Dynamic budget analysis of multiple parameters in a lithography system based on the superposition of light intensity fluctuations.

Lithography is one of the most critical processes in the manufacturing of micro- and nano-devices. As device critical dimensions continue to shrink, variations in system parameters during the lithography process often result in heavy deviations from the intended targets, making control of these parameters crucial to ensure that lithography results meet process requirements. Gaining a thorough comprehension of how various parameters interact and contribute to lithography errors is significant, and it is equally important to offer precise suggestions for managing these parameters in extreme ultraviolet lithography (EUVL) scanners. This paper analyzes the key physical factors in the light source, illumination system and projection system of EUVL scanners and proposes what we believe to be a new methodology of budget analysis utilizing the superposition of light intensity fluctuations. Then the corresponding characteristics of light intensity fluctuations are analyzed when these parameters have fluctuated through theoretical formula derivation. A mapping model was established between parameter fluctuations and imaging outcomes through the distribution of light intensity. The yield requirements for critical dimension and pattern shift in EUVL are used to determine the exact budget range for each parameter in the proposed methodology. By controlling the parameters according to the budget analysis method proposed in this paper, the deviation between the experimental results from the yield requirements is no more than 0.1% in average. This approach allows for dynamic updating of the control range of relevant parameters based on their distinct characteristics to accommodate the unique fingerprints of various EUVL scanners. Furthermore, based on this adaptive budget range of multiple parameters, it can offer distinct direction for the development of lithography equipment or serve as a clear indication for parameter monitoring.

A gravity-driven membrane bioreactor in treating the real decentralized domestic wastewater: Flux stability and membrane fouling.

Domestic wastewater in decentralized sites is capturing increasing attention. However, conventional treatment technology is not sufficiently cost-effective. In this study, real domestic wastewater was treated directly using a gravity-driven membrane bioreactor (GDMBR) at 45 mbar without backwashing or chemical cleaning, and the effects of different membrane pore sizes (0.22 µm, 0.45 µm, and 150 kDa) on flux development and contaminants removal were examined. The results showed that the flux initially decreased and then stabilized throughout long-term filtration and that the stabilized flux level of the GDMBR equipped the membranes with the pore size of 150 kDa and 0.22 µm was higher than that of 0.45 µm membrane and was in the range of 3-4 L m-2h-1. The flux stability was related to spongelike and permeable biofilm generation on the membrane surface in the GDMBR system. The presence of aeration shear on the membrane surface would cause the slough off of biofilm from the membrane surface, especially in the scenarios of GDMBR with the membrane pore size of 150 kDa and 0.22 µm, contributing to lower accumulation of extracellular polymeric substance (EPS) and smaller biofilm thickness compared to that of 0.45 µm membrane. Furthermore, the GDMBR system achieved efficient removals of chemical oxygen demand (COD), and ammonia, with average removal efficiencies of 60-80% and 70%. The high biological activity and microbial community diversity within the biofilm would improve its biodegradation and should be responsible for the efficient removal performance of contaminants. Interestingly, the membrane effluent could effectively retain total nitrogen (TN) and total phosphorus (TP). Therefore, it's feasible to adopt the GDMBR process to treat the actual domestic wastewater in the decentralized locations, and these findings could be expected to develop some simple and environmentally friendly strategies for decentralized wastewater treatment with fewer inputs.

Precise anatomical localization and classification of rib fractures on CT using a convolutional neural network.

OBJECTIVE: To develop a convolutional neural network (CNN) model for the detection, precise anatomical localization (right 1-12th and left 1-12th) and classification (fresh, healing and old fractures) of rib fractures automatically, and to compare the performance with the experienced radiologists. MATERIALS AND METHODS: A total of 640 rib fracture patients with 340,501 annotations were retrospectively collected from three hospitals. They consisted of a classification training dataset (n = 482), a localization training dataset (n = 30), an internal testing dataset (n = 90) and an external testing dataset (n = 38). RetinaNet with rib localization postprocessing and the result merging technique were employed to structure the CNN model. ROC curve, free-response ROC curve, AUC, precision, recall, and F1-score were calculated to choose the better option between model I (training classification and localization data together) and model II (adding an additional classification model to model I). RESULTS: The detection and classification performance of rib fractures was better in model II than in model I. The sensitivity of localization reached 97.11% and 94.87% on the right and left ribs, respectively. In the external dataset with different CT scanner and slice thickness, model II showed better diagnostic performance. Moreover, the CNN model was superior in diagnosing fresh and healing fractures to 5 radiologists and consumed shorter diagnosis time. CONCLUSIONS: Our CNN model was capable of detection, precise anatomical localization, and classification of rib fractures automatically.

Community Structure and Function of Epiphytic Bacteria Associated With Myriophyllum spicatum in Baiyangdian Lake, China.

Epiphytic bacteria on the surfaces of submerged macrophytes play important roles in the growth of the host plant, nutrient cycling, and the conversion of pollutants in aquatic systems. A knowledge of the epiphytic bacterial community structure could help us to understand these roles. In this study, the abundance, diversity, and functions of the epiphytic bacterial community of Myriophyllum spicatum collected from Baiyangdian Lake in June, August, and October 2019 were studied using quantitative PCR (qPCR), high-throughput sequencing, and the prediction of functions. An analysis using qPCR showed that the epiphytic bacteria were the most abundant in October and the least abundant in August. High-throughput sequencing revealed that Proteobacteria, Gammaproteobacteria, and Aeromonas were the dominant phylum, class, and genus in all the samples. The common analyses of operational taxonomic units (OTUs), NMDS, and LDA showed that the epiphytic bacterial communities were clustered together based on the seasons. The results of a canonical correlation analysis (CCA) showed that the key water quality index that affected the changes of epiphytic bacterial community of M. spicatum was the total phosphorus (TP). The changes in abundance of Gammaproteobacteria negatively correlated with the TP. Predictive results from FAPROTAX showed that the predominant biogeochemical cycle functions of the epiphytic bacterial community were chemoheterotrophy, nitrate reduction, and fermentation. These results suggest that the epiphytic bacterial community of M. spicatum from Baiyangdian Lake varies substantially with the seasons and environmental conditions.

Liver injury after antiviral treatment of critically ill patients with COVID-19: a single-centered retrospective cohort study.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) is the causative agent of coronavirus disease 2019 (COVID-19). Lung lesions are considered to be the main damage caused by SARSCoV-2 infection. In addition, liver injury has also been reported to occur during the course of the disease in severe cases. However, the effect of antiviral treatment on liver injury in critically ill patients is not yet clear. METHODS: We retrospectively evaluated the effect of antiviral treatment and antiviral drug arbidol on liver injury in COVID-19 critically ill patients. Baseline characteristics were collected from patients who were admitted to intensive care units of Tongji Hospital in Wuhan, China, and confounders were balanced by propensity score matching (PSM) and inverse probability of treatment weighting (IPTW) analyses. RESULTS: Both the PSM (OR=2.77; 95% CI: 1.03, 7.48; P=0.045) and the IPTW-adjusted (OR=2.33; 95% CI: 1.02, 5.34; P=0.047) results showed that COVID-19 critically ill patients receiving antiviral treatment had a significantly higher risk of liver injury. However, arbidol treatment did not have a significant effect on liver injury (IPTW: OR=2.11; 95% CI: 0.79, 5.67; P=0.14). CONCLUSIONS: Our results show that although arbidol treatment does not seem to be significantly associated with liver injury complications, the overall use of antiviral drugs increases the risk of liver injury for critically ill patients with COVID-19. Antiviral drugs are widely used to treat COVID-19, but we recommend that for critically ill patients, antiviral treatment should be used with caution considering both effectiveness and potential adverse effects.

Automatic detection and classification of rib fractures based on patients' CT images and clinical information via convolutional neural network.

OBJECTIVE: To develop a convolutional neural network (CNN) model for the automatic detection and classification of rib fractures in actual clinical practice based on cross-modal data (clinical information and CT images). MATERIALS: In this retrospective study, CT images and clinical information (age, sex and medical history) from 1020 participants were collected and divided into a single-centre training set (n = 760; age: 55.8 ± 13.4 years; men: 500), a single-centre testing set (n = 134; age: 53.1 ± 14.3 years; men: 90), and two independent multicentre testing sets from two different hospitals (n = 62, age: 57.97 ± 11.88, men: 41; n = 64, age: 57.40 ± 13.36, men: 35). A Faster Region-based CNN (Faster R-CNN) model was applied to integrate CT images and clinical information. Then, a result merging technique was used to convert 2D inferences into 3D lesion results. The diagnostic performance was assessed on the basis of the receiver operating characteristic (ROC) curve, free-response ROC (fROC) curve, precision, recall (sensitivity), F1-score, and diagnosis time. The classification performance was evaluated in terms of the area under the ROC curve (AUC), sensitivity, and specificity. RESULTS: The CNN model showed improved performance on fresh, healing, and old fractures and yielded good classification performance for all three categories when both clinical information and CT images were used compared to the use of CT images alone. Compared with experienced radiologists, the CNN model achieved higher sensitivity (mean sensitivity: 0.95 > 0.77, 0.89 > 0.61 and 0.80 > 0.55), comparable precision (mean precision: 0.91 > 0.87, 0.84 > 0.77, and 0.95 > 0.70), and a shorter diagnosis time (average reduction of 126.15 s). CONCLUSIONS: A CNN model combining CT images and clinical information can automatically detect and classify rib fractures with good performance and feasibility in actual clinical practice. KEY POINTS: • The developed convolutional neural network (CNN) performed better in fresh, healing, and old fractures and yielded a good classification performance in three categories, if both (clinical information and CT images) were used compared to CT images alone. • The CNN model had a higher sensitivity and matched precision in three categories than experienced radiologists with a shorter diagnosis time in actual clinical practice.

Automatic Detection and Classification of Rib Fractures on Thoracic CT Using Convolutional Neural Network: Accuracy and Feasibility.

OBJECTIVE: To evaluate the performance of a convolutional neural network (CNN) model that can automatically detect and classify rib fractures, and output structured reports from computed tomography (CT) images. MATERIALS AND METHODS: This study included 1079 patients (median age, 55 years; men, 718) from three hospitals, between January 2011 and January 2019, who were divided into a monocentric training set (n = 876; median age, 55 years; men, 582), five multicenter/multiparameter validation sets (n = 173; median age, 59 years; men, 118) with different slice thicknesses and image pixels, and a normal control set (n = 30; median age, 53 years; men, 18). Three classifications (fresh, healing, and old fracture) combined with fracture location (corresponding CT layers) were detected automatically and delivered in a structured report. Precision, recall, and F1-score were selected as metrics to measure the optimum CNN model. Detection/diagnosis time, precision, and sensitivity were employed to compare the diagnostic efficiency of the structured report and that of experienced radiologists. RESULTS: A total of 25054 annotations (fresh fracture, 10089; healing fracture, 10922; old fracture, 4043) were labelled for training (18584) and validation (6470). The detection efficiency was higher for fresh fractures and healing fractures than for old fractures (F1-scores, 0.849, 0.856, 0.770, respectively, p = 0.023 for each), and the robustness of the model was good in the five multicenter/multiparameter validation sets (all mean F1-scores > 0.8 except validation set 5 [512 × 512 pixels; F1-score = 0.757]). The precision of the five radiologists improved from 80.3% to 91.1%, and the sensitivity increased from 62.4% to 86.3% with artificial intelligence-assisted diagnosis. On average, the diagnosis time of the radiologists was reduced by 73.9 seconds. CONCLUSION: Our CNN model for automatic rib fracture detection could assist radiologists in improving diagnostic efficiency, reducing diagnosis time and radiologists' workload.

Correlation between clinical risk factors and tracheal intubation difficulty in infants with Pierre-Robin syndrome: a retrospective study.

BACKGROUND: Difficult tracheal intubation is a common problem encountered by anesthesiologists in the clinic. This study was conducted to assess the difficulty of tracheal intubation in infants with Pierre Robin syndrome (PRS) by incorporating computed tomography (CT) to guide airway management for anesthesia. METHODS: In this retrospective study, we analyzed case-level clinical data and CT images of 96 infants with PRS. First, a clinically experienced physician labeled CT images, after which the color space conversion, binarization, contour acquisition, and area calculation processing were performed on the annotated files. Finally, the correlation coefficient between the seven clinical factors and tracheal intubation difficulty, as well as the differences in each risk factor under tracheal intubation difficulty were calculated. RESULTS: The absolute value of the correlation coefficient between the throat area and tracheal intubation difficulty was 0.54; the observed difference was statistically significant. Body surface area, weight, and gender also showed significant difference under tracheal intubation difficulty. CONCLUSIONS: There is a significant correlation between throat area and tracheal intubation difficulty in infants with PRS. Body surface area, weight and gender may have an impact on tracheal intubation difficulty in infants with PRS.

A Pre-operative Nomogram for Prediction of Lymph Node Metastasis in Bladder Urothelial Carcinoma.

The status of lymph node (LN) metastases plays a decisive role in the selection of surgical procedures and post-operative treatment. Several histopathologic features, known as predictors of LN metastasis, are commonly available post-operatively. Medical imaging improved pre-operative diagnosis, but the results are not fully satisfactory due to substantial false positives. Thus, a reliable and robust method for pre-operative assessment of LN status is urgently required. We developed a prediction model in a training set from the TCGA-BLCA cohort including 196 bladder urothelial carcinoma samples with confirmed LN metastasis status. Least absolute shrinkage and selection operator (LASSO) regression was harnessed for dimension reduction, feature selection, and LNM signature building. Multivariable logistic regression was used to develop the prognostic model, incorporating the LNM signature, and a genomic mutation of MLL2, and was presented with a LNM nomogram. The performance of the nomogram was assessed with respect to its calibration, discrimination, and clinical usefulness. Internal validation was evaluated by the testing set from the TCGA cohort and independent validation was assessed by two independent cohorts. The LNM signature, which consisted of 48 selected features, was significantly associated with LN status (p < 0.005 for both the training and testing sets of the TCGA cohort). Predictors contained in the individualized prediction nomogram included the LNM signature and MLL2 mutation status. The model demonstrated good discrimination, with an area under the curve (AUC) of 98.7% (85.3% for testing set) and good calibration with p = 0.973 (0.485 for testing set) in the Hosmer-Lemeshow goodness of fit test. Decision curve analysis demonstrated that the LNM nomogram was clinically useful. This study presents a pre-operative nomogram incorporating a LNM signature and a genomic mutation, which can be conveniently utilized to facilitate pre-operative individualized prediction of LN metastasis in patients with bladder urothelial carcinoma.

Immune Signature-Based Subtypes of Cervical Squamous Cell Carcinoma Tightly Associated with Human Papillomavirus Type 16 Expression, Molecular Features, and Clinical Outcome.

Substantial heterogeneity exists within cervical cancer that is generally infected by human papillomavirus (HPV). However, the most common histological subtype of cervical cancer, cervical squamous cell carcinoma (CSCC), is poorly characterized regarding the association between its heterogeneity and HPV oncoprotein expression. We filtered out 138 CSCC samples with infection of HPV16 only as the first step; then we compressed HPV16 E6/E7 expression as HPVpca and correlated HPVpca with the immunological profiling of CSCC based on supervised clustering to discover subtypes and to characterize the differences between subgroups in terms of the HPVpca level, pathway activity, epigenetic dysregulation, somatic mutation frequencies, and likelihood of responding to chemo/immunotherapies. Supervised clustering of immune signatures revealed two HPV16 subtypes (namely, HPV16-IMM and HPV16-KRT) that correlated with HPVpca and clinical outcomes. HPV16-KRT is characterized by elevated expression of genes in keratinization, biological oxidation, and Wnt signaling, whereas HPV16-IMM has a strong immune response and mesenchymal features. HPV16-IMM exhibited much more epigenetic silencing and significant mutation at FBXW7, while MUC4 and PIK3CA were mutated frequently for HPV16-KRT. We also imputed that HPV16-IMM is much more sensitive to chemo/immunotherapy than is HPV16-KRT. Our characterization tightly links the expression of HPV16 E6/E7 with biological and clinical outcomes of CSCC, providing valuable molecular-level information that points to decoding heterogeneity. Together, these results shed light on stratifications of CSCC infected by HPV16 and shall help to guide personalized management and treatment of patients.

Paenibacillus cymbidii sp. nov., isolated from Cymbidium goeringii roots.

A Gram-stain-variable, aerobic, rod-shaped, endospore-forming strain R196T) was isolated from internal tissues of roots of Cymbidium goeringii. Cells were motile with peritrichous flagella. The colonies were light pink on tryptone soya agar medium. Phylogenetic analysis based on 16S rRNA gene sequences showed that strain R196T fell into a phylogenetic cluster belonging to the genus Paenibacillus. Strain R196T was closely related to Paenibacillus cavernae C4-5T and Paenibacillus contaminans CKOBP-6T with 93.6 and 93.3% sequence similarities, respectively. The major cellular polar lipids were diphosphatidylglycerol, phosphatidylethanolamine, phosphatidylglycerol, three unidentified phospholipids, two unidentified aminophospholipids and an unidentified aminolipid. The dominant respiratory quinone was MK-7. The main cellular fatty acids were anteiso-C15 : 0 (53.01%), C16 : 0 (13.04%) and iso-C16 : 0 (10.80%). The genome size of R196T was 9.45 Mb, containing 7617 predicted protein-coding genes, with a DNA G+C content of 57.7 mol%. Based on the results of phenotypic, chemotaxonomic and whole-genome analyses, strain R196T represents a novel species of the genus Paenibacillus, for which the name Paenibacillus cymbidii sp. nov. is proposed. The type strain is R196T (=ACCC 61713T=KCTC 33718T).