Evaluation of the Use and Reasons for Not Using a Helmet by Motorcyclists Admitted to the Emergency Ward of Shahid Bahonar Hospital in Kerman

Background: Motorcycle crashes are the cause of severe morbidity and mortality especially because of head injuries. It seems that wearing a helmet has an effective role in protection against head injuries. Nevertheless, motorcyclists usually have no tendency to wear a helmet when driving in cities and have several reasons for this behavior. Objectives: This study aimed to evaluate the use and reasons for not using a helmet by motorcyclists admitted to an emergency ward of a trauma hospital due to accident in Kerman, Iran. Patients and Methods: This study was carried out by recoding the opinions of motorcyclists who had been transferred to the emergency ward of Shahid Bahonar Hospital (Kerman/Iran). Since no data was available on the frequency of the use of helmets, a pilot study was carried out and a sample size of 377 was determined for the main study. Then a researcher-made questionnaire was used to investigate the motorcyclists’ reasons for not using a helmet. Results: Only 21.5% of the motorcyclists had been wearing helmets at the time of the accident. The most frequent reasons for not using a helmet were the heavy weight of the helmet (77%), feeling of heat (71.4%), pain in the neck (69.4%), feeling of suffocation (67.7%), limitation of head and neck movements (59.6%) and all together, physical discomfort was the main cause of not wearing a helmet during motorcycle rides. Conclusions: In general, it appears that it is possible to increase the use of helmets by eliminating its physical problems, and increasing the knowledge of community members in relation to the advantages of helmet use, which will result in a significant decrease in traumas resulting from motorcycle accidents

Intervention in gene regulatory networks via greedy control policies based on long-run behavior

<p>Abstract</p> <p>Background</p> <p>A salient purpose for studying gene regulatory networks is to derive intervention strategies, the goals being to identify potential drug targets and design gene-based therapeutic intervention. Optimal stochastic control based on the transition probability matrix of the underlying Markov chain has been studied extensively for probabilistic Boolean networks. Optimization is based on minimization of a cost function and a key goal of control is to reduce the steady-state probability mass of undesirable network states. Owing to computational complexity, it is difficult to apply optimal control for large networks.</p> <p>Results</p> <p>In this paper, we propose three new greedy stationary control policies by directly investigating the effects on the network long-run behavior. Similar to the recently proposed mean-first-passage-time (MFPT) control policy, these policies do not depend on minimization of a cost function and avoid the computational burden of dynamic programming. They can be used to design stationary control policies that avoid the need for a user-defined cost function because they are based directly on long-run network behavior; they can be used as an alternative to dynamic programming algorithms when the latter are computationally prohibitive; and they can be used to predict the best control gene with reduced computational complexity, even when one is employing dynamic programming to derive the final control policy. We compare the performance of these three greedy control policies and the MFPT policy using randomly generated probabilistic Boolean networks and give a preliminary example for intervening in a mammalian cell cycle network.</p> <p>Conclusion</p> <p>The newly proposed control policies have better performance in general than the MFPT policy and, as indicated by the results on the mammalian cell cycle network, they can potentially serve as future gene therapeutic intervention strategies.</p

Optimal In Silico Target Gene Deletion through Nonlinear Programming for Genetic Engineering

Optimal selection of multiple regulatory genes, known as targets, for deletion to enhance or suppress the activities of downstream genes or metabolites is an important problem in genetic engineering. Such problems become more feasible to address in silico due to the availability of more realistic dynamical system models of gene regulatory and metabolic networks. The goal of the computational problem is to search for a subset of genes to knock out so that the activity of a downstream gene or a metabolite is optimized.Based on discrete dynamical system modeling of gene regulatory networks, an integer programming problem is formulated for the optimal in silico target gene deletion problem. In the first result, the integer programming problem is proved to be NP-hard and equivalent to a nonlinear programming problem. In the second result, a heuristic algorithm, called GKONP, is designed to approximate the optimal solution, involving an approach to prune insignificant terms in the objective function, and the parallel differential evolution algorithm. In the third result, the effectiveness of the GKONP algorithm is demonstrated by applying it to a discrete dynamical system model of the yeast pheromone pathways. The empirical accuracy and time efficiency are assessed in comparison to an optimal, but exhaustive search strategy.Although the in silico target gene deletion problem has enormous potential applications in genetic engineering, one must overcome the computational challenge due to its NP-hardness. The presented solution, which has been demonstrated to approximate the optimal solution in a practical amount of time, is among the few that address the computational challenge. In the experiment on the yeast pheromone pathways, the identified best subset of genes for deletion showed advantage over genes that were selected empirically. Once validated in vivo, the optimal target genes are expected to achieve higher genetic engineering effectiveness than a trial-and-error procedure

The Pediatric Cell Atlas: defining the growth phase of human development at single-cell resolution

Single-cell gene expression analyses of mammalian tissues have uncovered profound stage-specific molecular regulatory phenomena that have changed the understanding of unique cell types and signaling pathways critical for lineage determination, morphogenesis, and growth. We discuss here the case for a Pediatric Cell Atlas as part of the Human Cell Atlas consortium to provide single-cell profiles and spatial characterization of gene expression across human tissues and organs. Such data will complement adult and developmentally focused HCA projects to provide a rich cytogenomic framework for understanding not only pediatric health and disease but also environmental and genetic impacts across the human lifespan

The Pediatric Cell Atlas:Defining the Growth Phase of Human Development at Single-Cell Resolution

Single-cell gene expression analyses of mammalian tissues have uncovered profound stage-specific molecular regulatory phenomena that have changed the understanding of unique cell types and signaling pathways critical for lineage determination, morphogenesis, and growth. We discuss here the case for a Pediatric Cell Atlas as part of the Human Cell Atlas consortium to provide single-cell profiles and spatial characterization of gene expression across human tissues and organs. Such data will complement adult and developmentally focused HCA projects to provide a rich cytogenomic framework for understanding not only pediatric health and disease but also environmental and genetic impacts across the human lifespan

Comparison of False Memory among Patients with Post Traumatic Stress Disorders (PTSD) based on the Received Psychological Treatment

Abstract: Background & Aims: False memory is more prevalent among PTSD patients. This memory can be affected by group and intensifies the symptoms of the disorder. Psychological Debriefing (PD) and Eye Movement Desensitization and Reprocessing (EMDR) are widely used for the treatment of PTSD patients. The efficacy of these treatments is controversial. Method: A total of 219 PTSD patients were randomly selected and divided into three groups based on the received treatment type (EMDR, PD, control group). All groups were evaluated and compared by using Rodiger & McDremott False Memory Scale. Results: The EMDR group in comparison to the PD and control groups and the control group in comparison to the PD group showed lower rates of false memory (P<0.01). η square showed that 21 percent of the variance of false memory could be explained by the type of received treatment. Conclusion: Considering lower level of false memory in EMDR group compared with other groups and the negative effects of false memory in identification of PTSD, EMDR is better than PD in the treatment of PTSD patients. Keywords: False memory syndrome, PTSD, Treatmen

Post-growth modification of electrical properties of ZnTe nanowires

ZnTe nanowires, grown by a vapor-liquid-solid technique are p-type and show a very high intrinsic resistivity. Enhancement of the nanowire conductivity was investigated by vacuum annealing, doping and Joule heating. The current-voltage (I-V) characteristics were measured in all cases and electrical parameters such as resistivity, carrier concentration and mobility were computed from the I-V curves. An improvement of five orders of magnitude in the electrical conductivity was seen after thermal annealing and Joule heating, comparable to the enhancement in conductivity obtained by doping. Published by Elsevier B.V.X1167sciescopu