Insulin-like growth factor-1 in myocardial ischemia-reperfusion injury: A review.

Myocardial ischemia-reperfusion injury (MIRI) is a severe damage inflicted on the ischemic myocardium when blood flow is restored, and it commonly occurs in a wide range of cardiovascular diseases. Presently, no effective clinical treatment exists for MIRI. Accumulating evidence indicates that insulin-like growth factor-1 (IGF-1) plays a role in the intricate chain of cardiovascular events, in addition to its well-recognized growth-promoting and metabolic effects. IGF-1, a member of the insulin family, exhibits a broad spectrum of protective effects against ischemia/reperfusion injury in various tissues, especially the myocardium. In particular, earlier research has demonstrated that IGF-1 reduces cellular oxidative stress, improves mitochondrial function, interacts with noncoding RNAs, and activates cardiac downstream protective genes and protective signaling channels. This review aimed to summarize the role of IGF-1 in MIRI and elucidate its related mechanisms of action. In addition, IGF-1-related interventions for MIRI, such as ischemic preconditioning and post-conditioning, were discussed. The purpose of this review was to provide evidence supporting the activation of IGF-1 in MIRI and advocate its use as a therapeutic target.

Albumin combined with neutrophil-to-lymphocyte ratio score and outcomes in patients with acute coronary syndrome treated with percutaneous coronary intervention.

BACKGROUND: Evidence about the association between albumin combined with neutrophil-to-lymphocyte ratio score (ANS) and survival outcomes in patients with acute coronary syndrome (ACS) undergoing percutaneous coronary intervention (PCI) is rare. This study aimed to evaluate the prognostic value of ANS in patients with ACS undergoing PCI by propensity score matching (PSM) analysis. PATIENTS AND METHODS: Patients with ACS undergoing PCI were consecutively enrolled in this prospective cohort study from January 2016 to December 2018. The albumin and neutrophil-to-lymphocyte ratio cutoff values for predicting major adverse cardiovascular events (MACEs) were calculated using receiver operating characteristic curves. Survival analysis was performed using Kaplan-Meier estimates, the Cox proportional hazard regression models and PSM. The study endpoint was the occurrence of a MACE, which included all-cause mortality and rehospitalization for severe heart failure during follow-up. RESULTS: Overall, 1549 patients with adequate specimens were identified and assigned into different groups for comparison. Before and after PSM, the Kaplan-Meier curves showed that a higher ANS value was associated with a higher risk of MACEs (all P  < 0.001). The multivariate Cox proportional hazard regression model showed that the ANS (per 1 score increase) [hazard ratio (HR), 2.016; 95% confidence interval (CI), 1.329-3.057; P  = 0.001 vs. HR, 2.166; 95% CI, 1.344-3.492; P  = 0.002] was an independent predictor for MACEs. CONCLUSION: This study tentatively confirms that ANS may be a valuable clinical indicator to identify high-risk ACS patients after PCI. More high-quality prospective studies are needed in the future.

Lnc-PLCB1 is stabilized by METTL14 induced m6A modification and inhibits Helicobacter pylori mediated gastric cancer by destabilizing DDX21.

Helicobacter pylori (H. pylori) infection is considered to be an important factor in gastric cancer (GC). Long noncoding RNA (lncRNA) and m6A modification are involved in the occurrence and development of GC, but the role of lncRNA m6A modification in the development of GC mediated by H. pylori is still unclear. Here, we found that H. pylori infection downregulated the expression of lnc-PLCB1 through METTL14-mediated m6A modification and IRF2-mediated transcriptional regulation. Overexpression of lnc-PLCB1 inhibited the proliferation and migration of GC cells, while downregulation of lnc-PLCB1 promoted the proliferation and migration ability of GC cells. In addition, clinical analysis showed that lnc-PLCB1 is lower in GC tissues than in normal tissues. Further study found that lnc-PLCB1 reduced the protein stability of its binding protein DEAD-box helicase 21 (DDX21) and then downregulated the expression of CCND1 and Slug, thereby playing tumour suppressing role in the occurrence and development of GC. In conclusion, the METTL14/lnc-PLCB1/DDX21 axis plays an important role in H. pylori-mediated GC, and lnc-PLCB1 can be used as a new target for GC treatment.

Secondary lens with hybrid structure of freeform surface and microstructure for ultra-thin backlight unit.

Ultra-thin has become the development trend of the direct-lit backlight unit (BLU). Double freeform surface lenses are commonly used in direct-lit BLUs to reduce thickness. However, for an ultra-thin BLU with quite small optical distance (OD) and a large LED pitch distance, the curvature of the designed lens would be quite large, which would make the final optical performance heavily affected by fabrication errors. This paper proposes a lens with freeform surfaces and microstructures. The rays from LEDs are first collimated by the freeform surfaces and the collimated rays are then reflected by the microstructures to the bottom of the BLU, which can effectively enlarge the spot size and reduce the OD. The simulation results show that the uniformity can be improved from 41.3% of the conventional double freeform surface lens to 83% when OD is 3 mm. Such hybrid lenses can avoid the fabrication of freeform surfaces with large curvature and the advantages of easy design and easy fabrication.

Advanced Lung Cancer Inflammation Index for Predicting Prognostic Risk for Patients with Acute Coronary Syndrome Undergoing Percutaneous Coronary Intervention.

Purpose: The decreased advanced lung cancer inflammation index (ALI), defined as body mass index (BMI) * albumin (Alb)/neutrophil-to-lymphocyte ratio (NLR), is an independent prognostic risk factor for overall survival in gastric, lung, and colorectal cancers. This study aimed to investigate the value of ALI in predicting the risk of major adverse cardiovascular events (MACEs) in patients with acute coronary syndrome (ACS). Patients and Methods: A total of 1624 patients with ACS undergoing percutaneous coronary intervention (PCI) were consecutively enrolled between January 2016 and December 2018. Follow-up data were collected at 1, 3, 6, and 12 months and annually thereafter. The primary endpoints were MACEs. All endpoints were defined as all-cause mortality, recurrent angina pectoris, restenosis/intra stent thrombosis, stroke, heart failure, and all-cause bleeding. Results: The MACEs group and non-MACEs group showed significant differences in patients with age >65 years (28 [50.0%] vs 319 [23.7%]), history of heart failure (16 [28.6%] vs 127 [9.4%]), history of ischemic stroke (14 [25.0%] vs 186 [13.8%]), history of cardiogenic shock (6 [10.71%] vs 16 [1.19%]), left ventricular ejection fraction <40% (8 [14.29%] vs 33 [2.46%]), and ALI <343.96 (44 [78.65%] vs 680 [50.60%]) (all p<0.001). The optimal cut-off value for ALI was 334.96. The area under the curve (AUC) of the 1-, 2-, 3-, and 5-year was 0.560, 0.577, 0.665, and 0.749, respectively. The survival rate was significantly lower in the low ALI group than in the high ALI group (log-rank p<0.001). Low ALI was an independent risk factor for the long-term prognosis of patients with ACS after PCI, univariate HR: 3.671, 95% CI: 1.938-6.953, p<0.001; multivariate HR: 3.009, 95% CI: 1.57-5.769, p=0.001. Conclusion: ALI score less than 334.96 is an independent prognostic risk factor for patients with ACS undergoing PCI and may be a novel marker for clinical practice.

Learn to Adapt for Self-Supervised Monocular Depth Estimation.

Monocular depth estimation is one of the fundamental tasks in environmental perception and has achieved tremendous progress by virtue of deep learning. However, the performance of trained models tends to degrade or deteriorate when employed on other new datasets due to the gap between different datasets. Though some methods utilize domain adaptation technologies to jointly train different domains and narrow the gap between them, the trained models cannot generalize to new domains that are not involved in training. To boost the transferability of self-supervised monocular depth estimation models and mitigate the issue of meta-overfitting, we train the model in the pipeline of meta-learning and propose an adversarial depth estimation task. We adopt model-agnostic meta-learning (MAML) to obtain universal initial parameters for further adaptation and train the network in an adversarial manner to extract domain-invariant representations for easing meta-overfitting. In addition, we propose a constraint to impose upon cross-task depth consistency to compel the depth estimation to be identical in different adversarial tasks, which improves the performance of our method and smoothens the training process. Experiments on four new datasets demonstrate that our method adapts quite fast to new domains. Our method trained after 0.5 epoch achieves comparable results with the state-of-the-art methods trained at least 20 epochs.

Molecular Joint Representation Learning via Multi-Modal Information of SMILES and Graphs.

In recent years, artificial intelligence has played an important role on accelerating the whole process of drug discovery. Various of molecular representation schemes of different modals (e.g., textual sequence or graph) are developed. By digitally encoding them, different chemical information can be learned through corresponding network structures. Molecular graphs and Simplified Molecular Input Line Entry System (SMILES) are popular means for molecular representation learning in current. Previous works have done attempts by combining both of them to solve the problem of specific information loss in single-modal representation on various tasks. To further fusing such multi-modal imformation, the correspondence between learned chemical feature from different representation should be considered. To realize this, we propose a novel framework of molecular joint representation learning via Multi-Modal information of SMILES and molecular Graphs, called MMSG. We improve the self-attention mechanism by introducing bond-level graph representation as attention bias in Transformer to reinforce feature correspondence between multi-modal information. We further propose a Bidirectional Message Communication Graph Neural Network (BMC GNN) to strengthen the information flow aggregated from graphs for further combination. Numerous experiments on public property prediction datasets have demonstrated the effectiveness of our model.

Perception and Navigation in Autonomous Systems in the Era of Learning: A Survey.

Autonomous systems possess the features of inferring their own state, understanding their surroundings, and performing autonomous navigation. With the applications of learning systems, like deep learning and reinforcement learning, the visual-based self-state estimation, environment perception, and navigation capabilities of autonomous systems have been efficiently addressed, and many new learning-based algorithms have surfaced with respect to autonomous visual perception and navigation. In this review, we focus on the applications of learning-based monocular approaches in ego-motion perception, environment perception, and navigation in autonomous systems, which is different from previous reviews that discussed traditional methods. First, we delineate the shortcomings of existing classical visual simultaneous localization and mapping (vSLAM) solutions, which demonstrate the necessity to integrate deep learning techniques. Second, we review the visual-based environmental perception and understanding methods based on deep learning, including deep learning-based monocular depth estimation, monocular ego-motion prediction, image enhancement, object detection, semantic segmentation, and their combinations with traditional vSLAM frameworks. Then, we focus on the visual navigation based on learning systems, mainly including reinforcement learning and deep reinforcement learning. Finally, we examine several challenges and promising directions discussed and concluded in related research of learning systems in the era of computer science and robotics.

Diagnostic and prognostic value of miR-486-5p, miR-451a, miR-21-5p and monocyte to high-density lipoprotein cholesterol ratio in patients with acute myocardial infarction.

Acute myocardial infarction (AMI) is one of the most serious complications of coronary heart disease. Although morbidity and mortality have been decreasing year by year, acute coronary syndrome still has a high mortality rate and disability rate. To search for accurate and effective biomarkers, we explore the diagnostic and prognostic value of microRNAs (miRNAs) and the monocyte to high-density lipoprotein cholesterol ratio (MHR) in patients with AMI. By referring to the relevant literature, miR-486-5p, miR-451a and miR-21-5p were reportedly altered in the blood of patients with ischemic heart disease. These miRNAs were selected and validated in 40 AMI patients, 22 unstable angina pectoris (UAP) and 22 healthy groups (HC) by real-time fluorescent quantitative polymerase chain reaction (RT-qPCR). All patients with AMI underwent primary percutaneous coronary intervention (PCI) and were followed up 3 months after the operation. MHR and miR-451a expression were markedly elevated in plasma samples of AMI patients compared with the UAP and HC groups, but the expressions of miR-486-5p and miR-21-5p were significantly decreased. The expression level of miRNA-451a increased gradually among the three groups (p < 0.05). However, the expression of miRNA-21-5p showed a downward trend (p < 0.05). More importantly, MHR was significantly different before and after PCI in AMI patients (p < 0.05). Receiver operating characteristic (ROC) analysis indicated that MHR, miR-486-5p, miR-451a and miR-21-5p could diagnose and predict AMI. MiR-451a was a more reliable biomarker for AMI diagnosis among these miRNAs. Moreover, the combination of MHR and miRNAs had higher diagnostic value for AMI. We further demonstrated that miR-21-5p had a strong predictive ability for the occurrence of major adverse cardiovascular events (MACE) after 3 months. The results showed that circulating miR-486-5p, miR-451a, miR-21-5p and MHR may play critical roles in the early phase of AMI, and may be used as potential predictors for AMI diagnosis. Importantly, miR-451a was a more reliable biomarker in diagnosing AMI patients. Circulating miR-21-5p may be used as a predictor of MACE occurrence.

The Prognostic Value of Hematologic Inflammatory Markers in Patients With Acute Coronary Syndrome Undergoing Percutaneous Coronary Intervention.

The aggregate index of systemic inflammation (AISI), systemic inflammation response index (SIRI), and neutrophil-to-lymphocyte*platelet ratio (NLRP) are novel indices that simultaneously reflect the inflammatory and immune status. However, the role of these indices in acute coronary syndrome (ACS) patients undergoing percutaneous coronary intervention (PCI) remains unclear. We aimed to elucidate the predictive value of AISI, SIRI, and NLRP in patients with ACS undergoing PCI. A total of 1558 patients with ACS undergoing PCI were consecutively enrolled from January 2016 to December 2018. The AISI, SIRI, NLRP, systemic immune-inflammatory index, derived neutrophil-to-lymphocyte ratio, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and monocyte-to-lymphocyte ratio cutoff values for predicting major adverse cardiovascular events (MACE) were calculated using receiver-operating characteristic curves, and Spearman's test was used to analyze correlations between these indices. Kaplan-Meier curves and Cox regression models were used for survival analyses, and the endpoint was a MACE, which included all-cause mortality and rehospitalization for severe heart failure during the follow-up period. The Kaplan-Meier curves showed that higher AISI, SIRI, and NLRP values were associated with a higher risk of MACE (all P < .001). The association between AISI, SIRI, and NLRP and ACS prognosis was stable in various subgroups according to sex, age, smoking, dyslipidemia, hypertension, diabetes mellitus, history of stroke, and heart failure (P for interaction > .05). Increasing tertiles of AISI, SIRI, and NLRP significantly increased the MACE risk (P for trend < .05). AISI, SIRI, and NLRP may be suitable laboratory markers for identifying high-risk patients with ACS after PCI.

A visualization analysis of hotspots and frontiers of cardiovascular diseases with frailty.

Cardiovascular diseases (CVD) and frailty are common health problems among the elderly. This research aims to investigate the hotspots and frontiers of the field of CVD with frailty. Data of publications between 2000 and 2021 were collected from the Web of Science Core Collection (WoSCC) and CiteSpace was used for analyzing the hotspots and frontiers of cardiovascular diseases with frailty research from high-impact countries/regions, institutions, authors, cited references, cited journals, high-frequency keywords, and burst keywords. The results showed that the USA, England, and Canada were the leading countries/regions in research on CVD with frailty. Other countries/regions and regions lagged behind these developed countries/regions. There is a need to establish cooperation between developed countries/regions and developing countries/regions. Research hotspots focused on frailty in the elderly with CVD, exercise intervention, assessment for CVD patients with frailty, quality of life, and common diseases related to CVD with frailty. The frontier fields include care and intervention of CVD patients with frailty, social frailty, and validation of CVD with frailty.

Nursing Skill Assessment of Hospital Nurses in Management of Critically Ill Patients.

Objective: To analyze the application of standardized nursing procedures in critically ill patients' nursing evaluation. Methods: 90 cases of critically ill patients aged from 18 to 65 who were treated in our hospital from April 2020 to April 2021 were selected and divided into the control group and observation group, respectively, with 45 cases according to the drawing method. The rescue time, blood pressure, heart rate before and after nursing, adverse mood, length of stay, incidence of adverse events, ICU transfer and death, and satisfaction of 2 groups were statistically analyzed and compared. Results: The rescue time of cardiopulmonary resuscitation, oxygen inhalation, venous opening, and endotracheal resuscitation in the observation group was 3.24 ± 1.01, which is lower than that in the control group, 6.65 ± 2.11, with statistical significance (P < 0.05). Similarly, the vital signs in the observation group were 2.45 ± 0.44, which is also significantly lower than that in the control group, 5.67 ± 1.56. After nursing, the blood pressure and heart rate in the observation group were lower than those in control group, with statistical significance (P < 0.05). The adverse mood of the observation group after nursing was lower than that of the control group, with statistical significance (P < 0.05). The length of stay, incidence of adverse events, intensive care unit (ICU) transfer, and death in the observation group were lower than those in the control group, with statistical significance (P < 0.05). The length of stay in the observation group was 8.87 ± 2.11, while 11.34 ± 2.45 in the observation group. The incidence of adverse events in the observation group was 1, while 8 in the observation group. The length of stay in the observation group was 8.87 ± 2.11, while 11.34 ± 2.45 in the observation group. The ICU transfer in the observation group were 2, while 9 in the observation group. There was no death in the observation group, however, 4 in the observation group. Nursing satisfaction in the observation group was higher than that in the control group, with statistical significance (P < 0.05). The number of patients that are very satisfied in the observation group was 28, while 20 in the control group. The number of patients that are satisfied in the observation group was the same as in the control group, both 15. However, the number of patients that are dissatisfied in the observation group was 2, while 10 in the control group. Conclusion: The application of the standardized nursing process in the nursing of critically ill patients can not only effectively reduce the self-rating anxiety scale (SAS) and sarcoidosis diagnostic score (SDS) of patients but also reduce the incidence of complications and improve the nursing satisfaction of patients.

Understanding learners' metacognitive experiences in learning to write in English as a foreign language: A structural equation modeling approach.

Many researchers have acknowledged the role of metacognition in facilitating learning to write in English as a foreign language (EFL). Although research on metacognition has explored learners' metacognitive knowledge and metacognitive strategies in the field of EFL writing, little is known about the nature of learners' metacognitive experiences in EFL writing. To fill such an important gap, this study was designed to assess EFL learners' metacognitive experiences before, during, and after writing. Data were collected from a total of 760 undergraduates through three self-report questionnaires and a writing task. Results from quantitative analyses showed four subcategories of EFL learners' metacognitive experiences in writing: metacognitive feeling, metacognitive judgments/estimates, online task-specific metacognitive knowledge, and online task-specific metacognitive strategies. Based on the empirical evidence, we propose a model of metacognitive experiences in EFL writing. Theoretical, methodological, and pedagogical implications are discussed.

Tumour-suppressing functions of the lncRNA MBNL1-AS1/miR-889-3p/KLF9 axis in human breast cancer cells.

This study aimed to explore the role and potential mechanism of the long non-coding (lncRNA) MBNL1-AS1 in human breast cancer. We included 80 patients with breast cancer in this study. Breast cancer cell lines, including MCF7, SKBR3, MDA-MB-231 and MDA-MB-415, and the normal human breast cell line MCF10A were used in this study. MBNL1-AS1, miR-889-3p mimics, si-Krüppel-like factor 9 (KLF9) or their controls were transfected in the cells. Quantitative reverse transcription polymerase chain reaction (qRT-PCR), Western blotting and immunohistochemistry assay were performed to detect the expression of MBNL1-AS1, miR-889-3p and KLF9. Cell proliferation, invasion and migration were detected. Luciferase reporter gene and pull-down assay were performed to verify the target relationship among MBNL1-AS1, miR-889-3p and KLF9. Glycolysis was also detected after transfection. The expression of the lncRNA MBNL1-AS1 was low in the breast cancer tissues and cells. Lower expression levels of the lncRNA MBNL1-AS1 were associated with poor prognosis of breast cancer. Overexpression of the lncRNA MBNL1-AS1 decreased proliferation, invasion, migration and glycolysis of breast cancer cells. The lncRNA MBNL1-AS1 could interact with miR-889-3p, and KLF9 was the downstream target of miR-889-3p. Moreover, miR-889-3p was negatively correlated with KLF9 and lncRNA MBNL1-AS1. Both miR-889-3p and si-KLF9 could reverse the overexpression of lncRNA MBNL1-AS1 in breast cancer development. The lncRNA MBNL1-AS1 decreased proliferation, invasion, migration and glycolysis of breast cancer via the miR-889-3p/KLF9 axis, which might be a potential biomarker for the diagnosis of breast cancer.

The role and features of peer assessment feedback in college English writing.

Peer assessment is a common pedagogical practice for evaluating students' writing in college English classrooms. However, in-depth research on the learning outcomes after peer assessment is scarce and inconsistent; how peer feedback is used has not been fully explored either. This study compared peer feedback to teacher feedback and explored the different features of feedback and its impact on draft revision. Two specific research questions were answered in this study: (1) In which aspects can peer feedback supplement teacher feedback in improving the linguistic features in writing? (2) What are the differences in features of peer feedback versus teacher feedback? And how do they connect to feedback take-in? Two writing tasks were assigned to 94 students. One received teacher feedback and the other peer feedback. Pre-feedback and post-feedback writings in both tasks (4 sets in total) were scored and human ratings were adjusted using Many-Facet Rasch modeling to control for differences in leniency. Drawing on three natural language processing (NLP) tools, this study also assessed writing qualities by comparing 22 selected indices related to the scoring rubrics for human raters, which involve three dimensions: cohesion, lexical quality and syntactic complexity. Peer and teacher feedback was then coded based on features of feedback to explore their influence on draft revision. The results showed that both peer and teacher feedback had positive effects on rating scores. We confirmed peer feedback as an effective classroom approach to improve writing, though limited compared to teacher feedback as reflected in the indices. In terms of feedback features, students often stopped at identifying the language problems, while the teacher provided more explanations, solutions or suggestions regarding the problems identified. Implications for peer feedback research and implementation of peer assessment activities are provided.

Unsupervised Estimation of Monocular Depth and VO in Dynamic Environments via Hybrid Masks.

Deep learning-based methods mymargin have achieved remarkable performance in 3-D sensing since they perceive environments in a biologically inspired manner. Nevertheless, the existing approaches trained by monocular sequences are still prone to fail in dynamic environments. In this work, we mitigate the negative influence of dynamic environments on the joint estimation of depth and visual odometry (VO) through hybrid masks. Since both the VO estimation and view reconstruction process in the joint estimation framework is vulnerable to dynamic environments, we propose the cover mask and the filter mask to alleviate the adverse effects, respectively. As the depth and VO estimation are tightly coupled during training, the improved VO estimation promotes depth estimation as well. Besides, a depth-pose consistency loss is proposed to overcome the scale inconsistency between different training samples of monocular sequences. Experimental results show that both our depth prediction and globally consistent VO estimation are state of the art when evaluated on the KITTI benchmark. We evaluate our depth prediction model on the Make3D dataset to prove the transferability of our method as well.

Circulating microRNA-208 family as early diagnostic biomarkers for acute myocardial infarction: A meta-analysis.

OBJECTIVE: Many recent studies have demonstrated that serum miRNA-208 (miR-208) could be a powerful biomarker in the early diagnosis of acute myocardial infarction (AMI). However, the result of previous studies was not accurate due to the small sample sizes and controversial issues. Therefore, this study was performed to investigate the relationship between the expression levels of miR-208 and AMI. MATERIALS AND METHODS: According to the inclusion and exclusion criteria, a preliminary literature search was performed. The study was based on articles published in PubMed, Embase, Cochrane databases before September 30, 2019. Two staff members extracted data from the included articles for meta-analysis. These data were analyzed for sensitivity, specificity, diagnostic odds ratio, and summary receiver operator curve (SROC) analyses. RESULTS: This study included 13 pieces of literature, which contains 1703 patients with AMI and 1589 controls. The main results of our meta-analysis were as follows: The pool sensitivity and specificity of miR-208 for diagnosing AMI was 83% and 97%. The area under the SROC curve (AUC) was 93%. Mir-208 had a highly effective diagnostic capacity to distinguish AMI from chest pain patients with an AUC of 93%. CONCLUSIONS: The results showed that circulating miR-208 was a reliable biomarker both for diagnosting ST-elevation myocardial infarction (STEMI) and non-ST elevation myocardial infarction (NSTEMI). MiR-208 was sufficient to distinguish AMI patients with chest pain from healthy controls.

Investigating Students' Metacognitive Experiences: Insights From the English as a Foreign Language Learners' Writing Metacognitive Experiences Questionnaire (EFLLWMEQ).

While research on metacognitive knowledge and metacognitive strategies in second language (L2) writing has proliferated, little attention has been paid to metacognitive experiences in learning to write. This study contributes a novel 6-point Likert scale questionnaire, EFL Learners' Writing Metacognitive Experiences Questionnaire (EFLLWMEQ), and reports insights into learners' metacognitive experiences gathered from its use. The questionnaire was designed to investigate, first, the nature of students' metacognitive experiences when they learn to write in English as a foreign language (EFL) and, secondly, the relationship between students' metacognitive experiences and their writing performance. To this end, the questionnaire was developed and validated with two independent samples of 340 and 540 Chinese undergraduates whose metacognitive experiences were measured as they learned to write in EFL. Data were subjected to exploratory factor analysis (EFA) and confirmatory factor analysis (CFA), respectively. Findings of EFA and CFA revealed a four-factor structure of students' metacognitive experiences of EFL writing: Metacognitive estimates, metacognitive feelings, online task-specific metacognitive knowledge, and online task-specific metacognitive strategies. Results showed that students' metacognitive experiences had positive correlations with their EFL writing test scores. Importantly, the CFA results from the sample of 540 students supported the four-factor correlated model with the best model fit, confirming the validity and reliability of the EFLLWMEQ. This study has theoretical and pedagogical implications for how learners' metacognitive experiences can be managed in learning to write, particularly in EFL classrooms.

Multitask GANs for Semantic Segmentation and Depth Completion With Cycle Consistency.

Semantic segmentation and depth completion are two challenging tasks in scene understanding, and they are widely used in robotics and autonomous driving. Although several studies have been proposed to jointly train these two tasks using some small modifications, such as changing the last layer, the result of one task is not utilized to improve the performance of the other one despite that there are some similarities between these two tasks. In this article, we propose multitask generative adversarial networks (Multitask GANs), which are not only competent in semantic segmentation and depth completion but also improve the accuracy of depth completion through generated semantic images. In addition, we improve the details of generated semantic images based on CycleGAN by introducing multiscale spatial pooling blocks and the structural similarity reconstruction loss. Furthermore, considering the inner consistency between semantic and geometric structures, we develop a semantic-guided smoothness loss to improve depth completion results. Extensive experiments on the Cityscapes data set and the KITTI depth completion benchmark show that the Multitask GANs are capable of achieving competitive performance for both semantic segmentation and depth completion tasks.

Masked GAN for Unsupervised Depth and Pose Prediction With Scale Consistency.

Previous work has shown that adversarial learning can be used for unsupervised monocular depth and visual odometry (VO) estimation, in which the adversarial loss and the geometric image reconstruction loss are utilized as the mainly supervisory signals to train the whole unsupervised framework. However, the performance of the adversarial framework and image reconstruction is usually limited by occlusions and the visual field changes between the frames. This article proposes a masked generative adversarial network (GAN) for unsupervised monocular depth and ego-motion estimations. The MaskNet and Boolean mask scheme are designed in this framework to eliminate the effects of occlusions and impacts of visual field changes on the reconstruction loss and adversarial loss, respectively. Furthermore, we also consider the scale consistency of our pose network by utilizing a new scale-consistency loss, and therefore, our pose network is capable of providing the full camera trajectory over a long monocular sequence. Extensive experiments on the KITTI data set show that each component proposed in this article contributes to the performance, and both our depth and trajectory predictions achieve competitive performance on the KITTI and Make3D data sets.