Decision Tree for Measuring the Interaction of Hyper-Saline Lake and Coastal Aquifer in Lake Urmia

© 2015 ASCE.Lake Urmia is located in the North West of Iran. The hyper saline lake is drying up very fast and more than seventy percent of the water in the lake has vanished in recent years. In this research, the West and South banks of the lake's basin which is known as the West Azerbaijan province of Iran are studied. During the period from March 2001 to August 2011, six pilot stations for ground water near the lake shore were monitored. Correlation, cross-correlation, distribution, and regression analysis were done for lake and pilot stations. Several decision trees were fitted to the model and the most proper one was selected to test the hypothesis. Results show that the North West of the basin is the most interactive part of the ground water and the fitted decision tree model with randomly selected data is performing well

Assessment of a conceptual hydrological model and artificial neural networks for daily outflows forecasting

Artificial neural networks (ANNs) are used by hydrologists and engineers to forecast flows at the outlet of a watershed. They are employed in particular where hydrological data are limited. Despite these developments, practitioners still prefer conventional hydrological models. This study applied the standard conceptual HEC-HMS’s soil moisture accounting (SMA) algorithm and the multi layer perceptron (MLP) for forecasting daily outflows at the outlet of Khosrow Shirin watershed in Iran. The MLP [optimized with the scaled conjugate gradient] used the logistic and tangent sigmoid activation functions resulting into 12 ANNs. The R2 and RMSE values for the best trained MPLs using the tangent and logistic sigmoid transfer function were 0.87, 1.875 m3 s−1 and 0.81, 2.297 m3 s−1, respectively. The results showed that MLPs optimized with the tangent sigmoid predicted peak flows and annual flood volumes more accurately than the HEC-HMS model with the SMA algorithm, with R2 and RMSE values equal to 0.87, 0.84 and 1.875 and 2.1 m3 s−1, respectively. Also, an MLP is easier to develop due to using a simple trial and error procedure. Practitioners of hydrologic modeling and flood flow forecasting may consider this study as an example of the capability of the ANN for real world flow forecasting

The causative variants of amyloidosis in the autism

Purpose: Autism spectrum disorders (ASD) consist of a group of neurodevelopmental disorders that include autistic behavior, Asperger�s syndrome and pervasive developmental disabilities. According to the increasing observations that patients with mitochondrial disorders have symptoms associated with ASD, we have aimed to analyze the role of mitochondrial DNA (mtDNA) variation in autistic patients. Material and methods: We selected children with autistic behaviors (15�60 CARS Score). The mitochondrial DNA extraction process was done by GeNet Bio DNA extraction kit. The regions of interest were amplified using independent PCR runs. After purification of PCR products, both strands were sequenced by Big Dye Termination system in a directly determined automated sequencing on an ABI 3700 capillary sequencer machine using both primers. All sequencing results were analyzed using bioinformatics� tools sequencher software 5. Results: In this study, 31 samples were examined, which 15 unique variants were detected in genes related to COXI-III. The most frequent variant (30.76) were related to COX1 with amino acid change A � A. The only significant pathogenic variant was C8264G, except for C8264G, all variants seemed to be homoplasmic substitution. Conclusion: In our study, among the variations we found, one variant what probably had an interesting association with possible amyloidosis, had been reported in patient with autism previously. It is hoped that with finding more definable genetic and biological markers, the autistic children diagnosis and treatment will be more effective. © 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group

Cyclotron-based production of 68Ga, [68Ga]GaCl3, and [68Ga]Ga-PSMA-11 from a liquid target

Abstract Purpose To optimize the direct production of 68Ga on a cyclotron, via the 68Zn(p,n)68Ga reaction using a liquid cyclotron target. We Investigated the yield of cyclotron-produced 68Ga, extraction of [68Ga]GaCl3 and subsequent [68Ga]Ga-PSMA-11 labeling using an automated synthesis module. Methods Irradiations of a 1.0 M solution of [68Zn]Zn(NO3)2 in dilute (0.2–0.3 M) HNO3 were conducted using GE PETtrace cyclotrons and GE 68Ga liquid targets. The proton beam energy was degraded to a nominal 14.3 MeV to minimize the co-production of 67Ga through the 68Zn(p,2n)67Ga reaction without unduly compromising 68Ga yields. We also evaluated the effects of varying beam times (50–75 min) and beam currents (27–40 μA). Crude 68Ga production was measured. The extraction of [68Ga]GaCl3 was performed using a 2 column solid phase method on the GE FASTlab Developer platform. Extracted [68Ga]GaCl3 was used to label [68Ga]Ga-PSMA-11 that was intended for clinical use. Results The decay corrected yield of 68Ga at EOB was typically > 3.7 GBq (100 mCi) for a 60 min beam, with irradiations of [68Zn]Zn(NO3)2 at 0.3 M HNO3. Target/chemistry performance was more consistent when compared with 0.2 M HNO3. Radionuclidic purity of 68Ga was typically > 99.8% at EOB and met the requirements specified in the European Pharmacopoeia ( 50% (~ 1.85 GBq, 50 mCi); yields improved as processes were optimized. Labeling yields for [68Ga]Ga-PSMA-11 were near quantitative (~ 1.67 GBq, 45 mCi) at EOS. Cyclotron produced [68Ga]Ga-PSMA-11 underwent full quality control, stability and sterility testing, and was implemented for human use at the University of Michigan as an Investigational New Drug through the US FDA and also at the Royal Prince Alfred Hospital (RPA). Conclusion Direct cyclotron irradiation of a liquid target provides clinically relevant quantities of [68Ga]Ga-PSMA-11 and is a viable alternative to traditional 68Ge/68Ga generators.http://deepblue.lib.umich.edu/bitstream/2027.42/174048/1/41181_2020_Article_106.pd