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The purpose of this scoping review was to outline the current gait interventions used in biomechanics-based research focused on persons with autism spectrum disorder. A review of articles was conducted using the PRISMA methodology for reporting guidance with an a priori PICO framework. The selected articles were identified, reviewed, evaluated for risk of bias, and used to group biomechanics-based gait interventions that have been implemented in research involving persons with autism spectrum disorder. Titles and abstracts of 573 articles were reviewed; seven articles describing gait interventions met the inclusionary criteria. The interventions reported in the literature were hippotherapy, weighted vests, and exercise programs. Additional orthopedic interventions (serial casting and foot orthoses) were utilized for individuals who primarily walk on their forefoot. Findings from this review reveal changes in spatiotemporal characteristics post-hippotherapy, changes in ground reaction forces and foot pressures after participation in exercise intervention programs, and no change in parameters via weighted vests. For persons with autism spectrum disorder who are forefoot strikers, the use of serial casting and foot orthoses reportedly improved the overall kinematic and spatiotemporal parameters during gait.

Autism spectrum disorder (ASD) is a mental developmental disorder associated with social and communicational defects and Stereotypical Motor Movements (SMM). SMM is a set of repetitive motor activities associated with several mental developmental disorders like Autism. SMM has several forms like arm flapping, head banging, ear covering, and spinning with various degrees of severity that might lead to self-injury in severe cases.

Developing a computer-vision-based technology to detect noticeable SMM behaviors can help in the early diagnosis of autism. In this paper, a computer vision-based neural network model was proposed to detect and recognize repetitive motor behaviors. The proposed model went through three main stages: First, data preparation. Second, human body features extraction using deep learning pose estimation and the skeleton representation model, and finally, multiclass classification to distinguish between several classes of headbanging. The proposed solution was evaluated using the Self Stimulatory Behavior Dataset (SSBD) which is a public dataset of three classes of repetitive motor behaviors associated with autism. We also collected a set of 40 videos of autistic children exhibiting headbanging from public domains like YouTube. In addition to that, we captured 25 videos of typically developing subjects mimicking headbanging. The collected and the videoed videos were used to evaluate the proposed model. This work proves the applicability of diagnosing mental developmental syndrome symptoms using vision-based techniques in cooperation with neural networks. The produced results prove that the used techniques can operate well in real-world challenging applications. The proposed model achieved 85.5% accuracy on SSBD and 93% on the collected and recorded videos.

Currently, Extreme Programming, Scrum, and Kanban are the three most commonly used methods in agile software development (ASD) projects. Each method has different practices and shares a set of agile principles, where quality, time, and cost are the three project performance indicators. Companies may focus on and prioritize certain indicators based on industry or project differences. Therefore, choosing appropriate practices that fit the specific performance indicator is an important decision for organizations. This study utilizes a hierarchical consensus model to examine the correlation between four agile practice groups, six agile principle categories, and three project performance indicators. The modified Delphi method was applied to collect the pairwise comparison data, and the analytic hierarchy process was utilized to analyze the data. A Delphi panel of experts from both academia and industry was established to reach a consensus on the correlation priority using pairwise comparison matrices. The principle of cooperation between customer and developer is considered the most important principle related to project time and cost performance, while the technical excellence principle is the most important principle related to project quality performance. These results can assist organizations and practitioners in adopting the ASD practices that will best enhance their competitive advantage.

Functional Magnetic Resonance Imaging (fMRI), for many decades acts as a potential aiding method for diagnosing medical problems. Several successful machine learning algorithms have been proposed in literature to extract valuable knowledge from fMRI. One of these algorithms is the convolutional neural network (CNN) that competent with high capabilities for learning optimal abstractions of fMRI. This is because the CNN learns features similarly to human brain where it preserves local structure and avoids distortion of the global feature space. Focusing on the achievements of using the CNN for the fMRI, and accordingly, the Deep Convolutional Auto-Encoder (DCAE) benefits from the data-driven approach with CNN’s optimal features to strengthen the fMRI classification. In this paper, a new two consequent multi-layers DCAE deep discriminative approach for classifying fMRI Images is proposed. The first DCAE is unsupervised sub-model that is composed of four CNN. It focuses on learning weights to utilize discriminative characteristics of the extracted features for robust reconstruction of fMRI with lower dimensional considering tiny details and refining by its deep multiple layers. Then the second DCAE is a supervised sub-model that focuses on training labels to reach an outperformed results. The proposed approach proved its effectiveness and improved literately reported results on a large brain disorder fMRI dataset.

Peri-implant debris certainly lead to osteolysis, necrosis, pseudotumor formation, tissue granulation, fibrous capsule contractions, and even implant failure. For the three-dimensional (3D) printed cage, impaction during cage insertion is one of the most potential sources of fracture debris. A finite-element study was carried out to reduce the impact-induced debris of the 3D-printed cage. This study focused on the design strategy of solid and cellular structures along the load-transferring path. Using the finite-element method, the cellular structure of the transforaminal lumbar interbody fusion (TLIF) cage was systematically modified in the following four variations: a noncellular cage (NC), a fully cellular (FC) cage, a solid cage with a cellular structure in the middle concave (MC) zone, and a strengthened cage (SC) in the MC zone. Three comparison indices were considered: the stresses at the cage-instrument interfaces, in the MC zone, and along the specific load-transferring path. The NC and FC were the least and most highly stressed variations at the cage-instrument interfaces and in the MC zone, respectively. Along the entirely load-transferring path, the FC was still the most highly stressed variation. It showed a higher risk of stress fracture for the FC cage. For the MC and SC, the MC zone was consistently more stressed than the directly impacted zone. The further strengthened design of the SC had a lower peak stress (approximately 29.2%) in the MC zone compared with the MC. Prior to 3D printing, the load-transferring path from the cage-instrument interfaces to the cage-tissue interfaces should be determined. The cage-instrument interfaces should be printed as a solid structure to avoid impact-induced fracture. The other stress-concentrated zones should be cautiously designed to optimize the coexistence strategy of the solid and cellular structures.

National Science and Technology Prizes.

Virologist Hou Yunde won the State Preeminent Science and Technology Award.

Highlights from the State Natural Science Award.

Highlights from the State Technological Invention Award.

Breakthrough: Chinese researchers cloned monkeys using Dolly’s cloning method.

Chinese GM rice approved by U.S. FDA.

Chinese Government supports TCM innovations.

First bio-safety level four lab put into operation in Wuhan.

New way to develop flu vaccines.

New genome research project in China.

Chinese scientists enhance e-skin sensory capability.

Gene technology start-up offers genetic testing to trace ancestry.

Genetic basis for biological motion perception and linkage to autistic traits.

Novel drug efflux pump in gram-positive bacteria.

China FDA approves new once-weekly Bydureon to improve glycemic control in patients with Type-2 Diabetes.

Illumina and KingMed Diagnostics partner to develop next-generation sequencing technology for Chinese FDA approval.

Varian and Ping An sign MoU to expand access to high quality cancer care in China.

Eisai completes construction of oral solid dose production facility at new Suzhou plant in China.

Ping An Technology sets world records in international medical imaging evaluation.

Marken opens new kit building facility in Shanghai.

ASLAN Pharmaceuticals announces shortened timeline to commercialisation for varlitinib in China.

ASLAN Pharmaceuticals appoints Stephen Doyle as head of China.

HKUST Scientists reveal how human brains keep balance.

The purpose of this paper is to compare the design equation of Allowable Stress Design (ASD) with that of Load and Resistance Factor Design (LRFD) concerning the member stability for the economic design of cable-stayed bridges. Both elastic and inelastic buckling analyses are carried out for the cable-stayed bridges with the effective buckling lengths of the key members calculated. The axial-flexural interaction equations prescribed in ASD and LRFD are used to check the stability of main members in cable-stayed bridges. Parametric studies are performed for the bridges with a center span of 300, 600, 900, and 1200 m of different girder depths. Peak values of the interaction equations are calculated at the intersection between the girder and towers. Since the peak values of the interaction equations by inelastic buckling analysis are less than those by elastic buckling analysis, a more economical design of the girder and towers can be achieved using the inelastic buckling analysis. In addition, the use of LRFD specifications can result in a more economical design by about 20% on average than the ASD specifications for steel cable-stayed bridges.

Children suffering from Autism Spectrum Disorder (ASD) have impaired social communication, interaction and restricted and repetitive behaviors. ASD is caused by abnormal brain developments which give rise to the behavioral characteristics associated with ASD. The clinical diagnosis of ASD is performed on the basis of behavioral assessment and it causes a time delay in early intervention, as there is a time gap between abnormal brain developments and associated behavioral characteristics. Electroencephalography (EEG) is a technique which measures the electrical activity produced by the brain and it has been used to detect several neurological disorders. Studies have shown that there is a variation in the EEG signals of a normal subject and EEG signals of ASD subjects. In this study, we obtained scalograms of EEG signals by using Continuous Wavelet Transform (CWT). Pre-trained deep Convolutional Neural Networks (CNNs) such as GoogLeNet, AlexNet, MobileNet and SqueezeNet were used for extracting the features from scalograms and classification of obtained scalograms from EEG signals of normal and ASD subjects. We also used Support Vector Machine (SVM) algorithm and Relevance Vector Machine (RVM) for classification of the features extracted by the deep CNNs. The GoogLeNet, AlexNet, MobileNet and SqueezeNet deep CNNs achieved a validation accuracy of 75%, 75.84%, 79.45% and 82.98% in classifying the scalograms generated from EEG signals. The SVM achieved an accuracy of 71.6%, 74.76%, 70.70% and 81.47% using GoogleNet, Mobilenet, AlexNet and SqueezeNet for scalogram feature extraction. The RVM achieved an accuracy of 65.5%, 69.9%, 65.3% and 72.59% when used for classification using the features generated from GoogLeNet, AlexNet, MobileNet and SqueezeNet.The SqueezeNet deep CNN performed better than GoogLeNet, AlexNet and MobileNet for classification of the EEG scalograms. The feature extraction using SqueezeNet also resulted in better classification accuracy obtained by SVM and RVM. The results indicate that pre-trained models can be used for classifying the ASD using scalograms of the EEG signals.

There is an acknowledged global shortage in qualified and skilled cyber security practitioners, so much so that governments, employers and educational establishments are developing new routes and opportunities to encourage interest and applications from demographics that would not normally apply for cyber security roles. These demographics include women, young children and people on the autistic spectrum. The potential employment of people from this last demographic — people on the Autistic Spectrum — in cyber security roles will be the focus of this research. Two areas are of interest, the ethical considerations in employing people on the Autistic Spectrum and the identification of a strategy to support the employee and employer relationship. Both are discussed here. In Europe the shortfall is expected to be in the region of 350,000 employees by the year 2020 and in the United States the number is expected to be 1.2 million by the same date. This research looks at how specific autistic traits and strengths can be matched to cyber security vacancies and discusses ethical considerations and a potential support process. A qualitative research methodology was used to identify suitable traits and potential cyber security vacancies. Ethical principles and a proposed support process are put forward to allow potential employers and autistic employees to engage in equitable employment opportunities. The autistic demographic does indeed offer skilled and capable resources to help fill cyber security vacancies; however, work is needed to allow both sides to benefit from the opportunities.