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Purpose It is of great significance to accurately identify the KRAS gene mutation status for patients in tumor prognosis and personalized treatment. Although the computer-aided diagnosis system based on deep learning has gotten all-round development, its performance still cannot meet the current clinical application requirements due to the inherent limitations of small-scale medical image dataset and inaccurate lesion feature extraction. Therefore, our aim is to propose a deep learning model based on T2 MRI of colorectal cancer patients to identify whether KRAS gene is mutated. Methods In this research, a multi-task attentive model is proposed to identify KRAS gene mutations in patients, which is mainly composed of a segmentation sub-network and an identification sub-network. Specifically, at first, the features extracted by the encoder of segmentation model are used as guidance information to guide the two attention modules in the identification network for precise activation of the lesion area. Then the original image of the lesion and the segmentation result are concatenated for feature extraction. Finally, features extracted from the second step are combined with features activated by the attention modules to identify the gene mutation status. In this process, we introduce the inter-layer loss function to encourage the similarity of the two sub-network parameters and ensure that the key features are fully extracted to alleviate the overfitting problem caused by small dataset to some extent. Results The proposed identification model is benchmarked primarily using 15-fold cross validation. 382 images from 36 clinical cases were used to test the model. For the identification of KRAS mutation status, the average accuracy is 89.95±1.23%, the average sensitivity is 89.29±1.79%, the average specificity is 90.53±2.45%, and the average AUC is 95.73±5.02%. For segmentation of lesions, the average dice is 88.11 ± 0.86% CONCLUSIONS: We developed a novel deep learning-based model to identify the KRAS status in colorectal cancer. We demonstrated the excellent properties of the proposed identification through comparison with ground truth gene mutation status of 36 clinical cases. And all these results show that the novel method has great potential for clinical application. This article is protected by copyright. All rights reserved.

Traditional Chinese medicine (TCM) prescriptions have made great contributions to the treatment of diseases and health preservation. To alleviate the shortage of TCM resources and improve the professionalism of automatically generated prescriptions, this paper deeply explores the connection between symptoms and herbs through deep learning technology, and realizes the automatic generation of TCM prescriptions. Particularly, this paper considers the significance of referring to similar underlying prescriptions as herbal candidates in the TCM prescribing process. Moreover, this paper incorporates the idea of referring to the potential guidance information of corresponding prescriptions when model extracts symptoms representations. To provide a reference for inexperienced young TCM doctors when they prescribe, this paper proposes a dual-branch guidance strategy combined with candidate attention model (DGSCAM) to automatically generate TCM prescriptions based on symptoms text. The format of the data used this paper is the “symptoms-prescription” data pair. The specific method is as follows. First, DGSCAM realizes the extraction of key information of prescription-guided symptoms through a dual-branch network. Then, herbal candidates in the form of prescriptions that can treat symptoms are proposed in view of the compatibility knowledge of TCM prescriptions. To our knowledge, this is the first attempt to use prescriptions as herbal candidates in the prescription generation process. We conduct extensive experiments on a mixed public and clinical dataset, achieving 37.39% precision, 25.04% recall, and 29.99% F1 score, with an average doctor score of 7.7 out of 10. The experimental results show that our proposed model is valid and can generate more specialized TCM prescriptions than the baseline models. The DGSCAM developed by us has broad application scenarios and greatly promotes the research on intelligent TCM prescribing.

Kirsten Ras (KRAS) mutation identification has great clinical significance to formulate the rectal cancer treatment scheme. Recently, the development of deep learning does much help to improve the computer-aided diagnosis technology. However, deep learning models are usually designed for only one task, ignoring the potential benefits in jointly performing both tasks. In this paper, we proposed a joint network named segmentation-based multi-scale attention model (SMSAM) to predict the mutation status of KRAS gene in rectal cancer. More specifically, the network performs segmentation and prediction tasks at the same time. The two tasks mutually transfer knowledge between each other by sharing the same encoder. Meanwhile, two universal multi-scale attention blocks are introduced to ensure that the network more focuses on the region of interest. Besides, we also proposed an entropy branch to provide more discriminative features for the model. Finally, the method is evaluated on internal and external datasets. The results show that the comprehensive performance of SMSAM is better than the existing methods. The code and model have been publicly available.

To solve the problem that the safety data in the process of coal mine production are easy to be maliciously tampered with and deleted, a mine consortium blockchain data security monitoring system is proposed. The coal mine consortium blockchain includes supervision department, builds favourable centralized and decentralized production mode, and improves PBFT (Practical Byzantine Fault Tolerance) consensus mechanism to implement practical coal mine safety production. The evaluation shows that the architecture we proposed is more appropriate and efficient for the mine Internet of Things than the traditional blockchain architecture. The Hyperledger Fabric platform is used to build the mine consortium blockchain system to achieve the sensor data reliability, node consensus, safe operation automation management, and major equipment traceability.

The authors would like to make the following corrections about the published paper [...]

At the end of 2019, the COVID-19 virus spread worldwide, infecting millions of people. Infectious diseases induced by pathogenic microorganisms such as the influenza virus, hepatitis virus, and Mycobacterium tuberculosis are also a major threat to public health. The high mortality caused by infectious pathogenic microorganisms is due to their strong virulence, which leads to the excessive counterattack by the host immune system and severe inflammatory damage of the immune system. This paper reviews the efficacy, mechanism and related immune regulation of epigallocatechin-3-gallate (EGCG) as an anti-pathogenic microorganism drug. EGCG mainly shows both direct and indirect anti-infection effects. EGCG directly inhibits early infection by interfering with the adsorption on host cells, inhibiting virus replication and reducing bacterial biofilm formation and toxin release; EGCG indirectly inhibits infection by regulating immune inflammation and antioxidation. At the same time, we reviewed the bioavailability and safety of EGCG in vivo. At present, the bioavailability of EGCG can be improved to some extent using nanostructured drug delivery systems and molecular modification technology in combination with other drugs. This study provides a theoretical basis for the development of EGCG as an adjuvant drug for anti-pathogenic microorganisms.

Among the various types of lung nodules, ground glass nodules (GGNs) are difficult to segment accurately due to complex morphological characteristics. Moreover, GGNs are associated with a higher malignancy probability. Three-dimensional (3D) segmentation and reconstruction techniques can help physicians intuitively elucidate the relationship between lung nodules and their surrounding tissues. We propose an improved supervoxel 3D region growing approach based on positron emission tomography/computed tomography (PET/CT) multimodal data for the segmentation and reconstruction of GGNs. First, the seed point is automatically located with PET information and a 3D mask is generated. Then, a fuzzy connectivity (FC) map is generated based on the 3D mask, and an improved supervoxel 3D region growing is utilized on a fuzzy connectivity map under the constraints of the 3D mask. Finally, 3D GGNs segmentation and reconstruction results are obtained. Qualitative and quantitative comparisons between our proposed method and other region growing methods shows great superiority of our proposed method, with the Jaccard similarity coefficient between our proposed method and physician manual segmentation reaching 95.61%; the average processing time is 16.38 s. Experimental results show that our proposed supervoxel-based 3D region growing method is very promising for assisting physicians in diagnosis.

Background Lung cancer is one of the most common types of cancer, among which lung adenocarcinoma accounts for the largest proportion. Currently, accurate staging is a prerequisite for effective diagnosis and treatment of lung adenocarcinoma. Previous research has used mainly single-modal data, such as gene expression data, for classification and prediction. Integrating multi-modal genetic data (gene expression RNA-seq, methylation data and copy number variation) from the same patient provides the possibility of using multi-modal genetic data for cancer prediction. A new machine learning method called gcForest has recently been proposed. This method has been proven to be suitable for classification in some fields. However, the model may face challenges when applied to small samples and high-dimensional genetic data. Results In this paper, we propose a multi-weighted gcForest algorithm (MLW-gcForest) to construct a lung adenocarcinoma staging model using multi-modal genetic data. The new algorithm is based on the standard gcForest algorithm. First, different weights are assigned to different random forests according to the classification performance of these forests in the standard gcForest model. Second, because the feature vectors generated under different scanning granularities have a diverse influence on the final classification result, the feature vectors are given weights according to the proposed sorting optimization algorithm. Then, we train three MLW-gcForest models based on three single-modal datasets (gene expression RNA-seq, methylation data, and copy number variation) and then perform decision fusion to stage lung adenocarcinoma. Experimental results suggest that the MLW-gcForest model is superior to the standard gcForest model in constructing a staging model of lung adenocarcinoma and is better than the traditional classification methods. The accuracy, precision, recall, and AUC reached 0.908, 0.896, 0.882, and 0.96, respectively. Conclusions The MLW-gcForest model has great potential in lung adenocarcinoma staging, which is helpful for the diagnosis and personalized treatment of lung adenocarcinoma. The results suggest that the MLW-gcForest algorithm is effective on multi-modal genetic data, which consist of small samples and are high dimensional.

Acute myocardial infarction (AMI) is one of the most serious and dangerous cardiovascular diseases. In recent years, the number of patients around the world has been increasing significantly, among which people under the age of 45 have become the high-risk group for sudden death of AMI. AMI occurs quickly and does not show obvious symptoms before onset. In addition, postonset clinical testing is also a complex and invasive test, which may cause some postoperative complications. Therefore, it is necessary to propose a noninvasive and convenient auxiliary diagnostic method. In traditional Chinese medicine (TCM), it is an effective auxiliary diagnostic strategy to complete the disease diagnosis through some body surface features. It is helpful to observe whether the palmar thenar undergoes hypertrophy and whether the metacarpophalangeal joint is swelling in detecting acute myocardial infarction. Combined with deep learning, we propose a depth model based on traditional palm image (MTIALM), which can help doctors of traditional Chinese medicine to predict myocardial infarction. By building the shared network, the model learns information that covers all the tasks. In addition, task-specific attention branch networks are built to simultaneously detect the symptoms of different parts of the palm. The information interaction module (IIM) is proposed to further integrate the information between task branches to ensure that the model learns as many features as possible. Experimental results show that the accuracy of our model in the detection of metacarpophalangeal joints and palmar thenar is 83.16% and 84.15%, respectively, which are significantly improved compared with the traditional classification methods.

Regional crop layout has changed significantly due to climate, policy, and other factors, which has impacted farmland nutrient balance. Here, we evaluated the impact of crop layout changes on N and P nutrient balance in the West Liaohe River Basin from 2000–2015. The study area has long been in a N and P surplus state. The unit N surplus exhibited a downward trend and the unit P surplus showed an increasing trend. Significant correlations existed between planting areas and nutrient surplus. The N and P surplus layout was mainly concentrated in the West Liaohe River lower reaches basin. The planting area of wheat must be reduced and the areas of maize and soybean must be controlled to adjust the N and P balance and reduce the environmental pollution risk. Chemical fertilizer and seed inputs are the main sources of N input. Furthermore, combining farming and pastoral farming is conducive to improving N and P use efficiency. Manure can be absorbed by farmland, the ratio of organic and chemical fertilizers can be reasonably set, and chemical fertilizer application can be reduced.

Estimating regional soils Nitrogen and phosphorus balance in cropland is essential to improve management practices, reduce environmental risks and develop sustainable agriculture. In this study,spatial and temporal variations in crop layout, the impact on soil N and P nutrient balance were assessed from 2000 to 2015 in the West Liaohe River Basin between 2000 and 2015. The result shows that the area of cropland is on the rise, and the spatial distribution of arable land is consistent with the distribution of the main tributaries of the West Liaohe River basin. The change in planting layout for maize and soybeans has a significant impact on the nutrient balance of farmland, which plays a critical role in modifying surplus nutrients. Nutrient surpluses on farmland were mostly concentrated in areas where maize planting layout changed between 2000 and 2015. The N nutrient surplus rate decreased by 39.3%, N utilization efficiency, increased by 70.7%; P nutrient surplus rate decreased by 3.8%, and P utilization efficiency increased by 49.3%. The average utilization efficiencies of N and P nutrients were 27.8% and 9.1%, respectively, and the utilization efficiency was low. Chemical manure is the main source of nutrients. The risk of phosphorus pollution was higher than the risk of nitrogen pollution in the West Liaohe River Basin. The lower Liaohe River Basin (below the Sujiapu) was the region with the most violent changes in nitrogen and phosphorus nutrient balance. It is recommended that reduce the amount of chemical fertilizer application, especially, reduce the amount of P application, improve the ef-ficiency of nutrient use, and focus on strengthening pollution control in key areas such as the West Liaohe River lower reaches basin (below Sujiabao), reducing the risk of agricultural nonpoint source pollution.

In view of the repeated investment in the current medical lung cancer early warning system, 'information island' and the low rate of lung cancer early warning, this paper proposes a mobile lung cancer early warning system based on Windows Azure cloud computing with the application requirements of medical information system. The system is applied to the smart phones of Windows system, and the Windows Azure Microsoft cloud platform is accessed through wireless network, using SQL Azure to store medical data (such as lung CT image, ECG curve and high-resolution colour ultrasound image), embed the medical lung cancer early warning model based on deep learning in the cloud computing platform to process and analyse the patient's medical data, and realise the real-time synchronous update of doctors, patients and cloud platform data. The accuracy of lung cancer early-warning system on LIDC-IDRI is high, and the detection results are uploaded to doctors and patients' smart phones in real time. The results show that the mobile lung cancer early warning system based on Windows Azure cloud computing not only effectively integrates medical resources, but also provides patients with medical services and consultation at home, which integrates the advantages of all aspects and adapts to the development of the times.

Patient stratification has made a great contribution to efficient and personalized medicine. An important task in patient stratification is to discover quite distinct disease subtypes for effective treatment. In this paper, we propose a new deep learning and clustering model which combines Deep Belief Network (DBN) and Fuzzy C-Means(FCM), called Unsupervised Deep Fuzzy C-Means clustering Network(UDFCMN), to cluster lung cancer patients from lung CT images. In our deep clustering network, images after preprocessing are first encoded into multiple layers of hidden variables to extract hierarchical features and feature distribution and form the high-level representations. Here, to solve the problem of feature homogenization in DBN, we introduce the Winner-Take-All (WTA) idea to meliorate the traditional DBN structure, called WTADBN. Then FCM is used to produce the initial cluster labels with the new representations learnt by stacked WTARBM. Therefore, the FCM-generated cluster labels are used for the fine-tuning of the DBN as ground-truth labels. And an unsupervised image clustering and patient stratification process is completed by cross iteration. We tested our deep FCM clustering algorithm to do experiment on both public dataset from the internet and private dataset from cooperate hospital. For the latter one, the clinical and biological verification was also performed. Experimental results reveal outperformance of UDFCMN as compared to the state-of-the-art unsupervised classification methods. These results also indicate that our approach may have practical applications in lung cancer pathogenesis studies and provide useful guidelines for personalized cancer therapy.

In cricket, the region plays a significant role in ranking teams. The International Cricket Council (ICC) uses an ad-hoc points system to rank cricket teams, which entirely based on the number of wins and losses a match. The ICC ignores the strength and weaknesses of the team across the region. Even though the relative accuracy in the ad-hoc ranking is high, but they do not provide a clearly defined method of ranking. We proposed Region-wise Team Rank (RTR) and a Region-wise Weighted Team Rank (RWTR) to rank cricket teams. The intuition is to get more points to a team that wins a match from a stronger team as compared to a team that wins against a weaker team & vice versa. The proposed method considers not only the number of region-wise wins and losses but also incorporates the region-wise strength and weakness of a team while assigning them the ranking score. In conclusion, the ranking list of the teams compares to the ICC official ranking.

Protein-peptide interaction is crucial for many cellular processes. It is difficult to determine the interaction by experiments as peptides are often very flexible in structure. Accurate sequence-based prediction of peptide-binding residues can facilitate the study of this interaction. In this work, we developed two novel sequence-based methods SVMpep and PepBind to identify the peptide-binding residues. Recent studies demonstrate that the protein-peptide binding is closely associated with intrinsic disorder. We thus introduced intrinsic disorder in our feature design and developed the ab initio method SVMpep. Experiments show that intrinsic disorder contributes to 1.2-5.2% improvement in area under the receiver operating characteristic curve (AUC). Comparison to the recent sequence-based method SPRINT-Seq reveals that SVMpep improves the AUC and Matthews correlation coefficient (MCC) by at least 7.7% and 70%, respectively. In addition, by combining SVMpep with two template-based methods S-SITE and TM-SITE, we next proposed the consensus-based method PepBind. Remarkably, compared with the latest structure-based method SPRINT-Str, PepBind improves the AUC and MCC by 1.7% and 28.3%, respectively, on the same independent test set of SPRINT-Str. The success of PepBind is attributed to the improved prediction of the ab initio method SVMpep by introducing intrinsic disorder and the consensus prediction by combining three complementary methods. A web server that implements the proposed methods is freely available at http://yanglab.nankai.edu.cn/PepBind/ .

Owing to the large amount of transmission data and transmission paths between equipment and applications in the power grid control centre, it is easy to cause problems such as packet loss and incomplete data transmission. An improved Apriori algorithm is proposed to mine multi-dimensional data association rules when multiple service devices are running in parallel, then optimise the discontinuous data path transmission based on the ant colony algorithm. The ant colony migration probability is used to calculate the pheromone concentration value of the ant on the discontinuous data path in a fixed period, plan the ant travel path, use the direction factor to adjust the discontinuous data transmission path appropriately, determine the optimal path, and realise the mining optimisation. Simulation results show that the proposed method can effectively improve the convergence speed of discontinuous data transmission, reduce the delay time of the data transmission network, and ensure the integrity of data transmission.