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Yellow leaf disease (YLD) is an economically important disease affecting betel palm in several countries, the cause of which remains unclear despite associations with putative agents, including phytoplasmas. In this study, we screened the potential casual agents associated with YLD in Hainan, China using next-generation sequencing and revealed the association of areca palm velarivirus 1 (APV1) with the YLD-affected palm. The complete genome of the APV1-WNY isolate was determined to be 17,546 nucleotides in length, approximately 1.5 kb longer than the previously reported APV1_HN genome. Transmission electron microscopy showed that APV1 particles are flexuous and filamentous, a typical morphology of species in the Closteroviridae family. Comparison of symptomatic and symptomless tree populations showed a strong association between APV1 and YLD. APV1 was detected in Pseudococcus sp. mealybugs sampled from YLD-affected trees in many locations, suggesting that mealybugs are a potential transmission vector for APV1. Although further studies are needed to confirm a causal relationship, these results provide timely information for the prevention and management of YLD associated with APV1.

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In order to facilitate the broad applications of molecular recognition materials in biomedical areas, it is critical to enhance their adsorption capacity while maintaining their excellent recognition performance. In this work, we designed and synthesized well-defined peptide-imprinted mesoporous silica (PIMS) for specific recognition of an immunostimulating hexapeptide from human casein (IHHC) by using amphiphilic ionic liquid as the surfactant to anchor IHHC via a combination of one-step sol-gel method and docking oriented imprinting approach. Thereinto, theoretical calculation was employed to reveal the multiple binding interactions and dual-template configuration between amphiphilic ionic liquid and IHHC. The fabricated PIMS was characterized and an in-depth analysis of specific recognition mechanism was conducted. Results revealed that both adsorption and recognition capabilities of PIMS far exceeded that of the NIMS's. More significantly, the PIMS exhibited a superior binding capacity (60.5 mg g), which could increase 18.9% than the previous work. The corresponding imprinting factor and selectivity coefficient could reach up to 4.51 and 3.30, respectively. The PIMS also possessed lickety-split kinetic binding for IHHC, where the equilibrium time was only 10 min. All of these merits were due to the high surface area and the synergistic effect of multiple interactions (including hydrogen bonding, π-π stacking, ion-ion electrostatic interactions and van der Waals interactions, etc) between PIMS and IHHC in imprinted sites. The present work suggests the potential application of PIMS for large-scale and high-effective separation of IHHC, which may lead to their broad applications in drug/gene deliver, biosensors, catalyst and so on.

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Prostate cancer can be diagnosed by prostate biopsy using transectal ultrasound guidance. The high number of pathology images from biopsy tissues is a burden on pathologists, and analysis is subjective and susceptible to inter-rater variability. The use of machine learning techniques could make prostate histopathology diagnostics more precise, consistent, and efficient overall. This paper presents a new classification fusion network model that was created by fusing eight advanced image features: seven hand-crafted features and one deep-learning feature. These features are the scale-invariant feature transform (SIFT), speeded up robust feature (SURF), oriented features from accelerated segment test (FAST) and rotated binary robust independent elementary features (BRIEF) (ORB) of local features, shape and texture features of the cell nuclei, the histogram of oriented gradients (HOG) feature of the cavities, a color feature, and a convolution deep-learning feature. Matching, integrated, and fusion networks are the three essential components of the proposed deep-learning network. The integrated network consists of both a backbone and an additional network. When classifying 1100 prostate pathology images using this fusion network with different backbones (ResNet-18/50, VGG-11/16, and DenseNet-121/201), we discovered that the proposed model with the ResNet-18 backbone achieved the best performance in terms of the accuracy (95.54%), specificity (93.64%), and sensitivity (97.27%) as well as the area under the receiver operating characteristic curve (98.34%). However, each of the assessment criteria for these separate features had a value lower than 90%, which demonstrates that the suggested model combines differently derived characteristics in an effective manner. Moreover, a Grad-CAM++ heatmap was used to observe the differences between the proposed model and ResNet-18 in terms of the regions of interest. This map showed that the proposed model was better at focusing on cancerous cells than ResNet-18. Hence, the proposed classification fusion network, which combines hand-crafted features and a deep-learning feature, is useful for computer-aided diagnoses based on pathology images of prostate cancer. Because of the similarities in the feature engineering and deep learning for different types of pathology images, the proposed method could be used for other pathology images, such as those of breast, thyroid cancer.

BackgroundSpinal tuberculosis (TB) has the highest incidence in remote plateau areas, particularly in Tibet, China, due to inadequate local healthcare services, which not only facilitates the transmission of TB bacteria but also increases the burden on grassroots hospitals. Computer-aided diagnosis (CAD) is urgently required to improve the efficiency of clinical diagnosis of TB using computed tomography (CT) images. However, classical machine learning with handcrafted features generally has low accuracy, and deep learning with self-extracting features relies heavily on the size of medical datasets. Therefore, CAD, which effectively fuses multimodal features, is an alternative solution for spinal TB detection.MethodsA new deep learning method is proposed that fuses four elaborate image features, specifically three handcrafted features and one convolutional neural network (CNN) feature. Spinal TB CT images were collected from 197 patients with spinal TB, from 2013 to 2020, in the People’s Hospital of Tibet Autonomous Region, China; 3,000 effective lumbar spine CT images were randomly screened to our dataset, from which two sets of 1,500 images each were classified as tuberculosis (positive) and health (negative). In addition, virtual data augmentation is proposed to enlarge the handcrafted features of the TB dataset. Essentially, the proposed multimodal feature fusion CNN consists of four main sections: matching network, backbone (ResNet-18/50, VGG-11/16, DenseNet-121/161), fallen network, and gated information fusion network. Detailed performance analyses were conducted based on the multimodal features, proposed augmentation, model stability, and model-focused heatmap.ResultsExperimental results showed that the proposed model with VGG-11 and virtual data augmentation exhibited optimal performance in terms of accuracy, specificity, sensitivity, and area under curve. In addition, an inverse relationship existed between the model size and test accuracy. The model-focused heatmap also shifted from the irrelevant region to the bone destruction caused by TB.ConclusionThe proposed augmentation effectively simulated the real data distribution in the feature space. More importantly, all the evaluation metrics and analyses demonstrated that the proposed deep learning model exhibits efficient feature fusion for multimodal features. Our study provides a profound insight into the preliminary auxiliary diagnosis of spinal TB from CT images applicable to the Tibetan area.