Organotypic stromal cells impact endothelial cell transcriptome in 3D microvessel networks

Endothelial cells line all major blood vessels and serve as integral regulators of many functions including vessel diameter, cellular trafficking, and transport of soluble mediators. Despite similar functions, the phenotype of endothelial cells is highly organ-specific, yet our understanding of the mechanisms leading to organ-level differentiation is incomplete. We generated 3D microvessel networks by combining a common naïve endothelial cell with six different stromal cells derived from the lung, skin, heart, bone marrow, pancreas, and pancreatic cancer. Single cell RNA-Seq analysis of the microvessel networks reveals five distinct endothelial cell populations, for which the relative proportion depends on the stromal cell population. Morphologic features of the organotypic vessel networks inversely correlate with a cluster of endothelial cells associated with protein synthesis. The organotypic stromal cells were each characterized by a unique subpopulation of cells dedicated to extracellular matrix organization and assembly. Finally, compared to cells in 2D monolayer, the endothelial cell transcriptome from the 3D in vitro heart, skin, lung, and pancreas microvessel networks are more similar to the in vivo endothelial cells from the respective organs. We conclude that stromal cells contribute to endothelial cell and microvessel network organ tropism, and create an endothelial cell phenotype that more closely resembles that present in vivo

Hygroscopic Growth of Ammonium Sulfate/Dicarboxylic Acids

Recent studies have shown that tropospheric sulfate aerosols commonly contain 50% by mass organic species. The influence of these organics on the chemical and physical properties of sulfate aerosols is not fully established. We have measured the water activity of pure dicarboxylic acids and eutonic mixtures of ammonium sulfate/dicarboxylic acids at 25°C and have calculated van\u27t Hoff factors for each individual system. We have also used the vapor pressure data to determine the hygroscopic growth curves for pure dicarboxylic acids and eutonic mixtures and provide power law fits to the data. For the systems studied we find that the presence of soluble dicarboxylic acids at the eutonic proportion depresses hygroscopic growth when compared to pure ammonium sulfate. In addition, we find that the presence of low-solubility dicarboxylic acids at the eutonic proportion has no effect on the hygroscopic growth when compared to pure ammonium sulfate. To model the hygroscopic growth curves of the eutonic solutions, we employed the Zdanovskii, Stokes, and Robinson method. It was found that this approximation was accurate to within 17% for all the systems studied

Identifying Important Juvenile Dusky Shark Habitat in the Northwest Atlantic Ocean Using Acoustic Telemetry and Spatial Modeling

Highly mobile species can be challenging for fisheries management and conservation due to large home ranges combined with dependence on discrete habitat areas where they can be easily targeted or vulnerable to anthropogenic disturbances. Management of the Dusky Shark Carcharhinus obscurus in the northwest Atlantic Ocean has been particularly challenging due to the species\u27 inherent vulnerability to overfishing and poorly understood habitat associations. To better understand habitat associations and seasonal distributions, we combined telemetry and remotely sensed environmental data to spatially model juvenile Dusky Shark presence probability in the northwest Atlantic Ocean. To accomplish this, 22 juvenile Dusky Sharks (107-220 cm TL) that were tagged with acoustic transmitters at different locations within the U.S. Middle Atlantic Bight region were tracked through networked arrays of acoustic receivers. Tag detections were summarized as daily presence records, and data describing environmental conditions, including depth, chlorophyll-a concentration, salinity, and sea surface temperature, were extracted at detection locations. These data were used in boosted regression tree models to predict juvenile Dusky Shark presence probability based on environmental parameters during fall 2017 and summer 2018. Telemetry observations and modeled presence probability showed consistent associations with temperatures between 16 degrees C and 26 degrees C and chlorophyll-a concentrations between 2 and 7 mg/m(3), which were associated with seasonal migration timing and monthly spatial distributions. Dusky Shark tag detections and predicted distributions during summer and early fall overlapped areas in the Middle Atlantic Bight that were affected by fisheries and potential offshore energy development. Our methodology provides a framework for assessing climate change effects on distribution

Aseptic meningitis in a patient taking etanercept for rheumatoid arthritis: a case report

Background \ud We report a case of a 53 year old lady recently commenced on etanercept, an anti-TNF (tumour necrosis factor) therapy for rheumatoid arthritis presenting with \ud confusion, pyrexia and an erythematous rash. \ud \ud Case presentation \ud A lumbar puncture was highly suggestive of bacterial meningitis, but CSF cultures produced no growth, and polymerase chain reactions (PCR) for all previously reported bacterial, fungal and viral causes of meningitis were negative. \ud \ud Conclusions \ud This case report describes aseptic meningitis as a previously unreported complication of etanercept therapy, and serves as a reminder of the rare but potentially lifethreatening risk of serious infections in patients taking anti-TNF therapy for a variety of conditions

Scalable fault tolerant algorithms for linear-scaling coupled-cluster electronic structure methods.

By means of coupled-cluster theory, molecular properties can be computed with an accuracy often exceeding that of experiment. The high-degree polynomial scaling of the coupled-cluster method, however, remains a major obstacle in the accurate theoretical treatment of mainstream chemical problems, despite tremendous progress in computer architectures. Although it has long been recognized that this super-linear scaling is non-physical, the development of efficient reduced-scaling algorithms for massively parallel computers has not been realized. We here present a locally correlated, reduced-scaling, massively parallel coupled-cluster algorithm. A sparse data representation for handling distributed, sparse multidimensional arrays has been implemented along with a set of generalized contraction routines capable of handling such arrays. The parallel implementation entails a coarse-grained parallelization, reducing interprocessor communication and distributing the largest data arrays but replicating as many arrays as possible without introducing memory bottlenecks. The performance of the algorithm is illustrated by several series of runs for glycine chains using a Linux cluster with an InfiniBand interconnect

New Constraints on Cosmic Reionization from the 2012 Hubble Ultra Deep Field Campaign

Understanding cosmic reionization requires the identification and characterization of early sources of hydrogen-ionizing photons. The 2012 Hubble Ultra Deep Field (UDF12) campaign has acquired the deepest infrared images with the Wide Field Camera 3 aboard Hubble Space Telescope and, for the first time, systematically explored the galaxy population deep into the era when cosmic microwave background (CMB) data indicates reionization was underway. The UDF12 campaign thus provides the best constraints to date on the abundance, luminosity distribution, and spectral properties of early star-forming galaxies. We synthesize the new UDF12 results with the most recent constraints from CMB observations to infer redshift-dependent ultraviolet (UV) luminosity densities, reionization histories, and electron scattering optical depth evolution consistent with the available data. Under reasonable assumptions about the escape fraction of hydrogen ionizing photons and the intergalactic medium clumping factor, we find that to fully reionize the universe by redshift z~6 the population of star-forming galaxies at redshifts z~7-9 likely must extend in luminosity below the UDF12 limits to absolute UV magnitudes of M_UV\sim -13 or fainter. Moreover, low levels of star formation extending to redshifts z~15-25, as suggested by the normal UV colors of z\simeq7-8 galaxies and the smooth decline in abundance with redshift observed by UDF12 to z\simeq10, are additionally likely required to reproduce the optical depth to electron scattering inferred from CMB observations.Comment: Version accepted by ApJ (originally submitted Jan 5, 2013). The UDF12 website can be found at http://udf12.arizona.ed

Systematic pathological component scores for skin-containing vascularized composite allografts

Clinical management of skin-containing vascularized composite allografts (VCA) requires accurate assessment of the graft status, typically based on skin biopsies. The Banff 2007 Working Classification proposed 4 grades of acute rejection, but did not score individual features or include vascular rejection. Here we report a systematic scoring system developed from MHC-mismatched porcine skin-containing VCA. Biopsies from 20 VCA, 9 autologous skin flaps and 9 normal skin were analyzed to optimize the methodology and set thresholds. The components quantified were: perivascular cells/dermal vessel (pc), perivascular dermal infiltrate area (pa), luminal leukocytes/capillary or venule (c), epidermal infiltrate (ei), epidermal apoptosis or necrosis (e), endarteritis (v), and chronic allograft vasculopathy (cav). To evaluate prognostic value, we scored a separate group of 28 serial biopsies from 8 recipients (4 that were ultimately accepted and 4 that rejected. Parameters on the initial biopsies predicting later graft rejection included pc (p < 0.02), pa (p < 0.03), ei (p < 0.0005), e (p < 0.003) and c (p < 0.005). Reproducibility between 2 pathologists blinded to clinical data was acceptable, with weighted kappa scores for pc (0.673), pa (0.399), ei (0.464), e (0.663), v (0.766), and c (0.642). This component scoring system can be adapted clinically, since human and porcine skin are highly similar. Vascular lesions in VCA are also highlighted in this system and could impact graft outcome. The component score approach complements Banff 2007 grades and will enable the establishment of clinically significant thresholds

CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison

Large, labeled datasets have driven deep learning methods to achieve expert-level performance on a variety of medical imaging tasks. We present CheXpert, a large dataset that contains 224,316 chest radiographs of 65,240 patients. We design a labeler to automatically detect the presence of 14 observations in radiology reports, capturing uncertainties inherent in radiograph interpretation. We investigate different approaches to using the uncertainty labels for training convolutional neural networks that output the probability of these observations given the available frontal and lateral radiographs. On a validation set of 200 chest radiographic studies which were manually annotated by 3 board-certified radiologists, we find that different uncertainty approaches are useful for different pathologies. We then evaluate our best model on a test set composed of 500 chest radiographic studies annotated by a consensus of 5 board-certified radiologists, and compare the performance of our model to that of 3 additional radiologists in the detection of 5 selected pathologies. On Cardiomegaly, Edema, and Pleural Effusion, the model ROC and PR curves lie above all 3 radiologist operating points. We release the dataset to the public as a standard benchmark to evaluate performance of chest radiograph interpretation models. The dataset is freely available at https://stanfordmlgroup.github.io/competitions/chexpert .Comment: Published in AAAI 201