130 research outputs found
Effect of Selected Antiretroviral Drugs on Malondialdehyde (MDA)and Catalase Levels in Healthy Rat Tissues
No full textThe study assessed the effect of selected antiretroviral drugs used in the management of HIV/AIDS on the oxidative stress marker malondialdehyde (as TBARs) and the antioxidant enzyme, Catalase, of the liver and kidney tissues of healthy albino rats. 0.43, 0.43, 0.27, and 0.21mg/kg of Efavirenz, Abacavir, SNP 40 and Lamivudine respectively, were orally administered to four different groups of albino rats for seven days. The control group received normal saline. On the eighth day, the rats were sacrificed and the liver and kidney tissues were collected for Lipid peroxidation and Catalase activity analysis. Efavirenz and Lamivudine caused significant decrease (P0.05) compared to the control. All the drugs caused significant increase (P<0.05) in Catalase activity in the liver and a significant decrease (P<0.05) in Catalase activity in the kidney. Taken together, the present observation suggests that the effects of antiretroviral drugs on oxidative stress markers (such as MDA) and on antioxidant enzymes (such as catalase) in healthy as well as in HIV infected humans (by way of extrapolation) may vary from drug to drug and from organ to organ. We therefore advocate for extensive clinical research to investigate the influence of antiretroviral drugs on antioxidants enzymes in HIV and HIV/AIDS patients. Keywords: antiretroviral drugs, lipid peroxidation, liver, kidney, catalase
Ketogenic diet improves and restores redox status and biochemical indices in monosodium glutamate-induced rat testicular toxicity
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Solid Verifiable Credentials
No full textThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 63-66).Credentials are an integral part of our lives, as they express our capabilities and enable access to restricted services and benefits. In the early 2010s, the Verifiable Claims Working Group of the World Wide Web Consortium (W3C) proposed a specification for what is now the Verifiable Credentials Data Model. This living specification, which is still in development, outlines a cogent framework for the issuance, storage, presentation, and verification of credentials on the Web. Many of the leading Verifiable Credentials projects leverage Distributed Ledger Technology (DLT), potentially compromising Web interoperability and sometimes exposing otherwise personal data. SolidVC is a decentralized Verifiable Credentials platform built with the open protocols of the Web. It is implemented on top of Solid, a Web framework developed at MIT in 2016 that allows decentralized applications to interact with personal user data to provide services in an access controlled environment.by Kayode Yadilichi Ezike.M. Eng.M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Scienc
Status of Selected Hematological and Biochemical Parameters in Malaria and Malaria-typhoid Co-infection
No full textAPPLICATION OF GEOPHYSICAL AND GEOCHEMICAL METHODS FOR SOIL CHARACTERISATION IN SUSTAINABLE PRECISION AGRICULTURE IN SELECTED FARMS
No full textAll soils have potential for high yield for specific crops. Nigerian soils have potential for medium to high yield, but poor farming practices including the misuse of chemical fertilizers result in a number of constraints such as soil salinity, degradation and declining
fertility, which militate against high crop yields. Nigeria, currently battling with food insecurity because population growth is not commensurate with agricultural production. Thus, there is need for urgent intervention in the agricultural sector. The aim of this study
was to integrate geophysical and geochemical methods for sustainable precision agriculture in two farm sites of Covenant University and Landmark University, Nigeria. In this study, electrical resistivity, geochemical and satellite imagery methods were used for soil characterisation in farm sites at Covenant University, Ota, Southwest and Landmark University, Omu-Aran, North-central Nigeria between June, 2018 and January, 2019. The
electrical resistivity data were processed using RES2DINV and Win-Resist software. Geochemical analysis of soil samples from the sites was conducted using ICP-MS in
ACME laboratory, Canada. Monthly MERRA satellite data was used to determine the soil temperature and soil moisture content while soil salinity was estimated from Landsat-8
satellite imagery. The study showed that electrical resistivity of the topsoil in Covenant University farm ranged from 120 -500 Ωm, while that of Landmark University farm ranged from 345-527 Ωm. The soil types delineated at the Covenant University farm were clayey
sand and lateritic clay; sand/lateritic gravelly sand was delineated at Landmark University farm. Potentially toxic elements were detected in the soil samples of both sites; arsenic (As), chromium (Cr), lead (Pb) and copper (Cu) exceeded FAO/WHO recommended standard limits in Covenant University farm. The pollution indices of Co, Cr, Ni, Pb and Mn in the Covenant University farm were within low to high contamination, while As was within medium to high contamination. In Landmark University farm, the pollution indices of Pb, Cu, Zn, Co and Cd ranged from low to medium, while As has pollution index within low to high contamination. Results showed elevated concentrations of As in all samples. Ca-Mg, P-Mg, Fe-Al, Ca-K, Mg-K and Na-K paired nutrients were positively correlated at
5% level of significance in both farmlands, indicating similar increase in both farmlands. Also, the geospatial maps revealed zones of high and low accumulation of essential macro nutrients within the farmlands. Landmark University farmland indicated higher soil salinity
than Covenant University farm land. Soil temperature (ST) data at Covenant University farm ranged from 296 - 314 K, while ST at Landmark University farm ranged from 289 -
317 K. Soil moisture content data for both farms ranged from 23 – 113 3 3 mmand 10 - 110 3 3 mmin Covenant and Landmark University farms, respectively. The sandy gravelly soil of Landmark University farm is suitable for the planting of root and tuber crops such as carrot, yam, potatoes, turmeric and beets. Cabbage, leafy vegetables and lemon grass can be grown successfully in Covenant University farm. The ecological risk assessment of toxic
metals, showed that arsenic may present a moderate to very high biological risk to both plants and animals that feed on the soil of both farm lands. The site-specific information of the farm sites has been provided. This study provides database that can serve as useful guide in soil management decision making for better yiel
Lead-cadmium accumulation in agricultural soils and health implications
No full textThe use of pesticides in farms has made agricultural soils often contaminated with heavy metals,
excess of which the soils could pollute the environment and potentially damage human health through
accumulation in the food chain. The geochemical compositions in agricultural soils from two commercial
farms were analysed using ICP-MS. Trace elements are needed in minute quantities in soils but can be toxic
to plants and humans with health implications at elevated rates. Soil samples from the study area were
analysed and the result revealed that lead (Pb) and cadmium (Cd) concentrations in the study sites were
within the FAO/WHO standard limits. The implications of elevated rates on crops and humans alike are
highlighted in this paper
Residual Soils Derived from Charnockite and Migmatite as Road Pavement Layer Materials
No full textSuitable soils for road pavement layer construction are getting depleted. Highway Engineers are consistently in search of locally-available and suitable soils for road construction. This paper investigates the potential use of residual soils derived from charnockite and migmatite as road pavement layer materials. Natural moisture content, sieve and hydrometer analyses, specific gravity, Atterberg limits, compaction, California bearing ratio (CBR), and permeability characteristics of soil samples collected from six different locations in Ekiti State, Nigeria were determined. The soils derived from charnockite have their average unsoaked CBR, soaked CBR and permeability to be higher than those of the soils derived from migmatite by 64.7%, 73.5% and 1750.9%, respectively. Consequently, some of the soils derived from charnockite satisfy the requirements by the Nigerian General Specification for use as subgrade and subbase materials, while those derived from migmatite generally have poor geotechnical properties. The soils derived from charnockite are recommended for use as pavement layer materials, while those derived from migmatite need to be stabilized before being used
Modelling and prediction of water current using artificial neural networks: A case study of the commodore channel
No full textWater current modelling and prediction techniques along coastal inlets have attracted growing concern in recent years. This is largely so because water current component continues to be a major contributor to movement of sediments, tracers and pollutants, and to a whole range of offshore applications in engineering, environmental observations, exploration and oceanography. However, most research works are lacking adequate methods for developing precise prediction models along the commodore channel in Lagos State. This research work presents water current prediction using Artificial Neural Networks (ANNs). The Back Propagation (BP) technique with feed forward architecture and optimized training algorithm known as Levenbergq-Marquardt was used to develop a Neural Network Water Current Prediction model-(NNWLM) in a MATLAB programming environment. It was passed through model sensitivity analysis and afterwards tested with data from the Commodore channel (Lagos Lagoon). The result revealed prediction accuracy ranging from 0.012 to 0.045 in terms of Mean Square Error (MSE) and 0.80 to 0.83 in terms of correlation coefficient (R-value). With this high performance, the Neural network developed in this work can be used as a veritable tool for water current prediction along the Commodore channel and in extension a wide variety of coastal engineering and development, covering sediment management program: dredging, sand bypassing, beach-contingency plans, and protection of beaches vulnerable to storm erosion and monitoring and prediction of long-term water current variations in coastal inlets.
Keywords: Artificial Neural Network, Commodore Channel, Coastal Inlet, Water Current, Back Propagation
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