Data mining methods for the prediction of intestinal absorption using QSAR

Oral administration is the most common route for administration of drugs. With the growing cost of drug discovery, the development of Quantitative Structure-Activity Relationships (QSAR) as computational methods to predict oral absorption is highly desirable for cost effective reasons. The aim of this research was to develop QSAR models that are highly accurate and interpretable for the prediction of oral absorption. In this investigation the problems addressed were datasets with unbalanced class distributions, feature selection and the effects of solubility and permeability towards oral absorption prediction. Firstly, oral absorption models were obtained by overcoming the problem of unbalanced class distributions in datasets using two techniques, under-sampling of compounds belonging to the majority class and the use of different misclassification costs for different types of misclassifications. Using these methods, models with higher accuracy were produced using regression and linear/non-linear classification techniques. Secondly, the use of several pre-processing feature selection methods in tandem with decision tree classification analysis – including misclassification costs – were found to produce models with better interpretability and higher predictive accuracy. These methods were successful to select the most important molecular descriptors and to overcome the problem of unbalanced classes. Thirdly, the roles of solubility and permeability in oral absorption were also investigated. This involved expansion of oral absorption datasets and collection of in vitro and aqueous solubility data. This work found that the inclusion of predicted and experimental solubility in permeability models can improve model accuracy. However, the impact of solubility on oral absorption prediction was not as influential as expected. Finally, predictive models of permeability and solubility were built to predict a provisional Biopharmaceutic Classification System (BCS) class using two multi-label classification techniques, binary relevance and classifier chain. The classifier chain method was shown to have higher predictive accuracy by using predicted solubility as a molecular descriptor for permeability models, and hence better final provisional BCS prediction. Overall, this research has resulted in predictive and interpretable models that could be useful in a drug discovery context

Machine learning for predicting lifespan-extending chemical compounds

Increasing age is a risk factor for many diseases; therefore developing pharmacological interventions that slow down ageing and consequently postpone the onset of many age‐related diseases is highly desirable. In this work we analyse data from the DrugAge database, which contains chemical compounds and their effect on the lifespan of model organisms. Predictive models were built using the machine learning method random forests to predict whether or not a chemical compound will increase Caenorhabditis elegans’ lifespan, using as features Gene Ontology (GO) terms annotated for proteins targeted by the compounds and chemical descriptors calculated from each compound’s chemical structure. The model with the best predictive accuracy used both biological and chemical features, achieving a prediction accuracy of 80%. The top 20 most important GO terms include those related to mitochondrial processes, to enzymatic and immunological processes, and terms related to metabolic and transport processes. We applied our best model to predict compounds which are more likely to increase C. elegans’ lifespan in the DGIdb database, where the effect of the compounds on an organism’s lifespan is unknown. The top hit compounds can be broadly divided into four groups: compounds affecting mitochondria, compounds for cancer treatment, anti‐inflammatories, and compounds for gonadotropin‐ releasing hormone therapies

Three-way Imbalanced Learning based on Fuzzy Twin SVM

Three-way decision (3WD) is a powerful tool for granular computing to deal with uncertain data, commonly used in information systems, decision-making, and medical care. Three-way decision gets much research in traditional rough set models. However, three-way decision is rarely combined with the currently popular field of machine learning to expand its research. In this paper, three-way decision is connected with SVM, a standard binary classification model in machine learning, for solving imbalanced classification problems that SVM needs to improve. A new three-way fuzzy membership function and a new fuzzy twin support vector machine with three-way membership (TWFTSVM) are proposed. The new three-way fuzzy membership function is defined to increase the certainty of uncertain data in both input space and feature space, which assigns higher fuzzy membership to minority samples compared with majority samples. To evaluate the effectiveness of the proposed model, comparative experiments are designed for forty-seven different datasets with varying imbalance ratios. In addition, datasets with different imbalance ratios are derived from the same dataset to further assess the proposed model's performance. The results show that the proposed model significantly outperforms other traditional SVM-based methods

The impact of agricultural innovations on poverty, vulnerability and resilience to food insecurity of smallholders in Ethiopia

Ethiopia has adopted agriculture centered growth strategies over the last three decades that give more emphasis on improving agricultural production and productivity with the ultimate goal to transform the countrys economy. The strategies have mainly aimed at improving smallholder agriculture through introducing improved technologies intended to boost agricultural production and thus alleviate poverty and food insecurity. Although agriculture centered growth strategies contributed to sustained growth in the country over the last two decades, the benefits of growth have not been evenly distributed with observed rising income inequality and a still significant proportion of smallholders remaining under the poverty line. Similarly, despite considerable yield progress over the last three decades due to the introduction of improved inputs Ethiopian farmers yield gap compared with other developing countries is quite high. Moreover, the frequent occurrences of shocks such as drought and flooding adversely affect smallholders substantially and thereby exacerbate the existing poverty and food insecurity problems in the country. This thesis applied different econometric techniques to analyze the impact of the adoption of multiple agricultural technologies on crop yield, poverty, vulnerability, and resilience to food insecurity in Ethiopia. The study uses four rounds of household level panel data collected between 2012 and 2019 to assess the link between the adoption of the different combinations of five productivity-enhancing technologies: chemical fertilizer, improved seed, pesticide, and soil and water conservation practices: terracing and contour ploughing on consumption, poverty, vulnerability, and yields of smallholders. To solve the endogeneity problem in the regression models, we applied two-stage multinomial endogenous switching regression model combined with the Mundlak approach. Additionally, the thesis examines the role of the adoption of chemical fertilizer and improved seeds on household resilience to food insecurity amid the occurrence of adverse shocks. The findings are presented in three chapters of the cumulative thesis (Chapters two to four). Chapter two analyses the effect of productivity enhancing technologies and soil and water conservation measures and their possible combinations on consumption, poverty, and vulnerability to poverty. Per capita consumption expenditure for food and other essential non-food items, such as clothing and footwear, is used as a proxy variable to measure poverty. Using the national poverty line in 2011 prices, sample households are grouped into poor and non-poor households and the movement of sample households in and out of poverty between 2012 and 2016 is analyzed using a poverty transition matrix. By employing the ordered logit model, the study additionally examined the dynamics of poverty and vulnerability as well as their drivers. The results show that the adoption of the different combinations of agricultural technology sets including single technology adoption has considerable impacts on consumption expenditure and the greatest impact is attained when farmers combine multiple complementary inputs. Similarly, we find that the likelihood of households remaining poor or vulnerable decreased with adoption. In addition, the study revealed that poorer households are the least adopters of the technology combinations considered in the study, thereby being the least to benefit from adoption. We, therefore, conclude that the adoption of multiple complementary technologies has substantial dynamic benefits that improve the poverty and vulnerability status of households, and given the observed low level of adoption rates, we suggest that much more intervention is warranted, with a special focus on poorer and vulnerable households, to ensure smallholders get support to improve their input use. Chapter three assesses the impacts of multiple technology adoption on the yield of Ethiopias four staple crops, namely teff, wheat, maize and barley. Regarding the empirical estimation, we specified yield equations for each of the four crops and five to six possible input combinations that are included in the analysis indicating the presence of slope effect of technology choice other than the intercept of the outcome equations. The findings suggest that the application of two or more complementary inputs is considerably linked with higher maize, teff, barley, and wheat yield. Specifically, barley yield is highest for farmers who have adopted a combination of at least three of the technologies. Maize producers are the largest beneficiaries of the technologies. The impact of the technology choice sets tends to have an inconclusive effect on wheat and teff yields. However, a significant yield gap in all of the four crops was observed. Socio-economic characteristics of the household head such as age and gender as well as the households access to infrastructure and spatial characteristics of the household are other important determinants of crop yield. The implications are that more publicly funded efforts could be worthwhile for easing adoption constraints, which would in turn help smallholders to increase their crop yields that indirectly improve their livelihood. Chapter four aims to identify the determinants of household resilience to food insecurity which is the households ability to absorb or cope with the negative effects of shocks and bounce back to at least their initial livelihood status and assess its role on future household food security when hit by adverse shocks. Furthermore, the study analyzes the role of single or joint adoption of chemical fertilizer and improved seed on household food security. The household food security indicators used in the analysis are dietary diversity and per capita food consumption and uses data from the last three waves out of our four survey rounds. In terms of empirical estimation, the household resilience capacity index is estimated by combining factor analysis and structural equation modeling. Then different regression models are executed to assess the causal link between technology adoption and resilience capacity and household food security indicators in the face of adverse shocks. Our findings reveal that the most important pillars contributing to the building of household resilience capacity are assets followed by access to basic services. We find that the initial level of the household resilience score is significantly and positively associated with future household food security status. Moreover, the results reveal that the adoption of chemical fertilizer and improved seed is significantly and positively associated with household resilience capacity index, dietary diversity, and food consumption over time. Shocks such as drought appear to be significant contributors to the loss of household food security. Overall, it is revealed that the adoption of improved inputs significantly and positively increases household food security. However, the results show no evidence that supports the current level of adoption that helps households to shield themselves from the adverse effects of shocks. Finally, this study gives insights on examining the impacts and impact pathways of adoption of improved technologies on smallholder welfare which guide decision-makers for intervention as well as pave a way for future research that contributes to the fight against rural poverty and food insecurity. This thesis also concludes that public intervention in terms of investment in providing improved agricultural practices is crucial in improving rural livelihood, but it has to be inclusive and provide opportunities for the poor and vulnerable.Äthiopien hat in den letzten drei Jahrzehnten agrarzentrierte Wachstumsstrategien verfolgt, die den Schwerpunkt auf die Verbesserung der landwirtschaftlichen Produktion und Produktivität legen, um die Wirtschaft des Landes zu transformieren. Die Strategien zielten hauptsächlich darauf ab, die kleinbäuerliche Landwirtschaft durch die Einführung verbesserter Technologien zu verbessern, um die landwirtschaftliche Produktion zu steigern und damit Armut und Ernährungsunsicherheit zu lindern. Obwohl die auf die Landwirtschaft ausgerichteten Wachstumsstrategien in den letzten zwei Jahrzehnten zu einem nachhaltigen Wachstum im Land beigetragen haben, war diesesWachstum nicht gleichmäßig verteilt. Es wurde eine steigende Einkommensungleichheit beobachtet und ein immer noch erheblicher Anteil der Kleinbauern lebt unterhalb der Armutsgrenze. Ebenso ist die Ertragslücke der äthiopischen Bauern im Vergleich zu anderen Entwicklungsländern trotz der Einführung verbesserter Betriebsmittel recht hoch. Darüber hinaus beeinträchtigen häufige Schocks wie Dürre und Überschwemmungen insbesondere die Kleinbauern erheblich und verschärfen dadurch die bestehenden Probleme der Armut und Ernährungsunsicherheit im Land. In dieser Arbeit wurden verschiedene ökonometrische Methoden angewandt, um die Auswirkungen der Einführung mehrerer landwirtschaftlicher Technologien auf Ernteerträge, Armut, Anfälligkeit und Widerstandsfähigkeit gegenüber Ernährungsunsicherheit in Äthiopien zu analysieren. Die Studie verwendet vier Runden von Paneldaten auf Haushaltsebene. Diese wurden zwischen 2012 und 2019 wurden, um den Zusammenhang zwischen der Einführung verschiedener Kombinationen von fünf produktivitätssteigernden Technologien - chemischer Dünger, verbessertes Saatgut, Pestizide sowie Boden- und Wasserschutzpraktiken wie Terrassierung und Konturpflügen - auf Konsum, Armut, Vulnerabilität gegenüber Armutsgefährdung und Erträgen von Kleinbauern zu untersuchen. Um das Endogenitätsproblem in den Regressionsmodellen zu lösen, haben wir ein zweistufiges multinomiales endogenes Switching-Regressionsmodell in Kombination mit dem Mundlak-Ansatz verwendet. Zusätzlich untersucht die Arbeit die Rolle der Adoption von chemischem dünger und verbessertem Saatgut auf die Resilienz der Haushalte gegenüber Ernährungsunsicherheit. Die Ergebnisse werden in drei Kapiteln einer kumulativen Dissertation vorgestellt (Kapitel zwei bis vier). In Kapitel zwei werden die Auswirkungen von produktivitätssteigernden Technologien und Boden- und Wasserschutzmaßnahmen sowie deren mögliche Kombinationen auf Konsum, Armut und Armutsgefährdung analysiert. Die Pro-Kopf-Konsumausgaben für Nahrungsmittel und andere wichtige Güter des täglichen Bedarfs, wie Kleidung und Schuhe, werden als Proxy-Variable zur Messung von Armut verwendet. Unter Verwendung der nationalen Armutsgrenze mit Preisen von 2011 werden die Stichprobenhaushalte in arme und nicht arme Haushalte eingeteilt. Die Bewegung der Stichprobenhaushalte in und aus der Armut zwischen 2012 und 2016 wird mithilfe einer Armutsübergangsmatrix analysiert. Durch den Einsatz eines geordneten Logit-Modells wurden in der Studie zusätzlich die Dynamik von Armut und Armutsgefährdung sowie deren Treiber untersucht. Die Ergebnisse zeigen, dass die Adoption verschiedener Kombinationen von landwirtschaftlichen Technologien, sowie die Adoption von einzelnen Technologien, erhebliche Auswirkungen auf die Konsumausgaben haben. Die größte Auswirkung wird erreichtwenn Landwirte mehrere komplementäre Betriebsmittel kombinieren. Ebenso stellen wir fest, dass die Wahrscheinlichkeit, dass Haushalte arm oder armutsgefährdet bleiben, mit der Adoption von Technologien abnimmt. Darüber hinaus ergab die Studie, dass ärmere Haushalte die wenigsten der in der Studie betrachteten Technologiekombinationen nutzen und somit am wenigsten davon profitieren. Wir kommen daher zu der Schlussfolgerung, dass die Anwendung mehrerer komplementärer Technologien erhebliche Vorteile hat, die den Armutsstatus und Armutsgefährdungsstatus der Haushalte verbessern Angesichts der beobachteten niedrigen Adoptionsraten empfehlen wir, dass viel mehr Interventionen gerechtfertigt sind. Mit einem besonderen Fokus auf ärmere und armutsgefährdete Haushalte sollten diese sicherzustellen, dass Kleinbauern Unterstützung erhalten, um ihre Betriebsmittel- und Technologienutzung zu verbessern. In Kapitel drei werden die Auswirkungen des Einsatzes mehrerer Technologien auf den Ertrag der vier äthiopischen Grundnahrungsmittel Teff, Weizen, Mais und Gerste untersucht. Für die empirische Schätzung haben wir Ertragsgleichungen für jede der vier Kulturen und fünf bis sechs mögliche Betriebsmittel-Kombinationen spezifiziert, die in die Analyse einfließen und auf das Vorhandensein eines Neigungseffekts der Technologiewahl neben dem Achsenabschnitt der Ergebnisgleichungen hinweisen. Die Ergebnisse deuten darauf hin, dass die Anwendung von zwei oder mehr komplementären Inputs signifikant mit höheren Mais-, Teff-, Gersten- und Weizenerträgen zusammenhängt. Insbesondere der Gerstenertrag ist bei Landwirten am höchsten, die eine Kombination von mindestens drei der Technologien eingesetzt haben. Maisproduzenten sind die größten Nutznießer der Technologien. Die Auswirkung der Technologiekombinationen auf die Weizen- und Tefferträge ist tendenziell nicht eindeutig. Es wurde jedoch ein signifikanter Ertragsunterschied bei allen vier Feldfrüchten beobachtet. Sozioökonomische Merkmale des Haushaltsvorstands wie Alter und Geschlecht sowie der Zugang des Haushalts zur Infrastruktur und räumliche Merkmale des Haushalts sind weitere wichtige Determinanten des Ernteertrags. Die Implikationen sind, dass mehr öffentlich finanzierte Anstrengungen lohnenswert sein könnten, um Adoptionsbeschränkungen abzubauen. Dies würde den Kleinbauern helfen, ihre Ernteerträge zu steigern, was indirekt ihren Lebensunterhalt verbessern würde. Kapitel vier zielt darauf ab, die Determinanten der Resilienz der Haushalte gegenüber Ernährungsunsicherheit zu identifizieren, d.h. die Fähigkeit der Haushalte, die negativen Auswirkungen von Schocks zu absorbieren oder zu bewältigen und zu ihrer normalen Situation zurückzukehren, und ihre Rolle für die zukünftige Ernährungssicherheit der Haushalte zu bewerten, wenn sie von widrigen Schocks betroffen sind. Darüber hinaus analysiert die Studie die Rolle der alleinigen oder gemeinsamen Anwendung von chemischem Dünger und verbessertem Saatgut auf die Ernährungssicherheit der Haushalte. Die Indikatoren für die Ernährungssicherheit der Haushalte, die in der Analyse verwendet werden, sind die Ernährungsvielfalt und der Pro-Kopf-Verbrauch an Nahrungsmitteln. Es werden Daten aus den letzten drei Runden der vier Erhebungsrunden verwendet. Was die empirische Schätzung betrifft, so wird ein Index für die Resilienzfähigkeit der Haushalte durch eine Kombination von Faktorenanalyse und Strukturgleichungsmodellierung geschätzt. Anschließend werden verschiedene Regressionsmodelle durchgeführt, um den kausalen Zusammenhang zwischen Technologieadoption und Resilienzkapazität und den Indikatoren der Ernährungssicherheit von Haushalten angesichts widriger Schocks zu bewerten. Unsere Ergebnisse zeigen, dass die wichtigsten Säulen, die zum Aufbau von Resilienzkapazitäten von Haushalten beitragen, Vermögenswerte sind, gefolgt vom Zugang zu Basisdienstleistungen. Wir stellen fest, dass die Resilienzfähigkeit der Haushalte signifikant und positiv mit dem zukünftigen Status der Ernährungssicherheit der Haushalte verbunden ist. Darüber hinaus zeigen die Ergebnisse, dass der Einsatz von chemischem Dünger und verbessertem Saatgut signifikant und positiv mit dem Resilienzindex der Haushalte, der Ernährungsvielfalt und dem Nahrungsmittelkonsum im Zeitverlauf zusammenhängt. Schocks wie Dürre scheinen signifikant zum Verlust der Ernährungssicherheit der Haushalte beizutragen. Insgesamt zeigt sich, dass der Einsatz von verbesserten Betriebsmitteln die Ernährungssicherheit der Haushalte signifikant und positiv erhöht. Es wird jedoch auch beobachtet, dass die Haushalte nicht in der Lage sind, sich vor den negativen Auswirkungen von Schocks zu schützen. Abschließend gibt diese Studie Einblicke in die Untersuchung der Auswirkungen und Wirkungspfade der Einführung verbesserter Technologien auf das Wohlergehen von Kleinbauern, die Entscheidungsträgern eine Anleitung für Interventionen geben und einen Weg für zukünftige Forschung ebnen, die zum Kampf gegen ländliche Armut und Ernährungsunsicherheit beiträgt. Diese Arbeit kommt auch zu der Schlussfolgerungdass öffentliche Interventionen in Form von Investitionen in die Bereitstellung verbesserter landwirtschaftlicher Praktiken von entscheidender Bedeutung für die Verbesserung der ländlichen Lebensbedingungen sind. Jedoch müssen diese inklusiv sein und Möglichkeiten für arme und armutsgefährdete Haushalte bieten

Decision trees to characterise the roles of permeability and solubility on the prediction of oral absorption

Oral absorption of compounds depends on many physiological, physiochemical and formulation factors. Two important properties that govern oral absorption are in vitro permeability and solubility, which are commonly used as indicators of human intestinal absorption. Despite this, the nature and exact characteristics of the relationship between these parameters are not well understood. In this study a large dataset of human intestinal absorption was collated along with in vitro permeability, aqueous solubility, melting point, and maximum dose for the same compounds. The dataset allowed a permeability threshold to be established objectively to predict high or low intestinal absorption. Using this permeability threshold, classification decision trees incorporating a solubility-related parameter such as experimental or predicted solubility, or the melting point based absorption potential (MPbAP), along with structural molecular descriptors were developed and validated to predict oral absorption class. The decision trees were able to determine the individual roles of permeability and solubility in oral absorption process. Poorly permeable compounds with high solubility show low intestinal absorption, whereas poorly water soluble compounds with high or low permeability may have high intestinal absorption provided that they have certain molecular characteristics such as a small polar surface or specific topology. © 2014 Published by Elsevier Masson SAS

2012 Abstract Booklet

Complete Schedule of Events for the 14th Annual Undergraduate Research Symposium at Minnesota State University, Mankato

Nutrient content and carcass composition of South African mutton with a focus on bioavailability of selected nutrients

South Africans frequently consume red meat as part of their diet. However the nutrient content of South African sheep meat is derived from other countries. The Red Meat Industry considered it essential to have more reliable data and thus the nutrient content of A2 South African lamb was recently determined and published. This is the next phase of the study in which the right sides of C2 mutton carcasses were used to determine the nutrient and physical (carcass) composition of each raw cut as well as the whole carcass by calculation. Eighteen mutton carcasses of the most commonly consumed breeds, namely Dorper and Merino, in South Africa were selected. The carcasses were obtained from large abattoirs form three mutton producing regions in South Africa namely Ermelo, the Karoo and Kalahari. Chilled carcass sides were subdivided into ten primal cuts. Three cuts (shoulder, loin and leg) from the left side were cooked in order to determine the nutrient composition thereof. The cuts were dissected into meat which consists of muscle and intramuscular fat, intermuscular - plus subcutaneous fat and bone in order to determine the physical composition per cut and for the whole carcass. Meat compromise of 63.2% of the carcass, with bone contributing to 20.5% and fat to 16.9%. Results showed differences in the physical composition of South African C2 mutton as it contains on average 47% less fat and 19% more lean muscle, when compared to previous published composition data. Three cuts (shoulder, loin and leg) from the left side were cooked in order to determine the nutrient composition thereof. Cooking resulted in an increase in the protein and cholesterol concentrations of the cooked cuts. Iron content was higher in the cooked loin and leg but decreased in the cooked shoulder. According to nutrient density, a 100g edible portion of the leg, loin and shoulder have a nutrient density higher than one for protein, iron, zinc and vitamin B12 indicating that these cuts are a good source of these specific nutrients. A 100g edible portion of the loin cut contained higher fat quantities than the cooked shoulder and leg cuts. The loin cut also had a higher cholesterol content at 70.8mg compared the 58.5mg cholesterol content in the shoulder and 57.9mg in the leg cut. However, these values were calculated with all associated subcutaneous fat and it is known that many consumers trim on plate, especially the loin cut. Considering the fact that significant differences were apparent between the current study and previous data derived from other countries, it emphasizes the importance of determining the nutrient composition of South African food products in order to increase the validity of the SA food composition tables. Food-based approaches targeting the relief of micronutrient deficiency usually encourage the consumption of animal foods together with the consumption of green leafy vegetables (GLV). The inclusion of GLV and red meat, two micronutrient rich foods, can be a strategy based on mutual supplementation to combat nutritional deficiencies as it has the potential to alleviate numerous micronutrient deficiencies including iron and vitamin A deficiency.Dissertation (MSc)--University of Pretoria, 2009.Food Scienceunrestricte

2023- The Twenty-seventh Annual Symposium of Student Scholars

The full program book from the Twenty-seventh Annual Symposium of Student Scholars, held on April 18-21, 2023. Includes abstracts from the presentations and posters.https://digitalcommons.kennesaw.edu/sssprograms/1027/thumbnail.jp

Public Health

Public health can be thought of as a series of complex systems. Many things that individual living in high income countries take for granted like the control of infectious disease, clean, potable water, low infant mortality rates require a high functioning systems comprised of numerous actors, locations and interactions to work. Many people only notice public health when that system fails. This book explores several systems in public health including aspects of the food system, health care system and emerging issues including waste minimization in nanosilver. Several chapters address global health concerns including non-communicable disease prevention, poverty and health-longevity medicine. The book also presents several novel methodologies for better modeling and assessment of essential public health issues