The Impact of Data Agglomeration on Export Structure Upgrading in Cities: A Factor Mobility Perspective

The digital economy and export upgrading are important topics of common concern for policymakers and academics during the period of high-quality economic development. From the perspective of factor mobility, this paper constructs the two-way fixed effect, mediation effect, and spatial Durbin models to analyze the impacts of data agglomeration on urban export structure upgrading. Using panel data of 280 cities at the prefecture level and above in China from 2005 to 2018, the empirical analysis reveals a positive impact of data agglomeration on urban export structure upgrading, with capital transfer and technology diffusion further reinforcing this impact. Additionally, data agglomeration promotes urban export structure upgrading by optimizing innovation resource allocation and exhibits a spatial spillover effect on urban export structure upgrading. Finally, the facilitating effect on urban export structure upgrading is heterogeneous. Consequently, it is imperative to expedite the construction of new digital infrastructure, foster the integration and symbiotic evolution of data and traditional production factors, and implement distinct innovation development pathways based on regional comparative advantages

SSiT: Saliency-guided Self-supervised Image Transformer for Diabetic Retinopathy Grading

Self-supervised Learning (SSL) has been widely applied to learn image representations through exploiting unlabeled images. However, it has not been fully explored in the medical image analysis field. In this work, Saliency-guided Self-Supervised image Transformer (SSiT) is proposed for Diabetic Retinopathy (DR) grading from fundus images. We novelly introduce saliency maps into SSL, with a goal of guiding self-supervised pre-training with domain-specific prior knowledge. Specifically, two saliency-guided learning tasks are employed in SSiT: (1) Saliency-guided contrastive learning is conducted based on the momentum contrast, wherein fundus images' saliency maps are utilized to remove trivial patches from the input sequences of the momentum-updated key encoder. Thus, the key encoder is constrained to provide target representations focusing on salient regions, guiding the query encoder to capture salient features. (2) The query encoder is trained to predict the saliency segmentation, encouraging the preservation of fine-grained information in the learned representations. To assess our proposed method, four publicly-accessible fundus image datasets are adopted. One dataset is employed for pre-training, while the three others are used to evaluate the pre-trained models' performance on downstream DR grading. The proposed SSiT significantly outperforms other representative state-of-the-art SSL methods on all downstream datasets and under various evaluation settings. For example, SSiT achieves a Kappa score of 81.88% on the DDR dataset under fine-tuning evaluation, outperforming all other ViT-based SSL methods by at least 9.48%

PRIOR: Prototype Representation Joint Learning from Medical Images and Reports

Contrastive learning based vision-language joint pre-training has emerged as a successful representation learning strategy. In this paper, we present a prototype representation learning framework incorporating both global and local alignment between medical images and reports. In contrast to standard global multi-modality alignment methods, we employ a local alignment module for fine-grained representation. Furthermore, a cross-modality conditional reconstruction module is designed to interchange information across modalities in the training phase by reconstructing masked images and reports. For reconstructing long reports, a sentence-wise prototype memory bank is constructed, enabling the network to focus on low-level localized visual and high-level clinical linguistic features. Additionally, a non-auto-regressive generation paradigm is proposed for reconstructing non-sequential reports. Experimental results on five downstream tasks, including supervised classification, zero-shot classification, image-to-text retrieval, semantic segmentation, and object detection, show the proposed method outperforms other state-of-the-art methods across multiple datasets and under different dataset size settings. The code is available at https://github.com/QtacierP/PRIOR.Comment: Accepted by ICCV 202

TriFormer: A Multi-modal Transformer Framework For Mild Cognitive Impairment Conversion Prediction

The prediction of mild cognitive impairment (MCI) conversion to Alzheimer's disease (AD) is important for early treatment to prevent or slow the progression of AD. To accurately predict the MCI conversion to stable MCI or progressive MCI, we propose Triformer, a novel transformer-based framework with three specialized transformers to incorporate multi-model data. Triformer uses I) an image transformer to extract multi-view image features from medical scans, II) a clinical transformer to embed and correlate multi-modal clinical data, and III) a modality fusion transformer that produces an accurate prediction based on fusing the outputs from the image and clinical transformers. Triformer is evaluated on the Alzheimer's Disease Neuroimaging Initiative (ANDI)1 and ADNI2 datasets and outperforms previous state-of-the-art single and multi-modal methods

Competing risk nomogram predicting cause-specific mortality in older patients with testicular germ cell tumors

BackgroundTesticular germ cell tumor (TGCT) is the most common type of malignancy in young men, but rarely in older adults. We aimed to construct a competing risk model to predict the prognosis for older patients with TGCT.MethodsWe collected TGCT patients aged 50 years or older diagnosed between 2004 and 2015 from the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) database. We estimated the cumulative incidences of cause-specific death (CSD) and other causes of death and established a nomogram predicting cause-specific mortality in older patients with TGCT by Fine-Gray competing risk regression. The concordance index (C-index), calibration curves, area under the receiver operating characteristic curve (AUC), and decision analysis curves (DCA) were used to evaluate the differentiation, accuracy, and clinical significance of the nomogram.ResultsA total of 2,751 older TGCT patients were included in the study. The 3-, 5-, and 10-year cumulative incidences were 4.4, 5.0 and 6.1%, respectively, for cause-specific death, and 3.8, 6.2, 13.1%, respectively, for other causes of death. Predictors of cause-specific mortality in older TGCT included age, marital status, annual household income, histology, tumor size, stage and surgery. In the training and validation sets, the C-indexes were greater than 0.8, indicating that the nomogram had good discrimination. The AUC revealed the same result. The calibration curves showed good agreement between the predicted and observed results of the nomogram. DCA curves indicated that the nomogram had more clinical significance than the conventional American Joint Committee on Cancer (AJCC) staging. Based on the total nomogram score of each case, all patients were categorized into low-risk and high-risk groups, and risk categorization allowed the identification of cases with a high risk of death.ConclusionWe established a competing risk nomogram with good performance that may help clinicians accurately predict the prognosis of older TGCT patients

Experimental insights on the development of buoyant plumes injected into a porous media

We describe a series of new laboratory experiments which examine the rise of a two-dimensional buoyancy-driven plume of freshwater through a porous layer initially saturated with aqueous saline solution. Measurements show that the plume head accounts for a constant fraction of about 0.7 of the buoyancy supplied at the source and that it grows as it rises through the porous layer. However, the morphology of the plume head becomes increasingly complex as the ratio of the injection speed to the buoyancy rise speed increases, with the fluid spreading laterally and developing localized buoyant fingers which intermingle with the ambient fluid. Behind the plume head, a tail of nearly constant width develops providing a pathway from the source to the plume head. These starting plume dynamics may be relevant for buoyancy-driven contaminant dispersal and also for the convection which develops during CO2 sequestration as CO2 dissolves into aquifer water.This work was partially supported by a grant from the Chinese Ministry of Education

Identifying the Key Components in ResNet-50 for Diabetic Retinopathy Grading from Fundus Images : A Systematic Investigation

Although deep learning-based diabetic retinopathy (DR) classification methods typically benefit from well-designed architectures of convolutional neural networks, the training setting also has a non-negligible impact on prediction performance. The training setting includes various interdependent components, such as an objective function, a data sampling strategy, and a data augmentation approach. To identify the key components in a standard deep learning framework (ResNet-50) for DR grading, we systematically analyze the impact of several major components. Extensive experiments are conducted on a publicly available dataset EyePACS. We demonstrate that (1) the DR grading framework is sensitive to input resolution, objective function, and composition of data augmentation; (2) using mean square error as the loss function can effectively improve the performance with respect to a task-specific evaluation metric, namely the quadratically weighted Kappa; (3) utilizing eye pairs boosts the performance of DR grading and; (4) using data resampling to address the problem of imbalanced data distribution in EyePACS hurts the performance. Based on these observations and an optimal combination of the investigated components, our framework, without any specialized network design, achieves a state-of-the-art result (0.8631 for Kappa) on the EyePACS test set (a total of 42,670 fundus images) with only image-level labels. We also examine the proposed training practices on other fundus datasets and other network architectures to evaluate their generalizability. Our codes and pre-trained model are available online.Applied Science, Faculty ofNon UBCBiomedical Engineering, School ofReviewedFacultyGraduat

Does ICU admission dysphagia independently contribute to delirium risk in ischemic stroke patients? Results from a cohort study

Abstract Background Delirium is prevalent in ischemic stroke patients, particularly those in the intensive care unit (ICU), and it poses a significant burden on patients and caregivers, leading to increased mortality rates, prolonged hospital stays, and impaired cognitive function. Dysphagia, a common symptom in critically ill patients with ischemic stroke, further complicates their condition. However, the association between dysphagia and delirium in this context remains unclear. The objective of this study was to investigate the correlation between dysphagia and delirium in ICU patients with ischemic stroke. Methods A retrospective analysis was conducted on adult patients diagnosed with ischemic stroke at a medical center in Boston. Ischemic stroke cases were identified using the ninth and tenth revisions of the International Classification of Diseases. Dysphagia was defined as a positive bedside swallowing screen performed by medical staff on the day of ICU admission, while delirium was assessed using the ICU Confusion Assessment Method and review of nursing notes. Logistic regression models were used to explore the association between dysphagia and delirium. Causal mediation analysis was employed to identify potential mediating variables. Results The study comprised 1838 participants, with a median age of approximately 70 years, and 50.5% were female. Among the total study population, the prevalence of delirium was 43.4%, with a higher prevalence observed in the dysphagia group (60.7% vs. 40.8%, p < 0.001) compared to the non-dysphagia group. After adjusting for confounding factors including age, sex, race, dementia, depression, sedative medications, history of falls, visual or hearing deficit, sequential organ failure score, and Glasgow coma score, multifactorial logistic regression analysis demonstrated a significant association between dysphagia and an increased likelihood of delirium (odds ratio [OR]: 1.48; 95% confidence interval [CI]: 1.07–2.05; p = 0.018; E-value = 1.73). Causal mediation analysis revealed that serum albumin levels partially mediated the association between dysphagia and delirium in critically ill patients with ischemic stroke (average causal mediated effect [ACME]: 0.02, 95% CI: 0.01 to 0.03; p < 0.001). Conclusion ICU admission dysphagia may independently contribute to the risk of delirium in patients with ischemic stroke. Early identification and intervention in ischemic stroke patients with dysphagia may help mitigate the risk of delirium and improve patient prognosis

Targeted DNA methylation profiling reveals epigenetic signatures in peanut allergy

DNA methylation (DNAm) has been shown to play a role in mediating food allergy; however, the mechanism by which it does so is poorly understood. In this study, we used targeted next-generation bisulfite sequencing to evaluate DNAm levels in 125 targeted highly informative genomic regions containing 602 CpG sites on 70 immune-related genes to understand whether DNAm can differentiate peanut allergy (PA) versus nonallergy (NA). We found PA-associated DNAm signatures associated with 12 genes (7 potentially novel to food allergy, 3 associated with Th1/Th2, and 2 associated with innate immunity), as well as DNAm signature combinations with superior diagnostic potential compared with serum peanut–specific IgE for PA versus NA. Furthermore, we found that, following peanut protein stimulation, peripheral blood mononuclear cell (PBMCs) from PA participants showed increased production of cognate cytokines compared with NA participants. The varying responses between PA and NA participants may be associated with the interaction between the modification of DNAm and the interference of environment. Using Euclidean distance analysis, we found that the distances of methylation profile comprising 12 DNAm signatures between PA and NA pairs in monozygotic (MZ) twins were smaller than those in randomly paired genetically unrelated individuals, suggesting that PA-related DNAm signatures may be associated with genetic factors