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The simplification of lines plays a crucial role in map generalization and multiscale representation; however, addressing inconsistent sidedness relationships between the simplified lines and their neighboring features poses a persistent challenge. In order to preserve correct and consistent sidedness relationships following simplification, this study introduces a novel line simplification method. This method incorporates topological constraints pertaining to left–right sidedness relationships, formulated as an optimization procedure based on the proposed partial total least squares method with constraints. The primary objective is to minimize the positional difference between the line features before and after simplification, with the sidedness relationship represented as an inequality constraint. Moreover, an optimization algorithm is derived to address the simplification problem effectively while adhering to the specified constraints. The proposed method is then applied to simplify the lines within three distinct datasets. Experimental results validate the efficacy of the proposed method in maintaining sidedness consistency across all tested datasets. In comparison to the Douglas–Peucker (DP) method, the proposed method exhibits minimal vertical displacement variance in the line feature points post‐simplification. Additionally, it achieves a smaller overall positional difference between the simplified and original lines compared to the DP method. These findings underscore the superior performance of the proposed method in maintaining sidedness relationships and minimizing positional differences during the line simplification process. [ABSTRACT FROM AUTHOR]

A small pigmented flagellate, Micromonas, is prevalently distributed in coastal and pelagic waters. However, there have been few studies conducted to quantify their abundance in the marginal seas of the Northwest Pacific Ocean. In this study, we used fluorescent in situ hybridization with tyramide signal amplification (TSA‐FISH) to reveal the spatial distribution of Micromonas in the northern South China Sea (SCS). On average, the abundance of Micromonas was 317 cells mL−1, with the average proportions in the nanoflagellates (NF) and photosynthetic picoeukaryotes (PPE) communities being 10.94% and 15.39%, respectively. This indicates a wide distribution and dominance of this genus in the studied area. The relationships between Micromonas abundance and various environmental factors suggested that biotic correlations play more important roles than physicochemical filtering on Micromonas assemblage. This may indicate a broad environmental adaptation spectrum of this genus through its flexibility in terms of resource acquisition strategies. In summary, this study provides insight into the spatial distribution pattern of Micromonas and highlights its crucial contribution to the composition of NFs and PPE communities, which rely on biological interaction to respond to the changing environmental conditions in the northern SCS. [ABSTRACT FROM AUTHOR]

Summary: During pregnancy many women may experience negative emotions and sleep disturbances. This systematic review and meta‐analysis was conducted to assess the efficacy of cognitive behavioural therapy for insomnia (CBT‐I) or sleep disturbance in pregnant women. From the earliest available publications to 15 April 2022, seven electronic literature databases were searched: PubMed, Web of Science, Cochrane Library, Embase, Chinese National Knowledge Infrastructure, Wanfang Data, and VIP Database for Chinese Science and Technology Journal. Randomised controlled trials of CBT‐I in pregnant women with insomnia or sleep disorders were included. The methodological bias of the included studies was assessed using the Cochrane risk of bias tool. The meta‐analysis was performed using RevMan 5.4 software. Stata Statistical Software: Release 15 was used for sensitivity analysis and publication bias. We included eight randomised controlled trials involving 743 pregnant women. Meta‐analysis showed that, compared with the control group, CBT‐I significantly improved the Insomnia Severity Index (mean difference [MD] = −4.25, 95% confidence interval [CI, −6.32, −2.19], p < 0.001), The Pittsburgh Sleep Quality Index (MD = −3.30, 95% CI [−4.81, −1.79], p < 0.001), sleep onset latency (standardised mean difference [SMD] = −1.25, 95% CI [−2.01, −0.50], p = 0.001), anxiety (SMD = −0.99, 95% CI [−1.32, −0.67], p < 0.001), and depression (SMD = −0.40, 95% CI [−0.72, −0.07], p = 0.02). No significant differences were found in total sleep time (SMD = 0.31, 95% CI [−0.54, 1.17], p = 0.47) and sleep efficiency (SMD = 0.80, 95% CI [−0.53, 2.13], p = 0.24). CBT‐I significantly improved pregnant women's sleep quality, insomnia severity, depression, and anxiety. This meta‐analysis provides evidence that CBT‐I is valid for insomnia or sleep disturbances during pregnancy. [ABSTRACT FROM AUTHOR]

The performance of lithium–sulfur (Li–S) battery is strongly dependent on the sulfur electrodes materials. Due to the chemical interaction between electrodes and intermediates produced in charging and discharging process, the Janus‐polar two dimensional (2D) electrode materials can enhance the electrochemical performance of Li–S battery by overcoming shuttle effect and increasing diffusion rates. Here, it is explored the structural, electronic, and dynamic characteristics of Li ion, S8 cluster, and lithium polysulfides adsorbed over Janus Phosphorus Nitride monolayer with blue phosphorene phase (b‐PN) by using first‐principles calculations. The reported findings highlight potential of the b‐PN monolayer as a good host material, given that the large absorption energies of different clusters vary from −0.47 to −3.78 eV, which can suppress shuttle effect effectively. These strong adsorptions are origin from the chemical interaction between nitrogen atoms of the b‐PN and Li atoms in lithium polysulfides in present work. Particularly, it is predicted that b‐PN monolayer has extremely small diffusion barriers for Li ion and Li2S8 clusters of 0.10 and 0.03 eV, which leading to a higher battery charge and discharge rate than reported graphene and MoS2. It is hoped that this efficient strategy for promising host materials of sulfur‐electrode guides the development of next‐generation lithium–sulfur batteries. [ABSTRACT FROM AUTHOR]

Traditional histopathology performed by pathologists by the naked eye is insufficient for accurate and efficient diagnosis of bladder cancer (BCa). We collected 643 H&E‐stained BCa images from Shanghai General Hospital and The Cancer Genome Atlas (TCGA). We constructed and cross‐verified automatic diagnosis and prognosis models by performing a machine learning algorithm based on pathomics data. Our study indicated that high diagnostic efficiency of the machine learning‐based diagnosis model was observed in patients with BCa, with area under the curve (AUC) values of 96.3%, 89.2%, and 94.1% in the training cohort, test cohort, and external validation cohort, respectively. Our diagnosis model also performed well in distinguishing patients with BCa from patients with glandular cystitis, with an AUC value of 93.4% in the General cohort. Significant differences were found in overall survival in TCGA cohort (hazard ratio (HR) = 2.09, 95% confidence interval (CI): 1.56‐2.81, P <.0001) and the General cohort (HR = 5.32, 95% CI: 2.95‐9.59, P <.0001) comparing patients with BCa of high risk vs low risk stratified by risk score, which was proved to be an independent prognostic factor for BCa. The integration nomogram based on our risk score and clinicopathologic characters displayed higher prediction accuracy than current tumor stage/grade systems, with AUC values of 77.7%, 83.8%, and 81.3% for 1‐, 3‐, and 5‐y overall survival prediction of patients with BCa. However, prospective studies are still needed for further verifications. [ABSTRACT FROM AUTHOR]

Background: Identifying patients who may develop severe coronavirus disease 2019 (COVID‐19) will facilitate personalized treatment and optimize the distribution of medical resources. Methods: In this study, 590 COVID‐19 patients during hospitalization were enrolled (Training set: n = 285; Internal validation set: n = 127; Prospective set: n = 178). After filtered by two machine learning methods in the training set, 5 out of 31 clinical features were selected into the model building to predict the risk of developing severe COVID‐19 disease. Multivariate logistic regression was applied to build the prediction nomogram and validated in two different sets. Receiver operating characteristic (ROC) analysis and decision curve analysis (DCA) were used to evaluate its performance. Results: From 31 potential predictors in the training set, 5 independent predictive factors were identified and included in the risk score: C‐reactive protein (CRP), lactate dehydrogenase (LDH), Age, Charlson/Deyo comorbidity score (CDCS), and erythrocyte sedimentation rate (ESR). Subsequently, we generated the nomogram based on the above features for predicting severe COVID‐19. In the training cohort, the area under curves (AUCs) were 0.822 (95% CI, 0.765–0.875) and the internal validation cohort was 0.762 (95% CI, 0.768–0.844). Further, we validated it in a prospective cohort with the AUCs of 0.705 (95% CI, 0.627–0.778). The internally bootstrapped calibration curve showed favorable consistency between prediction by nomogram and the actual situation. And DCA analysis also conferred high clinical net benefit. Conclusion: In this study, our predicting model based on five clinical characteristics of COVID‐19 patients will enable clinicians to predict the potential risk of developing critical illness and thus optimize medical management. [ABSTRACT FROM AUTHOR]

Disubstituted cyanamides are prepared by the title arylation of aryl and alkyl cyanamides with good yields. [ABSTRACT FROM AUTHOR]