Metagenomic insights into the dynamic degradation of brown algal polysaccharides by kelp-associated microbiota.

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 .

Performance Prediction of Cement Stabilized Soil Incorporating Solid Waste and Propylene Fiber.

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.

Deep Learning for Variational Multimodality Tumor Segmentation in PET/CT.

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.

Multi-to-binary network (MTBNet) for automated multi-organ segmentation on multi-sequence abdominal MRI images.

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.

Tumor co-segmentation in PET/CT using multi-modality fully convolutional neural network.

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.

Length-dependent thermal conductivity in suspended single-layer graphene.

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.