Proposal and evaluation of online medical services expansion mode for specialties: a patient perceived value perspective

There is a great imbalance and difference in the distribution of Chinese medical resources in urban and rural areas, as most medical resources are concentrated in urban areas. Against the backdrop of China’s promotion of "Internet + medical healthcare", medical institutions are encouraged to apply Internet and other information technologies to expand the space and content of medical services, but patients in remote places lack independent choice of consultation platform. Based on the theory of Maslow's hierarchy of needs, customer perception theory, Synergy theory, TAM and ACSIM, the model building of remote patients' perceived value satisfaction with online medical services for specialties is hypothesized. Take F hospital as the subject, The research provides an empirical research on the process rebuilding and redesigning specialized online health services based on the perceived value of remote patients. To obtain the perceived value needs of remote patients’ visits, this study carries out questionnaire survey to understand the main needs of remote patients visiting. The results show that: social contact and respect value need > safety and survival value need > self-value need > cost losses value. Meanwhile, the preliminary evaluation indicators of patients' perceived value are derived based on the results of the questionnaire. The research is mainly to verify the effect of the implementation of the Internet-based specialized medical partnership medical service access model for remote patients. The post-test questionnaire is designed to understand the overall level of remote patients’ perceived value of online medical services, including the level of perceived ease of use, the level of perceived usefulness, the level of perceived value, the level of satisfaction, and the level of synergy. Among them, the perceived usefulness scores the highest, It is found that the cost of the new model in terms of time, distance, expense, and energy has been significantly reduced. The research, through building the SEM model, tests the path relationships of relevant dimensions and mediating effect of the model of remote patients’ perceived value satisfaction with online medical services for specialties.Verifica-se um grande desequilíbrio na distribuição dos recursos médicos chineses nas áreas urbanas e rurais, visto que a maioria dos recursos médicos está concentrada nas áreas urbanas. No contexto da promoção chinesa de "Internet + saúde médica", as instituições médicas são incentivadas a recorrer à Internet e a outras tecnologias de informação para expandir o espaço e o conteúdo dos serviços médicos, mas os pacientes em lugares remotos não têm escolha independente da plataforma de consulta. Com base na teoria da hierarquia de necessidades de Maslow, teoria da perceção do cliente, teoria da sinergia, TAM e ACSIM, realizou-se a construção do modelo de satisfação do valor percebido de pacientes remotos, com serviços médicos online para especialidades. Considerou-se o hospital F como caso de estudo. A investigação fornece uma pesquisa empírica sobre o processo de reconstrução e redesenho de serviços de saúde online especializados, com base no valor percebido de pacientes remotos. Para obter as necessidades de valor percebido das visitas de pacientes remotos, neste estudo realizou-se uma pesquisa por questionário para entender as principais necessidades das visitas de pacientes remotos. Os resultados mostram que: contato social e respeito valor necessidade e > segurança e valor de sobrevivência necessidade > necessidade de valor próprio > valor de perdas de custo. Enquanto isso, os indicadores de avaliação preliminar do valor percebido dos pacientes são derivados com base nos resultados do questionário. O objetivo principal do presente trabalho é verificar o efeito da implementação do modelo de acesso a serviços médicos especializados, baseada na Internet para pacientes remotos. O questionário pós-teste foi projetado para compreender o nível geral de valor percebido de pacientes remotos de serviços médicos online, incluindo o nível de facilidade de uso percebida, o nível de utilidade percebida, o nível de valor percebido, o nível de satisfação e o nível de sinergia. Entre eles, a utilidade percebida pontua mais alto. Verifica-se que o custo do novo modelo em termos de tempo, distância, despesa e energia foi reduzido significativamente. Por meio da construção do modelo SEM, testaram-se as relações do caminho de dimensões relevantes, e o efeito mediador do modelo de satisfação de valor percebido de pacientes remotos, com serviços médicos online para especialidades

Axially Expanded Windows for Local-Global Interaction in Vision Transformers

Recently, Transformers have shown promising performance in various vision tasks. A challenging issue in Transformer design is that global self-attention is very expensive to compute, especially for the high-resolution vision tasks. Local self-attention performs attention computation within a local region to improve its efficiency, which leads to their receptive fields in a single attention layer are not large enough, resulting in insufficient context modeling. When observing a scene, humans usually focus on a local region while attending to non-attentional regions at coarse granularity. Based on this observation, we develop the axially expanded window self-attention mechanism that performs fine-grained self-attention within the local window and coarse-grained self-attention in the horizontal and vertical axes, and thus can effectively capturing both short- and long-range visual dependencies

RSIR Transformer: Hierarchical Vision Transformer using Random Sampling Windows and Important Region Windows

Recently, Transformers have shown promising performance in various vision tasks. However, the high costs of global self-attention remain challenging for Transformers, especially for high-resolution vision tasks. Local self-attention runs attention computation within a limited region for the sake of efficiency, resulting in insufficient context modeling as their receptive fields are small. In this work, we introduce two new attention modules to enhance the global modeling capability of the hierarchical vision transformer, namely, random sampling windows (RS-Win) and important region windows (IR-Win). Specifically, RS-Win sample random image patches to compose the window, following a uniform distribution, i.e., the patches in RS-Win can come from any position in the image. IR-Win composes the window according to the weights of the image patches in the attention map. Notably, RS-Win is able to capture global information throughout the entire model, even in earlier, high-resolution stages. IR-Win enables the self-attention module to focus on important regions of the image and capture more informative features. Incorporated with these designs, RSIR-Win Transformer demonstrates competitive performance on common vision tasks

Positional Label for Self-Supervised Vision Transformer

Positional encoding is important for vision transformer (ViT) to capture the spatial structure of the input image. General effectiveness has been proven in ViT. In our work we propose to train ViT to recognize the positional label of patches of the input image, this apparently simple task actually yields a meaningful self-supervisory task. Based on previous work on ViT positional encoding, we propose two positional labels dedicated to 2D images including absolute position and relative position. Our positional labels can be easily plugged into various current ViT variants. It can work in two ways: (a) As an auxiliary training target for vanilla ViT (e.g., ViT-B and Swin-B) for better performance. (b) Combine the self-supervised ViT (e.g., MAE) to provide a more powerful self-supervised signal for semantic feature learning. Experiments demonstrate that with the proposed self-supervised methods, ViT-B and Swin-B gain improvements of 1.20% (top-1 Acc) and 0.74% (top-1 Acc) on ImageNet, respectively, and 6.15% and 1.14% improvement on Mini-ImageNet

Vision Big Bird: Random Sparsification for Full Attention

Recently, Transformers have shown promising performance in various vision tasks. However, the high costs of global self-attention remain challenging for Transformers, especially for high-resolution vision tasks. Inspired by one of the most successful transformers-based models for NLP: Big Bird, we propose a novel sparse attention mechanism for Vision Transformers (ViT). Specifically, we separate the heads into three groups, the first group used convolutional neural network (CNN) to extract local features and provide positional information for the model, the second group used Random Sampling Windows (RS-Win) for sparse self-attention calculation, and the third group reduces the resolution of the keys and values by average pooling for global attention. Based on these components, ViT maintains the sparsity of self-attention while maintaining the merits of Big Bird (i.e., the model is a universal approximator of sequence functions and is Turing complete). Moreover, our results show that the positional encoding, a crucial component in ViTs, can be safely removed in our model. Experiments show that Vision Big Bird demonstrates competitive performance on common vision tasks.Comment: arXiv admin note: substantial text overlap with arXiv:2304.0625

A Short Note on Aberrant Responses Bias in Item Response Theory

Item response models often cannot calculate true individual response probabilities because of the existence of response disturbances (such as guessing and cheating). Many studies on aberrant responses under item response theory (IRT) framework had been conducted. Some of them focused on how to reduce the effect of aberrant responses, and others focused on how to detect aberrant examinees, such as person fit analysis. The purpose of this research was to derive a generalized formula of bias with/without aberrant responses, that showed the effect of both non-aberrant and aberrant response data on the bias of capability estimation mathematically. A new evaluation criterion, named aberrant absolute bias (|ABIAS|), was proposed to detect aberrant examinees. Simulation studies and application to a real dataset were conducted to demonstrate the efficiency and the utility of |ABIAS|

The order of expression is a key factor in the production of active transglutaminase in Escherichia coli by co-expression with its pro-peptide

<p>Abstract</p> <p>Background</p> <p><it>Streptomyces </it>transglutaminase (TGase) is naturally synthesized as zymogen (pro-TGase), which is then processed to produce active enzyme by the removal of its N-terminal pro-peptide. This pro-peptide is found to be essential for overexpression of soluble TGase in <it>E. coli</it>. However, expression of pro-TGase by <it>E. coli </it>requires protease-mediated activation <it>in vitro</it>. In this study, we developed a novel co- expression method for the direct production of active TGase in <it>E. coli</it>.</p> <p>Results</p> <p>A TGase from <it>S. hygroscopicus </it>was expressed in <it>E. coli </it>only after fusing with the pelB signal peptide, but fusion with the signal peptide induced insoluble enzyme. Therefore, alternative protocol was designed by co-expressing the TGase and its pro-peptide as independent polypeptides under a single T7 promoter using vector pET-22b(+). Although the pro-peptide was co-expressed, the TGase fused without the signal peptide was undetectable in both soluble and insoluble fractions of the recombinant cells. Similarly, when both genes were expressed in the order of the TGase and the pro-peptide, the solubility of TGase fused with the signal peptide was not improved by the co-expression with its pro-peptide. Interestingly, active TGase was only produced by the cells in which the pro-peptide and the TGase were fused with the signal peptide and sequentially expressed. The purified recombinant and native TGase shared the similar catalytic properties.</p> <p>Conclusions</p> <p>Our results indicated that the pro-peptide can assist correct folding of the TGase inter-molecularly in <it>E. coli</it>, and expression of pro-peptide prior to that of TGase was essential for the production of active TGase. The co-expression strategy based on optimizing the order of gene expression could be useful for the expression of other functional proteins that are synthesized as a precursor.</p

Vetting undesirable behaviors in android apps with permission use analysis

Android platform adopts permissions to protect sensitive resources from untrusted apps. However, after permissions are granted by users at install time, apps could use these permissions (sensitive resources) with no further restrictions. Thus, recent years have witnessed the explosion of undesirable behaviors in Android apps. An important part in the defense is the accurate analysis of Android apps. However, traditional syscall-based analysis techniques are not well-suited for Android, because they could not capture critical interactions between the application and the Android system. This paper presents VetDroid, a dynamic analysis platform for reconstructing sensitive behaviors in Android apps from a novel permission use perspective. VetDroid features a systematic frame-work to effectively construct permission use behaviors, i.e., how applications use permissions to access (sensitive) system resources, and how these acquired permission-sensitive resources are further utilized by the application. With permission use behaviors, security analysts can easily examine the internal sensitive behaviors of an app. Using real-world Android malware, we show that VetDroid can clearly reconstruct fine-grained malicious behaviors to ease malware analysis. We further apply VetDroid to 1,249 top free apps in Google Play. VetDroid can assist in finding more information leaks than TaintDroid [24], a state-of-the-art technique. In addition, we show howwe can use VetDroid to analyze fine-grained causes of information leaks that TaintDroid cannot reveal. Finally, we show that VetDroid can help identify subtle vulnerabilities in some (top free) applications otherwise hard to detect

Myocardial strain analysis of echocardiography based on deep learning

BackgroundStrain analysis provides more thorough spatiotemporal signatures for myocardial contraction, which is helpful for early detection of cardiac insufficiency. The use of deep learning (DL) to automatically measure myocardial strain from echocardiogram videos has garnered recent attention. However, the development of key techniques including segmentation and motion estimation remains a challenge. In this work, we developed a novel DL-based framework for myocardial segmentation and motion estimation to generate strain measures from echocardiogram videos.MethodsThree-dimensional (3D) Convolutional Neural Network (CNN) was developed for myocardial segmentation and optical flow network for motion estimation. The segmentation network was used to define the region of interest (ROI), and the optical flow network was used to estimate the pixel motion in the ROI. We performed a model architecture search to identify the optimal base architecture for motion estimation. The final workflow design and associated hyperparameters are the result of a careful implementation. In addition, we compared the DL model with a traditional speck tracking algorithm on an independent, external clinical data. Each video was double-blind measured by an ultrasound expert and a DL expert using speck tracking echocardiography (STE) and DL method, respectively.ResultsThe DL method successfully performed automatic segmentation, motion estimation, and global longitudinal strain (GLS) measurements in all examinations. The 3D segmentation has better spatio-temporal smoothness, average dice correlation reaches 0.82, and the effect of target frame is better than that of previous 2D networks. The best motion estimation network achieved an average end-point error of 0.05 ± 0.03 mm per frame, better than previously reported state-of-the-art. The DL method showed no significant difference relative to the traditional method in GLS measurement, Spearman correlation of 0.90 (p &lt; 0.001) and mean bias −1.2 ± 1.5%.ConclusionIn conclusion, our method exhibits better segmentation and motion estimation performance and demonstrates the feasibility of DL method for automatic strain analysis. The DL approach helps reduce time consumption and human effort, which holds great promise for translational research and precision medicine efforts