Farmers’ perceptions of the effectiveness of the Cocoa Disease and Pest Control Programme (CODAPEC) in Ghana and its effects on poverty reduction

The study examined the contribution of the Cocoa Disease and Pest Control Programme (CODAPEC), which is a cocoa production-enhancing government policy, to reducing poverty and raising the living standards of cocoa farmers in Ghana. One hundred and fifty (150) cocoa farmers were randomly selected from five communities in the Bibiani-Anhwiaso-Bekwai district of the Western Region of Ghana and interviewed using structured questionnaires. Just over half of the farmers (53%) perceived the CODAPEC programme as being effective in controlling pests and diseases, whilst 56.6% felt that their yields and hence livelihoods had improved. In some cases pesticides or fungicides were applied later in the season than recommended and this had a detrimental effect on yields. To determine the level of poverty amongst farmers, annual household consumption expenditure was used as a proxy indicator. The study found that 4.7% of cocoa farmers were extremely poor having a total annual household consumption expenditure of less than GH¢ 623.10 ($310.00) while 8.0% were poor with less than GH¢ 801.62 ($398.81). An amount of money ranging from GH¢ 20.00 ($9.95) to GH¢ 89.04 ($44.29) per annum was needed to lift the 4.7% of cocoa farmers out of extreme poverty, which could be achieved through modest increases in productivity. The study highlighted how agricultural intervention programmes, such as CODAPEC, have the potential to contribute to improved farmer livelihoods.

Networks of stored grain diseases and pests: Strategies for sampling and mitigation

Pathogens and pests of stored grains move through complex dynamic networks linking fields, farms, and bulk storage facilities. Human transport and other forms of dispersal link the components of this network. A network model for pathogen and pest movement through stored grain systems is a first step toward new sampling and mitigation strategies that utilize information about the network structure. An understanding of network structure can be applied to identifying the key network components for pathogen or pest movement through the system. For example, it may be useful to identify a network node, such as a local grain storage facility, through which grain from a large number of fields will be accumulated and move through the network. This node may be particularly important for sampling and mitigation. In some cases more detailed information about network structure can identify key nodes that link two large sections of the network, such that management at the key nodes will greatly reduce the risk of spread between the two sections. In addition to the spread of particular species of pathogens and pests, we also evaluate the spread of problematic subpopulations, such as subpopulations with pesticide resistance. We present an analysis of stored grain pathogen and pest networks for Australia and the United States.

RESPONSE OF LOCALLY ADAPTED PEARL MILLET POPULATIONS TO S1 PROGENY RECURRENT SELECTION FOR GRAIN YIELD AND RESISTANCE TO RUST

In the semi-arid zones of Uganda, pearl millet ( Pennisetum glaucum (L.) R. Br.) is mainly grown for food and income; but rust (Puccinia substriata var indica (L.) R. Br.) is the main foliar constraint lowering yield. The objective of the study was to genetically improve grain yield and rust resistance of two locally adapted populations (Lam and Omoda), through two cycles of modified phenotypic S1 progeny recurrent selection. Treatments included three cycles of two locally adapted pearl millet populations, evaluated at three locations. Significant net genetic gain for grain yield (72 and 36%) were achieved in Lam and Omoda populations, respectively. This led to grain yield of 1,047 from 611 kg ha-1 in Lam population and 943 from 693 kg ha-1 in Omoda population. Significant improvement in rust resistance was achieved in the two populations, with a net genetic gain of -55 and -71% in Lam and Omoda populations, respectively. Rust severity reduced from 30 to 14% in Lam population and from 57 to 17% in Omoda population. Net positive genetic gains of 68 and 8% were also achieved for 1000-grain weight in Lam and Omoda, respectively. Traits with a net negative genetic gain in both populations were days to 50% flowering, days to 50% anthesis, days to 50% physiological maturity, flower-anthesis interval, plant height and leaf area.

Rapid [Gamma]-amino acid synthesis and the inhibition of the growth and development of oblique banded leaf-roller larvae

The hypothesis that rapid y-aminobutyric acid (GABA) accumulation is a plant defense against phytophagous insects was investigated. Simulation of mechanical damage resulting from phytophagous insect activity increased soybean (Glycine max L.) leaf GABA 10- to 25-fold within 1 to 4 min. Pulverizing leaf tissue resulted in a value of 2. 15 (±O. 11 SE) ~mol GABA per gram fresh weight. Increasing the GABA levels in a synthetic diet from 1.6 to 2.6 Jlffiol GABA per gram fresh weight reduced the growth rates, developmental rates, total biomass (50% reduction), and survival rates (30% reduction) of cultured Oblique banded leaf-roller (OBLR) (Choristonellra rosacealla Harris) larvae. In field experiments OBLR larvae were found predominantly on young terminal leaves which have a reduced capacity to produce GABA in response to mechanical damage. Glutamate decarboxylase (GAD) is a cytosolic enzyme which catalyses the decarboxylation of L-Glu to GABA. GAD is a calmodulin binding enzyme whose activity is stimulated dramatically by increased cytosolic H+ or Ca2 + ion concentrations. Phytophagous insect activity will disrupt the cellular compartmentation of H+ and Ca2 +, activate GAD and subsequent GABA accumulation. In animals GABA is a major inhibitory neurotransmitter. The possible mechanisms resulting in GABA inhibited growth and development of insects are discussed.

The identification, distribution and persistence of oxamyl and its degradation products in planted corn seed, seedling root and soil from oxamyl-treated corn seeds

A simple method was developed for treating corn seeds with oxamyl. It involved soaking the seeds to ensure oxamyl uptake, centrifugation to draw off excess solution, and drying under a stream of air to prevent the formation of fungus. The seeds were found to have an even distribution of oxamyl. Seeds remained fungus-free even 12 months after treatment. The highest nonphytotoxic treatment level was obtained by using a 4.00 mg/mL oxamyl solution. Extraction methods for the determination of oxamyl (methyl-N'N'-dimethyl-N-[(methylcarbamoyl)oxy]-l-thiooxamimidate), its oxime (methyl-N',N'-dimethyl-N-hydroxy-1-thiooxamimidate), and DMCF (N,N-dimethyl-1-cyanoformanade) in seed" root, and soil were developed. Seeds were processed by homogenizing, then shaking in methanol. Significantly more oxamyl was extracted from hydrated seeds as opposed to dry seeds. Soils were extracted by tumbling in methanol; recoveries range~ from 86 - 87% for oxamyl. Root was extracted to 93% efficiency for oxamyl by homogenizing the tissue in methanol. NucharAttaclay column cleanup afforded suitable extracts for analysis by RP-HPLC on a C18 column and UV detection at 254 nm. In the degradation study, oxamyl was found to dissipate from the seed down into the soil. It was also detected in the root. Oxime was detected in both the seed and soil, but not in the root. DMCF was detected in small amounts only in the seed.

DATA DRIVEN APPROACHES TO IDENTIFY DETERMINANTS OF HEART DISEASES AND CANCER RESISTANCE

Cancer and cardio-vascular diseases are the leading causes of death world-wide. Caused by systemic genetic and molecular disruptions in cells, these disorders are the manifestation of profound disturbance of normal cellular homeostasis. People suffering or at high risk for these disorders need early diagnosis and personalized therapeutic intervention. Successful implementation of such clinical measures can significantly improve global health. However, development of effective therapies is hindered by the challenges in identifying genetic and molecular determinants of the onset of diseases; and in cases where therapies already exist, the main challenge is to identify molecular determinants that drive resistance to the therapies. Due to the progress in sequencing technologies, the access to a large genome-wide biological data is now extended far beyond few experimental labs to the global research community. The unprecedented availability of the data has revolutionized the capabilities of computational researchers, enabling them to collaboratively address the long standing problems from many different perspectives. Likewise, this thesis tackles the two main public health related challenges using data driven approaches. Numerous association studies have been proposed to identify genomic variants that determine disease. However, their clinical utility remains limited due to their inability to distinguish causal variants from associated variants. In the presented thesis, we first propose a simple scheme that improves association studies in supervised fashion and has shown its applicability in identifying genomic regulatory variants associated with hypertension. Next, we propose a coupled Bayesian regression approach -- eQTeL, which leverages epigenetic data to estimate regulatory and gene interaction potential, and identifies combinations of regulatory genomic variants that explain the gene expression variance. On human heart data, eQTeL not only explains a significantly greater proportion of expression variance in samples, but also predicts gene expression more accurately than other methods. We demonstrate that eQTeL accurately detects causal regulatory SNPs by simulation, particularly those with small effect sizes. Using various functional data, we show that SNPs detected by eQTeL are enriched for allele-specific protein binding and histone modifications, which potentially disrupt binding of core cardiac transcription factors and are spatially proximal to their target. eQTeL SNPs capture a substantial proportion of genetic determinants of expression variance and we estimate that 58% of these SNPs are putatively causal. The challenge of identifying molecular determinants of cancer resistance so far could only be dealt with labor intensive and costly experimental studies, and in case of experimental drugs such studies are infeasible. Here we take a fundamentally different data driven approach to understand the evolving landscape of emerging resistance. We introduce a novel class of genetic interactions termed synthetic rescues (SR) in cancer, which denotes a functional interaction between two genes where a change in the activity of one vulnerable gene (which may be a target of a cancer drug) is lethal, but subsequently altered activity of its partner rescuer gene restores cell viability. Next we describe a comprehensive computational framework --termed INCISOR-- for identifying SR underlying cancer resistance. Applying INCISOR to mine The Cancer Genome Atlas (TCGA), a large collection of cancer patient data, we identified the first pan-cancer SR networks, composed of interactions common to many cancer types. We experimentally test and validate a subset of these interactions involving the master regulator gene mTOR. We find that rescuer genes become increasingly activated as breast cancer progresses, testifying to pervasive ongoing rescue processes. We show that SRs can be utilized to successfully predict patients' survival and response to the majority of current cancer drugs, and importantly, for predicting the emergence of drug resistance from the initial tumor biopsy. Our analysis suggests a potential new strategy for enhancing the effectiveness of existing cancer therapies by targeting their rescuer genes to counteract resistance. The thesis provides statistical frameworks that can harness ever increasing high throughput genomic data to address challenges in determining the molecular underpinnings of hypertension, cardiovascular disease and cancer resistance. We discover novel molecular mechanistic insights that will advance the progress in early disease prevention and personalized therapeutics. Our analyses sheds light on the fundamental biological understanding of gene regulation and interaction, and opens up exciting avenues of translational applications in risk prediction and therapeutics.

Avaliação de metil jasmonato na indução de resistência de plantas de Eucalyptus spp. ao psilídeo-de-concha Glycaspis brimblecombei Moore (Hemiptera: Psyllidae

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Resistência de genótipos de feijoeiro a Chrysodeixis includens (Walker) (Lepidoptera: Noctuidae)

Pós-graduação em Agronomia (Proteção de Plantas) - FCA

Identificação e caracterização de Xanthomonas euvesicatoria de pimentão no Brasil

Pós-graduação em Agronomia (Proteção de Plantas) - FCA

Resistência de genótipos de Cucurbita spp. à Bemisia tabaci biótipo B (Hemiptera: aleyrodidae) e interação com extratos vegetais

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Resistência de genótipos de soja a Chrysodeixis includens (Walker) (Lepdoptera: Noctuidae)

Pós-graduação em Agronomia (Proteção de Plantas) - FCA