Inborn errors of immunity (IEI) associated with immune dysregulation are not sufficiently addressed in shared recommendation, resulting in delayed diagnosis and high morbidity. The availability of precision medicine for some of these immune defects makes it urgent to evaluate effective strategies to diagnose and treat such defects before the occurrence of severe complications. A diagnosis of an IEI in these patients enabled the use of a more specific treatment in most cases, and these have the potential to prevent further disease progression. We studied immune dysregulation diseases in 30 patients with autoimmune or allergic phenotypes, exploiting data from clinics and immunophenotype, genetic and transcriptome investigations, and 6 of them were diagnosed with a monogenic disorder. Our results confirm that a non-negligible number of children with IEIs may present with signs and symptoms of immune dysregulation and share many features with common multifactorial immune conditions. Reaching a genetic diagnosis becomes more likely in the presence of multiple clinical manifestations, especially when in association with abnormalities of lymphocytes subsets and/or immunoglobulins levels. Moreover, 5 of 6 patients that obtained a diagnosis of monogenic disorder received precision therapy, in four cases with a good or moderate response. The last two decades have witnessed a rising awareness that primary immune defects may present with signs and symptoms due to immune dysregulation, even in the absence of infections ,2. AccorThe aim of this study was to explore the feasibility of defining a priori criteria for selecting patients who may deserve a deep immunological and genomic study to detect immune dysregulation disorders and to assess the therapeutic impact of a definite diagnosis. Indeed, the well-established warning signs proposed by the Jeffrey Modell Foundation (JMF) to raise the suspicion of a primary immunodeficiency are mostly related to the risk of infections and may not help addressing the possibility of an immune dysregulation disease if it is not associated with serious or recurrent infections. To consider autoimmunity, allergy or lymphoproliferation in addition to the 10 JMF warning signs may enable the encompassing of a wider range of IEIs, but it also risks widening the study population too much. For this reason, we combined clinical signs of disease with immune disturbance  to improve the detection of subjects with IEIs. We report the results of our experience in the immunologic and genetic evaluation of children presenting with multiple clinical manifestations of immune dysregulation. Within this series, we recorded which clinical or immunologic features impacted the final diagnosis of an IEI. Moreover, we described cases in which the diagnosis affected the treatment, enabling the use of drugs targeted to the underlying pathogenesis mechanisms of the disease.One or more autoimmune disorders associated with at least one of the following: hypergammaglobulinemia or hypogammaglobulinemia, hypereosinophilia (>1500/mcL) or chronic lymphoproliferation ;Severe dermatitis plus hyper-IgE (>2000 UI/mL) and at least one of the following: recurrent infections, skeletal and joint disorders, recurrent pneumonia or pyogenic cutaneous abscesses;Idiopathic hypereosinophilia (>5000/mcL) plus hypergammaglobulinemia  and at least one of dermatitis, enteropathy and chronic lymphoproliferation;Severe dermatitis refractory to topical glucocorticoid treatment; dermatitis is considered severe if it affects at least 10 percent of the body surface and it affects quality of life and disrupts sleep, despite continuous topical treatment with glucocorticoid-based ointments;Severe food anaphylaxis refractory to oral desensitizing treatments. At our hospital, oral desensitization is proposed to all patients who experienced episodes of anaphylaxis to foods for which it is not easy to avoid the risk of allergic reactions for accidental exposures, such as for cow’s milk and egg proteins and that contain high levels of food-specific IgE antibodies.Within a research project, we enrolled subjects meeting at least one of the following set of criteria, which we considered possibly supportive of an immune dysregulation:Symptoms and signs were intentionally broad to encompass a relatively large spectrum of clinical presentations.Patients in group 1 were defined, only for purposes of the present study, as having a predominantly “autoimmune” phenotype, while the others from group 2 to group 5 were broadly defined as having an “allergic” phenotype. The study was approved by the local Ethics Committee . We enrolled consecutive patients meeting inclusion criteria from January 2018 to December 2021. After obtaining patients/caregivers’ informed consent, we collected DNA, RNA and peripheral blood mononuclear cells from each patient, and recorded clinical and laboratory data on a structured database.++CD19+IgD/IgM+CD27−; IgM B memory cells (B mem), defined as CD45++CD19+IgD/IgM+CD27+; switched memory B cells (B swi), defined as CD45++CD19+IgD/IgM-CD27+ and transitional B cells/recent bone marrow emigrants (RBE), defined as CD45++CD19+IgD/IgM+CD10+. Among T cells, we examined recent thymic emigrants (RTE), defined as CD45++CD3+CD4+CD31+CD45RA+; double negative T cells (DNT), defined as CD45++CD3+CD4-CD8-TCR a/b+; CD4/CD8 ratio; senescent CD8 T cells, defined as CD45++CD3+CD8+CD57+CD45RA+ and activated T cells, defined as CD45++CD3+CD4+CD69+ and as CD45++CD3+CD4+HLA-DR+.For most patients, we performed immunophenotyping with an extended cytometry panel in order to evaluate various lymphocyte subpopulations. Among B cell subpopulations we distinguished naïve follicular B cells (B naive), defined as CD45+ T cells and in resting lymphocytes with flow cytometry.Peripheral blood mononuclear cells (PBMCs) were left unstimulated or were treated with PMA and ionomycin for 2 h or with PHA for 24 h. Cells were then stained with anti-CD3 VioBlue, anti-CD4 APC and anti-CTLA4 PE antibodies to analyze CTLA4 expression in CD42. Then, cells were stained with surface antibodies CD3 VioBlue, CD4 APC and CD69 PE-Vio770. Finally, cells were fixed and permeabilized and then marked with anti-LRBA antibody for 30 min and then with secondary antibody for another 30 min to analyze LRBA expression in CD3+CD69+ T cells and in resting lymphocytes with flow cytometry.PBMCs were left unstimulated or were treated with PHA and incubated for 72 h at 37 °C, 5% CO2 at 37 °C, cells were stained with antibodies to cell surface antigens CD45 V500-C, CD3 VioBlue, CD4 PE and then fixed and permeabilized. PBMCs were finally stained with anti-STAT1 antibody or with an isotype control antibody. The expression level of STAT1 was analyzed in CD4+ T cells with flow cytometry.PBMCs were left unstimulated or were treated with IFN-alpha2a. After 24 h of incubation at 5% CO2. Cells were surface-stained with CD4 APC and CD3 VioBlue and then fixed and permeabilized with paraformaldehyde 4% and methanol 90%, respectively, to stain with anti-pS6 PE or corresponding isotype PE. The level of S6 phosphorylation was analyzed in CD4+ T cells activated in flow cytometry.PBMCs were left unstimulated or were treated with Dynabeads Human T-activator CD3/CD28 and incubated for 4 h at 37 °C, 5% COFor all analyses conducted in flow cytometry, samples were acquired with MACSQuant Analyzer 10 cytometer and analyzed with FlowLogic 7.2.1 software.Genetic analysis by target gene panels , useful for data visualization, was conducted with DESeq2 (v. 1.32.0)  to definOverall, 30 patients were enrolled; 14 patients met the “autoimmune phenotype” criteria, 13 met the “allergic phenotype” criteria and 3 showed an overlapping presentation. For most patients, we performed immunophenotyping with an extended cytometry panel. Target gene panels or whole exome sequencing were also analyzed in 22 selected patients  gene, respectively, associated with Signal Transducer and Activator of Transcription 1 (STAT1) gain-of-function (GOF) syndrome, as well as Common Variable Immune Deficiency (CVID), were found in one patient each; both patients met both the autoimmune and the allergic phenotype criteria. STAT1 expression was also studied with flow cytometry to functionally confirm the diagnosis in the patient with STAT1 GOF syndrome  standards .Genetic diagnosis impacted therapeutic choices in some cases, allowing for the choice of a molecularly targeted treatment . In partWe also investigated gene expression in peripheral blood cells from subjects with confirmed monogenic disorders to search for profiles associated with specific genotypes or phenotypes. The bioinformatic analysis was carried out for each patient on the 1000 most variable genes compared with healthy controls. In this analysis, samples tend to cluster near each other when they share a similar gene expression profile. Patients tended to cluster separately from healthy controls, with the exception of the patient with IPEX, who, however, still showed a mild expression of the disease. Notably, the patient with LRBA deficiency clustered close to the patient with CTLA4 haploinsufficiency, as expected, considering the involvement of shared pathways in the two diseases. Unfortunately, a comparison with patients with negative genetic results was not performed due to the low number of available RNA samples from this group. Thus, we cannot assess whether monogenic-disease-specific-like profiles may impact the classification of diseases for therapeutic purposes in individuals with negative genetic results.Overall, our data confirm that a non-negligible number of subjects with IEIs may present with signs and symptoms of immune dysregulation, often without a history of infections. While these patients share many features with common multifactorial immune conditions arising in the general population, reaching a genetic diagnosis becomes more likely in the presence of multiple clinical manifestations, especially when in association with abnormalities of lymphocyte subsets and/or hypogammaglobulinemia. In fact, this latter sign seemed to represent a strong predictor of a genetic diagnosis of IEI.Thus, recommendations to improve an early diagnosis of IEIs might be developed by weighting the number of immune complaints, the age at their onset and the presence of immune abnormalities. Of note, several laboratory anomalies in immune parameters appear worthy of being considered as supportive for a diagnosis of IEI, in particular, hypogammaglobulinemia and lymphocyte subset anomalies. Furthermore, the assessment of immunophenotype must not be limited to the main lymphocyte subsets, but should include specific subsets depending on the clinical picture.One characteristic shared by several patients in our series concerned their referrals to multiple specialties, because of complex clinical pictures encompassing multiple organ involvement. Even if comorbidities are not unusual in adults, they are much more rare the younger the subjects are. For this reason, the multi-organ involvement of an immune disease in children and young adults should prompt the suspicion of a dysregulatory PID and should warrant proper immunological and genetic investigations. This is even more important as some dysregulatory IEI can have precision treatments that can prevent a worse disease progression.Another issue raised by our experience concerns the importance of referring subjects with a suspected IEI to specialized centers, where different diagnostic approaches can be integrated, for example, to determine the significance of unclear genomic analysis with functional immune assays.A limitation of our study relies on its monocentric nature. This may represent a limitation both for the limited number of patients identified and for the possible existence of selection bias due to the main areas of expertise at our institute. However, this is also an added value for our study, since it describes a real-life experience of diagnosis and care of rare diseases in the field of immune defects. In fact, the study may be the starting point of novel multicenter research to explore the potential of multi-omic analysis in the field of monogenic and multifactorial immune dysregulation disorders.A diagnosis of an IEI in our study allowed for the use of a more specific treatment in most cases. This is of particular importance since more effective treatments have the potential to prevent further disease progression ,19. Our


This study presents the development of rigid polyurethane foams (RPUFs) filled with pine seed shells and yerba mate at 5, 10 and 15 wt%. The fillers were characterized for chemical properties using bench chemistry analyses, and the RPUFs were investigated in terms of chemical, morphological, mechanical, thermal and colorimetric characteristics. The main results indicated that yerba mate showed good compatibility with the polyurethane system, probably because its available hydroxyl groups reacted with isocyanate groups to form urethane bonds, producing increases in mechanical and thermal properties. However, pine seed shell did not appear to be compatible. Anisotropy increased slightly, as there was an increase in the percentage of reinforcement. The mechanical properties of the yerba mate reinforced foams proved stable, while there was a loss of overall up to ~50% for all mechanical properties in those reinforced with pine seed shell. Thermal properties were improved up to ~40% for the yerba mate reinforced foams, while those reinforced with pine nuts were stable. It was possible to observe a decrease in the glass transition temperature (T Among the most popular thermosetting plastics, rigid polyurethane foams (RPUFs) are applied in a wide variety of industries, such as building engineering, transport and thermal insulation ,2. This The use of natural fillers from agricultural or forestry wastes has been successfully incorporated in RPUFs, leading to satisfactory performances for maintaining and even increasing the RPUF properties . IncreasIn this sense, Członka et al.  reportedIn Brazil and other South American countries, pine seed shells and yerba mate are commonly used for various purposes. Pine seed shells are a popular designation for the seeds of several tree species, including both Pinaceae and Araucariaceae families, and are a popular ingredient for typical dishes from Brazilian gastronomy. Similarly, yerba mate is a traditional South American beverage prepared by infusion, which is widely consumed in countries such as Argentina, Paraguay and Uruguay ,18. AlthAraucaria angustifolia seeds) and yerba mate leaves (Ilex paraguariensis) were acquired from local commerce in southern Brazil. Post-consumed yerba mate tea and pine seed shells were oven-dried (at 50 °C until reaching constant mass) and ground in a Wiley mill coupled to a 100-mesh screen (<150 mm). Both these wastes were prepared (Tappi 257 cm-02) using wet chemical analyses, and then the ashes (T211 om-93), ethanol–toluene extractives (Tappi T204 om-97), acid-insoluble (Klason) lignin (Tappi T222 om-98) and holocellulose (remaining mass up to 100%) contents were determined.Pine seeds , was used as an NCO source. Moreover, poly-ethylene glycol (PEG-400), silicon oil and dimethylbenzylamine were used as chain extender, surfactant and catalyst, respectively. For the RPUFs manufacture, castor oil (24 parts/g), distilled water, glycerol (8 parts/g), PEG-400 (3.5 parts/g), silicon oil (1 parts/g) and filler were mechanically mixed at 1000 rpm for 120 s. Thereafter, p-MDI (63 part/g) and amine (0.4 part/g) were added to the other components, which were then mechanically stirred for an extra 60 s, keeping a constant NCO/OH stoichiometric ratio of 1.2. The detailed formulation of the manufactured RPUFs is presented in n is number of measured cells.The morphology of the fabricated RPUFs was analysed perpendicular to the rise direction by scanning electron microscopy (SEM) in an MA10 equipment (Zeiss Evo brand) operating at 3 kV. ImageJ software was used to measure average cell width and length using the SEM images. Anisotropy index of the RPUFs was calculated through Equation (1), according to Kirpluks and coworkers , where h® brand) outfitted with a diamond attenuated total reflection (ATR) accessory was used to record the spectra in the range of 500–4000 cm−1 over 64 scans with a resolution of 4 cm−1. Moreover, the thermal stability of the RPUFs was evaluated using a TGA-1000 thermogravimetric analyser . Differential scanning calorimetry (DSC) runs were performed using a Q20 calorimeter (TA Instruments) under a nitrogen atmosphere at a 50 mL/min flow rate from room temperature to 240 °C. The second-order DSC curve, in the range between −25 and 0 °C, was used to determine the glass transition temperature (Tg) [Fourier-transform infrared spectroscopy (FTIR) was used to identify chemical groups of the fabricated RPUFs and the fillers. IRSpirit equipment (Shimadzu3) for each group. The tests were performed in a 23-5D universal testing machine (Emic brand) using a crosshead speed of 2.5 mm/min, and the maximum compressive strength was determined at a 3.3 mm displacement, according to ASTM D1622. Apparent density was determined for the same samples, using an analytical scale (0.001 g resolution) and a digital calliper (0.01 mm resolution).The RPUFs were tested under a compression load parallel to the rise direction using seven prismatic samples , which reported brightness (L*), green–red (a*) and blue–yellow (b*) coordinates, Chroma (C*) and hue angle (h°). The apparatus was configured to use a light source (illuminant) D65 and a 10° viewing angle, according to the method known as CIELab.Chemical properties and thermal analysis were performed on one representative specimen. All other data were subjected to ANOVA tests. Whenever the null hypothesis was rejected, Tukey tests were used to compare the means. Before that, homogeneity of variances and data normality were verified using Leven and Shapiro–Wilk tests, respectively. All statistical analyses were implemented at a significance level of 5%.As expected, both vegetables presented high holocellulose contents (above 60%) , indicat−1, which can be attributed to the presence of polyphenols and the -OH bending vibrations from absorbed water molecules [−1), N-H amide II (at 1520 cm−1), H-C=O urethane linkages (at 1720 cm−1), and CH2 and CH3 groups (at 2850–2970 cm−1) [−1) included the free urethane and was lower in the pine seed shell samples. The peak at 2800–3000 cm−1 is attributed to symmetric and asymmetric vibrations from methyl (CH3) and methylene (CH2) groups, as well as C-H groups, associated with amorphous and crystalline polysaccharides [−1, while the peak at 2400 cm−1 indicated the presence of NCO groups.Compared to the pine seed shell, olecules ,29. Polyolecules . All RPU70 cm−1) . In thischarides ,33. Thes−1 region. According to Xu and coworkers [6H4OH), which may be involved in the interaction with the PU system. Finally, the appearance of a peak at 2270 cm−1 related to unreacted NCO groups from the p-MDI was found for the RPUFs filled with 10 and 15 wt% of yerba mate. According to Acosta and coworkers [In contrast to the RPUFs incorporated with pine seed shells, the ones filled with yerba mate exhibited an increase in the intensity of the broad and intense peak in the 3200–3500 cmoworkers , this broworkers , this inThe hygroscopic, thermal and mechanical properties of an RPUF are highly influenced by its morphology . SEM imaOn the other hand, the filled RPUFs presented a high number of open cells, probably due to the disruption of some cells induced by the filler insertion. In fact, it is known that the addition of fillers during the manufacture of RPUFs may cause a change in polymer viscosity, which can negatively impact foam expansion reaction and hinder cell formation and growth, ultimately resulting in an increase in heterogeneity and the emergence of open cells in the filled foams .In general, the insertion of the fillers yielded decreases in cell size, accompanied by unchanged levels of the anisotropy index. The morphological properties of the RPUF incorporated with 5% pine seed shells did not differ from those of the neat RPUF, which indicates a good filler interaction with the PU system in this case. In the study reported by Mosiewicki and coworkers , the resIn fact, when the cell size of one RPUF is reduced, its surface area increases, leading to more contact points between the filler and the RPUF matrix. This increased contact may lead to weak bonding between the RPUF matrix and the filler, resulting in low mechanical performance . On the The SEM images also indicate that the RPUFs incorporated with yerba mate exhibited a larger cell size than the ones filled with pine seed shells. This corroborates the results obtained by FTIR, and therefore also indicates that there was a better interaction between the yerba mate and its respective RPUF.As can be seen in The influences of the pine seed shell and yerba mate wastes on the compressive strength of the RPUFs can be seen in Regarding the compressive modulus , the intIn all, the shape of the neat RPUF curve remained unchanged after the insertion of the different filler weight fractions, which means that the structural chemical composition of the neat RPUF was almost unchanged. The first important downturn was observed at 240–260 °C. This thermal event was probably due to filler/RPUF crosslinking, since this temperature range is related to the breaking of urethane linkages . In applThe high residue content observed at temperatures above 700 °C in pine seed shells is associated with the presence of lignin and hemicelluloses, which may become fixed carbon until 600 °C . This mag of the filled RPUFs shifted to slightly lower values compared with neat RPUF, which may indicate changes in phase separation [g values, the RPUFs filled with yerba mate presented values closer to the neat case, which suggests that filler incorporation decreased the Tg values, but that crosslinking bonds had the contrary effect. Additionally, the indicated Tg values were in the range found in the literature for RPUFs [paration  and dampparation . Althougor RPUFs . The curTherefore, the addition of the filler in these cases probably changed the way the foam cells formed and grew during the RPUF production process. These fillers may have influenced the size, shape, and distribution of the cells. Additionally, these fillers may have provided sites for the formation of new RPUF cells, resulting in the formation of a more uniform cell structure. A filler that acts as a nucleating agent may also promote the formation of new cells by lowering the energy required for cell formation, which can result in the formation of smaller, more uniform cells.The photographs shown in All RPUFs incorporated with pine seed shells presented increases in a*, accompanied by decreases in L*, C and h°. On the other hand, the yerba mate being inserted led to decreases in L* and h°, accompanied by increases in a*, b* and C. These results can be explained by the presence of specific compounds in both the pine seed shells and the yerba mate. These complexes may interact with the RPUF matrix, altering their colour properties. For instance, the phenolic compounds  of pine seed shells may be causing the shift in hue from green to red ,42. On tRPUF composites incorporating different filler levels (5–15% wt.) of pine seed shells and yerba mate were successfully manufactured. Yerba mate exhibited greater chemical affinity compared to the pine seed shell filler, as confirmed by wet chemical and FTIR results, resulting in more uniform cells found by SEM images. This difference in host compatibility with the RPUF system also significantly affected DSC and mechanical results. The hygroscopic properties, apparent density, compression strength and thermal stability were only slightly influenced by both filler type and content. Although there were evident gains in aesthetic characteristics due to the insertion of both fillers in the RPUFs, the ones incorporated with yerba mate showed mechanical properties similar to neat RPUF, while the RPUFs incorporated with pine seed shells showed losses in the compression modulus.


Evaluating crop health and forecasting yields in the early stages are crucial for effective crop and market management during periods of biotic stress for both farmers and policymakers. Field experiments were conducted during 2017–18 and 2018–19 with objective to evaluate the effect of yellow rust on various biophysical parameters of 24 wheat cultivars, with varying levels of resistance to yellow rust and to develop machine learning (ML) models with improved accuracy for predicting yield by integrating thermal and RGB indices with crucial plant biophysical parameters. Results revealed that as the level of rust increased, so did the canopy temperature and there was a significant decrease in crop photosynthesis, transpiration, stomatal conductance, leaf area index, membrane stability index, relative leaf water content, and normalized difference vegetation index due to rust, and the reductions were directly correlated with levels of rust severity. The yield reduction in moderate resistant, low resistant and susceptible cultivars as compared to resistant cultivars, varied from 15.9–16.9%, 28.6–34.4% and 59–61.1%, respectively. The ML models were able to provide relatively accurate early yield estimates, with the accuracy increasing as the harvest approached. The yield prediction performance of the different ML models varied with the stage of the crop growth. Based on the validation output of different ML models, Cubist, PLS, and SpikeSlab models were found to be effective in predicting the wheat yield at an early stage (55-60 days after sowing) of crop growth. The KNN, Cubist, SLR, RF, SpikeSlab, XGB, GPR and PLS models were proved to be more useful in predicting the crop yield at the middle stage (70 days after sowing) of the crop, while RF, SpikeSlab, KNN, Cubist, ELNET, GPR, SLR, XGB and MARS models were found good to predict the crop yield at late stage (80 days after sowing). The study quantified the impact of different levels of rust severity on crop biophysical parameters and demonstrated the usefulness of remote sensing and biophysical parameters data integration using machine-learning models for early yield prediction under biotically stressed conditions. Triticum aestivum L., is a highly significant staple crop with immense global economic value. Wheat holds the distinction of being the largest cultivated crop worldwide, occupying approximately 225 million hectares of land with a total production of around 772.64 million metric tons and a productivity rate of 3.25 tons per hectare1. Wheat plays a crucial role in providing an affordable source of protein (20%) and calories (19%) through consumption to majority of the world population2. Given its status as a staple food and its ability to cater to the needs of a large population, particularly those in poverty, wheat carries special importance in ensuring global food security. With the projected increase in the world's population, the demand for wheat is expected to rise by 60% by 20503. India, being the second largest wheat producer globally, contributes approximately 13.5% pf the global wheat production1. According to an estimate 10–16% of world wheat produce is lost due to pest and diseases4.Yellow rust, also called stripe rust of wheat is considered most economically important disease of wheat and threat to world food security5. The yellow rust infection most commonly occurs on the wheat leaves, which reduces the light interception and photosynthesis resulting lower yields. Understanding and quantifying the changes in plant biophysical parameters in reponse to different levels of plant disease severity is essential for the effective management of resources.Wheat, scientifically known as 6 studied the alterations in leaf area, photosynthetic rate, stomatal conductance, transpiration rate of oat to understand the plant response towards leaf blight disease. Francesconi and Balestra7 studied the changes in photosynthesis and stomatal conductance to describe wheat response for fusarium head blight disease. In order to comprehend how plants react to stressors, the relative leaf water content (RWC) is a significant parameter. Several researchers have employed RWC to examine how plants respond to various plant diseases, such as vascular wilt of tomato8, fusarium canker in almond9, root rot of pea10, and sharp eyespot and fusarium head blight of wheat11. Membrane stability index (MSI) is a widely used physiological index to evaluate plant stress under biotic and abiotic stressors11. The leaf area index (LAI) is another fundamental variable that has a strong correlation with crop photosynthesis and the accumulation of dry matter in crops and hence it is an important parameter for tracking the growth status of crops and predicting their yield13. Several researchers have used LAI as a key indicator for monitoring the growth conditions of crop like maize14, wheat15, rice16 etc. The Normalized difference vegetation index (NDVI) is a highly effective indicator of photosynthetically active biomass and is proficient at precisely discerning shifts in crop conditions17. It is also one of the most frequently utilized vegetation index and finds broad application in identifying nutrient deficiencies, indirect photosynthesis estimation, and the detection of both biotic and abiotic stress conditions18. Additionally, the NDVI is the most commonly employed vegetative index for identifying diseased tissue19. Several researcher have used NDVI for monitoring and evaluating impacts of yellow rust on wheat crop21.Wang et al.5. To best of our knowledge none of studies till date quantified plant biophysical responses in wheat cultivars under different levels of rust severity and hence there is a notable scarcity of research examining the effects of different yellow rust severity levels on diverse wheat cultivars under field conditions.Though researchers have explored various biophysical parameters of wheat under diseased conditions, a notable gap in these assessments is that they are predominantly focused on distinguishing only between diseased and non-diseased conditions. To enhance wheat yield and economic returns, a crucial step is to comprehend how plant biophysical parameters are affected by varying degrees of disease severity. Applying a uniform approach to combat yellow rust frequently leads to excessive chemical use, diminishing farmers' profits and causing environmental harm22. The major limitations of traditional techniques of yield prediction such as crop growth models and statistical methods lies in their inability to effectively account for the constantly changing biotic and abiotic factors that affect crop production. Furthermore, these conventional models necessitate a substantial amount of data relating to soil, climate, crop, and management practices, as well as significant user proficiency and expertise to fine-tune the model23. The progress made in machine learning (ML) has introduced a novel and enhanced approach to overcome the above limitations in yield prediction of agricultural crops24. Several researchers used machine learning as well as deep learning approaches to predict yield in crops like wheat26, pigeon pea27, maize28, oil palm29 etc. under varying environmental conditions. However, researchers focusing on machine learning for yield prediction primarily rely on remote sensing data, which offers limited accuracy in early yield prediction. Augmenting remote sensing data with actual plant biophysical information has the potential to enhance the accuracy of early yield prediction. An evident research gap exists in the integrated utilization of plant biophysical data and image-based information for yield prediction. No prior study has undertaken the combination of thermal and visible image-derived indices with biophysical parameters to forecast wheat yield under biotic stress conditions.Predicting crop yield is another crucial task in the current scenario for policymakers and farmers in order to ensure food security and sustainability. Nevertheless, this task poses significant challenges due to the complex interrelationships among soil, plant, and environmental factors that impact crop yieldWith this background, the current study is planned to (a) investigate and quantify the effect of yellow rust on the biophysical parameters of wheat cultivars under different levels of yellow rust severity (b) coupling plant biophysical parameters data with image-based visible and thermal indices using ML models for improving yield prediction under biotically stressed conditions and (c) comparing the ability of different ML models to predict wheat yield under biotically stressed conditions.2 concentration. These measurements were taken alongside the NDVI. To compare the impact of yellow rust on different cultivar categories, we used a one-way ANOVA with a post hoc Tukey test at a significance level of 5%. The crop yields were predicted using ML models at 3 different growth stages by using image based indices and biophysical parameters as model inputs. The performances of ML models were evaluated using coefficient of determination (R2), d-index, root mean square error (RMSE), normalized root mean square error (n-RMSE) and mean bias error (MBE). Standardized ranking performance index (sRPI) derived from R2, d-index, RMSE and MBE of calibration and validation was utilized to finally rank models for their ability of yield prediction. A flow chart describing the methodology is presented in Fig. Thermal and visible images were collected for two years at different crop growth stages, along with biophysical parameters of the crop, including the LAI, RWC, MSI, photosynthesis, stomatal conductance, transpiration, and intercellular COHaplustepts). Twenty-four different wheat cultivars with varying levels of resistance and susceptibility were planted in plots measuring 2.5 × 2.5 m with a row spacing of 25 cm. The planting occurred on November 24th in the first year and November 25th in the second year. In order to create an artificial epiphytotic in field conditions, a mixture of six virulent and most predominant pathotypes of P. striiformis tritici were used as urediospore inoculum. Disease severity was recorded on a weekly basis after when susceptible checks reached 25–30% severity according to the modified Cobb's scale30, and the final disease severity (FDS) was taken for yellow rust on March 8th during the first year and March 4th during the second year. The cultivars were divided and classified into four different categories viz. High , Moderate , Low  and Susceptible (> 60% FDS) based on FDS as described by Singh et al.31.Field experiments were conducted over a two-year period (2017–2018 and 2018–2019) at the Indian Agricultural Research Institute's research farm in New Delhi, which has a semi-arid climate with hot, dry summers and dry winters. The crop was grown from the middle of November to the first week of April. The soil at the experimental site is non-calcareous and slightly alkaline, classified as Indo-Gangetic alluvium  was utilized to determine the LAI of the plants. To obtain the LAI of the plot, three internal subsamples were taken for each plot for each cultivar and the results were averaged32. The observations were taken at 1700 h IST, when the diffused radiation was maximum, with a quarter view cap on the sensor to exclude the operator and the other part of the hemispherical view. A detailed protocol for LAI measurement is given in Liu et al.33. In the 2017–18, measurements were taken at 55, 70, 80, 95, and 110 days after sowing (DAS), while during the 2018–19, LAI readings were recorded at 60, 70, 80, 92, and 105 DAS.NDVI: NDVI data was collected using a handheld NDVI sensor  between 1300 and 1400 h IST to minimize sun angle effects34. The Green-Seeker sensor estimates the NDVI values using of reflectance data in the red and near infrared regions of the electromagnetic spectrum. The sensor was swept over the middle section of the wheat plots of each cultivar and three readings were taken. The average NDVI value was then calculated for each wheat cultivar. In the 2017–18, measurements were taken at 55, 70, 80, and 95 DAS, while during the 2018–19 the NDVI readings were recorded at 60, 70, 80 and 92 DAS.RWC: The RWC was determined using the method described by Barrs and Weatherley35. The second leaflet from top of the plants were collected carefully to avoid any damage. To prevent loss of any moisture from the leaves, the samples were kept in icebox and the fresh weight (F) of the leaves were taken as quickly as possible. Subsequently, the leaves were saturated by adding enough water and kept for 6 h for attaining a constant weight by absorbing water and the weight of the saturated leaf or the turgid weight (S) was recorded. Then the leaves were dried in hot air oven at 65 °C until constant dry weight (D) was obtained. Subsequently, using the F, D and S, RWC was calculated with the following formula: MSI: The measurement of MSI was conducted by taking 0.2 g of cut leaf samples and placing them in test tubes containing 20 ml of double-distilled water. These test tubes were then incubated at 30 °C overnight, and the electrical conductivity (EC) reading of the water containing the leaf samples was recorded as C1. Subsequently, the test tubes were subjected to boiling in a water bath at 100 °C for 15 min, followed by cooling to room temperature. The EC was again recorded for the solution containing the leaf samples as C236. The MSI was then calculated using the equation : MSI (%) = (1 − (C1/C2)) × 100. As in the previous cases, three internal replications were taken for each cultivar and the values were averaged to obtain the average MSI values. In the 2017–18, MSI measurements were taken at 55, 70, 80, and 95 DAS, while during the 2018–19 the MSI readings were recorded at 60, 70, 80 and 92 DAS.Leaf photosynthesis, stomatal conductance, transpiration and intercellular CO2 concentration: Leaf photosynthesis rate, stomatal conductance, transpiration rate and intercellular CO2 concentration were measured using Infrared Gas Analyzer . Mature leaflets of wheat were analyzed using an Infrared Gas Analyzer  under a constant light of 1000 µmol m-2 s-1. The IRGA was set to operate in closed method to measure leaf photosynthesis rate, stomatal conductance, transpiration rate, and intercellular CO2 concentration37. Three observations were taken for each cultivar on the uppermost completely extended leaves during the optimum period of photosynthetic active radiation from 1000 to 1230 h IST. The observations were recorded at 55, 70, 80, and 95 DAS in 2017–18, while during 2018–19 the readings were recorded at 60, 70, 80 and 92 DAS.Yield: When the crops reached maturity, a manual harvest of 1 square meter was conducted. The grains were separated from the rest of the plant through threshing and beating and the yield was calculated based on the dry weight.38. Thermal and visible images were taken at three different locations in each plot from 1 m above the crop canopy, during various stages of crop growth. In the 2017–18, the images were taken at 55, 70, 80, and 95 DAS, while during the 2018–19 the images were captured at 60, 70, 80 and 92 DAS.The thermal and visible images were acquired simultaneously using the Testo (890-1) handheld camera. This camera is equipped with thermal detector of resolution of 640 × 480 pixels and a high-quality wide-angle lens that covers a field of view of 42° × 32°. With a thermal sensitivity of less than 40 mK at 86 °F, the camera can detect even subtle temperature variations. It has a minimum focusing distance of 0.1 m. The camera's geometric resolution is 1.13 mrad when the emissivity is set at 0.95, and it operates within a spectral range of 8–14 µm. The temperature estimation capability of the camera ranges from − 20 to 100 °C, with an accuracy of ± 2%. The thermal and visible images were taken each year at four different dates between stem elongation to flowering period of the crop viz. 55, 70, 80 and 95 DAS in 2017–18 and 60, 70, 80 and 92 DAS in 2018–19. Images were captured from a nadir view angle at peak daytime temperatures and low wind conditions between 1330 and 1430 h ISTCanopy temperature: The average temperature of the canopy at various crop growth stages was derived using the thermal images. Testo IR soft analysis software was used to calculate the average temperature of the canopy by averaging the temperature values of each pixel in the image39.Visible and thermal indices: Visible indices were calculated by extracting RGB tristimulus values using R software version 4.1.2. The RGB values were normalized in order to minimize the impact of illumination and variations in color40. Thermal indices were computed by utilizing temperature values extracted from the pixel data of thermal images using Testo IR software, in conjunction with the concurrent measurement of wet and dry leaf temperatures taken while capturing the thermal image42. We generated 45 visible and 3 thermal indices for each of the three stages for yield prediction. These indices along with their respective formulas are provided in Supplementary Table 42. For yield prediction, we utilized dataset generated from collecting data of 24 cultivars at three growth stages resulting 48 data points for calibration and validation at each stage. The predictor variables for yield included RGB and thermal image indices, LAI, RWC, NDVI, MSI, photosynthesis rate, stomatal conductance, transpiration rate, intercellular CO2 concentration and image derived canopy temperature. Out of the total data at available at each stage, 70% of the data points were used in calibration and the remaining 30% were used for validation. The parameters of the ML models were fine-tuned and adjusted through tenfold cross-validation with five repetitions, employing the 'caret' package43 in R software version 4.1.244. To evaluate the predictive capability of the models several statistical parameters including the R2, d-index, MBE, RMSE and n-RMSE were calculated. n-RMSE (%) values within the range of 0–10% are considered “excellent”, those between 10–20% are classified as “good”, values from 20–30% fall into the “fair” category, and any values exceeding 30% are deemed “poor”46. To rank the models by combining the results of calibration and validation of multiple statistical criteria, standardized Ranking Performance Index (sRPI) was calculated47. The sRPI ranges between 0 and 1, where the worst-performing model gets 0, while the best model gets value of 1. The yield predictions were made at three different stages before crop harvest viz, Stage 1: 55–60 DAS, Stage 2: 70 DAS, and Stage 3: 80 DAS.The biophysical parameters and the image based indices of first three dates of observations of each year were used to develop 12 ML models, which include Elastic Net (ELNET), Support Vector Machine (SVM), Gaussian Process Regression (GPR), Generalized Linear Model (GLM), Spike and Slab Regression (SpikeSlab), Multivariate Adaptive Regression Spline (MARS), Partial Least Square Regression (PLS), Random Forest (RF), K-Nearest Neighbours (KNN), Stepwise Linear Regression (SLR), eXtreme Gradient Boosting (XGB), and Cubist. Details on the ML models used in this study is also described in Singh et al.The planting material used in this study was procured from the Division of Plant Pathology, ICAR-Indian Agricultural Research Institute, New Delhi, India. We confirm that the current study complies with relevant institutional, national, and international guidelines and legislation for experimental research and field studies on plants (either cultivated or wild), including the collection of plant materials.The FDS in twelve wheat cultivars  remained below 20% which are categorized as highly resistant cultivars. Six wheat cultivars  have FDS between 21 and 40% and these are categorized as moderate resistant cultivars. Similarly, three cultivars  have FDS between 41 and 60%, and are categorized as low resistance cultivars. Finally, in three cultivars  the FDS was more than 80% during both years and they are categorized as susceptible cultivars , the maximum values of MSI for high and moderate category cultivars were 79.72 and 72.76%, respectively at 60 DAS, while for low and susceptible category cultivars the maximum values of MSI were 67.50 and 61.56%, respectively at 70 DAS. The trend of MSI variation in both years was same at all growth stages viz. High > Moderate > Low > Susceptible. In both years, when comparing with the resistant category cultivars, the MSI also displayed significant reductions across various growth stages.Considering the average of reductions in both years, at 55–60 DAS, the MSI declined by 8.63%, 14.24%, and 21.18% for moderate, low, and susceptible category cultivars, respectively. Similarly, at 70 DAS, these reductions averaged 7.95%, 14.12%, and 23.84%, and by 80 DAS, the reductions were 15.37%, 32.14%, and 54.11%, respectively. Progressing to 92–95 DAS, the average MSI reductions were 36.78%, 54.58%, and 74.47% for moderate, low, and susceptible category cultivars, respectively , the adjacent categories were somewhat similar statistically, however after 80 DAS all the categories were significantly different at 5% level of significance in both years. During 2017–18, the maximum values of stomatal conductance achieved in high, moderate, low and susceptible category cultivars were 0.456, 0.408, 0.346 and 0.262 mol H2O m−2 s−1, respectively at 80 DAS. Similarly, in 2018–19, the maximum values of photosynthesis rate for high and moderate category cultivars were 0.609 and 0.435 mol H2O m−2 s−1, respectively at 80 DAS, while for low and susceptible category the maximum values were 0.363 and 0.316 mol H2O m−2 s−1, respectively at 70 DAS. The trend of stomatal conductance in both years was same at all growth stages viz., High > Moderate > Low > Susceptible. The maximum difference in stomatal conductance of high resistance and susceptible cultivars was observed at 95 DAS and at 80 DAS in 2017–18 and 2018–19, respectively  the maximum values of RWC for high, moderate, low and susceptible category cultivars were 89.55, 81.31, 75.11 and 67.86%, respectively at 70 DAS. The trend of RWC variation in both years was same at all growth stages viz. High > Moderate > Low > Susceptible. Considering the average of reductions as compared to resistant category in both years the RWC at 55–60 DAS decreased by 3.56%, 6.45%, and 12.82% for moderate, low, and susceptible category cultivars, respectively. Likewise, at 70 DAS, these reductions averaged 9.40%, 16.80%, and 25.62%, and by 80 DAS, they amounted to 9.88%, 18.3%, and 29.23%, respectively. Progressing to 92–95 DAS, the average RWC reductions were 10.27%, 20.1%, and 32.61% for moderate, low, and susceptible category cultivars, respectively  of 0.97, the maximum model agreement index (d-index) of 0.99 and the lowest RMSE of 1.77 q/ha. Based on the n-RMSE value (3.78%), the prediction accuracy of this model comes under the category of excellent prediction (0–10%). The PLS model was found to be the poorest among all in terms of crop yield prediction with the lowest coefficient of determination (R2) of 0.12, the least model agreement index (d-index) of 0.45, and a maximum RMSE of 9.40 q/ha. Based on the n-RMSE value (20.09%), the prediction accuracy of this model comes under the category of fair prediction (20–30%). Considering the prediction accuracy indicator (n-RMSE), the models GLM, XGB, and Cubist come under excellent prediction class (0–10%), ELNET, RF, GPR, SVM, SpikeSlab, SLR and KNN models come under good prediction class (10–20%) and MARS and PLS models come under fair prediction class (20–30%).Using the data collected at 55 DAS in 2017–18 and at 60 DAS in 2018–19, some of the ML models were able to predict the wheat yield with acceptable accuracies Table a,b. The The validation output of different ML models in terms of the prediction of crop yields is presented in Table 2) of 1.00, the maximum model agreement index (d-index) of 1.00 and the lowest RMSE of 0.59 q/ha. Based on the n-RMSE value (1.27%), the prediction accuracy of this model comes under the category of excellent prediction (0–10%). The MARS model was found to be the poorest among all in terms of crop yield prediction with the lowest coefficient of determination (R2) of 0.37, and a maximum RMSE of 7.95 q/ha. Considering the prediction accuracy indicator (n-RMSE), the models GLM, XGB, RF, and Cubist come under the excellent prediction class (0–10%) and the rest of the models come under the good prediction class (10–20%).Using the data collected at 70 DAS in 2017–18 and 2018–19, the ML models were able to predict the wheat yield with higher accuracies as compared to the yield predictions using stage 1 (55–60 DAS) data. The model calibration output using stage 2 data was presented in Table The validation output of different ML models in terms of the prediction of crop yields using the stage 2 data set is presented in Table 2) of 0.99, the maximum model agreement index (d-index) of 1.00 and the lowest RMSE of 1.22 q/ha. Based on the n-RMSE value (2.62%), the prediction accuracy of this model comes under the category of excellent prediction (0–10%). Considering the prediction accuracy indicator (n-RMSE), the models GLM, ELNET, RF, SVM,XGB, and Cubist can be categorized as the excellent prediction class (0–10%) and the rest of the models come under the good prediction class (10–20%).Using the data collected at 80 DAS in 2017–18 and 2018–19, the prediction accuracies of ML models were further improved as compared to the yield predictions made using data of 55–60 and 70 DAS. The model calibration output using stage 3 data was presented in Table The validation output of different ML models in terms of the prediction of crop yields using the stage 3 data set is presented in Table This research set out to investigate the effects of different intensities of wheat yellow rust on crop biophysical parameters, and to estimate yield through the integration of thermal and visual imaging with machine learning algorithms. Wheat yellow rust has a significant negative influences on crop growth and physiology, and these negative impacts are in direct proportion to the degree of rust severity. As the disease severity increased, the biophysical parameters such as photosynthesis rate, transpiration rate, stomatal conductance, LAI, MSI and RWC were significantly reduced. The discussion has been structured into subsections to improve the understanding of the findings.48. In wheat, the yellow rust fungus causes damage to the plant by growing in the leaves and producing spores that erupt through the leaf surface49, which reduces the effective green leaf area of the crop resulting in reduced photosynthesis rate in the plants. The reduced photosynthesis due to yellow rust is also attributed to the reduced chlorophyll content in the crop51. Abdulbagiyeva et al.52 also studied the effect of yellow rust in six bread wheat cultivars of Peninsula, they reported that the yellow rust resulted significant reduction in the photosynthesis rate, mainly due to reduction in the net assimilating area of the plant. The reduced stomatal conductance in wheat might be attributed to the damaged stomata due to the eruption of pathogen through the stomata49. According to findings of Smith et al.53, the transpiration rate of wheat affected by yellow rust of wheat decreased by 10%. Their study also noted an initial surge in the transpiration rate due to the disruption of the leaf epidermis by the rust pathogen, which hindered stomatal closure, however, as the disease progresses the effect is reversed owing to the decrease in effectively transpiring leaf area.Stressful environmental conditions have long been known to cause significant harm to the photosynthetic pigments of plants2 concentration went up in the susceptible cultivars due to reduced stomatal conductance, which is slightly compensated by the reduced photosynthesis with disease progress, resulting higher or equivalent concentrations in some cases where no significant differences in adjacent categories cultivars were observed. However, numerically the intercellular CO2 concentration of the susceptible cultivars always remained significantly higher than the resistant cultivars. Mandal et al.54, in their research revealed that psyllium's downy mildew decreased plant photosynthesis and stomatal conductance, while increasing the intercellular CO2 concentration. Additionally, Zhao et al.55 also observed a similar increase in intercellular CO2 concentration in sugarcane leaves affected by orange rust.The intercellular CO53 noted a temperature rise of 0.2–1.0 °C in foliage due to yellow rust lesions ten days after infection. This temperature increase was caused by rupturing of the epidermis, which prevented stomatal closure. Likewise, Wang et al.56 observed a similar trend in cucumber plants affected by wilt pathogen, where reduced stomatal conductance and transpiration led to an increase in temperature.As per our findings, yellow rust disease severity is linked with an increased canopy temperature and a negative correlation with the transpiration rate. Smith et al.57 and Oerke et al.58. In addition, several studies have reported similar trends of higher canopy temperature after pathogen infection, which were used to identify diseases in various crops. For example, verticillium in Olive59, powdery mildew of tomato60, head blight of wheat61, downy mildew of grapes62 etc.Our findings of temperature increase with reduced transpiration and stomatal conductance are supported by other researchers, such as Lindenthal et al.51. The LAI is key parameter linked with photosynthesis and transpiration rate of the canopy63 and as we have already mentioned the reduction of these basic physiological parameters of the crop due to the disease incidence, it was expected to observe LAI reduction with increasing disease severity. The reduced photosynthesis and green leaf area are responsible for poor and stunted growth of the crop. This reduces the leaf area per unit ground area which is captured in the LAI measurements, that showed a continuous decline with increasing levels of disease severity. The yellow rust reduces the green leaf area of the crop, induces chlorosis and necrosis. These changes in plants induces foliar shrinkage and senescence which results in poor LAI of the crop under diseased conditions64. The NDVI also showed a decreasing value as the disease severity increases. The NDVI measures the reflectance of the crop in NIR and Red wavelengths and the yellowing of crops tend to reduce the NDVI65. Su et al.3 discovered that the yellow rust of wheat increases the Red and decreases the NIR reflectance, due to chlorophyll reduction, which makes NDVI as a useful index for monitoring yellow rust in wheat. Several researchers have reported reduced NDVI due to yellow rust and used it for monitoring rust field using aerial images of the field66.The LAI followed trend of decreasing values with increasing disease severity, which is due to the poor crop growth and canopy development due to yellow rust infection67. Recently, researchers have reported increased electrolyte leakage due to yellow rust in wheat cultivars, which might have resulted in low MSI of the crops with increasing disease severity51. The wheat yellow rust pathogen directly damages the cell while erupting from the leaf to the surface, which might have attributed to low MSI of wheat cultivars. Sabir et al.68 also reported reduced MSI of wheat cultivars due to yellow rust. The RWC showed decreasing trends as the disease severity increases. Chen et al.51 in their study reported a lower value of RWC in yellow rust susceptible cultivar as compared to the resistant wheat cultivars, however the differences were not statistically significant in their study.The MSI is linked with cell membrane damage due to stress that results in change in membrane permeability and leakage of electrolytes49. The yellow rust on wheat reduces the sugar supply to the developing seed resulting in smaller seeds. The flag leaf and the upper second leaf are considered most important for producing sugars for the developing grain and as soon as these are affected from the rust, the yield of wheat crop falls substantially69. The yield loss due to the yellow rust infection is attributed to the reduced kernels per spike and low-test weight70, which is due to the serious destruction of the photosynthetic function of the leaves affecting the assimilation of the products71. Smith et al.53 reported a yield reduction of 46–51% due to yellow rust of wheat. Park et al.72 reported a yield loss of 15–25% in plants with some level of resistance to yellow rust, whereas in susceptible cultivars the yield loss reported was 45–50%. Jindal et al.73 assessed the losses due to yellow rust in some common wheat varieties of India, out of  which few are also part of our study, and reported a loss ranging from 4.2–68.3%. All the above findings on wheat yield loss due to yellow rust is in line with our findings.The crop yield was significantly reduced with increasing disease severity. The yield reduction is attributed to several factors. The reduced green leaf area has reduced the total photosynthesis, light interception as well as its use efficiency, which is one of the primary reasons behind the reduced crop yieldThe ML models used in our study were able to make good predictions of yield under yellow rust affected conditions using the image indices and biophysical parameters as training dataset.75 were effectively captured by RGB and thermal image indices. The variations in the values of these indices were utilized to quantify crop yield through machine learning models. In comparison to similar studies, our results show better values for R2, d-index, RMSE, n-RMSE and MBE. This may be due to the inclusion of biophysical parameters of the crop that are strongly correlated with plant health. We also observed that, while prediction accuracy improves as we move closer to harvesting, the initial predictions made during first stage are more crucial for decision making, despite having acceptable but lower accuracies, compared to the predictions made closer to harvesting. The validation output of different models indicated that Cubist, PLS, and SpikeSlab models were found to be the effective models to predict the wheat yield at an early stage of the crop. The KNN, Cubist, SLR, RF, SpikeSlab, XGB, GPR and PLS models proved to be more useful in predicting crop yield at middle stage of the crop. Further, this research also demonstrated that at late stage of the crop, the RF, SpikeSlab, KNN, Cubist, ELNET, GPR, SLR, XGB and MARS models were found good to predict the crop yield. As there are no comparable studies to ours, we have instead compared our research to similar work on yield prediction that used machine learning. Ruan et al.26 recently employed several ML algorithms, such as ELNET, SVM, KNN, RF, and XGB, in combination with proximal sensing and weather data to predict wheat yield. Their findings indicate that RF and XGB were the top two models for predicting wheat yield, with R2 of over 0.70 and RMSE ranging from 0.75–0.85 t/ha. Li et al.76 used remote sensing-based vegetation indices to predict wheat yield in China with reasonable accuracy using RF and SVM. Han et al.77 tested KNN, SVM, GPR, ANN and DT (decision tree) methods for predicting wheat yield in China. Their results suggested that the models were able to predict yield with good accuracy with R2 > 0.75 and n-RMSE value less than 10%. SVM, GPR and RF were top three models for yield prediction in their study. Fei et al.78 used UAV based multi-sensor data to predict wheat yield using ML algorithms. They tested Cubist, SVM, DNN, ridge regression and RF methods with RGB, multispectral and thermal camera data. Results of their study revealed that the model performance improves using muti-sensor data as compared to the single sensor. Using single sensor data, they achieved R2 values of 0.527–0.670, while in the ensemble approach the R2 up to 0.692 were observed. This falls in line with results of our study, though we do not test the single sensor approach, however from the literature we observed that using the thermal and visible images with actual biophysical parameters of the crop improved the prediction accuracy considerably. Kang et al.79 tested the Lasso, SVM, RF XGB and CNN models to predict maize yield in United States. They found that the XGB outperforms all the models in terms of accuracy and stability. Going forward, the prediction of yield could be expanded to include satellite-level data, and the inclusion of additional agroclimatic and soil variables could enhance the accuracy of the results by utilizing machine learning and deep learning models.As evident from our results, following the rust infections, cell membranes become more damaged, resulting in water loss, causing dehydration, and wilting in plants. This situation also leads to the closure of stomata, altering heat loss from the leaf surface and affecting leaf surface temperature. When crop diseases begin, the shifts in heat radiation due to increased plant water loss, stomata closure, and heightened respiration can be observed in thermal image derived canopy temperature. These alterations along with changes in plant pigmentation due to yellow rustThis study investigated the influence of yellow rust on the biophysical parameters of wheat cultivars with varying degrees of rust resistance. Using thermal and RGB image indices in combination with biophysical parameters, ML models were constructed to estimate yield under diseased conditions. The biophysical parameters, including photosynthesis rate, transpiration rate, stomatal conductance, LAI, MSI and RWC were dropped significantly due to rust, and the reductions were directly correlated with levels of rust severity. The yield reduction in moderate resistant, low resistant and susceptible cultivars as compared to resistant cultivars, varied from 15.9 to 16.9%, 28.6 to 34.4% and 59.0 to 61.1%, respectively. The ML models were able to predict the yield of rust-affected wheat crop before harvest with reasonable accuracy. The Cubist, PLS, and SpikeSlab models were found to be effective models for predicting the wheat yield at an early stage of crop growth (55–60 DAS). The KNN, Cubist, SLR, RF, SpikeSlab, XGB, GPR and PLS models were proved to be more useful in predicting crop yield at middle stage of crop growth (70 DAS) and at late stage of crop growth (80 DAS) RF, SpikeSlab, KNN, Cubist, ELNET, GPR, SLR, XGB and MARS models were found good to predict the crop yield. This study quantifies the changes in biophysical parameters in wheat cultivars with different rust resistant levels and suggests the potential of ML models developed using biophysical parameters and image-based indices to assess the crop yield, which can be used for sustainable management of resources.Supplementary Figures.Supplementary Tables.


Riboflavin deficiency can lead to premature farrowing, stillborn piglets, weak-born piglets and neonatal death. Riboflavin (vitamin B2) is considered essential for reproductive function. The longer the period on riboflavin-deficient diets, the more severe the clinical signs become. Litter size as well as body size of piglets can also be considered risk factors that may contribute to the problem.This case report involved two organic farms of 320 (farm A) and 250 sows (farm B). Between 2019 and 2020, premature farrowing with weak-born or stillborn piglets and severe intra-litter mortality, ranging from 60 to 100% were observed. Investigations for infectious causes of reproductive disease, drinking water quality and general feed composition were performed, but showed no significant results. Feed composition was subsequently evaluated more in detail. Riboflavin levels were very low specifically 1.25 mg/kg of diet (3.75 mg/kg of diet is the NRC minimum recommended level). Riboflavin as a vitamin complex supplement (B complex) was administered to sows one month before the farrowing date and this led to a rapid improvement of the problem such that no stillbirth or intra-litter mortality was observed.ex juvantibus diagnosis of this deficiency condition. This case report highlights that riboflavin deficiency during gestation should be considered in case of premature parturition and stillborn litters.The clinical presentation, the low riboflavin levels in the feed below the recommended levels for gestating sows and the effectiveness of the riboflavin supplementation, led to an  Perinatal piglet mortality is a multifactorial problem leading to major economic and animal welfare problems in pig farms around the world. Stillborn piglets may be caused by infections and be associated with many non-infectious risk factors. The risk factors can be further categorized as genetic, maternal, piglet and environmental factors, with a possibility of interaction between them . Many inIncreasing litter size is a risk factor for stillbirths –6 and liRiboflavin, also known as vitamin B2, is considered essential for reproductive function. In pregnant sows, riboflavin deficiency can lead to premature births, stillbirths, and death . It is nIn fact, riboflavin is required as part of many enzymes essential to the utilisation of carbohydrates, fat, and protein. More than 100 enzymes are known to bind flavin adenine dinucleotide (FAD) or flavin mononucleotide (FMN) in animal and microbial systems. Riboflavin and related natural flavins participate in numerous and diverse reactions, perhaps more than any other vitamin-coenzyme group.Riboflavin functions in flavoprotein-enzyme systems to help regulate cellular metabolism, and is also specifically involved in the metabolism of carbohydrates. Riboflavin is also an essential factor in amino acid metabolism as part of amino acid oxidases .This case report describes an episode of increased premature birth and stillbirth rate in two organic pig farms. This case shows that riboflavin deficiency in pregnant sows should be considered in the differential diagnosis when such reproductive problems occur.The case report involved two organic farms of 320 (farm A) and 250 sows (farm B), both practising a three-week batch production. The genetics were Topigs 20 and 60, with Topigs Fomeva11 terminal boar and with in-house production of great grandparent (GGP) and grandparent (GP) gilts. In both farms, the same organic pelleted sow feed was used. Sows were fed individually with electronic sow feeders during gestation and lactation. Lactating diet started about a month before entering the farrowing room. They were fed ad libitum in the farrowing room. Body condition scores (BCS) were evaluated by measuring backfat and by weighing the sows. The scores were always within reference levels. Sows were housed in farrowing pens, each pen measured 7.5 square metres and also had an external box of 2.5 square metres with a solid floor, and straw as a bedding material. The ambient temperature was 21–22 °C, while the nest for the piglets was kept at 26–27 °C. Sows were moved from the gestation unit to the farrowing unit five days prior the expected farrowing day. As the farms were organic, birth induction was not allowed and the use of uterotonics was only possible in specific sporadic cases in order to guarantee animal welfare. Farrowing management was not modified before or after the onset of the problem, while the length of gestation remained the same for the whole period of time.Breeding animals (gilts and sows) were vaccinated against Suid Herpesvirus 1 (every 3 months), Parvovirus, Leptospira and Erysipelas (every 4 months) and gilts were vaccinated against neonatal colibacillosis and clostridiosis some weeks prior to first farrowing. The farms were infected with porcine reproductive and respiratory syndrome (PRRS), but there were no clinical problems.In farm A sows had a litter size that ranged from 15 to 23 with an average litter size of 19 piglets per litter , 89.6% of piglets were live-born while the stillborn rate was 10.4%, the pre-weaning mortality was around 18% and the number of weaned piglets per sow per year was 28. The average gestation length was 114.5 days and the return to oestrus was around 15% , presented premature farrowing during October 2019. This occurred 3–4 days before the expected date of farrowing, and resulted in weak-born or stillborn piglets, in particular stillbirth type II. To control premature farrowing, from November to the end of December 2019, progestogen (Regumate 5 ml/sow) was administered at day 110, 111 and 112 of gestation. This treatment decreased the number of sows with premature farrowing, but the incidence of weak-born or stillborn piglets remained the same. As soon as the administration of progestogens was suspended, premature births were once again observed. During 2020, the situation worsened with premature farrowing and high piglet mortality 15–24 h after birth, and intra-litter mortality ranging from 60 to 100%. The total number of piglets born remained the same, that is between 15 and 23 piglets per litter. Subsequently, approximately 20% of the multiparous sows showed similar problems. In farm B, a newly established herd, a high incidence (48%) of premature farrowing in first parity sows (3–4 days before the due date of delivery) was observed in March 2020. This was systematically observed in subsequent batches; more specifically 30–40% of the sows exhibited premature farrowing. During August and September 2020, the second parity sows experienced the same problems.In both farms, the problem persisted throughout 2020 and led to the loss of 1600 (farm A) and 970 (farm B) piglets respectively. The sows were not clinically diseased and did not show other fertility problems such as repeat breeding or abortions.Between 2019 and 2020, 88 piglets from farm A and 117 piglets from farm B were sent to the Diagnostic Laboratory of Parma at Istituto Zooprofilattico Sperimentale of Lombardia and Emilia-Romagna for post-mortem examination and laboratory investigations. Necropsies were conducted on stillbirth piglets, weak born piglets and also on piglets that died during lactation. At necropsy, piglets did not show significant gross lesions. Bacteriological examination performed for each piglet on samples of liver, kidney, spleen and brain did not show pathogenic bacteria.Chlamydia spp. [Leptospira spp. [Brucella spp. [Toxoplasma gondii [Investigations for PRRSV, Virotype PRRS RT-PCR Kit , PCV2 , Parvovidia spp. , Leptospira spp. , Brucelllla spp.  and Toxoa gondii  were perFive pooled samples belonging to 3 different litters from farm A were positive for PCV3. The cycle quantity (Cq) values were 18.3, 37.2, 32.3 and 19.2. Cq values higher than 38 were considered negative. Two pools of piglets belonging to the same litter from farm B tested positive for PSV.Samples from brain, cerebellum, kidney, spleen, liver, lungs, heart, and intestines were collected during necropsies from all cases and fixed in 10% neutral buffered formalin and a sample of them were submitted for histopathological evaluation following standardised methods. Samples were submitted for histopathological evaluation to confirm the results of PCRs  or to investigate possible microscopic lesions in samples that were PCR negative. No significant histopathological lesions were found in any of the samples.The feed administered to sows in both farms was tested to assess levels of zearalenone, using an ELISA test. The levels were very low, namely between 36 and 84 ppb, and not considered to be as clinically relevant .The drinking water in both farms (ground water) was tested for chemical, physical and microbiological parameters. The results of these investigations did not show any abnormalities. In the absence of reference values for livestock, we took into consideration reference values for human consumption Table .Table 4Dper os, for 30 days until farrowing  methods. These 50 samples were collected from different silos and stocking units, and at different timepoints from the end of 2019 throughout 2020. The analysis results showed nutrient levels above the National Research Council (NRC) minimum recommended values, including the levels of different vitamins, with the exception of riboflavin, for which the detected level was 1.25 mg/kg of diet Tables . Consideg Tables  and 8. IThis supplementation led to the resolution of the problem, and no problems with premature farrowing or increased stillbirths were observed during a monitoring period, specifically 18 months, from January 2021 to June 2022.This clinical case described a severe outbreak characterised by premature farrowing and weak-born and/or stillborn piglets in two organic sow farms. The screening for different pathogens to be considered in the differential diagnosis in case of reproductive problems in sows demonstrated sporadic positivity to PCV3 (farm A) and PSV (farm B).PCV3 was first described in 2015  and, sinPSV has been associated with several diseases, primarily gastrointestinal, neurologic, reproductive, and respiratory disorders, but also with subclinical infections. However, for most serotypes, proof of a causal relationship between viral infection and clinical signs is still lacking . The preIn addition to the investigations for infectious problems, attention was paid to the investigation of non-infectious causes such as zearalenone and to assess drinking water quality and feed ion. As reported above, the nutrient levels were within the NRC recommended ranges, with the exception of riboflavin, for which the levels were three times lower than those recommended by the NRC.Animals and humans are unable to synthesise riboflavin within tissues and, therefore requirements should be basically met primarily by dietary sources, along with some intestinal microbial synthesis. Riboflavin plays many essential roles in the release of food energy and the assimilation of nutrients, so it is easy to understand why deficiency is reflected in a wide variety of signs among animal species. A decreased growth rate and lowered feed efficiency are common signs in all affected species . RiboflaRiboflavin requirements vary with growth, environment, age, activity, health, other dietary components and synthesis by the host. Only a few old studies have reported the riboflavin requirement in pigs. As an example, Hughes proposed in 1940 that the daily minimum requirement of riboflavin for young growing pigs ranged between 2.2 and 6.6 mg per 100 kg body weight . Krider More recently, the NRC reported that pigs have a riboflavin requirement of between 2 and 4 mg/kg of diet  and as rZintzen et al.  reportedAn important information that is missing is the evaluation of erythrocyte glutathione reductase activity coefficient (EGRAC), a sensitive biochemical indicator of riboflavin deficiency. Frank et al.  reportedThe clinical presentation, the results of laboratory investigations that ruled out infectious agents, the low level of vitamin B2 in the feed which was far below the NRC’s minimum values, and the effectiveness of the multivitamin supplementation led to an ex juvantibus diagnosis of this deficiency condition. The treatment administered to sows proved to be successful that it could be suggested as a protocol.This case report highlights that riboflavin deficiency during gestation should be considered in cases of premature parturition and stillborn litters.


Shorebirds migrate long distances twice annually, which requires intense physiological and morphological adaptations, including the ability to rapidly gain weight via fat deposition at stopover locations. The role of the microbiome in weight gain in avian hosts is unresolved, but there is substantial evidence to support the hypothesis that the microbiome is involved with host weight from mammalian microbiome literature. Here, we collected 100 fecal samples of Ruddy Turnstones to investigate microbiome composition and function during stopover weight gain in Delaware Bay, USA. Using 16S rRNA sequencing on 90 of these samples and metatranscriptomic sequencing on 22, we show that taxonomic composition of the microbiome shifts during weight gain, as do functional aspects of the metatranscriptome. We identified 10 genes that are associated with weight class, and polyunsaturated fatty acid biosynthesis in the microbiota is significantly increasing as birds gain weight. Our results support that the microbiome is a dynamic feature of host biology that interacts with both the host and the environment and may be involved in the rapid weight gain of shorebirds.IMPORTANCE Many animals migrate long distances annually, and these journeys require intense physiological and morphological adaptations. One such adaptation in shorebirds is the ability to rapidly gain weight at stopover locations in the middle of their migrations. The role of the microbiome in weight gain in birds is unresolved but is likely to play a role. Here, we collected 100 fecal samples from Ruddy Turnstones to investigate microbiome composition (who is there) and function (what they are doing) during stopover weight gain in Delaware Bay, USA. Using multiple molecular methods, we show that both taxonomic composition and function of the microbiome shifts during weight gain. We identified 10 genes that are associated with weight class, and polyunsaturated fatty acid biosynthesis in the microbiota is significantly increasing as birds gain weight. Our results support that the microbiome is a dynamic feature of host biology that interacts with both the host and the environment and may be involved in the rapid weight gain of shorebirds. Within every major vertebrate clade, there are species that undertake annual seasonal migrations to find optimal environmental conditions for survival or breeding: from lampreys to sea turtles to elephants to many species of bird. Migratory animals go through a myriad of physiological changes throughout their annual cycles to prepare for, accomplish, and recover from migration itself or to prepare for, accomplish, and recover from reproduction; for long-distance migratory birds, these changes associated with migration are the most extreme changes of their annual cycle , 2. ShorBacteroidetes) are associated with lean or normal weight  hibernate for 6 to 9 months and rely completely on the fat mass accumulated during their active season. Although the role of the microbiome in fattening is unknown in this species, the microbiome was recently shown to be involved in maintaining lean mass during hibernation (Ursus arctos) is another hibernator whose microbiome undergoes annual changes: during the summer, while the bears are gaining weight, the microbiome is relatively higher in Firmicutes, which can increase fat deposition under controlled conditions when transplanted into germfree mice (The role of the microbiota (the microorganisms that live on and inside a host) in the anatomical and physiological changes associated with vertebrate weight gains and losses of migration is a major outstanding question with both basic and applied implications. A vertebrate’s microbiome is intimately involved in many aspects of vertebrate biology, including development, immunity, behavior, and digestion  and relatively anhydrous, meaning the birds do not need to carry water weight to access the energy during flight . MigratoIn wild shorebirds, the increased food input combined with internal physiological shifts raises the question of how the microbiome affects and is affected by these changes. The taxa within the microbiome are frequently decoupled from the functional potential of the community  with drThe compositional dynamics of the microbial taxa in the gut microbiome of shorebirds has been studied using 16S rRNA gene amplicon sequencing at different stages of the annual cycle. Geographic location is significantly correlated with the taxonomic composition of the microbiome across multiple host species , but in Arenaria interpres) fecal microbiome, collected in Delaware Bay at different stages of fattening during the spring-migration staging period, using both 16S rRNA gene amplicon and metatranscriptome sequencing. Ruddy Turnstones stop in Delaware Bay for approximately 2 weeks during the month of May on their northwards migration and double their body mass during this time period by intensive foraging on the eggs of the horseshoe crab, Limulus polyphemus . Body mass significantly differed among our three sampling sites , with the lowest bird weights at Swains and the highest weights at Back North  and 41 male (M) Turnstones. Four individuals could not reliably be assigned a sex and are referred to as unknown (U); birds belonged to the following weight class sample sizes: light (N = 34), medium (N = 28), and heavy (N = 28). After quality control, we retained 3,700,042 high quality sequences across the 90 samples with an average of 40,660 ± 1,733 SE sequences per sample.(i) Alpha diversity. Shannon’s diversity index (here “Shannon”) significantly differed among weight classes . Light and medium weight birds differed significantly from heavy birds, but not from each other . Observed number of ASVs did not significantly differ between weight classes . Alpha diversity significantly differed among sampling sites , but did not differ between sexes .(ii) Beta diversity. The NMDS plot showed clustering in microbiome communities by weight class and showed directional change from light to heavy birds; medium weight class birds appeared to be located in between the light and heavy weight classes (R2 = 12%) and sampling site (R2 = 3.6%) were significantly associated with variation in microbiome composition (P < 0.001). Sex of the birds was not significantly correlated with microbiome composition . Homogeneity of variance (beta dispersion) did not significantly differ among weight classes , sexes , or sampling sites . We were unable to separate the effects of weight and collection date due to the strong correlation between these variables. We compared the R2 between models that included weight only, date only, and the date*weight interaction. All three variables were significant (P < 0.001) with the highest R2 represented by the date*weight interaction (0.1685), followed by date (0.1588) and weight (0.1408). classes . PERMANO(iii) Community composition. Twenty-six phyla were identified across all samples; five phyla comprised 97.3% of all sequences. The dominant phylum was Fusobacteria (40.7%), followed by Proteobacteria (26.7%), Firmicutes (18.9%), Bacteroidetes (5.8%), and Tenericutes  Differential abundance of taxa. Pairwise tests identified many genera associated with weight class  in which the genus was overrepresented is shown and also denoted by color . Download Table S2, XLSX file, 0.01 MB.All genera identified as significantly overexpressed in the pairwise DESeq differential abundance analyses using amplicon dataCopyright © 2023 Grond et al.2023Grond et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the After ribodepletion and library preparation, 22 out of 40 initially selected RNA samples were suitable for metatranscriptome sequencing, including 14 males and eight females in the following weight class sample sizes: light (N = 7), medium (N = 9), and heavy (N = 5). A total of 143,039 unique transcripts, 30,057 UniProt IDs, and 6, 481 KEGG Orthologys were detected (before filtering low represented transcripts).F2,19 = 2.78, R2 = 0.227, P = 0.02), but not between sexes . Light birds and medium birds differed significantly from each other (Padj = 0.018), but no difference in functional community was detected between light birds and heavy birds (Padj = 0.366) or between medium birds and heavy birds (Padj = 0.226). PCA beta dispersion differed among weight classes , which was driven by a significant difference between light birds and heavy birds (TukeyHSD: Padj = 0.020). Beta dispersion did not differ between light birds and medium birds (TukeyHSD: Padj = 0.074) or between medium birds and heavy birds (TukeyHSD: Padj = 0.588).We detected a significant difference in the functional gene community  among the three weight classes ((i) Differential expression.Several KOs were differentially expressed among weight classes  production during cell growth.10.1128/msystems.01128-22.4TABLE S3Table S3, XLSX file, 0.01 MB.All KEGG Orthology information for the functions identified as significant in the differential expression analysis. Download Copyright © 2023 Grond et al.2023Grond et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the R2 = 0.19, P = 0.024), alpha-linoleic acid metabolism , arachidonic acid metabolism . Biosynthesis of unsaturated fatty acids was not significantly associated with weight .Expression of three polyunsaturated fatty acids were significantly associated with weight : linoleiP < 0.05  and fatty acid producing marine bacteria  may be Flavobacterium (Phylum: Flavobacteria) was significantly higher in heavy birds compared to medium birds and Vibrio (Phylum: Proteobacteria) were significantly higher in the heavy birds compared to both the light and medium birds . Additionally, there appeared to be separation of the light birds into two clusters on PC1. These clusters did not correspond to any variable we could identify, including site, sex, or relative weight. To identify genes that may be associated with weight or weight gain, we used multiple methods: an unbiased pairwise significance test  and as the only significant difference between males and females . K06422 is associated with sspE, a small acid-soluble spore protein. As sporulation is a response to starvation in some bacteria, it may be that this protein is overrepresented in the light birds because they are underweight , and the microbiota had entered a stress/starvation response. The other functions associated with the light birds, K01886 and K08139 are also generally associated with cell growth and metabolism. The functions associated with the medium weight birds were all potentially associated with proteins . K13993 is also associated with tissue remodeling and is significantly higher in both the medium and heavy birds compared to light. This result seems counterintuitive as fattening shorebirds are known to switch from protein recovery postmigration to fat deposition around medium weight  and structural proteins (K07625). Of particular note is K08720, which is associated with Vibrio biofilm formation, and may specifically involve iron balance  , genes ia priori hypothesis that polyunsaturated fatty acid biosynthesis would increase as the birds fatten. “Essential” fatty acids are those an animal needs but cannot produce; the diet of horseshoe crab eggs provides the essential fatty acids birds need and PUFAs in particular are an extremely efficient way to store energy that shorebirds use to power their migrations. Alpha-Linoleic acid is an n-3 PUFA, whereas Lineoleic acid and Arachidonic acid are n-6 PUFAs. These different categories of PUFA can have multiple and antagonistic effects  from 7 to 31 May 2018 at three beaches in Delaware Bay, DE  were collected and birds were sexed and aged based on plumage characteristics , medium 100 to 150 g), and heavy (>150 g). The medium weight category starts at 100 g, as this is the average weight of Turnstones during the wintering period , following the parallel extraction protocol. Extracted RNA and DNA were stored at –80°C until sequencing.For the 16S rRNA gene sequencing, the V4 region of the 16S rRNA gene was PCR amplified and sequenced at the University of Connecticut Microbial Analysis, Resources, and Services facility, following the standard operating procedure. Quant-iT PicoGreen kit was used to quantify DNA concentrations, and 30 ng of extracted DNA was used as the template to amplify the V4 region of the 16S rRNA gene. V4 primers (515F and 806R) with Illumina adapters and dual barcodes were used for amplification . PCR conFor the metatranscriptomes, total RNA was quantified, and purity ratios determined for each sample using the NanoDrop 2000 spectrophotometer . To assess RNA quality, total RNA was analyzed on the Agilent TapeStation 4200  using the RNA High Sensitivity assay following the manufacturers protocol. Ribosomal Integrity Numbers (RIN) were recorded for each sample. Total RNA samples  were prepared for prokaryotic transcriptome sequencing by first ribodepleting bacterial rRNA using the RiboMinus Transcriptome isolation kit, Bacteria . Ribodepletion efficiency was analyzed prior to the start of library preparation on the Agilent TapeStation 4200  using the RNA High Sensitivity assay following the manufacturer’s protocol. Efficient ribodepletion is supported by the disappearance of the 16S and 23S rRNA peaks , with the sample’s electropherogram trace now showing a smear of shorter molecules .Purified ribodepleted RNA underwent library preparation using the Illumina TruSeq Stranded mRNA Sample Preparation kit following the manufacturer’s protocol modification for purified mRNA as input . Libraries were validated for length and adapter dimer removal using the Agilent TapeStation 4200 D1000 High Sensitivity assay  then quantified and normalized using the dsDNA High Sensitivity Assay for Qubit 3.0 . Sample libraries of sufficient quality were sequenced  with a sequencing depth targeted at 7 to 10 M total paired end reads/sample at the Center for Genome Innovation at the University of Connecticut.For the 16S rRNA gene amplicon data, during standard Illumina demultiplexing, sequences were quality checked and trimmed to remove adaptors and barcodes. The DADA2 (v. 3.11) pipeline in R (v3.6.0) was used to quality control and process the reads , 54; lowde novo assembled using Trinity (v2.2.0 .https://doi.org/10.6084/m9.figshare.11337716.v1).Sequence and metadata are available on Figshare (