Automatic 3D reconstruction of vertebrae from orthogonal bi-planar radiographs.

When conducting spine-related diagnosis and surgery, the three-dimensional (3D) upright posture of the spine under natural weight bearing is of significant clinical value for physicians to analyze the force on the spine. However, existing medical imaging technologies cannot meet current requirements of medical service. On the one hand, the mainstream 3D volumetric imaging modalities (e.g. CT and MRI) require patients to lie down during the imaging process. On the other hand, the imaging modalities conducted in an upright posture (e.g. radiograph) can only realize 2D projections, which lose the valid information of spinal anatomy and curvature. Developments of deep learning-based 3D reconstruction methods bring potential to overcome the limitations of the existing medical imaging technologies. To deal with the limitations of current medical imaging technologies as is described above, in this paper, we propose a novel deep learning framework, ReVerteR, which can realize automatic 3D Reconstruction of Vertebrae from orthogonal bi-planar Radiographs. With the utilization of self-attention mechanism and specially designed loss function combining Dice, Hausdorff, Focal, and MSE, ReVerteR can alleviate the sample-imbalance problem during the reconstruction process and realize the fusion of the centroid annotation and the focused vertebra. Furthermore, aiming at automatic and customized 3D spinal reconstruction in real-world scenarios, we extend ReVerteR to a clinical deployment-oriented framework, and develop an interactive interface with all functions in the framework integrated so as to enhance human-computer interaction during clinical decision-making. Extensive experiments and visualization conducted on our constructed datasets based on two benchmark datasets of spinal CT, VerSe 2019 and VerSe 2020, demonstrate the effectiveness of our proposed ReVerteR. In this paper, we propose an automatic 3D reconstruction method of vertebrae based on orthogonal bi-planar radiographs. With the 3D upright posture of the spine under natural weight bearing effectively constructed, our proposed method is expected to better support doctors make clinical decision during spine-related diagnosis and surgery.

A survey of label-noise deep learning for medical image analysis.

Several factors are associated with the success of deep learning. One of the most important reasons is the availability of large-scale datasets with clean annotations. However, obtaining datasets with accurate labels in the medical imaging domain is challenging. The reliability and consistency of medical labeling are some of these issues, and low-quality annotations with label noise usually exist. Because noisy labels reduce the generalization performance of deep neural networks, learning with noisy labels is becoming an essential task in medical image analysis. Literature on this topic has expanded in terms of volume and scope. However, no recent surveys have collected and organized this knowledge, impeding the ability of researchers and practitioners to utilize it. In this work, we presented an up-to-date survey of label-noise learning for medical image domain. We reviewed extensive literature, illustrated some typical methods, and showed unified taxonomies in terms of methodological differences. Subsequently, we conducted the methodological comparison and demonstrated the corresponding advantages and disadvantages. Finally, we discussed new research directions based on the characteristics of medical images. Our survey aims to provide researchers and practitioners with a solid understanding of existing medical label-noise learning, such as the main algorithms developed over the past few years, which could help them investigate new methods to combat with the negative effects of label noise.

A solution and practice for combining multi-source heterogeneous data to construct enterprise knowledge graph.

The knowledge graph is one of the essential infrastructures of artificial intelligence. It is a challenge for knowledge engineering to construct a high-quality domain knowledge graph for multi-source heterogeneous data. We propose a complete process framework for constructing a knowledge graph that combines structured data and unstructured data, which includes data processing, information extraction, knowledge fusion, data storage, and update strategies, aiming to improve the quality of the knowledge graph and extend its life cycle. Specifically, we take the construction process of an enterprise knowledge graph as an example and integrate enterprise register information, litigation-related information, and enterprise announcement information to enrich the enterprise knowledge graph. For the unstructured text, we improve existing model to extract triples and the F1-score of our model reached 72.77%. The number of nodes and edges in our constructed enterprise knowledge graph reaches 1,430,000 and 3,170,000, respectively. Furthermore, for each type of multi-source heterogeneous data, we apply corresponding methods and strategies for information extraction and data storage and carry out a detailed comparative analysis of graph databases. From the perspective of practical use, the informative enterprise knowledge graph and its timely update can serve many actual business needs. Our proposed enterprise knowledge graph has been deployed in HuaRong RongTong (Beijing) Technology Co., Ltd. and is used by the staff as a powerful tool for corporate due diligence. The key features are reported and analyzed in the case study. Overall, this paper provides an easy-to-follow solution and practice for domain knowledge graph construction, as well as demonstrating its application in corporate due diligence.

A Skeleton-Based Rehabilitation Exercise Assessment System With Rotation Invariance.

Automated exercise assessment is of great importance for patients under rehabilitation exercise who require professional guidance. Among the existing approaches, the skeleton-based assessment model that classifies the correctness of the exercise has attracted much attention due to its relative ease of implementation and convenience in use. However, there are two problems with this approach. The first problem is its sensitivity to the orientation of the human skeleton. To solve this problem, we propose a novel rotation-invariant descriptor, the dot product matrix of the human skeleton, and prove mathematically that this descriptor discards only the orientation message that we do not expect while preserving all other useful information. Lack of feedback from the system is the second problem, because the exercisers do not know which parts of their exercises are incorrect. Therefore, we develop a visualization method for our system based on Gradient-Weighted Class Activation Mapping (Grad-CAM) and an quantitative metric called Overlap Ratio (OvR) to measure the quality of the visualization result. To demonstrate the effect of our method, we conduct experiments on two public datasets and a self-generated push-up dataset. The experimental results show that our rotation-invariant descriptor can achieve absolute robustness to orientation even under severe angle perturbations. In terms of accuracy and OvR, our method even outperforms previous works in most cases, indicating that the rotation-invariant descriptor helps the assessment model to extract more stable features. The visualization results are also informative to correct the movements; some examples are presented in this paper. The code of this paper and our push-up dataset are publicly available at https://github.com/Kelly510/RehabExerAssess.

Post-pandemic healthcare for COVID-19 vaccine: Tissue-aware diagnosis of cervical lymphadenopathy via multi-modal ultrasound semantic segmentation.

With the widespread deployment of COVID-19 vaccines all around the world, billions of people have benefited from the vaccination and thereby avoiding infection. However, huge amount of clinical cases revealed diverse side effects of COVID-19 vaccines, among which cervical lymphadenopathy is one of the most frequent local reactions. Therefore, rapid detection of cervical lymph node (LN) is essential in terms of vaccine recipients' healthcare and avoidance of misdiagnosis in the post-pandemic era. This paper focuses on a novel deep learning-based framework for the rapid diagnosis of cervical lymphadenopathy towards COVID-19 vaccine recipients. Existing deep learning-based computer-aided diagnosis (CAD) methods for cervical LN enlargement mostly only depend on single modal images, e.g., grayscale ultrasound (US), color Doppler ultrasound, and CT, while failing to effectively integrate information from the multi-source medical images. Meanwhile, both the surrounding tissue objects of the cervical LNs and different regions inside the cervical LNs may imply valuable diagnostic knowledge which is pending for mining. In this paper, we propose an Tissue-Aware Cervical Lymph Node Diagnosis method (TACLND) via multi-modal ultrasound semantic segmentation. The method effectively integrates grayscale and color Doppler US images and realizes a pixel-level localization of different tissue objects, i.e., lymph, muscle, and blood vessels. With inter-tissue and intra-tissue attention mechanisms applied, our proposed method can enhance the implicit tissue-level diagnostic knowledge in both spatial and channel dimension, and realize diagnosis of cervical LN with normal, benign or malignant state. Extensive experiments conducted on our collected cervical LN US dataset demonstrate the effectiveness of our methods on both tissue detection and cervical lymphadenopathy diagnosis. Therefore, our proposed framework can guarantee efficient diagnosis for the vaccine recipients' cervical LN, and assist doctors to discriminate between COVID-related reactive lymphadenopathy and metastatic lymphadenopathy.

Association of Increased Amygdala Activity with Stress-Induced Anxiety but not Social Avoidance Behavior in Mice.

Chronic stress leads to many psychiatric disorders, including social and anxiety disorders that are associated with over-activation of neurons in the basolateral amygdala (BLA). However, not all individuals develop psychiatric diseases, many showing considerable resilience against stress exposure. Whether BLA neuronal activity is involved in regulating an individual's vulnerability to stress remains elusive. In this study, using a mouse model of chronic social defeat stress (CSDS), we divided the mice into susceptible and resilient subgroups based on their social interaction behavior. Using in vivo fiber photometry and in vitro patch-clamp recording, we showed that CSDS persistently (after 20 days of recovery from stress) increased BLA neuronal activity in all the mice regardless of their susceptible or resilient nature, although impaired social interaction behavior was only observed in susceptible mice. Increased anxiety-like behavior, on the other hand, was evident in both groups. Notably, the CSDS-induced increase of BLA neuronal activity correlated well with the heightened anxiety-like but not the social avoidance behavior in mice. These findings provide new insight to our understanding of the role of neuronal activity in the amygdala in mediating stress-related psychiatric disorders.

MPF-net: An effective framework for automated cobb angle estimation.

In clinical practice, the Cobb angle is the gold standard for idiopathic scoliosis assessment, which can provide an important reference for clinicians to make surgical plan and give medical care to patients. Currently, the Cobb angle is measured manually on both anterior-posterior(AP) view X-rays and lateral(LAT) view X-rays. The clinicians first find four landmarks on each vertebra, and then they extend the line from landmarks and measure the Cobb angle by rules. The whole process is time-consuming and subjective, so that the automated Cobb angle estimation is required for efficient and reliable Cobb angle measurement. The noise in X-rays and the occlusion of vertebras are the main difficulties for automated Cobb angle estimation, and it is challenging to utilize the information between the multi-view X-rays of the same patient. Addressing these problems, in this paper, we propose an effective framework named MPF-net by using deep learning methods for automated Cobb angle estimation. We combine a vertebra detection branch and a landmark prediction branch based on the backbone convolutional neural network, which can provide the bounded area for landmark prediction. Then we propose a proposal correlation module to utilize the information between neighbor vertebras, so that we can find the vertebras hidden by ribcage and arms on LAT X-rays. We also design a feature fusion module to utilize the information in both AP and LAT X-rays for better performance. The experiment results on 2738 pair of X-rays show that our proposed MPF-net achieves precise vertebra detection and landmark prediction performance, and we get impressive 3.52 and 4.05 circular mean absolute errors on AP and LAT X-rays respectively, which is much better than previous methods. Therefore, we can provide clinicians with automated, efficient and reliable Cobb angle measurement.

Harmine inhibits the proliferation and migration of glioblastoma cells via the FAK/AKT pathway.

AIMS: Glioblastoma is one of the most invasive tumors of the central nervous system, and has a high degree of malignancy and poor prognosis. Harmine, an active ingredient extracted from perennial herbs, has been reported to have obvious antitumor effects on various tumors. However, the effects of harmine on glioblastoma growth remain unknown. We here explored the effects of harmine on glioblastoma and its underlying molecular mechanisms related to tumorigenesis. MATERIALS AND METHODS: CCK-8 and immunofluorescent assay were performed to measure anti-proliferative effect of harmine on U251-MG and U373-MG cells. Wound healing assay was performed to measure the effects of harmine on cell migration. qRT-PCR and western blot were performed to detect the protein/gene expression. BALB/c nude mice bearing U251-MG xenografts was used to measure the effects of harmine on the growth of glioblastoma in vivo. KEY FINDINGS: Harmine treatment significantly suppressed the proliferation of U251-MG and U373-MG cells in a dose and time-dependent way. Mechanistically, harmine reduced the basal and EGF-enhanced the phosphorylation level of FAK and AKT. Moreover, harmine inhibited the cell viability of U251-MG and U373-MG cells by downregulating the phosphorylation of the FAK/AKT pathway. Besides, harmine significantly suppressed the migration of U251-MG cells by suppressing the expression of MMP2, MMP9 and VEGF. Subsequently, orthotopic xenograft models revealed that harmine treatment dramatically inhibited the growth of glioblastoma in vivo. SIGNIFICANCE: In conclusion, these results suggest that harmine suppresses the proliferation and migration of U251-MG and U373-MG cells by inhibiting the FAK/AKT signaling pathway. Our findings elucidate harmine could be a promising drug for glioblastoma therapy.

Evolutionary selection of biofilm-mediated extended phenotypes in Yersinia pestis in response to a fluctuating environment.

Yersinia pestis is transmitted from fleas to rodents when the bacterium develops an extensive biofilm in the foregut of a flea, starving it into a feeding frenzy, or, alternatively, during a brief period directly after feeding on a bacteremic host. These two transmission modes are in a trade-off regulated by the amount of biofilm produced by the bacterium. Here by investigating 446 global isolated Y. pestis genomes, including 78 newly sequenced isolates sampled over 40 years from a plague focus in China, we provide evidence for strong selection pressures on the RNA polymerase ω-subunit encoding gene rpoZ. We demonstrate that rpoZ variants have an increased rate of biofilm production in vitro, and that they evolve in the ecosystem during colder and drier periods. Our results support the notion that the bacterium is constantly adapting-through extended phenotype changes in the fleas-in response to climate-driven changes in the niche.

Genetic and population analyses of Vibrio parahaemolyticus isolates from three major coastal regions in China.

AIM: This study aims to evaluate the genetic and population structure of Vibrio parahaemolyticus in the major coastal regions of China. MATERIALS & METHODS: Multilocus sequence typing was performed. RESULTS: Insertion of large sequence into recA happened in nearly 30 strains, which were untypeable by multilocus sequence typing. A collection of 307 V. parahaemolyticus isolates were typed into 160 sequence types, including 117 novel ones. eBURST analysis revealed five clonal complexes, 11 doublets, and 108 singletons. The 160 sequence types formed two main lineages in the phylogenetic analysis. CONCLUSION: V. parahaemolyticus along the Chinese coastal regions exhibits high levels of genetic diversity and has undergone significant purifying selection and frequent recombination. A deeper understanding of V. parahaemolyticus genetic diversity could be obtained at the level of genome sequences.

Genetic diversity and population structure of Lactobacillus delbrueckii subspecies bulgaricus isolated from naturally fermented dairy foods.

Lactobacillus delbrueckii subsp. bulgaricus is one of the most widely used starter culture strains in industrial fermented dairy manufacture. It is also common in naturally fermented dairy foods made using traditional methods. The subsp. bulgaricus strains found in naturally fermented foods may be useful for improving current industrial starter cultures; however, little is known regarding its genetic diversity and population structure. Here, a collection of 298 L. delbrueckii strains from naturally fermented products in Mongolia, Russia, and West China was analyzed by multi-locus sequence typing based on eight conserved genes. The 251 confirmed subsp. bulgaricus strains produced 106 unique sequence types, the majority of which were assigned to five clonal complexes (CCs). The geographical distribution of CCs was uneven, with CC1 dominated by Mongolian and Russian isolates, and CC2-CC5 isolates exclusively from Xinjiang, China. Population structure analysis suggested six lineages, L1-L6, with various homologous recombination rates. Although L2-L5 were mainly restricted within specific regions, strains belonging to L1 and L6 were observed in diverse regions, suggesting historical transmission events. These results greatly enhance our knowledge of the population diversity of subsp. bulgaricus strains, and suggest that strains from CC1 and L4 may be useful as starter strains in industrial fermentation.

Expanding the biotechnology potential of lactobacilli through comparative genomics of 213 strains and associated genera.

Lactobacilli are a diverse group of species that occupy diverse nutrient-rich niches associated with humans, animals, plants and food. They are used widely in biotechnology and food preservation, and are being explored as therapeutics. Exploiting lactobacilli has been complicated by metabolic diversity, unclear species identity and uncertain relationships between them and other commercially important lactic acid bacteria. The capacity for biotransformations catalysed by lactobacilli is an untapped biotechnology resource. Here we report the genome sequences of 213 Lactobacillus strains and associated genera, and their encoded genetic catalogue for modifying carbohydrates and proteins. In addition, we describe broad and diverse presence of novel CRISPR-Cas immune systems in lactobacilli that may be exploited for genome editing. We rationalize the phylogenomic distribution of host interaction factors and bacteriocins that affect their natural and industrial environments, and mechanisms to withstand stress during technological processes. We present a robust phylogenomic framework of existing species and for classifying new species.

Comparative genomic analysis of 45 type strains of the genus Bifidobacterium: a snapshot of its genetic diversity and evolution.

Bifidobacteria are well known for their human health-promoting effects and are therefore widely applied in the food industry. Members of the Bifidobacterium genus were first identified from the human gastrointestinal tract and were then found to be widely distributed across various ecological niches. Although the genetic diversity of Bifidobacterium has been determined based on several marker genes or a few genomes, the global diversity and evolution scenario for the entire genus remain unresolved. The present study comparatively analyzed the genomes of 45 type strains. We built a robust genealogy for Bifidobacterium based on 402 core genes and defined its root according to the phylogeny of the tree of bacteria. Our results support that all human isolates are of younger lineages, and although species isolated from bees dominate the more ancient lineages, the bee was not necessarily the original host for bifidobacteria. Moreover, the species isolated from different hosts are enriched with specific gene sets, suggesting host-specific adaptation. Notably, bee-specific genes are strongly associated with respiratory metabolism and are potential in helping those bacteria adapt to the oxygen-rich gut environment in bees. This study provides a snapshot of the genetic diversity and evolution of Bifidobacterium, paving the way for future studies on the taxonomy and functional genomics of the genus.

MLST-based inference of genetic diversity and population structure of clinical Klebsiella pneumoniae, China.

Multilocus sequence typing was applied to a collection of 327 clinical isolates of Klebsiella pneumoniae from China, which was proven to be a good representative of the global diversity of K. pneumoniae. Three lineages L1 to L3 are presented in the population with limited genetic flow across different lineages. However, extremely high levels of recombination can be observed within lineages to the extent at which the alleles are associated almost randomly. Lineages L2 and L3 most likely represent highly specific subgroups of less-virulent K. pneumoniae with modified metabolic networks, while lineage L1 contains not only hypervirulent clones with massive acquisition of virulent genes but also 'primitive and intermediate forms' during evolution of hypervirulent K. pneumoniae.

Epidemic Clones, Oceanic Gene Pools, and Eco-LD in the Free Living Marine Pathogen Vibrio parahaemolyticus.

We investigated global patterns of variation in 157 whole-genome sequences of Vibrio parahaemolyticus, a free-living and seafood associated marine bacterium. Pandemic clones, responsible for recent outbreaks of gastroenteritis in humans, have spread globally. However, there are oceanic gene pools, one located in the oceans surrounding Asia and another in the Mexican Gulf. Frequent recombination means that most isolates have acquired the genetic profile of their current location. We investigated the genetic structure in the Asian gene pool by calculating the effective population size in two different ways. Under standard neutral models, the two estimates should give similar answers but we found a 27-fold difference. We propose that this discrepancy is caused by the subdivision of the species into a hundred or more ecotypes which are maintained stably in the population. To investigate the genetic factors involved, we used 51 unrelated isolates to conduct a genome-wide scan for epistatically interacting loci. We found a single example of strong epistasis between distant genome regions. A majority of strains had a type VI secretion system associated with bacterial killing. The remaining strains had genes associated with biofilm formation and regulated by cyclic dimeric GMP signaling. All strains had one or other of the two systems and none of isolate had complete complements of both systems, although several strains had remnants. Further "top down" analysis of patterns of linkage disequilibrium within frequently recombining species will allow a detailed understanding of how selection acts to structure the pattern of variation within natural bacterial populations.

Influenza H7N9 and H9N2 viruses: coexistence in poultry linked to human H7N9 infection and genome characteristics.

UNLABELLED: Avian influenza virus A of the novel H7N9 reassortant subtype was recently found to cause severe human respiratory infections in China. Live poultry markets were suspected locations of the human H7N9 infection sources, based on the cases' exposure histories and sequence similarities between viral isolates. To explore the role of live poultry markets in the origin of the novel H7N9 virus, we systematically examined poultry and environmental specimens from local markets and farms in Hangzhou, using real-time reverse transcription-PCR (RT-PCR) as well as high-throughput next-generation sequencing (NGS). RT-PCR identified specimens positive for the H7 and N9 genomic segments in all of the 12 poultry markets epidemiologically linked to 10 human H7N9 cases. Chickens, ducks, and environmental specimens from the markets contained heavily mixed subtypes, including H7, N9, H9, and N2 and sometimes H5 and N1. The idea of the coexistence of H7N9 and H9N2 subtypes in chickens was further supported by metagenomic sequencing. In contrast, human H7N9 infection cases (n = 31) were all negative for H9N2 virus according to real-time RT-PCR. The six internal segments were indistinguishable for the H7N9 and H9N2 viruses. The H9, N2, and internal-segment sequences were very close to the sequence of the H9N2 virus circulating in chickens in China recently. Our results provide direct evidence that H9N2 strains coexisted with the novel human-pathogenic H7N9 influenza virus in epidemiologically linked live poultry markets. Avian influenza A virus of the H9N2 subtype likely made a recent contribution to the evolution of the H7N9 virus and continues to do so. IMPORTANCE: Our results suggest that avian influenza A virus of the H9N2 subtype likely made a recent contribution to the evolution of the H7N9 virus, a novel reassortant avian influenza virus A subtype, and continues to do so. The finding helps shed light on how the H7N9 virus emerged, spread, and transmitted to humans. It is of considerable interest for assessing the risk of the possible emergence of novel reassortant viruses with enhanced transmissibility to humans.

Two-factor designs unable to examine the interactions (part 1).

Two-factor designs are quite commonly used in scientific research. If the two factors have interactions, research designs like the factorial design and the orthogonal design can be adopted; however, these designs usually require many experiments. If the two factors have no interaction or the interaction is not statistically significant on result in theory and in specialty, and the measuring error of the experimental data under a certain condition (usually it is one of the experimental conditions which is formed by the complete combination of the levels of two factors) is allowed in specialty, researchers can use random block design without repeated experiments, balanced non-complete random block design without repeated experiments, single factor design with a repeatedly measured factor, two-factor design without repeated experiments and two-factor nested design. This article introduced the first three design types with examples.