Transforming growth factor beta1 (TGFB1) acts as a growth inhibitor of normal colonic epithelial cells, however, as a tumor promoter of colorectal cancer (CRC) cells. To explore the association between genetic polymorphisms in the TGFB1 pathway and CRC susceptibility and clinical outcome, we carried out a case-control study on a Swedish population of 308 CRC cases and 585 age- and gender-matched controls. The cases were sampled prospectively and had up to 16 years follow-up, making the study material particularly suitable for survival analysis. On the basis of their reported or predicted functional effect, nine single-nucleotide polymorphisms (TGFB1: Leu10Pro; TGFBR1: 9A/6A and IVS7G+24A; FURIN: C-229T; THBS1: T+42C; LTBP1L: C-256G; LTBP4: T-893G and Thr750Ala; BAMBI: T-779A) were selected for genotyping. We evaluated the associations between genotypes and CRC and Dukes' stage. Survival probabilities were compared between different subgroups. The observed statistically significant associations included a decreased CRC risk for TGFBR1 IVS7G+24A minor allele carriers (odds ratio (OR): 0.72, 95% confidence interval (CI): 0.53-0.97), less aggressive tumors with Dukes' stage A+B for carriers of LTBP4 Thr750Ala and BAMBI T-779A minor alleles (OR: 0.58, 95%CI: 0.36-0.93 and OR: 0.51, 95%CI: 0.29-0.89, respectively) and worse survival for FURIN C-229T heterozygotes (hazard ratio: 1.63, 95%CI: 1.08-2.46). As this is the first study about the influence of the polymorphisms in the TGFB1 pathway on CRC progression, further studies in large independent cohorts are warranted.

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The strawberry is a highly nutritional and beneficial cash crop, and its appearance quality sorting is a crucial step in the production process. Manual identifying and sorting, however, are subjective and more time consuming. In the field of image classification, vision transformers (ViT) have shown better performance. In this article, an extensive evaluation of the ViT models for the identification of strawberry appearance quality is presented. Moreover, to balance the accuracy and computation time of the ViT model, we propose a ViT-based method that uses fine-tuned ViT-B/32 to extract the class token and imports it into the support vector machine (SVM) to identify strawberry. Besides, the class token is imported into the SVM with different kernel functions to evaluate their performance. The experimental results show that the highest recognition accuracy of original ViT models is ViT-L/16, which can reach 97.38%. The application of the linear kernel function is more suitable in this work.The accuracy of original ViT-B/32 is 93.7% and the proposed method improves the accuracy by 4.4% up to 98.1%. Furthermore, the required computation time of the proposed method is only 62.13 s, which is faster than other models. Therefore, the proposed method demonstrates enhanced robustness and universality, especially for abnormal and ripe strawberries.Practical Applications: The identification of strawberry appearance quality is a laborintensive task. Traditional methods mainly rely on manpower, which has high cost and low efficiency. Therefore, this work uses the fine-tuned ViT-B/32 to extract features and import them into SVM to identify the appearance quality of strawberries, which obtains the identification results more accurate and efficient. This study could facilitate the development of smart picking equipment for strawberries.

The quality of the strawberry appearance is the most important indicator for consumers and modern fruit process engineering, which is closely associated with the ripeness and diseases. The continuous development of profound deep learning has greatly helped the recognition of strawberry appearance quality, granting a vigorous tool of relative precise outcomes, but the better performance of deep learning requires more time and more computation for training. This paper analysis the efficiency of different combination models that utilize deep feature plus classifiers for recognizing strawberry. The six convolutional neural networks are used to extract the deep feature and then the feature is imported into eight classifiers for classification.The average accuracy of six combination models is 1.53% higher than transfer learning. Besides, a method combining ResNet101 plus linear discriminant analysis (LDA) is proposed. The results show that the accuracy of the proposed method is 96.55%, which is superior to the transfer learning. Moreover, the training time of the method is 57.70 s faster than transfer learning. Therefore, this method has positive significance for the development of recognizing strawberry appearance quality. Practical ApplicationsThe goal of our study is to make it easier for farmers to accurately classify the appearance of strawberries in real situations. This paper analysis the efficiency of different combination models that utilize deep feature plus classifiers for recognizing strawberry. Meanwhile, we have proposed a method combining ResNet101 with linear discriminant analysis (LDA), which obtain the classification task more accurate and efficient. This method has an important role in recognizing strawberry, which will facilitate the development of efficient sorting equipment and provide a viable strategy for future smart sorting methods. Therefore, this work has practical application value.

With the rapid development of information and communication technologies, various e-health solutions have been proposed. The digitized medical images as well as the mono-dimension medical signals are two major forms of medical information that are stored and manipulated within an electronic medical environment. Though a variety of industrial and international standards such as DICOM and HL7 have been proposed, many proprietary formats are still pervasively used by many Hospital Information System (HIS) and Picture Archiving and Communication System (PACS) vendors. Those proprietary formats are the big hurdle to form a nationwide or even worldwide e-health network. Thus there is an imperative need to solve the medical data integration problem. Moreover, many small clinics, many hospitals in developing countries and some regional hospitals in developed countries, which have limited budget, have been shunned from embracing the latest medical information technologies due to their high costs. In this paper, we propose an XML based middleware which acts as a translation engine to seamlessly integrate clinical ECG data from a variety of proprietary data formats. Furthermore, this ECG translation engine is designed in a way that it can be integrated into an existing PACS to provide a low cost medical information integration and storage solution.

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