The knowledge of the variability of antigens of blood groups is essential in the transfusional practice, mainly to avoid grave alloimmunizations. This study had as objective to evaluate the prevalence of the phenotypes from the blood donors from Santa Maria´s Blood Center. These donors were evaluated for the mainly antigens of ABO, Rh and Kell systems. About the phenotyped samples with Rh system, 1274 samples (54.18%) were phenotyped as positives Rh and 1077 samples (45.82%) phenotyped as negative Rh. From the phenotyped donors as negatives Rh, 103 samples (9.5%) were positives for the ―C‖ or ―E‖ antigen. Relating the percentage of the Kell system positive in donors with negative Rh, was gotten 8.3%. We concluded that the negative Rh donor must be analyzed for other antigens of Rh system and for the Kell antigen, exactly being less immunogenics, these antigens are able to cause Grave Hemolytic Disease and Alloimmunizations. The D antigen Phenotypic expression may vary due to changes qualitative/quantitative: weak D, partial D. This study analyzed a sample of donors by genotyping to identify the most common D variants in this region was found (44%) weak D, (3%) and partial D. Strengthens thus the importance of established protocols for use of rare blood and ensure the proper use of blood products, as well as the safety of blood transfusion. / O conhecimento da variabilidade dos antígenos de grupos sanguíneos é essencial na prática transfusional, principalmente para evitar aloimunizações graves. Assim, este estudo teve como objetivo avaliar a prevalência dos fenótipos dos doadores de sangue do Hemocentro de Santa Maria, os quais foram avaliados para os principais antígenos dos sistemas ABO, Rh e Kell. Das amostras fenotipadas quanto ao sistema Rh, 1274 amostras (54.18%) foram fenotipadas como Rh positivos e 1077 amostras (45,82%) fenotipadas como Rh negativo. Dos doadores fenotipados como Rh negativos, 103 amostras (9,5%) foram positivas para o antígeno ―C‖ e/ou ―E‖. Relacionando o percentual do sistema Kell positivo em doadores Rh negativos foi de 8,3%. Conclui-se, então, que o doador Rh negativo deve ser analisado para os demais antígenos do sistema Rh e para o antígeno Kell, pois, mesmo sendo menos imunogênicos, estes antígenos são capazes de causar doença hemolítica graves e aloimunizações. O antígeno D pode variar de expressão fenotípica, devido a alterações qualitativas/quantitativas: D fraco, D parcial. Este trabalho analisou uma amostragem de doadores, através de genotipagem para identificar quais as variantes de D mais freqüentes nesta região, foi encontrado (44%) D fraco, (3%) D parcial. Reforça, dessa forma, a importância de ser estabelecidos protocolos para utilização destes sangues raros e garantir o uso correto destes hemocomponentes, assim como a segurança transfusional.

Field data collection involves time and money consuming processes, additionally carrying possible measurement errors. With the technological advance in the last years, low cost remote sensing tools have emerged to facilitate procedures for in-field measurements, being one of the most known techniques the use of multispectral cameras coupled to RPA. These tools are complemented by the implementation of procedures in GIS and image-processing software, from which are developed methodologies leading to extract target values from a certain original set of data. In this work, multispectral images were used in two case studies: (1) for yield estimation in pea plots for breeding research, and (2) for plant counting in an apple nursery planted directly on the soil; both fields are located in Washington State, USA. In the first case, a reliable and replicable methodology for yield estimation was created as a high throughput phenotyping technique; while in the second case an algorithm capable of identifying the number of apple plants with more than 95% accuracy was developed. In both studies, remote sensing is used as an efficient and practical way to improve field operations under the specified conditions of each case. / A coleta de dados de campo envolve processos de grande consumo em tempo e dinheiro, ademais de levar o risco de possíveis erros de medição. Com o avanço tecnológico nos últimos anos, surgiram ferramentas de sensoriamento remoto de baixo custo para facilitar procedimentos de medição em campo, sendo uma das técnicas mais conhecidas o uso de câmeras multiespectrales acopladas a um ARP. Essas ferramentas são complementadas pela implementação de procedimentos em programas SIG e de processamento de imagens, a partir dos quais são desenvolvidas metodologias que visam extrair valores alvo desde um determinado conjunto original de dados. Neste trabalho, foram utilizadas imagens multiespectrais no desenvolvimento de dois estudos de caso: (1) para estimativa de produtividade em parcelas para pesquisa de ervilha, e (2) para contagem de plantas em um viveiro de maçã plantado diretamente no solo; ambos os campos localizados no estado de Washington, EUA. No primeiro caso, foi criada uma metodologia confiável e replicável para estimativa de produtividade como técnica de fenotipagem de alto rendimento; enquanto no segundo caso, foi desenvolvido um algoritmo capaz de identificar o número de plantas de maçã com mais de 95% de exatidão. Em ambos os estudos, o sensoriamento remoto é usado como uma ferramenta eficiente e prática na melhora de operações de campo.

O gÃnero Malassezia abrange leveduras lipofÃlicas e lipodependentes que, apÃs vÃrias mudanÃas em sua classificaÃÃo taxonÃmica, compreende na atualidade 13 espÃcies, incluindo M. pachydermatis, M. furfur, M. globosa, M. obtusa, M. sympodialis, M. slooffiae, M. restricta, M. dermatis, M. japonica, M. yamatoensis, M. nana, M. caprae e M. eqÃina. Estas leveduras estÃo associadas a vÃrias enfermidades que incluem infecÃÃes, como a pitirÃase versicolor ou dermatoses, dermatite seborrÃica e dermatite atÃpica, entre outras. O objetivo geral deste trabalho foi contribuir para melhor entendimento sobre a identificaÃÃo fenotÃpica, manutenÃÃo em micoteca e sensibilidade a antifÃngicos in vitro de Malassezia spp.. A fenotipagem baseou-se nas caracterÃsticas macro e micromorfolÃgicas, bem como anÃlises em bioquÃmicas e nutricionais. Doze cepas de diferentes espÃcies de Malassezia spp. sofreram estocagem a -80ÂC sob Ãleos vegetais. A tÃcnica de microdiluiÃÃo foi realizada em caldo RPMI 1640, suplementado com bile, glicerol e Tween 20, sendo complementada com subcultivo em Ãgar Dixon, para determinaÃÃo da concentraÃÃo inibitÃria mÃnima (CIM) e da concentraÃÃo fungicida mÃnima (CFM). As drogas testadas foram Cetoconazol (CET), Itraconazol (ITR), Fluconazol (FLU), Voriconazol (VOR), Anfotericina B (ANB) e Caspofungina (CAS). Com a anÃlise fenotÃpica convencional das cepas (n=38), pode-se sugerir a presenÃa de M. furfur/ M. dermatis (n=17), M. sympodialis (n=8), M. slooffiae (n=5) e M. pachydermatis (n=8). O estoque realizado em Ãleos vegetais a -80ÂC demonstrou significativas taxas de recuperaÃÃo, no entanto caracterÃsticas fisiolÃgicas foram alteradas como _-glicosidase e assimilaÃÃo de Chremophor EL. A maioria das cepas estudadas (84,21%), M. pahydermatis e cepas lipodependentes, foram sensÃveis ao CET e ITR, obtendo valores de CIM &#61603;&#61472;0,03&#956;g/mL. Para o FLU, os valores de CIM variaram de 4 a 64&#956;g/mL e frente ao VOR as cepas de M. pachydermatis obtiveram CIMs que variaram de <0,03 a 2&#956;g/mL, enquanto as cepas lipodependentes de Malassezia spp. obtiveram resultados mais dispersos que variaram de <0,03 a >16&#956;g/mL. Perante ANB, o intervalo de CIM encontrado foi de 1 a >16&#956;g/mL. NÃo foi possÃvel determinar os valores de CIM e CFM frente à caspofungina. Os extratos oriundos das sementes do abacate foram ativos contra as cepas de M. pachydermatis. / The genus Malassezia enclose lipophylic and lipid-dependent yeast that after many changes in its taxonomic classification, comprises in the atuality 13 species, including M. pachydermatis, M. furfur, M. sympodialis, M. globosa, M. obtusa, M. restricta, M. slooffiae, M. dermatis, M. japonica, M. yamatoensis, M. nana, M. equine, and M. caprae. These yeasts are associated to several diseases including, such as pityriasis versicolor, or dermatoses, seborrheic dermatitis and atopic dermatitis, among others. The general aim of this study was to contribute to better knowledgement about the phenotypical identification, storage in fungal collection and in vitro antifungal susceptibility of Malassezia spp. The phenotiping was based on macro and micromorphologics characteristics, as well as biochemistry and nutritional analisys. Twenteen strains suffers storage at -80ÂC in vegetables oils. The microdilution technique was accomplished in RPMI 1640 broth, supplemented with ox bile, Tween 20 and glycerol, being complemented with subculture on Dixon agar, for determination of the minimal inhibitory concentration (MIC) and minimal fungicidal concentration (CFM). The drugs tested were ketoconazole (KET), itraconazole (ITR), fluconazole (FLC), amphotericin B (AMB) and caspofungine (CAS). With the conventional phenotypical analysis of the strains (n=38), could suggest the presence of the M. furfur/ M. dermatis (n=17), M. sympodialis (n=8), M. slooffiae (n=5) and M. pachydermatis (n=8). The stored accomplished in vegetable oils at -80ÂC showed meaningful rate of recorver, but physiologic characteristics was modified, such as _-glicosidase activity and Chremophor EL assimilation. Most of the strains (84,21%) was sensitive for KET and ITR, obtaining values of MIC &#61603;&#61472;0.03&#956;g/mL. For FLC the MIC range was 4 to 64&#956;g/mL, against VOR the strains of M. pachydermatis obtained MIC range <0,03 a 2&#956;g/mL, therefore the lipid-dependents strains of Malassezia spp. obtained dispersive results from <0.03 to >16&#956;g/mL. Against ANB, the MIC range was 1 to >16&#956;g/mL. It was not possible determinate the MIC and MFC values for caspofungine. The extracts from avocado seeds were active against strains of M. pachydermatis.

Os testes pré-transfusionais são o conjunto de técnicas imuno-hematológicas aplicadas nas amostras do receptor e do doador com a finalidade de fornecer uma unidade de hemácias compatível e segura para a transfusão. Além da fenotipagem ABO e RhD obrigatória nos testes pré-transfusionais, a determinação do perfil antigênico de outros antígenos eritrocitários de importância clínica, melhora a segurança transfusional, especialmente para pacientes previamente aloimunizados ou candidatos à transfusões crônicas. Para tais pacientes, conhecer o fenótipo eritrocitário permite a seleção de concentrados de hemácias com o mesmo fenótipo, o que previne reações transfusionais hemolíticas, assim como nova aloimunização. Indivíduos que apresentam as hemácias sensibilizadas \"in vivo\" por anticorpos anti-eritrocitários, como ocorre na anemia hemolítica autoimune (AHAI), terão o teste direto da antiglobulina (TDA) positivo, dificultando a realização da fenotipagem. O TDA positivo provoca resultados falsos positivos especialmente quando são utilizados para a fenotipagem soros comerciais da classe IgG que exigem a fase de antiglobulina humana, ou teste indireto da antiglobulina (TIA), para leitura. Em 1982 Edwards, Moulds e Judd propuseram a utilização da técnica da cloroquina para dissociar os complexos de antígenoanticorpo preservando a membrana eritrocitária e permitindo a fenotipagem. Atualmente é a técnica mais utilizada nos centros brasileiros. A técnica da glicina-ácida/EDTA foi primeiramente descrita por Louie, Jieng e Zeroulis em 1986, porém é pouco utilizada por não haver os reagentes prontos comercialmente no mercado brasileiro. O presente estudo avaliou a técnica da glicina-ácida-EDTA com reagentes preparados \"in house\" em paralelo à técnica da cloroquina, que é atualmente a técnica aplicada no Hemocentro de Ribeirão Preto. Para isso, amostras de hemácias de doadores (n=50) com fenótipo conhecido foram sensibilizadas com anticorpos humanos de especificidade conhecida e ambas as técnicas foram aplicadas. Foi verificado ainda o efeito da técnica sobre a expressão dos antígenos eritrocitários (Fya, Fyb, Jka, Jkb, S e s) através da fenotipagem das amostras que tiveram o TDA negativado. A viabiliadade dos anticorpos presentes no eluato recuperado após tratamento com a glicina- ácida/EDTA também foi testada. A glicina-ácida/EDTA foi efetiva em negativar o TDA em 33 amostras (66%), comparável com a cloroquina que negativou 28 amostras (56%). O eluato apresentou-se viável no tratamento com a glicina-ácida/EDTA e não foi constatada destruição dos antígenos eritrocitários após o tratamento. Concluímos que a técnica da glicina- ácida/EDTA pode ser utilizada como uma opção no tratamento de hemácias com TDA positivo com a vantagem de seu tempo de execução ser inferior ao da cloroquina (2 minutos x 30 a 120 minutos), o que lhe torna útil em situações de maior urgência. Quando ambas as técnicas são utilizadas em hemácias diferentes de uma mesma amostra a efetividade está significativamente aumentada (p<0,05). / The pre-transfusion tests are the group of immunohematology techniques applied in the receptor and donor samples in order to provide a unit of red blood cells compatible and safe for transfusion. In addition to the obligatory ABO and RhD phenotyping in the pretransfusion tests, the determination of the antigenic profile of other red cells antigens of clinical importance improves transfusion safety, especially for patients previously alloimmunized or candidates for chronic transfusion. For such patients, knowing the phenotype allows the selection of red blood cell concentrates with the same phenotype, which prevents hemolytic transfusion reactions, as well as new alloimmunization. Individuals who have antibody-coated red blood cells sensitized in vivo, such as in autoimmune hemolytic anemia (AHAI), will have a direct antiglobulin test (DAT) positive, making it difficult to perform phenotyping. Positive DAT causes false positive results especially when commercially available IgG serums which require the human antiglobulin phase or indirect antiglobulin test (IAT) for reading are used for phenotyping. In 1982 Edwards, Moul ds and Judd proposed the use of the chloroquine technique to dissociate antigen-antibody complexes by preserving the erythrocyte membrane and allowing phenotyping. It is currently the most used technique in Brazilian centers. The EDTA/glycine-acid technique was first described by Louie, Jieng and Zeroulis in 1986, but it is little used because commercially available reagents are not available in the Brazilian market. The present study evaluated the EDTA/glycine-acid technique with reagents prepared in house in parallel to the chloroquine technique, which is currently the technique applied at the Hemocentro de Ribeirão Preto. For this, donor red cells samples (n = 50) with known phenotype were sensitized with human antibodies of known specificity and both techniques were applied. The effect of the technique on the expression of erythrocyte antigens (Fya, Fyb, Jka, Jkb, S and s) was also verified by the phenotyping of the samples that had negative DAT. The viability of the antibodies present in the recovered eluate after treatment with EDTA/glycine-acid. A was also tested EDTA/glycine-acid was effective in negatively affecting DAT in 33 samples (66%), comparable to chloroquine that negatived 28 samples (56%). The eluate was viable in treatment with EDTA/glycine-acid and no destruction of erythrocyte antigens after treatment. We conclude that the EDTA/ glycine-acid technique can be used as an option in the treatment of red cells with TDA positive, with the advantage that its execution time is inferior to that of chloroquine (2 minutes x 30 to 120 minutes), which makes it useful in emergency situations. When both techniques are used in different erythrocytes of the same sample, the effectiveness is significantly increased (p <0.05).

Many systems for field-based, high-throughput phenotyping (FB-HTP) quantify and characterize the reflected radiation from the crop canopy to derive phenotypes, as well as infer plant function and health status. However, given the technology's nascent status, it remains unknown how biophysical and physiological properties of the plant canopy impact downstream interpretation and application of canopy reflectance data. In that light, we assessed relationships between leaf thickness and several canopy-associated traits, including normalized difference vegetation index (NDVI), which was collected via active reflectance sensors carried on a mobile FB-HTP system, carbon isotope discrimination (CID), and chlorophyll content. To investigate the relationships among traits, two distinct cotton populations, an upland (Gossypium hirsutum L.) recombinant inbred line (RIL) population of 95 lines and a Pima (G, barbaderise L.) population composed of 25 diverse cultivars, were evaluated under contrasting irrigation regimes, water-limited (WL) and well-watered pm conditions, across 3 years. We detected four quantitative trait loci (QTL) and significant variation in both populations for leaf thickness among genotypes as well as high estimates of broad-sense heritability (on average, above 0.7 for both populations), indicating a strong genetic basis for leaf thickness. Strong phenotypic correlations (maximum r = -0.73) were observed between leaf thickness and NDVI in the Pima population, but not the RIL population. Additionally, estimated genotypic correlations within the RIL population for leaf thickness with CID, chlorophyll content, and nitrogen discrimination (r(gij) = -0.32, 0.48, and 0.40, respectively) were all significant under WW but not WL conditions. Economically important fiber quality traits did not exhibit significant phenotypic or genotypic correlations with canopy traits. Overall, our results support considering variation in leaf thickness as a potential contributing factor to variation in NDVI or other canopy traits measured via proximal sensing, and as a trait that impacts fundamental physiological responses of plants.

La production agricole doit augmenter plus rapidement pour répondre à la demande alimentaire mondiale dans un avenir proche. Le phénotypage, c'est-à-dire la surveillance quantitative des variables de l'état des cultures et du fonctionnement quantitatif de la canopée, a été reconnu comme le goulot d'étranglement pour accélérer le progrès génétique et augmenter le rendement. Le phénotypage sur le terrain est obligatoire car il permet d'évaluer les génotypes dans des conditions naturelles de champ. Les progrès technologiques des capteurs, de la communication et de l'informatique favorisent le développement de systèmes de phénotypage à haut débit au cours de la dernière décennie. Toutefois, l'interprétation des mesures de phénotypage n' a fait l'objet que d'une attention limitée, ce qui a entraîné une sous-exploitation des potentiels des systèmes actuels. Cette thèse se concentre sur l'interprétation des mesures de phénotypage au champ sur les cultures de blé. Il comprend trois aspects complémentaires qui illustrent les potentiels du traitement d'image avancé, de l'inversion du modèle et de l'assimilation des données pour l'interprétation des mesures de phénotypage afin d'accéder à de nouveaux caractères ou d'améliorer la précision avec laquelle les caractères déjà accessibles ont été récupérés. Plusieurs plateformes (phénotypette, phénomobile, drones) et capteurs (caméras haute résolution RVB, LiDAR) ont été utilisés tout au long de cette étude. Les positions précises des plantes le long et à travers la rangée ont été décrites à partir d'images RVB haute résolution. Des modèles statistiques pour l'espacement des plantes le long du rang et la distance au centre du rang ont ensuite été proposés et calibrés. L'influence du profil de semis sur la fraction verte, facilement mesurable avec les techniques de phénotypage, a ensuite été évaluée. Le modèle statistique utilisé pour décrire la distribution de l'espacement des plantes le long de la rangée a été utilisé pour étudier la taille d'échantillonnage optimale et la méthode d'estimation de la densité des plantes. Enfin, une méthode a été mise au point pour estimer automatiquement la densité végétale à partir des images RVB haute résolution. Les résultats montrent une précision relativement élevée lorsque la résolution spatiale est suffisamment élevée et lorsque les observations sont effectuées avant que les plantes n'aient atteint trois stades de feuilles. Il est relativement facile d'obtenir une estimation précise du DG en utilisant des observations passives à un stade précoce. Toutefois, les performances se dégradent en cas de conditions DG élevées en raison du problème de saturation. L'utilisation du LiDAR avec sa capacité à apporter des informations sur la troisième dimension a été étudiée comme un moyen possible d'atténuer l'effet de saturation basé sur les régularités entre les couches supérieures et plus profondes de la canopée, comme décrit par le modèle ADEL_Wheat. Le LiDAR utilisé équipe la plate-forme de phénotypage phénomobile. Les résultats montrent une amélioration significative des performances lors de l'utilisation des observations LiDAR par rapport à l'estimation classique basée sur la fraction verte, assimilation de l'évolution temporelle des fractions vertes dans le modèle ADEL-Wheat. La surveillance de la dynamique de l'architecture de la canopée pour obtenir les premiers traits de vigueur de la culture est très recherchée par les sélectionneurs. Les résultats montrent que peu de paramètres du modèle ADEL-Wheat sont effectivement accessibles à partir de cette technique d'assimilation. De plus, il permet également d'estimer avec une bonne précision les propriétés émergentes de la canopée telles que le GAI et le nombre de tiges avec plus de 3 feuilles. Sur la base de ces résultats novateurs, des conclusions sont finalement tirées sur les limites de cette étude et sur les travaux futurs à entreprendre pour un phénotypage efficace sur le terrain à haut débit. / Crop production has to increase faster to meet the global food demand in the near future. Phenotyping, i.e. the monitoring crop state variables and canopy functioning quantitatively, was recognized as the bottleneck to accelerate genetic progress to increase the yield. Field phenotyping is mandatory since it allows evaluating the genotypes under natural field conditions. The technological advances of sensors, communication and computing foster the development of high-throughput phenotyping systems during the last decade. However, only limited attentions was paid in the interpretation of phenotyping measurements, leading to an under-exploitation of the potentials of current systems. This thesis focuses on advancing the interpretation of field phenotyping measurements over wheat crops. It includes three complementary aspects that illustrate the potentials of advanced image processing, model inversion and data assimilation for the interpretation of phenotyping measurements to access new traits or improve the accuracy with which already accessible traits have been retrieved. Several platforms (phenotypette, phenomobile, UAV) and sensors (RGB high resolution cameras, LiDAR) were used along this study.Characterization of the sowing pattern and density. The precise plant positions along and across the row was described from high resolution RGB images. Statistical models for the spacing of plants along the row and distance to the row center were then proposed and calibrated. The influence of the sowing pattern on the green fraction that can be easily measured with phenotyping techniques was then evaluated. The statistical model used to describe the distribution of plant spacing along the row was exploited to investigate the optimal sampling siz and method for plant density estimation. Finally, a method was developed to automatically estimate the plant density from the high resolution RGB images. Results show a relatively high accuracy when the spatial resolution is high enough and when observations are made before plants have reached 3 leaves stages.ADEL-Wheat model assisted Estimation of GAI from LiDAR measurements. It is relatively easy to achieve accurate GAI estimate using passive observations at early stages. However, the performances degrade for high GAI conditions due to the saturation problem. The use of LiDAR with its capacity to bring information on the third dimension was investigated as a possible way to alleviate the saturation effect based on the regularities between top and deeper canopy layers as described by the ADEL_Wheat model. The LiDAR used is equipping the phenomobile phenotyping platform. Focus was put on the stage of maximum GAI development when saturation effects are the largest. Results show a significant improvement of performances when using LiDAR observations as compared to classical green fraction based estimation.Assimilation of green fractions temporal evolution into ADEL-Wheat model. Monitoring the dynamics of canopy architecture to get early vigor traits of the crop is highly desired by breeders. The feasibility and interest of a phenotyping data assimilation approach was evaluated based on in silico experiments using the ADEL_Wheat model simulations. The green fraction observed from several view directions and dates is the variable that is assimilated. A sensitivity analysis was conducted to evaluate the effect of the number and spacing of the observation dates as well as the number of view directions used. Results show that few parameters of the ADEL-Wheat model are actually accessible from this assimilation technique. Further, it allows also estimating with a good accuracy emerging canopy properties such as the GAI and the number of stems with more than 3 leaves. Based on these innovative results, conclusions are finally drawn on the limits of this study and on the future work to undertake for efficient field high-throughput phenotyping

Increasing salinity is a worldwide problem, but the knowledge on how salt enters the roots of plants remains largely unknown. Non-selective cation channels (NSCCs) have been suggested to be the major pathway for the entry of sodium ions (Na+) in several species. The hypothesis tested in this research is that PQ loop (PQL) proteins could form NSCCs, mediate some of the Na+ influx into the root and contribute to ion accumulation and the inhibition of growth in saline conditions. This is based on previous preliminary evidence indicating similarities in the properties of NSCC currents and currents mediated by PQL proteins, such as the inhibition of an inward cation current mediated by PQL proteins by high external calcium and pH acidification. PQL family members belonging to clade one in Arabidopsis and barley were characterized using a reverse genetics approach, electrophysiology and high-throughput phenotyping. Expression of AtPQL1a and HvPQL1 in HEK293 cells increased Na+ and K+ inward currents in whole cell membranes. However, when GFP-tagged PQL proteins were transiently overexpressed in tobacco leaf cells, the proteins appeared to localize to intracellular membrane structures. Based on q-RT-PCR, the levels of mRNA of AtPQL1a, AtPQL1b and AtPQL1c is higher in salt stressed plants compared to control plants in the shoot tissue, while the mRNA levels in the root tissue did not change in response to stress. Salt stress responses of lines with altered expression of AtPQL1a, AtPQL1b and AtPQL1c were examined using RGB and chlorophyll fluorescence imaging of plants growing in soil in a controlled environment chamber. Decreases in the levels of expression of AtPQL1a, AtPQL1b and AtPQL1c resulted in larger rosettes, when measured seven days after salt stress imposition. Interestingly, overexpression of AtPQL1a also resulted in plants having larger rosettes in salt stress conditions. Differences between the mutants and the wild-type plants were not observed at earlier stages, suggesting that PQLs might be involved in long-term responses to salt stress. These results contribute towards a better understanding of the role of PQLs in salinity tolerance and provide new targets for crop improvement.

<p>High-throughput phenotyping using high spatial, spectral, and temporal resolution remote sensing (RS) data has become a critical part of the plant breeding chain focused on reducing the time and cost of the selection process for the “best” genotypes with respect to the trait(s) of interest. In this study, the potential of accurate and reliable sorghum biomass prediction using hyperspectral and LiDAR data acquired by sensors mounted on UAV platforms is investigated. Experiments comprised multiple varieties of grain and forage sorghum, including some photoperiod sensitive varieties, providing an opportunity to evaluate a wide range of genotypes and phenotypes. </p><p>Feature extraction is investigated, where various novel features, as well as traditional features, are extracted directly from the hyperspectral imagery and LiDAR point cloud data and input to classical machine learning (ML) regression based models. Predictive models are developed for multiple experiments conducted during the 2017, 2018, and 2019 growing seasons at the Agronomy Center for Research and Education (ACRE) at Purdue University. The impact of the regression method, data source, timing of RS and field-based biomass reference data acquisition, and number of samples on the prediction results are investigated. R2 values for end-of-season biomass ranged from 0.64 to 0.89 for different experiments when features from all the data sources were included. Using geometric based features derived from the LiDAR point cloud and the chemistry-based features extracted from hyperspectral data provided the most accurate predictions. The analysis of variance (ANOVA) of the accuracies of the predictive models showed that both the data source and regression method are important factors for a reliable prediction; however, the data source was more important with 69% significance, versus 28% significance for the regression method. The characteristics of the experiments, including the number of samples and the type of sorghum genotypes in the experiment also impacted prediction accuracy. </p><p>Including the genomic information and weather data in the “multi-year” predictive models is also investigated for prediction of the end of season biomass. Models based on one and two years of data are used to predict the biomass yield for the future years. The results show the high potential of the models for biomass and biomass rank predictions. While models developed using one year of data are able to predict biomass rank, using two years of data resulted in more accurate models, especially when RS data, which encode the environmental variation, are included. Also, the possibility of developing predictive models using the RS data collected until mid-season, rather than the full season, is investigated. The results show that using the RS data until 60 days after sowing (DAS) in the models can predict the rank of biomass with R2 values of around 0.65-0.70. This not only reduces the time required for phenotyping by avoiding the manual sampling process, but also decreases the time and the cost of the RS data collections and the associated challenges of time-consuming processing and analysis of large data sets, and particularly for hyperspectral imaging data.</p><p>In addition to extracting features from the hyperspectral and LiDAR data and developing classical ML based predictive models, supervised and unsupervised feature learning based on fully connected, convolutional, and recurrent neural networks is also investigated. For hyperspectral data, supervised feature extraction provides more accurate predictions, while the features extracted from LiDAR data in an unsupervised training yield more accurate prediction. </p><p>Predictive models based on Recurrent Neural Networks (RNNs) are designed and implemented to accommodate high dimensional, multi-modal, multi-temporal data. RS data and weather data are incorporated in the RNN models. Results from multiple experiments focused on high throughput phenotyping of sorghum for biomass predictions are provided and evaluated. Using proposed RNNs for training on one experiment and predicting biomass for other experiments with different types of sorghum varieties illustrates the potential of the network for biomass prediction, and the challenges relative to small sample sizes, including weather and sensitivity to the associated ground reference information.</p>

<p>  </p> <p>Modern agriculture must address the massive challenge of providing food for the increasing population. The challenge lies in increasing crop yield and reducing losses caused by abiotic and biotic stresses. In fact, for some crops, such as wheat and maize, over 40% of the production is lost due to environmental conditions (abiotic stresses) or pests and pathogens (biotic stresses). Specialists in the area are suggesting a need for a second green revolution to meet the increasing demand in food production. While in the first green revolution was focused on breeding and genetics to produce crops' genetic lines with a higher yield. The second green revolution will utilize cutting-edge technologies to increase yield and reduce crop losses. The development of remote sensing technologies and their applications is the main driving force of modern agricultural practices. Currently, farmers are relying more on automation, data collection, and data analysis to manage farming operations. The reliance on remote sensor technologies is a game-changer for traditional agricultural practices, and it is contributing tremendously to increasing production and avoiding yield losses. Hyperspectral phenotyping is an emerging remote sensing technology that utilizes the light's reflectance to provide insightful information about plant traits. For several years, research groups have been applying hyperspectral phenotyping techniques to detect plant traits information, such as nitrogen content, photosynthesis rates, pests infestation, and abiotic stress detection. Although this is not a novel approach to plant traits detection, this technology application is not mature yet. Several challenges are associated with using hyperspectral information for phenotyping, such as model transferability, data collection scalability, and the heritability of plant traits retrieved using hyperspectral data. In my thesis dissertation, I addressed some of those challenges contributing to advances in hyperspectral phenotyping. My results demonstrate that using full-range hyperspectral reflectance data (400-2400nm) to retrieve nitrogen in winter wheat increases the model transferability across years and genotypes. Predicting nitrogen content using hyperspectral data can be used as a surrogate to calculate nitrogen use efficiency traits. My research highlights the hurdles associated with spectral detection of stresses interaction, such as drought stress, which can mask western corn rootworm detection in maize. Finally, I explored the correlation among spectral, functional, and field traits in a soybean NAM (Nested Association Mapping) population to understand the relationship among those traits' variability and how that information can be used for soybean breeding programs. The outcomes of my thesis dissertation advance the knowledge in the hyperspectral phenotyping field and its application to modern agriculture. Consequently, my study also contributes to food security programs by providing insightful information about the hyperspectral assessment of plant health status, which is essential to increase yield production and reduce crop losses. </p>

Indiana University-Purdue University Indianapolis (IUPUI) / Phenotyping definitions are essential in cohort identification when conducting clinical research, but they become an obstacle when they are not readily available. Developing new definitions manually requires expert involvement that is labor-intensive, time-consuming, and unscalable. Moreover, automated approaches rely mostly on electronic health records’ data that suffer from bias, confounding, and incompleteness. Limited efforts established in utilizing text-mining and data-driven approaches to automate extraction and literature-based knowledge discovery of phenotyping definitions and to support their scalability. In this dissertation, we proposed a text-mining pipeline combining rule-based and machine-learning methods to automate retrieval, classification, and extraction of phenotyping definitions’ information from literature. To achieve this, we first developed an annotation guideline with ten dimensions to annotate sentences with evidence of phenotyping definitions' modalities, such as phenotypes and laboratories. Two annotators manually annotated a corpus of sentences (n=3,971) extracted from full-text observational studies’ methods sections (n=86). Percent and Kappa statistics showed high inter-annotator agreement on sentence-level annotations. Second, we constructed two validated text classifiers using our annotated corpora: abstract-level and full-text sentence-level. We applied the abstract-level classifier on a large-scale biomedical literature of over 20 million abstracts published between 1975 and 2018 to classify positive abstracts (n=459,406). After retrieving their full-texts (n=120,868), we extracted sentences from their methods sections and used the full-text sentence-level classifier to extract positive sentences (n=2,745,416). Third, we performed a literature-based discovery utilizing the positively classified sentences. Lexica-based methods were used to recognize medical concepts in these sentences (n=19,423). Co-occurrence and association methods were used to identify and rank phenotype candidates that are associated with a phenotype of interest. We derived 12,616,465 associations from our large-scale corpus. Our literature-based associations and large-scale corpus contribute in building new data-driven phenotyping definitions and expanding existing definitions with minimal expert involvement.