Hierarchical Vertex Regression-Based Segmentation of Head and Neck CT Images for Radiotherapy Planning

Segmenting organs at risk from head and neck CT images is a prerequisite for the treatment of head and neck cancer using intensity modulated radiotherapy. However, accurate and automatic segmentation of organs at risk is a challenging task due to the low contrast of soft tissue and image artifact in CT images. Shape priors have been proved effective in addressing this challenging task. However, conventional methods incorporating shape priors often suffer from sensitivity to shape initialization and also shape variations across individuals. In this paper, we propose a novel approach to incorporate shape priors into a hierarchical learning-based model. The contributions of our proposed approach are as follows: 1) a novel mechanism for critical vertices identification is proposed to identify vertices with distinctive appearances and strong consistency across different subjects; 2) a new strategy of hierarchical vertex regression is also used to gradually locate more vertices with the guidance of previously located vertices; and 3) an innovative framework of joint shape and appearance learning is further developed to capture salient shape and appearance features simultaneously. Using these innovative strategies, our proposed approach can essentially overcome drawbacks of the conventional shape-based segmentation methods. Experimental results show that our approach can achieve much better results than state-of-the-art methods

Learning-based deformable image registration for infant MR images in the first year of life

Many brain development studies have been devoted to investigate dynamic structural and functional changes in the first year of life. To quantitatively measure brain development in such a dynamic period, accurate image registration for different infant subjects with possible large age gap is of high demand. Although many state-of-the-art image registration methods have been proposed for young and elderly brain images, very few registration methods work for infant brain images acquired in the first year of life, because of (1) large anatomical changes due to fast brain development and (2) dynamic appearance changes due to white matter myelination

Acupuncture treatment for ischaemic stroke in young adults: protocol for a randomised, sham-controlled clinical trial

INTRODUCTION: Stroke in young adults is not uncommon. Although the overall incidence of stroke has been recently declining, the incidence of stroke in young adults is increasing. Traditional vascular risk factors are the main cause of young ischaemic stroke. Acupuncture has been shown to benefit stroke rehabilitation and ameliorate the risk factors for stroke. The aims of this study were to determine whether acupuncture treatment will be effective in improving the activities of daily living (ADL), motor function and quality of life (QOL) in patients of young ischaemic stroke, and in preventing stroke recurrence by controlling blood pressure, lipids and body weight. METHODS AND ANALYSIS: In this randomised, sham-controlled, participant-blinded and assessor-blinded clinical trial, 120 patients between 18 and 45 years of age with a recent (within 1 month) ischaemic stroke will be randomised for an 8-week acupuncture or sham acupuncture treatment. The primary outcome will be the Barthel Index for ADL. The secondary outcomes will include the Fugl-Meyer Assessment for motor function; the World Health Organization Quality of Life BREF (WHOQOL-BREF) for QOL; and risk factors that are measured by ambulatory blood pressure, the fasting serum lipid, body mass index and waist circumference. Incidence of adverse events and long-term mortality and recurrence rate during a 10-year and 30-year follow-up will also be investigated. ETHICS AND DISSEMINATION: Ethics approval was obtained from the Ethics Committee of The Third Affiliated Hospital of Zhejiang Chinese Medical University. Protocol V.3 was approved in June 2013. The results will be disseminated in a peer-reviewed journal and presented at international congresses. The results will also be disseminated to patients by telephone during follow-up calls enquiring on the patient's post-study health status. TRIAL REGISTRATION NUMBER: ChiCTR-TRC- 13003317; Pre-results

CausalCellSegmenter: Causal Inference inspired Diversified Aggregation Convolution for Pathology Image Segmentation

Deep learning models have shown promising performance for cell nucleus segmentation in the field of pathology image analysis. However, training a robust model from multiple domains remains a great challenge for cell nucleus segmentation. Additionally, the shortcomings of background noise, highly overlapping between cell nucleus, and blurred edges often lead to poor performance. To address these challenges, we propose a novel framework termed CausalCellSegmenter, which combines Causal Inference Module (CIM) with Diversified Aggregation Convolution (DAC) techniques. The DAC module is designed which incorporates diverse downsampling features through a simple, parameter-free attention module (SimAM), aiming to overcome the problems of false-positive identification and edge blurring. Furthermore, we introduce CIM to leverage sample weighting by directly removing the spurious correlations between features for every input sample and concentrating more on the correlation between features and labels. Extensive experiments on the MoNuSeg-2018 dataset achieves promising results, outperforming other state-of-the-art methods, where the mIoU and DSC scores growing by 3.6% and 2.65%.Comment: 10 pages, 5 figures, 2 tables, MICCA

Comparative Genomics of Bacillus thuringiensis Reveals a Path to Specialized Exploitation of Multiple Invertebrate Hosts

This is the final version of the article. Available from American Society for Microbiology via the DOI in this record.Understanding the genetic basis of host shifts is a key genomic question for pathogen and parasite biology. The Bacillus cereus group, which encompasses Bacillus thuringiensis and Bacillus anthracis, contains pathogens that can infect insects, nematodes, and vertebrates. Since the target range of the essential virulence factors (Cry toxins) and many isolates is well known, this group presents a powerful system for investigating how pathogens can diversify and adapt to phylogenetically distant hosts. Specialization to exploit insects occurs at the level of the major clade and is associated with substantial changes in the core genome, and host switching between insect orders has occurred repeatedly within subclades. The transfer of plasmids with linked cry genes may account for much of the adaptation to particular insect orders, and network analysis implies that host specialization has produced strong associations between key toxin genes with similar targets. Analysis of the distribution of plasmid minireplicons shows that plasmids with orf156 and orf157, which carry genes encoding toxins against Lepidoptera or Diptera, were contained only by B. thuringiensis in the specialized insect clade (clade 2), indicating that tight genome/plasmid associations have been important in adaptation to invertebrate hosts. Moreover, the accumulation of multiple virulence factors on transposable elements suggests that cotransfer of diverse virulence factors is advantageous in terms of expanding the insecticidal spectrum, overcoming insect resistance, or through gains in pathogenicity via synergistic interactions between toxins.IMPORTANCE Population genomics have provided many new insights into the formation, evolution, and dynamics of bacterial pathogens of humans and other higher animals, but these pathogens usually have very narrow host ranges. As a pathogen of insects and nematodes, Bacillus thuringiensis, which produces toxins showing toxicity to many orders of insects and other invertebrates, can be used as a model to study the evolution of pathogens with wide host ranges. Phylogenomic analysis revealed that host specialization and switching occur at the level of the major clade and subclade, respectively. A toxin gene co-occurrence network indicates that multiple toxins with similar targets were accumulated by the same cell in the whole species. This accumulation may be one of the strategies that B. thuringiensis has used to fight against host resistance. This kind of formation and evolution of pathogens represents a different path used against multiple invertebrate hosts from that used against higher animals.This work was supported by the National Key Research and Development Program of China (2017YFD0201201), the China 948 Program of the Ministry of Agriculture (2016-X21), the National Natural Science Foundation of China (NSFC) (31500003 and 31670085), the China Postdoctoral Science Foundation-funded project (2015M580649 and 2016T90700), and Chinese Fundamental Research Funds for the Central Universities (2662016QD039, 2662015PY123, and 2662017PY094)

TensorMD: Scalable Tensor-Diagram based Machine Learning Interatomic Potential on Heterogeneous Many-Core Processors

Molecular dynamics simulations have emerged as a potent tool for investigating the physical properties and kinetic behaviors of materials at the atomic scale, particularly in extreme conditions. Ab initio accuracy is now achievable with machine learning based interatomic potentials. With recent advancements in high-performance computing, highly accurate and large-scale simulations become feasible. This study introduces TensorMD, a new machine learning interatomic potential (MLIP) model that integrates physical principles and tensor diagrams. The tensor formalism provides a more efficient computation and greater flexibility for use with other scientific codes. Additionally, we proposed several portable optimization strategies and developed a highly optimized version for the new Sunway supercomputer. Our optimized TensorMD can achieve unprecedented performance on the new Sunway, enabling simulations of up to 52 billion atoms with a time-to-solution of 31 ps/step/atom, setting new records for HPC + AI + MD

Spatiotemporally controllable plasma lattice structures in dielectric barrier discharge

A method to generate spatiotemporally controllable plasma lattice structures via dielectric barrier discharge is proposed by utilizing a latticed water electrode. A variety of plasma lattice structures including triangle, hexagon, honeycomb, and complex superlattices are obtained by changing three parameters including the applied voltage, the gas pressure, and the gas compositions. Moreover, these plasma lattices can be quickly reconstructed, allowing for active control both in space and time. Two-dimensional particle-in-cell simulations are carried out to demonstrate the formation of plasma structures under different voltages, which are in good agreement with experimental observations. Our method provides a unique way for the fabrication of controllable plasma lattice structures, which may enable tunable control of microwave radiation for wide applications

Maternal OGTT Glucose Levels at 26–30 Gestational Weeks with Offspring Growth and Development in Early Infancy

Aims. We aim to evaluate the association of maternal gestational oral glucose tolerance test (OGTT) glucose concentrations with anthropometry in the offspring from birth to 12 months in Tianjin, China. Methods:. A total of 27,157 pregnant women underwent OGTT during 26–30 weeks gestation, and their children had body weight/length measured from birth to 12 months old. Results:. Maternal OGTT glucose concentrations at 26–30 gestational weeks were positively associated with Z-scores for birth length-for-gestational age and birth weight-for-length. Compared with infants born to mothers with normal glucose tolerance, infants born to mothers with gestational diabetes mellitus (impaired glucose tolerance/new diabetes) had higher mean values of Z-scores for birth length-for-gestational age (0.07/0.23; normal group −0.08) and birth weight-for-length (0.27/0.57; normal group −0.001), smaller changes in mean values of Z-scores for length-for-age (0.75/0.62; normal group 0.94) and weight-for-length (0.18/−0.17; normal group 0.37) from birth to month 3, and bigger changes in mean values in Z-scores for weight-for-length (0.07/0.12; normal group 0.02) from month 9 to 12. Conclusions:. Abnormal maternal glucose tolerance during pregnancy was associated with higher birth weight and birth length, less weight and length gain in the first 3 months of life, and more weight gain in the months 9–12 of life