Health literacy associated differences of medication use in Crohn's disease

PURPOSE: The aim of this study was to explore a difference in medication use (defined by medication selection and medication self-discontinuation) between patients with limited and adequate health literacy. This study also investigated whether the association between medication use and clinical remission of CD was different across the health literacy spectrum. METHODS: A cross-sectional study was conducted by analyzing an existing dataset from the IBD Health Literacy study in the Department of Gastroenterology at Boston Medical Center (BMC). Among 61 patients who were enrolled, 26 had limited health literacy and 35 had adequate health literacy. Medication use was defined by medication selection and medication self-discontinuation. Medication selection was further defined as whether patients with monotherapy were on one of the following medications including 5-ASAs, immunomodulators (including thiopurines and methotrexate), biologics and prednisone; medication self-discontinuation was further defined as whether patients had ever discontinued medications (including thiopurines and biologics) without physician recommendation. Harvey Bradshaw Index score was the assessment of clinical remission of CD. Newest Vital Sign scores were applied to assess health literacy. RESULTS: The odds ratios for patients who were on 5-ASAs and immune modulators (including thiopurines and methotrexate) monotherapy at the time of visit to have limited health literacy, compared to patients who were on monotherapy of biologic agent, were 3.75 (95%CI (0.46-38.26), p = 0.22) and 1.25 (95%CI (0.13-9.67), p = 0.83), respectively. The odds ratio for those whoever self-discontinued any medications to have limited health literacy versus those who did not was 1.62 (95%CI (0.42-6.24), p = 0.48). The odds ratio for patients whoever self-discontinued any medications to be in clinical remission against those who did not was 0.46 (95%CI (0.1-1.85), p = 0.27). The odds ratio for associations between medication self-discontinuation and clinical remission were 0.6 (95%CI (0.06-4.58), p = 0.63) in patients with limited health literacy and 0.5 (95%CI (0.06-4.62), p = 0.51) in patients with adequate health literacy. CONCLUSION: There were no differences of medication use between limited and adequate health literacy. The association between medication self-discontinuation and clinical remission of CD was indifferent across the health literacy levels. The results of this study provides a foundation for future studies on health literacy associated differences in CD populations and helps promote the effectiveness of treatment for CD by arousing more attention to different health literacy populations

Semi-supervised Road Updating Network (SRUNet): A Deep Learning Method for Road Updating from Remote Sensing Imagery and Historical Vector Maps

A road is the skeleton of a city and is a fundamental and important geographical component. Currently, many countries have built geo-information databases and gathered large amounts of geographic data. However, with the extensive construction of infrastructure and rapid expansion of cities, automatic updating of road data is imperative to maintain the high quality of current basic geographic information. However, obtaining bi-phase images for the same area is difficult, and complex post-processing methods are required to update the existing databases.To solve these problems, we proposed a road detection method based on semi-supervised learning (SRUNet) specifically for road-updating applications; in this approach, historical road information was fused with the latest images to directly obtain the latest state of the road.Considering that the texture of a road is complex, a multi-branch network, named the Map Encoding Branch (MEB) was proposed for representation learning, where the Boundary Enhancement Module (BEM) was used to improve the accuracy of boundary prediction, and the Residual Refinement Module (RRM) was used to optimize the prediction results. Further, to fully utilize the limited amount of label information and to enhance the prediction accuracy on unlabeled images, we utilized the mean teacher framework as the basic semi-supervised learning framework and introduced Regional Contrast (ReCo) in our work to improve the model capacity for distinguishing between the characteristics of roads and background elements.We applied our method to two datasets. Our model can effectively improve the performance of a model with fewer labels. Overall, the proposed SRUNet can provide stable, up-to-date, and reliable prediction results for a wide range of road renewal tasks.Comment: 22 pages, 8 figure

Refined Equivalent Pinhole Model for Large-scale 3D Reconstruction from Spaceborne CCD Imagery

In this study, we present a large-scale earth surface reconstruction pipeline for linear-array charge-coupled device (CCD) satellite imagery. While mainstream satellite image-based reconstruction approaches perform exceptionally well, the rational functional model (RFM) is subject to several limitations. For example, the RFM has no rigorous physical interpretation and differs significantly from the pinhole imaging model; hence, it cannot be directly applied to learning-based 3D reconstruction networks and to more novel reconstruction pipelines in computer vision. Hence, in this study, we introduce a method in which the RFM is equivalent to the pinhole camera model (PCM), meaning that the internal and external parameters of the pinhole camera are used instead of the rational polynomial coefficient parameters. We then derive an error formula for this equivalent pinhole model for the first time, demonstrating the influence of the image size on the accuracy of the reconstruction. In addition, we propose a polynomial image refinement model that minimizes equivalent errors via the least squares method. The experiments were conducted using four image datasets: WHU-TLC, DFC2019, ISPRS-ZY3, and GF7. The results demonstrated that the reconstruction accuracy was proportional to the image size. Our polynomial image refinement model significantly enhanced the accuracy and completeness of the reconstruction, and achieved more significant improvements for larger-scale images.Comment: 24 page

Sea Ice Extraction via Remote Sensed Imagery: Algorithms, Datasets, Applications and Challenges

The deep learning, which is a dominating technique in artificial intelligence, has completely changed the image understanding over the past decade. As a consequence, the sea ice extraction (SIE) problem has reached a new era. We present a comprehensive review of four important aspects of SIE, including algorithms, datasets, applications, and the future trends. Our review focuses on researches published from 2016 to the present, with a specific focus on deep learning-based approaches in the last five years. We divided all relegated algorithms into 3 categories, including classical image segmentation approach, machine learning-based approach and deep learning-based methods. We reviewed the accessible ice datasets including SAR-based datasets, the optical-based datasets and others. The applications are presented in 4 aspects including climate research, navigation, geographic information systems (GIS) production and others. It also provides insightful observations and inspiring future research directions.Comment: 24 pages, 6 figure

RESEARCH ON VISUAL ANALYSIS METHODS OF TERRORISM EVENTS

Resumo: Este artigo analisa as interlocuções entre memória, patrimônio, artes do saber-fazer e as relações de gênero na Unidade Prisional de Goiás com enfoque no projeto Cabocla: bordando cidadania, o modo como ele tem contribuído para uma outra formatação da experiência feminina no cárcere, a economia simbólica e a patrimonialização de objetos através da eleição da cultura vilaboense, reproduzida em bordados feitos por mulheres encarceradas. Por meio de entrevistas com a idealizadora do projeto e com uma esposa de reeducando, uma ex-reeducanda e uma mulher que cumpre pena privativa de liberdade, alinhavamos um painel sobre os impactos da atividade manual no encarceramento e na trajetória de vida dessas mulheres.Palavras-chave: bordado; memória; cárcere; patrimônio.Abstract: This article examines the dialogues between memory, heritage, arts know-how and gender relations in Prison Unit Goiás focusing on Cabocla project: embroidering citizenship, the way he has contributed to a other formatting of the female experience in prison the symbolic economy and patrimony of objects through the election of vilaboense culture, reproduced in embroidery made by women prisoners. Through interviews with the creator of the project and re-educating with a wife, an ex-convict and a woman who still meets custodial sentence, sew a panel on the impact of incarceration on manual activity and the life course of these women.Keyword: embroidery; memory; jail; patrimony

From Video to Hyperspectral: Hyperspectral Image-Level Feature Extraction with Transfer Learning

Hyperspectral image classification methods based on deep learning have led to remarkable achievements in recent years. However, these methods with outstanding performance are also accompanied by problems such as excessive dependence on the number of samples, poor model generalization, and time-consuming training. Additionally, the previous patch-level feature extraction methods have some limitations, for instance, non-local information is difficult to model, etc. To solve these problems, this paper proposes an image-level feature extraction method with transfer learning. Firstly, we look at a hyperspectral image with hundreds of contiguous spectral bands from a sequential image perspective. We attempt to extract the global spectral variation information between adjacent spectral bands by using the optical flow estimation method. Secondly, we propose an innovative data adaptation strategy to bridge the gap between hyperspectral and video data, and transfer the optical flow estimation network pre-trained with video data to the hyperspectral feature extraction task for the first time. Thirdly, we utilize the traditional classifier to achieve classification. Simultaneously, a vote strategy combined with features at different scales is proposed to improve the classification accuracy further. Extensive, well-designed experiments on four scenes of public hyperspectral images demonstrate that the proposed method (Spe-TL) can obtain results that are competitive with advanced deep learning methods under various sample conditions, with better time effectiveness to adapt to new target tasks. Moreover, it can produce more detailed classification maps that subtly reflect the authentic distribution of ground objects in the original image

From Video to Hyperspectral: Hyperspectral Image-Level Feature Extraction with Transfer Learning

Hyperspectral image classification methods based on deep learning have led to remarkable achievements in recent years. However, these methods with outstanding performance are also accompanied by problems such as excessive dependence on the number of samples, poor model generalization, and time-consuming training. Additionally, the previous patch-level feature extraction methods have some limitations, for instance, non-local information is difficult to model, etc. To solve these problems, this paper proposes an image-level feature extraction method with transfer learning. Firstly, we look at a hyperspectral image with hundreds of contiguous spectral bands from a sequential image perspective. We attempt to extract the global spectral variation information between adjacent spectral bands by using the optical flow estimation method. Secondly, we propose an innovative data adaptation strategy to bridge the gap between hyperspectral and video data, and transfer the optical flow estimation network pre-trained with video data to the hyperspectral feature extraction task for the first time. Thirdly, we utilize the traditional classifier to achieve classification. Simultaneously, a vote strategy combined with features at different scales is proposed to improve the classification accuracy further. Extensive, well-designed experiments on four scenes of public hyperspectral images demonstrate that the proposed method (Spe-TL) can obtain results that are competitive with advanced deep learning methods under various sample conditions, with better time effectiveness to adapt to new target tasks. Moreover, it can produce more detailed classification maps that subtly reflect the authentic distribution of ground objects in the original image

A Deep few-shot learning algorithm for hyperspectral image classification

For hyperspectral image classification problem of small sample, this paper proposes a depth of less sample learning algorithm, this algorithm through the simulation of the small sample classification in the process of training is to train the depth 3D convolution neural network feature extraction, the extraction of characteristic with smaller class span and large spacing between classes, more suitable for small sample classification problem, and can be used for different hyperspectral data, has better generalization ability. The trained model is used to extract the features of the target data set, and then the nearest neighbor classifier and support vector machine classifier are combined for supervised classification. Three groups of hyperspectral image data of Pavia university, Indian Pines and Salinas were used in the classification experiment. The experimental results showed that the algorithm could achieve a better classification accuracy than the traditional semi-supervised classification method under the condition of fewer training samples (only 5 marked samples were selected for each type of feature as training samples)