Ideal Architecture Research Articles

Transcription factor OsWRKY72 controls rice leaf angle by regulating LAZY1-mediated shoot gravitropism.

Leaf angle is a major trait of ideal architecture, which is considered to influence rice (Oryza sativa) cultivation and grain yield. Although a few mutants with altered rice leaf inclination angles have been reported, the underlying molecular mechanism remains unclear. In this study, we showed that a WRKY transcription factor gene, OsWRKY72, was highly expressed in the leaf sheath and lamina joint. Phenotypic analyses showed that oswrky72 mutants had smaller leaf angles than the wild type, while OsWRKY72 overexpression lines exhibited an increased leaf angle. This observation suggests that OsWRKY72 functions as a positive regulator, promoting the enlargement of the leaf angle. Our bioinformatics analysis identified LAZY1 as the downstream gene of OsWRKY72. Electrophoretic mobility shift assays and dual-luciferase analysis revealed that OsWRKY72 directly inhibited LAZY1 by binding to its promoter. Moreover, knocking out OsWRKY72 enhanced shoot gravitropism, which contrasted with the phenotype of lazy1 plants. These results imply that OsWRKY72 regulates the leaf angle through gravitropism by reducing the expression of LAZY1. In addition, OsWRKY72 could directly regulate the expression of other leaf angle-related genes such as FLOWERING LOCUS T-LIKE 12 (OsFTL12) and WALL-ASSOCIATED KINASE 11 (OsWAK11). Our study indicates that OsWRKY72 contributes positively to the expansion of the leaf angle by interfering with shoot gravitropism in rice.

Precise fine-turning of GhTFL1 by base editing tools defines ideal cotton plant architecture

BackgroundCRISPR/Cas-derived base editor enables precise editing of target sites and has been widely used for basic research and crop genetic improvement. However, the editing efficiency of base editors at different targets varies greatly.ResultsHere, we develop a set of highly efficient base editors in cotton plants. GhABE8e, which is fused to conventional nCas9, exhibits 99.9% editing efficiency, compared to GhABE7.10 with 64.9%, and no off-target editing is detected. We further replace nCas9 with dCpf1, which recognizes TTTV PAM sequences, to broaden the range of the target site. To explore the functional divergence of TERMINAL FLOWER 1 (TFL1), we edit the non-coding and coding regions of GhTFL1 with 26 targets to generate a comprehensive allelic population including 300 independent lines in cotton. This allows hidden pleiotropic roles for GhTFL1 to be revealed and allows us to rapidly achieve directed domestication of cotton and create ideotype germplasm with moderate height, shortened fruiting branches, compact plant, and early-flowering. Further, by exploring the molecular mechanism of the GhTFL1L86P and GhTFL1K53G+S78G mutations, we find that the GhTFL1L86P mutation weakens the binding strength of the GhTFL1 to other proteins but does not lead to a complete loss of GhTFL1 function.ConclusionsThis strategy provides an important technical platform and genetic information for the study and creation of ideal plant architecture.

Strigolactone and abscisic acid synthesis and signaling pathways are enhanced in the wheat oligo-tillering mutant ot1

Tiller number greatly contributes to grain yield in wheat. Using ethylmethanesulfonate mutagenesis, we previously discovered the oligo-tillering mutant ot1. The tiller number was significantly lower in ot1 than in the corresponding wild type from the early tillering stage until the heading stage. Compared to the wild type, the thousand-grain weight and grain length were increased by 15.41% and 31.44%, respectively, whereas the plant height and spike length were decreased by 26.13% and 37.25%, respectively. Transcriptomic analysis was conducted at the regreening and jointing stages to identify differential expressed genes (DEGs). Functional enrichment analysis with the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) databases showed differential expression of genes associated with ADP binding, transmembrane transport, and transcriptional regulation during tiller development. Differences in tiller number in ot1 led to the upregulation of genes in the strigolactone (SL) and abscisic acid (ABA) pathways. Specifically, the SL biosynthesis genes DWARF (D27), D17, D10, and MORE AXILLARY GROWTH 1 (MAX1) were upregulated by 3.37- to 8.23-fold; the SL signal transduction genes D14 and D53 were upregulated by 1.81- and 1.32-fold, respectively; the ABA biosynthesis genes 9-CIS-EPOXICAROTENOID DIOXIGENASE 3 (NCED3) and NCED5 were upregulated by 1.66- and 3.4-fold, respectively; and SNF1-REGULATED PROTEIN KINASE2 (SnRK2) and PROTEIN PHOSPHATASE 2C (PP2C) genes were upregulated by 1.30- to 4.79-fold. This suggested that the tiller number reduction in ot1 was due to alterations in plant hormone pathways. Genes known to promote tillering growth were upregulated, whereas those known to inhibit tillering growth were downregulated. For example, PIN-FORMED 9 (PIN9), which promotes tiller development, was upregulated by 8.23-fold in ot1; Ideal Plant Architecture 1 (IPA1), which inhibits tiller development, was downregulated by 1.74-fold. There were no significant differences in the expression levels of TILLER NUMBER 1 (TN1) or TEOSINTE BRANCHED 1 (TB1), indicating that the tiller reduction in ot1 was not controlled by known genes. Our findings provide valuable data for subsequent research into the genetic bases and regulatory mechanisms of wheat tillering.

Breeding maize of ideal plant architecture for high-density planting tolerance through modulating shade avoidance response and beyond.

Maize is a major staple crop widely used as food, animal feed, and raw materials in industrial production. High-density planting is a major factor contributing to the continuous increase of maize yield. However, high planting density usually triggers a shade avoidance response and causes increased plant height and ear height, resulting in lodging and yield loss. Reduced plant height and ear height, more erect leaf angle, reduced tassel branch number, earlier flowering, and strong root system architecture are five key morphological traits required for maize adaption to high-density planting. In this review, we summarize recent advances in deciphering the genetic and molecular mechanisms of maize involved in response to high-density planting. We also discuss some strategies for breeding advanced maize cultivars with superior performance under high-density planting conditions.

A critical analysis of Zero Trust Architecture (ZTA)

Zero Trust (ZT) has become a very hot approach for building secure systems, promoted by industry and government as a new way to produce systems with a high degree of security. ZT is based on not trusting any request for accessing resources. Because of the possibility of increasing the security of enterprise systems there has been a large amount of publication on different aspects of this strategy. It is then important to evaluate if its claims are true. We have used security patterns to design and evaluate security architectures and we apply here this method to analyze the expectations of this strategy. We relate the ideas behind ZT to the accumulated knowledge of security and attempt to answer some questions about the value and possibilities of this technology. In general, industry publications are vague about the technical aspects of these systems, ignore past security knowledge, and there are few reports describing actual experience building and using ZT architectures. Is Zero Trust Architecture (ZTA) the ideal architecture to build secure systems? To obtain a deeper understanding of this architecture, we analyze its pattern structure and provide a sketch of its reference architecture built as an aggregation of security patterns. As any system architecture, regardless of the way it has been constructed, represents a system, we also consider its threats. Finally, we provide directions for research on this area.

A multi-trait GWAS-based genetic association network controlling soybean architecture and seed traits.

Ideal plant architecture is essential for enhancing crop yields. Ideal soybean (Glycine max) architecture encompasses an appropriate plant height, increased node number, moderate seed weight, and compact architecture with smaller branch angles for growth under high-density planting. However, the functional genes regulating plant architecture are far not fully understood in soybean. In this study, we investigated the genetic basis of 12 agronomic traits in a panel of 496 soybean accessions with a wide geographical distribution in China. Analysis of phenotypic changes in 148 historical elite soybean varieties indicated that seed-related traits have mainly been improved over the past 60 years, with targeting plant architecture traits having the potential to further improve yields in future soybean breeding programs. In a genome-wide association study (GWAS) of 12 traits, we detected 169 significantly associated loci, of which 61 overlapped with previously reported loci and 108 new loci. By integrating the GWAS loci for different traits, we constructed a genetic association network and identified 90 loci that were associated with a single trait and 79 loci with pleiotropic effects. Of these 79 loci, 7 hub-nodes were strongly linked to at least three related agronomic traits. qHub_5, containing the previously characterized Determinate 1 (Dt1) locus, was associated not only with plant height and node number (as determined previously), but also with internode length and pod range. Furthermore, we identified qHub_7, which controls three branch angle-related traits; the candidate genes in this locus may be beneficial for breeding soybean with compact architecture. These findings provide insights into the genetic relationships among 12 important agronomic traits in soybean. In addition, these studies uncover valuable loci for further functional gene studies and will facilitate molecular design breeding of soybean architecture.

Bio-functional hydroxyapatite-coated 3D porous polyetherketoneketone scaffold for enhanced osteogenesis and osteointegration in orthopedic applications.

Polyetherketoneketone (PEKK), a high-performance thermoplastic special engineering material, maintains bone-like mechanical properties and has received considerable attention in the biomedical field. The 3D printing technique enables the production of porous scaffolds with a honeycomb structure featuring precisely controlled pore size, porosity and interconnectivity, which holds significant potential for applications in tissue engineering. The ideal pore architecture of porous PEKK scaffolds has yet to be elucidated. Porous PEKK scaffolds with five pore sizes P200 (225 ± 9.8 μm), P400 (411 ± 22.1 μm), P600 (596 ± 23.4 μm), P800 (786 ± 24.2 μm) and P1000 (993 ± 26.0 μm) were produced by a 3D printer. Subsequently, the optimum pore size, the P600, for mechanical properties and osteogenesis was selected based on in vitro experiments. To improve the interfacial bioactivity of porous PEKK scaffolds, hydroxyapatite (HAp) crystals were generated via in situ biomimetic mineralization induced by the phase-transited lysozyme coating. Herein, a micro/nanostructured surface showing HAp crystals on PEKK scaffold was developed. In vitro and in vivo experiments confirmed that the porous PEKK-HAp scaffolds exhibited highly interconnected pores and functional surface structures that were favorable for biocompatibility and osteoinductivity, which boosted bone regeneration. Therefore, this work not only demonstrates that the pore structure of the P600 scaffold is suitable for PEKK orthopedic implants but also sheds light on a synergistic approach involving 3D printing and biomimetic mineralization, which has the potential to yield customized 3D PEKK-HAp scaffolds with enhanced osteoinductivity and osteogenesis, offering a promising strategy for bone tissue engineering.

A methodology to customise commercial products of SMEs

Product development durations are being drastically reduced, thanks, amongst other reasons, to the use of design automation techniques. But this trend is observed in large companies and to a much lesser extent in SMEs. This article seeks to research the possibility of finding suitable automation technologies for this type of enterprises. Automating the development of commercial offers in SMEs can be a good starting point to the change in this trend. Focusing on this objective, the necessary requirements that a tool of this type must have are established. Different models of assistants have been developed and analysed on different products, deducing from their results an ideal system architecture and methodology. This solution is based on design tables, a technology that links data from a spreadsheet, implements the company’s design process, with parameters of a three-dimensional CAD model, which represents the solution. The rest of the documents that constitute the commercial offer and the possibility of linking them with verification tools are derived from this design core. The main conclusion is the current viability of this method, anticipating the improvement of many aspects, mainly highlighting the link with the rest of the life cycle phases through interoperability tools.

SCAG: A Stratified, Clustered, and Growing-Based Algorithm for Soybean Branch Angle Extraction and Ideal Plant Architecture Evaluation.

Three-dimensional (3D) phenotyping is important for studying plant structure and function. Light detection and ranging (LiDAR) has gained prominence in 3D plant phenotyping due to its ability to collect 3D point clouds. However, organ-level branch detection remains challenging due to small targets, sparse points, and low signal-to-noise ratios. In addition, extracting biologically relevant angle traits is difficult. In this study, we developed a stratified, clustered, and growing-based algorithm (SCAG) for soybean branch detection and branch angle calculation from LiDAR data, which is heuristic, open-source, and expandable. SCAG achieved high branch detection accuracy (F-score = 0.77) and branch angle calculation accuracy (r = 0.84) when evaluated on 152 diverse soybean varieties. Meanwhile, the SCAG outperformed 2 other classic algorithms, the support vector machine (F-score = 0.53) and density-based methods (F-score = 0.55). Moreover, after applying the SCAG to 405 soybean varieties over 2 consecutive years, we quantified various 3D traits, including canopy width, height, stem length, and average angle. After data filtering, we identified novel heritable and repeatable traits for evaluating soybean density tolerance potential, such as the ratio of average angle to height and the ratio of average angle to stem length, which showed greater potential than the well-known ratio of canopy width to height trait. Our work demonstrates remarkable advances in 3D phenotyping and plant architecture screening. The algorithm can be applied to other crops, such as maize and tomato. Our dataset, scripts, and software are public, which can further benefit the plant science community by enhancing plant architecture characterization and ideal variety selection.

Genetic mapping of dynamic control of leaf angle across multiple canopy levels in maize.

Optimizing leaf angle and other canopy architecture traits has helped modern maize (Zea mays L.) become adapted to higher planting densities over the last 60 years. Traditional investigations into genetic control of leaf angle have focused on one leaf or the average of multiple leaves; as a result, our understanding of genetic control across multiple canopy levels is still limited. To address this, genetic mapping across four canopy levels was conducted in the present study to investigate the genetic control of leaf angle across the canopy. We developed two populations of doubled haploid lines derived from three inbreds with distinct leaf angle phenotypes. These populations were genotyped with genotyping-by-sequencing and phenotyped for leaf angle at four different canopy levels over multiple years. To understand how leaf angle changes across the canopy, the four measurements were used to derive three additional traits. Composite interval mapping was conducted with the leaf-specific measurements and the derived traits. A set of 59 quantitative trait loci (QTLs) were uncovered for seven traits, and two genomic regions were consistently detected across multiple canopy levels. Additionally, seven genomic regions were found to contain consistent QTLs with either relatively stable or dynamic effects at different canopy levels. Prioritizing the selection of QTLs with dynamic effects across the canopy will aid breeders in selecting maize hybrids with the ideal canopy architecture that continues to maximize yield on a per area basis under increasing planting densities.

Different contributions of PROG1 and TAC1 to the angular kinematics of the main culm and tillers of wild rice (Oryza rufipogon).

PROG1is necessary but insufficient for the main culm inclination while TAC1partially takes part in it, and both genes promote tiller inclination in Asian wild rice. Asian wild rice (Oryza rufipogon), the ancestor of cultivated rice (O. sativa), has a prostrate architecture, with tillers branching from near the ground. The main culm of each plant grows upward and then tilts during the vegetative stage. Genes controlling tiller angle have been reported; however, their genetic contributions to the culm movement have not been quantified. Here, we quantified their genetic contributions to angular kinematics in the main culm and tillers. For the main culm inclination, one major QTL surrounding the PROG1 region was found. In cultivated rice, tillers firstly inclined and lately rose, while it kept inclining in wild rice. It was suggested that PROG1 affected the tiller elevation angle in the later kinematics, whereas TAC1 was weakly associated with the tiller angle in the whole vegetative stage. Micro-computed tomography (micro-CT) suggested that these angular changes are produced by the bending of culm bases. Because near-isogenic lines (NILs) of wild rice-type Prog1 and Tac1 alleles in the genetic background of cultivated rice did not show the prostrate architecture, the involvement of another gene(s) for inclination of the main culm was suggested. Our findings will not only contribute to the understanding of the morphological transition during domestication but also be used in plant breeding to precisely reproduce the ideal plant architecture by combining the effects of multiple genes.

High canopy photosynthesis before anthesis explains the outstanding yield performance of rice cultivars with ideal plant architecture

ContextThe majority of investigations on mechanisms underlying yield difference between superior and conventional cultivars has focused on the grain-filling stage after anthesis. However, the physiological process before anthesis has been less explored. ObjectiveWe aimed to quantify the contribution of canopy photosynthesis at pre-anthesis stage to grain yield formation and clarify the mechanism underlying the yield advantage of the superior cultivars. MethodsThis study selected representative cultivars of indica and japonica rice, each type having a pair of cultivars with ideal plant architecture (IPA) and conventional architecture. Field experiments over four years (2019–2022) were conducted to compare the relevance of canopy photosynthesis to yield formation between the phases before and after anthesis. ResultsThe two IPA cultivars, JYZK-6 and ZZY-1, produced higher grain yield than the two conventional cultivars, NJ-5055 and HHZ. JYZK-6 and ZZY-1 also had good distribution of light interception inside the canopy, with the middle and lower part receiving more light than NJ-5055 and HHZ. At leaf level, there was no significant difference in photosynthetic rate between the two types of cultivars. At canopy level, the IPA cultivars had stronger photosynthetic activities before anthesis, outperforming NJ-5055 by 35.56% in averaged canopy photosynthesis, and by 46.65% in averaged daily net photosynthetic accumulation in 2021–2022. By contrast, canopy photosynthesis after anthesis was not significantly different between the two types. The IPA cultivars stored heavier biomass in internodes and sheaths before anthesis and remobilized more reserves to the developing grains, with the remobilization rate being 16.42% (internodes) and 35.34% (sheaths) higher than NJ-5055 and HHZ. ConclusionThe higher canopy productivity before anthesis is one of the main reasons accounting for the superior IPA cultivars, suggesting the necessity of optimizing plant growth and development during the vegetative and reproductive phases.

UV-B irradiation-activated E3 ligase GmILPA1 modulates gibberellin catabolism to increase plant height in soybean

Plant height is a key agronomic trait that affects yield and is controlled by both phytohormone gibberellin (GA) and ultraviolet-B (UV-B) irradiation. However, whether and how plant height is modulated by UV-B-mediated changes in GA metabolism are not well understood. It has not been reported that the E3 ubiquitin ligase Anaphase Promoting Complex/Cyclosome (APC/C) is involved in the regulation of plant growth in response to environmental factors. We perform a forward genetic screen in soybean and find that a mutation in Glycine max Increased Leaf Petiole Angle1 (GmILPA1), encoding a subunit of the APC/C, lead to dwarfism under UV-B irradiation. UV-B promotes the accumulation of GmILPA1, which ubiquitinate the GA catabolic enzyme GA2 OXIDASE-like (GmGA2ox-like), resulting in its degradation in a UV-B-dependent manner. Another E3 ligase, GmUBL1, also ubiquitinate GmGA2ox-like and enhance the GmILPA1-mediated degradation of GmGA2ox-like, which suggest that GmILPA1-GmGA2ox-like module counteract the UV-B-mediated reduction of bioactive GAs. We also determine that GmILPA1 is a target of selection during soybean domestication and breeding. The deletion (Indel-665) in the promoter might facilitate the adaptation of soybean to high UV-B irradiation. This study indicates that an evolutionary GmILPA1 variant has the capability to develop ideal plant architecture with soybean cultivars.

Integrating genome-wide association study with transcriptomic data to predict candidate genes influencing Brassica napus root and biomass-related traits under low phosphorus conditions

BackgroundRapeseed (Brassica napus L.) is an essential source of edible oil and livestock feed, as well as a promising source of biofuel. Breeding crops with an ideal root system architecture (RSA) for high phosphorus use efficiency (PUE) is an effective way to reduce the use of phosphate fertilizers. However, the genetic mechanisms that underpin PUE in rapeseed remain elusive. To address this, we conducted a genome-wide association study (GWAS) in 327 rapeseed accessions to elucidate the genetic variability of 13 root and biomass traits under low phosphorus (LP; 0.01 mM P +). Furthermore, RNA-sequencing was performed in root among high/low phosphorus efficient groups (HP1/LP1) and high/low phosphorus stress tolerance groups (HP2/LP2) at two-time points under control and P-stress conditions.ResultsSignificant variations were observed in all measured traits, with heritabilities ranging from 0.47 to 0.72, and significant correlations were found between most of the traits. There were 39 significant trait–SNP associations and 31 suggestive associations, which integrated into 11 valid quantitative trait loci (QTL) clusters, explaining 4.24–24.43% of the phenotypic variance observed. In total, RNA-seq identified 692, 1076, 648, and 934 differentially expressed genes (DEGs) specific to HP1/LP1 and HP2/LP2 under P-stress and control conditions, respectively, while 761 and 860 DEGs common for HP1/LP1 and HP2/LP2 under both conditions. An integrated approach of GWAS, weighted co-expression network, and differential expression analysis identified 12 genes associated with root growth and development under LP stress. In this study, six genes (BnaA04g23490D, BnaA09g08440D, BnaA09g04320D, BnaA09g04350D, BnaA09g04930D, BnaA09g09290D) that showed differential expression were identified as promising candidate genes for the target traits.Conclusion11 QTL clusters and 12 candidate genes associated with root and development under LP stress were identified in this study. Our study's phenotypic and genetic information may be exploited for genetic improvement of root traits to increase PUE in rapeseed.

A Deep Convolutional Model for Heart Disease Prediction based on ECG Data with Explainable AI

Heart disease (HD) prediction is crucial in realizing the notion of intelligent healthcare owing to the exploding number of heart diseases being reported on a daily basis. However, in a domain like healthcare, accountability is key for a medical practitioner to completely adopt the decisions of an intelligent model. Accordingly, the proposed model develops a convolutional model for heart disease prediction based on ECG data in a supervised manner. Moreover, the easily accessible and economical ECG data is utilized in the model in the form of image data. The incorporation of ECG data as images has provided amazing results in the recent researches compared to being considered as signals. The architecture follows a stacked Convolutional Neural Network for extracting features from ECG images followed by fully connected network for classification. The evaluation of the proposed model on customized public datasets demonstrates its ability to achieve impressive outcomes by leveraging the characteristics of convolutional neural networks (CNNs) and supervised learning. Similarly, Explainability in the form of interpretability has been incorporated into the framework thus ensuring accountability of the model which is crucial in medical domain. Detailed experiments for identification of ideal model architecture are conducted. Further, local and vision based Explainability has been explored in detail using LIME and Grad-CAM. The model could achieve a precision, recall and f1-score of 0.982, 0.982, and 0.981 respectively proving the superiority of the model. Moreover, Explainability visualization based on popular algorithms for true positive and false positive results have shown promising results on the PhysioNet ECG dataset.

Natural alleles of Mediator subunit genes modulate plant height in chickpea.

SUMMARYPlant height (PH) is an important plant architectural trait targeted during Green Revolution to enhance crop yields. Identification of genes and natural alleles governing plant height without compromising agronomic performance can fill the lacuna of knowledge connecting ideal plant architecture with maximum achievable yield in chickpea. Through coherent strategy involving genome‐wide association study, QTL/fine mapping, map‐based cloning, molecular haplotyping, and downstream functional genomics, the current study identified two Mediator subunit genes namely, CaMED23 and CaMED5b and their derived natural alleles/haplotypes underlying the major QTLs and trans‐acting eQTLs regulating plant height in chickpea. Differential accumulation of haplotype‐specific transcripts of these two Mediator genes in corresponding haplotype‐introgressed near‐isogenic lines (NILs) correlates negatively with the plant height trait. Quantitative as well as qualitative estimation based on histology, scanning electron microscopy, and histochemical assay unraveled the reduced lengths and cell sizes of internodes along with compromised lignin levels in dwarf/semi‐dwarf chickpea NILs introgressed with superior CaMED23 and CaMED5b gene haplotypes. This observation, supported by global transcriptome profiling‐based diminished expression of various phenylpropanoid pathway genes upstream of lignin biosynthesis in dwarf/semi‐dwarf NILs, essentially links plant height with lignin accumulation. The identified molecular signatures in the Mediator subunit genes can be efficiently utilized to develop desirable dwarf/semi‐dwarf‐type chickpea cultivars without affecting their yield per plant via modulating lignin/phenylpropanoid biosynthesis.

Prediction of Viscosity of Cobalt Ferrite/SAE50 Engine Oil based Nanofluids using well Trained Artificial Neutral Network (ANN) and Response Surface Methodology (RSM)

Heat transmission by ordinary fluids such as pure water, oil, and ethylene glycol is inefficient due to their low viscosity. To boost the efficiency of conventional fluids, very small percent of nanoparticles are added to the base fluids to prepare nanofluid. The impact of changing in viscosity can be used to investigate the rheological properties of nanofluids. In this paper, (CoFe2O4)/engine oil based nanofluids were prepared using two steps standard methodology. In first step, CoFe2O4 (CF) were synthesized using the sol-gel wet chemical process. The crystalline structure and morphology were confirmed using X-Ray diffraction analysis (XRD) and scanning electron microscopy (SEM), respectively. In second step, the standard procedure was adapted by taking several solid volume fractions of CF as Ø = 0, 0.25, 0.50, 0.75, and 1.0 %. Such percent of concentrations were dispersed in appropriate volume of engine oil using the ultrasonication for 5 h. After date, the viscosity of prepared five different nanofluids were determined at temperatures ranging from 40 to 80 °C. According to the findings, the viscosity of nanofluids (µnf) decreased as temperature increased while increased when the volume percentage of nanofluids Ø raised. Furthermore, total 25 experimental observations were considered to predict viscosity using an artificial neural network (ANN) and response surface methodology (RSM). The algorithm for building the ideal ANN architecture has been recommended in order to predict the fluid velocity of the CF/SAE-50 oil based nanofluid using MATLAB software. In order to determine the correctness of the predicted model, the mean square error (MSE) was calculated 0.0136.

Neural networks for fatigue crack propagation predictions in real-time under uncertainty

Crack propagation analyses are fundamental for all mechanical structures for which safety must be guaranteed, e.g. as for the aviation and aerospace fields. The estimation of life for structures in presence of defects is a process inevitably affected by numerous and unavoidable uncertainty and variability sources, whose effects need to be quantified to avoid unexpected failures or excessive conservativism.In this work, residual fatigue life prediction models have been created through neural networks for the purpose of performing probabilistic life predictions of damaged structures in real-time and under stochastically varying input parameters. In detail, five different neural network architectures have been compared in terms of accuracy, computational runtimes and minimum number of samples needed for training, so to determine the ideal architecture with the strongest generalization power. The networks have been trained, validated and tested by using the fatigue life predictions computed by means of simulations developed with FEM and Monte Carlo methods. A real-world case study has been presented to show how the proposed approach can deliver accurate life predictions even when input data are uncertain and highly variable.Results demonstrated that the “H1-L1” neural network has been the best model, achieving an accuracy (Mean Square Error) of 4.8e-7 on the test dataset, and the best and the most stable results when decreasing the amount of data. Additionally, since requiring only very few parameters, its potential applicability for Structural Health Monitoring purposes in small cost-effective GPU devices resulted to be attractive.

Curling Leaf 1, Encoding a MYB-Domain Protein, Regulates Leaf Morphology and Affects Plant Yield in Rice.

The leaf is the main site of photosynthesis and is an important component in shaping the ideal rice plant architecture. Research on leaf morphology and development will lay the foundation for high-yield rice breeding. In this study, we isolated and identified a novel curling leaf mutant, designated curling leaf 1 (cl1). The cl1 mutant exhibited an inward curling phenotype because of the defective development of sclerenchymatous cells on the abaxial side. Meanwhile, the cl1 mutant showed significant reductions in grain yield and thousand-grain weight due to abnormal leaf development. Through map-based cloning, we identified the CL1 gene, which encodes a MYB transcription factor that is highly expressed in leaves. Subcellular localization studies confirmed its typical nuclear localization. Transcriptome analysis revealed a significant differential expression of the genes involved in photosynthesis, leaf morphology, yield formation, and hormone metabolism in the cl1 mutant. Yeast two-hybrid assays demonstrated that CL1 interacts with alpha-tubulin protein SRS5 and AP2/ERF protein MFS. These findings provide theoretical foundations for further elucidating the mechanisms of CL1 in regulating leaf morphology and offer genetic resources for practical applications in high-yield rice breeding.

The transcription factor SPL13 mediates strigolactone suppression of shoot branching by inhibiting cytokinin synthesis in Solanum lycopersicum.

Plant architecture imposes a large impact on crop yield. IDEAL PLANT ARCHITECTURE 1 (IPA1), which encodes a SQUAMOSA PROMOTER BINDING PROTEIN-LIKE (SPL) transcription factor, is a target of molecular design for improving grain yield. However, the roles of SPL transcription factors in regulating tomato (Solanum lycopersicum) plant architecture are unclear. Here, we show that the expression of SPL13 is down-regulated in the lateral buds of strigolactone (SL)-deficient ccd mutants and is induced by GR24 (a synthetic analog of SL). Knockout of SPL13 by CRISPR/Cas9 resulted in higher levels of cytokinins (CKs) and transcripts of the CK synthesis gene ISOPENTENYL TRANSFERASES 1 (IPT1) in the stem nodes, and more growth of lateral buds. GR24 suppresses CK synthesis and lateral bud growth in ccd mutants, but is not effective in spl13 mutants. On the other hand, silencing of the IPT1 gene inhibited bud growth of spl13 mutants. Interestingly, SL levels in root extracts and exudates are significantly increased in spl13 mutants. Molecular studies indicated that SPL13 directly represses the transcription of IPT1 and the SL synthesis genes CAROTENOID CLEAVAGE DIOXYGENASE 7 (CCD7) and MORE AXILLARY GROWTH 1 (MAX1). The results demonstrate that SPL13 acts downstream of SL to suppress lateral bud growth by inhibiting CK synthesis in tomato. Tuning the expression of SPL13 is a potential approach for decreasing the number of lateral shoots in tomato.