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Marine bacteria play important roles in the degradation and cycling of algal polysaccharides. However, the dynamics of epiphytic bacterial communities and their roles in algal polysaccharide degradation during kelp decay are still unclear. Here, we performed metagenomic analyses to investigate the identities and predicted metabolic abilities of epiphytic bacterial communities during the early and late decay stages of the kelp Saccharina japonica. During kelp decay, the dominant epiphytic bacterial communities shifted from Gammaproteobacteria to Verrucomicrobia and Bacteroidetes. In the early decay stage of S. japonica, epiphytic bacteria primarily targeted kelp-derived labile alginate for degradation, among which the gammaproteobacterial Vibrionaceae (particularly Vibrio) and Psychromonadaceae (particularly Psychromonas), abundant in alginate lyases belonging to the polysaccharide lyase (PL) families PL6, PL7, and PL17, were key alginate degraders. More complex fucoidan was preferred to be degraded in the late decay stage of S. japonica by epiphytic bacteria, predominantly from Verrucomicrobia (particularly Lentimonas), Pirellulaceae of Planctomycetes (particularly Rhodopirellula), Pontiellaceae of Kiritimatiellota, and Flavobacteriaceae of Bacteroidetes, which depended on using glycoside hydrolases (GHs) from the GH29, GH95, and GH141 families and sulfatases from the S1_15, S1_16, S1_17, and S1_25 families to depolymerize fucoidan. The pathways for algal polysaccharide degradation in dominant epiphytic bacterial groups were reconstructed based on analyses of metagenome-assembled genomes. This study sheds light on the roles of different epiphytic bacteria in the degradation of brown algal polysaccharides.IMPORTANCEKelps are important primary producers in coastal marine ecosystems. Polysaccharides, as major components of brown algal biomass, constitute a large fraction of organic carbon in the ocean. However, knowledge of the identities and pathways of epiphytic bacteria involved in the degradation process of brown algal polysaccharides during kelp decay is still elusive. Here, based on metagenomic analyses, the succession of epiphytic bacterial communities and their metabolic potential were investigated during the early and late decay stages of Saccharina japonica. Our study revealed a transition in algal polysaccharide-degrading bacteria during kelp decay, shifting from alginate-degrading Gammaproteobacteria to fucoidan-degrading Verrucomicrobia, Planctomycetes, Kiritimatiellota, and Bacteroidetes. A model for the dynamic degradation of algal cell wall polysaccharides, a complex organic carbon, by epiphytic microbiota during kelp decay was proposed. This study deepens our understanding of the role of epiphytic bacteria in marine algal carbon cycling as well as pathogen control in algal culture.

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Cement stabilized soil (CSS) yields wide application as a routine cementitious material due to cost-effectiveness. However, the mechanical strength of CSS impedes development. This research assesses the feasible combined enhancement of unconfined compressive strength (UCS) and flexural strength (FS) of construction and demolition (C&D) waste, polypropylene fiber, and sodium sulfate. Moreover, machine learning (ML) techniques including Back Propagation Neural Network (BPNN) and Random Forest (FR) were applied to estimate UCS and FS based on the comprehensive dataset. The laboratory tests were conducted at 7-, 14-, and 28-day curing age, indicating the positive effect of cement, C&D waste, and sodium sulfate. The improvement caused by polypropylene fiber on FS was also evaluated from the 81 experimental results. In addition, the beetle antennae search (BAS) approach and 10-fold cross-validation were employed to automatically tune the hyperparameters, avoiding tedious effort. The consequent correlation coefficients (R) ranged from 0.9295 to 0.9717 for BPNN, and 0.9262 to 0.9877 for RF, respectively, indicating the accuracy and reliability of the prediction. K-Nearest Neighbor (KNN), logistic regression (LR), and multiple linear regression (MLR) were conducted to validate the BPNN and RF algorithms. Furthermore, box and Taylor diagrams proved the BAS-BPNN and BAS-RF as the best-performed model for UCS and FS prediction, respectively. The optimal mixture design was proposed as 30% cement, 20% C&D waste, 4% fiber, and 0.8% sodium sulfate based on the importance score for each variable.

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Positron emission tomography/computed tomography (PET/CT) imaging can simultaneously acquire functional metabolic information and anatomical information of the human body. How to rationally fuse the complementary information in PET/CT for accurate tumor segmentation is challenging. In this study, a novel deep learning based variational method was proposed to automatically fuse multimodality information for tumor segmentation in PET/CT. A 3D fully convolutional network (FCN) was first designed and trained to produce a probability map from the CT image. The learnt probability map describes the probability of each CT voxel belonging to the tumor or the background, and roughly distinguishes the tumor from its surrounding soft tissues. A fuzzy variational model was then proposed to incorporate the probability map and the PET intensity image for an accurate multimodality tumor segmentation, where the probability map acted as a membership degree prior. A split Bregman algorithm was used to minimize the variational model. The proposed method was validated on a non-small cell lung cancer dataset with 84 PET/CT images. Experimental results demonstrated that: 1). Only a few training samples were needed for training the designed network to produce the probability map; 2). The proposed method can be applied to small datasets, normally seen in clinic research; 3). The proposed method successfully fused the complementary information in PET/CT, and outperformed two existing deep learning-based multimodality segmentation methods and other multimodality segmentation methods using traditional fusion strategies (without deep learning); 4). The proposed method had a good performance for tumor segmentation, even for those with Fluorodeoxyglucose (FDG) uptake inhomogeneity and blurred tumor edges (two major challenges in PET single modality segmentation) and complex surrounding soft tissues (one major challenge in CT single modality segmentation), and achieved an average dice similarity indexes (DSI) of 0.86 ± 0.05, sensitivity (SE) of 0.86 ± 0.07, positive predictive value (PPV) of 0.87 ± 0.10, volume error (VE) of 0.16 ± 0.12, and classification error (CE) of 0.30 ± 0.12.

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Fully convolutional neural network (FCN) has achieved great success in semantic segmentation. However, the performance of the FCN is generally compromised for multi-object segmentation. Multi-organ segmentation is very common while challenging in the field of medical image analysis, where organs largely vary with scales. Different organs are often treated equally in most segmentation networks, which is not quite optimal. In this work, we propose to divide a multi-organ segmentation task into multiple binary segmentation tasks by constructing a multi-to-binary network (MTBNet). The proposed MTBNet is based on the FCN for pixel-wise prediction. Moreover, we build a plug-and-play multi-to-binary block (MTB block) to adjust the influence of the feature maps from the backbone. This is achieved by parallelizing multiple branches with different convolutional layers and a probability gate (ProbGate). The ProbGate is set up for predicting whether the class exists, which is supervised clearly via an auxiliary loss without using any other inputs. More reasonable features are achieved by summing branches' features multiplied by the probability from the accompanying ProbGate and fed into a decoder module for prediction. The proposed method is validated on a challenging task dataset of multi-organ segmentation in abdominal MRI. Compared to classic medical and other multi-scale segmentation methods, the proposed MTBNet improves the segmentation accuracy of small organs by adjusting features from different organs and reducing the chance of missing or misidentifying these organs.

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Automatic tumor segmentation from medical images is an important step for computer-aided cancer diagnosis and treatment. Recently, deep learning has been successfully applied to this task, leading to state-of-the-art performance. However, most of existing deep learning segmentation methods only work for a single imaging modality. PET/CT scanner is nowadays widely used in the clinic, and is able to provide both metabolic information and anatomical information through integrating PET and CT into the same utility. In this study, we proposed a novel multi-modality segmentation method based on a 3D fully convolutional neural network (FCN), which is capable of taking account of both PET and CT information simultaneously for tumor segmentation. The network started with a multi-task training module, in which two parallel sub-segmentation architectures constructed using deep convolutional neural networks (CNNs) were designed to automatically extract feature maps from PET and CT respectively. A feature fusion module was subsequently designed based on cascaded convolutional blocks, which re-extracted features from PET/CT feature maps using a weighted cross entropy minimization strategy. The tumor mask was obtained as the output at the end of the network using a softmax function. The effectiveness of the proposed method was validated on a clinic PET/CT dataset of 84 patients with lung cancer. The results demonstrated that the proposed network was effective, fast and robust and achieved significantly performance gain over CNN-based methods and traditional methods using PET or CT only, two V-net based co-segmentation methods, two variational co-segmentation methods based on fuzzy set theory and a deep learning co-segmentation method using W-net.

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Graphene exhibits extraordinary electronic and mechanical properties, and extremely high thermal conductivity. Being a very stable atomically thick membrane that can be suspended between two leads, graphene provides a perfect test platform for studying thermal conductivity in two-dimensional systems, which is of primary importance for phonon transport in low-dimensional materials. Here we report experimental measurements and non-equilibrium molecular dynamics simulations of thermal conduction in suspended single-layer graphene as a function of both temperature and sample length. Interestingly and in contrast to bulk materials, at 300 K, thermal conductivity keeps increasing and remains logarithmically divergent with sample length even for sample lengths much larger than the average phonon mean free path. This result is a consequence of the two-dimensional nature of phonons in graphene, and provides fundamental understanding of thermal transport in two-dimensional materials.