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Reflectance confocal microscopy (RCM) is a noninvasive optical imaging technique that uses a laser to capture cellular-level resolution images based on differing refractive indices of tissue elements. RCM image interpretation is challenging and requires training to interpret and correlate the grayscale output images that lack nuclear features with tissue pathology. Here, we utilize a deep learning-based framework that uses a convolutional neural network to transform grayscale images into virtually-stained hematoxylin and eosin (H&E)-like images enabling the visualization of various skin layers. To train the deep-learning framework, a series of a minimum of 7 time-lapsed, successive “stacks” of RCM images of excised tissue, spaced 1.52μm apart to a depth of 60.96μm were obtained using the Vivascope 1500. The tissue samples were stained with a 50% acetic acid solution to enhance cell nuclei. These images served as the “ground truth” to train a deep convolutional neural network with a conditional generative adversarial network (GAN)-based machine learning algorithm to digitally convert the images into GAN-based H&E-stained digital images. The machine learning algorithm was initially trained and subsequently retrained with new samples, specifically focusing on squamous neoplasms. The trained algorithm was applied to skin lesions that had a clinical differential diagnosis of squamous neoplasms including squamous cell carcinoma, actinic keratosis, seborrheic keratosis, and basal cell carcinoma. Through continuous training and refinement, the algorithm was able to produce high-resolution, histological quality images of different squamous neoplasms. This algorithm may be used in the future to facilitate earlier diagnosis of cutaneous neoplasms and enable greater uptake of noninvasive imaging technology within the medical community. 

Defining the presence of residual tumor and margins may enhance tissue sparing in dermatologic surgery, but no device serves this role. Reflectance Confocal Microscopy (RCM) provides non-invasive cellular-level resolution of the skin, but the FDA-approved RCM device is rigid and requires a tissue cap making tissue mapping difficult. We previously applied “virtual histology”, a deep-learning algorithm to RCM images to generate biopsy-free histology, however, whether virtual histology can be applied to images obtained with a portable, handheld RCM device to scan for residual tumor and margins is unknown. We hypothesize that combining a handheld device with virtual histology could provide accurate tumor assessment and these virtual histology images would correlate with traditional histology. The study was conducted as a prospective, consecutive non-randomized trial at a VA Medical Center dermatologic surgery clinic. Patients over 18 years old with confirmed BCC, SCC, or SCCis were included. Successive in-vivo confocal images from the epidermis and dermis were obtained with the handheld device and processed through a conditional generative adversarial network-based algorithm to create H&E pseudo-stained virtual histology. The algorithm produced similar virtual histology of in-vivo RCM images from the handheld and standard device, demonstrating successful application to the handheld device. Virtual histology applied to handheld RCM images capturing residual tumor, precancerous lesions (actinic keratosis) and scar tissue correlated with Mohs frozen section histology from excised tissue. The combination of machine-learning based virtual histology with handheld RCM images may provide histology-quality data in real time for tumor evaluation to assist the surgeon, improving clinical efficiency by decreasing unnecessary surgeries/layers and cosmesis through better margin assessment. 

Reflectance confocal microscopy (RCM) is a noninvasive optical imaging modality that allows for cellular-level resolution, in vivo images of skin without performing a traditional skin biopsy. RCM image interpretation currently requires specialized training to interpret the grayscale output images that are difficult to correlate with tissue pathology. Here, we use a deep learning-based framework that uses a convolutional neural network to transform grayscale output images into virtually-stained hematoxylin and eosin (H&E)-like images allowing for the visualization of various skin layers, including the epidermis, dermal-epidermal junction, and superficial dermis layers. To train the deep-learning framework, a stack of a minimum of 7 time-lapsed, successive RCM images of excised tissue were obtained from epidermis to dermis 1.52 microns apart to a depth of 60.96 microns using the Vivascope 3000. The tissue was embedded in agarose tissue and a curette was used to create a tunnel through which drops of 50% acetic acid was used to stain cell nuclei. These acetic acid-stained images were used as “ground truth” to train a deep convolutional neural network using a conditional generative adversarial network (GAN)-based machine learning algorithm to digitally convert the images into GAN-based H&E-stained digital images. We used the already trained machine learning algorithm and retrained the algorithm with new samples to include squamous neoplasms. Through further training and refinement of the algorithm, high-resolution, histological quality images can be obtained to aid in earlier diagnosis and treatment of cutaneous neoplasms. The overall goal of obtaining biopsy-free virtual histology images with this technology can be used to provide real-time outputs of virtually-stained H&E skin lesions, thus decreasing the need for invasive diagnostic procedures and enabling greater uptake of the technology by the medical community. 

The ability to accurately define tumor margins may enhance tissue sparing and increase efficiency in the dermatologic surgery process, but no device exists that serves this role. Reflectance Confocal Microscopy (RCM) provides non-invasive cellular resolution of the skin. The only clinically-approved RCM device is bulky, non-portable, and requires a tissue cap which makes mapping of the underlying tissue impossible. We recently combined “virtual histology”, a machine learning algorithm with RCM images from this standard RCM device to generate biopsy-free histology to overcome these limitations. Whether virtual histology can be used with a portable, handheld RCM device to scan for residual tumor and tumor margins is currently unknown. We hypothesize that combining a handheld RCM device with virtual histology could provide accurate tumor margin assessment. We determined whether our established virtual histology algorithm could be applied to images from a portable RCM device and whether these pseudo-stained virtual histology images correlated with histology from skin specimens. The study was conducted as a prospective, consecutive non-randomized trial at a Veterans Affairs Medical Center dermatologic surgery clinic. All patients greater than 18 years of age with previously biopsied BCC, SCC, or SCCis were included. Successive confocal images from the epidermis to the dermis were obtained 1.5 microns apart from the handheld RCM device to detect residual skin cancer. The handheld, in-vivo RCM images were processed through a conditional generative adversarial network-based machine learning algorithm to digitally convert the images into H&E pseudo-stained virtual histology images. Virtual histology of in-vivo RCM images from unbiopsied skin captured with the portable RCM device were similar to those obtained with the standard RCM device and virtual histology applied to portable RCM images correctly correlated with frozen section histology. Residual tumors detected with virtual histology generated from the portable RCM images accurately corresponded with residual tumors shown in the frozen surgical tissue specimen. Residual tumor was also not detected when excised tissue was clear of tumor following surgical procedure. Thus, the combination of virtual histology with portable RCM may provide accurate histology-quality data for evaluation of residual skin cancer prior to surgery. Combining machine learning-based virtual histology with handheld RCM images demonstrates promise in providing insights into tumor characteristics and has the potential to assist the surgeon and better guide practice decisions to more efficiently serve patients, leading to decreased layers and appointment times. Future work is needed to provide real-time virtual histology, convert horizontal/confocal sections into vertical or 3D sections, and to perform clinical studies to map tumors in tissue. 

Polycyclic aromatic hydrocarbons (PAHs) are organic molecules containing adjacent aromatic rings. Infrared emission bands show that PAHs are abundant in space, but only a few specific PAHs have been detected in the interstellar medium. We detected 1-cyanopyrene, a cyano-substituted derivative of the related four-ring PAH pyrene, in radio observations of the dense cloud TMC-1, using the Green Bank Telescope. The measured column density of 1-cyanopyrene is $\sim \text{1}{\text{.52×10}}^{\text{12}}$cm⁻², from which we estimate that pyrene contains up to 0.1% of the carbon in TMC-1. This abundance indicates that interstellar PAH chemistry favors the production of pyrene. We suggest that some of the carbon supplied to young planetary systems is carried by PAHs that originate in cold molecular clouds. 

Dogs (Canis familiaris) prefer the walk at lower speeds and the more economical trot at speeds ranging from 0.5 Fr up to 3 Fr. Important works have helped to understand these gaits at the levels of the center of mass, joint mechanics, and muscular control. However, less is known about the global dynamics for limbs and if these are gait or breed-specific. For walk and trot, we analyzed dogs’ global dynamics, based on motion capture and single leg kinetic data, recorded from treadmill locomotion of French Bulldog (N= 4), Whippet (N= 5), Malinois (N= 4), and Beagle (N= 5). Dogs’ pelvic and thoracic axial leg functions combined compliance with leg lengthening. Thoracic limbs were stiffer than the pelvic limbs and absorbed energy in the scapulothoracic joint. Dogs’ ground reaction forces (GRF) formed two virtual pivot points (VPP) during walk and trot each. One emerged for the thoracic (fore) limbs (VPP_TL) and is roughly located above and caudally to the scapulothoracic joint. The second is located roughly above and cranially to the hip joint (VPP_PL). The positions of VPPs and the patterns of the limbs’ axial and tangential projections of the GRF were gaits but not always breeds-related. When they existed, breed-related changes were mainly exposed by the French Bulldog. During trot, positions of the VPPs tended to be closer to the hip joint or the scapulothoracic joint, and variability between and within breeds lessened compared to walk. In some dogs, VPP_PLwas located below the pelvis during trot. Further analyses revealed that leg length and not breed may better explain differences in the vertical position of VPP_TLor the horizontal position of VPP_PL. The vertical position of VPP_PLwas only influenced by gait, while the horizontal position of VPP_TLwas not breed or gait-related. Accordingly, torque profiles in the scapulothoracic joint were likely between breeds while hip torque profiles were size-related. In dogs, gait and leg length are likely the main VPPs positions’ predictors. Thus, variations of VPP positions may follow a reduction of limb work. Stability issues need to be addressed in further studies. 

Abstract The Radiative‐Convective Equilibrium Model Intercomparison Project (RCEMIP) exhibits a large spread in the simulated climate across models, including in profiles of buoyancy and relative humidity. Here we use simple theory to understand the control of stability, relative humidity, and their responses to warming. Across the RCEMIP ensemble, temperature profiles are systematically cooler than a moist adiabat, and convective available potential energy (CAPE) increases with warming at a rate greater than that expected from the Clausius‐Clapeyron relation. There is higher CAPE (greater instability) in models that are on average moister in the lower‐troposphere. To more explicitly evaluate the drivers of the intermodel spread, we use simple theory to estimate values of entrainment and precipitation efficiency (PE) given the simulated values of CAPE and lower‐tropospheric relative humidity. We then decompose the intermodel spread in CAPE and relative humidity (and their responses to warming) into contributions from variability in entrainment, PE, the temperature of the convecting top, and the inverse water vapor scale height. Model‐to‐model variation in entrainment is a dominant source of intermodel spread in CAPE and its changes with warming, while variation in PE is the dominant source of intermodel spread in relative humidity. We also decompose the magnitude of the CAPE increase with warming and find that atmospheric warming itself contributes most strongly to the CAPE increase, but the indirect effect of increases in the water vapor scale height with warming also contribute to increasing CAPE beyond that expected from Clausius‐Clapeyron.