Radiosynthesis and biodistribution of [^18F]-tetracosactide using a semi-automated [^18F]SFB production module

In order to prepare a specific melanocortin type 2 receptor (MC2R) ligand, b1-24-corticotrophin was pre-pared in one-step reaction with [18F] SFB and b-1-24-corticotrophin pharmaceutical solution (1 mg/mL, pH=6.5). [18F]SFB was prepared in a semi-automated module in two steps with an overall radiochemical yield of 47% to EOB (not-decay corrected) in 90 min. The 18F-labeled intermediates and 18F-labeled peptide was checked by RTLC and HPLC. The results show that the radiochemical purity is >95% and the yield to EOB (not-decay corrected) is 29% for final 18F-labeled peptide at optimized conditions. Preliminary in vivo studies in normal mice were performed to deter-mine biodistribution of the 18F-labeled peptide for 150 min. The results show that the major tracer uptake is consistent with the natural distribution of MC2R receptors in mammals. Testes/blood and testes/muscle ratios for 18F-labeled peptide at 150 min were 184 and 1.56, respectively, and adipocyte/blood and adipocyte/muscle ratios at 120 min were 221 and 142, respectively. The data support the specific receptor binding of the radiolabeled peptide as reported for MC2R receptor accumulation in adipocytes and testes and demonstrates the retention of biological activity of the pep-tide. This tracer can be used in detection of MC2R distribution in malignancies and sex organ diseases.     

A new optimization method based on cellular automata for VVER-1000 nuclear reactor loading pattern

This paper presents a new and innovative optimization technique, which uses cellular automata for solving multi-objective optimization problems. Due to its ability in simulating the local information while taking neighboring effects into account, the cellular automata technique is a powerful tool for optimization. The fuel-loading pattern in nuclear reactor cores is a major optimization problem. Due to the immensity of the search space in fuel management optimization problems, finding the optimum solution requires a huge amount of calculations in the classical method. The cellular automata models, based on local information, can reduce the computations significantly. In this study, reducing the power peaking factor, while increasing the initial excess reactivity inside the reactor core of VVER-1000, which are two apparently contradictory objectives, are considered as the objective functions. The result is an optimum configuration, which is in agreement with the pattern proposed by the designer. In order to gain confidence in the reliability of this method, the aforementioned problem was also solved using neural network and simulated annealing, and the results and procedures were compared.     

Solving the stochastic support vector regression with probabilistic constraints by a high-performance neural network model

This paper offers a recurrent neural network to support vector machine (SVM) learning in stochastic support vector regression with probabilistic constraints. The SVM is first converted into an equivalent quadratic programming (QP) formulation in linear and nonlinear cases. An artificial neural network for SVM learning is then proposed. The presented neural network framework guarantees obtaining the optimal solution of the SVM problem. The existence and convergence of the trajectories of the network are studied. The Lyapunov stability for the considered neural network is also shown. The efficiency of the proposed method is shown by three illustrative examples.

     

The Impact of Sub-Millisecond Damage Fixation Kinetics on the In Vitro Sparing Effect at Ultra-High Dose Rate in UNIVERSE

The impact of the exact temporal pulse structure on the potential cell and tissue sparing of ultra-high dose-rate irradiation applied in FLASH studies has gained increasing attention. A previous version of our biophysical mechanistic model (UNIVERSE: UNIfied and VERSatile bio response Engine), based on the oxygen depletion hypothesis, has been extended in this work by considering oxygen-dependent damage fixation dynamics on the sub-milliseconds scale and introducing an explicit implementation of the temporal pulse structure. The model successfully reproduces in vitro experimental data on the fast kinetics of the oxygen effect in irradiated mammalian cells. The implemented changes result in a reduction in the assumed amount of oxygen depletion. Furthermore, its increase towards conventional dose-rates is parameterized based on experimental data from the literature. A recalculation of previous benchmarks shows that the model retains its predictive power, while the assumed amount of depleted oxygen approaches measured values. The updated UNIVERSE could be used to investigate the impact of different combinations of pulse structure parameters (e.g., dose per pulse, pulse frequency, number of pulses, etc.), thereby aiding the optimization of potential clinical application and the development of suitable accelerators.     

Application of Bayesian Decision Networks for Groundwater Resources Management Under the Conditions of High Uncertainty and Data Scarcity

This paper presents management of groundwater resource using a Bayesian Decision Network (BDN). The Kordkooy region in North East of Iran has been selected as study area. The region has been sub-divided into three zones based on transmissivity (T) and electrical conductivity (EC) values. The BDN parameters: prior probabilities and Conditional Probability Tables - CPTs) have been identified for each of the three zones. Three groups of management scenarios have been developed based on the two decision variables including “Crop pattern” and “Domestic water demand” across the three zones of the study area: 1) status quo management for all three zones represent current conditions; 2) the effect of change in cropping pattern on management endpoints and 3) the effect of future increased domestic water demand on management endpoints. The outcomes arising from implementing each scenario have been predicted by use of the constructed BDN for each of the zones. Results reveal that probability of drawdown in groundwater levels of southern areas is relatively high compared with other zones. Groundwater withdrawal from northern and northwestern areas of the study area should be limited due to the groundwater quality problems associated with shallow groundwater of these two zones. The ability of the Bayesian Decision Network to take into account key uncertainties in natural resources and perform meaningful analysis in cases where there is not a vast amount of information and observed data available – and opportunities for enabling inputs for the analysis based partly on expert elicitation,emphasizes key advantages of this approach for groundwater management and addressing the groundwater related problems in a data-scarce area.     

Whodunnit – Searching for the most important feature types signalling emotion-related user states in speech

In this article, we describe and interpret a set of acoustic and linguistic features that characterise emotional/emotion-related user states – confined to the one database processed: four classes in a German corpus of children interacting with a pet robot. To this end, we collected a very large feature vector consisting of more than 4000 features extracted at different sites. We performed extensive feature selection (Sequential Forward Floating Search) for seven acoustic and four linguistic types of features, ending up in a small number of ‘most important’ features which we try to interpret by discussing the impact of different feature and extraction types. We establish different measures of impact and discuss the mutual influence of acoustics and linguistics.     

A novel method to investigate tensile properties of woven fabrics in all-direction

In this research, we propose a new method and instrument to measure load-extension curve of the fabric samples under all-directional extension. Using this method, fabric sample is loaded under extension in the warp, weft, and all other directions in between. Using this new method, we introduced a comprehensive tensile modulus which is affected by the fabric structures and densities. For this purpose different structures i.e. plain, Twill 2/2, Twill 1/3, and Twill 1/7 were produced with different densities. Then these samples were tested under the new method. Our results are then compared to those one would obtain under uniaxial tests, by performing independent uniaxial tests. It is shown that under all-directional test lowering the weft density decreases the tensile comprehensive modulus. Furthermore, a reduction in the floating yarns in the fabric structure decreases the tensile comprehensive modulus. It was found that among the samples tested here, plain fabric and Twill 1/7, respectively, possess the maximum and minimum comprehensive modulus. A similar trend was also observed through independent uniaxial extension, in warp or weft direction, test.     

A complexity tradeoff in ranking-function termination proofs

To prove that a program terminates, we can employ a ranking function argument, where program states are ranked so that every transition decreases the rank. Alternatively, we can use a set of ranking functions with the property that every cycle in the program’s flow-chart can be ranked with one of the functions. This “local” approach has gained interest recently on the grounds that local ranking functions would be simpler and easier to find. The current study is aimed at better understanding the tradeoffs involved, in a precise quantitative sense. We concentrate on a convenient setting, the Size-Change Termination framework (SCT). In SCT, programs are replaced by an abstraction whose termination is decidable. Moreover, sufficient classes of ranking functions (both global and local) are known. Our results show a tradeoff: either exponentially many local functions of certain simple forms, or an exponentially complex global function may be required for proving termination.     

New pulse mode neuro-fuzzy hardware architecture and application to image denoising

This paper focuses on a new digital architecture of pulse mode neuro-fuzzy system (PMNFS) with on-chip learning ability. The main purpose goal is to make use of the outstanding features of neuro-fuzzy in function approximation, and implement a reconfigurable architecture with on-chip learning on a field-programmable gate array (FPGA) platform. Details of the whole design with on-chip learning solutions are given. As an application illustrating the efficiency and scalability of the proposed PMNFS, we have considered the approximation of image denoising, which is a very important step in image processing. Experimental results show great efficiency of the proposed method, outperforming other denoising techniques. It was also demonstrated that such a system is strongly adaptive and gives good restored images independently of the kind of noises. Owing to learning, such feature cannot be met with conventional denoising techniques. Design synthesis results on a virtex II PRO FPGA platform are presented. Comparisons with conventional techniques as well as neural ones show higher performances of the designed PMNFS.