Genetic landscape of autism spectrum disorder in Vietnamese children

Autism spectrum disorder (ASD) is a complex disorder with an unclear aetiology and an estimated global prevalence of 1%. However, studies of ASD in the Vietnamese population are limited. Here, we first conducted whole exome sequencing (WES) of 100 children with ASD and their unaffected parents. Our stringent analysis pipeline was able to detect 18 unique variants (8 de novo and 10 ×-linked, all validated), including 12 newly discovered variants. Interestingly, a notable number of X-linked variants were detected (56%), and all of them were found in affected males but not in affected females. We uncovered 17 genes from our ASD cohort in which CHD8, DYRK1A, GRIN2B, SCN2A, OFD1 and MDB5 have been previously identified as ASD risk genes, suggesting the universal aetiology of ASD for these genes. In addition, we identified six genes that have not been previously reported in any autism database: CHM, ENPP1, IGF1, LAS1L, SYP and TBX22. Gene ontology and phenotype-genotype analysis suggested that variants in IGF1, SYP and LAS1L could plausibly confer risk for ASD. Taken together, this study adds to the genetic heterogeneity of ASD and is the first report elucidating the genetic landscape of ASD in Vietnamese children

Eco-friendly facile synthesis of Co3O4-Pt nanorods for ethylene detection towards fruit quality monitoring

Ethylene, a biomarker widely employed for evaluating fruit ripening during storage, exists at extremely low concentrations. Therefore a gas sensor with high sensitivity and a sub-ppm detection limit is needed. In this work, porous Co3O4 nanorods were synthesized through a hydrothermal method involving Co(NO3)2, Na2C2O4, H2O and ethylene glycol (EG), followed by annealing at 400 degrees C in air. The surface of the porous Co3O4 nanorods was functionalized with Pt nanoparticles to enhance the ethylene sensing performance. The effect of Co3O4 surface functionalisation with Pt nanoparticles was investigated by adding different amounts of nanoparticles. The sensor's outstanding performance at the optimum working temperature of 250 degrees C is attributed to the synergy between the high catalytic activity of Pt nanoparticles and the extensive surface area of the porous Co3O4 nanorods. Compared to pure Co3O4, the 0.031 wt% Pt sensor showed better ethylene sensing performance with a response 3.4 times that of pristine Co3O4. The device also demonstrated high selectivity, repeatability, long-term stability and a detection limit of 0.13 ppm for ethylene, which is adequate for fruit quality monitoring. The gas sensing mechanism of porous Co3O4 nanorods and the influence of Pt decoration on sensor performance are discussed

Clay fine fissuring monitoring using miniature geo-electrical resistivity arrays

Abstract This article describes a miniaturised electrical imaging (resistivity tomography) technique to map the cracking pattern of a clay model. The clay used was taken from a scaled flood embankment built to study the fine fissuring due to desiccation and breaching process in flooding conditions. The potential of using a miniature array of electrodes to follow the evolution of the vertical cracks and number them during the drying process was explored. The imaging technique generated two-dimensional contoured plots of the resistivity distribution within the model before and at different stages of the desiccation process. The change in resistivity associated with the widening of the cracks were monitored as a function of time. Experiments were also carried out using a selected conductive gel to slow down the transport process into the cracks to improve the scanning capabilities of the equipment. The main vertical clay fissuring network was obtained after inversion of the experimental resistivity measurements and validated by direct observations

The Role of Anisotropy in Distinguishing Domination of Néel or Brownian Relaxation Contribution to Magnetic Inductive Heating: Orientations for Biomedical Applications

Magnetic inductive heating (MIH) has been a topic of great interest because of its potential applications, especially in biomedicine. In this paper, the parameters characteristic for magnetic inductive heating power including maximum specific loss power (SLPmax), optimal nanoparticle diameter (Dc) and its width (ΔDc) are considered as being dependent on magnetic nanoparticle anisotropy (K). The calculated results suggest 3 different Néel-domination (N), overlapped Néel/Brownian (NB), and Brownian-domination (B) regions. The transition from NB- to B-region changes abruptly around critical anisotropy Kc. For magnetic nanoparticles with low K (K Kc) are opposite. The decreases of the SLPmax when increasing polydispersity and viscosity are characterized by different rates of d(SLPmax)/dσ and d(SLPmax)/dη depending on each domination region. The critical anisotropy Kc varies with the frequency of an alternating magnetic field. A possibility to improve heating power via increasing anisotropy is analyzed and deduced for Fe3O4 magnetic nanoparticles. For MIH application, the monodispersity requirement for magnetic nanoparticles in the B-region is less stringent, while materials in the N- and/or NB-regions are much more favorable in high viscous media. Experimental results on viscosity dependence of SLP for CoFe2O4 and MnFe2O4 ferrofluids are in good agreement with the calculations. These results indicated that magnetic nanoparticles in the N- and/or NB-regions are in general better for application in elevated viscosity media

Reduction of the size of datasets by using evolutionary feature selection: the case of noise in a modern city

Smart city initiatives have emerged to mitigate the negative effects of a very fast growth of urban areas. Most of the population in our cities are exposed to high levels of noise that generate discomfort and different health problems. These issues may be mitigated by applying different smart cities solutions, some of them require high accurate noise information to provide the best quality of serve possible. In this study, we have designed a machine learning approach based on genetic algorithms to analyze noise data captured in the university campus. This method reduces the amount of data required to classify the noise by addressing a feature selection optimization problem. The experimental results have shown that our approach improved the accuracy in 20% (achieving an accuracy of 87% with a reduction of up to 85% on the original dataset).Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. This research has been partially funded by the Spanish MINECO and FEDER projects TIN2016-81766-REDT (http://cirti.es), and TIN2017-88213-R (http://6city.lcc.uma.es)

Pulsed electromagnetic energy treatment offers no clinical benefit in reducing the pain of knee osteoarthritis: a systematic review

Background The rehabilitation of knee osteoarthritis often includes electrotherapeutic modalities as well as advice and exercise. One commonly used modality is pulsed electromagnetic field therapy (PEMF). PEMF uses electro magnetically generated fields to promote tissue repair and healing rates. Its equivocal benefit over placebo treatment has been previously suggested however recently a number of randomised controlled trials have been published that have allowed a systematic review to be conducted. Methods A systematic review of the literature from 1966 to 2005 was undertaken. Relevant computerised bibliographic databases were searched and papers reviewed independently by two reviewers for quality using validated criteria for assessment. The key outcomes of pain and functional disability were analysed with weighted and standardised mean differences being calculated. Results Five randomised controlled trials comparing PEMF with placebo were identified. The weighted mean differences of the five papers for improvement in pain and function, were small and their 95% confidence intervals included the null. Conclusion This systematic review provides further evidence that PEMF has little value in the management of knee osteoarthritis. There appears to be clear evidence for the recommendation that PEMF does not significantly reduce the pain of knee osteoarthritis

An Efficient Event-driven Neuromorphic Architecture for Deep Spiking Neural Networks

© 2019 IEEE. Deep Neural Networks (DNNs) have been successfully applied to various real-world machine learning applications. However, performing large DNN inference tasks in real-time remains a challenge due to its substantial computational costs. Recently, Spiking Neural Networks (SNNs) have emerged as an alternative way of processing DNN'fs task. Due to its eventbased, data-driven computation, SNN reduces both inference latency and complexity. With efficient conversion methods from traditional DNN, SNN exhibits similar accuracy, while leveraging many state-of-the-art network models and training methods. In this work, an efficient neuromorphic hardware architecture for image recognition task is presented. To preserve accuracy, the analog-to-spiking conversion algorithm is adopted. The system aims to minimize hardware area cost and power consumption, enabling neuromorphic hardware processing in edge devices. Simulation results have shown that, with the MNIST digit recognition task, the system has achieved × 20 reduction in terms of core area cost compared to the state-of-the-art works, with an accuracy of 94.4%, core area of 15 μ m2 at a maximum frequency of 250 MHz