Heterogeneity of immune cells and their communications unveiled by transcriptome profiling in acute inflammatory lung injury.

Background: Acute Respiratory Distress Syndrome (ARDS) or its earlier stage Acute lung injury (ALI), is a worldwide health concern that jeopardizes human well-being. Currently, the treatment strategies to mitigate the incidence and mortality of ARDS are severely restricted. This limitation can be attributed, at least in part, to the substantial variations in immunity observed in individuals with this syndrome. Methods: Bulk and single cell RNA sequencing from ALI mice and single cell RNA sequencing from ARDS patients were analyzed. We utilized the Seurat program package in R and cellmarker 2.0 to cluster and annotate the data. The differential, enrichment, protein interaction, and cell-cell communication analysis were conducted. Results: The mice with ALI caused by pulmonary and extrapulmonary factors demonstrated differential expression including Clec4e, Retnlg, S100a9, Coro1a, and Lars2. We have determined that inflammatory factors have a greater significance in extrapulmonary ALI, while multiple pathways collaborate in the development of pulmonary ALI. Clustering analysis revealed significant heterogeneity in the relative abundance of immune cells in different ALI models. The autocrine action of neutrophils plays a crucial role in pulmonary ALI. Additionally, there was a significant increase in signaling intensity between B cells and M1 macrophages, NKT cells and M1 macrophages in extrapulmonary ALI. The CXCL, CSF3 and MIF, TGFß signaling pathways play a vital role in pulmonary and extrapulmonary ALI, respectively. Moreover, the analysis of human single-cell revealed DCs signaling to monocytes and neutrophils in COVID-19-associated ARDS is stronger compared to sepsis-related ARDS. In sepsis-related ARDS, CD8+ T and Th cells exhibit more prominent signaling to B-cell nucleated DCs. Meanwhile, both MIF and CXCL signaling pathways are specific to sepsis-related ARDS. Conclusion: This study has identified specific gene signatures and signaling pathways in animal models and human samples that facilitate the interaction between immune cells, which could be targeted therapeutically in ARDS patients of various etiologies.

Corrigendum to "Optical measurements of dissolved organic matter as proxies for CODMn and BOD5 in plateau lakes" [Environ. Sci. Ecotech. 19 (2024) 100326].

[This corrects the article DOI: 10.1016/j.ese.2023.100326.].

Convolutional neural network with parallel convolution scale attention module and ResCBAM for breast histology image classification.

Breast cancer is the most common cause of female morbidity and death worldwide. Compared with other cancers, early detection of breast cancer is more helpful to improve the prognosis of patients. In order to achieve early diagnosis and treatment, clinical treatment requires rapid and accurate diagnosis. Therefore, the development of an automatic detection system for breast cancer suitable for patient imaging is of great significance for assisting clinical treatment. Accurate classification of pathological images plays a key role in computer-aided medical diagnosis and prognosis. However, in the automatic recognition and classification methods of breast cancer pathological images, the scale information, the loss of image information caused by insufficient feature fusion, and the enormous structure of the model may lead to inaccurate or inefficient classification. To minimize the impact, we proposed a lightweight PCSAM-ResCBAM model based on two-stage convolutional neural network. The model included a Parallel Convolution Scale Attention Module network (PCSAM-Net) and a Residual Convolutional Block Attention Module network (ResCBAM-Net). The first-level convolutional network was built through a 4-layer PCSAM module to achieve prediction and classification of patches extracted from images. To optimize the network's ability to represent global features of images, we proposed a tiled feature fusion method to fuse patch features from the same image, and proposed a residual convolutional attention module. Based on the above, the second-level convolutional network was constructed to achieve predictive classification of images. We evaluated the performance of our proposed model on the ICIAR2018 dataset and the BreakHis dataset, respectively. Furthermore, through model ablation studies, we found that scale attention and dilated convolution play an important role in improving model performance. Our proposed model outperforms the existing state-of-the-art models on 200 × and 400 × magnification datasets with a maximum accuracy of 98.74 %.

miR-451a was selectively sorted into exosomes and promoted the progression of esophageal squamous cell carcinoma through CAB39.

Exosomes are emerging mediators of cell-cell communication, which are secreted from cells and may be delivered into recipient cells in cell biological processes. Here, we examined microRNA (miRNA) expression in esophageal squamous cell carcinoma (ESCC) cells. We performed miRNA sequencing in exosomes and cells of KYSE150 and KYSE450 cell lines. Among these differentially expressed miRNAs, 20 of the miRNAs were detected in cells and exosomes. A heat map indicated that the level of miR-451a was higher in exosomes than in ESCC cells. Furthermore, miRNA pull-down assays and combined exosomes proteomic data showed that miR-451a interacts with YWHAE. Over-expression of YWHAE leads to miR-451a accumulation in the exosomes instead of the donor cells. We found that miR-451a was sorted into exosomes. However, the biological function of miR-451a remains unclear in ESCC. Here, Dual-luciferase reporter assay was conducted and it was proved that CAB39 is a target gene of miR-451a. Moreover, CAB39 is related to TGF-ß1 from RNA-sequencing data of 155 paired of ESCC tissues and the matched tissues. Western Blot and qPCR revealed that CAB39 and TGF-ß1 were positively correlated in ESCC. Over-expression of CAB39 were cocultured with PBMCs from the blood from healthy donors. Flow cytometry assays showed that apoptotic cells were significantly reduced after CAB39 over-expression and significantly increased after treated with TGF-ß1 inhibitors. Thus, our data indicate that CAB39 weakens antitumor immunity through TGF-ß1 in ESCC. In summary, YWHAE selectively sorted miR-451a into exosomes and it can weaken antitumor immunity promotes tumor progression through CAB39.

Employing hybrid deep learning for near-real-time forecasts of sensor-based algal parameters in a Microcystis bloom-dominated lake.

Harmful cyanobacterial blooms (CyanoHABs) are increasingly impacting the ecosystem of lakes, reservoirs and estuaries globally. The integration of real-time monitoring and deep learning technology has opened up new horizons for early warnings of CyanoHABs. However, unlike traditional methods such as pigment quantification or microscopy counting, the high-frequency data from in-situ fluorometric sensors display unpredictable fluctuations and variability, posing a challenge for predictive models to discern underlying trends within the time-series sequence. This study introduces a hybrid framework for near-real-time CyanoHABs predictions in a cyanobacterium Microcystis-dominated lake - Lake Dianchi, China. The proposed model was validated using hourly Chlorophyll-a (Chl a) concentrations and algal cell densities. Our results demonstrate that applying decomposition-based singular spectrum analysis (SSA) significantly enhances the prediction accuracy of subsequent CyanoHABs models, particularly in the case of temporal convolutional network (TCN). Comparative experiments revealed that the SSA-TCN model outperforms other SSA-based deep learning models for predicting Chl a (R2 = 0.45-0.93, RMSE = 2.29-5.89 µg/L) and algal cell density (R2 = 0.63-0.89, RMSE = 9489.39-16,015.37 cells/mL) at one to four steps ahead predictions. The forecast of bloom intensities achieved a remarkable accuracy of 98.56 % and an average precision rate of 94.04 % ± 0.05 %. In addition, scenarios involving various input combinations of environmental factors demonstrated that water temperature emerged as the most effective driver for CyanoHABs predictions, with a mean RMSE of 2.94 ± 0.12 µg/L, MAE of 1.55 ± 0.09 µg/L, and R2 of 0.83 ± 0.01. Overall, the newly developed approach underscores the potential of a well-designed hybrid deep-learning framework for accurately predicting sensor-based algal parameters. It offers novel perspectives for managing CyanoHABs through online monitoring and artificial intelligence in aquatic ecosystems.

A study on rapid simulation of mine roadway fires for emergency decision-making.

In traditional mine fire simulation, the FDS simulation software has been verified by large-scale and full-size fire experiments. The resulting calculations closely align with real-world scenarios, making it a valuable tool for simulating mine fires. However, when a fire occurs in a mine, utilizing FDS software to predict the fire situation in the mine entails a sequence of steps, including modeling, environmental parameter setting, arithmetic, and data processing, which takes time in terms of days, thus making it difficult to meet the demand for emergency decision-making timelines. To address the need for rapid predictions of mine tunnel fire development, a method for swiftly estimating environmental parameters and the concentration of causative factors at various times and locations post-fire has been devised. FDS software was employed to simulate numerous roadway fires under diverse conditions. Parameters such as fire source intensity, roadway cross-sectional area, roadway wind speed, roadway inclination angle, time, and others were utilized as the input layer for a neural network. In contrast, wind flow temperature, carbon monicide (CO) concentration, fire wind pressure, visibility, and others were designated as the output layer for training the neural network model. This approach established a fire prediction model to resolve issues related to time-consuming numerical simulations and the inability to provide a rapid response to disaster emergencies. The trained neural network model can instantaneously predict the environmental parameters and concentrations of the causative factors at different times and locations. The model exhibits an average relative error of 12.12% in temperature prediction, a mean absolute error of 0.87 m for visibility, a mean absolute error of 3.49 ppm for CO concentration, and a mean absolute error of 16.78 Pa for fire wind pressure. Additionally, the mean relative error in density is 2.9%. These predictions serve as crucial references for mine fire emergency decision-making.

The impact of delayed tracheostomy on critically ill patients receiving mechanical ventilation: a retrospective cohort study in a chinese tertiary hospital.

OBJECTIVES: The timing of tracheostomy for critically ill patients on mechanical ventilation (MV) is a topic of controversy. Our objective was to determine the most suitable timing for tracheostomy in patients undergoing MV. DESIGN: Retrospective cohort study. SETTING AND PARTICIPANTS: One thousand eight hundred eighty-four hospitalisations received tracheostomy from January 2011 to December 2020 in a Chinese tertiary hospital. METHODS: Tracheostomy timing was divided into three groups: early tracheostomy (ET), intermediate tracheostomy (IMT), and late tracheostomy (LT), based on the duration from tracheal intubation to tracheostomy. We established two criteria to classify the timing of tracheostomy for data analysis: Criteria I (ET ≤ 5 days, 5 days < IMT ≤ 10 days, LT > 10 days) and Criteria II (ET ≤ 7 days, 7 days < IMT ≤ 14 days, LT > 14 days). Parameters such as length of ICU stay, length of hospital stay, and duration of MV were used to evaluate outcomes. Additionally, the outcomes were categorized as good prognosis, poor prognosis, and death based on the manner of hospital discharge. Student's t-test, analysis of variance (ANOVA), Mann-Whitney U test, Kruskal-Wallis test, Chi-square test, and Fisher's exact test were employed as appropriate to assess differences in demographic data and individual characteristics among the ET, IMT, and LT groups. Univariate Cox regression model and multivariable Cox proportional hazards regression model were utilized to determine whether delaying tracheostomy would increase the risk of death. RESULTS: In both of two criterion, patients with delayed tracheostomies had longer hospital stays (p < 0.001), ICU stays (p < 0.001), total time receiving MV (p < 0.001), time receiving MV before tracheostomy (p < 0.001), time receiving MV after tracheostomy (p < 0.001), and sedation durations. Similar results were also found in sub-population diagnosed as trauma, neurogenic or digestive disorders. Multinomial Logistic regression identified LT was independently associated with poor prognosis, whereas ET conferred no clinical benefits compared with IMT. CONCLUSIONS: In a mixed ICU population, delayed tracheostomy prolonged ICU and hospital stays, sedation durations, and time receiving MV. Multinomial logistic regression analysis identified delayed tracheostomies as independently correlated with worse outcomes. TRIAL REGISTRATION: ChiCTR2100043905. Registered 05 March 2021. http://www.chictr.org.cn/listbycreater.aspx.

Influence of hydrological features on CO2 and CH4 concentrations in the surface water of lakes, Southwest China: A seasonal and mixing regime analysis.

Due to the large spatiotemporal variability in the processes controlling carbon emissions from lakes, estimates of global lake carbon emission remain uncertain. Identifying the most reliable predictors of CO2 and CH4 concentrations across different hydrological features can enhance the accuracy of carbon emission estimates locally and globally. Here, we used data from 71 lakes in Southwest China varying in surface area (0.01‒702.4 km2), mean depth (< 1‒89.6 m), and climate to analyze differences in CO2 and CH4 concentrations and their driving mechanisms between the dry and rainy seasons and between different mixing regimes. The results showed that the average concentrations of CO2 and CH4 in the rainy season were 23.9 ± 18.8 µmol L-1 and 2.5 ± 4.9 µmol L-1, respectively, which were significantly higher than in the dry season (10.5 ± 10.3 µmol L-1 and 1.8 ± 4.2 µmol L-1, respectively). The average concentrations of CO2 and CH4 at the vertically mixed sites were 24.1 ± 21.8 µmol L-1 and 2.6 ± 5.4 µmol L-1, being higher than those at the stratified sites (14.8 ± 13.4 µmol L-1 and 1.7 ± 3.5 µmol L-1, respectively). Moreover, the environmental factors were divided into four categories, i.e., system productivity (represented by the contents of total nitrogen, total phosphorus, chlorophyll a and dissolved organic matter), physicochemical factors (water temperature, Secchi disk depth, dissolved oxygen and pH value), lake morphology (lake area, water depth and drainage ratio), and geoclimatic factors (altitude, wind speed, precipitation and land-use intensity). In addition to the regression and variance partitioning analyses between the concentrations of CO2 and CH4 and environmental factors, the cascading effects of environmental factors on CO2 and CH4 concentrations were further elucidated under four distinct hydrological scenarios, indicating the different driving mechanisms between the scenarios. Lake morphology and geoclimatic factors were the main direct drivers of the carbon concentrations during the rainy season, while they indirectly affected the carbon concentrations via influencing physicochemical factors and further system productivity during the dry season; although lake morphology and geoclimatic factors directly contributed to the carbon concentrations at the vertically mixed and stratified sites, the direct effect of system productivity was only observed at the stratified sites. Our results emphasize that, when estimating carbon emissions from lakes at broad spatial scales, it is essential to consider the influence of precipitation-related seasons and lake mixing regimes.

Optical measurements of dissolved organic matter as proxies for CODMn and BOD5 in plateau lakes.

The presence of organic matter in lakes profoundly impacts drinking water supplies, yet treatment processes involving coagulants and disinfectants can yield carcinogenic disinfection by-products. Traditional assessments of organic matter, such as chemical oxygen demand (CODMn) and biochemical oxygen demand (BOD5), are often time-consuming. Alternatively, optical measurements of dissolved organic matter (DOM) offer a rapid and reliable means of obtaining organic matter composition data. Here we employed DOM optical measurements in conjunction with parallel factor analysis to scrutinize CODMn and BOD5 variability. Validation was performed using an independent dataset encompassing six lakes on the Yungui Plateau from 2014 to 2016 (n = 256). Leveraging multiple linear regressions (MLRs) applied to DOM absorbance at 254 nm (a254) and fluorescence components C1-C5, we successfully traced CODMn and BOD5 variations across the entire plateau (68 lakes, n = 271, R2 > 0.8, P < 0.0001). Notably, DOM optical indices yielded superior estimates (higher R2) of CODMn and BOD5 during the rainy season compared to the dry season and demonstrated increased accuracy (R2 > 0.9) in mesotrophic lakes compared to oligotrophic and eutrophic lakes. This study underscores the utility of MLR-based DOM indices for inferring CODMn and BOD5 variability in plateau lakes and highlights the potential of integrating in situ and remote sensing platforms for water pollution early warning.

Prediction of Radiation Treatment Response for Locally Advanced Rectal Cancer via a Longitudinal Trend Analysis Framework on Cone-Beam CT.

Locally advanced rectal cancer (LARC) presents a significant challenge in terms of treatment management, particularly with regards to identifying patients who are likely to respond to radiation therapy (RT) at an individualized level. Patients respond to the same radiation treatment course differently due to inter- and intra-patient variability in radiosensitivity. In-room volumetric cone-beam computed tomography (CBCT) is widely used to ensure proper alignment, but also allows us to assess tumor response during the treatment course. In this work, we proposed a longitudinal radiomic trend (LRT) framework for accurate and robust treatment response assessment using daily CBCT scans for early detection of patient response. The LRT framework consists of four modules: (1) Automated registration and evaluation of CBCT scans to planning CT; (2) Feature extraction and normalization; (3) Longitudinal trending analyses; and (4) Feature reduction and model creation. The effectiveness of the framework was validated via leave-one-out cross-validation (LOOCV), using a total of 840 CBCT scans for a retrospective cohort of LARC patients. The trending model demonstrates significant differences between the responder vs. non-responder groups with an Area Under the Curve (AUC) of 0.98, which allows for systematic monitoring and early prediction of patient response during the RT treatment course for potential adaptive management.

N-containing dissolved organic matter promotes dissolved inorganic carbon supersaturation in the Yangtze River, China.

Dissolved inorganic carbon (DIC) represents a major global carbon pool and the flux from rivers to oceans has been observed to be increasing. The effect of weathering with respect to increasing DIC has been widely studied in recent decades; however, the influence of dissolved organic matter (DOM) on increasing DIC in large rivers remains unclear. This study employed stable carbon isotopes and Fourier transform ion cyclotron mass spectrometry (FT-ICR MS) to investigate the effect of the molecular composition of DOM on the DIC in the Yangtze River. The results showed that organic matter is an important source of DIC in the Yangtze River, accounting for 40.0 ± 12.1 % and 32.0 ± 7.2 % of DIC in wet and dry seasons, respectively, and increased along the river by approximately three times. Nitrogen (N)-containing DOM, an important composition in DOM with a percentage of ∼40 %, showed superior oxidation state than non N-containing DOM, suggesting that the presence of N could improve the degradable potential of DOM. Positive relationship between organic sourced DIC (DICOC) and N-containing DOM formulae indicated that N-containing DOM is crucial to facilitate the mineralization of DOM to DICOC. N-containg molecular formular with low H/C and O/C ratio were positively correlated with DICOC further verified these energy-rich and biolabile compounds are preferentially decomposed by bacteria to produce DIC. N-containing components significantly accelerated the degradation of DOM to DICOC, which is important for understanding the CO2 emission and carbon cycling in large rivers.

Radiomics-clinical nomogram based on pretreatment 18F-FDG PET-CT radiomics features for individualized prediction of local failure in nasopharyngeal carcinoma.

To explore the prognostic significance of PET/CT-based radiomics signatures and clinical features for local recurrence-free survival (LRFS) in nasopharyngeal carcinoma (NPC). We retrospectively reviewed 726 patients who underwent pretreatment PET/CT at our center. Least absolute shrinkage and selection operator (LASSO) regression and the Cox proportional hazards model were applied to construct Rad-score, which represented the radiomics features of PET-CT images. Univariate and multivariate analyses were used to establish a nomogram model. The concordance index (C-index) and calibration curve were used to evaluate the predictive accuracy and discriminative ability. Receiver operating characteristic analysis was performed to stratify the local recurrence risk of patients. The nomogram was validated by evaluating its discrimination ability and calibration in the validation cohort. A total of eight features were selected to construct Rad-score. A radiomics-clinical nomogram was built after the selection of univariate and multivariable Cox regression analyses, including the Rad-score and maximum standardized uptake value (SUVmax). The C-index was 0.71 (0.67-0.74) in the training cohort and 0.70 (0.64-0.76) in the validation cohort. The nomogram also performed far better than the 8th T-staging system with an area under the receiver operating characteristic curve (AUC) of 0.75 vs. 0.60 for 2 years and 0.71 vs. 0.60 for 3 years. The calibration curves show that the nomogram indicated accurate predictions. Decision curve analysis (DCA) revealed significantly better net benefits with this nomogram model. The log-rank test results revealed a distinct difference in prognosis between the two risk groups. The PET/CT-based radiomics nomogram showed good performance in predicting LRFS and showed potential to identify patients at high-risk of developing NPC.

Clinical target volume and organs at risk segmentation for rectal cancer radiotherapy using the Flex U-Net network.

Purpose/Objectives: The aim of this study was to improve the accuracy of the clinical target volume (CTV) and organs at risk (OARs) segmentation for rectal cancer preoperative radiotherapy. Materials/Methods: Computed tomography (CT) scans from 265 rectal cancer patients treated at our institution were collected to train and validate automatic contouring models. The regions of CTV and OARs were delineated by experienced radiologists as the ground truth. We improved the conventional U-Net and proposed Flex U-Net, which used a register model to correct the noise caused by manual annotation, thus refining the performance of the automatic segmentation model. Then, we compared its performance with that of U-Net and V-Net. The Dice similarity coefficient (DSC), Hausdorff distance (HD), and average symmetric surface distance (ASSD) were calculated for quantitative evaluation purposes. With a Wilcoxon signed-rank test, we found that the differences between our method and the baseline were statistically significant (P< 0.05). Results: Our proposed framework achieved DSC values of 0.817 ± 0.071, 0.930 ± 0.076, 0.927 ± 0.03, and 0.925 ± 0.03 for CTV, the bladder, Femur head-L and Femur head-R, respectively. Conversely, the baseline results were 0.803 ± 0.082, 0.917 ± 0.105, 0.923 ± 0.03 and 0.917 ± 0.03, respectively. Conclusion: In conclusion, our proposed Flex U-Net can enable satisfactory CTV and OAR segmentation for rectal cancer and yield superior performance compared to conventional methods. This method provides an automatic, fast and consistent solution for CTV and OAR segmentation and exhibits potential to be widely applied for radiation therapy planning for a variety of cancers.

Trophic status and lake depth play important roles in determining the nutrient-chlorophyll a relationship: Evidence from thousands of lakes globally.

A fundamental problem in lake eutrophication management is that the nutrient-chlorophyll a (Chl a) relationship shows high variability due to diverse influences of for example lake depth, lake trophic status, and latitude. To accommodate the variability induced by spatial heterogeneity, a reliable and general insight into the nutrient-Chl a relationship may be achieved by applying probabilistic methods to analyze data compiled across a broad spatial scale. Here, the roles of two critical factors determining the nutrient-Chl a relationship, lake depth and trophic status, were explored by applying Bayesian networks (BNs) and a Bayesian hierarchical linear regression model (BHM) to a compiled global dataset from 2849 lakes and 25083 observations. We categorized the lakes into three groups (shallow, transitional, and deep) according to mean and maximum depth relative to mixing depth. We found that despite a stronger effect of total phosphorus (TP) and total nitrogen (TN) on Chl a when combined, TP played a dominant role in determining Chl a, regardless of lake depth. However, when the lake was hypereutrophic and/or TP was >40 µg/L, TN had a greater impact on Chl a, especially in shallow lakes. The response curve of Chl a to TP and TN varied with lake depth, with deep lakes having the lowest yield Chl a per unit of nutrient, followed by transitional lakes, while shallow lakes had the highest ratio. Moreover, we found a decrease of TN/TP with increasing Chl a concentrations and lake depth (represented as mixing depth/mean depth). Our established BHM may help estimating lake type and/or lake-specific acceptable TN and TP concentrations that comply with target Chl a concentrations with higher certainty than can be obtained when bulking all lake types.

Urbanization shifts long-term phenology and severity of phytoplankton blooms in an urban lake through different pathways.

Climate change can induce phytoplankton blooms (PBs) in eutrophic lakes worldwide, and these blooms severely threaten lake ecosystems and human health. However, it is unclear how urbanization and its interaction with climate impact PBs, which has implications for the management of lakes. Here, we used multi-source remote sensing data and integrated the Virtual-Baseline Floating macroAlgae Height (VB-FAH) index and OTSU threshold automatic segmentation algorithm to extract the area of PBs in Lake Dianchi, China, which has been subjected to frequent PBs and rapid urbanization in its vicinity. We further explored long-term (2000-2021) trends in the phenological and severity metrics of PBs and quantified the contributions from urbanization, climate change, and also nutrient levels to these trends. When comparing data from 2011-2021 to 2000-2010, we found significantly advanced initiation of PBs (28.6 days) and noticeably longer duration (51.9 days) but an insignificant trend in time of disappearance. The enhancement of algal nutrient use efficiency, likely induced by increased water temperature and reduced nutrient concentrations, presumably contributed to an earlier initiation and longer duration of PBs, while there was a negative correlation between spring wind speed and the initiation of PBs. Fortunately, we found that both the area of the PBs and the frequency of severe blooms (covering more than 19.8 km2 ) demonstrated downward trends, which could be attributed to increased wind speed and/or reduced nutrient levels. Moreover, the enhanced land surface temperature caused by urbanization altered the thermodynamic characteristics between the land and the lake, which, in turn, possibly caused an increase in local wind speed and water temperature, suggesting that urbanization can differently regulate the phenology and severity of PBs. Our findings have significant implications for the understanding of the impacts of urbanization on PB dynamics and for improving lake management practices to promote sustainable urban development under global change.

XBound-Former: Toward Cross-Scale Boundary Modeling in Transformers.

Skin lesion segmentation from dermoscopy images is of great significance in the quantitative analysis of skin cancers, which is yet challenging even for dermatologists due to the inherent issues, i.e., considerable size, shape and color variation, and ambiguous boundaries. Recent vision transformers have shown promising performance in handling the variation through global context modeling. Still, they have not thoroughly solved the problem of ambiguous boundaries as they ignore the complementary usage of the boundary knowledge and global contexts. In this paper, we propose a novel cross-scale boundary-aware transformer, XBound-Former, to simultaneously address the variation and boundary problems of skin lesion segmentation. XBound-Former is a purely attention-based network and catches boundary knowledge via three specially designed learners. First, we propose an implicit boundary learner (im-Bound) to constrain the network attention on the points with noticeable boundary variation, enhancing the local context modeling while maintaining the global context. Second, we propose an explicit boundary learner (ex-Bound) to extract the boundary knowledge at multiple scales and convert it into embeddings explicitly. Third, based on the learned multi-scale boundary embeddings, we propose a cross-scale boundary learner (X-Bound) to simultaneously address the problem of ambiguous and multi-scale boundaries by using learned boundary embedding from one scale to guide the boundary-aware attention on the other scales. We evaluate the model on two skin lesion datasets and one polyp lesion dataset, where our model consistently outperforms other convolution- and transformer-based models, especially on the boundary-wise metrics. All resources could be found in https://github.com/jcwang123/xboundformer.

NETs Promote Inflammatory Injury by Activating cGAS-STING Pathway in Acute Lung Injury.

Acute respiratory distress syndrome (ARDS) threatens the survival of critically ill patients, the mechanisms of which are still unclear. Neutrophil extracellular traps (NETs) released by activated neutrophils play a critical role in inflammatory injury. We investigated the role of NETs and the underlying mechanism involved in acute lung injury (ALI). We found a higher expression of NETs and cyclic GMP-AMP synthase-stimulator of interferon genes (cGAS-STING) in the airways, which was reduced by Deoxyribonuclease I (DNase I) in ALI. The administration of the STING inhibitor H-151 also significantly relieved inflammatory lung injury, but failed to affect the high expression of NETs in ALI. We isolated murine neutrophils from bone marrow and acquired human neutrophils by inducing HL-60 to differentiate. After the PMA interventions, exogenous NETs were obtained from such extracted neutrophils. Exogenous NETs intervention in vitro and in vivo resulted in airway injury, and such inflammatory lung injury was reversed upon degrading NETs with or inhibiting cGAS-STING with H-151 as well as siRNA STING. In conclusion, cGAS-STING participates in regulating NETs-mediated inflammatory pulmonary injury, which is expected to be a new therapeutic target for ARDS/ALI.

A generalization performance study on the boosting radiotherapy dose calculation engine based on super-resolution.

PURPOSE: During the radiation treatment planning process, one of the time-consuming procedures is the final high-resolution dose calculation, which obstacles the wide application of the emerging online adaptive radiotherapy techniques (OLART). There is an urgent desire for highly accurate and efficient dose calculation methods. This study aims to develop a dose super resolution-based deep learning model for fast and accurate dose prediction in clinical practice. METHOD: A Multi-stage Dose Super-Resolution Network (MDSR Net) architecture with sparse masks module and multi-stage progressive dose distribution restoration method were developed to predict high-resolution dose distribution using low-resolution data. A total of 340 VMAT plans from different disease sites were used, among which 240 randomly selected nasopharyngeal, lung, and cervix cases were used for model training, and the remaining 60 cases from the same sites for model benchmark testing, and additional 40 cases from the unseen site (breast and rectum) was used for model generalizability evaluation. The clinical calculated dose with a grid size of 2 mm was used as baseline dose distribution. The input included the dose distribution with 4 mm grid size and CT images. The model performance was compared with HD U-Net and cubic interpolation methods using Dose-volume histograms (DVH) metrics and global gamma analysis with 1%/1 mm and 10% low dose threshold. The correlation between the prediction error and the dose, dose gradient, and CT values was also evaluated. RESULTS: The prediction errors of MDSR were 0.06-0.84% of Dmean indices, and the gamma passing rate was 83.1-91.0% on the benchmark testing dataset, and 0.02-1.03% and 71.3-90.3% for the generalization dataset respectively. The model performance was significantly higher than the HD U-Net and interpolation methods (p < 0.05). The mean errors of the MDSR model decreased (monotonously by 0.03-0.004%) with dose and increased (by 0.01-0.73%) with the dose gradient. There was no correlation between prediction errors and the CT values. CONCLUSION: The proposed MDSR model achieved good agreement with the baseline high-resolution dose distribution, with small prediction errors for DVH indices and high gamma passing rate for both seen and unseen sites, indicating a robust and generalizable dose prediction model. The model can provide fast and accurate high-resolution dose distribution for clinical dose calculation, particularly for the routine practice of OLART.

Ultrafast Combustion Synthesis of Robust and Efficient Electrocatalysts for High-Current-Density Water Oxidation.

The scalable production of inexpensive, efficient, and robust catalysts for oxygen evolution reaction (OER) that can deliver high current densities at low potentials is critical for the industrial implementation of water splitting technology. Herein, a series of metal oxides coupled with Fe2O3 are in situ grown on iron foam massively via an ultrafast combustion approach for a few seconds. Benefiting from the three-dimensional nanosheet array framework and the heterojunction structure, the self-supporting electrodes with abundant active centers can regulate mass transport and electronic structure for prompting OER activity at high current density. The optimized Ni(OH)2/Fe2O3 with robust structure can deliver a high current density of 1000 mA cm-2 at the overpotential as low as 271 mV in 1.0 M KOH for up to 1500 h. Theoretical calculation demonstrates that the strong electronic modulation plays a crucial part in the hybrid by optimizing the adsorption energy of the intermediate, thereby enhancing the efficiency of oxygen evolution. This work proposes a method to construct cheap and robust catalysts for practical application in energy conversion and storage.

Tissues margin-based analytical anisotropic algorithm boosting method via deep learning attention mechanism with cervical cancer.

PURPOSE: Speed and accuracy are two critical factors in dose calculation for radiotherapy. Analytical Anisotropic Algorithm (AAA) is a rapid dose calculation algorithm but has dose errors in tissue margin area. Acuros XB (AXB) has high accuracy but takes long time to calculate. To improve the dose accuracy on the tissue margin area for AAA, we proposed a novel deep learning-based dose accuracy improvement method using Margin-Net combined with Margin-Loss. METHODS: A novel model 'Margin-Net' was designed with a Margin Attention Mechanism to generate special margin-related features. Margin-Loss was introduced to consider the dose errors and dose gradients in tissues margin area. Ninety-five VMAT cervical cancer cases with paired AAA and AXB dose were enrolled in our study: 76 cases for training and 19 cases for testing. Tissues Margin Masks were generated from RT contours with 6 mm extension. Tissues Margin Mask, AAA dose and CTs were input data; AXB dose was used as reference dose for model training and evaluation. Comparison experiments were performed to evaluated effectiveness of Margin-Net and Margin-Loss. RESULTS: Compared to AXB dose, the 3D gamma passing rate (1%/1 mm, 10% threshold) for 19 test cases 95.75 ± 1.05% using Margin-Net with Margin-Loss, which was significantly higher than the original AAA dose (73.64 ± 3.46%). The passing rate reduced to 94.07 ± 1.16% without Margin-Loss and 87.3 ± 1.18% if Margin-Net key structure 'MAM' was also removed. CONCLUSION: The proposed novel tissues margin-based dose conversion method can significantly improve the dose accuracy of Analytical Anisotropic Algorithm to be comparable to AXB algorithm. It can potentially improve the efficiency of treatment planning process with low demanding of computation resources.