Interpretation on Multi-modal Visual Fusion

In this paper, we present an analytical framework and a novel metric to shed light on the interpretation of the multimodal vision community. Our approach involves measuring the proposed semantic variance and feature similarity across modalities and levels, and conducting semantic and quantitative analyses through comprehensive experiments. Specifically, we investigate the consistency and speciality of representations across modalities, evolution rules within each modality, and the collaboration logic used when optimizing a multi-modality model. Our studies reveal several important findings, such as the discrepancy in cross-modal features and the hybrid multi-modal cooperation rule, which highlights consistency and speciality simultaneously for complementary inference. Through our dissection and findings on multi-modal fusion, we facilitate a rethinking of the reasonability and necessity of popular multi-modal vision fusion strategies. Furthermore, our work lays the foundation for designing a trustworthy and universal multi-modal fusion model for a variety of tasks in the future.Comment: This version was under review since 2023/3/

Fast and Accurate Neural Word Segmentation for Chinese

Neural models with minimal feature engineering have achieved competitive performance against traditional methods for the task of Chinese word segmentation. However, both training and working procedures of the current neural models are computationally inefficient. This paper presents a greedy neural word segmenter with balanced word and character embedding inputs to alleviate the existing drawbacks. Our segmenter is truly end-to-end, capable of performing segmentation much faster and even more accurate than state-of-the-art neural models on Chinese benchmark datasets.Comment: To appear in ACL201

Brachial-ankle pulse wave velocity trajectories in a middle-aged population

ObjectiveThe “trajectory” phenotype was observed in several cardiovascular risk factors with aging. We aim to identify multiple brachial-ankle Pulse Wave Velocity (baPWV) trajectory phenotypes and assess their determinants.MethodsAmong 5,182 participants with baPWV measurements (2010–2016) at no less than three time points in Kailuan Study, we derived baPWV trajectory pattern using SAS Proc Traj program. We applied the lowest Bayesian information criterion to identify the best typing model, related the identified trajectory pattern to baseline and changes in characteristics.ResultsAmong 5.3 ± 1.7 years follow-up, four distinct baPWV trajectories were identified as low (1,961,37.8%), medium-low (1,846,35.6%), medium-high (1,024,19.8%), and high (351,6.8%) groups. In the stepwise models, mean arterial pressure and age were the main determinators of the trajectory patterns, with a Δpseudo-R2 of 0.335 and 0.164, respectively. With the low trajectory group as reference and multivariable adjustment, odd ratios of medium low, medium high and high associated with 1 mmHg increment of mean arterial pressure were 1.08(95%CI: 1.07–1.09), 1.13(1.12–1.14), and 1.16(1.15–1.18). The estimates for age were 1.08(1.07–1.10), 1.20(1.18–1.21) and 1.28(1.26–1.31). Additionally, baseline resting heart rate, low-density lipoprotein cholesterol, fasting blood glucose, hypersensitive C-reaction protein and uric acid, and changes in mean arterial pressure, resting heart rate, fasting blood glucose, and uric acid were positively associated with the trajectory, while BMI was negatively associated.ConclusionsThe changes in baPWV overtime followed a “trajectory” pattern, mainly determined by mean arterial pressure and age

Serum proteome analysis for profiling protein markers associated with carcinogenesis and lymph node metastasis in nasopharyngeal carcinoma

Nasopharyngeal carcinoma (NPC), one of the most common cancers in population with Chinese or Asian progeny, poses a serious health problem for southern China. It is unfortunate that most NPC victims have had lymph node metastasis (LNM) when first diagnosed. We believe that the 2D based serum proteome analysis can be useful in discovering new biomarkers that may aid in the diagnosis and therapy of NPC patients. To filter the tumor specific antigen markers of NPC, sera from 42 healthy volunteers, 27 non-LNM NPC patients and 37 LNM NPC patients were selected for screening study using 2D combined with MS. Pretreatment strategy, including sonication, albumin and immunoglobulin G (IgG) depletion, was adopted for screening differentially expressed proteins of low abundance in serum. By 2D image analysis and MALDI-TOF-MS identification, twenty-three protein spots were differentially expressed. Three of them were further validated in the sera using enzyme-linked immunosorbent assay (ELISA). Our research demonstrates that HSP70, sICAM-1 and SAA, confirmed with ELISA at sera and immunohistochemistry, are potential NPC metastasis-specific serum biomarkers which may be of great underlying significance in clinical detection and management of NPC

Actively implementing an evidence-based feeding guideline for critically ill patients (NEED): a multicenter, cluster-randomized, controlled trial

Background: Previous cluster-randomized controlled trials evaluating the impact of implementing evidence-based guidelines for nutrition therapy in critical illness do not consistently demonstrate patient benefits. A large-scale, sufficiently powered study is therefore warranted to ascertain the effects of guideline implementation on patient-centered outcomes. Methods: We conducted a multicenter, cluster-randomized, parallel-controlled trial in intensive care units (ICUs) across China. We developed an evidence-based feeding guideline. ICUs randomly allocated to the guideline group formed a local "intervention team", which actively implemented the guideline using standardized educational materials, a graphical feeding protocol, and live online education outreach meetings conducted by members of the study management committee. ICUs assigned to the control group remained unaware of the guideline content. All ICUs enrolled patients who were expected to stay in the ICU longer than seven days. The primary outcome was all-cause mortality within 28 days of enrollment. Results: Forty-eight ICUs were randomized to the guideline group and 49 to the control group. From March 2018 to July 2019, the guideline ICUs enrolled 1399 patients, and the control ICUs enrolled 1373 patients. Implementation of the guideline resulted in significantly earlier EN initiation (1.20 vs. 1.55 mean days to initiation of EN; difference − 0.40 [95% CI − 0.71 to − 0.09]; P = 0.01) and delayed PN initiation (1.29 vs. 0.80 mean days to start of PN; difference 1.06 [95% CI 0.44 to 1.67]; P = 0.001). There was no significant difference in 28-day mortality (14.2% vs. 15.2%; difference − 1.6% [95% CI − 4.3% to 1.2%]; P = 0.42) between groups. Conclusions: In this large-scale, multicenter trial, active implementation of an evidence-based feeding guideline reduced the time to commencement of EN and overall PN use but did not translate to a reduction in mortality from critical illness. Trial registration: ISRCTN, ISRCTN12233792. Registered November 20th, 2017

Actively implementing an evidence-based feeding guideline for critically ill patients (NEED): a multicenter, cluster-randomized, controlled trial.

BackgroundPrevious cluster-randomized controlled trials evaluating the impact of implementing evidence-based guidelines for nutrition therapy in critical illness do not consistently demonstrate patient benefits. A large-scale, sufficiently powered study is therefore warranted to ascertain the effects of guideline implementation on patient-centered outcomes.MethodsWe conducted a multicenter, cluster-randomized, parallel-controlled trial in intensive care units (ICUs) across China. We developed an evidence-based feeding guideline. ICUs randomly allocated to the guideline group formed a local "intervention team", which actively implemented the guideline using standardized educational materials, a graphical feeding protocol, and live online education outreach meetings conducted by members of the study management committee. ICUs assigned to the control group remained unaware of the guideline content. All ICUs enrolled patients who were expected to stay in the ICU longer than seven days. The primary outcome was all-cause mortality within 28 days of enrollment.ResultsForty-eight ICUs were randomized to the guideline group and 49 to the control group. From March 2018 to July 2019, the guideline ICUs enrolled 1399 patients, and the control ICUs enrolled 1373 patients. Implementation of the guideline resulted in significantly earlier EN initiation (1.20 vs. 1.55 mean days to initiation of EN; difference - 0.40 [95% CI - 0.71 to - 0.09]; P = 0.01) and delayed PN initiation (1.29 vs. 0.80 mean days to start of PN; difference 1.06 [95% CI 0.44 to 1.67]; P = 0.001). There was no significant difference in 28-day mortality (14.2% vs. 15.2%; difference - 1.6% [95% CI - 4.3% to 1.2%]; P = 0.42) between groups.ConclusionsIn this large-scale, multicenter trial, active implementation of an evidence-based feeding guideline reduced the time to commencement of EN and overall PN use but did not translate to a reduction in mortality from critical illness.Trial registrationISRCTN, ISRCTN12233792 . Registered November 20th, 2017

Actively implementing an evidence-based feeding guideline for critically ill patients (NEED): a multicenter, cluster-randomized, controlled trial (vol 26, 46, 2022)

BackgroundPrevious cluster-randomized controlled trials evaluating the impact of implementing evidence-based guidelines for nutrition therapy in critical illness do not consistently demonstrate patient benefits. A large-scale, sufficiently powered study is therefore warranted to ascertain the effects of guideline implementation on patient-centered outcomes.MethodsWe conducted a multicenter, cluster-randomized, parallel-controlled trial in intensive care units (ICUs) across China. We developed an evidence-based feeding guideline. ICUs randomly allocated to the guideline group formed a local "intervention team", which actively implemented the guideline using standardized educational materials, a graphical feeding protocol, and live online education outreach meetings conducted by members of the study management committee. ICUs assigned to the control group remained unaware of the guideline content. All ICUs enrolled patients who were expected to stay in the ICU longer than seven days. The primary outcome was all-cause mortality within 28 days of enrollment.ResultsForty-eight ICUs were randomized to the guideline group and 49 to the control group. From March 2018 to July 2019, the guideline ICUs enrolled 1399 patients, and the control ICUs enrolled 1373 patients. Implementation of the guideline resulted in significantly earlier EN initiation (1.20 vs. 1.55 mean days to initiation of EN; difference - 0.40 [95% CI - 0.71 to - 0.09]; P = 0.01) and delayed PN initiation (1.29 vs. 0.80 mean days to start of PN; difference 1.06 [95% CI 0.44 to 1.67]; P = 0.001). There was no significant difference in 28-day mortality (14.2% vs. 15.2%; difference - 1.6% [95% CI - 4.3% to 1.2%]; P = 0.42) between groups.ConclusionsIn this large-scale, multicenter trial, active implementation of an evidence-based feeding guideline reduced the time to commencement of EN and overall PN use but did not translate to a reduction in mortality from critical illness.Trial registrationISRCTN, ISRCTN12233792 . Registered November 20th, 2017

The complete chloroplast genome and phylogenetic analysis of Cardamine circaeoides Hook. f. et Thoms., 1861 (Brassicaceae)

A species of Cardamine circaeoides Hook. f. et Thoms., 1861, which belongs to the Cardamine family (Brassicaceae), is an endemic species of the Wuling Mountains in Hunan and Hubei Provinces of China. Since there are many morphologically related species of C. circaeoides, the chloroplast (cp) genome characteristics of C. circaeoides were analyzed in order to explore the phylogenetic relationship between it and the closely related species. A cp genome totaling 154,838 base pairs exhibited a typical quadripartite structure with a pair of IRs (inverted repeats; 26,493 base pairs) separated by a small single-copy region of 17,938 base pairs and a large single-copy region of 83,914 base pairs. A total of 130 genes were found in the cp genome, including 85 protein-coding genes, 37 tRNA genes, and 8 rRNA genes. There were 36.21%, 33.96%, 29.09%, and 42.35% GC content in the entire cp genome, LSC region, SSC region, and IR region, respectively. According to phylogenetic analysis, C. circaeoides is evolutionarily closer to Cardamine hupingshanensis

Echocardiography-based machine learning algorithm for distinguishing ischemic cardiomyopathy from dilated cardiomyopathy

Abstract Background Machine learning (ML) can identify and integrate connections among data and has the potential to predict events. Heart failure is primarily caused by cardiomyopathy, and different etiologies require different treatments. The present study examined the diagnostic value of a ML algorithm that combines echocardiographic data to automatically differentiate ischemic cardiomyopathy (ICM) from dilated cardiomyopathy (DCM). Methods We retrospectively collected the echocardiographic data of 200 DCM patients and 199 ICM patients treated in the First Affiliated Hospital of Guangxi Medical University between July 2016 and March 2022. All patients underwent invasive coronary angiography for diagnosis of ICM or DCM. The data were randomly divided into a training set and a test set via 10-fold cross-validation. Four ML algorithms (random forest, logistic regression, neural network, and XGBoost [ML algorithm under gradient boosting framework]) were used to generate a training model for the optimal subset, and the parameters were optimized. Finally, model performance was independently evaluated on the test set, and external validation was performed on 79 patients from another center. Results Compared with the logistic regression model (area under the curve [AUC] = 0.925), neural network model (AUC = 0.893), and random forest model (AUC = 0.900), the XGBoost model had the best identification rate, with an average sensitivity of 72% and average specificity of 78%. The average accuracy was 75%, and the AUC of the optimal subset was 0.934. External validation produced an AUC of 0.804, accuracy of 78%, sensitivity of 64% and specificity of 93%. Conclusions We demonstrate that utilizing advanced ML algorithms can help to differentiate ICM from DCM and provide appreciable precision for etiological diagnosis and individualized treatment of heart failure patients