Diabetes and kidney cancer: A direct or indirect association?

A positive association between diabetes and kidney cancer has been reported in several investigations, but it is unclear whether diabetes or its complications account for this association. Recent advances in estimating direct associations may be useful for elucidating the association between diabetes and kidney cancer. Therefore, we performed a case-control analysis to evaluate whether the direct association between diabetes and kidney cancer is the primary concern in this exposure-outcome relation. Discharge data (with International Classification of Diseases – 9 codes) from 2001 for hospitals throughout Florida were used to construct a case-control population of inpatients aged ≥45 years. Cases (n=1,909) were inpatients with malignant kidney cancer and controls (n=6,451) were inpatients with motor vehicle injuries. Diabetes status was ascertained for cases and controls. Covariates that required adjustment to estimate the total (age, gender, ethnicity, obesity, and smoking) and direct (age, gender, ethnicity, obesity, smoking, hypertension, and kidney disease) associations were identified in a directed acyclic graph. Binary logistic regression was used to estimate the adjusted total and direct odds ratios (ORs) and corresponding 95% confidence intervals (CIs) of kidney cancer for diabetics. The odds of kidney cancer were higher for inpatients with diabetes than inpatients without diabetes when estimating the total association (OR=1.27, 95%CI: 1.10, 1.47) but attenuated when estimating the direct association (OR=1.08, 95%CI: 0.93, 1.25). Our findings provide preliminary insight that the direct association between diabetes and kidney cancer may not be the primary concern in this exposure-outcome relation; indirect pathways (i.e. diabetic complications) may have greater influence on this relation. A similar analysis using longitudinal data with appropriately measured covariates may provide more definitive conclusions and could have implications for kidney cancer prevention among diabetics

“Design, Synthesis, And Characterization Of Novel Benzimidazole Derivatives And Their Biological Evaluation”

Because of their various biological functions and possible therapeutic uses, benzimidazole derivatives have drawn a lot of interest in medicinal chemistry. Our goal was to create, manufacture, and assess a number of new benzimidazole derivatives with improved pharmacological properties in this work. In order to maximize target interactions with particular biological targets, the benzimidazole scaffold was logically modified during the design process. A multistep synthetic process was used to create the novel derivatives, allowing for the integration of various functional groups. The effective synthesis of the intended chemicals was confirmed by characterizing techniques such nuclear magnetic resonance (NMR) spectroscopy, mass spectrometry, and infrared spectroscopy. The produced compounds were then evaluated using an in vitro techniques. Human cell lines were used in preliminary cytotoxicity tests to get knowledge about the derivatives' possible safety characteristics. This study's findings identified a number of benzimidazole compounds with significant biological properties and possible medicinal uses. In conclusion, a number of substances with remarkable therapeutic potential have been produced as a result of the design, synthesis, and assessment of novel benzimidazole derivatives

Tools to assist in determining business values of individual minibus-taxi operations in Rustenburg, North-West, South Africa

A multi-phase municipal informal public transport survey was undertaken in the city of Rustenburg to collect public transport operational and business data of minibus taxi associations running their services within the municipal boundary. The methodology followed consisted of cordon counts coupled to onboard surveys. The onboard surveys were executed by means of the GoMetro Pro mapping application, a mobile application that enables field collectors to digitally map respective transport networks and operations. Operational data was captured over a full operating day by a representative sample of vehicles of the larger population on pre-determined days and periods of the month that were selected in collaboration with the taxi associations, to accurately reflect the monthly operations. The data collected formed part of an operations analysis of the existing minibus-taxi transport system for the spatial and business planning of the Rustenburg Rapid Transport (RRT) Yarona Bus Rapid Transit (BRT) system. Revenue data and operational data per vehicle for a full day of operations was collected by the mobile application, repeated on selected days in a month. The data was inferred by post-processing of the data and applied to individual business models for each sampled minibus taxi, as representative drivers of a value in a system. The individual business models were extrapolated to the population as a representative sample of the population to determine the systems revenue and systems operations plan of an association.Papers Presented at the 2018 37th Southern African Transport Conference 9-12 July 2018 Pretoria, South Africa. Theme "Towards a desired transport future: safe, sufficient and affordable"

Design and Implementation of Deep Learning Method for Disease Identification in Plant Leaf

In the whole agriculture plays a very important in country’s economic condition specially in Indian agriculture has a crucial role for raising the Indian economic structure and its level. India’s frequent changing climatic situation, various bacterial disease is much normal that drastically decreases the productivity of crop productivity. Most of the researcher is moving towards into this topic to find the early detection technique to identify the disease in small green leaves plants. A single, micro bacterial infectious disease can destroy all the agricultural small green leaves plants get damaged overnight and hence must be prevented and cured as earliest as possible so that agriculture production. In this research work, we had tried to developed a green small green leaves plants bacterial disease early detection system based on the deep learning network system which will detect the disease at very earlier state of symptoms observed. Deep learning technique is has various algorithms to detect the earliest stage of any of the procedural processing of any bacterial infections or disease. This paper consists of investigations and analysis of latest deep learning techniques. Initially we will explore the deep learning architecture, its various source of data and different types of image processing method that can be used for processing the images captured of leaf for data processing. Different DL architectures with various data visualization’s tools has recently developed to determine symptoms and classifications of different type of plant-based disease. We had observed some issue that was un identified in previous research work during our literature survey and their technique to resolve that issue in order to handle the functional auto-detection system for identifying the certain plant disease in the field where massive growth of green small green leaves plants production is mostly done. Recently various enhancement has been done in techniques in CNN (convolution neural network) that generates much accurate images classification of any object. Our research work is based on deep learning network that will observe and identifies the symptoms generated in leaflet of plant and identifies the type of bacterial infection in progress in that with the help of plant classification stated in the plant dataset. Our research work represents the implementation DCGAN and Hybrid Net Model using Deep learning algorithm for early-stage identification of green plant leaves disease in various environmental condition. Our result obtained shows that it has DCGAN accuracy 96.90% when compared withHybrid Net model disease detection methodologies

Direct observation of membrane insertion by enveloped virus matrix proteins by phosphate displacement

Enveloped virus release is driven by poorly understood proteins that are functional analogs of the coat protein assemblies that mediate intracellular vesicle trafficking. We used differential electron density mapping to detect membrane integration by membrane-bending proteins from five virus families. This demonstrates that virus matrix proteins replace an unexpectedly large portion of the lipid content of the inner membrane face, a generalized feature likely to play a role in reshaping cellular membranes

Medication Complications in Extracorporeal Membrane Oxygenation.

The need for extracorporeal membrane oxygenation (ECMO) therapy is a marker of disease severity for which multiple medications are required. The therapy causes physiologic changes that impact drug pharmacokinetics. These changes can lead to exposure-driven decreases in efficacy or increased incidence of side effects. The pharmacokinetic changes are drug specific and largely undefined for most drugs. We review available drug dosing data and provide guidance for use in the ECMO patient population

Precision farming and archaeology

With a significant growth in the agricultural technology industry a vast amount of agricultural data is now being collected on farms throughout the world. Farmers aim to utilise these technologies to regularly record and manage the variation of crops and soils within their fields, to reduce inputs, increase yields and enhance environmental sustainability. In this paper we aim to highlight the variety of different data types and methodological processes involved in modern precision farming and explore how potentially interconnected these systems are with the archaeological communit

PRODUCTIVITY, NUTRIENT UPTAKE AND ECONOMICS OF RABI SUNFLOWER (HELIANTHUS ANNUUS. L) AS INFLUENCED BY TILLAGE PRACTICES AND NITROGEN MANAGEMENT

ABSTRACT: A field experiment was conducted during Rabi 2011 at Research farm, RARS, Bijapur on a deep black soil under rainfed condition with an objective to study the effect of different tillage practices imposed during kharif and nitrogen management on productivity, nutrient uptake and economics of rabi sunflower. Tillage practices had no significant effect on seed yield and stalk yield of sunflower crop. Seed yield (1187.5 kg ha -1 ) and stalk yield (2483.8 kg ha -1 ) of sunflower were significantly higher with 100% Recommended dose through fertilizer (N 3 ) over N 4 -Farmers' practice (24:30:0 N:P 2 O 5: K 2 O kg ha -1 ) but was on par with rest of the treatments. Significantly higher N uptake was seen with conventional tillage over minimum tillage but was on par with reduced tillage. Neither P nor K uptake was significantly influenced due to tillage practices in sunflower during rabi season. Sunflower fertilized with 100% recommended dose of fertilizer (N 3 ) recorded significantly higher N, P and K uptake at harvest. Maximum gross returns (38061Rs ha -1 ) and net returns (28729 Rs ha -1 ) was realized for the treatment combination T 3 N 3 (Minimum tillage with 100% recommended dose of fertilizer). Reducing the tillage intensity does not significantly influence economics and substituting 50% nitrogen through organic sources produces comparable yields that of inorganic source of nitrogen application