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To determine distribution of severe acute respiratory syndrome coronavirus 2 in hospital wards in Wuhan, China, we tested air and surface samples. Contamination was greater in intensive care units than general wards. Virus was widely distributed on floors, computer mice, trash cans, and sickbed handrails and was detected in air ≈4 m from patients.

Fibrosis is characterized by the excessive production of collagen and other extracellular matrix (ECM) components and represents a leading cause of morbidity and mortality worldwide. Previous studies of nonalcoholic steatohepatitis (NASH) with fibrosis were largely restricted to bulk transcriptome profiles. Thus, our understanding of this disease is limited by an incomplete characterization of liver cell types in general and hepatic stellate cells (HSCs) in particular, given that activated HSCs are the major hepatic fibrogenic cell population. To help fill this gap, we profiled 17,810 non-parenchymal cells derived from six healthy human livers. In conjunction with public single-cell data of fibrotic/cirrhotic human livers, these profiles enable the identification of potential intercellular signaling axes (e.g., ITGAV–LAMC1, TNFRSF11B–VWF and NOTCH2–DLL4) and master regulators (e.g., RUNX1 and CREB3L1 ) responsible for the activation of HSCs during fibrogenesis. Bulk RNA-seq data of NASH patient livers and rodent models for liver fibrosis of diverse etiologies allowed us to evaluate the translatability of candidate therapeutic targets for NASH-related fibrosis. We identified 61 liver fibrosis-associated genes (e.g., AEBP1, PRRX1 and LARP6 ) that may serve as a repertoire of translatable drug target candidates. Consistent with the above regulon results, gene regulatory network analysis allowed the identification of CREB3L1 as a master regulator of many of the 61 genes. Together, this study highlights potential cell–cell interactions and master regulators that underlie HSC activation and reveals genes that may represent prospective hallmark signatures for liver fibrosis.

Gene-expression programs define shared and species-specific phenotypes, but their evolution remains largely uncharacterized beyond the transcriptome layer 1 . Here we report an analysis of the co-evolution of translatomes and transcriptomes using ribosome-profiling and matched RNA-sequencing data for three organs (brain, liver and testis) in five mammals (human, macaque, mouse, opossum and platypus) and a bird (chicken). Our within-species analyses reveal that translational regulation is widespread in the different organs, in particular across the spermatogenic cell types of the testis. The between-species divergence in gene expression is around 20% lower at the translatome layer than at the transcriptome layer owing to extensive buffering between the expression layers, which especially preserved old, essential and housekeeping genes. Translational upregulation specifically counterbalanced global dosage reductions during the evolution of sex chromosomes and the effects of meiotic sex-chromosome inactivation during spermatogenesis. Despite the overall prevalence of buffering, some genes evolved faster at the translatome layer—potentially indicating adaptive changes in expression; testis tissue shows the highest fraction of such genes. Further analyses incorporating mass spectrometry proteomics data establish that the co-evolution of transcriptomes and translatomes is reflected at the proteome layer. Together, our work uncovers co-evolutionary patterns and associated selective forces across the expression layers, and provides a resource for understanding their interplay in mammalian organs. An analysis using ribosome-profiling and matched RNA-sequencing data for three organs across five mammalian species and a bird enables the comparison of translatomes and transcriptomes, revealing patterns of co-evolution of these two expression layers.

Contrast-rebalanced dichoptic movies have been shown to be an effective binocular treatment for amblyopia in the laboratory. Yet, at-home therapy is a more practical approach. In a randomized clinical trial, we compared dichoptic movies, streamed at-home on a handheld 3D-enabled game console, versus patching as amblyopia treatment. Sixty-five amblyopic children (3–7 years; 20/32–125) were randomly assigned to one of two parallel arms, binocular treatment (3 movies/week) or patching (14 h/week). The primary outcome, change in best corrected visual acuity (BCVA) at the 2-week visit was completed by 28 and 30, respectively. After the primary outcome, both groups of children had the option to complete up to 6 weeks of binocular treatment. At the 2-week primary outcome visit, BCVA had improved in the movie (0.07 ± 0.02 logMAR; p  < .001) and patching (0.06 ± 0.01 logMAR; p  < 0.001) groups. There was no significant difference between groups (CI 95 %: − 0.02 to 0.04; p  = .48). Visual acuity improved in both groups with binocular treatment up to 6 weeks (0.15 and 0.18 logMAR improvement, respectively). This novel, at-home, binocular movie treatment improved amblyopic eye BCVA after 2 weeks (similar to patching), with additional improvement up to 6 weeks. Repeated binocular visual experience with contrast-rebalanced binocular movies provides an additional treatment option for amblyopia. Clincaltrials.gov identifier: NCT03825107 (31/01/2019).

Efficiently analyzing and classifying dynamically changing time series data remains a challenge. The main issue lies in the significant differences in feature distribution that occur between old and new datasets generated constantly due to varying degrees of concept drift, anomalous data, erroneous data, high noise, and other factors. Taking into account the need to balance accuracy and efficiency when the distribution of the dataset changes, we proposed a new robust, generalized incremental learning (IL) model ELM-KL-LSTM. Extreme learning machine (ELM) is used as a lightweight pre-processing model which is updated using the new designed evaluation metrics based on Kullback-Leibler (KL) divergence values to measure the difference in feature distribution within sliding windows. Finally, we implemented efficient processing and classification analysis of dynamically changing time series data based on ELM lightweight pre-processing model, model update strategy and long short-term memory networks (LSTM) classification model. We conducted extensive experiments and comparation analysis based on the proposed method and benchmark methods in several different real application scenarios. Experimental results show that, compared with the benchmark methods, the proposed method exhibits good robustness and generalization in a number of different real-world application scenarios, and can successfully perform model updates and efficient classification analysis of incremental data with varying degrees improvement of classification accuracy. This provides and extends a new means for efficient analysis of dynamically changing time-series data.

The manual segmentation of retinal layers from OCT scan images is time-consuming and costly. The deep learning approach has potential for the automatic delineation of retinal layers to significantly reduce the burden of human graders. In this study, we compared deep learning model (DLM) segmentation with manual correction (DLM-MC) to conventional manual grading (MG) for the measurements of the photoreceptor ellipsoid zone (EZ) area and outer segment (OS) volume in retinitis pigmentosa (RP) to assess whether DLM-MC can be a new gold standard for retinal layer segmentation and for the measurement of retinal layer metrics. Ninety-six high-speed 9 mm 31-line volume scans obtained from 48 patients with RPGR-associated XLRP were selected based on the following criteria: the presence of an EZ band within the scan limit and a detectable EZ in at least three B-scans in a volume scan. All the B-scan images in each volume scan were manually segmented for the EZ and proximal retinal pigment epithelium (pRPE) by two experienced human graders to serve as the ground truth for comparison. The test volume scans were also segmented by a DLM and then manually corrected for EZ and pRPE by the same two graders to obtain DLM-MC segmentation. The EZ area and OS volume were determined by interpolating the discrete two-dimensional B-scan EZ-pRPE layer over the scan area. Dice similarity, Bland–Altman analysis, correlation, and linear regression analyses were conducted to assess the agreement between DLM-MC and MG for the EZ area and OS volume measurements. For the EZ area, the overall mean dice score (SD) between DLM-MC and MG was 0.8524 (0.0821), which was comparable to 0.8417 (0.1111) between two MGs. For the EZ area > 1 mm2, the average dice score increased to 0.8799 (0.0614). When comparing DLM-MC to MG, the Bland–Altman plots revealed a mean difference (SE) of 0.0132 (0.0953) mm2 and a coefficient of repeatability (CoR) of 1.8303 mm2 for the EZ area and a mean difference (SE) of 0.0080 (0.0020) mm3 and a CoR of 0.0381 mm3 for the OS volume. The correlation coefficients (95% CI) were 0.9928 (0.9892–0.9952) and 0.9938 (0.9906–0.9958) for the EZ area and OS volume, respectively. The linear regression slopes (95% CI) were 0.9598 (0.9399–0.9797) and 1.0104 (0.9909–1.0298), respectively. The results from this study suggest that the manual correction of deep learning model segmentation can generate EZ area and OS volume measurements in excellent agreement with those of conventional manual grading in RP. Because DLM-MC is more efficient for retinal layer segmentation from OCT scan images, it has the potential to reduce the burden of human graders in obtaining quantitative measurements of biomarkers for assessing disease progression and treatment outcomes in RP.

Interferon lambda 3 (IFN-λ3) is a newly identified cytokine with antiviral activity, and its single nucleotide polymorphisms are strongly associated with the treatment effectiveness and development of chronic hepatitis C virus infection. We thus examined the potential of IFN-λ3 to inhibit HIV replication and the possible mechanisms of the anti-HIV action by IFN-λ3 in human macrophages. Under different conditions (before, during, and after HIV infection), IFN-λ3 significantly inhibited viral replication in macrophages, which was associated with the induction of multiple antiviral cellular factors (ISG56, MxA, OAS-1, A3G/F and tetherin) and IFN regulatory factors (IRF-1, 3, 5, 7 and 9). This anti-HIV action of IFN-λ3 could be compromised by the JAK-STAT inhibitor. In addition, IFN-λ3 treatment of macrophages induced the expression of toll-like receptor 3 (TLR3) and two key adaptors (MyD88 and TRIF) in type I IFN pathway activation. However, HIV infection compromised IFN-λ3-mediated induction of the key elements in JAK-STAT signaling pathway. These data indicate that IFN-λ3 exerts its anti-HIV function by activating JAK-STAT pathway-mediated innate immunity in macrophages. Future in vivo studies are necessary in order to explore the potential for developing IFN-λ3-based therapy for HIV disease.

PurposePreviously, we have shown the capability of a hybrid deep learning (DL) model that combines a U-Net and a sliding-window (SW) convolutional neural network (CNN) for automatic segmentation of retinal layers from OCT scan images in retinitis pigmentosa (RP). We found that one of the shortcomings of the hybrid model is that it tends to underestimate ellipsoid zone (EZ) width or area, especially when EZ extends toward or beyond the edge of the macula. In this study, we trained the model with additional data which included more OCT scans having extended EZ. We evaluated its performance in automatic measurement of EZ area on SD-OCT volume scans obtained from the participants of the RUSH2A natural history study by comparing the model’s performance to the reading center’s manual grading.Materials and MethodsDe-identified Spectralis high-resolution 9-mm 121-line macular volume scans as well as their EZ area measurements by a reading center were transferred from the management center of the RUSH2A study under the data transfer and processing agreement. A total of 86 baseline volume scans from 86 participants of the RUSH2A study were included to evaluate two hybrid models: the original RP240 model trained on 480 mid-line B-scans from 220 patients with retinitis pigmentosa (RP) and 20 participants with normal vision from a single site, and the new RP340 model trained on a revised RP340 dataset which included RP240 dataset plus an additional 200 mid-line B-scans from another 100 patients with RP. There was no overlap of patients between training and evaluation datasets. EZ and apical RPE in each B-scan image were automatically segmented by the hybrid model. EZ areas were determined by interpolating the discrete 2-dimensional B-scan EZ-RPE layer over the scan area. Dice similarity, correlation, linear regression, and Bland-Altman analyses were conducted to assess the agreement between the EZ areas measured by the hybrid model and by the reading center.ResultsFor EZ area > 1 mm2, average dice coefficients ± SD between the EZ band segmentations determined by the DL model and the manual grading were 0.835 ± 0.132 and 0.867 ± 0.105 for RP240 and RP340 hybrid models, respectively ( p < 0.0005; n = 51). When compared to the manual grading, correlation coefficients (95% CI) were 0.991 (0.987–0.994) and 0.994 (0.991–0.996) for RP240 and RP340 hybrid models, respectively. Linear regression slopes (95% CI) were 0.918 (0.896–0.940) and 0.995 (0.975–1.014), respectively. Bland-Altman analysis revealed a mean difference ± SD of -0.137 ± 1.131 mm2 and 0.082 ± 0.825 mm2, respectively.ConclusionAdditional training data improved the hybrid model’s performance, especially reducing the bias and narrowing the range of the 95% limit of agreement when compared to manual grading. The close agreement of DL models to manual grading suggests that DL may provide effective tools to significantly reduce the burden of reading centers to analyze OCT scan images. In addition to EZ area, our DL models can also provide the measurements of photoreceptor outer segment volume and thickness to further help assess disease progression and to facilitate the study of structure and function relationship in RP.

In addition to flat rheology to deal with the wellbore stabilization problem caused by narrow safety density window when drilling deepwater wells, upgrading the mud cake quality by fluid loss reducer and plugging to stabilize the wellbore is also an important measure to deal with this problem. For the existing fluid loss reducers for deepwater water-based drilling fluids, it is difficult to balance the plugging performance. In this study, a cationic micro-crosslinking polymer was synthesized as a fluid loss reducer by reversed-phase emulsion polymerization. N-isopropylacrylamide (NIPAM) and acrylamide (AM) were chosen as the main body of the synthesis to enhance the hydrophilicity of the products. Methacryloyloxyethyl trimethyl ammonium chloride (DMC) was selected to provide cationic groups to enhance the residency of the products in the formation. The molecular structure of ENAD was characterized using infrared spectroscopy (FT-IR), nuclear magnetic resonance hydrogen spectroscopy (NMR) and X-ray photoelectron spectroscopy (XPS), and the molecular structure of the product was as expected. The thermal stability of ENAD was analyzed by TGA, and the initial thermal decomposition temperature was 283 °C. The filtration effect of ENAD in BF under different media was evaluated. The experimental results show that ENAD can withstand temperature up to 150°C and has good filtration performance. 16h after aging at 150°C, the API filtration loss (FLAPI) is 8.8mL, the sand bed intrusion depth is 4.4cm, the high temperature and high pressure filtration loss (FLHTHP) is 32mL, the high temperature and high pressure filtration loss of plugging 10μm sand disc is 92mL. After comparing with sulfonated asphalt (FT), it is found that ENAD has better filtration performance. It is found that ENAD has better performance in reducing filtration loss. The mode of interaction between ENAD and the formation and its own mechanism of filtration loss reduction were analyzed by zeta potential analysis and SEM. With the increase of ENAD addition, the absolute values of zeta potential of BF before and after aging decreased from 48.7 and 32.1 to 43.3 and 27.1, respectively. ENAD enhances the interaction force with bentonite particles through its own adsorption properties. The strength of the mud cake is further strengthened by thermal deformation properties. It also enhances the force between the polymer and the formation through electrostatic gravitational adsorption, which further enhances the retention capacity of the polymer in the formation. Compared with conventional fluid loss reducers, ENAD has superior filtration effect and can be used as a fluid loss reducer for deepwater water-based drilling fluids.

Moisture content is an important factor in corn breeding and cultivation. A corn breed with low moisture at harvest is beneficial for mechanical operations, reduces drying and storage costs after harvesting and, thus, reduces energy consumption. Nondestructive measurement of kernel moisture in an intact corn ear allows us to select corn varieties with seeds that have high dehydration speeds in the mature period. We designed a sensor using a ring electrode pair for nondestructive measurement of the kernel moisture in a corn ear based on a high-frequency detection circuit. Through experiments using the effective scope of the electrodes’ electric field, we confirmed that the moisture in the corn cob has little effect on corn kernel moisture measurement. Before the sensor was applied in practice, we investigated temperature and conductivity effects on the output impedance. Results showed that the temperature was linearly related to the output impedance (both real and imaginary parts) of the measurement electrodes and the detection circuit’s output voltage. However, the conductivity has a non-monotonic dependence on the output impedance (both real and imaginary parts) of the measurement electrodes and the output voltage of the high-frequency detection circuit. Therefore, we reduced the effect of conductivity on the measurement results through measurement frequency selection. Corn moisture measurement results showed a quadric regression between corn ear moisture and the imaginary part of the output impedance, and there is also a quadric regression between corn kernel moisture and the high-frequency detection circuit output voltage at 100 MHz. In this study, two corn breeds were measured using our sensor and gave R2 values for the quadric regression equation of 0.7853 and 0.8496.