PlaneRecTR: Unified Query Learning for 3D Plane Recovery from a Single View

3D plane recovery from a single image can usually be divided into several subtasks of plane detection, segmentation, parameter estimation and possibly depth estimation. Previous works tend to solve this task by either extending the RCNN-based segmentation network or the dense pixel embedding-based clustering framework. However, none of them tried to integrate above related subtasks into a unified framework but treat them separately and sequentially, which we suspect is potentially a main source of performance limitation for existing approaches. Motivated by this finding and the success of query-based learning in enriching reasoning among semantic entities, in this paper, we propose PlaneRecTR, a Transformer-based architecture, which for the first time unifies all subtasks related to single-view plane recovery with a single compact model. Extensive quantitative and qualitative experiments demonstrate that our proposed unified learning achieves mutual benefits across subtasks, obtaining a new state-of-the-art performance on public ScanNet and NYUv2-Plane datasets. Codes are available at https://github.com/SJingjia/PlaneRecTR.Comment: To be published in Proceedings of IEEE International Conference on Computer Vision (ICCV 2023). Camera Ready Version. Codes: https://github.com/SJingjia/PlaneRecTR , Video: https://youtu.be/YBB7totHGJ

The Effects of Mixed Foliar Nutrients of Calcium and Magnesium on the Major Bypass Respiratory Pathways in the Pulp of ‘Feizixiao’ Litchi

During the period of ‘Feizixiao’ litchi fruit pericarp’s full coloring, there is a phenomenon of “sugar withdrawal” in the pulp, and the mixed foliar nutrients of calcium and magnesium (Ca+Mg) can effectively overcome this phenomenon. One of the reasons for this may be that it is related to the influence of the mixed nutrients of Ca+Mg on the bypass respiratory pathways of the pulp. The major fruit quality indicators, the rates of cytochrome and cyanide-resistant respiratory pathways (CP and AP) in the pulp and the activities of their key enzymes, were observed continuously in 2021 and 2022, and the deferentially expressed genes (DEGs) related to the two bypass respiratory pathways in the pulp were screened by RNA-seq analysis, with a qPCR of the random genes performed to verify the results. Ca+Mg treatment kept the content of the total soluble sugar in the pulp stable and higher than that the control in the ripening stage; Ca+Mg treatment increased the activities of electron-transferring enzymes in the electron transport chain, such as NADH dehydrogenase (ND), succinate dehydrogenase (SDH), cytochrome bc1 complex, and cytochrome c (Cyt c) through up-regulating their gene expression. In terms of the rate-limiting enzymes in the pulp, Ca+Mg treatment increased the activity of cytochrome oxidase (COX) in the CP pathway by up-regulating the expression of COX genes, then increased the CP respiratory rate and inhibited the CP respiratory rate decrease; meanwhile, it also inhibited the activity of AOX (alternate oxidase) in the pulp in the AP pathway by down-regulating the expression of AOX genes, then inhibited the increase in the AP respiration rate. The qPCR validation of randomly selected DEGs showed a significant unitary linear correlation between their expression levels and the results of the RNA-seq analysis. Therefore, one of the physiological mechanisms on the mixed foliar nutrients of Ca and Mg overcoming the phenomenon of “sugar withdrawal” in the ‘Feizixiao’ litchi pulp could be to promote CP and to inhibit AP, and then to delay the ripening and senescence of the pulp

Development and validation of a machine learning predictive model for perioperative myocardial injury in cardiac surgery with cardiopulmonary bypass

Abstract Background Perioperative myocardial injury (PMI) with different cut-off values has showed to be associated with different prognostic effect after cardiac surgery. Machine learning (ML) method has been widely used in perioperative risk predictions during cardiac surgery. However, the utilization of ML in PMI has not been studied yet. Therefore, we sought to develop and validate the performances of ML for PMI with different cut-off values in cardiac surgery with cardiopulmonary bypass (CPB). Methods This was a second analysis of a multicenter clinical trial (OPTIMAL) and requirement for written informed consent was waived due to the retrospective design. Patients aged 18–70 undergoing elective cardiac surgery with CPB from December 2018 to April 2021 were enrolled in China. The models were developed using the data from Fuwai Hospital and externally validated by the other three cardiac centres. Traditional logistic regression (LR) and eleven ML models were constructed. The primary outcome was PMI, defined as the postoperative maximum cardiac Troponin I beyond different times of upper reference limit (40x, 70x, 100x, 130x) We measured the model performance by examining the area under the receiver operating characteristic curve (AUROC), precision-recall curve (AUPRC), and calibration brier score. Results A total of 2983 eligible patients eventually participated in both the model development (n = 2420) and external validation (n = 563). The CatboostClassifier and RandomForestClassifier emerged as potential alternatives to the LR model for predicting PMI. The AUROC demonstrated an increase with each of the four cutoffs, peaking at 100x URL in the testing dataset and at 70x URL in the external validation dataset. However, it’s worth noting that the AUPRC decreased with each cutoff increment. Additionally, the Brier loss score decreased as the cutoffs increased, reaching its lowest point at 0.16 with a 130x URL cutoff. Moreover, extended CPB time, aortic duration, elevated preoperative N-terminal brain sodium peptide, reduced preoperative neutrophil count, higher body mass index, and increased high-sensitivity C-reactive protein levels were identified as risk factors for PMI across all four cutoff values. Conclusions The CatboostClassifier and RandomForestClassifer algorithms could be an alternative for LR in prediction of PMI. Furthermore, preoperative higher N-terminal brain sodium peptide and lower high-sensitivity C-reactive protein were strong risk factor for PMI, the underlying mechanism require further investigation

Recent Research Progress of Mn4+-Doped A2MF6 (A = Li, Na, K, Cs, or Rb; M = Si, Ti, Ge, or Sn) Red Phosphors Based on a Core–Shell Structure

White light emitting diodes (WLEDs) are widely used due to their advantages of high efficiency, low electricity consumption, long service life, quick response time, environmental protection, and so on. The addition of red phosphor is beneficial to further improve the quality of WLEDs. The search for novel red phosphors has focused mainly on Eu2+ ion- and Mn4+ ion-doped compounds. Both of them have emissions in the red region, absorption in blue region, and similar quantum yields. Eu2+-doped phosphors possess a rather broad-band emission with a tail in the deep red spectral range, where the sensitivity of the human eye is significantly reduced, resulting in a decrease in luminous efficacy of WLEDs. Mn4+ ions provide a narrow emission band ~670 nm in oxide hosts, which is still almost unrecognizable to the human eye. Mn4+-doped fluoride phosphors have become one of the research hotspots in recent years due to their excellent fluorescent properties, thermal stability, and low cost. They possess broad absorption in the blue region, and a series of narrow red emission bands at around 630 nm, which are suitable to serve as red emitting components of WLEDs. However, the problem of easy hydrolysis in humid environments limits their application. Recent studies have shown that constructing a core–shell structure can effectively improve the water resistance of Mn4+-doped fluorides. This paper outlines the research progress of Mn4+-doped fluoride A2MF6 (A = Li, Na, K, Cs, or Rb; M = Si, Ti, Ge or Sn), which has been based on the core–shell structure in recent years. From the viewpoint of the core–shell structure, this paper mainly emphasizes the shell layer classification, synthesis methods, luminescent mechanism, the effect on luminescent properties, and water resistance, and it also gives some applications in terms of WLEDs. Moreover, it proposes challenges and developments in the future

Prediction models for postoperative delirium in elderly patients with machine-learning algorithms and SHapley Additive exPlanations

Abstract Postoperative delirium (POD) is a common and severe complication in elderly patients with hip fractures. Identifying high-risk patients with POD can help improve the outcome of patients with hip fractures. We conducted a retrospective study on elderly patients (≥65 years of age) who underwent orthopedic surgery with hip fracture between January 2014 and August 2019. Conventional logistic regression and five machine-learning algorithms were used to construct prediction models of POD. A nomogram for POD prediction was built with the logistic regression method. The area under the receiver operating characteristic curve (AUC-ROC), accuracy, sensitivity, and precision were calculated to evaluate different models. Feature importance of individuals was interpreted using Shapley Additive Explanations (SHAP). About 797 patients were enrolled in the study, with the incidence of POD at 9.28% (74/797). The age, renal insufficiency, chronic obstructive pulmonary disease (COPD), use of antipsychotics, lactate dehydrogenase (LDH), and C-reactive protein are used to build a nomogram for POD with an AUC of 0.71. The AUCs of five machine-learning models are 0.81 (Random Forest), 0.80 (GBM), 0.68 (AdaBoost), 0.77 (XGBoost), and 0.70 (SVM). The sensitivities of the six models range from 68.8% (logistic regression and SVM) to 91.9% (Random Forest). The precisions of the six machine-learning models range from 18.3% (logistic regression) to 67.8% (SVM). Six prediction models of POD in patients with hip fractures were constructed using logistic regression and five machine-learning algorithms. The application of machine-learning algorithms could provide convenient POD risk stratification to benefit elderly hip fracture patients

Glacial-interglacial Indian summer monsoon dynamics

&nbsp;&nbsp;&nbsp;&nbsp; The modern Indian summer monsoon (ISM) is characterized by exceptionally strong interhemispheric transport, indicating the importance of both Northern and Southern Hemisphere processesdriving monsoon variability. Here, we present a high-resolution continental record from southwestern China that demonstrates the importance of interhemispheric forcing in driving ISM variability at the glacial-interglacial time scale as well. Interglacial ISM maxima are dominated by an enhanced Indian low associated with global ice volume minima. In contrast, the glacial ISM reaches a minimum, and actually begins to increase, before global ice volume reaches a maximum. We attribute this early strengthening to an increased cross-equatorial pressure gradient derived from Southern Hemisphere high-latitude cooling. This mechanism explains much of the nonorbital scale variance in the Pleistocene ISM record.</p