Genes involved in auxin biosynthesis, transport and signalling underlie the extreme adventitious root phenotype of the tomato aer mutant.

The use of tomato rootstocks has helped to alleviate the soaring abiotic stresses provoked by the adverse effects of climate change. Lateral and adventitious roots can improve topsoil exploration and nutrient uptake, shoot biomass and resulting overall yield. It is essential to understand the genetic basis of root structure development and how lateral and adventitious roots are produced. Existing mutant lines with specific root phenotypes are an excellent resource to analyse and comprehend the molecular basis of root developmental traits. The tomato aerial roots (aer) mutant exhibits an extreme adventitious rooting phenotype on the primary stem. It is known that this phenotype is associated with restricted polar auxin transport from the juvenile to the more mature stem, but prior to this study, the genetic loci responsible for the aer phenotype were unknown. We used genomic approaches to define the polygenic nature of the aer phenotype and provide evidence that increased expression of specific auxin biosynthesis, transport and signalling genes in different loci causes the initiation of adventitious root primordia in tomato stems. Our results allow the selection of different levels of adventitious rooting using molecular markers, potentially contributing to rootstock breeding strategies in grafted vegetable crops, especially in tomato. In crops vegetatively propagated as cuttings, such as fruit trees and cane fruits, orthologous genes may be useful for the selection of cultivars more amenable to propagation.

A chromosome-level genome assembly of Solanum chilense, a tomato wild relative associated with resistance to salinity and drought.

Introduction: Solanum chilense is a wild relative of tomato reported to exhibit resistance to biotic and abiotic stresses. There is potential to improve tomato cultivars via breeding with wild relatives, a process greatly accelerated by suitable genomic and genetic resources. Methods: In this study we generated a high-quality, chromosome-level, de novo assembly for the S. chilense accession LA1972 using a hybrid assembly strategy with ~180 Gbp of Illumina short reads and ~50 Gbp long PacBio reads. Further scaffolding was performed using Bionano optical maps and 10x Chromium reads. Results: The resulting sequences were arranged into 12 pseudomolecules using Hi-C sequencing. This resulted in a 901 Mbp assembly, with a completeness of 95%, as determined by Benchmarking with Universal Single-Copy Orthologs (BUSCO). Sequencing of RNA from multiple tissues resulting in ~219 Gbp of reads was used to annotate the genome assembly with an RNA-Seq guided gene prediction, and for a de novo transcriptome assembly. This chromosome-level, high-quality reference genome for S. chilense accession LA1972 will support future breeding efforts for more sustainable tomato production. Discussion: Gene sequences related to drought and salt resistance were compared between S. chilense and S. lycopersicum to identify amino acid variations with high potential for functional impact. These variants were subsequently analysed in 84 resequenced tomato lines across 12 different related species to explore the variant distributions. We identified a set of 7 putative impactful amino acid variants some of which may also impact on fruit development for example the ethylene-responsive transcription factor WIN1 and ethylene-insensitive protein 2. These variants could be tested for their ability to confer functional phenotypes to cultivars that have lost these variants.

Modeling the spatial-spectral characteristics of plants for nutrient status identification using hyperspectral data and deep learning methods.

Sustainable fertilizer management in precision agriculture is essential for both economic and environmental reasons. To effectively manage fertilizer input, various methods are employed to monitor and track plant nutrient status. One such method is hyperspectral imaging, which has been on the rise in recent times. It is a remote sensing tool used to monitor plant physiological changes in response to environmental conditions and nutrient availability. However, conventional hyperspectral processing mainly focuses on either the spectral or spatial information of plants. This study aims to develop a hybrid convolution neural network (CNN) capable of simultaneously extracting spatial and spectral information from quinoa and cowpea plants to identify their nutrient status at different growth stages. To achieve this, a nutrient experiment with four treatments (high and low levels of nitrogen and phosphorus) was conducted in a glasshouse. A hybrid CNN model comprising a 3D CNN (extracts joint spectral-spatial information) and a 2D CNN (for abstract spatial information extraction) was proposed. Three pre-processing techniques, including second-order derivative, standard normal variate, and linear discriminant analysis, were applied to selected regions of interest within the plant spectral hypercube. Together with the raw data, these datasets were used as inputs to train the proposed model. This was done to assess the impact of different pre-processing techniques on hyperspectral-based nutrient phenotyping. The performance of the proposed model was compared with a 3D CNN, a 2D CNN, and a Hybrid Spectral Network (HybridSN) model. Effective wavebands were selected from the best-performing dataset using a greedy stepwise-based correlation feature selection (CFS) technique. The selected wavebands were then used to retrain the models to identify the nutrient status at five selected plant growth stages. From the results, the proposed hybrid model achieved a classification accuracy of over 94% on the test dataset, demonstrating its potential for identifying nitrogen and phosphorus status in cowpea and quinoa at different growth stages.

Field phenotyping for African crops: overview and perspectives.

Improvements in crop productivity are required to meet the dietary demands of the rapidly-increasing African population. The development of key staple crop cultivars that are high-yielding and resilient to biotic and abiotic stresses is essential. To contribute to this objective, high-throughput plant phenotyping approaches are important enablers for the African plant science community to measure complex quantitative phenotypes and to establish the genetic basis of agriculturally relevant traits. These advances will facilitate the screening of germplasm for optimum performance and adaptation to low-input agriculture and resource-constrained environments. Increasing the capacity to investigate plant function and structure through non-invasive technologies is an effective strategy to aid plant breeding and additionally may contribute to precision agriculture. However, despite the significant global advances in basic knowledge and sensor technology for plant phenotyping, Africa still lags behind in the development and implementation of these systems due to several practical, financial, geographical and political barriers. Currently, field phenotyping is mostly carried out by manual methods that are prone to error, costly, labor-intensive and may come with adverse economic implications. Therefore, improvements in advanced field phenotyping capabilities and appropriate implementation are key factors for success in modern breeding and agricultural monitoring. In this review, we provide an overview of the current state of field phenotyping and the challenges limiting its implementation in some African countries. We suggest that the lack of appropriate field phenotyping infrastructures is impeding the development of improved crop cultivars and will have a detrimental impact on the agricultural sector and on food security. We highlight the prospects for integrating emerging and advanced low-cost phenotyping technologies into breeding protocols and characterizing crop responses to environmental challenges in field experimentation. Finally, we explore strategies for overcoming the barriers and maximizing the full potential of emerging field phenotyping technologies in African agriculture. This review paper will open new windows and provide new perspectives for breeders and the entire plant science community in Africa.

The ORGAN SIZE (ORG) locus modulates both vegetative and reproductive gigantism in domesticated tomato.

BACKGROUND AND AIMS: Gigantism is a key component of the domestication syndrome, a suite of traits that differentiates crops from their wild relatives. Allometric gigantism is strongly marked in horticultural crops, causing disproportionate increases in the size of edible parts such as stems, leaves or fruits. Tomato (Solanum lycopersicum) has attracted attention as a model for fruit gigantism, and many genes have been described controlling this trait. However, the genetic basis of a corresponding increase in size of vegetative organs contributing to isometric gigantism has remained relatively unexplored. METHODS: Here, we identified a 0.4-Mb region on chromosome 7 in introgression lines (ILs) from the wild species Solanum pennellii in two different tomato genetic backgrounds (cv. 'M82' and cv. 'Micro-Tom') that controls vegetative and reproductive organ size in tomato. The locus, named ORGAN SIZE (ORG), was fine-mapped using genotype-by-sequencing. A survey of the literature revealed that ORG overlaps with previously mapped quantitative trait loci controlling tomato fruit weight during domestication. KEY RESULTS: Alleles from the wild species led to lower cell number in different organs, which was partially compensated by greater cell expansion in leaves, but not in fruits. The result was a proportional reduction in leaf, flower and fruit size in the ILs harbouring the alleles from the wild species. CONCLUSIONS: Our findings suggest that selection for large fruit during domestication also tends to select for increases in leaf size by influencing cell division. Since leaf size is relevant for both source-sink balance and crop adaptation to different environments, the discovery of ORG could allow fine-tuning of these parameters.

Machine Learning Methods for Automatic Segmentation of Images of Field- and Glasshouse-Based Plants for High-Throughput Phenotyping.

Image segmentation is a fundamental but critical step for achieving automated high- throughput phenotyping. While conventional segmentation methods perform well in homogenous environments, the performance decreases when used in more complex environments. This study aimed to develop a fast and robust neural-network-based segmentation tool to phenotype plants in both field and glasshouse environments in a high-throughput manner. Digital images of cowpea (from glasshouse) and wheat (from field) with different nutrient supplies across their full growth cycle were acquired. Image patches from 20 randomly selected images from the acquired dataset were transformed from their original RGB format to multiple color spaces. The pixels in the patches were annotated as foreground and background with a pixel having a feature vector of 24 color properties. A feature selection technique was applied to choose the sensitive features, which were used to train a multilayer perceptron network (MLP) and two other traditional machine learning models: support vector machines (SVMs) and random forest (RF). The performance of these models, together with two standard color-index segmentation techniques (excess green (ExG) and excess green-red (ExGR)), was compared. The proposed method outperformed the other methods in producing quality segmented images with over 98%-pixel classification accuracy. Regression models developed from the different segmentation methods to predict Soil Plant Analysis Development (SPAD) values of cowpea and wheat showed that images from the proposed MLP method produced models with high predictive power and accuracy comparably. This method will be an essential tool for the development of a data analysis pipeline for high-throughput plant phenotyping. The proposed technique is capable of learning from different environmental conditions, with a high level of robustness.

Fact-based nutrition for infants and lactating mothers-The NUTRISHIELD study.

Background: Human milk (HM) is the ideal source of nutrients for infants. Its composition is highly variable according to the infant's needs. When not enough own mother's milk (OMM) is available, the administration of pasteurized donor human milk (DHM) is considered a suitable alternative for preterm infants. This study protocol describes the NUTRISHIELD clinical study. The main objective of this study is to compare the % weight gain/month in preterm and term infants exclusively receiving either OMM or DHM. Other secondary aims comprise the evaluation of the influence of diet, lifestyle habits, psychological stress, and pasteurization on the milk composition, and how it modulates infant's growth, health, and development. Methods and design: NUTRISHIELD is a prospective mother-infant birth cohort in the Spanish-Mediterranean area including three groups: preterm infants <32 weeks of gestation (i) exclusively receiving (i.e., >80% of total intake) OMM, and (ii) exclusively receiving DHM, and (iii) term infants exclusively receiving OMM, as well as their mothers. Biological samples and nutritional, clinical, and anthropometric characteristics are collected at six time points covering the period from birth and until six months of infant's age. The genotype, metabolome, and microbiota as well as the HM composition are characterized. Portable sensor prototypes for the analysis of HM and urine are benchmarked. Additionally, maternal psychosocial status is measured at the beginning of the study and at month six. Mother-infant postpartum bonding and parental stress are also examined. At six months, infant neurodevelopment scales are applied. Mother's concerns and attitudes to breastfeeding are registered through a specific questionnaire. Discussion: NUTRISHIELD provides an in-depth longitudinal study of the mother-infant-microbiota triad combining multiple biological matrices, newly developed analytical methods, and ad-hoc designed sensor prototypes with a wide range of clinical outcome measures. Data obtained from this study will be used to train a machine-learning algorithm for providing dietary advice to lactating mothers and will be implemented in a user-friendly platform based on a combination of user-provided information and biomarker analysis. A better understanding of the factors affecting milk's composition, together with the health implications for infants plays an important role in developing improved strategies of nutraceutical management in infant care. Clinical trial registration: https://register.clinicaltrials.gov, identifier: NCT05646940.

Genetic and Physiological Responses to Heat Stress in Brassica napus.

Given the current rise in global temperatures, heat stress has become a major abiotic challenge affecting the growth and development of various crops and reducing their productivity. Brassica napus, the second largest source of vegetable oil worldwide, experiences a drastic reduction in seed yield and quality in response to heat. This review outlines the latest research that explores the genetic and physiological impact of heat stress on different developmental stages of B. napus with a special attention to the reproductive stages of floral progression, organogenesis, and post flowering. Several studies have shown that extreme temperature fluctuations during these crucial periods have detrimental effects on the plant and often leading to impaired growth and reduced seed production. The underlying mechanisms of heat stress adaptations and associated key regulatory genes are discussed. Furthermore, an overview and the implications of the polyploidy nature of B. napus and the regulatory role of alternative splicing in forming a priming-induced heat-stress memory are presented. New insights into the dynamics of epigenetic modifications during heat stress are discussed. Interestingly, while such studies are scarce in B. napus, opposite trends in expression of key genetic and epigenetic components have been identified in different species and in cultivars within the same species under various abiotic stresses, suggesting a complex role of these genes and their regulation in heat stress tolerance mechanisms. Additionally, omics-based studies are discussed with emphasis on the transcriptome, proteome and metabolome of B. napus, to gain a systems level understanding of how heat stress alters its yield and quality traits. The combination of omics approaches has revealed crucial interactions and regulatory networks taking part in the complex machinery of heat stress tolerance. We identify key knowledge gaps regarding the impact of heat stress on B. napus during its yield determining reproductive stages, where in-depth analysis of this subject is still needed. A deeper knowledge of heat stress response components and mechanisms in tissue specific models would serve as a stepping-stone to gaining insights into the regulation of thermotolerance that takes place in this important crop species and support future breeding of heat tolerant crops.

A near-chromosome level genome assembly of the European hoverfly, Sphaerophoria rueppellii (Diptera: Syrphidae), provides comparative insights into insecticide resistance-related gene family evolution.

BACKGROUND: Sphaerophoria rueppellii, a European species of hoverfly, is a highly effective beneficial predator of hemipteran crop pests including aphids, thrips and coleopteran/lepidopteran larvae in integrated pest management (IPM) programmes. It is also a key pollinator of a wide variety of important agricultural crops. No genomic information is currently available for S. rueppellii. Without genomic information for such beneficial predator species, we are unable to perform comparative analyses of insecticide target-sites and genes encoding metabolic enzymes potentially responsible for insecticide resistance, between crop pests and their predators. These metabolic mechanisms include several gene families - cytochrome P450 monooxygenases (P450s), ATP binding cassette transporters (ABCs), glutathione-S-transferases (GSTs), UDP-glycosyltransferases (UGTs) and carboxyl/choline esterases (CCEs). METHODS AND FINDINGS: In this study, a high-quality near-chromosome level de novo genome assembly (as well as a mitochondrial genome assembly) for S. rueppellii has been generated using a hybrid approach with PacBio long-read and Illumina short-read data, followed by super scaffolding using Hi-C data. The final assembly achieved a scaffold N50 of 87Mb, a total genome size of 537.6Mb and a level of completeness of 96% using a set of 1,658 core insect genes present as full-length genes. The assembly was annotated with 14,249 protein-coding genes. Comparative analysis revealed gene expansions of CYP6Zx P450s, epsilon-class GSTs, dietary CCEs and multiple UGT families (UGT37/302/308/430/431). Conversely, ABCs, delta-class GSTs and non-CYP6Zx P450s showed limited expansion. Differences were seen in the distributions of resistance-associated gene families across subfamilies between S. rueppellii and some hemipteran crop pests. Additionally, S. rueppellii had larger numbers of detoxification genes than other pollinator species. CONCLUSION AND SIGNIFICANCE: This assembly is the first published genome for a predatory member of the Syrphidae family and will serve as a useful resource for further research into selectivity and potential tolerance of insecticides by beneficial predators. Furthermore, the expansion of some gene families often linked to insecticide resistance and selectivity may be an indicator of the capacity of this predator to detoxify IPM selective insecticides. These findings could be exploited by targeted insecticide screens and functional studies to increase effectiveness of IPM strategies, which aim to increase crop yields by sustainably and effectively controlling pests without impacting beneficial predator populations.

De novo genome assembly and functional annotation for Fusarium langsethiae.

BACKGROUND: Fusarium langsethiae is a T-2 and HT-2 mycotoxins producing species firstly characterised in 2004. It is commonly isolated from oats in Northern Europe. T-2 and HT-2 mycotoxins exhibit immunological and haemotological effects in animal health mainly through inhibition of protein, RNA and DNA synthesis. The development of a high-quality and comprehensively annotated assembly for this species is therefore essential in providing the molecular understanding and the mechanism of T-2 and HT-2 biosynthesis in F. langsethiae to help develop effective control strategies. RESULTS: The F. langsethiae assembly was produced using PacBio long reads, which were then assembled independently using Canu, SMARTdenovo and Flye. A total of 19,336 coding genes were identified using RNA-Seq informed ab-initio gene prediction. Finally, predicting genes were annotated using the basic local alignment search tool (BLAST) against the NCBI non-redundant (NR) genome database and protein hits were annotated using InterProScan. Genes with blast hits were functionally annotated with Gene Ontology. CONCLUSIONS: We developed a high-quality genome assembly of a total length of 59 Mb and N50 of 3.51 Mb. Raw sequence reads and assembled genome is publicly available and can be downloaded from: GenBank under the accession JAFFKB000000000. All commands used to generate this assembly are accessible via GitHub: https://github.com/FadyMohareb/fusarium_langsethiae .

A scaffold-level genome assembly of a minute pirate bug, Orius laevigatus (Hemiptera: Anthocoridae), and a comparative analysis of insecticide resistance-related gene families with hemipteran crop pests.

BACKGROUND: Orius laevigatus, a minute pirate bug, is a highly effective beneficial predator of crop pests including aphids, spider mites and thrips in integrated pest management (IPM) programmes. No genomic information is currently available for O. laevigatus, as is the case for the majority of beneficial predators which feed on crop pests. In contrast, genomic information for crop pests is far more readily available. The lack of publicly available genomes for beneficial predators to date has limited our ability to perform comparative analyses of genes encoding potential insecticide resistance mechanisms between crop pests and their predators. These mechanisms include several gene/protein families including cytochrome P450s (P450s), ATP binding cassette transporters (ABCs), glutathione S-transferases (GSTs), UDP-glucosyltransferases (UGTs) and carboxyl/cholinesterases (CCEs). METHODS AND FINDINGS: In this study, a high-quality scaffold level de novo genome assembly for O. laevigatus has been generated using a hybrid approach with PacBio long-read and Illumina short-read data. The final assembly achieved a scaffold N50 of 125,649 bp and a total genome size of 150.98 Mb. The genome assembly achieved a level of completeness of 93.6% using a set of 1658 core insect genes present as full-length genes. Genome annotation identified 15,102 protein-coding genes - 87% of which were assigned a putative function. Comparative analyses revealed gene expansions of sigma class GSTs and CYP3 P450s. Conversely the UGT gene family showed limited expansion. Differences were seen in the distributions of resistance-associated gene families at the subfamily level between O. laevigatus and some of its targeted crop pests. A target site mutation in ryanodine receptors (I4790M, PxRyR) which has strong links to diamide resistance in crop pests and had previously only been identified in lepidopteran species was found to also be present in hemipteran species, including O. laevigatus. CONCLUSION AND SIGNIFICANCE: This assembly is the first published genome for the Anthocoridae family and will serve as a useful resource for further research into target-site selectivity issues and potential resistance mechanisms in beneficial predators. Furthermore, the expansion of gene families often linked to insecticide resistance may be an indicator of the capacity of this predator to detoxify selective insecticides. These findings could be exploited by targeted pesticide screens and functional studies to increase effectiveness of IPM strategies, which aim to increase crop yields by sustainably, environmentally-friendly and effectively control pests without impacting beneficial predator populations.

Missense mutation of a class B heat shock factor is responsible for the tomato bushy root-2 phenotype.

The bushy root-2 (brt-2) tomato mutant has twisting roots, and slower plant development. Here we used whole genome resequencing and genetic mapping to show that brt-2 is caused by a serine to cysteine (S75C) substitution in the DNA binding domain (DBD) of a heat shock factor class B (HsfB) encoded by SolycHsfB4a. This gene is orthologous to the Arabidopsis SCHIZORIZA gene, also known as AtHsfB4. The brt-2 phenotype is very similar to Arabidopsis lines in which the function of AtHsfB4 is altered: a proliferation of lateral root cap and root meristematic tissues, and a tendency for lateral root cap cells to easily separate. The brt-2 S75C mutation is unusual because all other reported amino acid substitutions in the highly conserved DBD of eukaryotic heat shock factors are dominant negative mutations, but brt-2 is recessive. We further show through reciprocal grafting that brt-2 exerts its effects predominantly through the root genotype even through BRT-2 is expressed at similar levels in both root and shoot meristems. Since AtHsfB4 is induced by root knot nematodes (RKN), and loss-of-function mutants of this gene are resistant to RKNs, BRT-2 could be a target gene for RKN resistance, an important trait in tomato rootstock breeding.Gene & accession numbersSolycHsfB4a - Solyc04g078770.

Improving the Tea Withering Process Using Ethylene or UV-C.

Using a combination of biochemical, transcriptomic, and physiological analyses, we elucidated the mechanisms of physical and chemical withering of tea shoots subjected to UV-C and ethylene treatments. UV-C irradiation (15 kJ m-2) initiated oxidation of catechins into theaflavins, increasing theaflavin-3-monogallate and theaflavin digallate by 5- and 13.2-4.4-fold, respectively, at the end of withering. Concomitantly, a rapid change to brown/red, an increase in electrolyte leakage, and the upregulation of peroxidases (viz. Px2, Px4, and Px6) and polyphenol oxidases (PPO-1) occurred. Exogenous ethylene significantly increased the metabolic rate (40%) and moisture loss (30%) compared to control during simulated withering (12 h at 25 °C) and upregulated transcripts associated with responses to dehydration and abiotic stress, such as those in the ethylene signaling pathway (viz. EIN4-like, EIN3-FBox1, and ERFs). Incorporating ethylene during withering could shorten the tea manufacturing process, while UV-C could enhance the accumulation of flavor-related compounds.

Data Science in the Food Industry.

Food safety is one of the main challenges of the agri-food industry that is expected to be addressed in the current environment of tremendous technological progress, where consumers' lifestyles and preferences are in a constant state of flux. Food chain transparency and trust are drivers for food integrity control and for improvements in efficiency and economic growth. Similarly, the circular economy has great potential to reduce wastage and improve the efficiency of operations in multi-stakeholder ecosystems. Throughout the food chain cycle, all food commodities are exposed to multiple hazards, resulting in a high likelihood of contamination. Such biological or chemical hazards may be naturally present at any stage of food production, whether accidentally introduced or fraudulently imposed, risking consumers' health and their faith in the food industry. Nowadays, a massive amount of data is generated, not only from the next generation of food safety monitoring systems and along the entire food chain (primary production included) but also from the Internet of things, media, and other devices. These data should be used for the benefit of society, and the scientific field of data science should be a vital player in helping to make this possible.

Flavivirus integrations in Aedes aegypti are limited and highly conserved across samples from different geographic regions unlike integrations in Aedes albopictus.

Mosquitoes of the genus Aedes are the main vectors of many viruses, e.g. dengue and Zika, which affect millions of people each year and for which there are limited treatment options. Understanding how Aedes mosquitoes tolerate high viral loads may lead to better disease control strategies. Elucidating endogenous viral elements (EVEs) within vector genomes may give exploitable biological insights. Previous studies have reported the presence of a large number of EVEs in Aedes genomes. Here we investigated if flavivirus EVEs are conserved across populations and different Aedes species by using ~ 500 whole genome sequence libraries from Aedes aegypti and Aedes albopictus, sourced from colonies and field mosquitoes across continents. We found that nearly all flavivirus EVEs in the Ae. aegypti reference genome originate from four separate putative viral integration events, and that they are highly conserved across geographically diverse samples. By contrast, flavivirus EVEs in the Ae. albopictus reference genome originate from up to nine distinct integration events and show low levels of conservation, even within samples from narrow geographical ranges. Our analysis suggests that flaviviruses integrated as long sequences and were subsequently fragmented and shuffled by transposable elements. Given that EVEs of Ae. aegypti and Ae. albopictus belong to different phylogenetic clades and have very differing levels of conservation, they may have different evolutionary origins and potentially different functional roles.

Overproduction of ABA in rootstocks alleviates salinity stress in tomato shoots.

To determine whether root-supplied ABA alleviates saline stress, tomato (Solanum lycopersicum L. cv. Sugar Drop) was grafted onto two independent lines (NCED OE) overexpressing the SlNCED1 gene (9-cis-epoxycarotenoid dioxygenase) and wild type rootstocks. After 200 days of saline irrigation (EC = 3.5 dS m-1 ), plants with NCED OE rootstocks had 30% higher fruit yield, but decreased root biomass and lateral root development. Although NCED OE rootstocks upregulated ABA-signalling (AREB, ATHB12), ethylene-related (ACCs, ERFs), aquaporin (PIPs) and stress-related (TAS14, KIN, LEA) genes, downregulation of PYL ABA receptors and signalling components (WRKYs), ethylene synthesis (ACOs) and auxin-responsive factors occurred. Elevated SlNCED1 expression enhanced ABA levels in reproductive tissue while ABA catabolites accumulated in leaf and xylem sap suggesting homeostatic mechanisms. NCED OE also reduced xylem cytokinin transport to the shoot and stimulated foliar 2-isopentenyl adenine (iP) accumulation and phloem transport. Moreover, increased xylem GA3 levels in growing fruit trusses were associated with enhanced reproductive growth. Improved photosynthesis without changes in stomatal conductance was consistent with reduced stress sensitivity and hormone-mediated alteration of leaf growth and mesophyll structure. Combined with increases in leaf nutrients and flavonoids, systemic changes in hormone balance could explain enhanced vigour, reproductive growth and yield under saline stress.

Detection of Meat Adulteration Using Spectroscopy-Based Sensors.

Minced meat is a vulnerable to adulteration food commodity because species- and/or tissue-specific morphological characteristics cannot be easily identified. Hence, the economically motivated adulteration of minced meat is rather likely to be practiced. The objective of this work was to assess the potential of spectroscopy-based sensors in detecting fraudulent minced meat substitution, specifically of (i) beef with bovine offal and (ii) pork with chicken (and vice versa) both in fresh and frozen-thawed samples. For each case, meat pieces were minced and mixed so that different levels of adulteration with a 25% increment were achieved while two categories of pure meat also were considered. From each level of adulteration, six different samples were prepared. In total, 120 samples were subjected to visible (Vis) and fluorescence (Fluo) spectra and multispectral image (MSI) acquisition. Support Vector Machine classification models were developed and evaluated. The MSI-based models outperformed the ones based on the other sensors with accuracy scores varying from 87% to 100%. The Vis-based models followed in terms of accuracy with attained scores varying from 57% to 97% while the lowest performance was demonstrated by the Fluo-based models. Overall, spectroscopic data hold a considerable potential for the detection and quantification of minced meat adulteration, which, however, appears to be sensor-specific.

Spoilage assessment of chicken breast fillets by means of fourier transform infrared spectroscopy and multispectral image analysis.

The objective of this research was the evaluation of Fourier transforms infrared spectroscopy (FT-IR) and multispectral image analysis (MSI) as efficient spectroscopic methods in tandem with multivariate data analysis and machine learning for the assessment of spoilage on the surface of chicken breast fillets. For this purpose, two independent storage experiments of chicken breast fillets (n â€‹= â€‹215) were conducted at 0, 5, 10, and 15 â€‹°C for up to 480 â€‹h. During storage, samples were analyzed microbiologically for the enumeration of Total Viable Counts (TVC) and Pseudomonas spp. In addition, FT-IR and MSI spectral data were collected at the same time intervals as for microbiological analyses. Multivariate data analysis was performed using two software platforms (a commercial and a publicly available developed platform) comprising several machine learning algorithms for the estimation of the TVC and Pseudomonas spp. population of the surface of the samples. The performance of the developed models was evaluated by intra batch and independent batch testing. Partial Least Squares- Regression (PLS-R) models from the commercial software predicted TVC with root mean square error (RMSE) values of 1.359 and 1.029 log CFU/cm2 for MSI and FT-IR analysis, respectively. Moreover, RMSE values for Pseudomonas spp. model were 1.574 log CFU/cm2 for MSI data and 1.078 log CFU/cm2 for FT-IR data. From the implementation of the in-house sorfML platform, artificial neural networks (nnet) and least-angle regression (lars) were the most accurate models with the best performance in terms of RMSE values. Nnet models developed on MSI data demonstrated the lowest RMSE values (0.717 log CFU/cm2) for intra-batch testing, while lars outperformed nnet on independent batch testing with RMSE of 1.252 log CFU/cm2. Furthermore, lars models excelled with the FT-IR data with RMSE of 0.904 and 0.851 log CFU/cm2 in intra-batch and independent batch testing, respectively. These findings suggested that FT-IR analysis is more efficient than MSI to predict the microbiological quality on the surface of chicken breast fillets.

De Novo Genome Assembly Of Solanum Sitiens Reveals Structural Variation Associated With Drought And Salinity Tolerance.

MOTIVATION: Solanum sitiens is a self-incompatible wild relative of tomato, characterised by salt and drought resistance traits, with the potential to contribute through breeding programmes to crop improvement in cultivated tomato. This species has a distinct morphology, classification and ecotype compared to other stress resistant wild tomato relatives such as S. pennellii and S. chilense. Therefore, the availability of a reference genome for S. sitiens will facilitate the genetic and molecular understanding of salt and drought resistance. RESULTS: A high-quality de novo genome and transcriptome assembly for S. sitiens (Accession LA1974) has been developed. A hybrid assembly strategy was followed using Illumina short reads (∼159X coverage) and PacBio long reads (∼44X coverage), generating a total of ∼262 Gbp of DNA sequence. A reference genome of 1,245 Mbp, arranged in 1,483 scaffolds with a N50 of 1.826 Mbp was generated. Genome completeness was estimated at 95% using the Benchmarking Universal Single-Copy Orthologs (BUSCO) and the K-mer Analysis Tool (KAT). In addition, ∼63 Gbp of RNA-Seq were generated to support the prediction of 31,164 genes from the assembly, and to perform a de novo transcriptome. Lastly, we identified three large inversions compared to S. lycopersicum, containing several drought resistance related genes, such as beta-amylase 1 and YUCCA7. AVAILABILITY: S. sitiens (LA1974) raw sequencing, transcriptome and genome assembly have been deposited at the NCBI's Sequence Read Archive, under the BioProject number "PRJNA633104".All the commands and scripts necessary to generate the assembly are available at the following github repository: https://github.com/MCorentin/Solanum_sitiens_assembly. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Robust detection of point mutations involved in multidrug-resistant Mycobacterium tuberculosis in the presence of co-occurrent resistance markers.

Tuberculosis disease is a major global public health concern and the growing prevalence of drug-resistant Mycobacterium tuberculosis is making disease control more difficult. However, the increasing application of whole-genome sequencing as a diagnostic tool is leading to the profiling of drug resistance to inform clinical practice and treatment decision making. Computational approaches for identifying established and novel resistance-conferring mutations in genomic data include genome-wide association study (GWAS) methodologies, tests for convergent evolution and machine learning techniques. These methods may be confounded by extensive co-occurrent resistance, where statistical models for a drug include unrelated mutations known to be causing resistance to other drugs. Here, we introduce a novel 'cannibalistic' elimination algorithm ("Hungry, Hungry SNPos") that attempts to remove these co-occurrent resistant variants. Using an M. tuberculosis genomic dataset for the virulent Beijing strain-type (n = 3,574) with phenotypic resistance data across five drugs (isoniazid, rifampicin, ethambutol, pyrazinamide, and streptomycin), we demonstrate that this new approach is considerably more robust than traditional methods and detects resistance-associated variants too rare to be likely picked up by correlation-based techniques like GWAS.