Combining biophysical parameters with thermal and RGB indices using machine learning models for predicting yield in yellow rust affected wheat crop.

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.

Preparedness of dental under-graduate students towards practicing endodontics in rural and remote areas of India.

BACKGROUND: Undergraduate students can provide valuable opinions and suggestions for modifying the educational program for the enhancement of learning. Therefore, the aim of this study was to explore the preparedness of undergraduate dental students with regard to practice endodontics in the rural and remote areas of India. MATERIALS AND METHODS: The present cross-sectional questionnaire-based study was conducted among dental undergraduates (interns). The survey was conducted among the students of 3 private dental colleges. For the data collection, a close-ended questionnaire was designed to test the knowledge for performing endodontic therapy, root canal therapy (RCT), attitude, and behavior of performing these procedures in the rural set-ups. Unpaired t test and one way analysis of variance (ANOVA) was used to compare the significant difference based on demographics and Pearson's correlation was applied to find the associations among knowledge, attitude, and behavior of study participants. The level of significance was set at below 0.05. RESULTS: A total of 79 students participated, out of which 22 respondents were males (27.8%), and 57 were females (72.15%). Overall mean knowledge scores for endodontics was 9.72 ± 2.19, and the scores of female students were slightly higher than males, whereas the attitude and behavior were better in males but there was no statistical significant difference between both the genders (P > 0.05). A statistically significant positive correlation was observed between attitude and behavior on practicing endodontics in rural settings among study subjects (P < 0.05). CONCLUSION: These findings highlights the necessity for taking initiatives to enhance the community-based dental education programs by various dental colleges for improving oral health care access for rural and isolated populations.

Cone-beam Computed Tomographic Evaluation of the Anatomical Changes of Temporomandibular Joint Use of Pre-post Dentures: A Time-control Study.

AIM AND OBJECTIVE: The growth of the temporomandibular joint (TMJ) gets affected by multiple factors like aging, occlusion state, and by the movement of the jaw while masticating and swallowing. Radiographic imaging is often utilized as a vital diagnostic adjunct in the evaluation of certain examinations of the TMJ. MATERIALS AND METHODS: In this in vivo study, 30 male participants with mean age 55 years, having edentulous maxillary and mandibular arches from the Outpatient Department of Prosthodontics, were randomly selected. Group I (n = 30) patients who were edentulous for the last 4-5 years but without wearing dentures. Whereas group II (n = 30) patients who were edentulous for the last 4-5 years but were wearing dentures for this period. Maxillary and mandibular dentures were fabricated and delivered to subjects. Subjects were subjected to the TMJ analysis with the help of CBCT. Radiological images of dentomaxillofacial structures were analyzed by a specialist with a dual monitor inside a darkened silent room. On the monitor, three times measurements were recorded followed by calculation of mean value. The recordings were taken on both sides and thus, 210 sites were analyzed altogether, followed by the statistical analysis using SPSS software version 15.0. RESULTS: The mean ages of group I and II were 59.00 ± 6.74 and 58.27 ± 6.75 years, respectively. The intra- and intergroup comparisons were done using a one-sample t-test. Differences in mean intercondylar width in groups I and II were not found to be statistically significant. The difference in mean length of glenoid fossa was not statistically significant at any of the above observation periods. A continuous decline in mean length of glenoid fossa was observed with time in both groups. The range of change in articular eminence length was found to be statistically significant for both the groups (p < 0.05). CONCLUSION: This study shows that the articular eminence flattening is correlated with age; on the other hand, the rate of deformation was found significantly more in total edentulous subjects as compared to subjects having normally maintained occlusion. The anatomical changes inside the TMJ have been much greater expressed within the completely edentulous subjects in whom the angle of sagittal condyle path declines and so does the articular eminence height. CLINICAL SIGNIFICANCE: It is essential to provide the edentulous patient with early prosthetic and occlusal rehabilitation after extractions to prevent the anatomical changes in TMJ.

Molecular characterization of Indian pathotypes of Puccinia striiformis f. sp. tritici and multigene phylogenetic analysis to establish inter- and intraspecific relationships.

Stripe rust caused by Puccinia striiformis f. sp. tritici (Pst) is one of the most devastating diseases of wheat (Triticum spp.) worldwide. Indian isolates were characterised based on their phenotypic reaction on differential hosts carrying different Yr genes. Based on virulence/avirulence structure, isolates were characterised into ten different pathotypes viz. 70S0-2, 67S64, 70S4, 66S0, 70S64, 66S64-1, 38S102, 47S102, 46S119, and 78S84. These Indian pathotypes of P. striiformis f. sp. tritici and 38 pathotypes of other rust species (P. graminis tritici and P. triticina) were used in this study to analyze their molecular phylogenetic relationship. The nucleotides of rDNA-ITS, partial ß-tubulin and ketopantoate reductase genes of all the pathotypes were sequenced directly after PCR. Based on sequence data of rDNA-ITS and ß-tubulin, three phylogenetic groups corresponding to three different species of Puccinia were obtained. Asian isolates formed a distinct evolutionary lineage than from those derived from USA. The sequence similarity of Indian pathotypes with other Asian (China and Iran) isolates indicated the same origin of pathotypes. The results will allow rapid identification of Indian P.striiformis f. sp. tritici pathotypes causing stripe rust in wheat, assist in making predictions regarding potential rust pathotypes, and identifying sources of resistance to the disease in advance.

Harnessing genetic potential of wheat germplasm banks through impact-oriented-prebreeding for future food and nutritional security.

The value of exotic wheat genetic resources for accelerating grain yield gains is largely unproven and unrealized. We used next-generation sequencing, together with multi-environment phenotyping, to study the contribution of exotic genomes to 984 three-way-cross-derived (exotic/elite1//elite2) pre-breeding lines (PBLs). Genomic characterization of these lines with haplotype map-based and SNP marker approaches revealed exotic specific imprints of 16.1 to 25.1%, which compares to theoretical expectation of 25%. A rare and favorable haplotype (GT) with 0.4% frequency in gene bank identified on chromosome 6D minimized grain yield (GY) loss under heat stress without GY penalty under irrigated conditions. More specifically, the 'T' allele of the haplotype GT originated in Aegilops tauschii and was absent in all elite lines used in study. In silico analysis of the SNP showed hits with a candidate gene coding for isoflavone reductase IRL-like protein in Ae. tauschii. Rare haplotypes were also identified on chromosomes 1A, 6A and 2B effective against abiotic/biotic stresses. Results demonstrate positive contributions of exotic germplasm to PBLs derived from crosses of exotics with CIMMYT's best elite lines. This is a major impact-oriented pre-breeding effort at CIMMYT, resulting in large-scale development of PBLs for deployment in breeding programs addressing food security under climate change scenarios.