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Objective This study aims to develop a prognosis prediction model and visualization system for acute paraquat poisoning based on an improved machine learning model. Methods 101 patients with acute paraquat poisoning admitted to 6 hospitals from March 2020 to March 2022 were selected for this study. After expiry of the treatment period (one year of follow-up for survivors and up to the time of death for deceased patients) and they were categorized into the survival group (n = 37) and death group (n = 64). The biochemical indexes of the patients were analyzed, and a prognosis prediction model was constructed using HHO-XGBoost, an improved machine-learning algorithm. Multivariate logistic analysis was used to verify the value of the self-screening features in the model. Results Seven features were selected in the HHO-XGBoost model, including oral dose, serum creatinine, alanine aminotransferase (ALT), white blood cell (WBC) count, neutrophil count, urea nitrogen level, and thrombin time. Univariate analysis showed statistically significant differences between these features’ survival and death groups (P

To determine the effect of ignition height on indoor flame spread behavior and overpressure development, a comprehensive full-scale deflagration testing facility was established. Extensive experimental research was conducted within this facility. The findings indicate that indoor flame reignition and the occurrence of secondary explosions are most pronounced with intermediate ignition. Furthermore, the explosion overpressure generated during the reverse turn of shock wave propagation is greater than that produced by the forward turn. In comparison to the peak overpressure Pext in the master bedroom for top, middle, and bottom ignition, the peak overpressure Pext in the second bedroom increased by approximately 14.48 %, 15.04 %, and 19.20 %, respectively. When comparing middle ignition to top ignition, the propagation speed of shock waves in the kitchen balcony, restroom, second bedroom, and master bedroom was enhanced by 34.21 %, 40.85 %, 40.70 %, and 34.65 %, respectively. Furthermore, when comparing middle ignition to bottom ignition, the propagation speed of shock waves in these areas experienced a significant increase of 126.32 %, 124.39 %, 123.26 %, and 113.86 %, respectively. These research findings provide a theoretical foundation and empirical data to support the investigation and analysis of the causes of indoor gas explosion incidents.

ABSTRACT: Avian influenza, particularly the H9N2 subtype, presents significant challenges to poultry health, underscoring the need for effective antiviral interventions. This study explores the antiviral capabilities of Belamcanda extract, a traditional Chinese medicinal herb, against H9N2 Avian influenza virus (AIV) in specific pathogen-free (SPF) chicks. Through a comprehensive approach, we evaluated the impact of the extract on cytokine modulation and crucial immunological signaling pathways, essential for understanding the host-virus interaction. Our findings demonstrate that Belamcanda extract significantly modulates the expression of key inflammatory cytokines, including tumor necrosis factor alpha (TNF-α), interleukin-1 (IL-1), interleukin-2 (IL-2), and interleukin-6 (IL-6), which are pivotal to the host's response to H9N2 AIV infection. Western blot analysis further revealed that the extract markedly reduces the expression of critical immune signaling molecules such as toll-like receptor 3 (TLR3), TIR-domain-containing adapter-inducing interferon-β (TRIF), and nuclear factor kappa B (NF-κB). These insights into the mechanisms by which Belamcanda extract influences host immune responses and hinders viral replication highlight its potential as an innovative antiviral agent for poultry health management. The study advances our comprehension of natural compounds' antiviral mechanisms and lays the groundwork for developing strategies to manage viral infections in poultry. The demonstrated ability of Belamcanda extract to modulate immune responses and inhibit viral replication establishes it as a promising candidate for future antiviral therapy development, especially in light of the need for effective treatments against evolving influenza virus strains and the critical demand for enhanced poultry health management strategies.

BackgroundCannabidiol (CBD), as an important therapeutic property of the cannabis plants, is mainly produced in the flower organs. Auxin response factors (ARFs) are play a crucial role in flower development and secondary metabolite production. However, the specific roles of ARF gene family in cannabis remain unknown.MethodsIn this study, various bioinformatics analysis of CsARF genes were conducted using online website and bioinformatics, quantitative real time PCR technology was used to investigate the expression patterns of the CsARF gene family in different tissues of different cannabis varieties, and subcellular localization analysis was performed in tobacco leaf.ResultsIn this study, 22 CsARF genes were identified and found to be unevenly distributed across 9 chromosomes of the cannabis genome. Phylogenetic analysis revealed that the ARF proteins were divided into 4 subgroups. Duplication analysis identified one pair of segmental/whole-genome duplicated CsARF, and three pairs of tandemly duplicated CsARF. Collinearity analysis revealed that two CsARF genes, CsARF4 and CsARF19, were orthologous in both rice and soybean. Furthermore, subcellular localization analysis showed that CsARF2 was localized in the nucleus. Tissue-specific expression analysis revealed that six genes were highly expressed in cannabis male flowers, and among these genes, 3 genes were further found to be highly expressed at different developmental stages of male flowers. Meanwhile, correlation analysis between the expression level of CsARF genes and CBD content in two cultivars ‘H8’ and ‘Y7’ showed that the expression level of CsARF13 was negatively correlated with CBD content, while the expression levels of six genes were positively correlated with CBD content. In addition, most of CsARF genes were responsive to IAA treatment.ConclusionOur study laid a foundation for the further studies of CsARFs function in cannabis, and provides candidate genes for breeding varieties with high CBD yield in cannabis production.

The vapor cloud explosion in open space are greatly affected by the initial conditions of gas cloud and the surrounding environment, while in the gas compressor station, these factors are ubiquitous. While the researches of external influencing factors like the obstacle and initial turbulence effect on natural gas explosion in open space are rare. In this paper, a series of natural gas explosion experiments were conducted and a long-distance natural gas pipeline compressor station was taking as an example to simulate the natural gas leakage and explosion in the typical location. Results showed that the greater the initial turbulence, the greater the peak overpressure of gas explosion. When the initial wind speed is 5 m/s, the peak overpressure can promote 73.4 % higher than that without initial turbulence. However, the overpressure doesn't linearly increase with the increasing of initial wind speed. What's more, the flame speed showed the same law with the overpressure, and the effect of concentration on overpressure is lower than that of initial turbulence and obstacle density. The explosion damaging range in the condenser area is larger than that in the separator area, and the maximum overpressure can reach about 21 kPa and the temperature can reach above 2280K, which provides an important basis for the safety prevention and personnel protection in natural gas compressor station.

Variations in Marine Dissolved Oxygen Concentrations (MDOC) play a critical role in the study of marine ecosystems and global climate evolution. Although artificial intelligence methods, represented by deep learning, can enhance the precision of MDOC inversion, the uninterpretability of the operational mechanism involved in the “black-box” often make the process difficult to interpret. To address this issue, this paper proposes a high-precision interpretable framework (CDRP) for intelligent MDOC inversion, including Causal Discovery, Drift Detection, RuleFit Model, and Post Hoc Analysis. The entire process of the proposed framework is fully interpretable: (i) The causal relationships between various elements are further clarified. (ii) During the phase of concept drift analysis, the potential factors contributing to changes in marine data are extracted. (iii) The operational rules of RuleFit ensure computational transparency. (iv) Post hoc analysis provides a quantitative interpretation from both global and local perspectives. Furthermore, we have derived quantitative conclusions about the impacts of various marine elements, and our analysis maintains consistency with conclusions in marine literature on MDOC. Meanwhile, CDRP also ensures the precision of MDOC inversion: (i) PCMCI causal discovery eliminates the interference of weakly associated elements. (ii) Concept drift detection takes more representative key frames. (iii) RuleFit achieves higher precision than other models. Experiments demonstrate that CDRP has reached the optimal level in single point buoy data inversion task. Overall, CDRP can enhance the interpretability of the intelligent MDOC inversion process while ensuring high precision.

Objective The safe operation of transmission lines is of great significance for national economic construction and development, but there were few studies on the evaluation of geological hazards susceptibility to transmission lines. Methods This study focuses on the Beijing-Tianjin-Hebei region as an example, where eight index factors, including elevation, slope, aspect, terrain relief, stratigraphic lithology, distance from fault, distance from water system, and land use type were selected. The frequency ratio method was used to classify each index factor to construct a susceptibility evaluation system.Then used different machine learning models and grid of different spatial resolutions as evaluation units to evaluate the susceptibility of the study area.Finally, the machine learning model with the highest accuracy and the traditional Analytic Hierarchy Process (AHP) were selected to complete the susceptibility zoning map of the study area. Results The research results show that the Bayesian Network model (Bayesian Network, BN) had the best application effect and the strongest model performance in the susceptibility evaluation of regional transmission lines, and the maximum AUC value was 0.876. The BN model outperformed the traditional AHP model, displaying superior precision in susceptibility mapping in the study area. Conclusion In addition, emplpying 50 m grid as the evaluation unit had achieved the best application effect in the evaluation of transmission line geological disaster susceptibility, which provided ideas and references for transmission line geological disaster evaluation and grid resolution selection.

Due to the complexity of terrain and climate in the mountain–plain transition zone, it is difficult to simulate and forecast the flow discharge of river basins accurately. The poor regularity of the river thus leads to uncertain flood control scheduling. Meanwhile, reservoirs and flood detention areas are constructed to store and divert water when severe floods threaten the safety of the basin. In order to improve the accuracy of flood forecasts and the effectiveness of flood control, a hydrological and 1D/2D hydrodynamic coupling model was developed to enable the joint computation of multiple objects, including mountainous streams, plains river networks, hydraulic control structures, and flood detention areas. For the hydrological component, the Xin’anjiang model with the Muskingum module is employed to simulate mountainous flow discharge. For the hydrodynamic component, the Saint–Venant equations and shallow water equations are applied to estimate flood processes in rivers and on land surfaces, respectively. The Dongtiaoxi River Basin in Zhejiang Province, China, serves as the case study, where river flow is influenced by both upstream mountainous floods and downstream backwater effects. Using the integrated model, flood routing and scheduling are simulated and visualized. Both the Xin’anjiang model and the 1D hydrodynamic model demonstrate over 80% acceptability in calibration and validation, confirming their robustness and reliability. Meanwhile, inundation in flood detention areas can be effectively estimated by coupling the 1D and 2D hydrodynamic models with a flood diversion scheduling model. The coupled model proves capable of simulating flood routing in complex river basins that include mountains, plains, and hydraulic control structures, accounting for the interactions between hydrological elements. These findings provide a new perspective on flood simulation in other similarly complex river basins.

Oil field-associated gas is typically a mixture of light alkanes. To ensure production safety, it is essential to investigate the combustion characteristics and the reaction mechanism of light alkane gas mixtures to uncover the underlying mechanisms of the combustion reaction. This paper investigates the equilibrium gas component concentrations resulting from the reaction of a gas mixture of methane, ethane, and propane at specific ratios through experimental tests. The study's results demonstrate that the equilibrium concentrations of methane, ethane, and propane initially decrease and then increase with the rise of the equivalence ratio under the same initial pressure conditions. In oxygen-poor conditions, the equilibrium concentration ratio of methane gradually increases in proportion to the equilibrium ratio, confirming that ethane and propane are the preferred participants in the reaction. Furthermore, a study of the equilibrium concentration of the critical intermediate product, ethylene, revealed that it does not completely oxidize in the combustion reaction. It can persist under both oxygen-rich and oxygen-poor conditions, with an equilibrium concentration of approximately 0.02%. The findings of this paper can serve as a theoretical supplement to the research on explosive gas combustion and explosions.

IntroductionThis study focuses on evaluating the therapeutic efficacy of cecropin AD, an antimicrobial peptide, against H9N2 avian influenza virus (AIV) in chickens. Given the global impact of H9N2 AIV on poultry health, identifying effective treatments is crucial.MethodsTo assess the impact of cecropin AD, we conducted in vivo experiments involving 108 5-week-old chickens divided into control, infected, and various treatment groups based on cecropin AD dosage levels (high, medium, and low). The methodologies included hemagglutination (HA) tests for viral titers, histopathological examination and toluidine blue (TB) staining for lung pathology, real-time PCR for viral detection, and enzyme-linked immunosorbent assays for measuring serum levels of inflammatory markers.ResultsThe findings revealed that cecropin AD substantially reduced lung pathology and viral load, especially at higher dosages, comparing favorably with the effects seen from conventional treatments. Moreover, cecropin AD effectively modulated mast cell activity and the levels of inflammatory markers such as IL-6, TNF-α, IFN-γ, and 5-HT, indicating its potential to diminish inflammation and viral spread.DiscussionCecropin AD presents a significant potential as an alternative treatment for H9N2 AIV in chickens, as evidenced by its ability to lessen lung damage, decrease viral presence, and adjust immune responses. This positions cecropin AD as a promising candidate for further exploration in the management of H9N2 AIV infections in poultry.

In our research, we explored a natural substance called Oxymatrine, found in a traditional Chinese medicinal plant, to fight against a common bird flu virus known as H9N2. This virus not only affects birds but can also pose a threat to human health. We focused on how this natural compound can help in stopping the virus from spreading in cells that line the lungs of birds and potentially humans. Our findings show that Oxymatrine can both directly block the virus and boost the body’s immune response against it. This dual-action mechanism is particularly interesting because it indicates that Oxymatrine might be a useful tool in developing new ways to prevent and treat this type of bird flu. Understanding how Oxymatrine works against the H9N2 virus could lead to safer and more natural ways to combat viral infections in animals and humans, contributing to the health and well-being of society. The H9N2 Avian Influenza Virus (AIV) is a persistent health threat because of its rapid mutation rate and the limited efficacy of vaccines, underscoring the urgent need for innovative therapies. This study investigated the H9N2 AIV antiviral properties of Oxymatrine (OMT), a compound derived from traditional Chinese medicine, particularly focusing on its interaction with pulmonary microvascular endothelial cells (PMVECs). Employing an array of in vitro assays, including 50% tissue culture infectious dose, Cell Counting Kit-8, reverse transcription-quantitative polymerase chain reaction, enzyme-linked immunosorbent assay, and Western blot, we systematically elucidated the multifaceted effects of OMT. OMT dose-dependently inhibited critical antiviral proteins (PKR and Mx1) and modulated the expression of type I interferons and key cytokines (IFN-α, IFN-β, IL-6, and TNF-α), thereby affecting TLR3 signaling and its downstream elements (NF-κB and IRF-3). OMT’s antiviral efficacy extended beyond TLR3-mediated responses, suggesting its potential as a versatile antiviral agent. This study not only contributes to the growing body of research on the use of natural compounds as antiviral agents but also underscores the importance of further investigating the broader application of OMT for combating viral infections.

Programmed death ligand-1 (PD-L1) is highly expressed in a variety of cancer cells and suggests a poorer prognosis for patients. The natural compound isorhamnetin (ISO) shows promise in treating cancers and causing damage to canine mammary tumor (CMT) cells. We investigated the mechanism of ISO in reducing PD-L1 expression in CMT cells. Clustered, regularly interspaced short palindromic repeat-associated protein 9 (CRISPR/Cas9) was used to mediate CD274 knockout in U27 cells. Then, monoclonal cells were screened and cultured. Nucleotide sequencing and expression of PD-L1 were detected. Additionally, we examined cell migration, invasion, and damage. Immunofluorescent staining of PD-L1 was examined in U27 cells. The signaling pathways were measured by Western blotting. Murine xenotransplantation models and murine immunocompetent allograft mammary tumor models were established to evaluate the effect of ISO therapy. Expression of Ki-67, caspase3, and PD-L1 were analyzed by immunohistochemistry. A pull-down assay was used to explore which proteins could bind to ISO. Canine EGFR protein was purified and used to detect whether it directly binds to ISO using a surface plasmon resonance assay. ISO inhibited the EGFR-STAT3-PD-L1 signaling pathway and blocked cancer growth, significantly increasing the survival rate of healthy cells. The cell membrane receptor EGFR was identified as a direct target of ISO. ISO could be exploited as an antineoplastic treatment of CMT by targeting EGFR to suppress PD-L1 expression.

A chemical ignition mathematical model was established based on the analysis of the process of chemical ignition and thermal ignition theory . Then, the model was solved by the method of nets on a computer to explore the mechanism for in-situ combustion ignition with chemical additives. Also, the development course of in-situ combustion ignition was reviewed. In addition, the ignition process by chemical methods was analysed. It was shown that the mathematical model was able to calculate some parameters such as the critical duration of the outer source action and critical energy for ignition. The heat flux that ignites an oil layer is greater than 2.35, when its dimensionless initial activation energy ɛ and initial temperature θ0are 0.026 and 11. Moreover, the ignition process and temperature distribution of oil sands were described with the model. The results are of great theoretical significance for understanding chemically assisted ignition mechanism in-depth, controlling ignition parameters and guiding chemical additives formulation design and optimization, etc.

The paper presents the damage results of thick steel plates subjected to local blast loading using experimental and numerical approaches. Three steel plates with a thickness of 17 mm under the local contact explosion of trinitrotoluene (TNT) explosives were tested, and the damaged parts of the steel plates were scanned using a scanning electron microscope (SEM). ANSYS LS-DYNA software was used to simulate the damage results of the steel plate. By analyzing and comparing the experimental results with the numerical simulation results, the influence law of the TNT acting on the steel plate, the damage mode of the steel plate, the reliability verification of the numerical simulation, and the criterion for judging the damage mode of the steel plate were obtained. Results show that the damage mode of the steel plate changes with the changes in the explosive charge. The diameter of the crater on the surface of the steel plate is mainly related to the diameter of the contact surface between the explosive and the steel plate. The fracture mode of the steel plate in the process of generating cracks is a quasi-cleavage fracture, and the process of generating craters and perforations in the steel plate is a ductile fracture. The damage mode of the steel plates can be divided into three types. The numerical simulation results have minor errors and high reliability, and numerical simulation can be used as an auxiliary tool for experiments. A new criterion is proposed to predict the damage mode of the steel plates under contact explosion.

Autonomous driving applications use two types of sensor systems to detect vehicles - depth sensing LiDAR and radiance sensing cameras. We compare the performance (average precision) of a ResNet for vehicle detection in complex, daytime, driving scenes when the input is a depth map [D = d(x,y)], a radiance image [L = r(x,y)], or both [D,L]. (1) When the spatial sampling resolution of the depth map and radiance image are both equal to typical camera resolutions, a ResNet detects vehicles at higher average precision from depth than radiance. (2) When the spatial sampling of the depth map matches the range of current LiDAR devices, the average precision is higher for radiance than depth. (3) A hybrid system that combines depth and radiance has higher average precision than systems using depth or radiance alone. We confirm these observations in both simulation and real-world data. We explain the advantage of combining depth and radiance by noting that the two types of information have complementary weaknesses. The radiance data are limited by dynamic range, motion blur and illumination variation; the LiDAR data have low spatial resolution. The ResNet effectively combines the two data sources to improve vehicle detection.

We quantify the generalization of a convolutional neural network (CNN) trained to identify cars. First, we perform a series of experiments to train the network using one image dataset - either synthetic or from a camera - and then test on a different image dataset. We show that generalization between images obtained with different cameras is roughly the same as generalization between images from a camera and ray-traced multispectral synthetic images. Second, we use ISETAuto, a soft prototyping tool that creates ray-traced multispectral simulations of camera images, to simulate sensor images with a range of pixel sizes, color filters, acquisition and post-acquisition processing. These experiments reveal how variations in specific camera parameters and image processing operations impact CNN generalization. We find that (a) pixel size impacts generalization, (b) demosaicking substantially impacts performance and generalization for shallow (8-bit) bit-depths but not deeper ones (10-bit), (c) the network performs well using raw (not demosaicked) sensor data for 10-bit pixels.

Hydrogen sulfide (H2S) precipitation incidents often occur during pigging operations in crude oil pipelines, which result in the leakage of H2S with a high concentration at the site and seriously threaten the life and health of workers. To identify the main cause of H2S precipitation, this study designs a crude oil pipeline experimental system by which pigging operations could be simulated according to the on-site pipeline configuration. In addition, this paper determines the conditions at which a large amount of H2S could be stripped in an elbow pipeline when N2 passes through the pig to contact the oil sample and pushes to return the oil compared with those in a straight pipeline. Furthermore, this paper analyzes the effect of the N2 injection rate on H2S precipitation under repeated N2 contact with oil samples. Finally, this paper develops an empirical formula presenting the relationship among the H2S concentration ratio and its various influencing factors, which could give immediate guidance to prevent the occurrence of similar accidents.

[This corrects the article DOI: 10.1371/journal.pone.0149379.].

Region proposal network (RPN) based object detection, such as Faster Regions with CNN (Faster R-CNN), has gained considerable attention due to its high accuracy and fast speed. However, it has room for improvements when used in special application situations, such as the on-board vehicle detection. Original RPN locates multiscale anchors uniformly on each pixel of the last feature map and classifies whether an anchor is part of the foreground or background with one pixel in the last feature map. The receptive field of each pixel in the last feature map is fixed in the original faster R-CNN and does not coincide with the anchor size. Hence, only a certain part can be seen for large vehicles and too much useless information is contained in the feature for small vehicles. This reduces detection accuracy. Furthermore, the perspective projection results in the vehicle bounding box size becoming related to the bounding box position, thereby reducing the effectiveness and accuracy of the uniform anchor generation method. This reduces both detection accuracy and computing speed. After the region proposal stage, many regions of interest (ROI) are generated. The ROI pooling layer projects an ROI to the last feature map and forms a new feature map with a fixed size for final classification and box regression. The number of feature map pixels in the projected region can also influence the detection performance but this is not accurately controlled in former works. In this paper, the original faster R-CNN is optimized, especially for the on-board vehicle detection. This paper tries to solve these above-mentioned problems. The proposed method is tested on the KITTI dataset and the result shows a significant improvement without too many tricky parameter adjustments and training skills. The proposed method can also be used on other objects with obvious foreshortening effects, such as on-board pedestrian detection. The basic idea of the proposed method does not rely on concrete implementation and thus, most deep learning based object detectors with multiscale feature maps can be optimized with it.

The rat is a preferred model system over the mouse for neurological studies, and cell type-specific Cre expression in the rat enables precise ablation of gene function in neurons of interest, which is especially valuable for neurodegenerative disease modeling and optogenetics. Yet, few such Cre rats are available. Here we report the characterization of two Cre rats, tyrosine hydroxylase (TH)-Cre and dopamine active transporter (DAT or Slc6a3)-Cre, by using a combination of immunohistochemistry (IHC) and mRNA fluorescence in situ hybridization (FISH) as well as a fluorescent reporter for Cre activity. We detected Cre expression in expected neurons in both Cre lines. Interestingly, we also found that in Th-Cre rats, but not DAT-Cre rats, Cre is expressed in female germ cells, allowing germline excision of the floxed allele and hence the generation of whole-body knockout rats. In summary, our data demonstrate that targeted integration of Cre cassette lead to faithful recapitulation of expression pattern of the endogenous promoter, and mRNA FISH, in addition to IHC, is an effective method for the analysis of the spatiotemporal gene expression patterns in the rat brain, alleviating the dependence on high quality antibodies that are often not available against rat proteins. The Th-Cre and the DAT-Cre rat lines express Cre in selective subsets of dopaminergic neurons and should be particularly useful for researches on Parkinson's disease.

Iron overload cardiomyopathy results from iron accumulation in the myocardium that is closely linked to iron-mediated myocardial fibrosis. Salvia miltiorrhiza (SM, also known as Danshen), a traditional Chinese medicinal herb, has been widely used for hundreds of years to treat cardiovascular diseases. Here, we investigated the effect and potential mechanism of SM on myocardial fibrosis induced by chronic iron overload (CIO) in mice. Kunming male mice (8 weeks old) were randomized to six groups of 10 animals each: control (CONT), CIO, low-dose SM (L-SM), high-dose SM (H-SM), verapamil (VRP) and deferoxamine (DFO) groups. Normal saline was injected in the CONT group. Mice in the other five groups were treated with iron dextran at 50 mg/kg per day intraperitoneally for 7 weeks, and those in the latter four groups also received corresponding daily treatments, including 3 g/kg or 6 g/kg of SM, 100 mg/kg of VRP, or 100 mg/kg of DFO. The iron deposition was estimated histologically using Prussian blue staining. Myocardial fibrosis was determined by Masson's trichrome staining and hydroxyproline (Hyp) quantitative assay. Superoxide dismutase (SOD) activity, malondialdehyde (MDA) content and protein expression levels of type I collagen (COL I), type I collagen (COL III), transforming growth factor-β1 (TGF-β1) and matrix metalloproteinase-9 (MMP-9) were analyzed to investigate the mechanisms underlying the effects of SM against iron-overloaded fibrosis. Treatment of chronic iron-overloaded mice with SM dose-dependently reduced iron deposition levels, fibrotic area percentage, Hyp content, expression levels of COL I and COL III, as well as upregulated the expression of TGF- β1 and MMP-9 proteins in the heart. Moreover, SM treatment decreased MDA content and increased SOD activity. In conclusion, SM exerted activities against cardiac fibrosis induced by CIO, which may be attributed to its inhibition of iron deposition, as well as collagen metabolism and oxidative stress.

Mechanosensory hair cells (HCs) and surrounding supporting cells (SCs) in the mouse cochlea are important for hearing and are derived from the same prosensory progenitors. Notch1 signaling plays dual but contrasting and age-dependent roles in mouse cochlear development: early lateral induction and subsequent lateral inhibition. However, it has been difficult to directly visualize mouse cochlear cells experiencing various levels of Notch1 activity at single cell resolution. Here, we characterized two knock-in mouse lines, Notch1(Cre (Low)/+) and Notch1(Cre (High)/+) , with different Cre recombinase activities, that can detect Notch1 receptor proteolysis or Notch1 activity at high and low thresholds, respectively. Using both lines together with a highly sensitive Cre reporter line, we showed that Notch1 activity is nearly undetectable during lateral induction but increases to medium and high levels during lateral inhibition. Furthermore, we found that within the neonatal organ of Corti, the vast majority of cells that experience Notch1 activity were SCs not HCs, suggesting that HCs kept undetectable Notch1 activity during the entire lineage development. Furthermore, among SC subtypes, ∼85-99% of Deiters' and outer pillar cells but only ∼19-38% of inner pillar cells experience medium and high levels of Notch1 activity. Our results demonstrate that Notch1 activity is highly heterogeneous: 1) between lateral induction and inhibition; 2) between HC and SC lineages; 3) among different SC subtypes; 4) among different cells within each SC subtype. Such heterogeneity should elucidate how the development of the cochclear sensory epithelium is precisely controlled and how HC regeneration can be best achieved in postnatal cochleae.

Nature Communications 6: Article number: 8510 (2015); Published: 14 October 2015; Updated: 4 March 2016 The original version of this Article contained a typographical error in the spelling of the author Zhenyi Liu, which was incorrectly given as Zenhy Liu. This has now been corrected in both the PDFand HTML versions of the Article.

Epidermal keratinocytes form a highly organized stratified epithelium and sustain a competent barrier function together with dermal and hematopoietic cells. The Notch signaling pathway is a critical regulator of epidermal integrity. Here, we show that keratinocyte-specific deletion of total Notch signaling triggered a severe systemic B-lymphoproliferative disorder, causing death. RBP-j is the DNA binding partner of Notch, but both RBP-j-dependent and independent Notch signaling were necessary for proper epidermal differentiation and lipid deposition. Loss of both pathways caused a persistent defect in skin differentiation/barrier formation. In response, high levels of thymic stromal lymphopoietin (TSLP) were released into systemic circulation by Notch-deficient keratinocytes that failed to differentiate, starting in utero. Exposure to high TSLP levels during neonatal hematopoiesis resulted in drastic expansion of peripheral pre- and immature B-lymphocytes, causing B-lymphoproliferative disorder associated with major organ infiltration and subsequent death, a previously unappreciated systemic effect of TSLP. These observations demonstrate that local skin perturbations can drive a lethal systemic disease and have important implications for a wide range of humoral and autoimmune diseases with skin manifestations.

The atmosphere-induced interference reduction device (AIRD) shows promise in mitigating interferences in inductively coupled plasma-mass spectrometry (ICP-MS) analysis. The effectiveness of AIRD in reducing interferences caused by atmospheric gases such as H, C, N and O was evaluated by using argon (Ar) and helium (He) as shielding gases due to their inert properties. Krypton (82Kr) served as an internal standard for monitoring and correcting instrumental sensitivity drift. The shielding gas flow rate was optimized using gas dynamics simulations, revealing a threshold of 10 L min-1 for optimal interference reduction. Results indicate the superiority of He in reducing interferences, with a reduction of 32% for 12C+, 51% for 15N+, 56% for 16O+, 54% for 16O¹H¹H+, 51% for 40Ar140Ar16O+ compared to Ar.N+, and 42% for 4 Moreover, AIRD maintained low oxide yields even after shielding gas cessation, with oxide yield maintained at approximately 0.03% for 24 hours. Analyses of LA-ICP-MS coupled with AIRD demonstrate that ThO/Th can be reduced from 0.92% to 0.15% compared to normal analysis without AIRD. Experimental investigations further revealed that AIRD influenced elemental sensitivity, particularly with He as the shielding gas, with an ~25.7% enhancement observed in the signal intensity of 82Kr. [ABSTRACT FROM AUTHOR]