HairNet2: deep learning to quantify cotton leaf hairiness, a complex genetic and environmental trait.

BACKGROUND: Cotton accounts for 80% of the global natural fibre production. Its leaf hairiness affects insect resistance, fibre yield, and economic value. However, this phenotype is still qualitatively assessed by visually attributing a Genotype Hairiness Score (GHS) to a leaf/plant, or by using the HairNet deep-learning model which also outputs a GHS. Here, we introduce HairNet2, a quantitative deep-learning model which detects leaf hairs (trichomes) from images and outputs a segmentation mask and a Leaf Trichome Score (LTS). RESULTS: Trichomes of 1250 images were annotated (AnnCoT) and a combination of six Feature Extractor modules and five Segmentation modules were tested alongside a range of loss functions and data augmentation techniques. HairNet2 was further validated on the dataset used to build HairNet (CotLeaf-1), a similar dataset collected in two subsequent seasons (CotLeaf-2), and a dataset collected on two genetically diverse populations (CotLeaf-X). The main findings of this study are that (1) leaf number, environment and image position did not significantly affect results, (2) although GHS and LTS mostly correlated for individual GHS classes, results at the genotype level revealed a strong LTS heterogeneity within a given GHS class, (3) LTS correlated strongly with expert scoring of individual images. CONCLUSIONS: HairNet2 is the first quantitative and scalable deep-learning model able to measure leaf hairiness. Results obtained with HairNet2 concur with the qualitative values used by breeders at both extremes of the scale (GHS 1-2, and 5-5+), but interestingly suggest a reordering of genotypes with intermediate values (GHS 3-4+). Finely ranking mild phenotypes is a difficult task for humans. In addition to providing assistance with this task, HairNet2 opens the door to selecting plants with specific leaf hairiness characteristics which may be associated with other beneficial traits to deliver better varieties.

Scalable Optimal Transport Methods in Machine Learning: A Contemporary Survey.

Optimal Transport (OT) is a mathematical framework that first emerged in the eighteenth century and has led to a plethora of methods for answering many theoretical and applied questions. The last decade has been a witness to the remarkable contributions of this classical optimization problem to machine learning. This paper is about where and how optimal transport is used in machine learning with a focus on the question of scalable optimal transport. We provide a comprehensive survey of optimal transport while ensuring an accessible presentation as permitted by the nature of the topic and the context. First, we explain the optimal transport background and introduce different flavors (i.e. mathematical formulations), properties, and notable applications. We then address the fundamental question of how to scale optimal transport to cope with the current demands of big and high dimensional data. We conduct a systematic analysis of the methods used in the literature for scaling OT and present the findings in a unified taxonomy. We conclude with presenting some open challenges and discussing potential future research directions. A live repository of related OT research papers is maintained in https://github.com/abdelwahed/OT_for_big_data.git.

Characterisation of Antennal Sensilla and Electroantennography Responses of the Dung Beetles Bubas bison, Onitis aygulus and Geotrupes spiniger (Coleoptera: Scarabaeoidea) to Dung Volatile Organic Compounds.

Locating sporadically distributed food resources and mate finding are strongly aided by volatile cues for most insects, including dung beetles. However, there is limited information on the olfactory ecology of dung beetles. We conducted a scanning electron microscopy study on the morphology and distribution of the antennal sensilla of three introduced dung beetle species in Australia: Geotrupes spiniger (Coleoptera: Geotrupidae), Bubas bison and Onitis aygulus (Coleoptera: Scarabaeidae). Three main morphological types of antennal sensilla were identified: sensilla trichodea (ST), sensilla basiconica (SB) and sensilla chaetica (SCh). Distinct variations of SB distribution were observed in B. bison and G. spiniger and on different lamellar surfaces in both sexes of all three species. Sexual dimorphism in antennal sensilla distribution or their abundance was not evident. To complement the morphological characterisation of sensilla, electroantennography (EAG) was carried out to construct EAG response profiles of the three species to selected dung volatiles. An initial study revealed that antennae of all species were sensitive to a mix of phenol, skatole, indole, p-cresol, butanone and butyric acid, common components of livestock dung headspace. In addition to these six compounds, dimethyl sulfide, dimethyl disulfide, eucalyptol and toluene were tested for antennal activity. All compounds evoked measurable EAG responses, confirming antennal sensitivity. Geotrupes spiniger exhibited significant responses to all the compounds compared to the control, whereas B. bison and O. aygulus only responded to a subset of compounds. A comparison of relative EAG amplitudes revealed highly significant responses to p-cresol in G. spiniger and to skatole in B. bison. Geotrupes spiniger displayed differential responses to all the compounds. Pooled EAG data suggest highly significant differences in responses among the three species and among compounds. Our findings suggest that a blend of volatiles may offer potential for the trapping of dung beetles, thereby avoiding the use of dung baits that are inconvenient, inconsistent and may pose a threat to farm biosecurity.

A split GAL4 RUBY assay for visual in planta detection of protein-protein interactions.

Protein-protein interactions (PPIs) are a fundamental process in cellular biogenesis. Here we have developed a split GAL4 RUBY assay that enables macroscopically visual PPI detection in plant leaves in real time. Candidate interacting protein partners are fused to specific domains of the yeast GAL4 and herpes simplex virus VP16 transcription factors and transiently expressed in Nicotiana benthamina leaves by Agrobacterium infiltration. PPI, that may be either direct or indirect, results in transcriptional activation of a RUBY reporter gene leading to the production of the highly visual metabolite, betalain, in leaf tissue of living plants. Samples require no processing for in planta visual qualitative assessment, but with very simple processing steps the assay is quantitative. Its accuracy is demonstrated using a series of known interacting protein partners and mutant derivatives including transcription factors, signalling molecules and plant resistance proteins with cognate pathogen effectors. Using this assay, association between the wheat Sr27 stem rust disease resistance protein and corresponding AvrSr27 avirulence effector family produced by the rust pathogen is detected. Interaction is also observed between this resistance protein and the effector encoded by the corresponding avrSr27-3 virulence allele. However, this association appears weaker in the split GAL4 RUBY assay, which coupled with lower avrSr27-3 expression during stem rust infection, likely enables virulent races of the rust pathogen to avoid Sr27-mediated detection.

Non-covalent binding tags for batch and flow biocatalysis.

Enzyme immobilization offers considerable advantage for biocatalysis in batch and continuous flow reactions. However, many currently available immobilization methods require that the surface of the carrier is chemically modified to allow site specific interactions with their cognate enzymes, which requires specific processing steps and incurs associated costs. Two carriers (cellulose and silica) were investigated here, initially using fluorescent proteins as models to study binding, followed by assessment of industrially relevant enzyme performance (transaminases and an imine reductase/glucose oxidoreductase fusion). Two previously described binding tags, the 17 amino acid long silica-binding peptide from the Bacillus cereus CotB protein and the cellulose binding domain from the Clostridium thermocellum, were fused to a range of proteins without impairing their heterologous expression. When fused to a fluorescent protein both tags conferred high avidity specific binding with their respective carriers (low nanomolar Kd values). The CotB peptide (CotB1p) induced protein aggregation in the transaminase and imine reductase/glucose oxidoreductase fusions when incubated with the silica carrier. The Clostridium thermocellum cellulose binding domain (CBDclos) allowed immobilization of all the proteins tested, but immobilization led to loss of enzymatic activity in the transaminases (< 2-fold) and imine reductase/glucose oxidoreductase fusion (> 80%). A transaminase-CBDclos fusion was then successfully used to demonstrate the application of the binding tag in repetitive batch and a continuous-flow reactor.

The Chlamydomonas reinhardtii chloroplast envelope protein LCIA transports bicarbonate in planta.

LCIA (low CO2-inducible protein A) is a chloroplast envelope protein associated with the CO2-concentrating mechanism of the green alga Chlamydomonas reinhardtii. LCIA is postulated to be a HCO3- channel, but previous studies were unable to show that LCIA was actively transporting bicarbonate in planta. Therefore, LCIA activity was investigated more directly in two heterologous systems: an Escherichia coli mutant (DCAKO) lacking both native carbonic anhydrases and an Arabidopsis mutant (ßca5) missing the plastid carbonic anhydrase ßCA5. Neither DCAKO nor ßca5 can grow in ambient CO2 conditions, as they lack carbonic anhydrase-catalyzed production of the necessary HCO3- concentration for lipid and nucleic acid biosynthesis. Expression of LCIA restored growth in both systems in ambient CO2 conditions, which strongly suggests that LCIA is facilitating HCO3- uptake in each system. To our knowledge, this is the first direct evidence that LCIA moves HCO3- across membranes in bacteria and plants. Furthermore, the ßca5 plant bioassay used in this study is the first system for testing HCO3- transport activity in planta, an experimental breakthrough that will be valuable for future studies aimed at improving the photosynthetic efficiency of crop plants using components from algal CO2-concentrating mechanisms.

Lagovirus Non-structural Protein p23: A Putative Viroporin That Interacts With Heat Shock Proteins and Uses a Disulfide Bond for Dimerization.

The exact function(s) of the lagovirus non-structural protein p23 is unknown as robust cell culture systems for the Rabbit haemorrhagic disease virus (RHDV) and other lagoviruses have not been established. Instead, a range of in vitro and in silico models have been used to study p23, revealing that p23 oligomerizes, accumulates in the cytoplasm, and possesses a conserved C-terminal region with two amphipathic helices. Furthermore, the positional homologs of p23 in other caliciviruses have been shown to possess viroporin activity. Here, we report on the mechanistic details of p23 oligomerization. Site-directed mutagenesis revealed the importance of an N-terminal cysteine for dimerization. Furthermore, we identified cellular interactors of p23 using stable isotope labeling with amino acids in cell culture (SILAC)-based proteomics; heat shock proteins Hsp70 and 110 interact with p23 in transfected cells, suggesting that they 'chaperone' p23 proteins before their integration into cellular membranes. We investigated changes to the global transcriptome and proteome that occurred in infected rabbit liver tissue and observed changes to the misfolded protein response, calcium signaling, and the regulation of the endoplasmic reticulum (ER) network. Finally, flow cytometry studies indicate slightly elevated calcium concentrations in the cytoplasm of p23-transfected cells. Taken together, accumulating evidence suggests that p23 is a viroporin that might form calcium-conducting channels in the ER membranes.

Cotton Breeding in Australia: Meeting the Challenges of the 21st Century.

The Commonwealth Scientific and Industrial Research Organisation (CSIRO) cotton breeding program is the sole breeding effort for cotton in Australia, developing high performing cultivars for the local industry which is worth∼AU$3 billion per annum. The program is supported by Cotton Breeding Australia, a Joint Venture between CSIRO and the program's commercial partner, Cotton Seed Distributors Ltd. (CSD). While the Australian industry is the focus, CSIRO cultivars have global impact in North America, South America, and Europe. The program is unique compared with many other public and commercial breeding programs because it focuses on diverse and integrated research with commercial outcomes. It represents the full research pipeline, supporting extensive long-term fundamental molecular research; native and genetically modified (GM) trait development; germplasm enhancement focused on yield and fiber quality improvements; integration of third-party GM traits; all culminating in the release of new commercial cultivars. This review presents evidence of past breeding successes and outlines current breeding efforts, in the areas of yield and fiber quality improvement, as well as the development of germplasm that is resistant to pests, diseases and abiotic stressors. The success of the program is based on the development of superior germplasm largely through field phenotyping, together with strong commercial partnerships with CSD and Bayer CropScience. These relationships assist in having a shared focus and ensuring commercial impact is maintained, while also providing access to markets, traits, and technology. The historical successes, current foci and future requirements of the CSIRO cotton breeding program have been used to develop a framework designed to augment our breeding system for the future. This will focus on utilizing emerging technologies from the genome to phenome, as well as a panomics approach with data management and integration to develop, test and incorporate new technologies into a breeding program. In addition to streamlining the breeding pipeline for increased genetic gain, this technology will increase the speed of trait and marker identification for use in genome editing, genomic selection and molecular assisted breeding, ultimately producing novel germplasm that will meet the coming challenges of the 21st Century.

Selection for early shoot vigour in wheat increases root hair length but reduces epidermal cell size of roots and leaves.

Six cycles of recurrent selection for early shoot vigour in wheat resulted in significant increases in leaf width and shoot biomass. Here, in replicated controlled-environment studies, the effect of early shoot vigour on root biomass, rhizosheath size, root hair length, and cell size in the roots and leaves was examined across different cycles of selection. Increased shoot vigour was associated with greater root biomass, larger rhizosheath size, and longer root hairs. Our findings demonstrate that rhizosheath size was a reliable surrogate for root hair length in this germplasm. Examination of the root epidermis revealed that the 'cell body' of the trichoblasts (hair-forming cells) and the atrichoblasts (non-hair-forming cells) decreased in size as shoot vigour increased. Therefore, in higher vigour germplasm, longer root hairs emerged from smaller trichoblasts so that total trichoblast volume (root hair plus cell body) was generally similar regardless of shoot vigour. Similarly, the sizes of the four main cell types on the leaf epidermis became progressively smaller as shoot vigour increased, which also increased stomatal density. The relationship between shoot vigour and root traits is considered, and the potential contribution of below-ground root traits to performance and competitiveness of high vigour germplasm is discussed.

HairNet: a deep learning model to score leaf hairiness, a key phenotype for cotton fibre yield, value and insect resistance.

BACKGROUND: Leaf hairiness (pubescence) is an important plant phenotype which regulates leaf transpiration, affects sunlight penetration, and provides increased resistance or susceptibility against certain insects. Cotton accounts for 80% of global natural fibre production, and in this crop leaf hairiness also affects fibre yield and value. Currently, this key phenotype is measured visually which is slow, laborious and operator-biased. Here, we propose a simple, high-throughput and low-cost imaging method combined with a deep-learning model, HairNet, to classify leaf images with great accuracy. RESULTS: A dataset of [Formula: see text] 13,600 leaf images from 27 genotypes of Cotton was generated. Images were collected from leaves at two different positions in the canopy (leaf 3 & leaf 4), from genotypes grown in two consecutive years and in two growth environments (glasshouse & field). This dataset was used to build a 4-part deep learning model called HairNet. On the whole dataset, HairNet achieved accuracies of 89% per image and 95% per leaf. The impact of leaf selection, year and environment on HairNet accuracy was then investigated using subsets of the whole dataset. It was found that as long as examples of the year and environment tested were present in the training population, HairNet achieved very high accuracy per image (86-96%) and per leaf (90-99%). Leaf selection had no effect on HairNet accuracy, making it a robust model. CONCLUSIONS: HairNet classifies images of cotton leaves according to their hairiness with very high accuracy. The simple imaging methodology presented in this study and the high accuracy on a single image per leaf achieved by HairNet demonstrates that it is implementable at scale. We propose that HairNet replaces the current visual scoring of this trait. The HairNet code and dataset can be used as a baseline to measure this trait in other species or to score other microscopic but important phenotypes.

Comparison of non-subjective relative fungal biomass measurements to quantify the Leptosphaeria maculans-Brassica napus interaction.

BACKGROUND: Blackleg disease, caused by the fungal pathogen Leptosphaeria maculans, is a serious threat to canola (Brassica napus) production worldwide. Quantitative resistance to this disease is a highly desirable trait but is difficult to precisely phenotype. Visual scores can be subjective and are prone to assessor bias. Methods to assess variation in quantitative resistance more accurately were developed based on quantifying in planta fungal biomass, including the Wheat Germ Agglutinin Chitin Assay (WAC), qPCR and ddPCR assays. RESULTS: Disease assays were conducted by inoculating a range of canola cultivars with L. maculans isolates in glasshouse experiments and assessing fungal biomass in cotyledons, petioles and stem tissue harvested at different timepoints post-inoculation. PCR and WAC assay results were well correlated, repeatable across experiments and host tissues, and able to differentiate fungal biomass in different host-isolate treatments. In addition, the ddPCR assay was shown to differentiate between L. maculans isolates. CONCLUSIONS: The ddPCR assay is more sensitive in detecting pathogens and more adaptable to high-throughput methods by using robotic systems than the WAC assay. Overall, these methods proved accurate and non-subjective, providing alternatives to visual assessments to quantify the L. maculans-B. napus interaction in all plant tissues throughout the progression of the disease in seedlings and mature plants and have potential for fine-scale blackleg resistance phenotyping in canola.

Sesamum indicum Oleosin L improves oil packaging in Nicotiana benthamiana leaves.

Plant oil production has been increasing continuously in the past decade. There has been significant investment in the production of high biomass plants with elevated oil content. We recently showed that the expression of Arabidopsis thaliana WRI1 and DGAT1 genes increase oil content by up to 15% in leaf dry weight tissue. However, triacylglycerols in leaf tissue are subject to degradation during senescence. In order to better package the oil, we expressed a series of lipid droplet proteins isolated from bacterial and plant sources in Nicotiana benthamiana leaf tissue. We observed further increases in leaf oil content of up to 2.3-fold when we co-expressed Sesamum indicum Oleosin L with AtWRI1 and AtDGAT1. Biochemical assays and lipid droplet visualization with confocal microscopy confirmed the increase in oil content and revealed a significant change in the size and abundance of lipid droplets.

New methods for confocal imaging of infection threads in crop and model legumes.

BACKGROUND: The formation of infection threads in the symbiotic infection of rhizobacteria in legumes is a unique, fascinating, and poorly understood process. Infection threads are tubes of cell wall material that transport rhizobacteria from root hair cells to developing nodules in host roots. They form in a type of reverse tip-growth from an inversion of the root hair cell wall, but the mechanism driving this growth is unknown, and the composition of the thread wall remains unclear. High resolution, 3-dimensional imaging of infection threads, and cell wall component specific labelling, would greatly aid in our understanding of the nature and development of these structures. To date, such imaging has not been done, with infection threads typically imaged by GFP-tagged rhizobia within them, or histochemically in thin sections. RESULTS: We have developed new methods of imaging infection threads using novel and traditional cell wall fluorescent labels, and laser confocal scanning microscopy. We applied a new Periodic Acid Schiff (PAS) stain using rhodamine-123 to the labelling of whole cleared infected roots of Medicago truncatula; which allowed for imaging of infection threads in greater 3D detail than had previously been achieved. By the combination of the above method and a calcofluor-white counter-stain, we also succeeded in labelling infection threads and plant cell walls separately, and have potentially discovered a way in which the infection thread matrix can be visualized. CONCLUSIONS: Our methods have made the imaging and study of infection threads more effective and informative, and present exciting new opportunities for future research in the area.

I see the light! Fluorescent proteins suitable for cell wall/apoplast targeting in Nicotiana benthamiana leaves.

Correct subcellular targeting is crucial for protein function. Protein location can be visualized in vivo by fusion to a fluorescent protein (FP). Nevertheless, despite intense engineering efforts, most FPs are dim or completely quenched at low pH (<6). This is particularly problematic for the study of proteins targeted to acidic compartments such as vacuoles (pH ~ 3-6) or plant cell walls (pH ~ 3.5-8.3). Plant cell walls play important roles (e.g. structural/protective role, control of growth/morphogenesis), are diverse in structure and function, and are highly dynamic (e.g. during cell growth, in response to biotic/abiotic stresses). To study and engineer plant cell walls, it is therefore critical to identify robust tools which can be used to locate proteins expressed in the apoplast. Here we used a transient expression assay in Nicotiana benthamiana leaves to test a range of FPs in vivo, and determined which ones retained strong fluorescence in the acidic environment of the apoplast. We selected 10 fluorescent proteins with a range of in vitro properties; two historical FPs and eight FPs with in vitro properties suggesting lower pH sensitivity or improved brightness, some of which had never been tested in plants prior to our study. We targeted each FP to the cytosol or the apoplast and compared the fluorescence in both compartments, before testing the in vivo pH sensitivity of FPs across a pH 8-4 gradient. Our results suggest that mTurquoise2, mNeonGreen, and mCherry are suited to tracking proteins in the apoplast under dynamic pH conditions. These fluorescent proteins may also be useful in other acidic compartments such as vacuoles.

Identification of Genes Involved in Lipid Biosynthesis through de novo Transcriptome Assembly from Cocos nucifera Developing Endosperm.

Cocos nucifera (coconut), a member of the Arecaceae family, is an economically important woody palm that is widely grown in tropical and subtropical regions. The coconut palm is well known for its ability to accumulate large amounts of oil, approximately 63% of the seed weight. Coconut oil varies significantly from other vegetable oils as it contains a high proportion of medium-chain fatty acids (MCFA; 85%). The unique composition of coconut oil raises interest in understanding how the coconut palm produces oil of a high saturated MCFA content, and if such an oil profile could be replicated via biotechnology interventions. Although some gene discovery work has been performed there is still a significant gap in the knowledge associated with coconut's oil production pathways. In this study, a de novo transcriptome was assembled for developing coconut endosperm to identify genes involved in the synthesis of lipids, particularly triacylglycerol. Of particular interest were thioesterases, acyltransferases and oleosins because of their involvement in the processes of releasing fatty acids for assembly, esterification of fatty acids into glycerolipids and protecting oils from degradation, respectively. It is hypothesized that some of these genes may exhibit a strong substrate preference for MCFA and hence may assist the future development of vegetable oils with an enriched MCFA composition. In this study, we identified and confirmed functionality of five candidate genes from the gene families of interest. This study will benefit future work in areas of increasing vegetable oil production and the tailoring of oil fatty acid compositions.

Up-regulation of lipid biosynthesis increases the oil content in leaves of Sorghum bicolor.

Synthesis and accumulation of the storage lipid triacylglycerol in vegetative plant tissues has emerged as a promising strategy to meet the world's future need for vegetable oil. Sorghum (Sorghum bicolor) is a particularly attractive target crop given its high biomass, drought resistance and C4 photosynthesis. While oilseed-like triacylglycerol levels have been engineered in the C3 model plant tobacco, progress in C4 monocot crops has been lagging behind. In this study, we report the accumulation of triacylglycerol in sorghum leaf tissues to levels between 3 and 8.4% on a dry weight basis depending on leaf and plant developmental stage. This was achieved by the combined overexpression of genes encoding the Zea mays WRI1 transcription factor, Umbelopsis ramanniana UrDGAT2a acyltransferase and Sesamum indicum Oleosin-L oil body protein. Increased oil content was visible as lipid droplets, primarily in the leaf mesophyll cells. A comparison between a constitutive and mesophyll-specific promoter driving WRI1 expression revealed distinct changes in the overall leaf lipidome as well as transitory starch and soluble sugar levels. Metabolome profiling uncovered changes in the abundance of various amino acids and dicarboxylic acids. The results presented here are a first step forward towards the development of sorghum as a dedicated biomass oil crop and provide a basis for further combinatorial metabolic engineering.

Determining the Subcellular Localization of Fluorescently Tagged Proteins Using Protoplasts Extracted from Transiently Transformed Nicotiana benthamiana Leaves.

In plants, stable expression is arguably the method of choice to test transgene function but it is a slow and labor-intensive process. This bottleneck generally limits the number of transgenes that can be tested, and as such hinders construct optimization. In the face of this challenge, transient expression in Nicotiana benthamiana leaves has emerged as a powerful screening platform to test gene expression, as well as subcellular distribution and function of many proteins within a week. This system relies on the infiltration of Agrobacterium tumefaciens (Agrobacterium) carrying DNA of interest into the leaf air spaces of N. benthamiana plants. Agrobacterium rapidly transforms the plant cells and the leaves can be analyzed within a few days. Investigating the subcellular localization of a protein of interest often relies on its fusion to a fluorescent tag. While the amount of accumulation of such fusion proteins can often be gauged by observing the fluorescence of the tag at the whole-leaf level, subcellular protein distribution is best determined in protoplasts extracted from transformed leaves. Here I present a simple and effective method to transform N. benthamiana leaves with Agrobacterium and to prepare protoplasts from these leaves to characterize the subcellular localization of proteins of interest.

The splicing co-factor Barricade/Tat-SF1 is required for cell cycle and lineage progression in Drosophila neural stem cells.

Stem cells need to balance self-renewal and differentiation for correct tissue development and homeostasis. Defects in this balance can lead to developmental defects or tumor formation. In recent years, mRNA splicing has emerged as an important mechanism regulating cell fate decisions. Here we address the role of the evolutionarily conserved splicing co-factor Barricade (Barc)/Tat-SF1/CUS2 in Drosophila neural stem cell (neuroblast) lineage formation. We show that Barc is required for the generation of neurons during Drosophila brain development by ensuring correct neural progenitor proliferation and differentiation. Barc associates with components of the U2 small nuclear ribonucleoprotein (snRNP) complex, and its depletion causes alternative splicing in the form of intron retention in a subset of genes. Using bioinformatics analysis and a cell culture-based splicing assay, we found that Barc-dependent introns share three major traits: they are short, GC rich and have weak 3' splice sites. Our results show that Barc, together with the U2 snRNP complex, plays an important role in regulating neural stem cell lineage progression during brain development and facilitates correct splicing of a subset of introns.