonal group were offered immunisation on the first day of life before discharge from hospital. Infants in the catch-up and high-risk groups received electronic appointments to attend a public hospital or health-care centre for nirsevimab administration. For this interim analysis, we used data collected from Sept 25 to Dec 31, 2023, from children born up to Dec 15, 2023. Data were retrieved from public health registries. Nirsevimab effectiveness in preventing RSV-associated lower respiratory tract infection (LRTI) hospitalisations; severe RSV-related LRTI requiring intensive care unit admission, mechanical ventilation, or oxygen support; all-cause LRTI hospitalisations; and all-cause hospitalisations was estimated using adjusted Poisson regression models. Data from five past RSV seasons (2016–17, 2017–18, 2018–19, 2019–20, and 2022–23), excluding the COVID-19 pandemic period, were used to estimate the number of RSV-related LRTI hospitalisations averted along with its IQR. The number needed to immunise to avoid one case in the 2023–24 season was then estimated from the averted cases. Nirsevimab safety was routinely monitored. The NIRSE-GAL study protocol was registered on ClinicalTrials.gov (NCT06180993), and follow-up of participants is ongoing. 9408 (91·7%) of 10 259 eligible infants in the seasonal and catch-up groups received nirsevimab, including 6220 (89·9%) of 6919 in the catch-up group and 3188 (95·4%) of 3340 in the seasonal group. 360 in the high-risk group were offered nirsevimab, 348 (97%) of whom received it. Only infants in the seasonal and catch-up groups were included in analyses to estimate nirsevimab effectiveness and impact because there were too few events in the high-risk group. In the catch-up and seasonal groups combined, 30 (0·3%) of 9408 infants who received nirsevimab and 16 (1·9%) of 851 who did not receive nirsevimab were hospitalised for RSV-related LRTI, corresponding to an effectiveness of 82·0% (95% CI 65·6–90·2). Effectiveness was 86·9% (69·1–94·2) against severe RSV-related LRTI requiring oxygen support, 69·2% (55·9–78·0) against all-cause LRTI hospitalisations, and 66·2% (56·0–73·7) against all-cause hospitalisations. Nirsevimab effectiveness against other endpoints of severe RSV-related LRTI could not be estimated because of too few events. RSV-related LRTI hospitalisations were reduced by 89·8% (IQR 87·5–90·3), and the number needed to immunise to avoid one RSV-related LRTI hospitalisation was 25 (IQR 24–32). No severe adverse events related to nirsevimab were registered. Nirsevimab substantially reduced infant hospitalisations for RSV-associated LRTI, severe RSV-associated LRTI requiring oxygen, and all-cause LRTI when given in real-world conditions. These findings offer policy makers and health authorities robust, real-world, population-based evidence to guide the development of strategies for RSV prevention. Sanofi and AstraZeneca. For the Spanish translation of the abstract see Supplementary Materials section.

Smart grids are able to forecast customers’ consumption patterns, i.e., their energy demand, and consequently electricity can be transmitted after taking into account the expected demand. To face today’s demand forecasting challenges, where the data generated by smart grids is huge, modern data-driven techniques need to be used. In this scenario, Deep Learning models are a good alternative to learn patterns from customer data and then forecast demand for different forecasting horizons. Among the commonly used Artificial Neural Networks, Long Short-Term Memory networks—based on Recurrent Neural Networks—are playing a prominent role. This paper provides an insight into the importance of the demand forecasting issue, and other related factors, in the context of smart grids, and collects some experiences of the use of Deep Learning techniques, for demand forecasting purposes. To have an efficient power system, a balance between supply and demand is necessary. Therefore, industry stakeholders and researchers should make a special effort in load forecasting, especially in the short term, which is critical for demand response.

Smart farming employs intelligent systems for every domain of agriculture to obtain sustainable economic growth with the available resources using advanced technologies. Deep Learning (DL) is a sophisticated artificial neural network architecture that provides state-of-the-art results in smart farming applications. One of the main tasks in this domain is yield estimation. Manual yield estimation undergoes many hurdles such as labor-intensive, time-consuming, imprecise results, etc. These issues motivate the development of an intelligent fruit yield estimation system that offers more benefits to the farmers in deciding harvesting, marketing, etc. Semantic segmentation combined with DL adds promising results in fruit detection and localization by performing pixel-based prediction. This paper reviews the different literature employing various techniques for fruit yield estimation using DL-based semantic segmentation architectures. It also discusses the challenging issues that occur during intelligent fruit yield estimation such as sampling, collection, annotation and data augmentation, fruit detection, and counting. Results show that the fruit yield estimation employing DL-based semantic segmentation techniques yields better performance than earlier techniques because of human cognition incorporated into the architecture. Future directions like customization of DL architecture for smart-phone applications to predict the yield, development of more comprehensive model encompassing challenging situations like occlusion, overlapping and illumination variation, etc., were also discussed.

Concerns about depleting fossil fuels and global warming effects are pushing our society to search for new renewable sources of energy with the potential to substitute coal, natural gas, and petroleum. In this sense, biomass, the only renewable source of carbon available on Earth, is the perfect replacement for petroleum in producing renewable fuels. The aviation sector is responsible for a significant fraction of greenhouse gas emissions, and two billion barrels of petroleum are being consumed annually to produce the jet fuels required to transport people and goods around the world. Governments are pushing directives to replace fossil fuel-derived jet fuels with those derived from biomass. The present mini review is aimed to summarize the main technologies available today for converting biomass into liquid hydrocarbon fuels with a molecular weight and structure suitable for being used as aviation fuels. Particular emphasis will be placed on those routes involving heterogeneous catalysts.