Positive outcome with neurofeedback treatment in a case of child with mild Autism Spectrum Disorder

Autism is a neurological disorder characterized by a lack of appropriate eye contact, facial expression, social interaction, communication, and restricted repetitive behavior. Autism Spectrum Disorder represents a group of disorders, including Autism, PDD-NOS, Rett's Disorder, Child Disintegrative Disorder and Asperger?s Disorder (American Psychiatric Association, 1994). According to DSM-IV-TR (APA, 2000), qualitative impairments in social interaction is one of the defining characteristics for the diagnosis of Autistic Disorder. Social impairments can include: lack of use of nonverbal behaviors such as eye gaze, gestures, body postures and facial expressions; lack of social-emotional reciprocity; impairment in expression of pleasure in the happiness of others; and a lack of interaction with peers, including an absence of symbolic or imaginative play activities (APA, 2000). This core impairment has led some to identify social deficits as the “heart” of Autism Spectrum Disorders (Gutstein, 2005)

Impurities Detection in Intensity Inhomogeneous Edible Bird’s Nest (EBN) Using a U-Net Deep Learning Model

As an important export, cleanliness control on edible bird’s nest (EBN) is paramount. Automatic impurities detection is in urgent need to replace manual practices. However, effective impurities detection algorithm is yet to be developed due to the unresolved inhomogeneous optical properties of EBN. The objective of this work is to develop a novel U-net based algorithm for accurate impurities detection. The algorithm leveraged the convolution mechanisms of U-net for precise and localized features extraction. Output probability tensors were then generated from the deconvolution layers for impurities detection and positioning. The U-net based algorithm outperformed previous image processing-based methods with a higher impurities detection rate of 96.69% and a lower misclassification rate of 10.08%. The applicability of the algorithm was further confirmed with a reasonably high dice coefficient of more than 0.8. In conclusion, the developed U-net based algorithm successfully mitigated intensity inhomogeneity in EBN and improved the impurities detection rate

A Comparative Study of Different Kernel Functions Applied to LW-KPLS Model for Nonlinear Processes

Soft sensors are inferential estimators when the employment of hardware sensors is inapplicable, expensive, or difficult in industrial plant processes. Currently, a simple soft sensor, namely locally weighted partial least squares (LW-PLS), which can cope with the nonlinearity of the process, has been developed. However, LW-PLS exhibits the disadvantages of handling strong nonlinear process data. To address this problem, Kernel functions are integrated into LW-PLS to form locally weighted Kernel partial least squares (LW-KPLS). Notice that a minimal study was carried out on the impact of different kernel functions that have not been integrated with the LW-KPLS, in which this model has the potential to be applied to different chemical-related nonlinear processes. Thus, this study investigates the predictive performance of LW-KPLS with several different Kernel functions using three nonlinear case studies. As the results, the predictive performances of LW-KPLS with Polynomial Kernel are better than other Kernel functions. The values of root-mean-square errors (RMSE) and error of approximation (Ea) for the training and testing dataset by utilizing this Kernel function are the lowest in their respective case studies, which are 34.60% to 95.39% lower for RMSEs values and 68.20% to 95.49% smaller for Ea values

A Case of Ovarian Fibromatosis and Massive Ovarian Oedema Associated With Intra-Abdominal Fibromatosis, Sclerosing Peritonitis and Meig's Syndrome

Purpose:To discuss a case of ovarian fibromatosis/massive ovarian oedema, intra-abdominal fibromatosis, sclerosing peritonitis and Meig's syndrome. To review the reported therapeutic options

Redox regulation of metabolic syndrome: recent developments in skeletal muscle insulin resistance and non-alcoholic fatty liver disease (NAFLD)

Several new discoveries over the past decade have shown that metabolic syndrome, a cluster of metabolic disorders, including increased visceral obesity, hyperglycemia, hypertension, dyslipidemia and low HDL-cholesterol, is commonly associated with skeletal muscle insulin resistance. More recently, non-alcoholic fatty liver disease (NAFLD) was recognized as an additional condition that is strongly associated with features of metabolic syndrome. While the pathogenesis of skeletal muscle insulin resistance and fatty liver is multifactorial, the role of dysregulated redox signaling has been clearly demonstrated in the regulation of skeletal muscle insulin resistance and NAFLD. In this review, we aim to provide recent updates on redox regulation with respect to (a) pro-oxidant enzymes (e.g. NAPDH oxidase and xanthine oxidase); (b) mitochondrial dysfunction; (c) endoplasmic reticulum (ER) stress; (d) iron metabolism derangements; and (e) gut-skeletal muscle or gut-liver connection in the development of skeletal muscle insulin resistance and NAFLD. Furthermore, we discuss promising new therapeutic strategies targeting redox regulation currently under investigation for the treatment of skeletal muscle insulin resistance and NAFLD

Medicinal Properties of Clinacanthus nutans: A review

To date, medicinal plants are the most important resources in the discovery of new drugs. Clinacanthus nutans has been used traditionally in Thailand folk medicine to promote overall well-being. A few biological constituents of C. nutans and their physiological functions have been evaluated in previous studies. However, the mechanisms of action, potency and efficacy of the plant are still not well understood. In this review, the pharmacological properties of C. nutans such as anti-inflammatory effects, anti-proliferation, anti-venom and anti-bacterial activities, and their underlying mechanisms of action are presented and discussed.Keywords:  Clinacanthus nutans: Anti-inflammatory, Anti-proliferation, Anti-venom, Anti-bacterial propertie

How digitalisation can enable industrial symbiosis practices : a case study

Industrial Symbiosis (IS) encourages a collaborative approach aiming at recovering, reprocessing and reusing non-labour resources and it is a promising solution for mitigating the rising cost of non-labour resource. Introducing IS is a knowledge intensive process and researchers have developed various information and communication (ICT) tools to support the process. However, the use of these tools in the actual industrial practice has not been adequately investigated yet. This study investigates the role that ICT tools play in facilitating the process of creating IS through a case study of International Synergies – the company which facilitated the world’s first national-level IS programme (i.e. NISP UK). Results suggest that the role of digitalisation can increase practitioners’ productivity mainly through data analytics

Surveillance of Broad-Spectrum Antibiotic Prescription in Singaporean Hospitals: A 5-Year Longitudinal Study

10.1371/journal.pone.0028751PLoS ONE612

The application of machine learning in nanoparticle treated water: A review

Pollution from industrial effluents and domestic waste are two of the most common sources of environmental pollutants. Due to the rising population and manufacturing industries, large amounts of pollutants were produced daily. Therefore, enhancements in wastewater treatment to render treated wastewater and provide effective solutions are essential to return clean and safe water to be reused in the industrial, agricultural, and domestic sectors. Nanotechnology has been proven as an alternative approach to overcoming the existing water pollution issue. Nanoparticles exhibit high aspect ratios, large pore volumes, electrostatic properties, and high specific surfaces, which explains their efficiency in removing pollutants such as dyes, pesticides, heavy metals, oxygen-demanding wastes, and synthetic organic chemicals. Machine learning (ML) is a powerful tool to conduct the model and prediction of the adverse biological and environmental effects of nanoparticles in wastewater treatment. In this review, the application of ML in nanoparticle-treated water on different pollutants has been studied and it was discovered that the removal of the pollutants could be predicted through the mathematical approach which included ML. Further comparison of ML method can be carried out to assess the prediction performance of ML methods on pollutants removal. Moreover, future studies regarding the nanotoxicity, synthesis process, and reusability of nanoparticles are also necessary to take into consideration to safeguard the environment