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Introduction: Reunion Island, a French overseas department, is located in the southern hemisphere, close to the Capricorn tropic. This island has a multicultural and multiethnic population of 860 000 inhabitants, a quarter of whom are at high risk of developing skin cancer due to intense ultraviolet radiation. Melanoma is responsible for the majority of skin cancer deaths. The early prevention of melanoma is based on sun protection in childhood, but French regulations are not adapted to the environmental conditions of this tropical region.The main objective of our study is to evaluate the effectiveness of three sun protection programs conducted in Reunionese primary schools through a pupil knowledge questionnaire., Methods and Analysis: PRESOLRE is an interventional, open-label, cluster-randomised controlled trial, in four parallel arms, that is being conducted throughout 2022-2023 on Reunion Island. The trial design assumes an escalation interventional effect using: first, a control arm without proposed intervention (arm 1); second, an arm whose classes are encouraged to use the validated educational programme 'Living With the Sun' (LWS) (arm 2); third, an arm whose classes are encouraged to use both 'LWS' combined with 'Mission Soleil Réunion's sun protection awareness programme (arm 3); fourth, an arm benefiting from an intervention similar to arm 3, combined with the distribution of hats, sunglasses and sun creams (arm 4). In all, 1780 pupils from 18 classes of 20 pupils, on average, will be included. Randomisation applies to the classes of pupils (so defined as clusters). The primary outcome is based on the proportion of correct answers to the knowledge questions after the awareness programme, compared between the four arms using a linear mixed model with random intercept., Ethics and Dissemination: The study obtained ethics approval in 2022 (ID: 2022-A00350-43). Results will be published in peer-reviewed journals., Trial Registration Number: NCT05367180., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Objective: Hepatitis E virus (HEV) has been scarcely investigated in the Indian Ocean. Following a nationwide serosurvey among blood donors, we conducted a population-based serosurvey to assess the magnitude of HEV exposure on Reunion Island., Methods: Four hundred and sixty-six archived frozen human sera from the 2009 CoPanFlu-RUN cohort were analysed using the Wantai HEV IgG enzyme immunoassay. HEV seropositivity was defined as an IgG titre ≥5 UI/ml. Raw and weighted seroprevalences were assessed to account for the discrepancy between the CoPanFlu-RUN subset and the general community. Prevalence proportion ratios (PPR) were measured using log-binomial models., Results: The raw and the weighted seroprevalences of HEV were 9.01% (95% CI 6.41-11.61) and 6.73% (95% CI 4.47-8.98), respectively. The presence of HEV IgG antibodies was associated with increasing age ( P  < 0.001). In a survey-adjusted model minimizing the sampling bias and adjusting for age, males were more likely to be seropositive than females (adjusted PPR 2.59, 95% CI 1.07-6.25). Seropositivity was spatially heterogeneous across the island ( P  < 0.01). Living in the neighbourhood of a pig farm within a low to intermediate slope area was associated with seropositivity in several models adjusting for age, gender, altitude of residency and interaction between slope and pig farms., Conclusion: Reunion Island is a low endemic area for HEV exposure. Despite limitations related to the retrospective study design, our findings confirm the roles of cumulative lifetime exposure and male gender in HEV exposure. The risk associated with neighbouring pig farms might also suggest environmental contamination in this setting., Competing Interests: The authors declare that they have no competing interests., (© 2019 The Authors. Published by Elsevier B.V.)

Objective: To identify the genetic cause of hypomyelinating leukodystrophy in 2 consanguineous families., Methods: Homozygosity mapping combined with whole-exome sequencing of consanguineous families was performed. Mutation consequences were determined by studying the structural change of the protein and by the RNA analysis of patients' fibroblasts., Results: We identified a biallelic mutation in a gene coding for a Pol III-specific subunit, POLR3K (c.121C>T/p.Arg41Trp), that cosegregates with the disease in 2 unrelated patients. Patients expressed neurologic and extraneurologic signs found in POLR3A - and POLR3B -related leukodystrophies with a peculiar severe digestive dysfunction. The mutation impaired the POLR3K-POLR3B interactions resulting in zebrafish in abnormal gut development. Functional studies in the 2 patients' fibroblasts revealed a severe decrease (60%-80%) in the expression of 5S and 7S ribosomal RNAs in comparison with control., Conclusions: These analyses underlined the key role of ribosomal RNA regulation in the development and maintenance of the white matter and the cerebellum as already reported for diseases related to genes involved in transfer RNA or translation initiation factors.

Objectives: Lifestyle combined interventions are a key strategy for preventing type-2 diabetes (T2DM) in overweight or obese subjects. In this framework, LIPOXmax individualized training, based on maximal fat oxidation [MFO], may be a promising intervention to promote fat mass (FM) reduction and prevent T2DM. Our primary objective was to compare three training programs of physical activity combined with a fruit- and vegetable-rich diet in reducing FM in overweight or obese women., Design and Setting: A five months non-blinded randomized controlled trial (RCT) with three parallel groups in La Réunion Island, a region where metabolic diseases are highly prevalent., Subjects: One hundred and thirty-six non-diabetic obese (body mass index [BMI]: 27-40 kg/m2) young women (aged 20-40) were randomized (G1: MFO intensity; G2: 60% of VO2-peak intensity; G3: free moderate-intensity at-home exercise following good physical practices)., Outcomes: Anthropometry (BMI, bodyweight, FM, fat-free mass), glucose (fasting plasma glucose, insulin, HOMA-IR) and lipid (cholesterol and triglycerides) profiles, and MFO values were measured at month-0, month-3 and month-5., Results: At month-5, among 109 women assessed on body composition, the three groups exhibited a significant FM reduction over time (G1: -4.1±0.54 kg; G2: -4.7±0.53 kg; G3: -3.5±0.78 kg, p<0.001, respectively) without inter-group differences (p = 0.135). All groups exhibited significant reductions in insulin levels or HOMA-IR index, and higher MFO values over time (p<0.001, respectively) but glucose control improvement was higher in G1 than in G3 while MFO values were higher in G1 than in G2 and G3. Changes in other outcome measures and inter-group differences were not significant., Conclusion: In our RCT the LIPOXmax intervention did not show a superiority in reducing FM in overweight or obese women but is associated with higher MFO and better glucose control improvements. Other studies are required before proposing LIPOXmax training for the prevention of T2DM in overweight or obese women., Trial Registration: ClincialTrials.gov NCT01464073.

For quantitative analysis of histopathological images, such as the lymphoma grading systems, quantification of features is usually carried out on single cells before categorizing them by classification algorithms. To this end, we propose an integrated framework consisting of a novel supervised cell-image segmentation algorithm and a new touching-cell splitting method. For the segmentation part, we segment the cell regions from the other areas by classifying the image pixels into either cell or extra-cellular category. Instead of using pixel color intensities, the color-texture extracted at the local neighborhood of each pixel is utilized as the input to our classification algorithm. The color-texture at each pixel is extracted by local Fourier transform (LFT) from a new color space, the most discriminant color space (MDC). The MDC color space is optimized to be a linear combination of the original RGB color space so that the extracted LFT texture features in the MDC color space can achieve most discrimination in terms of classification (segmentation) performance. To speed up the texture feature extraction process, we develop an efficient LFT extraction algorithm based on image shifting and image integral. For the splitting part, given a connected component of the segmentation map, we initially differentiate whether it is a touching-cell clump or a single nontouching cell. The differentiation is mainly based on the distance between the most likely radial-symmetry center and the geometrical center of the connected component. The boundaries of touching-cell clumps are smoothed out by Fourier shape descriptor before carrying out an iterative, concave-point and radial-symmetry based splitting algorithm. To test the validity, effectiveness and efficiency of the framework, it is applied to follicular lymphoma pathological images, which exhibit complex background and extracellular texture with nonuniform illumination condition. For comparison purposes, the results of the proposed segmentation algorithm are evaluated against the outputs of superpixel, graph-cut, mean-shift, and two state-of-the-art pathological image segmentation methods using ground-truth that was established by manual segmentation of cells in the original images. Our segmentation algorithm achieves better results than the other compared methods. The results of splitting are evaluated in terms of under-splitting, over-splitting, and encroachment errors. By summing up the three types of errors, we achieve a total error rate of 5.25% per image.

Objective: To distinguish centroblast cells from non-centroblast cells using a novel automated method in follicular lymphoma cases and measure its performance on cases obtained by a consensus of six pathologists., Study Design: Geometric and color texture features were used in the training and testing of the supervised quadratic discriminant analysis classifier. The technique was trained and tested on a data set composed of 218 centroblast images and 218 non-centroblast images. Computer performance was tested by measuring sensitivity and specificity among cells classified as centroblast and non-centroblast by consensus of six board-certified hematopathologists., Results: Automated classification distinguished centroblast from non-centroblast cells with a classification accuracy of 82.56% and sensitivity and specificity of 86.67% and 86.96%, respectively, when the approach was tested., Conclusion: The novelty of our approach is the identification of the centroblast cells with prior information and the introduction of the principal component analysis in the spectral domain to extract texture color features.

While tissue microarrays (TMAs) are a form of high-throughput screening, they presently still require manual construction and interpretation. Because of predicted increasing demand for TMAs, we investigated whether their construction could be automated. We created both epithelial recognition algorithms (ERAs) and field of view (FOV) algorithms that could analyze virtual slides and select the areas of highest cancer cell density in the tissue block for coring (algorithmic TMA) and compared these to the cores manually selected (manual TMA) from the same tissue blocks. We also constructed TMAs with TMAker, a robot guided by these algorithms (robotic TMA). We compared each of these TMAs to each other. Our imaging algorithms produced a grid of hundreds of FOVs, identified cancer cells in a stroma background and calculated the epithelial percentage (cancer cell density) in each FOV. Those with the highest percentages guided core selection and TMA construction. Algorithmic TMA and robotic TMA were overall approximately 50% greater in cancer cell density compared with Manual TMA. These observations held for breast, colon, and lung cancer TMAs. Our digital image algorithms were effective in automating TMA construction.

In this paper, we are proposing a novel automated method to recognize centroblast (CB) cells from non-centroblast (non-CB) cells for computer-assisted evaluation of follicular lymphoma tissue samples. The method is based on training and testing of a quadratic discriminant analysis (QDA) classifier. The novel aspects of this method are the identification of the CB object with prior information, and the introduction of the principal component analysis (PCA) in the spectral domain to extract color texture features. Both geometric and texture features are used to achieve the classification. Experimental results on real follicular lymphoma images demonstrate that the combined feature space improved the performance of the system significantly. The implemented method can identify centroblast cells (CB) from non-centroblast cells (non-CB) with a classification accuracy of 82.56%.

The deep superior colliculus (DSC) integrates multisensory input and triggers an orienting movement toward the source of stimulation (target). It would seem reasonable to suppose that input of an additional modality should always increase the amount of information received by a DSC neuron concerning a target. However, of all DSC neurons studied, only about one half in the cat and one-quarter in the monkey were multimodal. The rest received only unimodal input. Multimodal DSC neurons show the properties of multisensory enhancement, in which the neural response to an input of one modality is augmented by input of another modality, and of inverse effectiveness, in which weaker unimodal responses produce a higher percentage enhancement. Previously, we demonstrated that these properties are consistent with the hypothesis that DSC neurons use Bayes' rule to compute the posterior probability that a target is present given their stochastic sensory inputs. Here we use an information theoretic analysis of our Bayesian model to show that input of an additional modality may indeed increase target information, but only if input received from the initial modality does not completely reduce uncertainty concerning the presence of a target. Unimodal DSC neurons may be those whose unimodal input fully reduces target uncertainty and therefore have no need for input of another modality.

The deep layers of the superior colliculus (SC) integrate multisensory inputs and initiate an orienting response toward the source of stimulation (target). Multisensory enhancement, which occurs in the deep SC, is the augmentation of a neural response to sensory input of one modality by input of another modality. Multisensory enhancement appears to underlie the behavioral observation that an animal is more likely to orient toward weak stimuli if a stimulus of one modality is paired with a stimulus of another modality. Yet not all deep SC neurons are multisensory. Those that are exhibit the property of inverse effectiveness: combinations of weaker unimodal responses produce larger amounts of enhancement. We show that these neurophysiological findings support the hypothesis that deep SC neurons use their sensory inputs to compute the probability that a target is present. We model multimodal sensory inputs to the deep SC as random variables and cast the computation function in terms of Bayes' rule. Our analysis suggests that multisensory deep SC neurons are those that combine unimodal inputs that would be more uncertain by themselves. It also suggests that inverse effectiveness results because the increase in target probability due to the integration of multisensory inputs is larger when the unimodal responses are weaker.