Galvanic vestibular stimulation produces cross-modal improvements in visual thresholds

Background: Stochastic resonance (SR) refers to a faint signal being enhanced with the addition of white noise. Previous studies have found that vestibular perceptual thresholds are lowered with noisy galvanic vestibular stimulation (i.e., "in-channel" SR). Auditory white noise has been shown to improve tactile and visual thresholds, suggesting "cross-modal" SR. Objective: We aimed to study the cross-modal impact of noisy galvanic vestibular stimulation (nGVS) (n=9 subjects) on visual and auditory thresholds. Methods: We measured auditory and visual perceptual thresholds of human subjects across a swath of different nGVS levels in order to determine if a subject-specific best nGVS level elicited a reduction in thresholds as compared the no noise condition (sham). Results: We found an 18% improvement in visual thresholds (p = 0.026). Among the 7 of 9 subjects with reduced thresholds, the average improvement was 26%. Subjects with higher (worse) visual thresholds with no stimulation (sham) improved more than those with lower thresholds (p = 0.005). Auditory thresholds were unchanged by vestibular stimulation. Conclusions: These results are the first demonstration of cross-modal improvement with nGVS, indicating galvanic vestibular white noise can produce cross-modal improvements in some sensory channels, but not all.Comment: 15 pages, 7 figure

Reducing Cancer Disparities through Community Engagement in Policy Development: The Role of Cancer Councils

Cancer is the second leading cause of death in the U.S and a source of large racial and ethnic disparities in population health. Policy development is a powerful but sometimes overlooked public health tool for reducing cancer burden and disparities. Along with other partners in the public health system, community-based organizations such as local cancer councils can play valuable roles in developing policies that are responsive to community needs and in mobilizing resources to support policy adoption and implementation. This paper examines the current and potential roles played by local cancer councils to reduce cancer burden and disparities. Responsive public health systems require vehicles for communities to engage in policy development. Cancer councils provide promising models of engagement. Untapped opportunities exist for enhancing policy development through cancer councils, such as expanding targets of engagement to include private-sector stakeholders and expanding methods of engagement utilizing the Affordable Care Act’s Prevention and Public Health Fund

The role of 44-methylgambierone in ciguatera fish poisoning: Acute toxicity, production by marine microalgae and its potential as a biomarker for Gambierdiscus spp.

Ciguatera fish poisoning (CFP) is prevalent around the tropical and sub-tropical latitudes of the world and impacts many Pacific island communities intrinsically linked to the reef system for sustenance and trade. While the genus Gambierdiscus has been linked with CFP, it is commonly found on tropical reef systems in microalgal assemblages with other genera of toxin-producing, epiphytic and/or benthic dinoflagellates - Amphidinium, Coolia, Fukuyoa, Ostreopsis and Prorocentrum. Identifying a biomarker compound that can be used for the early detection of Gambierdiscus blooms, specifically in a mixed microalgal community, is paramount in enabling the development of management and mitigation strategies. Following on from the recent structural elucidation of 44-methylgambierone, its potential to contribute to CFP intoxication events and applicability as a biomarker compound for Gambierdiscus spp. was investigated. The acute toxicity of this secondary metabolite was determined by intraperitoneal injection using mice, which showed it to be of low toxicity, with an LD50 between 20 and 38 mg kg-1. The production of 44-methylgambierone by 252 marine microalgal isolates consisting of 90 species from 32 genera across seven classes, was assessed by liquid chromatography-tandem mass spectrometry. It was discovered that the production of this secondary metabolite was ubiquitous to the eight Gambierdiscus species tested, however not all isolates of G. carpenteri, and some species/isolates of Coolia and Fukuyoa

H2G-Net: A multi-resolution refinement approach for segmentation of breast cancer region in gigapixel histopathological images

Over the past decades, histopathological cancer diagnostics has become more complex, and the increasing number of biopsies is a challenge for most pathology laboratories. Thus, development of automatic methods for evaluation of histopathological cancer sections would be of value. In this study, we used 624 whole slide images (WSIs) of breast cancer from a Norwegian cohort. We propose a cascaded convolutional neural network design, called H2G-Net, for segmentation of breast cancer region from gigapixel histopathological images. The design involves a detection stage using a patch-wise method, and a refinement stage using a convolutional autoencoder. To validate the design, we conducted an ablation study to assess the impact of selected components in the pipeline on tumor segmentation. Guiding segmentation, using hierarchical sampling and deep heatmap refinement, proved to be beneficial when segmenting the histopathological images. We found a significant improvement when using a refinement network for post-processing the generated tumor segmentation heatmaps. The overall best design achieved a Dice similarity coefficient of 0.933±0.069 on an independent test set of 90 WSIs. The design outperformed single-resolution approaches, such as cluster-guided, patch-wise high-resolution classification using MobileNetV2 (0.872±0.092) and a low-resolution U-Net (0.874±0.128). In addition, the design performed consistently on WSIs across all histological grades and segmentation on a representative × 400 WSI took ~ 58 s, using only the central processing unit. The findings demonstrate the potential of utilizing a refinement network to improve patch-wise predictions. The solution is efficient and does not require overlapping patch inference or ensembling. Furthermore, we showed that deep neural networks can be trained using a random sampling scheme that balances on multiple different labels simultaneously, without the need of storing patches on disk. Future work should involve more efficient patch generation and sampling, as well as improved clustering.publishedVersio

H2G-Net: A multi-resolution refinement approach for segmentation of breast cancer region in gigapixel histopathological images

Over the past decades, histopathological cancer diagnostics has become more complex, and the increasing number of biopsies is a challenge for most pathology laboratories. Thus, development of automatic methods for evaluation of histopathological cancer sections would be of value. In this study, we used 624 whole slide images (WSIs) of breast cancer from a Norwegian cohort. We propose a cascaded convolutional neural network design, called H2G-Net, for segmentation of breast cancer region from gigapixel histopathological images. The design involves a detection stage using a patch-wise method, and a refinement stage using a convolutional autoencoder. To validate the design, we conducted an ablation study to assess the impact of selected components in the pipeline on tumor segmentation. Guiding segmentation, using hierarchical sampling and deep heatmap refinement, proved to be beneficial when segmenting the histopathological images. We found a significant improvement when using a refinement network for post-processing the generated tumor segmentation heatmaps. The overall best design achieved a Dice similarity coefficient of 0.933±0.069 on an independent test set of 90 WSIs. The design outperformed single-resolution approaches, such as cluster-guided, patch-wise high-resolution classification using MobileNetV2 (0.872±0.092) and a low-resolution U-Net (0.874±0.128). In addition, the design performed consistently on WSIs across all histological grades and segmentation on a representative × 400 WSI took ~ 58 s, using only the central processing unit. The findings demonstrate the potential of utilizing a refinement network to improve patch-wise predictions. The solution is efficient and does not require overlapping patch inference or ensembling. Furthermore, we showed that deep neural networks can be trained using a random sampling scheme that balances on multiple different labels simultaneously, without the need of storing patches on disk. Future work should involve more efficient patch generation and sampling, as well as improved clustering

Predictive associations between lifestyle behaviours and dairy consumption: The IDEFICS study

Background and aim: Physical activity (PA) and sedentary behaviours (SB) are related to obesity and cardiometabolic risk; however, the literature is controversial regarding the effect of dairy consumption on the development of cardiovascular disease (CVD) risk factors. The aim of this study was to assess longitudinally the relationship between specific lifestyle behaviours (PA and SB) and dairy consumption in a sample of European children and adolescents. Methods and results: Children from the IDEFICS study were included in the analyses. Two measurements, with 2 years'' interval, were conducted. A total of 1688 (50.8% boys) children provided information regarding diet, measured by a 24-h dietary recall, PA measured by accelerometers and parent-reported sedentary screen time (SST) at both time points. Different combinations of these behaviours, at each survey and over time, were derived applying specific recommendations. Multilevel ordinal logistic regression and analysis of covariance were used to assess their association with dairy consumption, adjusted for potential confounders. Differences by gender were found regarding dairy product consumption and also adherence to SB and PA recommendations at T0 and T1. Children meeting both lifestyle recommendations, at the two measurement points, had higher probability to consume more milk and yogurt and less cheese than the rest of combinations. Conclusions: These results suggest that European children with a healthy lifestyle, especially regarding PA and SB over time, consumed more milk and yogurt. This study suggests that the protective effect of specific dairy products found in literature could be partially due to the association of their consumption with specific healthy lifestyles

Dairy consumption at snack meal occasions and the overall quality of diet during childhood. Prospective and cross-sectional analyses from the IDEFICS/I.Family cohort

There is scarce information on the influence of dairy consumption between main meals on the overall diet quality through childhood, constituting the main aim of this research. From the Identification and prevention of Dietary-and lifestyle induced health EFfects In Children and infantS (IDEFICS) study, and based on the data availability in each period due to drop outs, 8807 children aged 2 to 9.9 years from eight European countries at baseline (T0: 2007–2008); 5085 children after two years (T1); and 1991 after four years (T3), were included in these analyses. Dietary intake and the Diet Quality Index (DQI) were assessed by two 24 hours dietary recalls (24-HDR) and food frequency questionnaire. Consumption of milk and yogurt (p = 0.04) and cheese (p < 0.001) at snack meal occasions was associated with higher DQI scores in T0; milk and yogurt (p < 0.001), and cheese (p < 0.001) in T1; and cheese (p = 0.05) in T3. Consumers of milk (p = 0.02), yogurt (p < 0.001), or cheese (p < 0.001) throughout T0 and T1 at all snack moments had significantly higher scores of DQI compared to non-consumers. This was also observed with the consumption of cheese between T1 and T3 (p = 0.03). Consumption of dairy products at snack moments through childhood is associated with a better overall diet quality, being a good strategy to improve it in this period