Comparative Genomics Reveals Genetic Diversity and Variation in Metabolic Traits in Fructilactobacillus sanfranciscensis Strains.

Fructilactobacillus sanfranciscensis is a significant and dominant bacterial species of sourdough microbiota from ecological and functional perspectives. Despite the remarkable prevalence of different strains of this species in sourdoughs worldwide, the drivers behind the genetic diversity of this species needed to be clarified. In this research, 14 F. sanfranciscensis strains were isolated from sourdough samples to evaluate the genetic diversity and variation in metabolic traits. These 14 and 31 other strains (obtained from the NCBI database) genomes were compared. The values for genome size and GC content, on average, turned out to 1.31 Mbp and 34.25%, respectively. In 45 F. sanfranciscensis strains, there were 162 core genes and 0 to 51 unique genes present in each strain. The primary functions of core genes were related to nucleotide, lipid transport, and amino acid, as well as carbohydrate metabolism. The size of core genes accounted for 41.18% of the pan-genome size in 14 F. sanfranciscensis strains, i.e., 0.70 Mbp of 1.70 Mbp. There were genetic variations among the 14 strains involved in carbohydrate utilization and antibiotic resistance. Moreover, exopolysaccharides biosynthesis-related genes were annotated, including epsABD, wxz, wzy. The Type IIA & IE CRISPR-Cas systems, pediocin PA-1 and Lacticin_3147_A1 bacteriocins operons were also discovered in F. sanfranciscensis. These findings can help to select desirable F. sanfranciscensis strains to develop standardized starter culture for sourdough fermentation, and expect to provide traditional fermented pasta with a higher quality and nutritional value for the consumers.

Graph structure learning layer and its graph convolution clustering application.

To learn the embedding representation of graph structure data corrupted by noise and outliers, existing graph structure learning networks usually follow the two-step paradigm, i.e., constructing a "good" graph structure and achieving the message passing for signals supported on the learned graph. However, the data corrupted by noise may make the learned graph structure unreliable. In this paper, we propose an adaptive graph convolutional clustering network that alternatively adjusts the graph structure and node representation layer-by-layer with back-propagation. Specifically, we design a Graph Structure Learning layer before each Graph Convolutional layer to learn the sparse graph structure from the node representations, where the graph structure is implicitly determined by the solution to the optimal self-expression problem. This is one of the first works that uses an optimization process as a Graph Network layer, which is obviously different from the function operation in traditional deep learning layers. An efficient iterative optimization algorithm is given to solve the optimal self-expression problem in the Graph Structure Learning layer. Experimental results show that the proposed method can effectively defend the negative effects of inaccurate graph structures. The code is available at https://github.com/HeXiax/SSGNN.

BreastDM: A DCE-MRI dataset for breast tumor image segmentation and classification.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has shown high sensitivity to diagnose breast cancer. However, few computer-aided algorithms focus on employing DCE-MR images for breast cancer diagnosis due to the lack of publicly available DCE-MRI datasets. To address this issue, our work releases a new DCE-MRI dataset called BreastDM for breast tumor segmentation and classification. In particular, a dataset of 232 patients selected with DCE-MR images for benign and malignant cases is established. Each case consists of three types of sequences: pre-contrast, post-contrast, and subtraction sequences. To show the difficulty of breast DCE-MRI tumor image segmentation and classification tasks, benchmarks are achieved by state-of-the-art image segmentation and classification algorithms, including conventional hand-crafted based methods and recently-emerged deep learning-based methods. More importantly, a local-global cross attention fusion network (LG-CAFN) is proposed to further improve the performance of breast tumor images classification. Specifically, LG-CAFN achieved the highest accuracy (88.20%, 83.93%) and AUC value (0.9154,0.8826) in both groups of experiments. Extensive experiments are conducted to present strong baselines based on various typical image segmentation and classification algorithms. Experiment results also demonstrate the superiority of the proposed LG-CAFN to other breast tumor images classification methods. The related dataset and evaluation codes are publicly available at smallboy-code/Breast-cancer-dataset.

Parallelly Adaptive Graph Convolutional Clustering Model.

Benefiting from exploiting the data topological structure, graph convolutional network (GCN) has made considerable improvements in processing clustering tasks. The performance of GCN significantly relies on the quality of the pretrained graph, while the graph structures are often corrupted by noise or outliers. To overcome this problem, we replace the pre-trained and fixed graph in GCN by the adaptive graph learned from the data. In this article, we propose a novel end-to-end parallelly adaptive graph convolutional clustering (AGCC) model with two pathway networks. In the first pathway, an adaptive graph convolutional (AGC) module alternatively updates the graph structure and the data representation layer by layer. The updated graph can better reflect the data relationship than the fixed graph. In the second pathway, the auto-encoder (AE) module aims to extract the latent data features. To effectively connect the AGC and AE modules, we creatively propose an attention-mechanism-based fusion (AMF) module to weight and fuse the data representations of the two modules, and transfer them to the AGC module. This simultaneously avoids the over-smoothing problem of GCN. Experimental results on six public datasets show that the effectiveness of the proposed AGCC compared with multiple state-of-the-art deep clustering methods. The code is available at https://github.com/HeXiax/AGCC.

Application of logistic regression and convolutional neural network in prediction and diagnosis of high-risk populations of lung cancer.

OBJECTIVES: The early detection, early diagnosis, and early treatment of lung cancer are the best strategies to improve the 5-year survival rate. Logistic regression analysis can be a helpful tool in the early detection of high-risk groups of lung cancer. Convolutional neural network (CNN) could distinguish benign from malignant pulmonary nodules, which is critical for early precise diagnosis and treatment. Here, we developed a risk assessment model of lung cancer and a high-precision classification diagnostic model using these technologies so as to provide a basis for early screening of lung cancer and for intelligent differential diagnosis. METHODS: A total of 355 lung cancer patients, 444 patients with benign lung disease and 472 healthy people from The First Affiliated Hospital of Zhengzhou University were included in this study. Moreover, the dataset of 607 lung computed tomography images was collected from the above patients. The logistic regression method was employed to screen the high-risk groups of lung cancer, and the CNN model was designed to classify pulmonary nodules into benign or malignant nodules. RESULTS: The area under the curve of the lung cancer risk assessment model in the training set and the testing set were 0.823 and 0.710, respectively. After finely optimizing the settings of the CNN, the area under the curve could reach 0.984. CONCLUSIONS: This performance demonstrated that the lung cancer risk assessment model could be used to screen for high-risk individuals with lung cancer and the CNN framework was suitable for the differential diagnosis of pulmonary nodules.

Proteomic Analysis Explores Interactions between Lactiplantibacillus plantarum and Saccharomyces cerevisiae during Sourdough Fermentation.

Sourdough is a fermentation culture which is formed following metabolic activities of a multiple bacterial and fungal species on raw dough. However, little is known about the mechanism of interaction among different species involved in fermentation. In this study, Lactiplantibacillus plantarum Sx3 and Saccharomyces cerevisiae Sq7 were selected. Protein changes in sourdough, fermented with single culture (either Sx3 or Sq7) and mixed culture (both Sx3 and Sq7), were evaluated by proteomics. The results show that carbohydrate metabolism in mixed-culture-based sourdough is the most important metabolic pathway. A greater abundance of L-lactate dehydrogenase and UDP-glucose 4-epimerase that contribute to the quality of sourdough were observed in mixed-culture-based sourdough than those produced by a single culture. Calreticulin, enolase, seryl-tRNA synthetase, ribosomal protein L23, ribosomal protein L16, and ribosomal protein L5 that are needed for the stability of proteins were increased in mixed-culture-based sourdough. The abundance of some compounds which play an important role in enhancing the nutritional characteristics and flavour of sourdough (citrate synthase, aldehyde dehydrogenase, pyruvate decarboxylase, pyruvate dehydrogenase E1 and acetyl-CoA) was decreased. In summary, this approach provided new insights into the interaction between L. plantarum and S. cerevisiae in sourdough, which may serve as a base for further research into the detailed mechanism.

Radiomics Model for Evaluating the Level of Tumor-Infiltrating Lymphocytes in Breast Cancer Based on Dynamic Contrast-Enhanced MRI.

BACKGROUND: To help identify potential breast cancer (BC) candidates for immunotherapies, we aimed to develop and validate a radiology-based biomarker (radiomic score) to predict the level of tumor-infiltrating lymphocytes (TILs) in patients with BC. PATIENTS AND METHODS: This retrospective study enrolled 172 patients with histopathology-confirmed BC assigned to the training (n = 121) or testing (n = 51) cohorts. Radiomic features were extracted and selected using Analysis-Kit software. The correlation between TIL levels and clinical features and radiomic features was evaluated. The clinical features model, radiomic signature model, and combined prediction model were constructed and compared. Predictive performance was assessed by receiver operating characteristic analysis and clinical utility by implementing a nomogram. RESULTS: Seven radiomic features were selected as the best discriminators to construct the radiomic signature model, the performance of which was good in both the training and validation data sets, with an area under the curve (AUC) of 0.742 (95% confidence interval [CI], 0.642-0.843) and 0.718 (95% CI, 0.558-0.878), respectively. Estrogen receptor status and tumor diameter were confirmed to be significant features for building the clinical feature model, which had an AUC of 0.739 (95% CI, 0.632-0.846) and 0.824 (95% CI, 0.692-0.957), respectively. The combined prediction model had an AUC of 0.800 (95% CI, 0.709-0.892) and 0.842 (95% CI, 0.730-0.954), respectively. CONCLUSION: The radiomic signature could be an important predictor of the TIL level in BC, which, when validated, could be useful in identifying BC patients who can benefit from immunotherapies. The nomogram may help clinicians make decisions.

Quantitative evaluation of computed and voxelwise computed diffusion-weighted imaging in breast cancer.

OBJECTIVES: To assess the value of computed diffusion-weighted imaging (cDWI) and voxelwise computed diffusion-weighted imaging (vcDWI) in breast cancer. METHODS: This retrospective study involved 130 patients (age range, 25-70 years; mean age ± standard deviation, 48.6 ± 10.5 years) with 130 malignant lesions, who underwent MRI examinations, including a DWI sequence, prior to needle biopsy or surgery. cDWIs with higher b-values of 1500, 2000, 2500, 3000, 3500, and 4000 s/mm2, and vcDWI were generated from measured (m) DWI with two lower b-values of 0/600, 0/800, or 0/1000 s/mm2. The signal-to-noise ratio (SNR) and contrast ratio (CR) of all image sets were computed and compared among different DWIs by two experienced radiologists independently. To better compare the CR with the SNR, the CR value was multiplied by 100 (CR100). RESULTS: The CR of vcDWI, and cDWIs, except for cDWI1000, differed significantly from that of measured diffusion-weighted imaging (mDWI) (cDWI1000: CR = 0.4904, p = 0.394; cDWI1500: CR = 0.5503, p = 0.006; cDWI2000: CR = 0.5889, p < 0.001; cDWI2500: CR = 0.6109, p < 0.001; cDWI3000: mean = 0.6214, p < 0.001; cDWI3500: CR = 0.6245, p < 0.001; cDWI4000: CR = 0.6228, p < 0.001). The vcDWI provided the highest CR, while the CRs of all cDWI image sets improved with increased b-values. The SNR of neither cDWI1000 nor vcDWI differed significantly from that of mDWI, but the mean SNRs of the remaining cDWIs were significantly lower than that of mDWI. The SNRs of cDWIs declined with increasing b-values, and the initial decrease at low b-values was steeper than the gradual attenuation at higher b-values; the CR100 rose gradually, and the two converged on the b-value interval of 1500-2000 s/mm2 . CONCLUSIONS: The highest CR was achieved with vcDWI; this could be a promising approach easier detection of breast cancer. ADVANCES IN KNOWLEDGE: This study comprehensively compared and evaluated the value of the emerging post-processing DWI techniques (including a set of cDWIs and vcDWI) in breast cancer.

Copper-Catalyzed Asymmetric Hydroboration of 1,1-Disubstituted Alkenes.

A mild and efficient approach for highly regio- and enantioselective copper-catalyzed hydroboration of 1,1-diaryl substituted alkenes with bis(pinacolato)diboron (B2Pin2) was developed for the first time, providing facile access to a series of valuable ß,ß-diaryl substituted boronic esters with high enantiomeric purity. Moreover, this approach could also be suitable for hydroboration of α-alkyl styrenes for the synthesis of enantioenriched ß,ß-arylalkyl substituted boronic esters. Gram-scale reaction, stereospecific derivatizations, and the application of important antimuscarinic drug (R)-tolterodine for concise enantioselective synthesis further highlighted the attractiveness of this new approach.