Identification of streptomycin-resistant Erwinia amylovora in Iowa.

Erwinia amylovora is a bacterial pathogen that causes fire blight, an important disease in apples and pears. Applying the antibiotic streptomycin during the phenological bloom stage is considered the most effective management tactic for fire blight. Though streptomycin-resistant (SmR) E. amylovora populations have emerged in major U.S. apple-producing regions, antibiotic resistance data for medium to small-sized apple-producing regions like the Midwest is still lacking. This short communication collected symptomatic fire blight samples from Iowa apple orchards during 2022 and 2023, where recent fire blight outbreaks persisted despite streptomycin use. Among E. amylovora isolates from seven counties in central and eastern Iowa, around 90% of them were SmR. All SmR isolates exhibited a single base pair mutation in codon 43 of the rpsL gene, conferring resistance to streptomycin levels exceeding 1,000 µg/mL. Through clustered regularly interspaced short palindromic repeat (CRISPR) analysis, we characterized two E. amylovora genotypes unique to our region. Whole genome sequencing of one representative SmR isolate, IA01, confirmed its CRISPR genotype and subsequent phylogenetic analysis suggested that IA01 is genetically similar to Michigan isolates and distinct from those in eastern and western regions of North America. Furthermore, the disease-causing ability of IA01 was comparable to that of the highly virulent Ea110 strain, a streptomycin sensitive strain isolated from Michigan, in immature pears. Overall, this study underscores the urgent need for regional strategies to address antibiotic resistance and provide insights into its genetic basis and geographic distribution which are crucial for sustainable orchard management.

Multi-Label Chest X-Ray Image Classification with Single Positive Labels.

Deep learning approaches for multi-label Chest X-ray (CXR) images classification usually require large-scale datasets. However, acquiring such datasets with full annotations is costly, time-consuming, and prone to noisy labels. Therefore, we introduce a weakly supervised learning problem called Single Positive Multi-label Learning (SPML) into CXR images classification (abbreviated as SPML-CXR), in which only one positive label is annotated per image. A simple solution to SPML-CXR problem is to assume that all the unannotated pathological labels are negative, however, it might introduce false negative labels and decrease the model performance. To this end, we present a Multi-level Pseudo-label Consistency (MPC) framework for SPML-CXR. First, inspired by the pseudo-labeling and consistency regularization in semi-supervised learning, we construct a weak-to-strong consistency framework, where the model prediction on weakly-augmented image is treated as the pseudo label for supervising the model prediction on a strongly-augmented version of the same image, and define an Image-level Perturbation-based Consistency (IPC) regularization to recover the potential mislabeled positive labels. Besides, we incorporate Random Elastic Deformation (RED) as an additional strong augmentation to enhance the perturbation. Second, aiming to expand the perturbation space, we design a perturbation stream to the consistency framework at the feature-level and introduce a Feature-level Perturbation-based Consistency (FPC) regularization as a supplement. Third, we design a Transformer-based encoder module to explore the sample relationship within each mini-batch by a Batch-level Transformer-based Correlation (BTC) regularization. Extensive experiments on the CheXpert and MIMIC-CXR datasets have shown the effectiveness of our MPC framework for solving the SPML-CXR problem.

Learning From Incorrectness: Active Learning With Negative Pre-Training and Curriculum Querying for Histological Tissue Classification.

Patch-level histological tissue classification is an effective pre-processing method for histological slide analysis. However, the classification of tissue with deep learning requires expensive annotation costs. To alleviate the limitations of annotation budgets, the application of active learning (AL) to histological tissue classification is a promising solution. Nevertheless, there is a large imbalance in performance between categories during application, and the tissue corresponding to the categories with relatively insufficient performance are equally important for cancer diagnosis. In this paper, we propose an active learning framework called ICAL, which contains Incorrectness Negative Pre-training (INP) and Category-wise Curriculum Querying (CCQ) to address the above problem from the perspective of category-to-category and from the perspective of categories themselves, respectively. In particular, INP incorporates the unique mechanism of active learning to treat the incorrect prediction results that obtained from CCQ as complementary labels for negative pre-training, in order to better distinguish similar categories during the training process. CCQ adjusts the query weights based on the learning status on each category by the model trained by INP, and utilizes uncertainty to evaluate and compensate for query bias caused by inadequate category performance. Experimental results on two histological tissue classification datasets demonstrate that ICAL achieves performance approaching that of fully supervised learning with less than 16% of the labeled data. In comparison to the state-of-the-art active learning algorithms, ICAL achieved better and more balanced performance in all categories and maintained robustness with extremely low annotation budgets. The source code will be released at https://github.com/LactorHwt/ICAL.

Quaternion Cross-Modality Spatial Learning for Multi-Modal Medical Image Segmentation.

Recently, the Deep Neural Networks (DNNs) have had a large impact on imaging process including medical image segmentation, and the real-valued convolution of DNN has been extensively utilized in multi-modal medical image segmentation to accurately segment lesions via learning data information. However, the weighted summation operation in such convolution limits the ability to maintain spatial dependence that is crucial for identifying different lesion distributions. In this paper, we propose a novel Quaternion Cross-modality Spatial Learning (Q-CSL) which explores the spatial information while considering the linkage between multi-modal images. Specifically, we introduce to quaternion to represent data and coordinates that contain spatial information. Additionally, we propose Quaternion Spatial-association Convolution to learn the spatial information. Subsequently, the proposed De-level Quaternion Cross-modality Fusion (De-QCF) module excavates inner space features and fuses cross-modality spatial dependency. Our experimental results demonstrate that our approach compared to the competitive methods perform well with only 0.01061 M parameters and 9.95G FLOPs.

QMLS: quaternion mutual learning strategy for multi-modal brain tumor segmentation.

Objective.Due to non-invasive imaging and the multimodality of magnetic resonance imaging (MRI) images, MRI-based multi-modal brain tumor segmentation (MBTS) studies have attracted more and more attention in recent years. With the great success of convolutional neural networks in various computer vision tasks, lots of MBTS models have been proposed to address the technical challenges of MBTS. However, the problem of limited data collection usually exists in MBTS tasks, making existing studies typically have difficulty in fully exploring the multi-modal MRI images to mine complementary information among different modalities.Approach.We propose a novel quaternion mutual learning strategy (QMLS), which consists of a voxel-wise lesion knowledge mutual learning mechanism (VLKML mechanism) and a quaternion multi-modal feature learning module (QMFL module). Specifically, the VLKML mechanism allows the networks to converge to a robust minimum so that aggressive data augmentation techniques can be applied to expand the limited data fully. In particular, the quaternion-valued QMFL module treats different modalities as components of quaternions to sufficiently learn complementary information among different modalities on the hypercomplex domain while significantly reducing the number of parameters by about 75%.Main results.Extensive experiments on the dataset BraTS 2020 and BraTS 2019 indicate that QMLS achieves superior results to current popular methods with less computational cost.Significance.We propose a novel algorithm for brain tumor segmentation task that achieves better performance with fewer parameters, which helps the clinical application of automatic brain tumor segmentation.

Dissecting the molecular dance: c-di-GMP, cAMP-CRP, and VfmH collaboration in pectate lyase regulation for Dickeya dadantii-unveiling the soft rot pathogen's strategy.

IMPORTANCE: Bacteria respond to environmental changes and adapt to host systems. The response regulator VfmH of the Vfm quorum sensing system regulates a crucial virulence factor, pectate lyase (Pel), in Dickeya dadantii. At high c-di-GMP concentrations, VfmH binds c-di-GMP, resulting in the loss of its activation property in the Pel and virulence regulation in D. dadantii. VfmH binds to c-di-GMP via three conserved arginine residues, and mutations of these residues eliminate the c-di-GMP-related phenotypes of VfmH in Pel synthesis. Our data also show that VfmH interacts with CRP to regulate pelD transcription, thus integrating cyclic AMP and c-di-GMP signaling pathways to control virulence in D. dadantii. We propose that VfmH is an important intermediate factor incorporating quorum sensing and nucleotide signaling pathways for the collective regulation of D. dadantii pathogenesis.

QGD-Net: A Lightweight Model Utilizing Pixels of Affinity in Feature Layer for Dermoscopic Lesion Segmentation.

RESPONSE: Pixels with location affinity, which can be also called "pixels of affinity," have similar semantic information. Group convolution and dilated convolution can utilize them to improve the capability of the model. However, for group convolution, it does not utilize pixels of affinity between layers. For dilated convolution, after multiple convolutions with the same dilated rate, the pixels utilized within each layer do not possess location affinity with each other. To solve the problem of group convolution, our proposed quaternion group convolution uses the quaternion convolution, which promotes the communication between to promote utilizing pixels of affinity between channels. In quaternion group convolution, the feature layers are divided into 4 layers per group, ensuring the quaternion convolution can be performed. To solve the problem of dilated convolution, we propose the quaternion sawtooth wave-like dilated convolutions module (QS module). QS module utilizes quaternion convolution with sawtooth wave-like dilated rates to effectively leverage the pixels that share the location affinity both between and within layers. This allows for an expanded receptive field, ultimately enhancing the performance of the model. In particular, we perform our quaternion group convolution in QS module to design the quaternion group dilated neutral network (QGD-Net). Extensive experiments on Dermoscopic Lesion Segmentation based on ISIC 2016 and ISIC 2017 indicate that our method has significantly reduced the model parameters and highly promoted the precision of the model in Dermoscopic Lesion Segmentation. And our method also shows generalizability in retinal vessel segmentation.

SPA-Net: A Deep Learning Approach Enhanced Using a Span-Partial Structure and Attention Mechanism for Image Copy-Move Forgery Detection.

With the wide application of visual sensors and development of digital image processing technology, image copy-move forgery detection (CMFD) has become more and more prevalent. Copy-move forgery is copying one or several areas of an image and pasting them into another part of the same image, and CMFD is an efficient means to expose this. There are improper uses of forged images in industry, the military, and daily life. In this paper, we present an efficient end-to-end deep learning approach for CMFD, using a span-partial structure and attention mechanism (SPA-Net). The SPA-Net extracts feature roughly using a pre-processing module and finely extracts deep feature maps using the span-partial structure and attention mechanism as a SPA-net feature extractor module. The span-partial structure is designed to reduce the redundant feature information, while the attention mechanism in the span-partial structure has the advantage of focusing on the tamper region and suppressing the original semantic information. To explore the correlation between high-dimension feature points, a deep feature matching module assists SPA-Net to locate the copy-move areas by computing the similarity of the feature map. A feature upsampling module is employed to upsample the features to their original size and produce a copy-move mask. Furthermore, the training strategy of SPA-Net without pretrained weights has a balance between copy-move and semantic features, and then the module can capture more features of copy-move forgery areas and reduce the confusion from semantic objects. In the experiment, we do not use pretrained weights or models from existing networks such as VGG16, which would bring the limitation of the network paying more attention to objects other than copy-move areas.To deal with this problem, we generated a SPANet-CMFD dataset by applying various processes to the benchmark images from SUN and COCO datasets, and we used existing copy-move forgery datasets, CMH, MICC-F220, MICC-F600, GRIP, Coverage, and parts of USCISI-CMFD, together with our generated SPANet-CMFD dataset, as the training set to train our model. In addition, the SPANet-CMFD dataset could play a big part in forgery detection, such as deepfakes. We employed the CASIA and CoMoFoD datasets as testing datasets to verify the performance of our proposed method. The Precision, Recall, and F1 are calculated to evaluate the CMFD results. Comparison results showed that our model achieved a satisfactory performance on both testing datasets and performed better than the existing methods.

Evaluation of Plant Defense Inducers and Plant Growth Regulators for Fire Blight Management Using Transcriptome Studies and Field Assessments.

Fire blight, caused by Erwinia amylovora, is a destructive disease of pome fruit trees. In the United States, apple and pear growers rely on applications of copper and antibiotics during bloom to control fire blight, but such methods have already led to regional instances of resistance. In this study, we used transcriptome analyses and field trials to evaluate the effectiveness of three commercially available plant defense elicitors and one plant growth regulator for fire blight management. Our data indicated that foliar applications of acibenzolar-S-methyl (ASM; Actigard 50WG) triggered a strong defense-related response in apple leaves, whereas applications of Bacillus mycoides isolate J (LifeGard WG) or Reynoutria sachalinensis extract (Regalia) did not. Genes upregulated by ASM were enriched in the biological processes associated with plant immunity, such as defense response and protein phosphorylation. The expression of several pathogenesis-related (PR) genes was induced by ASM as well. Surprisingly, many differentially expressed genes in ASM-treated apple leaves overlapped with those induced by treatment with prohexadione-calcium (ProCa; Apogee), a plant growth regulator that suppresses shoot elongation. Further analysis suggested that ProCa likely acts similarly to ASM to stimulate plant immunity because genes involved in plant defense were shared and significantly upregulated (more than twofold) by both treatments. Our field trials agreed with the transcriptome study, demonstrating that ASM and ProCa exhibit the best control performance relative to the other biopesticides. Taken together, these data are pivotal for the understanding of plant response and shed light on future improvements of strategies for fire blight management.

Improving breast tumor segmentation via shape-wise prior-guided information on cone-beam breast CT images.

Objective. Due to the blurry edges and uneven shape of breast tumors, breast tumor segmentation can be a challenging task. Recently, deep convolution networks based approaches achieve satisfying segmentation results. However, the learned shape information of breast tumors might be lost owing to the successive convolution and down-sampling operations, resulting in limited performance.Approach. To this end, we propose a novel shape-guided segmentation (SGS) framework that guides the segmentation networks to be shape-sensitive to breast tumors by prior shape information. Different from usual segmentation networks, we guide the networks to model shape-shared representation with the assumption that shape information of breast tumors can be shared among samples. Specifically, on the one hand, we propose a shape guiding block (SGB) to provide shape guidance through a superpixel pooling-unpooling operation and attention mechanism. On the other hand, we further introduce a shared classification layer (SCL) to avoid feature inconsistency and additional computational costs. As a result, the proposed SGB and SCL can be effortlessly incorporated into mainstream segmentation networks (e.g. UNet) to compose the SGS, facilitating compact shape-friendly representation learning.Main results. Experiments conducted on a private dataset and a public dataset demonstrate the effectiveness of the SGS compared to other advanced methods.Significance. We propose a united framework to encourage existing segmentation networks to improve breast tumor segmentation by prior shape information. The source code will be made available athttps://github.com/TxLin7/Shape-Seg.

Erwinia amylovora Type III Secretion System Inhibitors Reduce Fire Blight Infection Under Field Conditions.

Fire blight, caused by Erwinia amylovora, is an economically important disease in apples and pears worldwide. This pathogen relies on the type III secretion system (T3SS) to cause disease. Compounds that inhibit the function of the T3SS (T3SS inhibitors) have emerged as alternative strategies for bacterial plant disease management, as they block bacterial virulence without affecting growth, unlike traditional antibiotics. In this study, we investigated the mode of action of a T3SS inhibitor named TS108, a plant phenolic acid derivative, in E. amylovora. We showed that adding TS108 to an in vitro culture of E. amylovora repressed the expression of several T3SS regulon genes, including the master regulator gene hrpL. Further studies demonstrated that TS108 negatively regulates CsrB, a global regulatory small RNA, at the posttranscriptional level, resulting in a repression of hrpS, which encodes a key activator of hrpL. Additionally, TS108 has no impact on the expression of T3SS in Dickeya dadantii or Pseudomonas aeruginosa, suggesting that its inhibition of the E. amylovora T3SS is likely species specific. To better evaluate the performance of T3SS inhibitors in fire blight management, we conducted five independent field experiments in four states (Michigan, New York, Oregon, and Connecticut) from 2015 to 2022 and observed reductions in blossom blight incidence as high as 96.7% compared with untreated trees. In summary, the T3SS inhibitors exhibited good efficacy against fire blight.

Quaternion attention multi-scale widening network for endoscopy image super-resolution.

Objective. In the field of endoscopic imaging, Super-Resolution (SR) plays an important role in Manufactured Diagnosis, physicians and machine Automatic Diagnosis. Although many recent studies have been performed, by using deep convolutional neural networks on endoscopic SR, most of the methods have large parameters, which limits their practical application. In addition, almost all of these methods treat each channel equally based on the real-valued domain, without considering the difference among the different channels. Our objective is to design a SR model named Quaternion Attention Multi-scale Widening Network (QAMWN) for endoscopy images to address the above problem.Approach. QAMWN contains a stacked Quaternion Attention Multi-Scale Widening Block, that composed of Multi-scale Feature Widening Aggregation Module (MFWAM) and Quaternion Residual Channel Attention (QRCA). The MFWAM adopts multi-scale architecture with step-wise widening on feature channels for better feature extraction; and in QRCA, quaternion is introduced to construct Residual Channel Attention Mechanism, which obtains adaptively scales features by considering compact cross-channel interactions in the hyper-complex domain.Main results. To verify the efficacy of our method, it is performed on two public endoscopic datasets, CVC ClinicDB and Kvasir dataset. The experimental results show that our proposed method can achieve a better trade-off in model size and performance. More importantly, the proposed QAMWN outperforms previous state-of-the-art methods in both metrics and visualization.Significance. We propose a lightweight SR network for endoscopy and achieves better performance with fewer parameters, which helps in clinical diagnosis of endoscopy.

Insufficient or excessive dietary carbohydrates affect gut health through change in gut microbiota and regulation of gene expression of gut epithelial cells in grass carp (Ctenopharyngodon idella).

Dietary carbohydrate levels can affect gut health, but the roles played by gut microbiota and gut epithelial cells, and their interactions remain unclear. In this experiment, we investigated gut health, gut microbiota, and the gene expression profiles of gut epithelial cells in grass carp consuming diets with different carbohydrate levels. Compared to the moderate-carbohydrate diet, low-carbohydrate diet significantly increased the relative abundance of pathogenic bacteria (Ralstonia and Elizabethkingia) and decreased the abundance of metabolism in cofactors and vitamins, implying a dysregulated gut microbiota and compromised metabolic function. Moreover, low-carbohydrate diet inhibited the expression levels of key genes in autophagy-related pathways in gut epithelial cells, which might directly lead to reduced clearance of defective organelles and pathogenic microorganisms. These aforementioned factors may be responsible for the imperfect organization of the intestinal tract. High-carbohydrate diet also significantly increased the abundance of pathogenic bacteria (Flavobacterium), which directly contributed to a decrease in the abundance of immune system of the microbiota. Furthermore, the active pathways of staphylococcus aureus infection and complement and coagulation cascades, as well as the inhibition of the glutathione metabolism pathway were observed. Above results implied that high-carbohydrate diet might ultimately cause severe gut damage by affecting immune function of microbiota, mentioned immune-related pathways, and the antioxidant capacity. Finally, the correlation network diagram revealed strong correlations of the differentially immune-related gene major histocompatibility complex class I antigen (MR1) with Enhydrobacter and Ruminococcus_gnavus_group in low-carbohydrate diet group, and Arenimonas in high-carbohydrate diet group, respectively, suggesting that MR1 might be a central target for immune responses in gut epithelial cells induced by gut microbiota at different levels of dietary carbohydrate. All these results provided insight in the development of antagonistic probiotics and target genes to improve the utilization of carbohydrate.

Weakly supervised semantic segmentation of histological tissue via attention accumulation and pixel-level contrast learning.

Objective. Histopathology image segmentation can assist medical professionals in identifying and diagnosing diseased tissue more efficiently. Although fully supervised segmentation models have excellent performance, the annotation cost is extremely expensive. Weakly supervised models are widely used in medical image segmentation due to their low annotation cost. Nevertheless, these weakly supervised models have difficulty in accurately locating the boundaries between different classes of regions in pathological images, resulting in a high rate of false alarms Our objective is to design a weakly supervised segmentation model to resolve the above problems.Approach. The segmentation model is divided into two main stages, the generation of pseudo labels based on class residual attention accumulation network (CRAANet) and the semantic segmentation based on pixel feature space construction network (PFSCNet). CRAANet provides attention scores for each class through the class residual attention module, while the Attention Accumulation (AA) module overlays the attention feature maps generated in each training epoch. PFSCNet employs a network model containing an inflated convolutional residual neural network and a multi-scale feature-aware module as the segmentation backbone, and proposes dense energy loss and pixel clustering modules are based on contrast learning to solve the pseudo-labeling-inaccuracy problem.Main results. We validate our method using the lung adenocarcinoma (LUAD-HistoSeg) dataset and the breast cancer (BCSS) dataset. The results of the experiments show that our proposed method outperforms other state-of-the-art methods on both datasets in several metrics. This suggests that it is capable of performing well in a wide variety of histopathological image segmentation tasks.Significance. We propose a weakly supervised semantic segmentation network that achieves approximate fully supervised segmentation performance even in the case of incomplete labels. The proposed AA and pixel-level contrast learning also make the edges more accurate and can well assist pathologists in their research.

Genetic and Environmental Investigation of a Novel Phenylamino Acetamide Inhibitor of the Pseudomonas aeruginosa Type III Secretion System.

Traditional antibiotics target essential cellular components or metabolic pathways conserved in both pathogenic and nonpathogenic bacteria. Unfortunately, long-term antibiotic use often leads to antibiotic resistance and disruption of the overall microbiota. In this work, we identified a phenylamino acetamide compound, named 187R, that strongly inhibited the expression of the type III secretion system (T3SS) encoding genes and the secretion of the T3SS effector proteins in Pseudomonas aeruginosa. T3SS is an important virulence factor, as T3SS-deficient strains of P. aeruginosa are greatly attenuated in virulence. We further showed that 187R had no effect on bacterial growth, implying a reduced selective pressure for the development of resistance. 187R-mediated repression of T3SS was dependent on ExsA, the master regulator of T3SS in P. aeruginosa. The impact of 187R on the host-associated microbial community was also tested using the Arabidopsis thaliana phyllosphere as a model. Both culture-independent (Illumina sequencing) and culture-dependent (Biolog) methods showed that the application of 187R had little impact on the composition and function of microbial community compared to the antibiotic streptomycin. Together, these results suggested that compounds that target virulence factors could serve as an alternative strategy for disease management caused by bacterial pathogens. IMPORTANCE New antimicrobial therapies are urgently needed, since antibiotic resistance in human pathogens has become one of the world's most urgent public health problems. Antivirulence therapy has been considered a promising alternative for the management of infectious diseases, as antivirulence compounds target only the virulence factors instead of the growth of bacteria, and they are therefore unlikely to affect commensal microorganisms. However, the impacts of antivirulence compounds on the host microbiota are not well understood. We report a potent synthetic inhibitor of the P. aeruginosa T3SS, 187R, and its effect on the host microbiota of Arabidopsis. Both culture-independent (Illumina sequencing) and culture-dependent (Biolog) methods showed that the impacts of the antivirulence compound on the composition and function of host microbiota were limited. These results suggest that antivirulence compounds can be a potential alternative method to antibiotics.

Ligands for Melanocortin Receptors: Beyond Melanocyte-Stimulating Hormones and Adrenocorticotropin.

The discovery of melanocortins in 1916 has resulted in more than 100 years of research focused on these peptides. Extensive studies have elucidated well-established functions of melanocortins mediated by cell surface receptors, including MSHR (melanocyte-stimulating hormone receptor) and ACTHR (adrenocorticotropin receptor). Subsequently, three additional melanocortin receptors (MCRs) were identified. Among these five MCRs, MC3R and MC4R are expressed primarily in the central nervous system, and are therefore referred to as the neural MCRs. Since the central melanocortin system plays important roles in regulating energy homeostasis, targeting neural MCRs is emerging as a therapeutic approach for treating metabolic conditions such as obesity and cachexia. Early efforts modifying endogenous ligands resulted in the development of many potent and selective ligands. This review focuses on the ligands for neural MCRs, including classical ligands (MSH and agouti-related peptide), nonclassical ligands (lipocalin 2, ß-defensin, small molecules, and pharmacoperones), and clinically approved ligands (ACTH, setmelanotide, bremelanotide, and several repurposed drugs).

Exopolysaccharides amylovoran and levan contribute to sliding motility in the fire blight pathogen Erwinia amylovora.

Erwinia amylovora, the causative agent of fire blight, uses flagella-based motilities to translocate to host plant natural openings; however, little is known about how this bacterium migrates systemically in the apoplast. Here, we reveal a novel surface motility mechanism, defined as sliding, in E. amylovora. Deletion of flagella assembly genes did not affect this movement, whereas deletion of biosynthesis genes for the exopolysaccharides (EPSs) amylovoran and levan resulted in non-sliding phenotypes. Since EPS production generates osmotic pressure that potentially powers sliding, we validated this mechanism by demonstrating that water potential positively contributes to sliding. In addition, no sliding was observed when the water potential of the surface was lower than -0.5 MPa. Sliding is a passive motility mechanism. We further show that the force of gravity plays a critical role in directing E. amylovora sliding on unconfined surfaces but has a negligible effect when cells are sliding in confined microcapillaries, in which EPS-dependent osmotic pressure acts as the main force. Although amylovoran and levan are both required for sliding, we demonstrate that they exhibit different roles in bacterial communities. In summary, our study provides fundamental knowledge for a better understanding of mechanisms that drive bacterial sliding motility.

Collaborative multi-feature extraction and scale-aware semantic information mining for medical image segmentation.

Objective.In recent years, methods based on U-shaped structure and skip connection have achieved remarkable results in many medical semantic segmentation tasks. However, the information integration capability of this structure is still limited due to the incompatibility of feature maps of encoding and decoding stages at corresponding levels and lack of extraction of valid information in the final stage of encoding. This structural defect is particularly obvious in segmentation tasks with non-obvious, small and blurred-edge targets. Our objective is to design a novel segmentation network to solve the above problems.Approach.The segmentation network named Global Context-Aware Network is mainly designed by inserting a Multi-feature Collaboration Adaptation (MCA) module, a Scale-Aware Mining (SAM) module and an Edge-enhanced Pixel Intensity Mapping (Edge-PIM) into the U-shaped structure. Firstly, the MCA module can integrate information from all encoding stages and then effectively acts on the decoding stages, solving the problem of information loss during downsampling and pooling. Secondly, the SAM module can further mine information from the encoded high-level features to enrich the information passed to the decoding stage. Thirdly, Edge-PIM can further refine the segmentation results by edge enhancement.Main results.We newly collect Magnetic Resonance Imaging of Colorectal Cancer Liver Metastases (MRI-CRLM) dataset in different imaging sequences with non-obvious, small and blurred-edge liver metastases. Our method performs well on the MRI-CRLM dataset and the publicly available ISIC-2018 dataset, outperforming state-of-the-art methods such as CPFNet on multiple metrics after boxplot analysis, indicating that it can perform well on a wide range of medical image segmentation tasks.Significance.The proposed method solves the problem mentioned above and improved segmentation accuracy for non-obvious, small and blurred-edge targets. Meanwhile, the proposed visualization method Edge-PIM can make the edge more prominent, which can assist medical radiologists in their research work well.

The RNA-Binding Protein ProQ Impacts Exopolysaccharide Biosynthesis and Second Messenger Cyclic di-GMP Signaling in the Fire Blight Pathogen Erwinia amylovora.

Erwinia amylovora is a plant-pathogenic bacterium that causes fire blight disease in many economically important plants, including apples and pears. This bacterium produces three exopolysaccharides (EPSs), amylovoran, levan, and cellulose, and forms biofilms in host plant vascular tissues, which are crucial for pathogenesis. Here, we demonstrate that ProQ, a conserved bacterial RNA chaperone, was required for the virulence of E. amylovora in apple shoots and for biofilm formation in planta. In vitro experiments revealed that the deletion of proQ increased the production of amylovoran and cellulose. Prc is a putative periplasmic protease, and the prc gene is located adjacent to proQ. We found that Prc and the associated lipoprotein NlpI negatively affected amylovoran production, whereas Spr, a peptidoglycan hydrolase degraded by Prc, positively regulated amylovoran. Since the prc promoter is likely located within proQ, our data showed that proQ deletion significantly reduced the prc mRNA levels. We used a genome-wide transposon mutagenesis experiment to uncover the involvement of the bacterial second messenger c-di-GMP in ProQ-mediated cellulose production. The deletion of proQ resulted in elevated intracellular c-di-GMP levels and cellulose production, which were restored to wild-type levels by deleting genes encoding c-di-GMP biosynthesis enzymes. Moreover, ProQ positively affected the mRNA levels of genes encoding c-di-GMP-degrading phosphodiesterase enzymes via a mechanism independent of mRNA decay. In summary, our study revealed a detailed function of E. amylovora ProQ in coordinating cellulose biosynthesis and, for the first time, linked ProQ with c-di-GMP metabolism and also uncovered a role of Prc in the regulation of amylovoran production. IMPORTANCE Fire blight, caused by the bacterium Erwinia amylovora, is an important disease affecting many rosaceous plants, including apple and pear, that can lead to devastating economic losses worldwide. Similar to many xylem-invading pathogens, E. amylovora forms biofilms that rely on the production of exopolysaccharides (EPSs). In this paper, we identified the RNA-binding protein ProQ as an important virulence regulator. ProQ played a central role in controlling the production of EPSs and participated in the regulation of several conserved bacterial signal transduction pathways, including the second messenger c-di-GMP and the periplasmic protease Prc-mediated systems. Since ProQ has recently been recognized as a global posttranscriptional regulator in many bacteria, these findings provide new insights into multitiered regulatory mechanisms for the precise control of virulence factor production in bacterial pathogens.