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OPAQUE is an Asymmetric Password-Authenticated Key Exchange (aPAKE) protocol being standardized by the IETF (Internet Engineering Task Force) as a more secure alternative to the traditional “password-over-TLS” mechanism prevalent in current practice. OPAQUE defends against a variety of vulnerabilities of password-over-TLS by dispensing with reliance on PKI and TLS security, and ensuring that the password is never visible to servers or anyone other than the client machine where the password is entered. In order to facilitate the use of OPAQUE in practice, integration of OPAQUE with TLS is needed. The main proposal for standardizing such integration uses the Exported Authenticators (TLS-EA) mechanism of TLS 1.3 that supports post-handshake authentication and allows for a smooth composition with OPAQUE. We refer to this composition as TLS-OPAQUE and present a detailed security analysis for it in the Universal Composability (UC) framework. Our treatment is general and includes the formalization of components that are needed in the analysis of TLS-OPAQUE but are of wider applicability as they are used in many protocols in practice. Specifically, we provide formalizations in the UC model of the notions of post-handshake authentication and channel binding. The latter, in particular, has been hard to implement securely in practice, resulting in multiple protocol failures, including major attacks against prior versions of TLS. Ours is the first treatment of these notions in a computational model with composability guarantees. We complement the theoretical work with a detailed discussion of practical considerations for the use and deployment of TLS-OPAQUE in real-world settings and applications. 

Image-based cell classification has become a common tool to identify phenotypic changes in cell populations. However, this methodology is limited to organisms possessing well characterized species-specific reagents (e.g., antibodies) that allow cell identification, clustering and convolutional neural network (CNN) training. In the absence of such reagents, the power of image-based classification has remained mostly off-limits to many research organisms. We have developed an image-based classification methodology we named Image3C (Image-Cytometry Cell Classification) that does not require species-specific reagents nor pre-existing knowledge about the sample. Image3C combines image-based flow cytometry with an unbiased, high-throughput cell cluster pipeline and CNN integration. Image3C exploits intrinsic cellular features and non-species-specific dyes to perform de novo cell composition analysis and to detect changes in cellular composition between different conditions. Therefore, Image3C expands the use of imaged-based analyses of cell population composition to research organisms in which detailed cellular phenotypes are unknown or for which species-specific reagents are not available. 

Abstract The resolution of fluorescence microscopy images is limited by the physical properties of light. In the last decade, numerous super-resolution microscopy (SRM) approaches have been proposed to deal with such hindrance. Here we present Mean-Shift Super Resolution (MSSR), a new SRM algorithm based on the Mean Shift theory, which extends spatial resolution of single fluorescence images beyond the diffraction limit of light. MSSR works on low and high fluorophore densities, is not limited by the architecture of the optical setup and is applicable to single images as well as temporal series. The theoretical limit of spatial resolution, based on optimized real-world imaging conditions and analysis of temporal image stacks, has been measured to be 40 nm. Furthermore, MSSR has denoising capabilities that outperform other SRM approaches. Along with its wide accessibility, MSSR is a powerful, flexible, and generic tool for multidimensional and live cell imaging applications. 

Abstract Between 5 and 4 thousand years ago, crippling megadroughts led to the disruption of ancient civilizations across parts of Africa and Asia, yet the extent of these climate extremes in mainland Southeast Asia (MSEA) has never been defined. This is despite archeological evidence showing a shift in human settlement patterns across the region during this period. We report evidence from stalagmite climate records indicating a major decrease of monsoon rainfall in MSEA during the mid- to late Holocene, coincident with African monsoon failure during the end of the Green Sahara. Through a set of modeling experiments, we show that reduced vegetation and increased dust loads during the Green Sahara termination shifted the Walker circulation eastward and cooled the Indian Ocean, causing a reduction in monsoon rainfall in MSEA. Our results indicate that vegetation-dust climate feedbacks from Sahara drying may have been the catalyst for societal shifts in MSEA via ocean-atmospheric teleconnections.