L'urbanisme tactique propose des approches de planification alternatives aux méthodes traditionnelles de conception urbaine. Axés sur la satisfaction des besoins des utilisateurs et la résolution des problèmes socio-urbains, ces projets utilisent des méthodes inclusives, intuitives, collaboratives, expérimentales et sensibles pour aborder la ville. Essentiellement, ces projets "ascendants" inspirent une nouvelle façon de penser la ville en construisant de nouvelles valeurs urbaines collectives. Cette analyse documentaire synthétise diverses perspectives sur l'urbanisme tactique, en explorant sa nature adaptative, son rôle de catalyseur économique, sa dimension d'autonomisation sociale, l'influence narrative des médias et son applicabilité à l'échelle mondiale. Collectivement, ces dimensions décrivent l'urbanisme tactique comme une force à multiples facettes qui façonne les aspects économiques, sociaux et culturels du développement urbain.
The central Andean Precordillera, between 30-31°S, has experienced active faulting and deformation from the early Miocene to present driven by a flat-slab segment of the down-going Pacific Plate. Basic models for fault propagation, in this region, involve progressive eastward stepping of deformation; however, out-of-sequence faulting has been postulated. Furthermore, deformation appears to have started earlier in the northern part of this region and later in the southern part. We use apatite (U-Th-Sm)/He (AHe) low-temperature thermochronology to quantify timing of fault exhumation and fault growth patterns to test hypotheses about out-of-sequence thrusting and the southward propagation of deformation in the region. Nine vertical transects were collected in the eastern-most part of the Precordillera. Preliminary AHe data indicate complete and partial age resetting in middle to late Miocene sedimentary units, that were deposited, buried, and subsequently exhumed. AHe ages range between 30 - 2Ma and trend younger to the south, supporting previous suggestions of north to south deformation migration. Additionally, we use cosmogenic radionuclides (CRN) to assess modern erosion rates across the landscape. Initial erosion rates range from 22 – 1330 m/my, with generally lower rates in the north and higher rates to the south. Ongoing analysis and modeling of both thermochronologic and CRN data will help to constrain the recent exhumation and erosion history in the central Andean Precordillera and determine if combining these two techniques can be used to identify out-of-sequence faulting and changes in spatial patterns of tectonically driven deformation.
Microbiome and metabolic activity of the Soybean nodule changed by the Rhizobacteria and Fusarium complexM. Saleem1, Md Imam ul Khabir1 , Jannat Jhumur1 ,Z. H. Pervaiz21Department of Biological Sciences, Alabama State University2Department of Biological Sciences, Auburn UniversityKeyword : Microbiome, DNA Extraction,16S RNA,Sequencing, Microbial community, Microbial Diversity,OTU,Nodule Microbiome,BNF.Soybean plants develop symbiotic associations with rhizobia to form nodules in which the biological nitrogen fixation (BNF) occurs, which is an important nitrogen source for plant growth. Though soybean root-microbiome interactions are studies, we know little about nodule microbiome and metabolome, and how it is influenced by beneficial and pathogenic microbes. Here,we investigated the effect of rhizobacterial (growth promoting) and Fusarium spp .,(causing root rot disease) consortia on nodule microbiome and metabolome via seed inoculation in a field experiment.The soybean seeds were inoculated withbeneficial and pathogenic consortia while thecontrol represented the un-inoculated seeds. These seeds were sown in an experimental field soil managed by the University of Nebraska,Lincoln (UNL).The nodules were collected from soybean plants and DNA was extracted.The frozen nodules were crushed in the super-deionized water and metabolome analysis was done at the UNL.The 16S rRNA gene amplicon sequencing was done at the University of Minnisota.Though Proteobacteria dominanted the nodule microbiome, the abundance of other phyla was also significant across all treatments.Both consortia suppressed dominant bacterial families (including population of Bradyrhizobiumsp .)while rhizobacterial consortia incrased the diversity of nodule microbiome.The soybean nodules exhibited a rich community of bacterial phyla while microbiome was dominated by Proteobacteria in all treatments. Pathogen and beneficial consortia suppressed Proteobacteria though later increased OTU diversity and decreased the abundance ofBradyrhizobiumsp . Pathogen increased amino acids and organic compounds contents while rhizobacteria increased the contents of sugar acids, sugar alcohol, and organic acids.So,pathogen and benecifial consortia altered nodule microbiome and metabolome though their effects on BNF remain understudied.
Modern manufacturing enterprises have invested in a variety of sensors and IT infrastructure to increase plant floor information visibility. This offers an unprecedented opportunity to track performances of manufacturing systems from a dynamic, as opposed to static, sense. Conventional static models are inadequate to model manufacturing system performance variations in real-time from these large non-stationary data sources. This paper addresses a physics-based approach to model the performance outputs (e.g., throughputs, uptimes, and yield rates) from a multi-stage manufacturing system. Unlike previous methods, degradation and repair dynamics that influence downtime distributions in such manufacturing systems are explicitly considered. Sigmoid function theory is used to remove discontinuities in the models. The resulting model is validated using real-world datasets acquired from the General Motor's assembly lines, and it is found to capture dynamics of downtime better than traditional exponential distribution based simulation models. Index Terms-nonlinear stochastic differential equation (n-SDE) model, mean time between failure (MTBF), mean time to repair (MTTR), recurrence analysis, multi-stage manufacturing systems
Evaluation and improvement of Type III resistance (lower mycotoxin accumulation) is an integral part in developing wheat varieties with resistance to Fusarium Head Blight. Therefore, application of novel tools is necessary to increase selection accuracy and intensity. Here, we explored the application of phenomic prediction using hyperspectral imaging in predicting Deoxynivalenol (DON) content in soft winter wheat kernels. In all Bayesian prediction models used, phenomic prediction recorded higher accuracy (0.63-0.67) than genomic prediction (0.55-0.60). Following this, we proceeded to use the trained prediction models: Bayes C, Bayesian Ridge Regression, and Bayesian LASSO in a testing set of F4:5 breeding lines. Selection was carried out using Unsupervised K-Means Clustering. A large proportion of F4:5 breeding lines predicted to have low DON content were also observed to have low GC/MS-derived DON content. The results of this study revealed the potential application of hyperspectral imaging in predicting Deoxynivalenol accumulation in soft winter wheat kernels with increased selection intensity.
Dry bean (Phaseolus vulgaris L.) is the third largest pulse crop grown in Canada. Due to climate change and extreme weather, dry bean varieties are subjected to abiotic and biotic stresses, which affect yield stability and seed quality. Development of resilient cultivars is the most effective strategy to ensure productivity and environmental sustainability of dry bean crop. In this project, key phenotypic traits will be extracted for genetic improvement and development of elite cultivars with early maturity and high yield. Traditional phenotyping approaches are rigorous, time-consuming, and subject to human errors. Unmanned aerial vehicle (UAV)-based high-throughput phenotyping (HTP) has been changing the way of doing large-scale phenotyping in plant breeding. The use of aerial imaging systems offers a potential solution to provide an intensive tool for complex traits assessment to evaluate a large number of dry bean genotypes. By this, HTP technique will be optimized to improve selection efficiency of agronomic, physiological and disease resistance traits. In this study, two dry bean field trials, Advanced Yield Trial (AYT) consisting of F7 generation [yellow bean (5 entries), Pinto bean (20 entries)], and Performance Yield Trial (PeYT) of F8-F10 generation (49 entries) were grown in a randomized-block design at the Fairfield Research Farm at AAFC Lethbridge, AB. Both field trials were imaged at the specific developmental stages (vegetative, flowering, maturity) using UAV mounted RGB and multispectral sensors. The acquired imagery have been processed to accurately overlay images from different dates (time-series data comparison). We analyzed three-time point RGB and multispectral images to identify valuable traits such as canopy height, crop lodging, physiological maturity and accumulation of crop biomass over time. With the preliminary results, we found the utilization of UAV-based HTP has significant advantage in non-destructive measurements of canopy-level functional traits. Assessment of these traits at same climatic region can be used to identify crop characteristics that are important for screening of high-quality dry bean experimental lines and cultivars in field conditions. In the long term, it will provide a consistent and reliable information system to rapidly screen thousands of breeding populations individually that need to be genotyped for morphological and physiological functional traits.
Digital imaging technology has gained significant interest in recent decades, particularly in the field of high-throughput phenotyping (HTP) for plant breeding. Breeding programs generates thousands of new crop lines that require evaluation under multiple environments. Considerable efforts have been made in utilizing genome wide association studies (GWAS) and genomic selection (GS) to identify genetic markers and improve desirable crop characteristics. Selecting key phenotypes is an essential component of plant breeding, and traditional methods require considerable resources and are subjective. Therefore, breeders and geneticists are in an urge of a robust technology to identify desirable crop traits. HTP using advanced sensors is a promising approach to evaluate improved crop genotypes for traits of agronomic importance. In this project, six Research and development Centers (RDCs) of Agriculture and Agri-food Canada have been utilizing University of Saskatchewan built Field Phenotyping System ("UFPS Cart") to phenotype a heritage bread wheat panel. The UFPS cart is a proximal sensing mobile platform equipped with multiple payloads (RTK GPS, RGB, NIR, and LiDAR sensor). For diverse climatic data collection, the panel consisting of 30 Canadian western spring wheat varieties were grown under six environments. This study aims to develop large-scale data management and image analysis pipelines to quantify different crop growth characteristics representing agronomic and physiological traits. It support data-driven decision making under genotype × environment effect. The multi-location imagery and ground observation data from six environments are currently being processed using the internal General Public Science Cluster (GPSC) for deep learning training to develop prediction models and extract phenotypic traits of interest (canopy height, crop lodging, heading, maturity, grain yield and protein content). The developed tools and associated models will aid to accelerate advances in cereal breeding programs.
The Key facts of Translations are meant to validated the Fuzzy Weights. Moreover, In our case Fuzzy weights are based on Logical Reasoning and Logical Thinking, even though fuzzy logics are not concerned with data rather it depends on thought process based on human brain Imitation. Generally, at some point mankind depends on his own creations and tries to understand its usage through Machine Language or so called Machine Teaching and this is were humans try to understand fuzzy concepts based on binary language, moreover this translations are meant to become complicated the more we go deeper but here comes our originality of introducing fuzzy weights or logic based on Switching Theory Logical design which produces binary keys instead of values, we concentrated on reducing complexity of Machine Teaching through fuzzy weights.
ORCiD: [https://orcid.org/0000-0003-0655-2343] Keywords: Root imaging, root-system architecture (RSA), soybean, 2D-phenotyping & 3D-phenotyping Roots are a major part of plant systems and are essential to obtaining water and nutrients. Despite their importance, roots have not been extensively examined as compared to their aboveground counterparts, due primarily to the difficulties of access and lack of standard methods to quantify root morphology. While there have been several experiments performed under controlled environments, comparatively fewer studies have examined root architectures under field conditions. Here, we apply two imaging techniques to characterize variability in Root System Architecture (RSA) in diverse soybean genotypes under field settings with two contrasting soil conditions. Thus, our objectives are to (1) quantify root system architecture using 2D image techniques (e.g., Winrhizo and Image J) and (2) evaluate a contrasting subset of these samples (n = 30) using a novel 3D phenotyping approach. The research seeks to meet the need for enhanced methods in root system architecture analysis across diverse field conditions potentially leading to more resilient, high-yielding soybean varieties.
With increasing demands for sustainable food production, expediting innovation within the development of agricultural products is paramount for Bayer and similar companies. We used a novel gene-editing protocol that generates numerous events to increase our gene-editing capacity. TREDMIL was used create 800 distinct edits in soybeans at three Dt1 gRNA targets in over 1500 events sites distributed across 100 soy lines. With the ability to produce great numbers of edit events, our phenotypic testing had to evolve to keep pace. Using a hypothesis-based approach, we have refined phenotypic testing to measure relevant plant traits that impact yield. Our in-field phenotyping of the target set of plant traits feeds a machine learning model that adjusts small plot yield. Testing in both corn and soy has demonstrated small plots are predictive of large-scale yield testing (84% agreement) and that modeling with additional traits improved the predictive capacity to 93%.
In past global dust storms, no long lasting anomalies in the pressure cycle had been observed. The Global Dust Storm of Mars Year 34 (MY34), however, left behind an average surface pressure lower than what was expected based on the the values recorded on previous years by the Rover Environmental Monitoring Station (REMS) on Curiosity. The main signal contribution to the daily average surface pressure is the CO2 cycle, which is controlled by the Polar ice sublimation and freezing cycles. We used REMS and Mars Climate Sounder (MCS) data to search for correlations between the REMS anomaly and anomalies in the circulation compared to MCS observations from previous years. The findings include an early start of the retreat season for the Northern Polar cap, followed by the longest period of growth for the Southern Polar (SP) cap ice expansion since Curiosity had landed and then, during the dust storm, the longest retreat season of the Southern Polar cap. We also find a larger Northern Polar Cap extension after the storm, suggestive of a larger deposition of CO2 ice. The changes in length of the SP growth and retreat seasons might be consequence of the response of the zonal mean circulation to the dust storm. Changes in the structure of the zonal mean circulation compared to previous years are found in MCS data and presented. The combination of these anomalies constraint what physical processes may have caused this response in surface pressure after the dust storm.
In the realm of reservoir engineering, the application of machine learning has emerged as a transformative force, offering unprecedented insights into reservoir parameter characterization. In this study, we present a comprehensive analysis of four distinct machine learning models, namely Bagging, Extra Tree Regressor, XGBoost, and Ridge, to elucidate their efficacy in predicting permeability, a critical parameter for reservoir characterization. Our findings reveal an understanding of each model's performance. The Bagging model, while demonstrating an impressive trained accuracy of 0.99, exhibits some uncertainty in high permeability predictions, casting slight shadows on its applicability for reservoir characterization. In contrast, the Extra Tree Regressor model outshines the Bagging model with a trained accuracy of 100% and a prediction accuracy of 99.8%. It boasts lower absolute and absolute percentage errors, reinforcing its ability in permeability prediction. However, the XGBoost model takes a unique approach by emphasizing the density-corrected log over gamma-ray and sonic logs. Despite achieving remarkable trained and predicted data accuracy exceeding 99%, its reliance on the corrected density log introduces a mean absolute percentage error above 10, warranting closer scrutiny. In contrast, the Ridge model struggles, evident from its high AIC reading, signifying its limited compatibility with permeability prediction. Joint plots and LMplot analyses further showcases model behaviors. The Extra Tree model exhibits a 99% confidence interval, underscoring its reliability with minimal underpredictions. Conversely, the Bagging and Ridge models show susceptibility to high uncertainties in permeability predictions, particularly at extreme values. Our study concludes that the Extra Tree Regressor model excels in permeability prediction, with potential applications in reservoir interval assessments. The XGBoost model, while competent in sandstone reservoir prediction, bears a higher uncertainty burden. The Bagging and Ridge models, due to their uncertainty challenges, are less suitable for non-reservoir and sandstone reservoir interval predictions. High permeability correlations with elevated porosity, reduced water saturation, and lower gamma ray readings highlight the reservoir intervals' distinct characteristics. These observations underscore the reliability of our models and their potential contributions to reservoir engineering practices.
Non-destructive, real-time monitoring of root development can be helpful to farmers in improving crop resilience while minimizing resource use (Mervin et al., 2022). However, it is still an unexplored frontier in understanding root responses efficiently. In this study, we employed three in-soil fiber Bragg grating (FBG) based fiber sensors to generate root phenotyping data and developed an automated method using the deep learning architecture ResNet to monitor underground root development. In our preliminary study, we conducted a simulation experiment using two metal rods with diameters of 1mm and 5mm to mimic plant's roots. These rods were inserted to a depth of 15 cm in two different scenarios, 6 and 11 minutes, with the three in-soil FBG sensors continuously collecting data-two FBGs placed on the sides, and one placed at the bottom. The sensor data was preprocessed, resulting in 3228 samples for root diameter and 477 for root depth prediction models. We used an 80/20 split for training and testing the ResNet models to predict the artificial root diameter and ten different depth levels. The achieved accuracy was 0.95 for depth and 0.91 for diameter prediction. Overall, our study demonstrates the potential of ResNet architectures to accurately predict root depth and diameter with fiber optics-based sensors. Therefore, non-destructive root phenotyping in agricultural applications might be possible. Future work will involve evaluating these models in field experiments to assess their real-world performance.
Agriculture utilizes large quantities of freshwater resources to maintain crop production to a level that meets global demand. As the world population expands at a rapid rate, it is critical that we find more efficient ways to manage freshwater resources. The Plant DiTech Phenotyping Platform (Plant-DiTech LTD, Yavne, Israel) is a dynamic plant screening system that allows researchers to create controlled environments and rapidly collect a large quantity of physiological traits in real time. This platform opens the door to hundreds of potential experiments to explore growth trait responses to future environmental conditions and irrigation practices. Here, we test its utility on cultivated rice and upland cotton in a small pilot experiment. Cultivated rice (Oryza sativa ) and upland cotton (Gossypium hirsutim ) are both C3 crop species that are grown throughout the U.S. and many other countries and are critical for food and fiber production, respectively. Cotton is typically produced in the warm and dry regions of southern U.S. (USDA-ERS) where it heavily relies on irrigation practices to remain productive, while rice requires flooded conditions to maintain high productivity. Using these two test species for a preliminary experiment, a series of growth trait measurements in conjunction with system-recorded data by the Plant DiTech phenotyping platform are analyzed to test the linkages between organ- and canopy-level traits.
Prof. Roberto Grobman Keywords: skin, genetics, algorithms, biomarkers, wrinkles, aging, artificial intelligenceAbstractIntroduction: Skin, being the largest organ system in the body, is of utmost importance when it comes to timely diagnostics and treatment of skin conditions. Diagnostics, in history, have been dependent on symptoms and the doctor’s experience. Today, with advances in technology, is it possible to diagnose skin conditions more accurately and early. Skin imaging and deep learning have contributed immensely in very early diagnosis and hence a better prognosis. Artificial intelligence (AI) techniques have been applied in clinical genomics to identify genetic markers for predisposed conditions such as melanoma, psoriasis etc.Methods and results: Research and analysis of three studies were performed to obtain collective data on the current trends in skin disease diagnosis and mapping of genetic markers. AI shows a lot of promise in prediction of skin conditions and early treatment.Conclusion: Skin disease prognosis has been improved by the use of skinomics, microarray and AI techniques for accurate diagnostics and treatment.IntroductionThe skin is the largest organ of the body, composed of epidermis, dermis, and subcutaneous tissues, containing blood vessels, lymphatic vessels, nerves, and muscles, which can perspire, perceive the external temperature, and protect the body. Covering the entire body, the skin can protect multiple tissues and organs in the body from external invasions including artificial skin damage, chemical damage, adventitious viruses, and individuals’ immune system . Skin diseases have a big impact on everyday life and detecting underlying issues at the earliest is gaining importance. It is necessary to develop automatic methods in order to increase the accuracy of diagnosis for multitype skin diseases.Skin diseases and conditions are extremely prevalent, yet diagnostics are based on symptoms and the experience of the doctor. These are, often, not fool-proof and sometimes require a trial-and-error approach to diagnosis. Over the past few years, the image processing technique has achieved rapid development in medicine . A great example, the skin disease varicella was detected by Oyola and Arroyo through image processing technique’s colour transformation, equalization as well as edge detection, and the image of varicella was eventually collected and classified through Hough transform. The final empirical results demonstrated that a better diagnosis was received in terms of detection on varicella, and preliminary test was also conducted on varicella and herpes zoster on that basis. Sumithra et al. proposed a novel approach for automatic segmentation and classification of skin lesions by using SVM and k-nearest neighbor (k-NN) classifier. Kumar and Singh  established the relationship of skin cancer images across different types of neural network. Then, medical images were collected into this skin cancer classification system for training and testing based on the matlab image processing toolbox .Bioinformatics is a research field that uses computer‐based tools to investigate life sciences questions, employing “big data” results from large‐scale DNA sequencing, whole genomes, transcriptomes, metabolomes, populations, and biological systems, which can only be comprehensively viewed in silico. The epidermis was among the earliest targets of bioinformatics studies because it represents one of the most accessible targets for research. Consequently, bioinformatics methods in the fields of skin biology and dermatology generated a large volume of bioinformatics data, which led to origination of the term “skinomics.” Skinomics data are directed toward epidermal differentiation, malignancies, inflammation, allergens, and irritants, the effects of ultraviolet (UV) light, wound healing, the microbiome, stem cells, etc. Cultures of cutaneous cell types, keratinocytes, fibroblasts, melanocytes, etc., as well as skin from human volunteers and from animal models, have been extensively experimented on . We are presenting some combined research information on diagnostic imaging and application of bioinformatics in skin diseases through this article.Methods and resultsBioinformatics is an interdisciplinary field of knowledge that combines computer science, biology and biomedical sciences and statistics. Bioinformatics is oriented to the application and development of new computational methods to expand biological, biomedical or epidemiological knowledge.We used a data set provided by Transceptar Technologies/FullDNA, from Israel. The algorithm developed by Transceptar Technologies TRCPR18 has AI-based technology and allows the analysis of millions of data in a few seconds, taking into account the orientation of the gene and proceeding with various types of predisposition calculations. The Transceptar / FullDNA algorithm analyzes more than 61 skin-related conditions and this information was used to confirm previous research.Recent developments in high-speed technologies have led to a major revolution in biological and biomedical research and where today bioinformatics plays an increasingly central role in the analysis of large amounts of data.Literature from three studies were researched to summarise modern advances in skin disease diagnostics using Artificial Intelligence (AI), bioinformatics, skin imaging and machine learning.Imaging and deep learning applications:A study conducted by Patnaik et al. researched an approach to use various computer vision based techniques (deep learning) to automatically predict the various kinds of skin diseases. The system uses three publicly available image recognition architectures namely Inception V3, Inception Resnet V2, Mobile Net with modifications for skin disease application and successfully predicts the skin disease based on maximum voting from the three networks. The study approach involved development of a widespread plan to test the special features and general functionality on a range of platform combination, initiated by the test process. The method involves use of pre-trained image recognizers with modifications to identify skin images. The use of deep learning and ensembling features, results showed higher accuracy rate along with identification of more diseases. Previous models reported a maximum of six skin diseases with an accuracy level of 75% compared to as many as twenty diseases with an accuracy of 88%, in the study conducted by Patnaik et al. This proves that deep learning algorithms have a huge potential in the real world skin disease diagnosis .Microarray and skinomics applications:The most commonly used and highly preferred methodology in skinomics is DNA microarray technology, such as Affymetrix and Illumina. DNA microarrays are a perfect medium as they simultaneously measure the expression of the entire genome . Printed cDNA arrays, originated by Brown at Stanford , are often homemade, inexpensive, and can compare two samples on the same chip. Commercial alternatives such as oligonucleotide microarrays are available too, but a little expensive. These techniques offer personalized medication and find broad applications in the future. Microarray technology can be applied in skin ageing studies, UV damage studies, transcriptional studies in melanoma and wound healing studies. Genome‐wide association studies, GWAS, comprise examination of many common DNA polymorphisms in a large population cohort to detect association of polymorphisms with a given disease. Such polymorphisms can point to the genes where disease‐causing mutations may map. GWAS are particularly useful in the analysis of diseases, such as psoriasis, which are common and with a strong genetic component .Artificial intelligence in clinical genomics:Most artificial intelligence techniques have been adapted to address the various steps involved in clinical genomic analysis—including variant calling, genome annotation, variant classification, and phenotype-to-genotype correspondence—and perhaps eventually they can also be applied for genotype-to-phenotype predictions . AI has proven to be highly effective in the following areas:Variant Calling : The clinical interpretation of genomes is sensitive to the identification of individual genetic variants among the millions populating each genome, necessitating extreme accuracy. Standard variant-calling tools are prone to systematic errors that are associated with the subtleties of sample preparation, sequencing technology, sequence context, and the sometimes unpredictable influence of biology such as somatic mosaicism . AI algorithms can learn these biases from a single genome with a known gold standard of reference variant calls and produce superior variant calls .Phenotype-to-genotype mapping : The molecular diagnosis of skin disease often requires both the identification of candidate pathogenic variants and a determination of the correspondence between the diseased individual’s phenotype and those expected to result from each candidate pathogenic variant. AI algorithms can significantly enhance the mapping of phenotype to genotype, especially through the extraction of higher-level diagnostic concepts that are embedded in medical images and EHRs .Genotype-to-phenotype prediction : The ultimate purpose of clinical genetics is to provide diagnoses and forecasts of future disease risk. Although, not many successful predictions have been made in literature yet, this shows promise in the fact that a few simple studies have shown to accurately predict conditions .Conclusion:AI systems have surpassed the performance of state-of-the-art methods and have gained FDA clearance for a variety of clinical diagnostics, especially imaging-based diagnostics. The availability of large datasets for training, together with advances in AI algorithms is driving this surge of productivity. Deep-learning algorithms have shown tremendous promise in a variety of clinical genomics tasks such as variant calling, genome annotation, and functional impact prediction. It is possible that more generalized AI tools will become the standard in these areas, especially for clinical genomics tasks where inference from complex data is a frequently recurring task .The application of AI in medicine is a burgeoning area of development in light of the major impact it could potentially have on healthcare provision. The application of machine learning in medical imaging on skin lesions has been the most impactful, and demonstrates the potential for this technology in medical practice .
In recent years, the use of field-based high-throughput phenotyping (FHTP) has surged across diverse disciplines. Particularly, it has gained significant traction in agricultural research, enabling scientists to efficiently gather extensive data for a deeper understanding of plant biology in the context of plant growth dynamics. This abstract aims to demonstrate potential applications of data obtained through high-throughput phenotyping in the fields of plant biology and predictive plant breeding.The study utilized temporal phenotype data derived from repetitive drone flights equipped with various sensors. These data were incorporated into a novel mixed model, providing insights into the temporal genetic effects on different genotypes/plants. Gaussian or Lorentzian peak models, as well as Functional Principal Component analysis, were employed to characterize the growth patterns of various genotypes in diverse environments. The research revealed that Temporal Effect Sizes of Quantitative Trait Loci (QTLs) influence growth differently across time points, highlighting the dynamic nature of plant development. Furthermore, the study uncovered time-dependent associations between genotypes and their environments based on temporal phenotype values.The predictive capability of temporal phenomic data was found to surpass that of genomic data in predicting complex traits in maize. However, the combination of phenomic and genomic data consistently yielded the most accurate predictions for complex traits. By analyzing drone flights at specific growth stages, the study quantified physiological traits such as senescence progression across multiple time points. This analysis led to the calculation of new traits, including days to senescence and grain filing period, providing valuable insights into plant development and growth dynamics.
Wheat is an important primary crop that nourishes billions of people worldwide. Wheat diseases, particularly Fusarium head blight (FHB) disease, often have a severe effect on wheat yield in terms of both quantity and quality posing potential threats to the health of humans and livestock. Traditional methods such as field surveys for monitoring and assessing wheat diseases are time-consuming, costly and inefficient. In recent years, remote sensing approaches, particularly aerial imaging using Unmanned Aerial Vehicles (UAV), have become invaluable tools for rapid field scouting at larger scale, as well of crop growth and health status. This study aims to investigate the potential of combining high-resolution UAV multispectral imagery with machine learning (ML) methods for the estimation of FHB disease severity. Two experimental wheat fields were established at Volga, South Dakota, USA, in 2022. The severity of FHB disease was assessed and rated in the fields; and synchronous UAV flights were conducted to collect multispectral imagery. Canopy spectral and texture features were derived from the UAV multispectral imagery and used as input variables for ML models to predict FHB disease severity levels. Both classification and regression approaches were applied to estimation FHB severity using ML models such as Random Forest, Support Vector Machine, and Deep Neural Networks. The results show that both canopy spectral and texture features are important indicators for monitoring the severity of FHB wheat disease. Furthermore, the use of UAV remote sensing, combined with ML-based modeling, is a sustainable approach for rapid and accurate detection of wheat FHB disease severity.