Hawkinslist7932

Before placing a new cosmetic ingredient on the market, manufacturers must establish its safety profile, in particular assessing the skin sensitization potential, which is a mandatory requirement for topical applications. Since the ban on animal testing in Europe, and its extension to many parts of the world, a battery of in vitro tests covering the key steps of the Adverse Outcome Pathway (AOP) for skin sensitization is recommended. To date, three in vitro methods are validated in the OECD guidelines (442C, 442D, 442E), and many others are under validation by OECD (2019) and ECVAM. However, there is still no official strategy. Some industrial manufacturers have proposed in vitro strategies with good predictivity, but their studies were mainly based on the testing of simple and "easy to test" substances. see more This work therefore focused on "difficult to test" ingredients with particular physicochemical properties (i.e. poorly water-soluble components) or with particular intrinsic properties placing them outside thconsideration of the ingredient dermal penetration, is chosen as a starting point. The approach is completed, depending on the first response, by the h-CLAT model, assessing Key Event 3, and then potentially KeratinoSensTM assessing Key Event 2, but with a more direct application mode. This new testing strategy increases the accuracy to 88% on the selected ingredients and minimizes the risk of a false negative conclusion, which is crucial from the perspective of the ingredients' users and cosmetic consumers. BACKGROUND Feature selection is a crucial step in the machine learning methods that are currently used to assist with decoding brain states from fMRI data. This step can be based on either feature discrimination or feature reliability, but there is no clear evidence indicating which method is more suitable for fMRI data. METHODS We used ANOVA and Kendall's concordance coefficient as proxies for the two kinds of feature selection criteria. The performances of both methods were compared using different subject and feature numbers. The study included 987 subjects from the Human Connectome Project (HCP). RESULTS Classification performance suggested that features based on discrimination were more capable of distinguishing between various brain states for any number of subjects or extracted features. In addition, reliability-based features were always more stable than other features, and these properties (discernment and stability) of features, to some degree, related to the number of subjects and features. Furthermore, when the number of extracted features increased, the feature distributions also gradually extended from occipital lobe to more association regions of the brain. CONCLUSION The results from this study provide empirical guides for feature selection for the prediction of individual brain states. BACKGROUND Resting state fMRI has emerged as a popular neuroimaging method for automated recognition and classification of brain disorders. Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common brain disorders affecting young children, yet its underlying mechanism is not completely understood and its diagnosis is mainly dependent on behaviour analysis. NEW METHOD In this paper, we propose an end-to-end deep learning architecture to diagnose ADHD. Our aim is to (1) automatically classify a subject as ADHD or healthy control, and (2) demonstrate the importance of functional connectivity to increase classification accuracy and provide interpretable results. The proposed method, called DeepFMRI, is comprised of three sequential networks, namely (1) a feature extractor, (2) a functional connectivity network, and (3) a classification network. The model takes fMRI pre-processed time-series signals as input and outputs a diagnosis, and is trained end-to-end using back-propagation. RESULTS Experimental results on the publicly available ADHD-200 dataset demonstrate that this innovative method outperforms previous state-of-the-art. Different imaging sites contributed the data to the ADHD-200 dataset. For the New York University imaging site, our proposed method was able to achieve classification accuracy of 73.1% (specificity 91.6%, sensitivity 65.5%). COMPARISON WITH EXISTING METHODS In this work, we propose a novel end-to-end deep learning method incorporating functional connectivity for the classification of ADHD. To the best of our knowledge, this has not been explored by existing studies. CONCLUSIONS The results suggest that the proposed end-to-end deep learning architecture achieves better performance as compared to the other state-of-the-art methods. The findings suggest that the frontal lobe contains the most discriminative power towards the classification of ADHD. A recently developed high-throughput background membrane imaging (BMI) technique, the HORIZON, was assessed for its ability to quantify subvisible particulate (SVP) generated during protein therapeutic development. The HORIZON platform method was optimized and compared to three well-characterized SVP counting techniques light obscuration, micro-flow imaging (MFI), and FlowCam®. A head-to-head comparison was performed for precision, linearity, SVP concentration, and morphological output of BMI compared to the other three techniques using two unique enzymes under investigation. We found that dilution requirements for BMI are protein-specific, and membrane coverage is the critical instrument parameter to monitor for dilution suitability. The precision of BMI ranked similarly to all other techniques. Analysis of the same sample dilution, run in triplicate, across all four techniques indicated the BMI technique provides SVP concentrations that are comparable with the flow imaging techniques. Morphological information from BMI was generally less practical when compared with flow microscopy. The major drawback of BMI was that the current software indiscriminately clips large particles, potentially resulting in a misrepresentation of SVP size distribution. Despite this phenomenon, the concentration and size data generated corresponds well with current flow imaging techniques while decreasing time, cost, and sample requirements for SVP quantification.