High-level Screening Research Articles

OW-YOLO: An Improved YOLOv8s Lightweight Detection Method for Obstructed Walnuts

Walnut detection in mountainous and hilly regions often faces significant challenges due to obstructions, which adversely affect model performance. To address this issue, we collected a dataset comprising 2379 walnut images from these regions, with detailed annotations for both obstructed and non-obstructed walnuts. Based on this dataset, we propose OW-YOLO, a lightweight object detection model specifically designed for detecting small, obstructed walnuts. The model’s backbone was restructured with the integration of the DWR-DRB (Dilated Weighted Residual-Dilated Residual Block) module. To enhance efficiency and multi-scale feature fusion, we incorporated the HSFPN (High-Level Screening Feature Pyramid Network) and redesigned the detection head by replacing the original head with the more efficient LADH detection head while removing the head processing 32 × 32 feature maps. These improvements effectively reduced model complexity and significantly enhanced detection accuracy for obstructed walnuts. Experiments were conducted using the PyTorch framework on an NVIDIA GeForce RTX 4060 Ti GPU. The results demonstrate that OW-YOLO outperforms other models, achieving an mAP@0.5 (mean average precision) of 83.6%, mAP@[0.5:0.95] of 53.7%, and an F1 score of 77.9%. Additionally, the model’s parameter count decreased by 49.2%, weight file size was reduced by 48.1%, and computational load dropped by 37.3%, effectively mitigating the impact of obstruction on detection accuracy. These findings provide robust support for the future development of walnut agriculture and lay a solid foundation for the broader adoption of intelligent agriculture.

DGHR-YOLO: fabric defect detection based on High-level Screening -feature Pyramid Networks and deformable convolution

ABSTRACT Fabric defect detection is crucial for quality control in fabric manufacturing but remains a challenge due to the multi-scale characteristics of defects and their integration with the fabric background. To address this, we propose an efficient fabric defect detection model, DGHR-YOLO. First, the deformable convolution block (DCB) is introduced into the backbone network, leveraging its dynamic receptive field to effectively capture defect morphology and enhance feature extraction. Secondly, we propose the GS-SPPF module, designed to mitigate semantic information loss, optimize feature fusion, and improve both accuracy and inference speed. Thirdly, a lightweight High-level Screening Feature Pyramid Network (HS-FPN) is introduced, enabling effective multi-scale feature fusion while maintaining low complexity. Finally, a one-shot aggregation module based on channel shuffle and re-parameterized convolution is introduced, enhancing feature interaction across scales. Experimental results on the Tianchi textile dataset demonstrate that DGHR-YOLO achieves mAP0.5 and mAP0.5:0.95 scores of 85.8% and 72.7%, with respective improvements of 2.7% and 3.8% over YOLOv8m, while maintaining low parameter count and computational complexity.

APHS-YOLO: A Lightweight Model for Real-Time Detection and Classification of Stropharia Rugoso-Annulata.

The classification of Stropharia rugoso-annulata is currently reliant on manual sorting, which may be subject to bias. To improve the sorting efficiency, automated sorting equipment could be used instead. However, sorting naked mushrooms in real time remains a challenging task due to the difficulty of accurately identifying, locating and sorting large quantities of them simultaneously. Models must be deployable on resource-limited devices, making it challenging to achieve both a high accuracy and speed. This paper proposes the APHS-YOLO (YOLOv8n integrated with AKConv, CSPPC and HSFPN modules) model, which is lightweight and efficient, for identifying Stropharia rugoso-annulata of different grades and seasons. This study includes a complete dataset of runners of different grades in spring and autumn. To enhance feature extraction and maintain the recognition accuracy, the new multi-module APHS-YOLO uses HSFPNs (High-Level Screening Feature Pyramid Networks) as a thin-neck structure. It combines an improved lightweight PConv (Partial Convolution)-based convolutional module, CSPPC (Integration of Cross-Stage Partial Networks and Partial Convolution), with the Arbitrary Kernel Convolution (AKConv) module. Additionally, to compensate for the accuracy loss due to lightweighting, APHS-YOLO employs a knowledge refinement technique during training. Compared to the original model, the optimized APHS-YOLO model uses 57.8% less memory and 62.5% fewer computational resources. It has an FPS (frames per second) of over 100 and even achieves 0.1% better accuracy metrics than the original model. These research results provide a valuable reference for the development of automatic sorting equipment for forest farmers.

Multiple serous cavity effusion screening based on smear images using vision transformer

Serous cavity effusion is a prevalent pathological condition encountered in clinical settings. Fluid samples obtained from these effusions are vital for diagnostic and therapeutic purposes. Traditionally, cytological examination of smears is a common method for diagnosing serous cavity effusion, renowned for its convenience. However, this technique presents limitations that can compromise its efficiency and diagnostic accuracy. This study aims to overcome these challenges and introduce an improved method for the precise detection of malignant cells in serous cavity effusions. We have developed a transformer-based classification framework, specifically employing the vision transformer (ViT) model, to fulfill this objective. Our research involved collecting smear images and corresponding cytological reports from 161 patients who underwent serous cavity drainage. We meticulously annotated 4836 patches from these images, identifying regions with and without malignant cells, thus creating a unique dataset for smear image classification. The findings of our study reveal that deep learning models, particularly the ViT model, exhibit remarkable accuracy in classifying patches as malignant or non-malignant. The ViT model achieved an impressive area under the receiver operating characteristic curve (AUROC) of 0.99, surpassing the performance of the convolutional neural network (CNN) model, which recorded an AUROC of 0.86. Additionally, we validated our models using an external cohort of 127 patients. The ViT model sustained its high-level screening performance, achieving an AUROC of 0.98 at the patient level, compared to the CNN model’s AUROC of 0.84. The visualization of our ViT models confirmed their capability to precisely identify regions containing malignant cells in multiple serous cavity effusion smear images. In summary, our study demonstrates the potential of deep learning models, particularly the ViT model, in automating the screening process for serous cavity effusions. These models offer significant assistance to cytologists in enhancing diagnostic accuracy and efficiency. The ViT model stands out for its advanced self-attention mechanism, making it exceptionally suitable for tasks that necessitate detailed analysis of small, sparsely distributed targets like cellular clusters in serous cavity effusions.

Power system flexibility assessment for future flexibility needs: high-level screening method of the Macedonian power system

Power system flexibility assessment for future flexibility needs: high-level screening method of the Macedonian power system

Lifecycle energy solution of the electric propulsion ship with Live-Life cycle assessment for clean maritime economy

This study was planned to offer the roadmap for lifecycle clean shipping by addressing the fundamental question of ‘what are the promising energy solutions for the shipping sector?’. This goal was attempted to be achieved by a lifecycle comparative analysis of the viability of three zero-carbon fuels, ammonia, hydrogen, and inland electricity, based on the operational practicality as well as Well-to-Wake environmental impacts. Credible business scenarios were designed with a high-level screening of 27 short-route ferries currently engaged in 26 West-Scotland coastal routes. Then a series of comparative analyses between the diesel and the proposed alternative fuel sources was conducted. While carbon-free fuels are in the early stages of development in the UK, there are various views on how these fuels can be produced, distributed, and used onboard for the clean shipping economy. To determine the optimal energy solutions, all credible scenarios for the upstream pathways for these fuels were developed, based on the current and future prospected UK energy infrastructure and grids. Those scenarios were examined for West-Scotland shipping and extended to the UK targets. Their technical aspects for maritime application were also investigated in consideration of safety, regulation, infrastructural availability, supply chain constraints, barriers, and the downstream emission pathways to their uptake onboard. Ship conceptual designs were briefly conducted to evaluate the systems, technologies, and equipment required for onboard installation to utilise zero-carbon fuels. As a result of the study, when hydrogen was used as a fuel in fuel cells and electricity was supplied as a backup from batteries that store inland power and the solar PV system, GHG emissions were 25.7% of the conventional fossil fuel-using scenario. In addition, it was confirmed that GWP was 22.2% compared to MGO when ammonia was used as fuel without reforming into hydrogen and backup power was supplied from batteries and the solar PV system. It is believed that the analysis results can distinctively demonstrate the adequacy or the inadequacy of current maritime policies for decarbonising shipping. This research finally provides potential solutions for future UK maritime environmental protection and for enhancing the competitiveness of ship owners. In addition, this research concept can serve as a sample that suggests the direction of ideal research for future energy solutions in the marine sector.

Lesion identification and malignancy prediction from clinical dermatological images

We consider machine-learning-based lesion identification and malignancy prediction from clinical dermatological images, which can be indistinctly acquired via smartphone or dermoscopy capture. Additionally, we do not assume that images contain single lesions, thus the framework supports both focal or wide-field images. Specifically, we propose a two-stage approach in which we first identify all lesions present in the image regardless of sub-type or likelihood of malignancy, then it estimates their likelihood of malignancy, and through aggregation, it also generates an image-level likelihood of malignancy that can be used for high-level screening processes. Further, we consider augmenting the proposed approach with clinical covariates (from electronic health records) and publicly available data (the ISIC dataset). Comprehensive experiments validated on an independent test dataset demonstrate that (1) the proposed approach outperforms alternative model architectures; (2) the model based on images outperforms a pure clinical model by a large margin, and the combination of images and clinical data does not significantly improves over the image-only model; and (3) the proposed framework offers comparable performance in terms of malignancy classification relative to three board certified dermatologists with different levels of experience.

Using the WWF Water Risk Filter to Screen Existing and Projected Hydropower Projects for Climate and Biodiversity Risks

Climate change is predicted to drive various changes in hydrology that can translate into risks for river ecosystems and for those who manage rivers, such as for hydropower. Here we use the WWF Water Risk Filter (WRF) and geospatial analysis to screen hydropower projects, both existing (2488 dams) and projected (3700 dams), for a variety of risks at a global scale, focusing on biodiversity risks, hydrological risks (water scarcity and flooding), and how those hydrological risks may shift with climate change, based on three scenarios. Approximately 26% of existing hydropower dams and 23% of projected dams are within river basins that currently have medium to very high risk of water scarcity; 32% and 20% of the existing and projected dams, respectively, are projected to have increased risk by 2050 due to climate change. For flood risk, 75% of existing dams and 83% of projected dams are within river basins with medium to very high risk, and the proportion of hydropower dams in basins with the highest levels of flood risk is projected to increase by nearly twenty times (e.g., from 2% to 36% of dams). In addition, a large proportion of existing (76%) and projected hydropower dams (93%) are located in river basins with high or very high freshwater biodiversity importance. This is a high-level screening, intended to elucidate broad patterns of risk to increase awareness, highlight trends, and guide more detailed studies.

Brief report: Measures of effectiveness for single-question sleep problem screeners in children with autism spectrum disorder

BackgroundAlthough screening for sleep problems in children with ASD is recommended, primary care providers generally ask parents a single, high-level screening question about their child’s sleep. Can this capture whether a child has severe sleep problems? MethodParents of children with ASD ages 3–17 years recruited from a validated and verified US-based autism research registry completed an online survey on co-occurring conditions, including “degree of sleep problems”. The Composite Sleep Disturbance Index (CSDI) and its question on “parent satisfaction with current sleep pattern” were also incorporated. Results610 parent/child dyads were analyzed. 377 (62%) children had severe sleep problems per CSDI; 215 (57%) were parent-rated with Moderate/Severe sleep problems. 219 (93%) of 233 children without a severe sleep problem on CSDI were parent-rated as having None/Mild sleep problems. 94% with Moderate/Severe sleep problems per parents had severe sleep problems per CSDI. However, 15% of those whose parents rated as None had a severe sleep problem. 200 (33%) children had a Mild sleep problem rating; of these, 134 (67%) had CSDI severe sleep problem and 66 (33%) did not. ConclusionsA single question about sleep was good at classifying severe vs. non-severe sleep problems in children with ASD, especially with extreme responses. However, a single question was poor at classifying intermediate/non-extreme responses, and a significant number of children with severe sleep problems were missed. Screening with a short, validated questionnaire or asking more probing questions would assist in diagnosing most children with sleep problems. Parents may benefit from sleep education.

Tryptophan-like fluorescence as a high-level screening tool for detecting microbial contamination in drinking water

Regular monitoring of drinking water quality is vital to identify contamination of potable water supplies. Testing for microbial contamination is important to prevent transmission of waterborne disease, but establishing and maintaining a water quality monitoring programme requires sustained labour, consumables and resources. In low resource settings such as developing countries, this can prove difficult, but measuring microbial contamination is listed as a requirement of reaching the UN's Sustainable Development Goal 6 for water and sanitation. A nine-month water quality monitoring programme was conducted in rural Malawi to assess the suitability of tryptophan-like fluorescence (TLF), an emerging method for rapidly detecting microbial contamination, as a drinking water quality monitoring tool. TLF data was compared with thermotolerant coliforms (TTCs, E. coli) and inorganic hydrochemical parameters. A large (n = 235) temporal dataset was collected from five groundwater drinking water sources, with samples collected once or twice weekly depending on the season. The results show that TLF can indicate a broader contamination risk but is not as sensitive to short term variability when compared to other faecal indicators. This is likely due to a broad association of TLF with elevated DOC concentrations from a range of different sources. Elevated TLF may indicate preferential conditions for the persistence of TTCs and/or E. coli, but not necessarily a public health risk from microbial contamination. TLF is therefore a more precautionary risk indicator than microbial culturing techniques and could prove useful as a high-level screening tool for initial risk assessment. For widespread use of TLF to be successful, standardisation of TLF values associated with different levels of risk is required, however, this study highlights the difficulties of equating TLF thresholds to TTCs or E. coli data because of the influence of DOC/HLF on the TLF signal.

Automatic detection of various abnormalities in capsule endoscopy videos by a deep learning-based system: a multicenter study

A deep convolutional neural network (CNN) system could be a high-level screening tool for capsule endoscopy (CE) reading but has not been established for targeting various abnormalities. We aimed to develop a CNN-based system and compare it with the existing QuickView mode in terms of their ability to detect various abnormalities. We trained a CNN system using 66,028 CE images (44,684 images of abnormalities and 21,344 normal images). The detection rate of the CNN for various abnormalities was assessed per patient, using an independent test set of 379 consecutive small-bowel CE videos from 3 institutions. Mucosal breaks, angioectasia, protruding lesions, and blood content were present in 94, 29, 81, and 23 patients, respectively. The detection capability of the CNN was compared with that of QuickView mode. The CNN picked up 1,135,104 images (22.5%) from the 5,050,226 test images, and thus, the sampling rate of QuickView mode was set to 23% in this study. In total, the detection rate of the CNN for abnormalities per patient was significantly higher than that of QuickView mode (99% vs 89%, P< .001). The detection rates of the CNN for mucosal breaks, angioectasia, protruding lesions, and blood content were 100% (94 of 94), 97% (28 of 29), 99% (80 of 81), and 100% (23 of 23), respectively, and those of QuickView mode were 91%, 97%, 80%, and 96%, respectively. We developed and tested a CNN-based detection system for various abnormalities using multicenter CE videos. This system could serve as an alternative high-level screening tool to QuickView mode.

A GFP-fusion coupling FACS platform for advancing the metabolic engineering of filamentous fungi

BackgroundThe filamentous fungus Trichoderma reesei, the most widely used cellulase producer, also has promising applications in lignocellulose-based biorefinery: consolidated bioprocessing for the production of high value-added products. However, such applications are thwarted by the time-consuming metabolic engineering processes (design–build–test–learn cycle) for T. reesei, resulted from (i) the spore separation-mediated purification as the multinucleate hyphae, (ii) transformant screening for high expression levels since unavailable of episomal expression system, and (iii) cases of inexpressible heterologous proteins.ResultsIn this study, a GFP-fusion coupled fluorescence-activated cell sorting (FACS) platform was established to speed up the build and test process of the DBTL cycle, by enabling rapid selection for expressible heterologous genes and bypassing both laborious spore separation and transformant screening. Here, the feasibility of flow cytometry in analyzing and sorting T. reesei cells harboring GFP-fused expressible protein was proven, as well as the application of the platform for constitutive promoter strength evaluation. As a proof-of-concept, the platform was employed to construct the first T. reesei strain producing fatty alcohol, resulting in up to 2 mg hexadecanol being produced per gram biomass. Pathway construction was enabled through rapid selection of functional fatty acyl-CoA reductase encoding gene Tafar1 from three candidate genes and strains with high expression level from spore pools. As a result of using this method, the total costed time for the build and test cycle using T. reesei, subsequently, reduced by approx. 75% from 2 months to 2 weeks.ConclusionThis study established the GFP-fusion coupling FACS platform and the first filamentous fungal fatty alcohol-producing cell factory, and demonstrated versatile applications of the platform in the metabolic engineering of filamentous fungi, which can be harnessed to potentially advance the application of filamentous fungi in lignocellulose-based biorefinery.

“There is Nothing Inherently Mysterious about Assistive Technology”: A Qualitative Study about Blind User Experiences in US Academic Libraries

Eighteen academic library users who are blind were interviewed about their experiences with academic libraries and the libraries’ websites using an open-ended questionnaire and recorded telephone interviews. The study approaches these topics from a user-centered perspective, with the idea that blind users themselves can provide particularly reliable insights into the issues and potential solutions that are most critical to them. Most participants used reference librarians’ assistance, and most had positive experiences. High-level screen reader users requested help with specific needs. A larger number of participants reported contacting a librarian because of feeling overwhelmed by the library website. In some cases, blind users and librarians worked verbally without the screen reader. Users were appreciative of librarians’ help but outcomes were not entirely positive. Other times, librarians worked with users to navigate with a screen reader, which sometimes led to greater independence. Some users expressed satisfaction with working with librarians verbally, particularly if websites did not seem screen reader user friendly, but many users preferred independence. Participants agreed it would be helpful if librarians knew how to use screen readers, or at least if librarians were familiar enough with screen readers to provide relevant verbal cues. Many users liked and used chat reference and many preferred Purdue Online Writing Lab (OWL) to learn citation style, though learning citation style was challenging. Questions such as reference librarians’ role when e-resources are not equally accessible deserve wider discussion in the library literature and in practice. Given the challenges described by the research participants and legal requirements for equally effective electronic and information technologies, libraries and librarians should approach reference services for blind users more proactively. Recommendations are provided.

High-level Screening for Williston Basin Residual Oil Zones Using Location-independent Data

Residual oil zones (ROZs) are naturally waterflooded, traditionally noneconomic reservoirs in which potentially large amounts of CO2 could be stored in association with enhanced oil recovery. To date, ROZ exploration throughout the United States has primarily consisted of manual matching of hydrodynamics with structural data in preconceived, specific locations, assessing well- or field-scale analyses, followed by field tests. To locate potential ROZs more quickly en masse, Schlumberger Petrel software was used to build a 3-D geocellular model of the Williston Basin, and Schlumberger PetroMod was used to populate petrophysical properties and simulate hydrocarbon generation, migration, and accumulation.

Blueprints of Effective Biodiversity and Conservation Knowledge Products That Support Marine Policy

Biodiversity and conservation data are generally costly to collect, particularly in the marine realm. Hence, data collected for a given—often scientific—purpose are occasionally contributed towards secondary needs, such as policy implementation or other types of decision-making. However, while the quality and accessibility of marine biodiversity and conservation data have improved over the past decade, the ways in which these data can be used to develop and implement relevant management and conservation measures and actions are not always explicit. For this reason, there are a number of scientifically-sound datasets that are not used systematically to inform policy and decisions. Transforming these marine biodiversity and conservation datasets into knowledge products that convey the information required by policy- and decision-makers is an important step in strengthening knowledge exchange across the science-policy interface. Here, we identify seven characteristics of a selection of online biodiversity and conservation knowledge products that contribute to their ability to support policy- and decision-making in the marine realm (as measured by e.g. mentions in policy resolutions/decisions, or use for reporting under selected policy instruments; use in high-level screening for areas of biodiversity importance). These characteristics include: a clear policy mandate; established networks of collaborators; iterative co-design of a user-friendly interface; standardised, comprehensive and documented methods with quality assurance; consistent capacity and succession planning; accessible data and value-added products that are fit-for-purpose; and metrics of use collated and reported. The outcomes of this review are intended to: (a) support data creators/owners/providers in designing and curating biodiversity and conservation knowledge products that have greater influence, and hence impact, in policy- and decision-making, and (b) provide recommendations for how decision- and policy-makers can support the development, implementation, and sustainability of robust biodiversity and conservation knowledge products through the framing of marine policy and decision-making frameworks.

Adaptive High-Level Isolation Between Two Closely Placed Copolarized Antennas Using Tunable Screen

A novel design of high-level isolation screen composed of continuously tunable resonator columns is presented. The designed structure with the dimension of 200 $\times$ 125 mm $^2$ is fabricated and experimentally tested in an anechoic chamber for decoupling two closely placed copolarized monopoles. With the help of a self-contained optimization algorithm, the decoupling system achieves a high isolation of 110 dB. Experimental results also exhibit a strong robustness of the design in environmental adaptivity.

IDENTIFYING GENETIC ASSOCIATIONS WITH VARIABILITY IN METABOLIC HEALTH AND BLOOD COUNT LABORATORY VALUES: DIVING INTO THE QUANTITATIVE TRAITS BY LEVERAGING LONGITUDINAL DATA FROM AN EHR.

A wide range of patient health data is recorded in Electronic Health Records (EHR). This data includes diagnosis, surgical procedures, clinical laboratory measurements, and medication information. Together this information reflects the patient's medical history. Many studies have efficiently used this data from the EHR to find associations that are clinically relevant, either by utilizing International Classification of Diseases, version 9 (ICD-9) codes or laboratory measurements, or by designing phenotype algorithms to extract case and control status with accuracy from the EHR. Here we developed a strategy to utilize longitudinal quantitative trait data from the EHR at Geisinger Health System focusing on outpatient metabolic and complete blood panel data as a starting point. Comprehensive Metabolic Panel (CMP) as well as Complete Blood Counts (CBC) are parts of routine care and provide a comprehensive picture from high level screening of patients' overall health and disease. We randomly split our data into two datasets to allow for discovery and replication. We first conducted a genome-wide association study (GWAS) with median values of 25 different clinical laboratory measurements to identify variants from Human Omni Express Exome beadchip data that are associated with these measurements. We identified 687 variants that associated and replicated with the tested clinical measurements at p<5×10-08. Since longitudinal data from the EHR provides a record of a patient's medical history, we utilized this information to further investigate the ICD-9 codes that might be associated with differences in variability of the measurements in the longitudinal dataset. We identified low and high variance patients by looking at changes within their individual longitudinal EHR laboratory results for each of the 25 clinical lab values (thus creating 50 groups - a high variance and a low variance for each lab variable). We then performed a PheWAS analysis with ICD-9 diagnosis codes, separately in the high variance group and the low variance group for each lab variable. We found 717 PheWAS associations that replicated at a p-value less than 0.001. Next, we evaluated the results of this study by comparing the association results between the high and low variance groups. For example, we found 39 SNPs (in multiple genes) associated with ICD-9 250.01 (Type-I diabetes) in patients with high variance of plasma glucose levels, but not in patients with low variance in plasma glucose levels. Another example is the association of 4 SNPs in UMOD with chronic kidney disease in patients with high variance for aspartate aminotransferase (discovery p-value: 8.71×10-09 and replication p-value: 2.03×10-06). In general, we see a pattern of many more statistically significant associations from patients with high variance in the quantitative lab variables, in comparison with the low variance group across all of the 25 laboratory measurements. This study is one of the first of its kind to utilize quantitative trait variance from longitudinal laboratory data to find associations among genetic variants and clinical phenotypes obtained from an EHR, integrating laboratory values and diagnosis codes to understand the genetic complexities of common diseases.

A spatially distributed risk screening tool to assess climate and land use change impacts on water-related ecosystem services

To support the implementation of the European Water Framework Directive (WFD), and as part of a tiered approach to prioritise detailed modelling, a high-level screening methodology has been developed to assess the vulnerability of water-related ecosystem services (ES) to future change. The approach incorporates a range of spatially distributed scenarios of land use and climate, which are used as inputs to a qualitative risk assessment model underpinned by expert opinion. The method makes use of widely available datasets and provides a structured way of capturing and “codifying” expert knowledge, as well as for assessing the degree of consensus between different expert groups. The range of model output reflects uncertainty in both the expert-derived assumptions and the climate & land use simulations considered. The approach has been developed in collaboration with the Scottish Environment Protection Agency (SEPA) and applied in Scotland to support the second cycle of River Basin Management Planning.

Characteristics associated with the consumption of malted drinks among Malaysian primary school children: findings from the MyBreakfast study.

BackgroundThe consumption of beverages contributes to diet quality and overall nutrition. Studies on malted drinks, one of the widely consumed beverage choices among children in Asia, however, have received limited attention. This study aimed to examine the prevalence of malted drink consumption and explored associations of sociodemographic characteristics, nutrient intakes, weight status and physical activity levels with malted drink consumption among primary school children in Malaysia.MethodsData for this analysis were from the MyBreakfast Study, a national cross-sectional study conducted from April to October 2013 throughout all regions in Malaysia. A total of 2065 primary school children aged 6 to 12 years were included in the present analysis. Data on two days 24-h dietary recall or record, anthropometry, physical activity and screen time were recorded. Associations between malted drink consumption and related factors were examined using binary logistic regression, adjusting for region, area, gender, ethnicity and household income.ResultsAmong children aged 6 to12 years, 73.5 % reported consuming malted drinks for at least once per week. Consumption of malted drinks was significantly associated with region (χ2 = 45.64, p < 0.001), gender (χ2 = 4.41, p = 0.036) and ethnicity (χ2 = 13.74, p = 0.008). Malted drink consumers had similar total energy intake but higher micronutrient intakes compared to non-consumers. High physical activity level (OR = 1.77, 95 % CI = 1.06, 2.99) and lower screen time during weekends (OR = 0.93, 95 % CI = 0.86, 0.99) were independently associated with malted drink consumption among 6 to 9 year-old children, but not among 10 to 12 year-old children. No association was observed between malted drink consumption and weight status.ConclusionsMalted drink consumption is prevalent among Malaysian primary school children, particularly higher among boys, indigenous children and those who lived in the East Coast region of Malaysia. Consuming malted drinks is associated with higher micronutrient intakes and higher levels of physical activity, but not with body weight status.

DOES THE ICD IMPLANTATION RATE VARY BASED ON LOCAL HEALTH AUTHORITY IN BRITISH COLUMBIA?

DOES THE ICD IMPLANTATION RATE VARY BASED ON LOCAL HEALTH AUTHORITY IN BRITISH COLUMBIA?