The Concentration and Risk Assessment of Potentially Toxic Elements (PTEs) in Farmed and Wild Carps (Cyprinus carpio) in Hamadan Province of Iran.

This study focuses on measuring potentially toxic elements (PTEs) including mercury (Hg), lead (Pb), cadmium (Cd), arsenic (As), iron (Fe), copper (Cu), zinc (Zn), manganese (Mn) in farmed and wild carp by inductively coupled plasma-optical emission spectrometry (ICP-OES) and their intake amount and risk assessment. Estimated Daily Intake (EDI), Target Hazard Quotient (THQ) and the total THQ (TTHQ) were calculated for each element. In the present research, the concentration of As in farmed and wild carp samples was below the detection limit of ICP-OES (< 0.005 mg/kg). The mean concentrations of Pb, Cd, Fe, and Cu in farmed carp samples were significantly higher than that in wild carp samples, while Zn level was higher in the wild carp samples (P < 0.05). There is no significant difference between Hg and Mn in both fish (P > 0.05). The Monte Carlo simulation (MCS) results showed that the ranking order of PTEs based on their THQ was Hg > Pb > Zn > Cu > Fe > Cd > Mn. In the worst-case scenario (in the top 95 percentile) for both wild and farmed carp, the THQ of measured metals was less than one except Hg in children. Overall, this study demonstrated that the levels of PTEs in farmed and wild common carp had a potential non-carcinogenic risk for children (TTHQ > 1).

Prediction of outlet air characteristics and thermal performance of a symmetrical solar air heater via machine learning to develop a model-based operational control scheme-an experimental study.

This study develops reliable and robust machine learning (ML) models to predict the outlet air temperature and humidity and thermal efficiency of a solar air heater (SAH). Also, the application of predictive models for optimal control of the SAH operation is proposed. For this, the work contains three main parts: (a) a vertically-mounted symmetrical SAH was installed outside of a building room and operated throughout the winter of 2022. (b) By conducting experiments for five air mass flow rates, a large dataset with more than 62,500 sample points was collected. (c) Six input features containing time, environmental-related attributes, and SAH variables were applied to develop several state-of-the-art ML algorithms. To figure out the most accurate models for predicting output variables, the dataset was partitioned into three parts. Also, various modeling performance evaluation criteria were calculated and compared on the validation and test sets. Among these models, the gradient boosting machine algorithm based on LightGBM implementation achieved the best degree of generalization in modeling the target variables. The results demonstrated that the developed models obtained the lowest R-squared and the highest mean absolute percentage error of 0.9827 and 2.95%, respectively, on the test set. Moreover, the offline analysis of SAH operation based on the proposed control scheme demonstrated that 350 kWh of thermal energy can be generated during the application in the one-year winter season, 24% more than SAH operation without a model-based control strategy.

High gain/bandwidth off-chip antenna loaded with metamaterial unit-cell impedance matching circuit for sub-terahertz near-field electronic systems.

An innovative off-chip antenna (OCA) is presented that exhibits high gain and efficiency performance at the terahertz (THz) band and has a wide operational bandwidth. The proposed OCA is implemented on stacked silicon layers and consists of an open circuit meandering line. It is shown that by loading the antenna with an array of subwavelength circular dielectric slots and terminating it with a metamaterial unit cell, its impedance bandwidth is enhanced by a factor of two and its gain on average by about 4 dB. Unlike conventional antennas, where the energy is dissipated in a resistive load, the technique proposed here significantly reduces losses. The antenna is excited from underneath the antenna by coupling RF energy from an open-circuited feedline through a slot in the ground-plane of the middle substrate layer. The feedline is shielded with another substrate layer which has a ground-plane on its opposite surface to mitigate the influence of the structure on which the antenna is mounted. The antenna has the dimensions 12.3 × 4.5 × 0.905 mm3 and operates across the 0.137-0.158 THz band corresponding to a fractional bandwidth of 14.23%. Over this frequency range the average measured gain and efficiency are 8.6 dBi and 77%, respectively. These characteristics makes the proposed antenna suitable for integration in sub-terahertz near-field electronic systems such as radio frequency identification (RFID) devices with high spatial resolution.

An innovative antenna array with high inter element isolation for sub-6 GHz 5G MIMO communication systems.

A novel technique is shown to improve the isolation between radiators in antenna arrays. The proposed technique suppresses the surface-wave propagation and reduces substrate loss thereby enhancing the overall performance of the array. This is achieved without affecting the antenna's footprint. The proposed approach is demonstrated on a four-element array for 5G MIMO applications. Each radiating element in the array is constituted from a 3 × 3 matrix of interconnected resonant elements. The technique involves (1) incorporating matching stubs within the resonant elements, (2) framing each of the four-radiating elements inside a dot-wall, and (3) defecting the ground plane with dielectric slots that are aligned under the dot-walls. Results show that with the proposed approach the impedance bandwidth of the array is increased by 58.82% and the improvement in the average isolation between antennas #1&2, #1&3, #1&4 are 8 dB, 14 dB, 16 dB, and 13 dB, respectively. Moreover, improvement in the antenna gain is 4.2% and the total radiation efficiency is 23.53%. These results confirm the efficacy of the technique. The agreement between the simulated and measured results is excellent. Furthermore, the manufacture of the antenna array using the proposed approach is relatively straightforward and cost effective.

Protein docking refinement by convex underestimation in the low-dimensional subspace of encounter complexes.

We propose a novel stochastic global optimization algorithm with applications to the refinement stage of protein docking prediction methods. Our approach can process conformations sampled from multiple clusters, each roughly corresponding to a different binding energy funnel. These clusters are obtained using a density-based clustering method. In each cluster, we identify a smooth "permissive" subspace which avoids high-energy barriers and then underestimate the binding energy function using general convex polynomials in this subspace. We use the underestimator to bias sampling towards its global minimum. Sampling and subspace underestimation are repeated several times and the conformations sampled at the last iteration form a refined ensemble. We report computational results on a comprehensive benchmark of 224 protein complexes, establishing that our refined ensemble significantly improves the quality of the conformations of the original set given to the algorithm. We also devise a method to enhance the ensemble from which near-native models are selected.

Protein-ligand docking using FFT based sampling: D3R case study.

Fast Fourier transform (FFT) based approaches have been successful in application to modeling of relatively rigid protein-protein complexes. Recently, we have been able to adapt the FFT methodology to treatment of flexible protein-peptide interactions. Here, we report our latest attempt to expand the capabilities of the FFT approach to treatment of flexible protein-ligand interactions in application to the D3R PL-2016-1 challenge. Based on the D3R assessment, our FFT approach in conjunction with Monte Carlo minimization off-grid refinement was among the top performing methods in the challenge. The potential advantage of our method is its ability to globally sample the protein-ligand interaction landscape, which will be explored in further applications.

Development of an oxide-dispersion-strengthened steel by introducing oxygen carrier compound into the melt aided by a general thermodynamic model.

In general, melting process is not a common method for the production of oxide dispersion strengthened (ODS) alloys due to agglomeration and coarsening of oxide particles. However, vacuum casting process has recently been employed as a promising process to produce micro-scale oxide dispersed alloys. In this paper, we report the process and characterization of in situ formation and uniform dispersion of nano-scale Y-Ti oxide particles in Fe-10Ni-7Mn (wt.%) alloy. The processing route involves a solid-liquid reaction between the added TiO2 as an oxygen carrier and dissolved yttrium in liquid metal leading to an optimal microstructure with nano-sized dispersed oxide particles. The developed thermodynamic model shows the independence of the final phase constituents from experimental conditions such as melting temperature or vacuum system pressure which offers a general pathway for the manufacture of oxide dispersion strengthened materials.

Focused grid-based resampling for protein docking and mapping.

The fast Fourier transform (FFT) sampling algorithm has been used with success in application to protein-protein docking and for protein mapping, the latter docking a variety of small organic molecules for the identification of binding hot spots on the target protein. Here we explore the local rather than global usage of the FFT sampling approach in docking applications. If the global FFT based search yields a near-native cluster of docked structures for a protein complex, then focused resampling of the cluster generally leads to a substantial increase in the number of conformations close to the native structure. In protein mapping, focused resampling of the selected hot spot regions generally reveals further hot spots that, while not as strong as the primary hot spots, also contribute to ligand binding. The detection of additional ligand binding regions is shown by the improved overlap between hot spots and bound ligands.

The impact of side-chain packing on protein docking refinement.

We study the impact of optimizing the side-chain positions in the interface region between two proteins during the process of binding. Mathematically, the problem is similar to side-chain prediction, which has been extensively explored in the process of protein structure prediction. The protein-protein docking application, however, has a number of characteristics that necessitate different algorithmic and implementation choices. In this work, we implement a distributed approximate algorithm that can be implemented on multiprocessor architectures and enables a trade-off between accuracy and running speed. We report computational results on benchmarks of enzyme-inhibitor and other types of complexes, establishing that the side-chain flexibility our algorithm introduces substantially improves the performance of docking protocols. Furthermore, we establish that the inclusion of unbound side-chain conformers in the side-chain positioning problem is critical in these performance improvements. The code is available to the community under open source license.

Primary Adenocarcinoma of the Urethra: A Case Report and Review of the Literature.

Primary adenocarcinoma of the urethra is rarely reported. We report a case of a 47-year-old male with symptoms of urinary obstruction started 2 years before diagnosis. Video-assisted urethrocystoscopy revealed a papillary mass almost obstructing the entire lumen with bleeding. Pathology report was consistent with primary adenocarcinoma of the urethra.

Efficient Maintenance and Update of Nonbonded Lists in Macromolecular Simulations.

Molecular mechanics and dynamics simulations use distance based cutoff approximations for faster computation of pairwise van der Waals and electrostatic energy terms. These approximations traditionally use a precalculated and periodically updated list of interacting atom pairs, known as the "nonbonded neighborhood lists" or nblists, in order to reduce the overhead of finding atom pairs that are within distance cutoff. The size of nblists grows linearly with the number of atoms in the system and superlinearly with the distance cutoff, and as a result, they require significant amount of memory for large molecular systems. The high space usage leads to poor cache performance, which slows computation for large distance cutoffs. Also, the high cost of updates means that one cannot afford to keep the data structure always synchronized with the configuration of the molecules when efficiency is at stake. We propose a dynamic octree data structure for implicit maintenance of nblists using space linear in the number of atoms but independent of the distance cutoff. The list can be updated very efficiently as the coordinates of atoms change during the simulation. Unlike explicit nblists, a single octree works for all distance cutoffs. In addition, octree is a cache-friendly data structure, and hence, it is less prone to cache miss slowdowns on modern memory hierarchies than nblists. Octrees use almost 2 orders of magnitude less memory, which is crucial for simulation of large systems, and while they are comparable in performance to nblists when the distance cutoff is small, they outperform nblists for larger systems and large cutoffs. Our tests show that octree implementation is approximately 1.5 times faster in practical use case scenarios as compared to nblists.

A Subspace Semi-Definite programming-based Underestimation (SSDU) method for stochastic global optimization in protein docking.

We propose a new stochastic global optimization method targeting protein docking problems. The method is based on finding a general convex polynomial underestimator to the binding energy function in a permissive subspace that possesses a funnel-like structure. We use Principal Component Analysis (PCA) to determine such permissive subspaces. The problem of finding the general convex polynomial underestimator is reduced into the problem of ensuring that a certain polynomial is a Sum-of-Squares (SOS), which can be done via semi-definite programming. The underestimator is then used to bias sampling of the energy function in order to recover a deep minimum. We show that the proposed method significantly improves the quality of docked conformations compared to existing methods.

A New Distributed Algorithm for Side-Chain Positioning in the Process of Protein Docking*

Side-chain positioning (SCP) is an important component of computational protein docking methods. Existing SCP methods and available software have been designed for protein folding applications where side-chain positioning is also important. As a result they do not take into account significant special structure that SCP for docking exhibits. We propose a new algorithm which poses SCP as a Maximum Weighted Independent Set (MWIS) problem on an appropriately constructed graph. We develop an approximate algorithm which solves a relaxation of the MWIS and then rounds the solution to obtain a high-quality feasible solution to the problem. The algorithm is fully distributed and can be executed on a large network of processing nodes requiring only local information and message-passing between neighboring nodes. Motivated by the special structure in docking, we establish optimality guarantees for a certain class of graphs. Our results on a benchmark set of enzyme-inhibitor protein complexes show that our predictions are close to the native structure and are comparable to the ones obtained by a state-of-the-art method. The results are substantially improved if rotamers from unbound protein structures are included in the search. We also establish that the use of our SCP algorithm substantially improves docking results.

A Message Passing Approach to Side Chain Positioning with Applications in Protein Docking Refinement.

We introduce a message-passing algorithm to solve the Side Chain Positioning (SCP) problem. SCP is a crucial component of protein docking refinement, which is a key step of an important class of problems in computational structural biology called protein docking. We model SCP as a combinatorial optimization problem and formulate it as a Maximum Weighted Independent Set (MWIS) problem. We then employ a modified and convergent belief-propagation algorithm to solve a relaxation of MWIS and develop randomized estimation heuristics that use the relaxed solution to obtain an effective MWIS feasible solution. Using a benchmark set of protein complexes we demonstrate that our approach leads to more accurate docking predictions compared to a baseline algorithm that does not solve the SCP.

Investigating the efficiency of nursing education program from the perspective of graduate students of nursing and midwifery.

BACKGROUND: Continuous evaluation is required in order to ensure the university system's efficiency. One of the important aspects of evaluating the educational system's effectiveness is judging the system's ability in meeting environmental needs. The present research's goal has been to investigate nursing education's efficiency through investigating the graduate's condition and their views on education and studying in Isfahan University's School of Nursing and Midwifery in 2008. MATERIALS AND METHODS: This is a descriptive research which has investigated the nursing graduate's view on the nursing education efficiency. The sample of the present research contains one hundred graduates between the years 2001 and 2005 which have been chosen randomly to complete the questionnaire. The questionnaire is divided into five sections including; growth, demographic information, and satisfaction with professional development and the acquired scientific experiences during the education. The criteria of achieving educational goals, and acquiring individual and social development were used to determine the content of the questionnaire. Through further examination the validity of the questionnaire was calculated to be 0.85. The final analysis was done using the SPSS statistics software. FINDINGS: The majority of the participants were female and with an age range of 24 to 30. Among these, 55% were unemployed and 67% of them had no education higher than a bachelor degree. The mean scores of each of the efficiency fields were as following (the total score was 4): Professional growth 2.13 ± 0.36, Satisfaction with the obtained scientific achievement during studies 2.80 ± 0.48, achievement of the educational objectives1.95 ± 0.51 and individual and social improvement 2.70 ± 0.36, neither of which are desirable. There was no significant difference between the demographic information and education efficiency index. CONCLUSIONS: Considering the results of the present research, the nursing education system's efficiency level in Isfahan University is medium. Acquiring the educational goals is not satisfactory for the students. According to the results of this research, some changes have been suggested to change the lesson plans.