Perceptions on site-specific advanced practice roles for radiation therapists in Singapore : a single centre study

Perception of the radiation oncologists (ROs) and radiation therapists (RTTs) on site-specific advanced practice (SSAP) roles for RTTs, the establishment of SSAP in radiotherapy and the possible implication on current services in Singapore were assessed. Opinions of ROs and RTTs on management support, driving forces, restraints and implication upon successful establishment of SSAP were obtained. Main findings include strong RO’s support for SSAP development and RTTs' requisition for fair opportunities on role development. Other potential benefits include RTTs' career advancement, job satisfaction and retention. Enhancement of inter-professional relationship, service quality and patient satisfaction is anticipated with greater communication and collaboration

Combined CNN Transformer Encoder for Enhanced Fine-grained Human Action Recognition

Fine-grained action recognition is a challenging task in computer vision. As fine-grained datasets have small inter-class variations in spatial and temporal space, fine-grained action recognition model requires good temporal reasoning and discrimination of attribute action semantics. Leveraging on CNN's ability in capturing high level spatial-temporal feature representations and Transformer's modeling efficiency in capturing latent semantics and global dependencies, we investigate two frameworks that combine CNN vision backbone and Transformer Encoder to enhance fine-grained action recognition: 1) a vision-based encoder to learn latent temporal semantics, and 2) a multi-modal video-text cross encoder to exploit additional text input and learn cross association between visual and text semantics. Our experimental results show that both our Transformer encoder frameworks effectively learn latent temporal semantics and cross-modality association, with improved recognition performance over CNN vision model. We achieve new state-of-the-art performance on the FineGym benchmark dataset for both proposed architectures.Comment: The Ninth Workshop on Fine-Grained Visual Categorization (FGVC9) @ CVPR202

The Sign of Fourier Coefficients of Half-Integral Weight Cusp Forms

From a result of Waldspurger, it is known that the normalized Fourier coefficients $a(m)$ of a half-integral weight holomorphic cusp eigenform \f are, up to a finite set of factors, one of $\pm \sqrt{L(1/2, f, \chi_m)}$ when $m$ is square-free and $f$ is the integral weight cusp form related to \f by the Shimura correspondence. In this paper we address a question posed by Kohnen: which square root is $a(m)$? In particular, if we look at the set of $a(m)$ with $m$ square-free, do these Fourier coefficients change sign infinitely often? By partially analytically continuing a related Dirichlet series, we are able to show that this is so

The important of information & communications technology in Uber services

There is a growing interest and concern towards the concept of sustainable transport due to the low functionality of public transport, unregulated taxi pricing, lack of parking space, insufficient availability of taxies and the growing number of traffic congestion during peak hour in urban and sub-urban area.Uber services appear to be cost effective and a sustainable way to travel for public user especially commuter.The study aimed to explore the relationship between information & communications technology (ICT) and the effectiveness of Uber services.The factor that had been found that influence public ridership is information & communications technology.The application of convenience sampling with the usable data from 408 respondents who at least has the intention to use for future ride has been conducted by using online survey.In this study, there is a significant relationship between monthly salary & occupation with information & communications technology.This study contributed to city planner and local planner in developing or planning in order to have a smart city

Current Evidence for a Role of the Kynurenine Pathway of Tryptophan Metabolism in Multiple Sclerosis

The kynurenine pathway (KP) is the major metabolic pathway of the essential amino acid tryptophan (TRP). Stimulation by inflammatory molecules, such as interferon-γ (IFN-γ), is the trigger for induction of the KP, driving a complex cascade of production of both neuroprotective and neurotoxic metabolites, and in turn, regulation of the immune response and responses of brain cells to the KP metabolites. Consequently, substantial evidence has accumulated over the past couple of decades that dysregulation of the KP and the production of neurotoxic metabolites are associated with many neuroinflammatory and neurodegenerative diseases, including Parkinson's disease, AIDS-related dementia, motor neurone disease, schizophrenia, Huntington's disease, and brain cancers. In the past decade, evidence of the link between the KP and multiple sclerosis (MS) has rapidly grown and has implicated the KP in MS pathogenesis. KP enzymes, indoleamine 2,3-dioxygenase (IDO-1) and tryptophan dioxygenase (highest expression in hepatic cells), are the principal enzymes triggering activation of the KP to produce kynurenine from TRP. This is in preference to other routes such as serotonin and melatonin production. In neurological disease, degradation of the blood-brain barrier, even if transient, allows the entry of blood monocytes into the brain parenchyma. Similar to microglia and macrophages, these cells are highly responsive to IFN-γ, which upregulates the expression of enzymes, including IDO-1, producing neurotoxic KP metabolites such as quinolinic acid. These metabolites circulate systemically or are released locally in the brain and can contribute to the excitotoxic death of oligodendrocytes and neurons in neurological disease principally by virtue of their agonist activity at N-methyl-d-aspartic acid receptors. The latest evidence is presented and discussed. The enzymes that control the checkpoints in the KP represent an attractive therapeutic target, and consequently several KP inhibitors are currently in clinical trials for other neurological diseases, and hence may make suitable candidates for MS patients. Underpinning these drug discovery endeavors, in recent years, several advances have been made in how KP metabolites are assayed in various biological fluids, and tremendous advancements have been made in how specimens are imaged to determine disease progression and involvement of various cell types and molecules in MS.22 page(s

Evaluation of a Standardized Extract from Morus alba

To evaluate the antihyperglycemic effect of a standardized extract of the leaves of Morus alba (SEMA), the present study was designed to investigate the α-glucosidase inhibitory effect and acute single oral toxicity as well as evaluate blood glucose reduction in animals and in patients with impaired glucose tolerance in a randomized double-blind clinical trial. SEMA was found to inhibit α-glucosidase at a fourfold higher level than the positive control (acarbose), in a concentration-dependent manner. Moreover, blood glucose concentration was suppressed by SEMA in vivo. Clinical signs and weight changes were observed when conducting an evaluation of the acute toxicity of SEMA through a single-time administration, with clinical observation conducted more than once each day. After administration of the SEMA, observation was for 14 days; all of the animals did not die and did not show any abnormal symptoms. In addition, the inhibitory effects of rice coated with SEMA were evaluated in a group of impaired glucose tolerance patients on postprandial glucose and a group of normal persons, and results showed that SEMA had a clear inhibitory effect on postprandial hyperglycemia in both groups. Overall, SEMA showed excellent potential in the present study as a material for improving postprandial hyperglycemia

Deep learning system to predict the 5-year risk of high myopia using fundus imaging in children

Our study aims to identify children at risk of developing high myopia for timely assessment and intervention, preventing myopia progression and complications in adulthood through the development of a deep learning system (DLS). Using a school-based cohort in Singapore comprising 998 children (aged 6-12 years old), we train and perform primary validation of the DLS using 7456 baseline fundus images of 1878 eyes; with external validation using an independent test dataset of 821 baseline fundus images of 189 eyes together with clinical data (age, gender, race, parental myopia, and baseline spherical equivalent (SE)). We derive three distinct algorithms - image, clinical, and mix (image + clinical) models to predict high myopia development (SE ≤ -6.00 diopter) during teenage years (5 years later, age 11-17). Model performance is evaluated using the area under the receiver operating curve (AUC). Our image models (Primary dataset AUC 0.93-0.95; Test dataset 0.91-0.93), clinical models (Primary dataset AUC 0.90-0.97; Test dataset 0.93-0.94) and mixed (image + clinical) models (Primary dataset AUC 0.97; Test dataset 0.97-0.98) achieve clinically acceptable performance. The addition of 1 year SE progression variable has minimal impact on the DLS performance (clinical model AUC 0.98 versus 0.97 in the primary dataset, 0.97 versus 0.94 in the test dataset; mixed model AUC 0.99 versus 0.97 in the primary dataset, 0.95 versus 0.98 in test dataset). Thus, our DLS allows prediction of the development of high myopia by teenage years amongst school-going children. This has potential utility as a clinical decision support tool to identify "at-risk" children for early intervention.info:eu-repo/semantics/publishedVersio