Rotation with other crops slow down the fungal process in tobacco-growing soil.

Continuous cultivation of tobacco could cause serious soil health problems, which could cause bacterial soil to change to fungal soil. In order to study the diversity and richness of fungal community in tobacco-growing soil under different crop rotation, three treatments were set up in this study: CK (tobacco continuous cropping); B (barley-tobacco rotation cropping) and R (oilseed rape-tobacco rotation cropping). The results of this study showed that rotation with other crops significantly decreased the soil fungal OTUs, and also decreased the community richness, evenness, diversity and coverage of fungal communities. Among them, B decreased the most. In the analysis of the composition and structure of the fungal community, it was found that the proportion of plant pathogens Nectriaceae decreased from 19.67% in CK to 5.63% in B, which greatly reduced the possibility of soil-borne diseases. In the analysis of the correlation between soil environmental factors and fungal communities, it was found that Filobasidiaceae had a strong correlation with TP and AP, and Erysiphaceae had a strong correlation with TK and AK. NO3--N and NH4+-N were the two environmental factors with the strongest correlation with fungal communities. The results of this study showed that rotation with other crops slowed down the process of soil fungi in tobacco-growing soil and changed the dominant species of soil fungi community. At the same time, crop rotation changed the diversity and richness of soil fungal community by changing the physical and chemical properties of soil.

Variations in different preceding crops on the soil environment, bacterial community richness and diversity of tobacco-planting soil.

Tobacco (Nicotiana tabacum L.) is a major cash crop, and soil quality played a significant role in the yield and quality of tobacco. Most farmers cultivate tobacco in rotation with other crops to improve the soil characteristics. However, the effects of different previous crops on the soil's nutrient status and bacterial community for tobacco cultivation still need to be determined. Three treatments were assessed in this study, i.e., tobacco-planting soil without treatment (CK), soil with barley previously cultivated (T1), and soil with rapeseed previously cultivated (T2). The soil physical and chemical properties and the 16S rRNA gene sequence diversity of the bacterial community were analyzed. The effects of different crops on the physical and chemical properties of tobacco-planting soil and the diversity and richness of the bacterial community were comprehensively discussed. The results of this study showed that different previously cultivated crops altered the nutrient status of the soil, with changes in the ratio of NH4 +-N to NO3 --N having the most significant impact on tobacco. In CK, the ratio of NH4 +-N to NO3 --N was 1:24.2, T1-1:9.59, and T2-1:11.10. The composition of the bacterial community in tobacco-planting soil varied significantly depending on the previously cultivated crops. The richness and diversity of the bacterial community with different crops were considerably higher than without prior cultivation of different crops. The dominant bacteria in different treatments were Actinobacteriota, Proteobacteria, and Chloroflexi with their relative abundance differed. In conclusion, our study revealed significant differences in nutrient status, bacterial community diversity, and the richness of tobacco-planting soil after the preceding cultivation of different crops. Suitable crops should be selected to be previously cultivated in tobacco crop rotations in near future for sustainable agriculture.

CaraNet: context axial reverse attention network for segmentation of small medical objects.

Purpose: Segmenting medical images accurately and reliably is important for disease diagnosis and treatment. It is a challenging task because of the wide variety of objects' sizes, shapes, and scanning modalities. Recently, many convolutional neural networks have been designed for segmentation tasks and have achieved great success. Few studies, however, have fully considered the sizes of objects; thus, most demonstrate poor performance for small object segmentation. This can have a significant impact on the early detection of diseases. Approach: We propose a context axial reverse attention network (CaraNet) to improve the segmentation performance on small objects compared with several recent state-of-the-art models. CaraNet applies axial reserve attention and channel-wise feature pyramid modules to dig the feature information of small medical objects. We evaluate our model by six different measurement metrics. Results: We test our CaraNet on segmentation datasets for brain tumor (BraTS 2018) and polyp (Kvasir-SEG, CVC-ColonDB, CVC-ClinicDB, CVC-300, and ETIS-LaribPolypDB). Our CaraNet achieves the top-rank mean Dice segmentation accuracy, and results show a distinct advantage of CaraNet in the segmentation of small medical objects. Conclusions: We proposed CaraNet to segment small medical objects and outperform state-of-the-art methods.

CFPNet-M: A light-weight encoder-decoder based network for multimodal biomedical image real-time segmentation.

-Deep learning techniques are proving instrumental in identifying, classifying, and quantifying patterns in medical images. Segmentation is one of the important applications in medical image analysis. The U-Net has become the predominant deep-learning approach to medical image segmentation tasks. Existing U-Net based models have limitations in several respects, however, including: the requirement for millions of parameters in the U-Net, which consumes considerable computational resources and memory; the lack of global information; and incomplete segmentation in difficult cases. To remove some of those limitations, we built on our previous work and applied two modifications to improve the U-Net model: 1) we designed and added the dilated channel-wise CNN module and 2) we simplified the U-shape network. We then proposed a novel light-weight architecture, the Channel-wise Feature Pyramid Network for Medicine (CFPNet-M). To evaluate our method, we selected five datasets from different imaging modalities: thermography, electron microscopy, endoscopy, dermoscopy, and digital retinal images. We compared its performance with several models having a variety of complexities. We used the Tanimoto similarity instead of the Jaccard index for gray-level image comparisons. The CFPNet-M achieves segmentation results on all five medical datasets that are comparable to existing methods, yet require only 8.8 MB memory, and just 0.65 million parameters, which is about 2% of U-Net. Unlike other deep-learning segmentation methods, this new approach is suitable for real-time application: its inference speed can reach 80 frames per second when implemented on a single RTX 2070Ti GPU with an input image size of 256 × 192 pixels.

Breast cancer detection using synthetic mammograms from generative adversarial networks in convolutional neural networks.

The convolutional neural network (CNN) is a promising technique to detect breast cancer based on mammograms. Training the CNN from scratch, however, requires a large amount of labeled data. Such a requirement usually is infeasible for some kinds of medical image data such as mammographic tumor images. Because improvement of the performance of a CNN classifier requires more training data, the creation of new training images, image augmentation, is one solution to this problem. We applied the generative adversarial network (GAN) to generate synthetic mammographic images from the digital database for screening mammography (DDSM). From the DDSM, we cropped two sets of regions of interest (ROIs) from the images: normal and abnormal (cancer/tumor). Those ROIs were used to train the GAN, and the GAN then generated synthetic images. For comparison with the affine transformation augmentation methods, such as rotation, shifting, scaling, etc., we used six groups of ROIs [three simple groups: affine augmented, GAN synthetic, real (original), and three mixture groups of any two of the three simple groups] for each to train a CNN classifier from scratch. And, we used real ROIs that were not used in training to validate classification outcomes. Our results show that, to classify the normal ROIs and abnormal ROIs from DDSM, adding GAN-generated ROIs in the training data can help the classifier prevent overfitting, and on validation accuracy, the GAN performs about 3.6% better than affine transformations for image augmentation. Therefore, GAN could be an ideal augmentation approach. The images augmented by GAN or affine transformation cannot substitute for real images to train CNN classifiers because the absence of real images in the training set will cause over-fitting.

Optimization of wavelength selection for multispectral image acquisition: a case study of atrial ablation lesions.

In vivo autofluorescence hyperspectral imaging of moving objects can be challenging due to motion artifacts and to the limited amount of acquired photons. To address both limitations, we selectively reduced the number of spectral bands while maintaining accurate target identification. Several downsampling approaches were applied to data obtained from the atrial tissue of adult pigs with sites of radiofrequency ablation lesions. Standard image qualifiers such as the mean square error, the peak signal-to-noise ratio, the structural similarity index map, and an accuracy index of lesion component images were used to quantify the effects of spectral binning, an increased spectral distance between individual bands, as well as random combinations of spectral bands. Results point to several quantitative strategies for deriving combinations of a small number of spectral bands that can successfully detect target tissue. Insights from our studies can be applied to a wide range of applications.

Application of unsupervised learning to hyperspectral imaging of cardiac ablation lesions.

Atrial fibrillation is the most common cardiac arrhythmia. It is being effectively treated using the radiofrequency ablation (RFA) procedure, which destroys culprit tissue and creates scars that prevent the spread of abnormal electrical activity. Long-term success of RFA could be improved further if ablation lesions can be directly visualized during the surgery. We have shown that autofluorescence-based hyperspectral imaging (aHSI) can help to identify lesions based on spectral unmixing. We show that use of k -means clustering, an unsupervised learning method, is capable of detecting RFA lesions without a priori knowledge of the lesions' spectral characteristics. We also show that the number of spectral bands required for successful lesion identification can be significantly reduced, enabling the use of increased spectral bandwidth. Together, these findings can help with clinical implementation of a percutaneous aHSI catheter, since by reducing the number of spectral bands one can reduce hypercube acquisition and processing times, and by increasing the spectral width of individual bands one can collect more photons. The latter is of critical importance in low-light applications such as intracardiac aHSI. The ultimate goal of our studies is to help improve clinical outcomes for atrial fibrillation patients.