Liver Sinusoid on a Chip: Long-Term Layered Co-Culture of Primary Rat Hepatocytes and Endothelial Cells in Microfluidic Platforms

We describe the generation of microfluidic platforms for the co-culture of primary hepatocytes and endothelial cells; these platforms mimic the architecture of a liver sinusoid. This paper describes a progressional study of creating such a liver sinusoid on a chip system. Primary rat hepatocytes (PRHs) were co-cultured with primary or established endothelial cells in layers in single and dual microchannel configurations with or without continuous perfusion. Cell viability and maintenance of hepatocyte functions were monitored and compared for diverse experimental conditions. When primary rat hepatocytes were co-cultured with immortalized bovine aortic endothelial cells (BAECs) in a dual microchannel with continuous perfusion, hepatocytes maintained their normal morphology and continued to produce urea for at least 30 days. In order to demonstrate the utility of our microfluidic liver sinusoid platform, we also performed an analysis of viral replication for the hepatotropic hepatitis B virus (HBV). HBV replication, as measured by the presence of cell-secreted HBV DNA, was successfully detected. We believe that our liver model closely mimics the in vivo liver sinusoid and supports long-term primary liver cell culture. This liver model could be extended to diverse liver biology studies and liver-related disease research such as drug induced liver toxicology, cancer research, and analysis of pathological effects and replication strategies of various hepatotropic infectious agents

Enhancing Self-Consistency and Performance of Pre-Trained Language Models through Natural Language Inference

While large pre-trained language models are powerful, their predictions often lack logical consistency across test inputs. For example, a state-of-the-art Macaw question-answering (QA) model answers 'Yes' to 'Is a sparrow a bird?' and 'Does a bird have feet?' but answers 'No' to 'Does a sparrow have feet?'. To address this failure mode, we propose a framework, Consistency Correction through Relation Detection, or ConCoRD, for boosting the consistency and accuracy of pre-trained NLP models using pre-trained natural language inference (NLI) models without fine-tuning or re-training. Given a batch of test inputs, ConCoRD samples several candidate outputs for each input and instantiates a factor graph that accounts for both the model's belief about the likelihood of each answer choice in isolation and the NLI model's beliefs about pair-wise answer choice compatibility. We show that a weighted MaxSAT solver can efficiently compute high-quality answer choices under this factor graph, improving over the raw model's predictions. Our experiments demonstrate that ConCoRD consistently boosts accuracy and consistency of off-the-shelf closed-book QA and VQA models using off-the-shelf NLI models, notably increasing accuracy of LXMERT on ConVQA by 5% absolute. See https://ericmitchell.ai/emnlp-2022-concord/ for code and data.Comment: 16 pages. EMNLP 2022 Camera Ready. See https://ericmitchell.ai/emnlp-2022-concord/ for code and dat

Combined radiomics-clinical model to predict platinum-sensitivity in advanced high-grade serous ovarian carcinoma using multimodal MRI

IntroductionWe aimed to predict platinum sensitivity using routine baseline multimodal magnetic resonance imaging (MRI) and established clinical data in a radiomics framework.MethodsWe evaluated 96 patients with ovarian cancer who underwent multimodal MRI and routine laboratory tests between January 2016 and December 2020. The patients underwent diffusion-weighted, contrast-enhanced T1-weighted, and T2-weighted MRI. Subsequently, 293 radiomic features were extracted by manually identifying tumor regions of interest. The features were subjected to the least absolute shrinkage and selection operators, leaving only a few selected features. We built the first prediction model with a tree-based classifier using selected radiomics features. A second prediction model was built by combining the selected radiomic features with four established clinical factors: age, disease stage, initial tumor marker level, and treatment course. Both models were built and tested using a five-fold cross-validation.ResultsOur radiomics model predicted platinum sensitivity with an AUC of 0.65 using a few radiomics features related to heterogeneity. The second combined model had an AUC of 0.77, confirming the incremental benefits of the radiomics model in addition to models using established clinical factors.ConclusionOur combined radiomics-clinical data model was effective in predicting platinum sensitivity in patients with advanced ovarian cancer

Using present-day observations to detect when anthropogenic change forces surface ocean carbonate chemistry outside preindustrial bounds

© The Author(s), 2016. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Biogeosciences 13 (2016): 5065-5083, doi:10.5194/bg-13-5065-2016.One of the major challenges to assessing the impact of ocean acidification on marine life is detecting and interpreting long-term change in the context of natural variability. This study addresses this need through a global synthesis of monthly pH and aragonite saturation state (Ωarag) climatologies for 12 open ocean, coastal, and coral reef locations using 3-hourly moored observations of surface seawater partial pressure of CO2 and pH collected together since as early as 2010. Mooring observations suggest open ocean subtropical and subarctic sites experience present-day surface pH and Ωarag conditions outside the bounds of preindustrial variability throughout most, if not all, of the year. In general, coastal mooring sites experience more natural variability and thus, more overlap with preindustrial conditions; however, present-day Ωarag conditions surpass biologically relevant thresholds associated with ocean acidification impacts on Mytilus californianus (Ωarag < 1.8) and Crassostrea gigas (Ωarag < 2.0) larvae in the California Current Ecosystem (CCE) and Mya arenaria larvae in the Gulf of Maine (Ωarag < 1.6). At the most variable mooring locations in coastal systems of the CCE, subseasonal conditions approached Ωarag =  1. Global and regional models and data syntheses of ship-based observations tended to underestimate seasonal variability compared to mooring observations. Efforts such as this to characterize all patterns of pH and Ωarag variability and change at key locations are fundamental to assessing present-day biological impacts of ocean acidification, further improving experimental design to interrogate organism response under real-world conditions, and improving predictive models and vulnerability assessments seeking to quantify the broader impacts of ocean acidification.The CO2 and ocean acidification observations were funded by NOAA’s Climate Observation Division (COD) in the Climate Program Office and NOAA’s Ocean Acidification Program. The maintenance of the Stratus and WHOTS Ocean Reference Stations were also supported by NOAA COD (NA09OAR4320129). Additional support for buoy equipment, maintenance, and/or ancillary measurements was provided by NOAA through the US Integrated Ocean Observing System office: for the La Parguera buoy under a Cooperative Agreement (NA11NOS0120035) with the Caribbean Coastal Ocean Observing System, for the Chá b˘a buoy under a Cooperative Agreement (NA11NOS0120036) with the Northwest Association of Networked Ocean Observing System, for the Gray’s Reef buoy under a Cooperative Agreement (NA11NOS0120033) with the Southeast Coastal Ocean Observing Regional Association, and for the Gulf of Main buoy under a Cooperative Agreement (NA11NOS0120034) with the Northeastern Regional Association of Coastal and Ocean Observing Systems

Symmetry Control of Unconventional Spin–Orbit Torques in IrO\u3csub\u3e2\u3c/sub\u3e

Spin–orbit torques generated by a spin current are key to magnetic switching in spintronic applications. The polarization of the spin current dictates the direction of switching required for energy-efficient devices. Conventionally, the polarizations of these spin currents are restricted to be along a certain direction due to the symmetry of the material allowing only for efficient in-plane magnetic switching. Unconventional spin–orbit torques arising from novel spin current polarizations, however, have the potential to switch other magnetization orientations such as perpendicular magnetic anisotropy, which is desired for higher density spintronic-based memory devices. Here, it is demonstrated that low crystalline symmetry is not required for unconventional spin–orbit torques and can be generated in a nonmagnetic high symmetry material, iridium dioxide (IrO2), using epitaxial design. It is shown that by reducing the relative crystalline symmetry with respect to the growth direction large unconventional spin currents can be generated and hence spin–orbit torques. Furthermore, the spin polarizations detected in (001), (110), and (111) oriented IrO2 thin films are compared to show which crystal symmetries restrict unconventional spin transport. Understanding and tuning unconventional spin transport generation in high symmetry materials can provide a new route towards energy-efficient magnetic switching in spintronic devices

Dysregulated protocadherin-pathway activity as an intrinsic defect in induced pluripotent stem cell-derived cortical interneurons from subjects with schizophrenia.

We generated cortical interneurons (cINs) from induced pluripotent stem cells derived from 14 healthy controls and 14 subjects with schizophrenia. Both healthy control cINs and schizophrenia cINs were authentic, fired spontaneously, received functional excitatory inputs from host neurons, and induced GABA-mediated inhibition in host neurons in vivo. However, schizophrenia cINs had dysregulated expression of protocadherin genes, which lie within documented schizophrenia loci. Mice lacking protocadherin-α showed defective arborization and synaptic density of prefrontal cortex cINs and behavioral abnormalities. Schizophrenia cINs similarly showed defects in synaptic density and arborization that were reversed by inhibitors of protein kinase C, a downstream kinase in the protocadherin pathway. These findings reveal an intrinsic abnormality in schizophrenia cINs in the absence of any circuit-driven pathology. They also demonstrate the utility of homogenous and functional populations of a relevant neuronal subtype for probing pathogenesis mechanisms during development

Building a fault-tolerant quantum computer using concatenated cat codes

We present a comprehensive architectural analysis for a fault-tolerant quantum computer based on cat codes concatenated with outer quantum error-correcting codes. For the physical hardware, we propose a system of acoustic resonators coupled to superconducting circuits with a two-dimensional layout. Using estimated near-term physical parameters for electro-acoustic systems, we perform a detailed error analysis of measurements and gates, including CNOT and Toffoli gates. Having built a realistic noise model, we numerically simulate quantum error correction when the outer code is either a repetition code or a thin rectangular surface code. Our next step toward universal fault-tolerant quantum computation is a protocol for fault-tolerant Toffoli magic state preparation that significantly improves upon the fidelity of physical Toffoli gates at very low qubit cost. To achieve even lower overheads, we devise a new magic-state distillation protocol for Toffoli states. Combining these results together, we obtain realistic full-resource estimates of the physical error rates and overheads needed to run useful fault-tolerant quantum algorithms. We find that with around 1,000 superconducting circuit components, one could construct a fault-tolerant quantum computer that can run circuits which are intractable for classical supercomputers. Hardware with 32,000 superconducting circuit components, in turn, could simulate the Hubbard model in a regime beyond the reach of classical computing

Comparison of RNA-Seq and microarray in the prediction of protein expression and survival prediction

Gene expression profiling using RNA-sequencing (RNA-seq) and microarray technologies is widely used in cancer research to identify biomarkers for clinical endpoint prediction. We compared the performance of these two methods in predicting protein expression and clinical endpoints using The Cancer Genome Atlas (TCGA) datasets of lung cancer, colorectal cancer, renal cancer, breast cancer, endometrial cancer, and ovarian cancer. We calculated the correlation coefficients between gene expression measured by RNA-seq or microarray and protein expression measured by reverse phase protein array (RPPA). In addition, after selecting the top 103 survival-related genes, we compared the random forest survival prediction model performance across test platforms and cancer types. Both RNA-seq and microarray data were retrieved from TCGA dataset. Most genes showed similar correlation coefficients between RNA-seq and microarray, but 16 genes exhibited significant differences between the two methods. The BAX gene was recurrently found in colorectal cancer, renal cancer, and ovarian cancer, and the PIK3CA gene belonged to renal cancer and breast cancer. Furthermore, the survival prediction model using microarray was better than the RNA-seq model in colorectal cancer, renal cancer, and lung cancer, but the RNA-seq model was better in ovarian and endometrial cancer. Our results showed good correlation between mRNA levels and protein measured by RPPA. While RNA-seq and microarray performance were similar, some genes showed differences, and further clinical significance should be evaluated. Additionally, our survival prediction model results were controversial

Autonomous Seawater \u3ci\u3ep\u3c/i\u3eCO\u3csub\u3e2\u3c/sub\u3e and pH Time Series From 40 Surface Buoys and the Emergence of Anthropogenic Trends

Ship-based time series, some now approaching over 3 decades long, are critical climate records that have dramatically improved our ability to characterize natural and anthropogenic drivers of ocean carbon dioxide (CO2) uptake and biogeochemical processes. Advancements in autonomous marine carbon sensors and technologies over the last 2 decades have led to the expansion of observations at fixed time series sites, thereby improving the capability of characterizing sub-seasonal variability in the ocean. Here , we present a data product of 40 individual autonomous moored surface ocean pCO2 (partial pressure of CO2) time series established between 2004 and 2013, 17 also include autonomous pH measurements. These time series characterie a wide range of surface ocean carbonate conditions in diffferent oceanic (17 sites), coastal (13 sites), and coral reef (10 sites) regimes. A time of trend emergence (ToE) methodology applied ot the time series that exhibit well-constrained daily to interannual variability and an estimate of decadal variability indicates that the length of sustained observations necessary to detect statistically significant anthropogenic trends varies by marine environment. The ToE estisites, and 9 to 22 years at the coral reef sites. Only two open ocean pCO2 and pH range from 8 to 15 years at the open ocean sites, 16 to 41 years at the coastal sites, and 9 to 22 years at the coral reef sites. Only two open ocean pCO2 time series, Woods Hole Oceanographic Institution Hawaii Ocean Time-series Station (WHOTS) in the subtropical North Pacific and Stratus n the South Pacific gyre, have been deployed longer than the estimated trend detection time and, for these, deseasoned monthly means show estimated anthropogenic trends of 1.9 ± 0.3 and 1.6 ± 0.3 μatm yr-1, respectively. In the future, it is possible that updates to this product will allow for the estimation of anthropogenic trends at more sites; however, the product currently provides a valuable tool in an accessible format for evaluating climatology and natural variability of surface ocean carbonate chemistry in a variety of regions. Data are available at https://doi.org/10.7289/V5DB8043 and https://www.nodc.noaa.gov/ocads/oceans/Moorings/ndp097.html (Sutton et al., 2018)