Determination of Dosage Compensation of the Mammalian X Chromosome by RNA-seq is Dependent on Analytical Approach

Background An enduring question surrounding sex chromosome evolution is whether effective hemizygosity in the heterogametic sex leads inevitably to dosage compensation of sex-linked genes, and whether this compensation has been observed in a variety of organisms. Incongruence in the conclusions reached in some recent reports has been attributed to different high-throughput approaches to transcriptome analysis. However, recent reports each utilizing RNA-seq to gauge X-linked gene expression relative to autosomal gene expression also arrived at diametrically opposed conclusions regarding X chromosome dosage compensation in mammals. Results Here we analyze RNA-seq data from X-monosomic female human and mouse tissues, which are uncomplicated by genes that escape X-inactivation, as well as published RNA-seq data to describe relative X expression (RXE). We find that the determination of RXE is highly dependent upon a variety of computational, statistical and biological assumptions underlying RNA-seq analysis. Parameters implemented in short-read mapping programs, choice of reference genome annotation, expression data distribution, tissue source for RNA and RNA-seq library construction method have profound effects on comparing expression levels across chromosomes. Conclusions Our analysis shows that the high number of paralogous gene families on the mammalian X chromosome relative to autosomes contributes to the ambiguity in RXE calculations, RNA-seq analysis that takes into account that single- and multi-copy genes are compensated differently supports the conclusion that, in many somatic tissues, the mammalian X is up-regulated compared to the autosomes

Low-cost hardware in the loop for intelligent neural predictive control of hybrid electric vehicle

The design and investigation of an intelligent controller for hardware-in-the-loop (HIL) implementation of hybrid electric vehicles (HEVs) are proposed in this article. The proposed intelligent controller is adopted based on the enhancement of a model predictive controller (MPC) by an artificial neural network (ANN) approach. The MPC-based ANN (NNMPC) is proposed to control the speed of HEVs for a simulation system model and experimental HIL test systems. The HIL is established to assess the performance of the NNMPC to control the velocity of HEVs in an experimental environment. The real-time environment of HIL is implemented through a low-cost approach such as the integration of an Arduino Mega 2560 and a host Lenovo PC with a Core i7 @ 3.4 GHz processor. The NNMPC is compared with a proportional–integral (PI) controller, a classical MPC, and two different settings of the ANN methodology to verify the efficiency of the proposed intelligent NNMPC. The obtained results show a distinct behavior of the proposed NNMPC to control the speed of HEVs with good performance based on the distinct transient response, minimum error steady state, and system robustness against parameter perturbation

Low-cost hardware in the loop for intelligent neural predictive control of hybrid electric vehicle

The design and investigation of an intelligent controller for hardware-in-the-loop (HIL) implementation of hybrid electric vehicles (HEVs) are proposed in this article. The proposed intelligent controller is adopted based on the enhancement of a model predictive controller (MPC) by an artificial neural network (ANN) approach. The MPC-based ANN (NNMPC) is proposed to control the speed of HEVs for a simulation system model and experimental HIL test systems. The HIL is established to assess the performance of the NNMPC to control the velocity of HEVs in an experimental environment. The real-time environment of HIL is implemented through a low-cost approach such as the integration of an Arduino Mega 2560 and a host Lenovo PC with a Core i7 @ 3.4 GHz processor. The NNMPC is compared with a proportional–integral (PI) controller, a classical MPC, and two different settings of the ANN methodology to verify the efficiency of the proposed intelligent NNMPC. The obtained results show a distinct behavior of the proposed NNMPC to control the speed of HEVs with good performance based on the distinct transient response, minimum error steady state, and system robustness against parameter perturbation

A Systematic Review on the Extent and Quality of Pharmacoeconomic Publications in Egypt.

BACKGROUND: Egypt faces many challenges when matching patient needs with available resources. Consequently, there has been an increasing interest in pharmacoeconomics as an aid tool in health decision-making to better allocate resources. OBJECTIVES: To review and evaluate the volume and the quality of published pharmacoeconomic studies in Egypt. METHODS: A literature search was conducted in August 2018 using PubMed, Google Scholar, and Cochrane library to identify published Egyptian pharmacoeconomic studies. Articles were included if they were original economic studies, written and published in English, and conducted in Egypt. Each article was assessed independently by two reviewers using the 100-point Quality of Health Evaluation Studies (QHES) scale. RESULTS: Fifteen studies published between 2002 and 2017 were included in the review. Most of them were cost-effectiveness analyses (60%). The minority used secondary data (33.3%) or adopted modeling techniques (40%). The mean QHES score of the included studies was 70.1 ± 21.8, and approximately 40% of them had a QHES score of more than 80. CONCLUSION: Pharmacoeconomic evaluations in Egypt are still in their infancy. The Egyptian guidelines for economic evaluation should be adopted and the EQ-5D-5L value sets should be developed to increase the quality of economic research

Impact of moderate intensity aerobic exercise on chemotherapy-induced anemia in elderly women with breast cancer: A randomized controlled clinical trial

Exercises are often recommended for patients suffering from anemia to improve physical conditioning and hematologic parameters. Hence, the present study aimed to investigate the impact of moderate intensity aerobic exercise on chemotherapy-induced anemia. Thirty elderly women with breast cancer underwent chemotherapy and were randomly assigned into two equal groups; Group A received aerobic exercise for 25–40 min at 50–70% of the maximum heart rate, 3 times/week for 12 weeks in addition to usual daily living activities, medication and nutritional support. Group B who did not train served as controls. Hemoglobin (Hb), and red blood cell count (RBCs) were evaluated pre-treatment and after 12 weeks of training. There were significant declines of both Hb (t = 16.30; P < 0.001) and RBCs (t = 10.38; P < 0.001) in group B relative to group A. Regarding group A, Hb increased from 11.52 ± 0.62 to 12.10 ± 0.59 g/dL with a 5.03% change, while RBCs increased from 4.24 ± 0.37 to 4.49 ± 0.42 million cells/μL with a 5.89% change. Between-group differences were noteworthy regarding Hb (t = −5.34; P < 0.001) and RBCs (t = −5.314; P < 0.001). The results indicate that regular participation in moderate intensity aerobic exercise can enhance chemotherapy-induced anemia

Dual Proportional Integral Controller OF Two-Area Load Frequency Control Based Gravitational Search Algorithm

Gravitational Search Algorithm (GSA) has recently been explored to develop a novel algorithm for distributed optimization and control. This paper proposes a dual Proportional Integral (PI) controller of Load Frequency Control (LFC) based GSA to enhance the damping of oscillations in a two-area power system. A two-area non-reheat thermal system is considered to be equipped with dual PI controller. GSA is utilized to search for optimal controller parameters by minimizing a time-domain based objective function. The performance of the proposed controller has been evaluated with the performance of the conventional PI controller, and  PI  controller  tuned  by  GSA in  order  to  demonstrate  the  superior efficiency of the proposed dual PI controller tuned by GSA. Simulation results emphasis on the better performance of the optimized dual PI controller based on GSA in compare to optimized PI controller based on GSA and conventional one over wide range of operating conditions, and system parameters variations. DOI: http://dx.doi.org/10.11591/telkomnika.v15i3.845

An Improved Neural Network Algorithm to Efficiently Track Various Trajectories of Robot Manipulator Arms

The tuning of the robot actuator represents many challenges to follow a predefined trajectory on account of the uncertainties of parameters and the model nonlinearity. Furthermore, the controller gains require proper optimization to achieve good performance. In this paper, the use of a modified neural network algorithm (MNNA) is proposed as a novel adaptive tuning algorithm to optimize the controller gains. Furthermore, a new mathematical modulation is introduced to promote the exploration manner of the NNA without initial parameters. Specifically, the modulation is formed by using a polynomial mutation. The proposed algorithm is applied to select the proportional integral derivative (PID) controller gains of a robot manipulator arms in lieu of conventional procedures of designer expertise. Another vital contribution is formulating a new performance index that guarantees to improve the settling time and the overshoot of every arm output simultaneously. The proposed algorithm is evaluated with different intelligent techniques in the literature, including the genetic algorithm (GA) and the cuckoo search algorithm (CSA) with PID controllers, where its superiority to follow various trajectories is demonstrated. To affirm the robustness and efficiency of the proposed algorithm, several trajectories and uncertainties of parameters are considered for assessing the response of a robotic manipulator.Peer reviewe