λ³/1000 plasmonic nanocavities for biosensing fabricated by soft UV nanoimprint lithography

Arrays of plasmonic nanocavities with very low volumes, down to λ(3)/1000, have been fabricated by soft UV nanoimprint lithography. Nearly perfect omnidirectional absorption (3-70°) is demonstrated for the fundamental mode of the cavity (λ ? 1.15 μm). The second-order mode exhibits a sharper resonance with strong angular dependence and total optical absorption when the critical coupling condition is fulfilled (45-50°, λ ? 750 nm). It leads to high refractive index sensitivity (405 nm/RIU) and figure of merit (～21) and offers new perspectives for efficient biosensing experiments in ultralow volumes.     

Fungal diversity in cow, goat and ewe milk

Knowledge of fungal diversity in the environment is poor compared with bacterial biodiversity. In this study, we applied the denaturing high-performance liquid chromatography (D-HPLC) technique, combined with the amplification of the ITS1 region from fungal rDNA, for the rapid identification of major fungal species in 9 raw milk samples from cow, ewe and goat, collected at different periods of the year. A total of 27 fungal species were identified. Yeast species belonged to Candida, Cryptococcus, Debaryomyces, Geotrichum, Kluyveromyces, Malassezia, Pichia, Rhodotorula and Trichosporon genera; and mold species belonged to Aspergillus, Chrysosporium, Cladosporium, Engyodontium, Fusarium, Penicillium and Torrubiella genera. Cow milk samples harbored the highest fungal diversity with a maximum of 15 species in a single sample, whereas a maximum of 4 and 6 different species were recovered in goat and ewe milk respectively. Commonly encountered genera in cow and goat milk were Geotrichum candidum, Kluyveromyces marxianus and Candida spp. (C. catenulata and C. inconspicua); whereas Candida parapsilosis was frequently found in ewe milk samples. Most of detected species were previously described in literature data. A few species were uncultured fungi and others (Torrubiella and Malassezia) were described for the first time in milk.     

Dietary exposure estimates of 18 elements from the 1st French Total Diet Study.

To estimate the dietary exposure of the main minerals and trace elements from retail food typically consumed by the French population, samples were purchased and then prepared and cooked prior to analysis. A total of 1080 individual food composites samples were collected and analysed for 18 elements (arsenic, lead, cadmium, aluminium, mercury, antimony, chrome, calcium, manganese, magnesium, nickel, copper, zinc, lithium, sodium, molybdenum, cobalt and selenium). Intakes were calculated from different food consumption patterns found in France for average and high consumers among adults and children. Dietary exposures of those consumers estimated from the France 2000 Total Diet Study (FTDS) are reported, and compared with existing nutritional reference values (Lowest threshold Intake, LTI) or toxicological reference values (Provisional Tolerable Weekly Intake, PTWI or Upper Level, UL) of the respective element and from previous French studies. This study confirms for the populations concerned, the low probability of nutritional or health risks due to food consumption.     

Digital image forensics: a booklet for beginners

Digital visual media represent nowadays one of the principal means for communication. Lately, the reliability of digital visual information has been questioned, due to the ease in counterfeiting both its origin and content. Digital image forensics is a brand new research field which aims at validating the authenticity of images by recovering information about their history. Two main problems are addressed: the identification of the imaging device that captured the image, and the detection of traces of forgeries. Nowadays, thanks to the promising results attained by early studies and to the always growing number of applications, digital image forensics represents an appealing investigation domain for many researchers. This survey is designed for scholars and IT professionals approaching this field, reviewing existing tools and providing a view on the past, the present and the future of digital image forensics.     

Computer Aided Diagnosis of Melanoma Using Deep Neural Networks and Game Theory: Application on Dermoscopic Images of Skin Lesions

Early detection of melanoma remains a daily challenge due to the increasing number of cases and the lack of dermatologists. Thus, AI-assisted diagnosis is considered as a possible solution for this issue. Despite the great advances brought by deep learning and especially convolutional neural networks (CNNs), computer-aided diagnosis (CAD) systems are still not used in clinical practice. This may be explained by the dermatologist’s fear of being misled by a false negative and the assimilation of CNNs to a “black box”, making their decision process difficult to understand by a non-expert. Decision theory, especially game theory, is a potential solution as it focuses on identifying the best decision option that maximizes the decision-maker’s expected utility. This study presents a new framework for automated melanoma diagnosis. Pursuing the goal of improving the performance of existing systems, our approach also attempts to bring more transparency in the decision process. The proposed framework includes a multi-class CNN and six binary CNNs assimilated to players. The players’ strategies is to first cluster the pigmented lesions (melanoma, nevus, and benign keratosis), using the introduced method of evaluating the confidence of the predictions, into confidence level (confident, medium, uncertain). Then, a subset of players has the strategy to refine the diagnosis for difficult lesions with medium and uncertain prediction. We used EfficientNetB5 as the backbone of our networks and evaluated our approach on the public ISIC dataset consisting of 8917 lesions: melanoma (1113), nevi (6705) and benign keratosis (1099). The proposed framework achieved an area under the receiver operating curve (AUROC) of 0.93 for melanoma, 0.96 for nevus and 0.97 for benign keratosis. Furthermore, our approach outperformed existing methods in this task, improving the balanced accuracy (BACC) of the best compared method from 77% to 86%. These results suggest that our framework provides an effective and explainable decision-making strategy. This approach could help dermatologists in their clinical practice for patients with atypical and difficult-to-diagnose pigmented lesions. We also believe that our system could serve as a didactic tool for less experienced dermatologists.