Sevoflurane exposure in neonates induces long-term impairment of learning and memory; however, its effect on cognition in the later developmental period and the underlying mechanisms remain unclear. In the present study, we showed that multiple sevoflurane exposures impaired fear memory at long retention delays in neonatal (postnatal day 7) and preadolescent mice (postnatal day 22), but not in mice at older ages. After the fear memory test, expression of phosphorylated extracellular signaling-regulated kinase (p-ERK) and c-fos were elevated in the bed nucleus of the stria terminalis (BNST) and central amygdala, but not in the hippocampus or prefrontal cortex. The upregulation of p-ERK was restricted to populations of gamma-aminobutyric acid (GABAergic) neurons and was inhibited by multiple sevoflurane exposures. Intra-BNST injection of ERK inhibitor also impaired fear memory at long retention delays. In contrast, intra-BNST injection of ERK agonist attenuated impaired fear memory caused by repeated sevoflurane exposures. Injection of sevoflurane in the BNST but not the caudate putamen impaired the fear memory at long retention delays in preadolescent mice. Finally, chemogenetic activation of BNST GABAergic neurons by designer receptors exclusively activated by designer drug (DREADD) reversed the impaired fear memory at long retention delays by multiple sevoflurane exposures. These findings suggest that multiple sevoflurane exposures impaired fear memory at long retention delays in preadolescent mice by suppressing the ERK signaling in GABAergic neurons in the BNST.

Satellite laser altimetry offers an opportunity to accurately measure inland water levels over the Earth's surface. This paper described the validation and comparison of three spaceborne missions for inland water level retrievals, including the Geoscience Laser Altimeter System (GLAS) onboard the Ice, Cloud and Land Elevation Satellite-1 (ICESat-1), as well as the newly launched Advanced Topographic Laser Altimeter System (ATLAS) onboard ICESat-2 and the Global Ecosystem Dynamics Investigation (GEDI) lidar on the International Space Station. This study was conducted over the Great Lakes and lower Mississippi River using in-situ data from 22 gauging stations, where the potential for monitoring inland water dynamics was explored. The results showed that ICESat-2 provided lake water level retrievals with an unprecedented accuracy (RMSE = 0.06 m, biases = -0.01 +/- 0.05 m), followed by ICESat-1 (RMSE = 0.10 m, biases = -0.04 +/- 0.10 m), and then by GEDI (RMSE = 0.28 m, biases = -0.10 +/- 0.23 m). ICESat-2 also offered more accurate river water level measurements (RMSE = 0.12 m, biases = -0.08 +/- 0.07 m) than those of ICESat-1 (RMSE = 0.25 m, biases = -0.18 +/- 0.16 m) and GEDI (RMSE = 0.40 m, biases = 0.24 +/- 0.24 m). The analysis suggested that for all the altimeters, the strong beam achieved higher water level accuracies than those of the weak, and the nighttime acquisitions were slightly more accurate than those of the daytime. However, our result indicated no considerable differences between measurement accuracy and river width. Furthermore, the analysis demonstrated their great potential in monitoring inland water dynamics, especially with the combination of ICESat-2 and GEDI platforms. This study provided a comprehensive understanding of measurement accuracies of three recent missions and recommended that end users should employ the strong beam data acquired during the nighttime for accurate water level retrievals.

In the last 50 years there has been a tremendous progress in solving numerically the Navier-Stokes equations using finite differences, finite elements, spectral, and even meshless methods. Yet, in many real cases, we still cannot incorporate seamlessly (multi-fidelity) data into existing algorithms, and for industrial-complexity applications the mesh generation is time consuming and still an art. Moreover, solving ill-posed problems (e.g., lacking boundary conditions) or inverse problems is often prohibitively expensive and requires different formulations and new computer codes. Here, we employ physics-informed neural networks (PINNs), encoding the governing equations directly into the deep neural network via automatic differentiation, to overcome some of the aforementioned limitations for simulating incompressible laminar and turbulent flows. We develop the Navier-Stokes flow nets (NSFnets) by considering two different mathematical formulations of the Navier-Stokes equations: the velocity-pressure (VP) formulation and the vorticity-velocity (VV) formulation. Since this is a new approach, we first select some standard benchmark problems to assess the accuracy, convergence rate, computational cost and flexibility of NSFnets; analytical solutions and direct numerical simulation (DNS) databases provide proper initial and boundary conditions for the NSFnet simulations. The spatial and temporal coordinates are the inputs of the NSFnets, while the instantaneous velocity and pressure fields are the outputs for the VP-NSFnet, and the instantaneous velocity and vorticity fields are the outputs for the VV-NSFnet. This is unsupervised learning and, hence, no labeled data are required beyond boundary and initial conditions and the fluid properties. The residuals of the VP or VV governing equations, together with the initial and boundary conditions, are embedded into the loss function of the NSFnets. No data is provided for the pressure to the VP-NSFnet, which is a hidden state and is obtained via the incompressibility constraint without extra computational cost. Unlike the traditional numerical methods, NSFnets inherit the properties of neural networks (NNs), hence the total error is composed of the approximation, the optimization, and the generalization errors. Here, we empirically attempt to quantify these errors by varying the sampling (residual) points, the iterative solvers, and the size of the NN architecture. For the laminar flow solutions, we show that both the VP and the VV formulations are comparable in accuracy but their best performance corresponds to different NN architectures. The initial convergence rate is fast but the error eventually saturates to a plateau due to the dominance of the optimization error. For the turbulent channel flow, we show that NSFnets can sustain turbulence at Re-tau similar to 1, 000, but due to expensive training we only consider part of the channel domain and enforce velocity boundary conditions on the subdomain boundaries provided by the DNS data base. We also perform a systematic study on the weights used in the loss function for balancing the data and physics components, and investigate a new way of computing the weights dynamically to accelerate training and enhance accuracy. In the last part, we demonstrate how NSFnets should be used in practice, namely for ill-posed problems with incomplete or noisy boundary conditions as well as for inverse problems. We obtain reasonably accurate solutions for such cases as well without the need to change the NSFnets and at the same computational cost as in the forward well-posed problems. We also present a simple example of transfer learning that will aid in accelerating the training of NSFnets for different parameter settings. (C) 2020 Elsevier Inc. All rights reserved.

Background & Aims: There are currently limited therapeutic options for hepatocellular carcinoma (HCC), particularly when it is diagnosed at advanced stages. Herein, we examined the pathophysiological role of ROS1 and assessed the utility of ROS1-targeted therapy for the treatment of HCC. Methods: Recombinant ribonucleases (RNases) were purified, and the ligand-receptor relationship between RNase7 and ROS1 was validated in HCC cell lines by Duolink, immunofluores-cence, and immunoprecipitation assays. Potential interacting residues between ROS1 and RNase7 were predicted using a protein-protein docking approach. The oncogenic function of RNase7 was analyzed by cell proliferation, migration and inva-sion assays, and a xenograft mouse model. The efficacy of anti-ROS1 inhibitor treatment was evaluated in patient-derived xenograft (PDX) and orthotopic models. Two independent pa-tient cohorts were analyzed to evaluate the pathological rele-vance of RNase7/ROS1. Results: RNase7 associated with ROS1's N3-P2 domain and promoted ROS1-mediated oncogenic transformation. Patients with HCC exhibited elevated plasma RNase7 levels compared with healthy individuals. High ROS1 and RNase7 expression were strongly associated with poor prognosis in patients with HCC. In both HCC PDX and orthotopic mouse models, ROS1 inhibitor treatment markedly suppressed RNase7-induced tumorigenesis, leading to decreased plasma RNase7 levels and tumor shrinkage in mice. Conclusions: RNase7 serves as a high-affinity ligand for ROS1. Plasma RNase7 could be used as a biomarker to identify patients with HCC who may benefit from anti-ROS1 treatment. Lay summary: Receptor tyrosine kinases are known to be involved in tumorigenesis and have been targeted therapeuti-cally for a number of cancers, including hepatocellular carcinoma. ROS1 is the only such receptor with kinase activity whose ligand has not been identified. Herein, we show that RNase7 acts as a ligand to activate ROS1 signaling. This has important path-ophysiological and therapeutic implications. Anti-ROS1 in-hibitors could be used to treatment patients with hepatocellular carcinoma and high RNase7 levels. (C) 2020 European Association for the Study of the Liver. Published by Elsevier B.V. All rights reserved.

The recent pandemic outbreak of COVID-19 caused by a novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), poses a threat to public health globally. Thus, developing a rapid, accurate, and easy-to-implement diagnostic system for SARS-CoV-2 is crucial for controlling infection sources and monitoring illness progression. Here, we reported an ultrasensitive electrochemical detection technology using calixarene functionalized graphene oxide for targeting RNA of SARS-CoV-2. Based on a supersandwich-type recognition strategy, the technology was confirmed to practicably detect the RNA of SARS-CoV-2 without nucleic acid amplification and reverse-transcription by using a portable electrochemical smartphone. The biosensor showed high specificity and selectivity during in silico analysis and actual testing. A total of 88 RNA extracts from 25 SARS-CoV-2-confirmed patients and eight recovery patients were detected using the biosensor. The detectable ratios (85.5 % and 46.2 %) were higher than those obtained using RT-qPCR (56.5 % and 7.7 %). The limit of detection (LOD) of the clinical specimen was 200 copies/mL, which is the lowest LOD among the published RNA measurement of SARS-CoV-2 to date. Additionally, only two copies (10 mu L) of SARS-CoV-2 were required for per assay. Therefore, we developed an ultrasensitive, accurate, and convenient assay for SARS-CoV-2 detection, providing a potential method for point-of-care testing.

Aim: To emphasize the mechanism of the effect of exercise on lipid droplet (LD) metabolism disorder in nonalcoholic fatty liver disease (NAFLD). Main methods: C57BL/6J mice were randomly divided into three groups: The first group was fed with a normal diet (CON), the second group was fed a high-fat diet (HF), and finally group with a high-fat diet intervention and swim training (HF-EX). The total intervention period was 16 weeks. RT-PCR and Western blot were performed to evaluate the effect of exercise on LDs metabolism and the AMPK pathway. Histopathological examinations and immunofluorescence were performed to evaluate the lipid deposition and lipophagy in the liver. Key findings: Exercise reduced liver steatosis and insulin resistance along with the stimulation of AMPK/SIRT1 signaling and downstream regulation of lipid metabolism. In addition, exercise increased the expression of autophagy marker and colocalization of LC3 and LAMP1 with LDs. Significance: Exercise stimulated AMPK/SIRT1 and activated lipophagy in NAFLD. Enhancing lipophagy may be one of the key mechanisms of regulation and resolution of NAFLD by exercise.