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Worldwide obesity and cardiovascular diseases have encouraged the adoption of new and efficient dietary strategies. Among various proposed diets, ketogenic diets, both the Very-Low-Calorie Ketogenic Diet (VLCKD) and the Low-Calorie Ketogenic Diet (LCKD), have been suggested in recent years as an effective nutritional approach for obesity management. The VLCKD and the LCKD are characterized by a low carbohydrate content (50 g/day), 1-1.5 g of protein/kg of ideal body weight, less than 20-30 g of lipids, and a daily intake of about 800 calories for VLCKD and about 1200-1400 calories for LCKD. The purpose of our narrative review is to offer an overview of the most impactful studies in the scientific literature regarding VLCKD and LCKD to discuss their short- and long-term effects (less than 12 months and more than 12 months respectively) on weight loss, metabolic and cardiovascular aspects. Articles we focused on were cohort studies, case-control studies, cross-sectional studies, randomized controlled trials, and meta-analyses. Results indicate that VLCKD and LCKD could be helpful to ameliorate metabolic and cardiovascular risk factors such as weight loss, glucose, and cholesterol levels, both in the short and long- term. Further research in this area may include more randomized controlled trials to gather more data.

Relatively recent increased computational power and extensive traffic data availability have provided a unique opportunity to re-investigate drivers’ car-following (CF) behavior. Classic CF models assume drivers’ behavior is only influenced by their preceding vehicle. Recent studies have indicated that considering surrounding vehicles’ information (e.g., multiple preceding vehicles) could affect CF models’ performance. An in-depth investigation of surrounding vehicles’ contribution to CF modeling performance has not been reported in the literature. This study uses a deep-learning model with long short-term memory (LSTM) to investigate to what extent considering surrounding vehicles could improve CF models’ performance. This investigation helps to select the right inputs for traffic flow modeling. Five CF models are compared in this study (i.e., classic, multi-anticipative, adjacent-lanes, following-vehicle, and all-surrounding-vehicles CF models). Performance of the CF models is compared in relation to accuracy, stability, and smoothness of traffic flow. The CF models are trained, validated, and tested by a large publicly available dataset. The average mean square errors (MSEs) for the classic, multi-anticipative, adjacent-lanes, following-vehicle, and all-surrounding-vehicles CF models are 1.58 × 10−3, 1.54 × 10−3, 1.56 × 10−3, 1.61 × 10−3, and 1.73 × 10−3, respectively. However, the results show insignificant performance differences between the classic CF model and multi-anticipative model or adjacent-lanes model in relation to accuracy, stability, or smoothness. The following-vehicle CF model shows similar performance to the multi-anticipative model. The all-surrounding-vehicles CF model has underperformed all the other models.

Niloufar Shashaani,1 Reza Shiari,2 Abdullah Karimi,3 Shima Salehi,4 Roxana Ghanaei,3 Mehrnoush Hassas Yeganeh,2 Sara Shiari,1 Khosro Rahmani,2 Vadood Javadi Parvaneh2 1Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran; 2Department of Pediatric Rheumatology, Mofid Children’s Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran; 3Department of Infectious Diseases, Mofid Children’s Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran; 4Faculty of Medicine, Ali Asghar Children’s Hospital, Iran University of Medical Sciences, Tehran, IranCorrespondence: Reza Shiari Tel +98-21-22227033Email shiareza@yahoo.comIntroduction: Kawasaki disease (KD) is a systemic vasculitis that occurs mostly in children under five years old. Kawasaki affects the middle-size arteries, especially the coronary arteries. Therefore, without adequate treatment, it may cause coronary artery aneurysm in 25% of patients. The purpose of this study was to investigate the relationship between Kobayashi, Sano, and Egami criterions with coronary artery aneurysm in KD patients during the last ten years and to identify risk factors in patients with intravenous immunoglobulin (IVIG)-resistant and coronary artery aneurysms.Methodology: Medical records of 363 Kawasaki patients referred during 2008– 2017 were reviewed. Patients’ demographic data and Kobayashi, Sano, and Egami scores of each patient were calculated. Based on echocardiographic findings, cases of coronary artery aneurysm were determined. Sensitivity, specificity, positive and negative predictive value, and the accuracy of each criterion were determined to predicting IVIG resistance and detect coronary artery aneurysm.Results: There was a slight relationship between IVIG-resistance in Kawasaki children and its prediction based on the Kobayashi risk score, but no relationship was found between the Egami and Sano criteria. Sixty-three patients (17.4%) had coronary artery lesions (CALs) on time of diagnosis. There were no statistically significant differences between gender and mean age of children with and without CALs. Also, there was no significant relationship between coronary artery aneurysm in Kawasaki children and its prediction based on the above three risk factors. The area under the ROC-curve of all three risk measures of Kobayashi, Egami, and Sano indicated that all three criteria were not useful in predicting CALs.Conclusion: Despite the low accuracy of the three above criteria to predictive of patients with IVIG resistance, it seems that the variables of age, duration of fever, and C-reactive protein (CRP) are more useful than other variables and may be utilized to evaluate patients by establishing a more appropriate cut-off point.Keywords: Kawasaki disease, coronary artery aneurysm, IVIG-resistant, Kobayashi, Sano, Egami

Personalizing approaches to prevention and treatment of obesity will be a crucial aspect of precision health initiatives. However, in considering individual susceptibility to obesity, much remains to be learned about how to support healthy weight management in different population subgroups, environments and geographical locations.The International Weight Control Registry (IWCR) has been launched to facilitate a deeper and broader understanding of the spectrum of factors contributing to success and challenges in weight loss and weight loss maintenance in individuals and across population groups. The IWCR registry aims to recruit, enroll and follow a diverse cohort of adults with varying rates of success in weight management. Data collection methods include questionnaires of demographic variables, weight history, and behavioral, cultural, economic, psychological, and environmental domains. A subset of participants will provide objective measures of physical activity, weight, and body composition along with detailed reports of dietary intake. Lastly, participants will be able to provide qualitative information in an unstructured format on additional topics they feel are relevant, and environmental data will be obtained from public sources based on participant zip code.The IWCR will be a resource for researchers to inform improvements in interventions for weight loss and weight loss maintenance in different countries, and to examine environmental and policy-level factors that affect weight management in different population groups. This large scale, multi-level approach aims to inform efforts to reduce the prevalence of obesity worldwide and its associated comorbidities and economic impacts.NCT04907396 (clinicaltrials.gov) sponsor SB Roberts; Tufts University IRB #13075.

Surface transportation has evolved through technology advancements using parallel knowledge areas such as machine learning (ML). However, the transportation industry has not yet taken full advantage of ML. To evaluate this gap, we utilized a literature review approach to locate, categorize, and synthesize the principal concepts of research papers regarding surface transportation systems using ML algorithms, and we then decomposed them into their fundamental elements. We explored more than 100 articles, literature review papers, and books. The results show that 74% of the papers concentrate on forecasting, while multilayer perceptions, long short-term memory, random forest, supporting vector machine, XGBoost, and deep convolutional neural networks are the most preferred ML algorithms. However, sophisticated ML algorithms have been minimally used. The root-cause analysis revealed a lack of effective collaboration between the ML and transportation experts, resulting in the most accessible transportation applications being used as a case study to test or enhance a given ML algorithm and not necessarily to enhance a mobility or safety issue. Additionally, the transportation community does not define transportation issues clearly and does not provide publicly available transportation datasets. The transportation sector must offer an open-source platform to showcase the sector’s concerns and build spatiotemporal datasets for ML experts to accelerate technology advancements.

Background: Calcium is a necessary mineral for life to keep the body and bones healthy. Various factors including hormones, diet, age, and gender affect serum calcium status. The aim of this sturdy was to assess the serum calcium level (SCL) of Tehran population, which has about 10 million multi-Ethnic populations and represents from the whole country. Methods: In this retrospective study, the measured SCL of 105,128 individuals referred to different laboratories of Tehran, Iran were evaluated and its relationship with the age, gender, seasons, and different years during 2009-2018, were analyzed. Results: After excluding outliers, 91,257samples remained, which 61162 (58.64%) and 30,095 (41.36%) were female and male, respectively. The mean SCL was 9.36 (9.35, 9.37) mg/dl (95%CI). The highest and lowest SCLs were 3.1 and 18.2mg/dl, respectively. From the total study population, 74127 (81.23%) had normal SCLs, 14110 (15.46%) had hypocalcemia, and 3020 (3.31%) had hypercalcemia. SCLs were normal in 83.6% of men and 79.66% of women. Women had a significantly higher frequency of hypocalcemia compared to men (17.2% vs. 12.83%, p

Reduction of the transportation sector's air emissions has been an essential goal for decreasing the emissions' externalities and slowing climate change. This study evaluated the emission impacts of low-level automated vehicles (LAVs) in 86 metropolitan areas in the United States, based on county-specific emission costs of morbidity, mortality, and environmental damages. Twelve LAV technologies were investigated to estimate their impacts on CO2, CO, NOx, PM2.5, SOx, NH3, and VOC. The study analyzed the LAV technologies' direct and indirect emission impacts, including accident-related and non-accident-related congestion reductions, aerodynamic force reduction, the operational load of equipment, and traffic rebound. The results show that the LAV technologies could reduce the annual social costs of emissions by between 9 and 20 billion USD2019 in these metropolitan areas. This is equal to approximately 20% of the vehicles' emissions. A sensitivity analysis was performed on the technologies' market penetration rates (MPRs), demonstrating that the rate strongly influences the emission savings. This study revealed that LAVs' savings per vehicle-miles traveled (VMT) vary between areas, with the technologies performing differently in different areas due to both geographical factors (e.g., population) and technological factors (e.g., internal combustion engine performance). The study provides extensive resources for policymakers to develop targeted incentives and maximize the social benefits of the LAV technologies.

Jointly identifying a mixture of discrete and continuous factors of variability without supervision is a key problem in unraveling complex phenomena. Variational inference has emerged as a promising method to learn interpretable mixture representations. However, posterior approximation in high-dimensional latent spaces, particularly for discrete factors remains challenging. Here, we propose an unsupervised variational framework using multiple interacting networks called cpl-mixVAE that scales well to high-dimensional discrete settings. In this framework, the mixture representation of each network is regularized by imposing a consensus constraint on the discrete factor. We justify the use of this framework by providing both theoretical and experimental results. Finally, we use the proposed method to jointly uncover discrete and continuous factors of variability describing gene expression in a single-cell transcriptomic dataset profiling more than a hundred cortical neuron types., 10 pages, 6 figures, conference

Recursive Bayesian inference (RBI) provides optimal Bayesian latent variable estimates in real-time settings with streaming noisy observations. Active RBI attempts to effectively select queries that lead to more informative observations to rapidly reduce uncertainty until a confident decision is made. However, typically the optimality objectives of inference and query mechanisms are not jointly selected. Furthermore, conventional active querying methods stagger due to misleading prior information. Motivated by information theoretic approaches, we propose an active RBI framework with unified inference and query selection steps through Renyi entropy and $\alpha$-divergence. We also propose a new objective based on Renyi entropy and its changes called Momentum that encourages exploration for misleading prior cases. The proposed active RBI framework is applied to the trajectory of the posterior changes in the probability simplex that provides a coordinated active querying and decision making with specified confidence. Under certain assumptions, we analytically demonstrate that the proposed approach outperforms conventional methods such as mutual information by allowing the selections of unlikely events. We present empirical and experimental performance evaluations on two applications: restaurant recommendation and brain-computer interface (BCI) typing systems., Comment: 13 pages, 10 figures, 1 table

This article describes a data-driven approach to flood risk exposure evaluation and route delineation during heavy rainfall events. We cross-referenced diverse geospatial and drainage infrastructure datasets with the street network of Hoboken to uncover the factors that increase flood risk. Elevation, slope, precipitation level, imperviousness, and distance to the drainage system’s outlets were the most significant predictors to link flooding. We used the link flood risk patterns found in the data to train a reinforcement learning model that generates routes that avoid flood-prone areas. We benchmarked the route assistance model with shortest path and most reliable path algorithms, demonstrating our model has balanced path length and path reliability. We provided the flood risk model outputs at the link-level, which city authorities can use to plan road closures ahead of heavy precipitation events. The route assistance model can be used by drivers to better navigate flood-prone environments by detouring around riskier areas or canceling trips altogether.

Query selection for latent variable estimation is conventionally performed by opting for observations with low noise or optimizing information theoretic objectives related to reducing the level of estimated uncertainty based on the current best estimate. In these approaches, typically the system makes a decision by leveraging the current available information about the state. However, trusting the current best estimate results in poor query selection when truth is far from the current estimate, and this negatively impacts the speed and accuracy of the latent variable estimation procedure. We introduce a novel sequential adaptive action value function for query selection using the multi-armed bandit (MAB) framework which allows us to find a tractable solution. For this adaptive-sequential query selection method, we analytically show: (i) performance improvement in the query selection for a dynamical system, (ii) the conditions where the model outperforms competitors. We also present favorable empirical assessments of the performance for this method, compared to alternative methods, both using Monte Carlo simulations and human-in-the-loop experiments with a brain computer interface (BCI) typing system where the language model provides the prior information.

Gasoline is the main source of energy used for surface transportation in the United States. Reducing fuel consumption in light-duty vehicles can significantly reduce the transportation sector’s impact on the environment. Implementation of emerging automated technologies in vehicles could result in fuel savings. This study examines the effect of automated vehicle systems on fuel consumption using stochastic modeling. Automated vehicle systems examined in this study include warning systems such as blind spot warning, control systems such as lane keeping assistance, and information systems such as dynamic route guidance. We have estimated fuel savings associated with reduction of accident and non-accident-related congestion, aerodynamic force reduction, operation load, and traffic rebound. Results of this study show that automated technologies could reduce light-duty vehicle fuel consumption in the U.S. by 6% to 23%. This reduction could save $60 to $266 annually for the owners of vehicles equipped with automated technologies. Also, adoption of automated vehicles could benefit all road users (i.e., conventional vehicle drivers) up to $35 per vehicle annually (up to $6.2 billion per year).

Recursive Bayesian inference (RBI) provides optimal Bayesian latent variable estimates in real-time settings with streaming noisy observations. Active RBI attempts to effectively select queries that lead to more informative observations to rapidly reduce uncertainty until a confident decision is made. However, typically the optimality objectives of inference and query mechanisms are not jointly selected. Furthermore, conventional active querying methods stagger due to misleading prior information. Motivated by information theoretic approaches, we propose an active RBI framework with unified inference and query selection steps through Renyi entropy and $��$-divergence. We also propose a new objective based on Renyi entropy and its changes called Momentum that encourages exploration for misleading prior cases. The proposed active RBI framework is applied to the trajectory of the posterior changes in the probability simplex that provides a coordinated active querying and decision making with specified confidence. Under certain assumptions, we analytically demonstrate that the proposed approach outperforms conventional methods such as mutual information by allowing the selections of unlikely events. We present empirical and experimental performance evaluations on two applications: restaurant recommendation and brain-computer interface (BCI) typing systems., 13 pages, 10 figures, 1 table

The taxi business has been overly regulated for many decades. Regulations are supposed to ensure safety and fairness within a controlled competitive environment. By influencing both drivers and riders choices and behaviors, emerging e-hailing services (e.g., Uber and Lyft) have been reshaping the existing competition in the last few years. This study investigates the existing competition between the mainstream hailing services (i.e., Yellow and Green Cabs) and e-hailing services (i.e., Uber) in New York City. Their competition is investigated in terms of market segmentation, emerging demands, and regulations. Data visualization techniques are employed to find existing and new patterns in travel behavior. For this study, we developed a multi-agent model and applied reinforcement learning techniques to imitate drivers behaviors. The model is verified by the patterns recognized in our data visualization results. The model is then used to evaluate multiple new regulations and competition scenarios. Results of our study illustrate that e-hailers dominate low-travel-density areas (e.g., residential areas), and that e-hailers quickly identify and respond to change in travel demand. This leads to diminishing market size for hailers. Furthermore, our results confirm the indirect impact of Green Cabs regulations on the existing competition. This investigation, along with our proposed scenarios, can aid policymakers and authorities in designing policies that could effectively address demand while assuring a healthy competition for the hailing and e-haling sectors. Keywords: taxi; Uber, policy; E-hailing; multi-agent simulation; reinforcement learning, Comment: 12 pages, 10 figure

In dynamic state-space models, the state can be estimated through recursive computation of the posterior distribution of the state given all measurements. In scenarios where active sensing/querying is possible, a hard decision is made when the state posterior achieves a pre-set confidence threshold. This mandate to meet a hard threshold may sometimes unnecessarily require more queries. In application domains where sensing/querying cost is of concern, some potential accuracy may be sacrificed for greater gains in sensing cost. In this paper, we (a) propose a criterion based on a linear combination of state posterior and its changes, (b) show that for discrete-valued state estimation scenarios the proposed objective is more likely to sort correct and incorrect estimates appropriately compared to just looking at the posterior, and finally (c) demonstrate that the method can lead to significant human intent estimation speed increase without significant loss of accuracy in a brain-computer interface application.

Electroencephalography (EEG) is an effective non-invasive measurement method to infer user intent in brain-computer interface (BCI) systems for control and communication, however, these systems often lack sufficient accuracy and speed due to low separability of class-conditional EEG feature distributions. Many factors impact system performance, including inadequate training datasets and models’ ignorance of the temporal dependency of brain responses to serial stimuli. Here, we propose a signal model for event-related responses in the EEG evoked with a rapid sequence of stimuli in BCI applications. The model describes the EEG as a superposition of impulse responses time-locked to stimuli corrupted with an autoregressive noise process. The performance of the signal model is assessed in the context of RSVP keyboard, a language-model-assisted EEG-based BCI for typing. EEG data obtained for model calibration from 10 healthy participants are used to fit and compare two models: the proposed sequence-based EEG model and the trial-based feature-class-conditional distribution model that ignores temporal dependencies, which has been used in the previous work. The simulation studies indicate that the earlier model that ignores temporal dependencies may be causing drastic reductions in achievable information transfer rate (ITR). Furthermore, the proposed model, with better regularization, may achieve improved accuracy with fewer calibration data samples, potentially helping to reduce calibration time. Specifically, results show an average 8.6% increase in (cross-validated) calibration AUC for a single channel of EEG, and 54% increase in the ITR in a typing task.

The transportation sector is the largest producer of greenhouse gas (GHG) emissions in the United States. Energy-optimal algorithms are proposed to reduce the transportation sector’s fuel consumption and emissions. These algorithms optimize vehicles’ speed to lower energy consumption and emissions. However, recent studies argued that these algorithms could negatively impact traffic flow, create traffic congestions, and increase fuel consumption on the network-level. To overcome this problem, we propose a collective-energy-optimal adaptive cruise control (collective-ACC). Collective-ACC reduces fuel consumption and emissions by directly optimizing vehicles’ trajectories and indirectly by improving traffic flow. Collective-ACC is a bi-objective non-linear integer optimization. This optimization was solved by the Non-dominated Sorting Genetic Algorithm (NSGA-II). Collective-ACC was compared with manual driving and self-centered adaptive cruise control (i.e., conventional energy-optimal adaptive cruise controls (self-centered-ACC)) in a traffic simulation. We found that collective-ACC reduced fuel consumption by up to 49% and 42% compared with manual driving and self-centered-ACC, respectively. Collective-ACC also lowered CO2, CO, NOX, and PMX by up to 54%, 70%, 58%, and 64% from manual driving, respectively. Game theory analyses were conducted to investigate how adopting collective-ACC could impact automakers, consumers, and government agencies. We propose policy and business recommendations to accelerate adopting collective-ACC and maximize its environmental benefits.

Despite plenty of research being performed in the human movement science, less attention has been paid to the probable method used by the human brain in the higher-level motor planning. The previous studies suggest that the human brain may use a predictive approach to anticipate physical dynamics of the body and the environment to plan a short and collision-free movement trajectory. We propose that the human brain may use a model-based prediction procedure in path planning in which a finite prediction horizon is used to estimate the future state of the body and the environment. A goal-oriented driving task (GDT) in a virtual street was designed to consider the human path planning method in dynamic environments. Two groups of experiments were presented to consider the ability of the human brain in estimation of a dynamic object location and planning a collision-free path. The first group of study includes four GDTs, with different conditions to evaluate how the human planning strategy would change by varying the configuration of the environment. In the second group, the changes of human planning in a visually obscured and blurred situation were considered. The results are in compliance with the theory of using a model-based prediction approach by human brains and indicate that the subjects benefit from a prediction horizon to plan their paths. Our studies provide evidence to introduce possible factors which may be used by the human brain during path planning in dynamic environments.

This article addresses the impacts of flood events on urban street networks. Macro-traffic simulation techniques were used on disrupted and undisrupted scenarios to assess the increase on the network’s mobility and accessibility. Local topographical aspects of the terrain were analyzed to identify portions of the network more prone to disruption. Flood maps were used to systematically remove links from the network, generating its disrupted state for different scenarios. The traffic assignment model generated routes using k-shortest path methods with link impedance penalty functions, selecting them based on user equilibrium assumption. Simulation results indicated the viability of the method to analyze the impacts of flood events of different severity and duration. The successful validation of this method indicated its viability as a tool for benefit cost analysis of urban improvement projects including resilience plans for high risk cities. The analysis was validated using the City of Hoboken, New Jersey’s transportation network and flood models. Results can be applied to cities with a high chance of flooding and should help authorities to effectively review their infrastructure strategic plans as well as their short and long-term urban mobility plans.

Electroencephalography (EEG)-based brain-computer interfaces (BCIs) are developed to provide access channels for alternative communication and control systems to people with severe speech and physical impairments. Designs that exploit evoked response potentials (ERPs) in EEG constitute the majority of research efforts dedicated to noninvasive BCIs. Visual, auditory, and tactile stimulation paradigms are used to actively probe the user's brain to collect EEG evidence towards inferring intent in the context of the particular application. As assistive technology devices, however, existing EEG-based BCIs lack sufficient speed and accuracy to safely and reliably restore function at acceptable levels. This is mainly because the recorded EEG signals are not only noisy with a low signal-to-noise ratio, but are also nonstationary, due to physiological or environmental artifacts, sensor failure, and user fatigue. In this chapter, we address how reliable intent inference engines with reasonable speed and accuracy can be developed using parametric modeling. Examples of real-world data in the framework of the ERP-based BCI paradigm are provided to exemplify our detection and classification methods.

Electroencephalogram (EEG) signals have been shown very effective for inferring user intents in brain-computer interface (BCI) applications. However, existing EEG-based BCIs, in many cases, lack sufficient performance due to utilizing classifiers that operate on EEG signals induced by individual trials. While many factors influence the classification performance, an important aspect that is often ignored is the temporal dependency of these trial-EEG signals, in some cases impacted by interference of brain responses to consecutive target and non-target trials. In this study, the EEG signals are analyzed in a parametric sequence-based fashion, which considers all trials that induce brain responses in a rapid-sequence fashion, including a mixture of consecutive target and non-target trials. EEG signals are described as a linear combination of time-shifted cortical source activities plus measurement noise. Using a superposition of time invariant with an auto-regressive (AR) process, EEG signals are treated as a linear combination of a stationary Gaussian process and time-locked impulse responses to the stimulus (input events) onsets. The model performance is assessed in the framework of a rapid serial visualization presentation (RSVP) based typing task for three healthy subjects across two sessions. Signal modeling in this fashion yields promising performance outcomes considering a single EEG channel to estimate the user intent.

Introduction: Vehicle engine id one of the main sources of noise which its level is influenced by various parameters. The aim of this study was to investigate the impact of motor oils quality before and after oil change on the variability of vehicle engine induced noise level. In this study it is tried to follow-up the efficacy of motor oil quality on engines sound level. .Material and Method: First, engine noise of 94 vehicles were recorded for 30 seconds before and after oil change and all the vehicles technical information including mileage, type of motor oil, and type of vehicle were registered. Following, the recorded noises were calibrated in semi-anechoic chamber and the sound pressure levels were measured with A and C-weighting network and main octav bands, using a sound level meters. The obtained results analyzed using SPSS software version 17. . Results: The effects of motor oil quality on different noise levels of engines were determined and a significant reduction in noise level of vehicles engine was observed. Investigation of the relationship between mileage and motor oil quality on various engines sound level manifested that vehicles with mileage ranged 100000-150000 miles had significant reduction in their sound pressure levels in comparison with other vehicles. .Conclusion: The results revealed that engine oil is among factors reducing the vehicle engine induced noise level. Moreover, the engine oil type and the vehicle mileage are key variables which determine the impact of engine oil quality on reduction of the sound level of vehicles engine.

Lower extremity function recovery is one of the most important goals in stroke rehabilitation. Many paradigms and technologies have been introduced for the lower limb rehabilitation over the past decades, but their outcomes indicate a need to develop a complementary approach. One attempt to accomplish a better functional recovery is to combine bottom-up and top-down approaches by means of brain-computer interfaces (BCIs). In this study, a BCI-controlled robotic mirror therapy system is proposed for lower limb recovery following stroke. An experimental paradigm including four states is introduced to combine robotic training (bottom-up) and mirror therapy (top-down) approaches. A BCI system is presented to classify the electroencephalography (EEG) evidence. In addition, a probabilistic model is presented to assist patients in transition across the experiment states based on their intent. To demonstrate the feasibility of the system, both offline and online analyses are performed for five healthy subjects. The experiment results show a promising performance for the system, with average accuracy of 94% in offline and 75% in online sessions.

Background: This study was conducted to evaluate the incidence of medication discrepancies and its related factors using medication reconciliation method in patients admitted to the emergency department of Tehran University of Medical Sciences hospitals. Methods: In this cross-sectional study, 200 adult patients with at least one chronic disease that used two regular prescription medications were included in 2015. After 24 h of admission, demographic data and patient's home medications were collected. Medication discrepancies were assessed through comparison of a best possible medication history list with the physician's orders. Results: Out of 200 patients (mean age, 61.5 yr; 86 males, 114 women), 77.5% of patients had one or more medication discrepancies. The most common discrepancies were medication omission (35.49%), change (14.22%) and substitution (10.97%), respectively. The relationship between number of comorbid conditions (P=0.025), regular home medications (P=

The filter-bank based multicarrier FBMC system is a candidate for designing the physical layer of a cognitive radio because of its spectral efficiency and the spectral containment. The main drawback of such a system compared with orthogonal frequency division multiplexing systems is its high computation complexity, because each subcarrier is shaped by a non-rectangular prototype filter. Although poly-phase decomposition is suggested to decrease the sampling rate of filtering, the number of filtering operations multiplications is dramatically increased by the growing number of subcarriers and the amount of the desired spectral containment. Hence, hardware implementation of the FBMC system faces challenges such as high electrical power consumption and large silicon area occupation. In order to reduce computational complexity, a multiplierless filter design based on the canonical signed digit CSD representation is proposed. In this technique, at first, a prototype filter is designed. Then a pre-optimization step is employed to adapt the prototype filter coefficients to system objectives and produce an enriched initial seed for genetic algorithm GA optimization. Finally, a customized GA is employed to jointly optimize and synthesize filter coefficients into the finite precision CSD representation so that the system objectives such as intersymbol-interference, interchannel-interference, and stop-band attenuation remain unchanged against the full-precision representation of coefficients. Copyright © 2014 John Wiley & Sons, Ltd.

A biologically inspired two level method is proposed for real-time path planning in a complex and dynamic environment, employable in ground vehicles. This method takes the advantage of both global and local path finding procedures. In the first level, i.e., global level, the planner utilizes a neural network architecture as a sensory-motor map, similar to the cognitive map used by humans, and an optimization algorithm to produce a coarse path. In the second level, i.e., local level, the global path is improved by employing a model-based prediction method with a finite prediction horizon in a way that future information about the environment is involved in the planner's decision making. In the suggested method, the prediction horizon is variable and is adjusted in each step of the planning in agreement with the kinematic features of the closest obstacle in the visual field of the planner. We considered four different path planning tasks in a virtual dynamic environment to evaluate the performance of the proposed method against the human path planning strategy. The results demonstrate the ability of the method to plan a strategy comparable to the driving scenarios chosen by most subjects and to generate a real-time collision-free path in a dynamic environment with obstacles.

Background: Plant-origin foods are among the most important sources of food allergic reactions. An increase in the incidence of sesame seed allergy among children and adults has been reported in recent years. The aim of this preliminary study was to investigate the prevalence, importance and clinical manifestations of sesame allergy among Iranian patients.Methods: In a cross-sectional survey, 250 patients with suspected IgE-mediated food allergies completed a questionnaire and underwent skin prick tests with sesame extract as well as cross-reacting foods (walnut, soya and peanut). Total IgE and sesame-specific IgE levels were measured. Patients with positive skin test reactions and/or IgE specific for sesame without clinical symptoms were considered sensitive to sesame. The patients who also had clinical symptoms with sesame consumption were diagnosed as allergic to sesame.Results: Of the 250 patients enrolled in this study, 129 were male and 121 female, with a mean age of 11.7 years. The most common food allergens were cow's milk, egg, curry, tomato and sesame. Sesame sensitivity was found in 35 patients (14.1%). Only five patients (2%) had sesame allergy. Sesame-sensitive patients had a significantly higher frequency of positive prick test to cross-reacting foods when compared to non-sensitized patients (p=0.00). The type of symptom was independent of gender and age of the patients, but urticaria and dermatitis-eczema were significantly more frequent in sensitized patients (p=0.008).Conclusions: This is the first study addressing the prevalence of sesame seed allergy in Iranian population. We found sesame to be a common and important cause of food allergy. The panel of foods recommended for use in diagnostic allergy tests should be adjusted.

Transcranial current stimulation (tCS) is a non-invasive brain stimulation technique that has shown promise for studying and improving brain function. It can be applied with low-amplitude direct (tDCS), alternating (tACS) or random noise (tRNS) currents. EEG, with its high temporal resolution, portability, and affordability, offers great advantages in investigating the effects of tCS on brain activity. However, concurrent EEG acquisition and tCS stimulation suffers from the drawback that injected current induces significant artifacts on simultaneously acquired EEG. Furthermore, stimulus-current-induced artifacts in measured voltages have powers that are large compared to that of EEG, in the frequency range of interest for EEG analysis. While simple high-pass filtering of the EEG would eliminate artifacts from tDCS, it is not suitable when stimulating with frequencies in the range of significant EEG activity (1–40 Hz). This occurs both in low-frequency tACS/tRNS and in high-pass filtered tRNS, as even in the latter case substantial power will remain at EEG frequencies. In such cases, attenuating tRNS artifacts in EEG requires a more comprehensive model, such as the one we present here.

A new path planning strategy inspired by human path planning is proposed based on the dynamic wave expansion neural network (DWENN) for moving in dynamic environment. The proposed method performs in two phases. In the first phase, a coarse path is produced by using a DWENN and a cognitive map to represent the environment configuration. In the second phase, to improve the coarse path, a predictive approach is iteratively employed by combination of a locally recurrent neural network (LRNN) and a DWENN to plan a motion vector in a finite prediction horizon and executing it in a control horizon. A task is intended to evaluate the performance of the proposed method in crossing a street which includes a moving car as a dynamic obstacle. In this evaluation, different simulations with various prediction and control horizons have been performed. Our results imply that by applying a predictive method and adjusting the prediction and control horizons, DWENN can satisfactorily generate a collision-free path in dynamic environments.

Bu çalışma, 3-6 Ekim 2012'de Floransa[İtalya]'da düzenlenen 15. Biennial Meeting European-Society-for-Immunodeficiency (ESID)'de bildiri olarak sunulmuştur. European Soc Immunodeficiency (ESID) Int Nursing Grp Immunodeficiencies (INGID) Int Patient Org Primary Immunodeficiencies (IPOPI)

Acute lymphoblastic leukemia (ALL) is the most common subtype of leukemia in children. Although ALL and type 1 diabetes appear to be biologically unrelated, there are common threads in both epidemiology and etiology. Rising incidence rates of both ALL (1) and type 1 diabetes (2) observed over recent decades in many Western countries seem to support common etiological factors (3). In the current study, we report on diabetes-related autoantibodies (Abs) in a group of patients with ALL. Thirty-four consecutive children (19 males and 15 females, mean age 6.2 ± 4.6 years) were referred to our institution in 2004 for newly diagnosed ALL. Patients were tested for Abs to islet and thyroid antigens. After the initial investigation and treatment, 31/34 (91%) patients (3 died in the mean time) were followedup for 6 years to evaluate the evolution of the autoimmune markers and progression toward type 1 diabetes. Glutamic acid decarboxylase (GAD) Abs by direct …

This study was conducted to investigate the effect of plasmin and heparin on in vitro ovine spermoocyte interaction. Different concentrations of plasmin (0, 1, 10, 100 ng/ml) and heparin (0, 5, 10 IU/ml) were added alone or simultaneously into fertilization medium. After sperm and oocyte co-culture, binding and penetration of sperm to zona pellucida (ZP) were assayed. Treatment with 1 and 10 ng/ml plasmin resulted in higher sperm binding to ZP than those in control. The rates of sperm binding to ZP were increased with highest heparin concentrations (10 IU/ml). Heparin had no effect on penetration rate of sperm to ZP. Simultaneously effects of plasmin and heparin were not significant on penetration rate of sperm to ZP. But, 5 or 10 IU/ml heparin in the present of 1 ng/ml plasmin had higher effect on sperm binding to ZP than that in the other groups. These results suggest that plasmin and heparin (alone/simultaneously) might play a role in events related to fertilization in ovine.

The UNESCO Chair in Health Education in TUMS (TUMS), Iran, was established in Immunology, Asthma and Allergy Research Institute in April 2004. The purpose of this chair is to promote an integrated system of research, training, information and documentation activities in the field of health education. The target group includes the public, health care workers, students, trainees and researchers. During its one year existing, the Chair has supported financially eleven admitted proposals and supported technically eight submitted proposals in divers fields regarding health issues with the cooperation of other research institutes in TUMS. It has made connections with most of the national research institutes and universities to establish multi-centric collaborations. The activities of the Chair are being directed towards the health priorities of the country and region. The Chair has contributed to an important international congress on immunodeficiency disorders as well as another meeting on Asthma in near future. The Chair is to publish several educational books, booklets and CDs related to health education; it is also going to prepare a thorough proposal on HIV/AIDS prevention with collaboration of other institutions, which could be applicable regionally, and in neighboring countries and states.

5% (molar ratio) Fe doped TiO2 nanoparticles were prepared by a sol gel method and the post annealing of the samples was carried out at 400 °C, 600 °C and 800 °C in air. Structural characterization of the samples was carried out using High Resolution Transmission Electron Microscopy (HRTEM). HRTEM images of the samples revealed that the mean size of the nanoparticles changed from ∼8 nm to ∼100 nm as the annealing temperature was increased. Experimental investigation of the electronic structure of TiO2:Fe nanoparticles is important in order to understand the correlation between electronic and optical properties in these samples. X-ray Photoemission Spectroscopy (XPS) of the TiO2:Fe nanoparticles was performed to study the electronic structure. The results of XPS study confirmed the presence of Fe in all samples, which could not be detected by HRTEM and XRD, in spite of low doping levels, and revealed that Fe ions are predominantly in Fe3+ states. Photoluminescence (PL) measurements have been measured with an excitation energy of 250 nm to study optical properties of the TiO2:Fe nanoparticles. The active PL band at ∼440 nm has been observed.

Background: Hypomagnesaemia is one of the main side effects of cisplatin-based chemotherapy regimens in cancer patients. The aim of the current investigation was to evaluate the effect of oral magnesium oxide (MgO) supplementation on cisplatin-induced hypomagnesemia. Methods: This parallel-randomized controlled, open label trial was conducted in a hospital of Iran University of Medical Sciences in Tehran between December 2009 and May 2011. Participants were 69 adult patients with newly diagnosed non- leukemia neoplasms candidate for starting cisplatin-based chemotherapy. Oral MgO supplement according to cisplatin dose (500 mg MgO per 50 mg/m2 of cisplatin) as 2-3 divided daily doses was started after completion of each chemotherapy cycle and continued to the next cycle for the intervention group. Patients in the control group did not receive any supplementation. Serum magnesium (Mg) was measured before each chemotherapy cycle. The main outcome was measuring serum Mg change and hypomagnesaemia rate during chemotherapy treatment. Results: Sixty-two participants (31 intervention- 31 controls) enrolled into the study. Serum Mg levels showed significant difference between the two groups (P=0.01). There was a significant decrease in serum Mg of the control group (P=0.001). At the end of follow-up period prevalence of hypomagnesaemia in the intervention group was 10.7% versus 23.1% in the control group. Conclusion: Continuously oral supplementation with MgO according to cisplatin dose (500 mg MgO per 50 mg/m2 cisplatin) as 2-3 divided daily doses at rest days between chemotherapy cycles reduces the decline in serum Mg levels and also the prevalence of hypomagnesaemia in cancer patients.

Background Medullary thyroid carcinoma (MTC) occurs in both sporadic (75%) and hereditary (25%) forms. The missense mutations of the REarranged during Transfection (RET) proto-oncogene in MTC development have been well demonstrated. The aim of this study was to investigate frequency of G691S/S904S haplotype in MTC patients and their relatives. Methods In this research 293 participants were studied, including 181 patients (102 female, 79 male) and 112 their relatives (58 female, 54 male). Genomic DNA was extracted from peripheral blood leucocytes using the standard Salting Out/Proteinase K method. Nucleotide change detection was performed using PCR and direct DNA sequencing methods. Results According to DNA sequencing results, 159 individuals (104 patients, 55 relatives) had both G691S (rs1799939) missense mutation in exon11 and S904S (rs1800863) synonymous mutation in exon 15 of RET proto-oncogene. The allele frequency of G691S/S904S haplotype was 21.15% in patients and 10.75% in their relatives. Conclusion The obtained data showed the frequency of G691S/S904S RET gene haplotype among Iranian MTC patients and their relatives. The G691S and S904S nucleotide changes were in complete linkage disequilibrium, so the results were grouped together and referred to as G691S/S904S haplotype. Further analysis is need to demonstrate the association between this haplotype and MTC development.

Background: Medullary thyroid carcinoma (MTC) occurs in both sporadic (75%) and hereditary (25%) forms. The missense mutations of the rearranged during transfection (RET) proto-oncogene in MTC development have been well demonstrated. Several studies have been published that indicate the molecular analysis of RET gene may offer early identification of those patients at high risk to develop MTC and may provide the opportunity for early intervention. The aim of this study was to investigate frequency of G691S/S904S haplotype in MTC patients and their relatives. Methods: From 2004 to 2014, 358 participants were studied, including 213 patients (119 female, 94 male) and 145 their relatives (79 female, 66 male) in cellular and molecular research center of Shahid Beheshti Research Institute for Endocrine Sciences, Tehran, Iran. Genomic DNA was extracted from peripheral blood leucocytes using the standard Salting Out/Proteinase K method. Nucleotide change detection was performed using PCR and direct DNA sequencing methods. The RET mutations and SNPs, sequences were analyzed. Results: According to DNA sequencing results, 189 individuals (119 patients, 70 relatives) had both G691S (rs1799939) missense mutation in exon11 and S904S (rs1800863) synonymous mutation in exon 15 of RET proto-oncogene. The allele frequency of G691S/S904S haplotype was 35.02% in patients and 29.92% in their relatives. Conclusion: The obtained data showed the frequency of G691S/S904S RET gene haplotype among Iranian MTC patients and their relatives. The G691S and S904S nucleotide changes were in complete linkage disequilibrium, so the results were grouped together and referred to as G691S/S904S haplotype. This haplotype are not considered as oncogenic mutations at this time, its functional role should be investigated. Further analysis is needed to demonstrate the association between this haplotype and MTC development.

Autoimmune lymphoproliferative syndrome (ALPS) is a prototypic disorder of abnormal lymphocyte homeostasis. In the September 2005 issue of The Iranian Journal of Allergy, Asthma and Immunology, a patient with clinical features consistent with ALPS was described. Although the clinical presentation was in favor of ALPS, a precise diagnosis needed more laboratory evaluations.