Evaluation of Water Quality in Eutrophic Shallow Lakes: Case Study on Lake Uluabat, Turkey

Lake Uluabat is one of the most important shallow lakes in Turkey. The lake is located 15 km south of the Marmara Sea and 30 km west of the city of Bursa. The lake is very important in terms of biodiversity, but knowledge of its water quality is somewhat limited. The objective of this study was to assess water quality in Lake Uluabat and provide information for future management decisions. The temperature (T), pH, electrical conductivity (EC), dissolved oxygen (DO), alkalinity, chemical oxygen demand (COD), nitrogen species, phosphorus species, and chlorophyll-a (Chl-a) concentrations were monitored monthly at ten sampling points in the lake between August 2013 and July 2014. As a result, it was determined that the lake water has the characteristics of class 4 waters according to the Turkish Surface Water Quality Management Regulations (SWQMR). Also it was determined that Mustafakemalpaşa Brook carries significant amount of pollution loads into the lake. According to qualitative and quantitative observations, the effects of human impact and current status of the lake were determined. The physical and chemical characteristics of the lake water have changed according to human activities and nutrient loadings. According to the trophic level values and concentration values, the Lake exceeded the regulation limits

Predicting the Debonding of CAD/CAM Composite Resin Crowns with AI

A preventive measure for debonding has not been established and is highly desirable to improve the survival rate of computer-aided design/computer-aided manufacturing (CAD/CAM) composite resin (CR) crowns. The aim of this study was to assess the usefulness of deep learning with a convolution neural network (CNN) method to predict the debonding probability of CAD/CAM CR crowns from 2-dimensional images captured from 3-dimensional (3D) stereolithography models of a die scanned by a 3D oral scanner. All cases of CAD/CAM CR crowns were manufactured from April 2014 to November 2015 at the Division of Prosthodontics, Osaka University Dental Hospital (Ethical Review Board at Osaka University, approval H27-E11). The data set consisted of a total of 24 cases: 12 trouble-free and 12 debonding as known labels. A total of 8,640 images were randomly divided into 6,480 training and validation images and 2,160 test images. Deep learning with a CNN method was conducted to develop a learning model to predict the debonding probability. The prediction accuracy, precision, recall, F-measure, receiver operating characteristic, and area under the curve of the learning model were assessed for the test images. Also, the mean calculation time was measured during the prediction for the test images. The prediction accuracy, precision, recall, and F-measure values of deep learning with a CNN method for the prediction of the debonding probability were 98.5%, 97.0%, 100%, and 0.985, respectively. The mean calculation time was 2 ms/step for 2,160 test images. The area under the curve was 0.998. Artificial intelligence (AI) technology—that is, the deep learning with a CNN method established in this study—demonstrated considerably good performance in terms of predicting the debonding probability of a CAD/CAM CR crown with 3D stereolithography models of a die scanned from patients.Yamaguchi S., Lee C., Karaer O., et al. Predicting the Debonding of CAD/CAM Composite Resin Crowns with AI. Journal of Dental Research, 98(11), 1234-1238. © 2019 Sage Publications. DOI: 10.1177/0022034519867641

Characterization of greater middle eastern genetic variation for enhanced disease gene discovery

The Greater Middle East (GME) has been a central hub of human migration and population admixture. The tradition of consanguinity, variably practiced in the Persian Gulf region, North Africa, and Central Asia1-3, has resulted in an elevated burden of recessive disease4. Here we generated a whole-exome GME variome from 1,111 unrelated subjects. We detected substantial diversity and admixture in continental and subregional populations, corresponding to several ancient founder populations with little evidence of bottlenecks. Measured consanguinity rates were an order of magnitude above those in other sampled populations, and the GME population exhibited an increased burden of runs of homozygosity (ROHs) but showed no evidence for reduced burden of deleterious variation due to classically theorized ‘genetic purging’. Applying this database to unsolved recessive conditions in the GME population reduced the number of potential disease-causing variants by four- to sevenfold. These results show variegated genetic architecture in GME populations and support future human genetic discoveries in Mendelian and population genetics

Certify or not? An analysis of organic food supply chain with competing suppliers

Customers expect companies to provide clear health-related information for the products they purchase in a big data environment. Organic food is data-enabled with the organic label, but the certification cost discourages small-scale suppliers from certifying their product. This lack of a label means that product that satisfies the organic standard is regarded as conventional product. By considering the trade-off between the profit gained from organic label and additional costs of certification, this paper investigates an organic food supply chain where a leading retailer procures from two suppliers with different brands. Customers care about both the brand-value and quality (more specifically, if food is organic or not) when purchasing the product. We explore the organic certification and wholesale pricing strategies for suppliers, and the supplier selection and retail pricing strategies for the retailer. We find that when two suppliers adopt asymmetric certification strategy, the retailer tends to procure the product with organic label. The supplier without a brand name can compensate with organic certification, which leads to more profits than the branded rival. As the risk of being abandoned by the retailer increases, the supplier without a brand name is more eager than the rival to obtain the organic label. If both suppliers certify the product, however, they will fall into a prisoner’s dilemma under situation with low health utility from organic label and high certification cost

Hypervolemia and high tumor necrosis factor-?: An interesting finding during the first six months' posttransplant period in kidney transplant recipients with good renal function

PubMedID: 30152426Renal transplantation is the treatment of choice for the end-stage renal disease. Hypervolemia and inflammation are commonly overlooked. We investigated whether hyper-volemia develops in kidney transplant recipients with estimated glomerular filtration rate (eGFR) >70 mL/min and if there is there any correlation between inflammatory cytokines and hypervolemia in the 1st month and at six months after transplantation. We measured serum tumor necrosis factor-? (TNF-?), interleukin (IL)-1, IL-6, and body composition indices in 11 healthy volunteers and 19 kidney transplant recipients (KTRs) with eGFR >70 mL/min at one month and six months posttransplant. At baseline, body mass index (BMI) and fat tissue index (FTI) were significantly higher and overhydration improved (P = 0.006) at 6th month in KTRs. There was no difference in BMI, FTI, lean tissue index (LTI), and serum levels of IL-1 and IL-6 in controls and KTRs at six months posttransplant. Volume overload and serum TNF-? levels were significantly lower in controls than KTRs. There was positive correlation between volume overload and serum TNF-? levels in KTRs. Compared to baseline, parameters including volume overload, BMI, and FTI were found to be improved at six months' posttransplant in KTRs. Interestingly, volume overload and high levels of serum TNF-? continued at least six months after transplantation

Pleiotrophic Effects of Vitamin D in Proteinuric Chronic Kidney Disease Patients

OBJECTIVE: Proteinuria is an important marker that accelerates chronic kidney disease. Known anti-proteinuric treatments are not always sufficient. In recent years, animal studies have shown that Vitamin D has positive effects on inflammation in the kidney, glomerulosclerosis, interstitial fibrosis. The aim of our study was to investigate the effect of Vitamin D on proteinuria in patients with proteinuric chronic kidney disease (CKD). MATERIAL and METHODS: Thirty-five patients with proteinuric chronic kidney disease (CKDp) were included in the study [Study Group(SG)]. Vitamin D3 (300000 u / 21 d for 3 months) was added to therapy without any drugs modifications. Thirty-nine patients was consisted CKDp (CG). None of the patients had used Vitamin D and/or phosphorus-binding medication in the previous 6 months. The results of daily proteinuria and biochemical tests at the beginning of study and the end of 3 months were recorded. RESULTS: In the SG, there was significant improvement in proteinuria (median 1813 mg to 1395 mg p = 0.03), serum total protein and albumin (p=0.04 and p=0.02 respectively) at the end of 3 months. The BUN and creatinine values did not change. There was no significant difference in any parameter in CG. The percentage of patients with reduced proteinuria according to baseline was higher in SG (74%) when compared to CG (43%) (p=0.007). CONCLUSION: In CKDp patients, Vitamin D for 3 months significantly decreased proteinuria without causing a deterioration in renal function or an adverse event. Vitamin D may therefore be an alternative option to anti-proteinuric treatment in proteinuric CKD patients, in addition to its other known effects. © 2018 Turkish Society of Nephrology. All rights reserved