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The surge of graph neural networks has catalyzed significant advancements in recommendation systems by enabling more effective modeling of user-item interactions within undirected bipartite graphs. However, the proliferation of graph neural network architectures, coupled with the absence of standardized benchmarking frameworks, presents challenges in systematically evaluating and comparing different dynamic recommendation models. In response, we propose a comprehensive benchmarking study of bipartite graph neural network operators for recommendation systems using the PyTorch geometric library. Our contributions include the development of a flexible benchmarking framework encompassing data preprocessing, model training, and evaluation protocols, facilitating fair comparison across diverse dynamic recommendation scenarios. We rigorously assess the performance of various graph neural network models, ranging from traditional methods to state-of-the-art architectures, on the MovieLens100k dataset. Through insightful analysis of experimental results, we elucidate the strengths and weaknesses of different graph neural network operators and offer practical suggestions for model selection and configuration. Our work aims to foster transparency, reproducibility, and innovation in graph neural network-based dynamic recommendation systems, providing a valuable resource for researchers and practitioners in the field. [ABSTRACT FROM AUTHOR]

This article introduces an advanced nonlinear controller designed for optimizing the performance of a single‐link robot arm featuring a flexible joint. The proposed nonlinear control strategy incorporates a Proportional‐Integral (PI) controller in conjunction with a nonlinear velocity feedback component, aimed at providing effective nonlinear damping and suppressing vibrations. To validate the controller's performance, extensive simulations are conducted utilizing machine learning techniques within the Python environment. The performance of the proposed nonlinear damping controller is benchmarked against a conventional linear cascaded P‐PI control structure, with both controllers fine‐tuned using the Nelder‐Mead algorithm. Simulation results demonstrate that the nonlinear damping controller yields substantial improvements in the dynamic behavior of the robot axis arm, showcasing superior step and sinusoidal position tracking performance, along with active vibration damping capabilities. This research contributes valuable insights into the enhanced nonlinear control strategies for flexible‐joint robot arms, offering promising advancements in their overall dynamic performance. [ABSTRACT FROM AUTHOR]

Introduction: This study investigated the effectiveness of dialectical behavior therapy on hostile attributes and high-risk behaviors in adolescents. Materials and Methods: In the present study, 20 adolescents with high-risk behaviors and hostile documents who were referred to the Institute of Art Psychology in Mashhad, Iran, in the academic year 2020-2021, were randomly divided into experimental and control groups. Dialectical behavioral therapy was performed for 16 ninety-minute weekly sessions. The instruments used in this study were the high-risk behaviors questionnaire, hostile documents, and structured clinical interviews for clinical disorders. These questionnaires were completed by individuals in pre-test, post-test, and one month follow-up. The data were analyzed using descriptive statistics, repeated analysis of variance, and Tukey's post hoc test. Results: Based on the findings, the mean scores of hostile attributes and high-risk behaviors in the experimental group were reduced significantly compared to the controls (P< 0.05). Conclusion: Regarding the results, dialectical behavior therapy has positive effects on hostile attributes and high-risk behavior in adolescents. [ABSTRACT FROM AUTHOR]

Detecting the underlying human values within arguments is essential across various domains, ranging from social sciences to recent computational approaches. Identifying these values remains a significant challenge due to their vast numbers and implicit usage in discourse. This study explores the potential of emotion analysis as a key feature in improving the detection of human values and information extraction from this field. It aims to gain insights into human behavior by applying intensive analyses of different levels of human values. Additionally, we conduct experiments that integrate extracted emotion features to improve human value detection tasks. This approach holds the potential to provide fresh insights into the complex interactions between emotions and values within discussions, offering a deeper understanding of human behavior and decision making. Uncovering these emotions is crucial for comprehending the characteristics that underlie various values through data-driven analyses. Our experiment results show improvement in the performance of human value detection tasks in many categories. [ABSTRACT FROM AUTHOR]

We discuss application of methods from the Kraichnan model of turbulent advection to the study of non-equilibrium concentration fluctuations arising during diffusion in liquid mixtures at high Schmidt numbers. This approach treats nonlinear advection of concentration fluctuations exactly, without linearization. Remarkably, we find that static and dynamic structure functions obtained by this method reproduce precisely the predictions of linearized fluctuating hydrodynamics. It is argued that this agreement is an analogue of anomaly non-renormalization which does not, however, protect higher-order multi-point correlations. The latter should thus yield non-vanishing cumulants, unlike those for the Gaussian concentration fluctuations predicted by linearized theory. [ABSTRACT FROM AUTHOR]

Understanding the dynamics of charge carriers at the semiconductor surfaces and interfaces is fundamental to the further development of photocatalytic, photovoltaic, and optoelectronic devices. Here, we study the surface photovoltage (SPV) dynamics in intrinsic and doped silicon using scanning ultrafast electron microscopy (SUEM). SUEM is a surface sensitive technique that allows the direct imaging of carriers at ultrafast time scales, thereby elucidating their spatiotemporal response to optical excitation. We first discuss the mechanism of image formation in SUEM. We then use these images to show that carrier dynamics on the silicon surface depends strongly on the doping type and concentration, though not always dictated by SPV. The numerical simulation of the drift-diffusion model suggests that this is due to the formation of complex transport processes, driven by intrinsic and photoinduced fields in the excited volume. This work refines our current understanding of the surface-exclusive dynamics in semiconductors by introducing a means to study their evolution in space and time and providing a model to explain the underlying mechanism. [ABSTRACT FROM AUTHOR]

Generative adversarial networks (GANs) are one of the most powerful generative models, but always require a large and balanced dataset to train. Traditional GANs are not applicable to generate minority-class images in a highly imbalanced dataset. Balancing GAN (BAGAN) is proposed to mitigate this problem, but it is unstable when images in different classes look similar, e.g., flowers and cells. In this work, we propose a supervised autoencoder with an intermediate embedding model to disperse the labeled latent vectors. With the enhanced autoencoder initialization, we also build an architecture of BAGAN with gradient penalty (BAGAN-GP). Our proposed model overcomes the unstable issue in original BAGAN and converges faster to high-quality generations. Our model achieves high performance on the imbalanced scale-down version of MNIST Fashion, CIFAR-10, and one small-scale medical image dataset. https://github.com/GH920/improved-bagan-gp. [ABSTRACT FROM AUTHOR]

We assessed a panel of six essential oils (EOs) including Clausena anisata, Lippia alba, Pimpinella anisum, Syzygium aromaticum, Litchi chinensis and Heracleum sphondylium for their chemical constituents and scolicidal activity on Echinococcus granulosus. Protoscolices were collected from fertile hydatid cysts and exposed to different concentrations of the EOs. After 10 min of exposure, S. aromaticum and C. anisata EOs at the concentration of 100 μg/ml resulted in the mortality rate of 63.33 and 51.33%, respectively. EOs of S. aromaticum, L. alba and C. anisata were the most potent with LC50 values of 16.8, 23.9 and 24.3 µg/ml, respectively. EO of L. alba showed a LC90 value of 442.7 µg/ml, which was significantly higher than S. aromaticum (58.4) and C. anisata (76.7). The obtained results demonstrated that EOs of S. aromaticum and C. anisata can be further studied for the development of new therapeutics and have the potential to be tested under in vivo conditions. [ABSTRACT FROM AUTHOR]

Artificial intelligence computing adapted from biology is a suitable platform for the development of intelligent machines by imitating the functional mechanisms of the nervous system in creating high-level activities such as learning, decision making and cognition in today's systems. Here, the concentration is on improvement the cognitive potential of artificial intelligence network with a bio-inspired structure. In this regard, four spiking pattern recognition platforms for recognizing digits and letters of EMNIST, patterns of YALE, and ORL datasets are proposed. All networks are developed based on a similar structure in the input image coding, model of neurons (pyramidal neurons and interneurons) and synapses (excitatory AMPA and inhibitory GABA currents), and learning procedure. Networks 1–4 are trained on Digits, Letters, faces of YALE and ORL, respectively, with the proposed un-supervised, spatial–temporal, and sparse spike-based learning mechanism based on the biological observation of the brain learning. When the networks have reached the highest recognition accuracy in the relevant patterns, the main goal of the article, which is to achieve high-performance pattern recognition system with higher cognitive ability, is followed. The pattern recognition network that is able to detect the combination of multiple patterns which called intertwined patterns has not been discussed yet. Therefore, by integrating four trained spiking pattern recognition platforms in one system configuration, we are able to recognize intertwined patterns. These results are presented for the first time and could be the pioneer of a new generation of pattern recognition networks with a significant ability in smart machines. [ABSTRACT FROM AUTHOR]

Introduction: After lumpectomy, radiation therapy is used to control the tumor and increase patient survival. Following radiation therapy, the organs at risk are vulnerable to toxicity and secondary cancer. Material and Methods: Thirty-two patients with early-stage of left breast cancer were selected for this study. Intensity-modulated radiotherapy (IMRT) and three-dimensional conformal radiotherapy (3DCRT) were planned to deliver the prescribed dose to the target volume. Considering baseline risk of heart disease, the excess absolute risk (EAR) of heart disease was calculated using the Reynolds risk score for ages 50-70. Results: There was a significant difference in 10-year EAR of heart disease when comparing 3DCRT plans to IMRT (p <0.05). The 10-year EAR for IMRT in the low, median, and high-risk groups was superior to 3DCRT. Among factors involved in baseline risk, by increasing the age, the impact of smoking on increasing EAR was clearer compared to a family history of heart disease. Conclusion: IMRT had a more uniform dose distribution and a better conformity-homogeneity index than 3DCRT. However, the mean heart dose and subsequently the risk of heart disease significantly were lower in 3DCRT. Considering baseline risk leads to accurate estimates of the heart disease risk after breast cancer radiotherapy. [ABSTRACT FROM AUTHOR]

Background: Due to imprecise/missing data used for parameterization of ordinary differential equations (ODEs), model parameters are uncertain. Uncertainty of parameters has hindered the application of ODEs that require accurate parameters. Methods: We extended an available ODE model of tumor-immune system interactions via fuzzy logic to illustrate the fuzzification procedure of an ODE model. The fuzzy ODE (FODE) model assigns a fuzzy number to the parameters, to capture parametric uncertainty. We used the FODE model to predict tumor and immune cell dynamics and to assess the efficacy of 5-fluorouracil (5-FU) chemotherapy. Result: FODE model investigates how parametric uncertainty affects the uncertainty band of cell dynamics in the presence and absence of 5-FU treatment. In silico experiments revealed that the frequent 5-FU injection created a beneficial tumor microenvironment that exerted detrimental effects on tumor cells by enhancing the infiltration of CD8+ T cells, and natural killer cells, and decreasing that of myeloid-derived suppressor cells. The global sensitivity analysis was proved model robustness against random perturbation to parameters. Conclusion: ODE models with fuzzy uncertain kinetic parameters cope with insufficient/imprecise experimental data in the field of mathematical oncology and can predict cell dynamics uncertainty band. [ABSTRACT FROM AUTHOR]

The optimal arrangement of fuel assemblies inside a nuclear reactor core is one of the most challenging topics in nuclear engineering. Three different powerful optimization methods (particle swarm optimization, genetic algorithm method, and dragonfly algorithm) are developed and applied on the System-integrated Modular Advanced ReacTor (SMART). A new program named "DYNAMIC-FMO" is written in MATLAB language programming to solve a multi-objective NP-Hard problem of determining the fuel assemblies' optimal position inside the SMART core. To attain the required nuclear data and simulate the SMART core, DRAGON/PARCS codes are coupled and employed in our "DYNAMIC-FMO" program. Optimization of the SMART core loading pattern has led to a significant increase in cycle length by 185 days and a decrease in power peaking factor by 12.5%. According to the results, the dragonfly algorithm has better performance than the particle swarm optimization and the genetic algorithm for core loading pattern optimization of SMART. [ABSTRACT FROM AUTHOR]

Credit card fraud results in staggering financial losses amounting to billions of dollars annually, impacting both merchants and consumers. In light of the escalating prevalence of digital crime and online fraud, it is important for organizations to implement robust and advanced technology to efficiently detect fraud and mitigate the issue. Contemporary solutions heavily rely on classical machine learning (ML) and deep learning (DL) methods to handle such tasks. While these methods have been effective in many aspects of fraud detection, they may not always be sufficient for credit card fraud detection as they aren't adaptable to detect complex relationships when it comes to transactions. Fraudsters, for example, might set up many coordinated accounts to avoid triggering limitations on individual accounts. In the context of fraud detection, the ability of Graph Neural Networks (GNN's) to aggregate information contained within the local neighbourhood of a transaction enables them to identify larger patterns that may be missed by just looking at a single transaction. In this research, we conduct a thorough analysis to evaluate the effectiveness of GNNs in improving fraud detection over classical ML and DL methods. We first build an heterogeneous graph architecture with the source, transaction, and destination as our nodes. Next, we leverage Relational Graph Convolutional Network (RGCN) to learn the representations of nodes in our graph and perform node classification on the transaction node. Our experimental results demonstrate that GNN's outperform classical ML and DL methods. [ABSTRACT FROM AUTHOR]

The perturbation of a semiconductor from the thermodynamic equilibrium often leads to the display of non-linear dynamics and formation of spatiotemporal patterns due to the spontaneous generation of competing processes. Here, we use scanning ultrafast electron microscopy to show that the transport of hot carriers in the strongly excited semiconductor slows down by turning into an oscillatory process; this is evidenced by the expansions and contractions in the second moment of the distribution. We attribute such a response to the electric field generated by the spatial separation of photo-excited electrons and holes under intrinsic and photo-induced fields; we then introduce a transport model that mimics the experimental observation. Our finding provides a direct imaging evidence for the electrostatic oscillation of hot carriers in the highly excited semiconductor and offers new insights into their dynamics in space and time. [ABSTRACT FROM AUTHOR]

At any scale l in the turbulent inertial range, the magnetic field can be divided up into a large-scale component and a small-scale, high spatial frequency component which undergoes magnetic reversals. Such local reconnections, i.e., on any inertial scale l, seem to be an inseparable part of magnetohydrodynamic (MHD) turbulence, whose collective outcome can lead to global reconnection with a rate independent of the small-scale physics dominant at dissipative scales. We show that this picture, known as stochastic reconnection, is intimately related to nanoflare theory, proposed long ago to explain solar coronal heating. We argue that, due to stochastic flux freezing, a generalized version of magnetic flux freezing in turbulence, the field follows the flow in a statistical sense. Turbulence bends and stretches the field, increasing its spatial complexity. Strong magnetic shears associated with such a highly tangled field can trigger local reversals and field annihilations on a wide range of inertial scales which convert magnetic energy into kinetic and thermal energy. The former may efficiently enhance turbulence and the latter heat generation. We support this theoretical picture using scaling laws of MHD turbulence and also recent analytical and numerical studies which suggest a statistical correlation between magnetic spatial complexity and energy dissipation. Finally, using an MHD numerical simulation, we show that the time evolution of the magnetic complexity is statistically correlated with the rate of kinetic energy injection and/or magnetic-to-thermal energy conversion, in agreement with our proposed theoretical picture. [ABSTRACT FROM AUTHOR]

In this paper, we prove a generalization of a conjecture of Erdös, about the chromatic number of certain Kneser-type hypergraphs. For integers n , k , r , s with n ≥ r k and 2 ≤ s ≤ r , the r -uniform general Kneser hypergraph KG s r (n , k) , has all k -subsets of { 1 , ... , n } as the vertex set and all multi-sets { A 1 , ... , A r } of k -subsets with s -wise empty intersections as the edge set. The case r = s = 2 , was considered by Kneser [7] in 1955, where he conjectured that its chromatic number is n − 2 (k − 1). This was finally proved by Lovász [9] in 1978. The case r > 2 and s = 2 , was considered by Erdös in 1973, and he conjectured that its chromatic number is ⌈ n − r (k − 1) r − 1 ⌉. This conjecture was proved by Alon, Frankl and Lovász [2] in 1986. The case where s > 2 , was considered by Sarkaria [11] in 1990, where he claimed to prove a lower bound for its chromatic number which generalized all previous results. Unfortunately, an error was found by Lange and Ziegler [14] in 2006 in the induction method of Sarkaria on the number of prime factors of r , and Sarkaria's proof only worked when s is less than the smallest prime factor of r or s = 2. Finally in 2019, Aslam, Chen, Coldren, Frick and Setiabrata [3] were able to extend this by using methods different from Sarkaria to the case when r = 2 α 0 p 1 α 1 ... p t α t and 2 ≤ s ≤ 2 α 0 (p 1 − 1) α 1 ... (p t − 1) α t . In this paper, by applying the Z p -Tucker lemma of Ziegler [13] and Meunier [10] , we finally prove the general Erdös conjecture and prove the claimed result of Sarkaria for any 2 ≤ s ≤ r. We also provide another proof of a special case of this result, using methods similar to those of Alon, Frankl, and Lovász [2] and compute the connectivity of certain simplicial complexes that might be of interest in their own right. [ABSTRACT FROM AUTHOR]

Previous work has invoked kinetic and magnetic spatial complexities, associated with velocity and magnetic fields u (x , t) and B (x , t) , respectively, in order to study magnetic reconnection and diffusion in turbulent and magnetized fluids. In this paper, using the coarse-grained momentum equation, we argue that the fluid jets associated with magnetic reconnection events at an arbitrary scale l in the turbulence inertial range are predominantly driven by the Lorentz force N l = (j × B) l − j l × B l . This force is induced by the subscale currents and is analogous to the turbulent electromotive force E l = (u × B) l − u l × B l in dynamo theories. Typically, high (low) magnetic complexities during reconnection imply large (small) spatial gradients for the magnetic field, i.e., strong (weak) Lorentz forces Nl. Reconnection launches jets of fluid, hence the rate of change of kinetic complexity is expected to strongly correlate with the power injected by the Lorentz force Nl. We test this prediction using an incompressible, homogeneous magnetohydrodynamic (MHD) simulation and associate it with previous results. It follows that the stronger (weaker) the turbulence, the more (less) complex the magnetic field and the stronger (weaker) the driving Lorentz forces and thus the ensuing reconnection. [ABSTRACT FROM AUTHOR]

Let k be a positive integer. A k-tuple (total) dominating set in a graph G is a subset S of its vertices such that any vertex v in G has at least k vertices inside S among its neighbors and v itself (resp. among its neighbors only). The minimum size of a k-tuple (total) dominating set for G is called the k-tuple (total) domination number of G. It is shown in this article that the k-tuple total domination number of a connected (k + 1) -regular graph with n vertices is at most k 2 + k - 1 k 2 + k n , unless it is the incident graph of a finite projective plane of order k, where this number is k 2 + k k 2 + k + 1 n . We also show that the k-tuple domination number of a connected k-regular graph with n vertices is at most k 2 - 1 k 2 n , unless it is a Moore graph of diameter 2, where this number is k 2 k 2 + 1 n . [ABSTRACT FROM AUTHOR]

Let G be a connected graph of order n with vertex set V(G). For positive integers a and b, a subset S ⊆ V (G) is an (a, b)-dominating set if every vertex v ∈ S is adjacent to at least a vertices inside S and every vertex v ∈ V \ S is adjacent to at least b vertices inside S. The minimum cardinality of an (a, b)-dominating set for G is called the (a, b)-domination number of G and is denoted by γ a , b (G) . There are various results on upper bounds for γ a , b (G) when G is a regular graph or a and b are small numbers. In the first part of this paper, for a given graph G with the minimum degree of at least max { a , b } , we define a new graph G ′ associated to G and show that the independence number of this graph is related to γ a , b (G) . In the next part, using Lovász local lemma, we give a randomized approach to improve previous results on the upper bounds for γ a , b (G) in some special cases. [ABSTRACT FROM AUTHOR]

In this paper, we introduce new, more efficient, methods for training recurrent neural networks (RNNs) for system identification and Model Reference Control (MRC). These methods are based on a new understanding of the error surfaces of RNNs that has been developed in recent years. These error surfaces contain spurious valleys that disrupt the search for global minima. The spurious valleys are caused by instabilities in the networks, which become more pronounced with increased prediction horizons. The new methods described in this paper increase the prediction horizons in a principled way that enables the search algorithms to avoid the spurious valleys. The work also presents a novelty sampling method for collecting new data wisely. A clustering method determines when an RNN is extrapolating, which occurs when the RNN operates outside the region spanned by the training set, where adequate performance cannot be guaranteed. The new method presented in this paper uses a clustering method for extrapolation detection, and then the novel data is added to the original training set. The network performance is improved when additional training is performed with the augmented data set. The new techniques are applied to the model reference control of a magnetic levitation system. The techniques are tested on both simulated and experimental versions of the system. [ABSTRACT FROM AUTHOR]

Background: Measurement of oxygen consumption can give useful information of brain functionality in health and disease. To have a better understanding of normal physiology in all conditions (rest or functional tasks), development of accurate brain oxygen mapping is essential. This mapping could also be a great help in managing pathological conditions with disturbance in oxygen supply. One of the most important hemodynamic parameters of the brain is oxygen extraction fraction (OEF). By development of blood oxygen level dependent magnetic resonance imaging (BOLD MRI) in imaging, several methods have been proposed for measuring this parameter. The latest methods are mostly based on hypercapnic and hyperoxic stimulus. Objectives: Here,we presented a new method with hypercapnic and hyperoxic stimulus and using BOLD and dynamic susceptibility contrast (DSC) perfusion imaging instead of arterial spin labelling (ASL). Materials and Methods: A 3T Siemens Trio Tim Scanner was used for imaging five patients (three males and two females) who were recruited for this experiment. Four set of T1 data, BOLD data and two DSC-perfusions (two injections) were applied to each subject. Gas manipulation was applied while acquiring BOLD data and one of the DSC-perfusion was done in hyperoxic condition. Gas manipulation was a combination of hypercapnic, hyperoxic and normoxic conditions in a total of 13 minutes. The DSC data was analyzed using PMA (ASIST) software to calculate cerebral blood flow (CBF) and using its results with the BOLD data, OEF was calculated by MATLAB coding. All preprocessing was done with the SPM12 (MATLAB). Results: Comparable results were acquired for OEF with previous studies. The mean value of estimated OEF for gray matter (GM), white matter (WM) and whole brain is 0.46, 0.37, and 0.44, respectively which are within the expected range of normal condition. Conclusion: As the proposed method for OEF measurement uses a combination of hyperoxic and hypercapnic stimulus with BOLD and DSC-perfusion imaging, it could be considered the most available one in all MRI scanners and being a voxel wise method, it could give OEF value for any requested ROI. [ABSTRACT FROM AUTHOR]

Introduction: Various treatment methods for drug abusers will result in different success rates. This is partly due to different neural assumptions and partly due to various rate of relapse in abusers because of different circumstances. Investigating the brain activation networks of treated subjects can reveal the hidden mechanisms of the therapeutic methods. Methods: We studied three groups of subjects: heroin abusers treated with abstinent based therapy (ABT) method, heroin abusers treated with Methadone Maintenance Therapy (MMT) method, and a control group. They were all scanned with functional magnetic resonance imaging (fMRI), using a 6-block task, where each block consisted of the rest-craving-rest-neutral sequence. Using the dynamic causal modeling (DCM) algorithm, brain effective connectivity network (caused by the drug craving stimulation) was quantified for all groups. In this regard, 4 brain areas were selected for this analysis based on previous findings: ventromedial prefrontal cortex (VMPFC), dorsolateral prefrontal cortex (DLPFC), amygdala, and ventral striatum. Results: Our results indicated that the control subjects did not show significant brain activations after craving stimulations, but the two other groups showed significant brain activations in all 4 regions. In addition, VMPFC showed higher activations in the ABT group compared to the MMT group. The effective connectivity network suggested that the control subjects did not have any direct input from drug-related cue indices, while the other two groups showed reactions to these cues. Also, VMPFC displayed an important role in ABT group. In encountering the craving pictures, MMT subjects manifest a very simple mechanism compared to other groups. Conclusion: This study revealed an activation network similar to the emotional and inhibitory control networks observed in drug abusers in previous works. The results of DCM analysis also support the regulatory role of frontal regions on bottom regions. Furthermore, this study demonstrates the different effective connectivity patterns after drug abuse treatment and in this way helps the experts in the field. [ABSTRACT FROM AUTHOR]

In ultra-dense small cell networks, spatial multiplexing gain is a challenge because of the different propagation conditions. The channels associated with different transmit–receive pairs can be highly correlated due to the: 1) high probability of line-of-sight (LOS) communication between user equipment (UE) and base station (BS) and 2) insufficient spacing between antenna elements at both UE and BS. In this paper, we propose a novel transmission technique titled Diversity Pulse Shaped Transmission (DPST), to enhance the throughput over the correlated multiple-input multiple-output (MIMO) channels in an ultra-dense small cell network. The fundamental of DPST is to shape transmit signals at adjacent antennas with distinct interpolating filters, introducing pulse shaping diversity. In DPST, each antenna transmits its own data stream with a relative deterministic time offset—which must be a fraction of the symbol period—with respect to the adjacent antenna. The delay is interpolated with the pulse shaped signal generating a virtual MIMO channel that benefits from increased diversity from the receiver perspective. To extract the diversity, the receiver must operate in an oversampled domain, and hence, a fractionally spaced equalizer (FSE) is proposed. The joint impact of DPST and FSE helps the receiver to sense a less correlated channel, eventually enhancing the UE's throughput. Moreover, in order to minimize the spatial correlation, we aim to optimize the deterministic fractional delay. Simulation results show that applying DPST to a correlated channel can approximately enhance the UE throughput by 1.93\times and 3.76\times in \text2\,\times\, \text2 and \text4\,\times \,\text4 MIMO systems, respectively. [ABSTRACT FROM AUTHOR]

Background and Objective Epilepsy is a neurological disorder that causes recurrent and abrupt seizures which makes the patients insecure. Predicting seizures can reduce the burdens of this disorder. Methods A new approach in seizure prediction is presented that includes a novel technique in feature extraction from EEG. The algorithm firsts creates an embedding space from EEG time series. Then it takes samples with most of the information using an optimized and data specific Poincare plane. In order to quantify small dynamics on the Poincare plane, based on the order of locations of Poincaré samples in the sequence, 64 fuzzy rules in each channel are defined. Features are extracted based on the frequency distribution of these fuzzy rules in each minute. Then features with higher variance are selected as ictal features and again reduced using PCA. Finally, in order to evaluate how these innovative features can increase the performance of the seizure prediction algorithm, the transition from interictal to preictal state is scored utilizing SVM. Results The algorithm is tested on 460 h of EEG from 19 patients of Freiburg dataset who had at least 3 seizures. Considering maximum Seizure Prediction Horizon of 42 minutes, average sensitivity was 91.8 - 96.6% and average false prediction rate was 0.05 - 0.08/h. Conclusions The presented algorithm shows a better performance and more robustness compare to most of existing methods, and shows power in extracting optimal features from EEG. [ABSTRACT FROM AUTHOR]

Objective: Muscle synergy is the activation of a group of muscles that contribute to a particular movement. The goal of the present study is to examine the hypothesis that human reaching movements at different speeds share similar muscle synergies and to investigate the kinesiology basis and innervation of muscles.Methods: Electromyographic activity from six muscles of the upper limb and shoulder girdle were recorded during three movements at different speeds, i.e. slow, moderate and fast. The effect of window length on the RMS signal of the EMG was analyzed and then EMG envelope signals were decomposed using non-negative matrix factorization. For each of the ten subjects, three synergies were extracted which accounted for at least 99% of the VAF. For each movement, the muscle synergies and muscle activation coefficients of all participants were clustered in to three partitions. Investigation showed a high similarity and dependency of cluster members due to the cosine similarity and mutual information in muscle synergy clustering. For further verification, the EMG envelope signals for all subjects were reconstructed.Results: The results indicated a lower reconstruction error using the center of the muscle synergy clusters in comparison with the average of the activation coefficients, which confirms the current research's hypothesis. [ABSTRACT FROM AUTHOR]

Background: According to the World Health Organization, by the end of last year, about 37 million people throughout the world were diagnosed with AIDS and millions of people die each year from this disease.Objective: To develop an appropriate model which depicts the mechanism of the dynamics involved in the interactions between HIV and immune system in peripheral bloodstream of HIV infected individuals by considering the phenomena of virus mutation and taking into account the role of latently infected cells in speared of infection and considering the effects of antiretroviral drugs and occurrence of drug resistance in our model in order to assess the results obtained from applying different therapeutic methods.Methods: Two-dimensional CA model with Moor neighboring was developed. Various agents which they were referring to peripheral bloodstream particles of HIV infected individuals were defined. Then the biological rules were extracted from both expert knowledge and the authoritative articles. The extracted rules were applied for updating the states of these agents. The effects of using antiretroviral drug treatment were considered by applying drug's effectiveness of both of protease and reverse transcriptase inhibitors as two separate inputs of model.Results: Time evolution curves of concentrations of defined agents were shown as our results. In case of considering no treatment, our results showed that concentrations of healthy CD4+T cells reached the threshold of AIDS after a bout 250 weeks. By applying monotherapy method, the concentrations of these cells remained on the threshold of AIDS for a long time and applying combined antiretroviral therapy (cART) method leaded to increase the concentration of these cells 20% upper than threshold of AIDS. Also, by applying monotherapy and cART compared with no treatment, the concentrations of infected CD4+T cells 10% and 40% decreased further, respectively and for the level of viral load, leads to a reduction of almost 55% and 90%, respectively. Belated treatment, comparison with early treatment, caused almost 10% reduce (increase) in steady state concentrations of healthy (infected) cells. [ABSTRACT FROM AUTHOR]

Let n ≥ 1 , r ≥ 2 , and s ≥ 0 be integers and P = { P 1 , ... , P l } be a partition of [ n ] = { 1 , ... , n } with | P i | ≤ r for i = 1 , ... , l. Also, let F be a family of non-empty subsets of [ n ]. The r -uniform Kneser-type hypergraph KG r (F , P , s) is the hypergraph with the vertex set consisting of all P -admissible elements F ∈ F , that is | F ∩ P i | ≤ 1 for i = 1 , ... , l and the edge set consisting of all r -subsets { F 1 , ... , F r } of the vertex set that | F i ∩ F j | ≤ s for all 1 ≤ i < j ≤ r. In this article, we extend the equitable r -colorability defect ecd r (F) of Abyazi Sani and Alishahi to the case when one allows intersections among the vertices of an edge. It will be denoted by ecd r (F , s). We then prove that the chromatic number of KG r (F , P , s) is bounded from below by ecd r (F , ⌊ s / 2 ⌋) r − 1 , under the technical assumption that the pair (F , P) is ⌊ s / 2 ⌋ -good. This condition holds in the cases when s = 0 , or P consists of singletons, that is P -admissibility is automatically satisfied for all sets or the family F is the family of all k -sets in [ n ] for some integer s < k < n. This work generalizes many existing results in the literature on the Kneser hypergraphs. It generalizes the previous results of the current authors from the special family of all k -subsets of [ n ] to a general family F of subsets. [ABSTRACT FROM AUTHOR]

Efficient decision making in vertical handoff and network selection algorithms improves users' quality of service and helps users meet service requirements, anywhere and at any time. Hence, in this paper, a user-centric network selection algorithm is proposed, utilizing the estimated reputation of the available candidate networks based on user location and combined experienced users' utility. User utility is defined based on 1) quality of service, 2) monetary cost, and 3) energy consumption metrics. In the proposed history aware-based user location algorithm, the past experience of users for available networks is considered to estimate the future utility that a user can obtain from a candidate network. The reputation factor for networks is used based on knowledge of users from each other while receiving service. Simulation results indicate that the average obtained utility by users is improved and handoff criteria, i.e. handoff number and failed and unnecessary handoffs, decrease. It can be seen that users choose networks with good past operations and this can encourage operators to provide good quality services for increasing their revenue. [ABSTRACT FROM AUTHOR]

Background: The present study was designed to investigate the chronic effects of diazinon on oxidative stress markers in brain and heart of rats and the possible protective effects of tert butyl hydroquinone (TBHQ), as an antioxidant. Objectives: Therefore, the present study was undertaken to evaluate the protective role of TBHQ on oxidative stress induced by diazinon in brain and heart tissues of rats after 7 weeks exposure to sub-lethal dose of diazinon. Materials and Methods: In this experimental study, 28 male Wistar rats which were randomly divided into four groups: diazinon group (10 mg/kg, once a day), TBHQ group (0.028 g/kg of diet, once a day), diazinon + TBHQ group (diazinon, 10 mg/kg, once a day + TBHQ, 0.028 g/kg of diet, once a day) and control group (corn oil, as vehicle of diazinon and TBHQ). The animals were treated with diazinon, TBHQ or corn oil orally using a stomach tube for 7 weeks. Results: At the end of 7th week, total thiol groups, ferric reducing antioxidant power (FRAP) and malondialdehyde (MDA) levels in brain and heart tissues were investigated. A significant increase in MDA levels (P = 0.01) in heart tissue was evident in diazinon group, when compared to control group. Rats of the TBHQ and diazinon + TBHQ groups presented a significant increase in thiol groups (P = 0.01), when compared to control and diazinon groups. In addition, TBHQ administration significantly increased the FRAP level of brain tissue in TBHQ and TBHQ + diazinon groups, when compared with control and diazinon groups, respectively (P = 0.001). Conclusions: The results of the present study showed that TBHQ treatment could improve antioxidant status in brain and heart tissues of rats with chronic toxicity of diazinon. However, it could not ameliorate the lipid peroxidation sufficiently. [ABSTRACT FROM AUTHOR]

Objective: To examine if nonlinear feature extraction method yields appropriate results in complex brainstem response classification of three different consonant vowels diotically presented in normal Persian speaking adults.Methods: Speech-evoked auditory brainstem responses were obtained in 27 normal hearing young adults by using G.tec EEG recording system. 170ms synthetic consonant-vowel stimuli /ba/, /da/, /ga/ were presented binaurally and the recurrence quantification analysis was performed on the responses. The recurrence time of second type was proposed as a suitable feature. ANOVA was also used for testing the significance of extracted feature. Post-comparison statistical method was used for showing which means are significantly different from each other.Results: Dimension embedding and state space reconstruction were helpful for visualizing nonlinearity in auditory system. The proposed feature was successful in the objective classification of responses in window time 20.1-35.3ms, which belonged to formant transition period of stimuli. Also the p value behavior of recurrence time of second type feature as a discriminant feature was close to the nature of the response that includes transient and sustained parts. On the other hand, the /ba/ and /ga/ classification period was wider than the others.Conclusion: The extracted feature shown in this paper is helpful for the objective of distinguishing individuals with auditory processing disorders in the structurally similar voices. On the other hand, differing nonlinear feature is meaningful in a special region of response, equal to formant transition period, and this feature is related to the state space changes of brainstem response. It can be assumed that more information is within this region of signal and it is a sign of processing role of brainstem. The state changes of system are dependent on input stimuli, so the existence of top down feedback from cortex to brainstem forces the system to act differently. [ABSTRACT FROM AUTHOR]

In this paper, we develop a novel approach for bidirectional brain-machine interface (BMI). First, we propose a neural network model for sensory cortex (S1) connected to the neural network model of motor cortex (M1) considering the topographic mapping between S1 and M1. We use 4-box model in S1 and 4-box in M1 so that each box contains 500 neurons. Individual boxes are composed of two neural populations: inhibitory interneurons and pyramidal neurons. Next, we develop a new BMI algorithm based on neural firing. The main concept of these BMI algorithm is to close the loop between two components: the sensory interface and the motor interface. The sensory interface encodes some of the state parameters of the external device into an electrical stimulus delivered to the S1 model. The motor interface takes neural recordings from the M1 model and decodes them into a force applied to the object. We present the simulation results for the on line BMI which means that there is a real time information exchange between the S1-M1 network model and the external device. [ABSTRACT FROM AUTHOR]

Cognitive radio networks (CRNs) have been introduced in recent years to solve frequency leakage. In these networks, several challenges such as Lion and jamming attacks, giving false sensing information, low accuracy in detection of primary users, and sensing error can lead to decrease in CRN's performance. Selecting appropriate free bands by secondary users (SUs) can prevent interference and increase frequency use. In this paper, we present a decision making method based on reinforcement learning to find the optimal strategy for SU in best channel selection. In our model, effect of collision fraud and sensing error is considered simultaneously. In the proposed model, to overcome falsification of sensing reports, the abstract of spectrum sensing data falsification attack is extracted and modelled by using model-based reinforcement learning method. We formalize different states of system, actions of SUs, and probability transitions between states in the framework of Markov decision process (MDP). The proposed model is evaluated by numerical results and optimal strategies according to different effective parameters in network performance are studied. [ABSTRACT FROM AUTHOR]

Operators are currently considering the deployment of small cells to complement their macrocellular networks and increase their coverage and capacity. However, in order to roll-out a large number of small cells and allow anytime, anywhere wireless broadband connectivity through wireless technologies, operators must still face the challenge of backhauling the traffic from the small cells to the core network in a cost-effective manner. In this paper, backhaul challenges for small cell base stations (BSs) are discussed, and potential wired and wireless solutions together with their benefits and drawbacks are presented. The use of large scale antennas systems and free-space optics is also discussed. Moreover, a wireless backhaul planning tool targeted at finding the most cost-effective backhaul solution using a mixture of wireless technologies is presented. Simulation results confirm that the optimum backhaul solution is a combination of various options, which can overcome inherent scenario constraints while providing a cost-effective performance. [ABSTRACT FROM AUTHOR]

In this paper, a routing algorithm is proposed for access selection in a network to find the optimal paths among intermediate nodes with multiple interfaces. Markov Decision Process is applied in each node to find optimal policy and select proper paths to the best access point in a dynamic environment. A reward function is defined as environment feedback to optimize and adapt routing behavior of nodes based on the local information. Selection metrics in each node are interface load, link quality and destination condition. It is shown, by using the proposed algorithm, there are better management in the node which decreases interference and collision and selects links with better quality toward the best possible destination. The performance of the method is exemplified and it is shown how the throughput and average delay of the network with more interface in its nodes, improved while packet loss degrades. As an example a two-interface and a one-interface network are studied. It is shown when network load is increased, interface management will improve the throughput, in the network with two-interface nodes. Also, by considering the link quality factor in the reward function, packet dropping becomes less but average delay increases. [ABSTRACT FROM AUTHOR]

Background: In patients who undergoing PCI, association between right ventricular function and outcome of the procedure remained unclear. The present study aimed to determine association between echocardiography findings of systolic right ventricular function and functional status of patients following PCI. Methods: In a cross-sectional study conducted at Imam Ali hospital and heart center in Kermanshah, Iran in 2013, 40 patients with history of inferior wall myocardial infarction (Inf MI) according to previous electrocardiography (ECG) in past hospitalization for MI who were candidate for percutaneous coronary intervention (PCI) on right coronary artery (RCA) and had left ventricle ejection fraction (LVEF) less than 40% were included. The subjects underwent echocardiography on admission to assess echocardiography indices of systolic right ventricular function including tricuspid annular plane systolic excursion (TAPSE), tricuspid annular systolic velocity (TASV), Tei-index (Myocardial performance index), and RV fractional area (RVFA) change that was repeated one month later. Baseline functional status was assessed based on the New York Heart Association functional classification score (NYHA score) that divided to 4 grades. Results: NYHA score improved following PCI procedure (from 2.20±0.46 to 1.10±0.30, P<0.001). The mean score of TAPSE significantly increased from 18.68±2.12 to 20.40±2.11 (P<0.001). The mean of TASV also increased from 13.28±1.52 to 14.85±1.90 (P<0.001). Also, Tei-index was improved from 0.52±0.05 to 0.47±0.03 (P<0.001). Moreover, RVFA was significantly increased after PCI (from 35.02±2.40 to 38.25±2.57, P<0.001). There was no significant relationship between the changes in NYHA score and each of right ventricular systolic function indices. Conclusion: Although right ventricular systolic function considerably improved following PCI procedure, but the changes in this improvement is not associated with the improvement of function class after the procedure. [ABSTRACT FROM AUTHOR]

The effectiveness of added filters including conventional and rare earth materials for dental radiography tasks was investigated using a simulation approach. Current study focuses on the combination of a range of various filters to investigate the reduction of radiation absorbed dose and improving the quality of a radiography image. To simulate the X-ray beam spectrum, a MCNP5 code was applied. Relative intensity, beam quality, and mean energy were investigated for a typical dental radiography machine. The impact of different rare-earth materials with different thicknesses and tube voltages on the X-ray spectrum was investigated. For Aluminum as a conventional filter, the modeled X-ray spectra and HVL values were in a good agreement with those reported by IPEM. The results showed that for a 70 kVp voltage, with an increase of the thickness and atomic number of a given added filters, an increase of HVL values were observed. However, with the increase of the attenuator thickness, X-ray beam intensity decreases. For mean energy, different results were observed. It was also found that rare earth made filters reduce high energy X-ray radiation due to k-edge absorption. This leads to an ideal beam for intra-oral radiography tasks. However, as a disadvantage of rare earth added filters, the reduction of the tube output levels should also be considered. [ABSTRACT FROM AUTHOR]

Background: The liver is the major target organ for aflatoxin B1 (AFB1). Ingestion of aflatoxin causes hepatotoxicty. In this study, captopril as new agent to help the hepatotoxicity induced by aflatoxin was suggested. Materials and Methods: The isolated perfused rat liver (IPRL) was chosen for evaluating hepatic function. Sixteen rats were divided randomly into four experimental groups: control, captopril, AFB1 and AFB1 + captopril. The level of glutathione content and lipid peroxidation, as marker of oxidative stress and lactate dehydrogenase (LDH), alanine transaminase (ALT) and aspartate transaminase (AST) activities and pH of the perfusate medium were measured. Results: There was a significant decrease in lipid peroxidation and same increase was observed in glutathione level. Treatment with captopril also modulated the enzymes activity and pH of perfusate. Conclusion: This study showed that captopril protects the hepatotoxicty induced by AFB1. Therefore, this drug may provide an effective new strategy to reduce of aflatoxins toxicity. [ABSTRACT FROM AUTHOR]

In this paper, the control of a planar three-link musculoskeletal arm by using a revolutionary actor-critic reinforcement learning (RL) method during a reaching movement to a stationary target is presented. The arm model used in this study included three skeletal links (wrist, forearm, and upper arm), three joints (wrist, elbow, and shoulder without redundancy), and six non-linear monoarticular muscles (with redundancy), which were based on the Hill model. The learning control system was composed of actor, critic, and genetic algorithm (GA) parts. Two single-layer neural networks were used for each part of the actor and critic. This learning control system was used to apply six activation commands to six monoarticular muscles at each instant of time. It also used a reinforcement (reward) feedback for the learning process and controlling the direction of arm movement. Also, the GA was implemented to select the best learning rates for actor-critic neural networks. The results showed that mean square error (MSE) and average episode time gradually decrease and average reward gradually increases to constant values during the learning of the control policy. Furthermore, when learning was complete, optimal values of learning rates were selected. [ABSTRACT FROM AUTHOR]

Abstract: The aim of this paper is to create a model for mapping the surface electromyogram (EMG) signals to the force that generated by human arm muscles. Because the parameters of each person''s muscle are individual, the model of the muscle must have two characteristics: (1) The model must be adjustable for each subject. (2) The relationship between the input and output of model must be affected by the force–length and the force–velocity behaviors are proven through Hill''s experiments. Hill''s model is a kinematic mechanistic model with three elements, i.e. one contractile component and two nonlinear spring elements. In this research, fuzzy systems are applied to improve the muscle model. The advantages of using fuzzy system are as follows: they are robust to noise, they prove an adjustable nonlinear mapping, and are able to model the uncertainties of the muscle. Three fuzzy coefficients have been added to the relationships of force–length (active and passive) and force–velocity existing in Hill''s model. Then, a genetic algorithm (GA) has been used as a biological search method that can adjust the parameters of the model in order to achieve the optimal possible fit. Finally, the accuracy of the fuzzy genetic implementation Hill-based muscle model (FGIHM) is invested as following: the FGIHM results have 12.4% RMS error (in worse case) in comparison to the experimental data recorded from three healthy male subjects. Moreover, the FGIHM active force–length relationship which is the key characteristics of muscles has been compared to virtual muscle (VM) and Zajac muscle model. The sensitivity of the FGIHM has been evaluated by adding a white noise with zero mean to the input and FGIHM has proved to have lower sensitivity to input noise than the traditional Hill''s muscle model. [Copyright &y& Elsevier]

Background: Studying the behavior of a society of neurons, extracting the communication mechanisms of brain with other tissues, finding treatment for some nervous system diseases and designing neuroprosthetic devices, require an algorithm to sort neuralspikes automatically. However, sorting neural spikes is a challenging task because of the low signal to noise ratio (SNR) of the spikes. The main purpose of this study was to design an automatic algorithm for classifying neuronal spikes that are emitted from a specific region of the nervous system. Methods: The spike sorting process usually consists of three stages: detection, feature extraction and sorting. We initially used signal statistics to detect neural spikes. Then, we chose a limited number of typical spikes as features and finally used them to train a radial basis function (RBF) neural network to sort the spikes. In most spike sorting devices, these signals are not linearly discriminative. In order to solve this problem, the aforesaid RBF neural network was used. Results: After the learning process, our proposed algorithm classified any arbitrary spike. The obtained results showed that even though the proposed Radial Basis Spike Sorter (RBSS) reached to the same error as the previous methods, however, the computational costs were much lower compared to other algorithms. Moreover, the competitive points of the proposed algorithm were its good speed and low computational complexity. Conclusion: Regarding the results of this study, the proposed algorithm seems to serve the purpose of procedures that require real-time processing and spike sorting. [ABSTRACT FROM AUTHOR]

Variable Stiffness Actuators (VSAs) have been developed to address the safety and limited adaptability in interactions with uncertainties and energy efficiency issues which exist in traditional "stiff" robots. When desired performance of a VSA is given for a certain application, the question is how this desired performance can be achieved with minimum energy consumption and maximum robustness against uncertainties. This will lead to more compact, lighter but more powerful VSAs. This work develops an understanding of how to optimally design the parameters of the stiffness adjustment mechanisms by developing a framework that can robustly maximize the output performance of VSAs. Five VSA examples, each representing a different design set of stiffness adjustment mechanism, are being considered and evaluated based on the proposed optimization framework to perform a given periodic motion. The resultant optimal design of each set is then compared with the original design in terms of output performance and robustness. The proposed framework shows improvement of the output performance for the given periodic motion up to 546%, with robustness of up to 2.1% perturbation of the optimal design values. • Output performance of variable stiffness actuators is defined and formulated. • Different classes of variable stiffness actuators are being analyzed. • An interval-optimization problem for output performance is presented. • The proposed framework shows improvement of the output performance for the given periodic motions. [ABSTRACT FROM AUTHOR]

• An interval-based optimization framework to maximize the energy efficiency of variable stiffness actuators. • Different design parameters are involved in such optimization including continuous, integer and even categorical variables. • The proposed algorithm can deal with mixed-integer variables with a technique to convert categorical variables into integer ones. • Five different variable stiffness actuators have been taken into account. • Improvements in energy efficiency of all five representative actuators, as high as 354%. [Display omitted] Despite the necessity for being able to regulate the stiffness of the robotic platforms in physical contact with humans, and tremendous number of different variable stiffness actuators that have been developed so far, there is still no such actuator that has successfully passed the research lab phase and transferred into a real application. The main reason is due to the lack of understating on how to optimally design a stiffness adjustment mechanism based on desired performances of each application. If not optimally designed, the additional complexities within the actuators, prevent to win the trade-off between benefits of having a stiffness adjustment mechanisms versus its costs, such as the energy storage capacity of the elastic elements compared to how much energy can be actually released at the output of the actuator, i.e. the link. Currently, this trade-off criterion is not in favor of introducing a stiffness adjustment mechanism into the actuator. Therefor, generally, having a simple series elastic actuator with active compliance has been preferred over having a complex variable stiffness actuator, in many real applications. This work develops an understanding of how to optimally design the parameters of a stiffness adjustment mechanism by developing a framework that can robustly maximize the energy efficiency of variable stiffness actuators. Five different design sets of stiffness adjustment mechanism are being considered and evaluated based on the proposed optimization framework. The resultant optimal design of each set is then compared with the original design in terms of energy efficiency. The proposed framework shows improvement of energy efficiency up to 354%, while design constraints are all being satisfied. [ABSTRACT FROM AUTHOR]

This paper explores the application of Visual Question-Answering (VQA) technology, which combines computer vision and natural language processing (NLP), in the medical domain, specifically for analyzing radiology scans. VQA can facilitate medical decision-making and improve patient outcomes by accurately interpreting medical imaging, which requires specialized expertise and time. The paper proposes developing an advanced VQA system for medical datasets using the Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation (BLIP) architecture from Salesforce, leveraging deep learning and transfer learning techniques to handle the unique challenges of medical/radiology images. The paper discusses the underlying concepts, methodologies, and results of applying the BLIP architecture and fine-tuning approaches for VQA in the medical domain, highlighting their effectiveness in addressing the complexities of VQA tasks for radiology scans. Inspired by the BLIP architecture from Salesforce, we propose a novel multi-modal fusion approach for medical VQA and evaluating its promising potential. [ABSTRACT FROM AUTHOR]

Coronary Artery Bypass Graft (CABG) is a common cardiac surgery, but it continues to have many associated risks, including the need for blood transfusions. Previous research has shown that blood transfusion during CABG surgery is associated with an increased risk for infection and mortality. The current study aims to use modern techniques, such as deep neural networks and data synthesis, to develop models that can best predict the need for blood transfusion among CABG patients. Results show that neural networks with synthetic data generated by DataSynthesizer have the best performance. Implications of results and future directions are discussed. [ABSTRACT FROM AUTHOR]

Objectives: One of the well-accepted beliefs about natural products, considering the advances of recently appearing new edges and features of herbal medicine, is paying more attention to cancer treatments. However, they have not been properly studied with reasonable/reliable clinical trials in human subjects in most cases. Therefore, seeking in vitro effects of herbs like Melissa officinalis (MO) in cancer therapy to identify the involved possible mechanism in conjugation with configurative/morphological aspects of treated cells seems quite necessary. In this study, we evaluated the co-treatment effect of anti-cancer drug methotrexate (MTX) and MO on HeLa cancer cells. Methods: MTT assay was applied to assess the quantitative cytotoxicity effect of both MTX and Mo. Apoptosis assay via flow cytometry was used to determine the amount of apoptotic and necrotic cells. To further investigate the anti-cancer effects, DAPI staining and DNA ladder assays are used qualitatively to detect changes in the nuclei of cells that are a sign of apoptosis occurring and morphological modifications of DNA. Results: MTX and MO mixture showed high cytotoxicity and apoptosis rate compared to untreated cells. Furthermore, the morphological changes of MTX and MO mixture were more evident than that of single MO, MTX, and control groups. Conclusions: These data regarding cell growth reduction and apoptosis induction in HeLa cancer cells showed that MTX and MO mixture can be an appropriate platform for cancer therapy. [ABSTRACT FROM AUTHOR]

Lawsonia inermis L. (Henna) has been widely used in cosmetic, medicine and pharmacy industries. Henna is one of the ancient used plants and it can be absorbed into the blood and interact with plasma serum albumin. In this article, we investigated the interaction between henna and bovine serum albumin (BSA) through several spectroscopic techniques, which accompanied with molecular docking approach. According to the results, fluorescence intensity of BSA were decreased upon addition of different concentrations of henna and variation of the intensity illustrates the interaction between henna and BSA. Stern-Volmer quenching analysis along with bimolecular quenching rate results suggested that henna quenched the intensity of BSA through hybrid quenching mechanism, which was confirmed via UV–Vis measurement. As the temperature increase, values of binding constant increased from 5.17 × 102 to 6.17× 102 (M−1) demonstrating that the stability of henna–BSA complex increased with temperature rising. Thermodynamic investigations revealed that the henna molecule bind to BSA via hydrophobic bonds spontaneously. Furthermore, the molecular docking analysis demonstrated that henna has high affinity to bind a cavity in Site II (Domain III, subdomain IIIA) on BSA with binding energy score - 11.37 kcal/mol. Finally, it should be stated that distribution of the henna in the blood circulatory system can be done via BSA, which would accompany us to understand the different medicinal aspects of this natural product especially in drug delivery. • Henna interaction with BSA was investigated. • The fluorescence quenching mechanism was both static and dynamic. • There was an increase in UV absorption of BSA upon increasing henna concentration. • A cavity in Site II of BSA plays the main role in binding of henna. [ABSTRACT FROM AUTHOR]

Peripheral nerves injuries (PNIs) still associated with both clinical and social problems. Accordingly, tissue engineers' and surgeons' attentions have been drawn for finding efficient solutions. Herein, scaffolds based on silk fibroin (SF)/raffinose-grafted-GO (S.RafGO) nanocomposite were fabricated. Subsequently, PC12 cells growth in term of number and morphology were investigated on neat SF polymer, SF/GO (S.GO), and S.RafGO scaffolds. Characterization via scanning electron microscopy (SEM) exhibited more fibrous structures with few lamellar nanosheets for S.GO; although, S.RafGO showed extended lamellar with lower fibrous structure. Due to the incorporation of GO and raffinose-GO nanosheets into SF structure, electrical conductivity increased ~30 and 40%, respectively. Water contact angle data revealed that S.RafGO is more wettable than SF and S.GO. Real-time PCR technique detected higher expressions of the β -tubulin, MAP2 genes on S.RafGO scaffolds in comparison with S.GO and the control group. Immunocytochemistry staining studies confirmed the overexpression of neural-specific proteins including nestin, β -tubulin of S.GO, and S.RafGO nanocomposites in comparison with pure SF scaffolds. Unlabelled Image • The idea of this research is to investigate the modified wonder biomaterial (i.e. graphene oxide) through chemical grafts of raffinose trisaccharide for bio-application. • In this literature, it has been considered how pristine graphene oxide (GO) and its grafted form with raffinose (RafGO) in the silk fibroin structure effect on proliferation, attachment, and some eminent neuron specific gene expressions. [ABSTRACT FROM AUTHOR]