Ethics statement The research complies with all relevant ethical regulations and informed consent was obtained by all study participants (n=25, 16 females and 9 males). Study protocols were approved by the Ethikkomission beider Basel (EKBB; Basel, Switzerland; reference number 342/10), the Ifakara Health Institute Institutional Review Board (Reference number IHI/IRB/No

Yi, Y., Zhu, D., Guo, S., Zhang, Z. & Shi, C. A review on the deterioration and approaches to enhance the durability of concrete in the marine environment

From image collection to dataset creation The whole machine learning workflow is schematically represented in Fig.1. In brief, it starts with an algorithms training which consists of three main phases, namely: (i) live-islet autofluorescence intensity imaging by exciting at 740nm and collecting in the 420460-nm range, which is dominated by NAD(P)H and lipofuscin signals; (ii) NAD(P)H auto-fluorescence lifetime imaging at the same focal plane in live islets at both low (2.2mM) and high glucose (16.7mM), with subtraction of the lipofuscin intrinsic signal, to produce metabolic data in terms of balance between free and protein-bound NAD(P)H; (iii) islet fixation and immunostaining using antibodies against glucagon and insulin to identify single and cells and then extract single-cell information from both intensity and lifetime data through spatial matching of immunofluorescence and live-islet acquisitions (Fig.1a). At this point, we curate the manual processing of experimental data to extract a set of numerical features (Fig.1b) and store them in a feature matrix.

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Trust Stamp announces a provisional patent for a new AI-powered technology to counter Injection Attacks, including deep fake images and Videos Trust Stamp the Privacy-First Identity Company, announced that it has filed provisional patent #63/662,575 with the US Patent and Trademark Office for a new methodology to detect injection attacks in biometric authentication processes, including attacks executed using deep fake images and videos. Injection attacks targeting biometric processes typically bypass the camera on a users device or inject video or still images captured in a different context into the data stream between the users device and the server to which they are authenticating. Read:FriendliAI Integrates With Weights & Biases to Streamline Gen AI Deployment Workflows Dr Norman Poh, Trust Stamps Chief Science Officer, commented, We already have a number of liveness detection technologies implemented, but there are now billions of daily attacks being perpetrated with a growing number of injection attacks using genuine artifacts captured out of context as well as deep fake images and videos