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The emergence of a novel avian-origin influenza A virus strain (H7N9) causing severe human disease in China has raised serious concerns. In this report, key clinical and virologic features of this outbreak are presented. Sporadic human infections with avian influenza A viruses, which usually occur after recent exposure to poultry, have caused a wide spectrum of illness, ranging from conjunctivitis and upper respiratory tract disease to pneumonia and multiorgan failure. Low pathogenic avian influenza A (H7N2, H7N3, H9N2, or H10N7) 1 – 4 virus infections have caused lower respiratory tract illness that is mild (conjunctivitis or uncomplicated influenza-like illness) to moderate in severity. Most human infections with highly pathogenic avian influenza (HPAI) A (H7) viruses have resulted in conjunctivitis (H7N3) or uncomplicated influenza illness, but one case of fatal acute respiratory distress syndrome (ARDS) was reported . . .

The purpose of this study was to develop a fully-automated segmentation algorithm, robust to various density enhancing lung abnormalities, to facilitate rapid quantitative analysis of computed tomography images. A polymorphic training approach is proposed, in which both specifically labeled left and right lungs of humans with COPD, and nonspecifically labeled lungs of animals with acute lung injury, were incorporated into training a single neural network. The resulting network is intended for predicting left and right lung regions in humans with or without diffuse opacification and consolidation. Performance of the proposed lung segmentation algorithm was extensively evaluated on CT scans of subjects with COPD, confirmed COVID-19, lung cancer, and IPF, despite no labeled training data of the latter three diseases. Lobar segmentations were obtained using the left and right lung segmentation as input to the LobeNet algorithm. Regional lobar analysis was performed using hierarchical clustering to identify radiographic subtypes of COVID-19. The proposed lung segmentation algorithm was quantitatively evaluated using semi-automated and manually-corrected segmentations in 87 COVID-19 CT images, achieving an average symmetric surface distance of 0.495 ± 0.309 mm and Dice coefficient of 0.985 ± 0.011 . Hierarchical clustering identified four radiographical phenotypes of COVID-19 based on lobar fractions of consolidated and poorly aerated tissue. Lower left and lower right lobes were consistently more afflicted with poor aeration and consolidation. However, the most severe cases demonstrated involvement of all lobes. The polymorphic training approach was able to accurately segment COVID-19 cases with diffuse consolidation without requiring COVID-19 cases for training.

This paper addresses issues with monitoring systems that identify and track illegal drones. The development of drone technologies promotes the widespread commercial application of drones. However, the ability of a drone to carry explosives and other destructive materials may pose serious threats to public safety. In order to reduce these threats, we propose an acoustic-based scheme for positioning and tracking of illegal drones. Our proposed scheme has three main focal points. First, we scan the sky with switched beamforming to find sound sources and record the sounds using a microphone array; second, we perform classification with a hidden Markov model (HMM) in order to know whether the sound is a drone or something else. Finally, if the sound source is a drone, we use its recorded sound as a reference signal for tracking based on adaptive beamforming. Simulations are conducted under both ideal conditions (without background noise and interference sounds) and non-ideal conditions (with background noise and interference sounds), and we evaluate the performance when tracking illegal drones.

To improve the reliability and reduce the maintenance cost of transformer oil, a life prediction of transformer oil is needed so that the maintenance of transformer can be performed correctly. However, it is difficult to predict the reliable lifetime of transformer oil accurately because of its unkind operating condition at different environments. To solve this problem, based on the theory of accelerated life testing (ALT), a reliability assessment method for transformer insulating oil on a normal operational conditions is proposed. An inverse power Weibull distribution model for insulating oil lifetime with voltage is built. Numerical procedure of model parameter estimation is presented, the variances of model parameters and reliability indices, which including mean lifetime, reliability, reliable lifetime at given reliability and failure rate, are derived. The feasibility and correctness of the proposed method are validated by real lifetime data of transformer insulating oil in literature. The reliability of transformer insulating oil used at normal usage conditions are predicted by the proposed method, and the point and interval estimations of reliability indices are evaluated. The results show that reliable lifetime and mean lifetime under reliability limit should be considered simultaneously in repair or replacement of transformer insulating oil.

Background While increasing evidence suggests that alterations in the gut microbiota and metabolites are associated with ovarian cancer (OC) risk, whether these associations imply causation remains to be identified. Methods We conducted a two-sample Mendelian randomization (MR) study utilizing a large-scale genome-wide association study (GWAS) to explore the causal effects of the gut microbiota of 196/220 individuals and 1,400 plasma metabolites on OC and epithelial ovarian cancer (EOC) subtypes. Data on the gut microbiota were obtained from the MiBioGen consortium of 18,340 subjects and the Dutch Microbiome Project of 7,738 volunteers. Data on plasma metabolites were derived from a GWAS of plasma metabolites in 8,299 participants. Ovarian cancer ( n  = 25,509) and EOC subtypes were obtained from the Ovarian Cancer Association Consortium (OCAC). Metabolites and associated targets were analyzed via network pharmacology and molecular docking. Results At the genus and species levels, we identified seven risk factors for the gut microbiota: the genus Dialister ( P  = 0.024), genus Ruminiclostridium5 ( P  = 0.0004), genus Phascolarctobacterium ( P  = 0.0217), species Bacteroides massiliensis ( P  = 0.011), species Phascolarctobacterium succinatutens ( P  = 0.0212), species Paraprevotella clara ( P  = 0.0247) and species Bacteroides dorei ( P  = 0.0054). In addition, five gut microbes at the genus and species levels were found to be protective: genus Family XIII AD3011 group ( P  = 0.006), genus Butyrivibrio ( P  = 0.0095), genus Oscillibacter ( P  = 0.0206), species Roseburia hominis ( P  = 0.0241), and species Bifidobacterium bifidum ( P  = 0.0224). For plasma metabolites, we revealed five positive and four negative correlations with OC. Among these, caffeic acid and caffeine metabolites and sphingomyelin and ceramide metabolites were identified as risk factors, whereas phenylalanine metabolites, butyric acid metabolites, and some lipid metabolites were recognized as protective factors. A series of sensitivity analyses revealed no abnormalities, including pleiotropy and heterogeneity analyses. Conclusion Our MR analysis demonstrated that the gut microbiota and metabolites are causally associated with OC, which has significant potential for the early detection and diagnosis of OC and EOC subtypes, providing valuable insights into this area of research.

Deuterostomes are a morphologically disparate clade, encompassing the chordates (including vertebrates), the hemichordates (the vermiform enteropneusts and the colonial tube-dwelling pterobranchs) and the echinoderms (including starfish). Although deuterostomes are considered monophyletic, the inter-relationships between the three clades remain highly contentious. Here we report, Yanjiahella biscarpa , a bilaterally symmetrical, solitary metazoan from the early Cambrian (Fortunian) of China with a characteristic echinoderm-like plated theca, a muscular stalk reminiscent of the hemichordates and a pair of feeding appendages. Our phylogenetic analysis indicates that Y . biscarpa is a stem-echinoderm and not only is this species the oldest and most basal echinoderm, but it also predates all known hemichordates, and is among the earliest deuterostomes. This taxon confirms that echinoderms acquired plating before pentaradial symmetry and that their history is rooted in bilateral forms. Yanjiahella biscarpa shares morphological similarities with both enteropneusts and echinoderms, indicating that the enteropneust body plan is ancestral within hemichordates. The early evolution of the deuterostomes is not well resolved. Here, Topper and colleagues investigate the early Cambrian metazoan Yanjiahella biscarpa , concluding that it is a stem echinoderm, is among the oldest known deuterstomes, and supports an ancestral enteropneust body plan in hemichordates.

As one of the important parts of modern mechanical equipment, the accurate real-time diagnosis of rolling bearing is particularly important. Traditional fault diagnosis methods have some disadvantages, such as low diagnostic accuracy and difficult fault feature extraction. In this paper, a method combining Wavelet transform (WT) and Deformable Convolutional Neural Network (D-CNN) is proposed to realize accurate real-time fault diagnosis of end-to-end rolling bearing. The vibration signal of rolling bearing is taken as the monitoring target. Firstly, the Orthogonal Matching Pursuit (OMP) algorithm is used to remove the harmonic signal and retain the impact signal and noise. Secondly, the time-frequency map of the signal is obtained by time-frequency transform using Wavelet analysis. Finally, the D-CNN is used for feature extraction and classification. The experimental results show that the accuracy of the method can reach 99.9% under various fault modes, and it can accurately identify the fault of rolling bearing.

Multidetector row computed tomography (MDCT) is increasingly taking a central role in identifying subphenotypes within chronic obstructive pulmonary disease (COPD), asthma, and other lung-related disease populations, allowing for the quantification of the amount and distribution of altered parenchyma along with the characterization of airway and vascular anatomy. The embedding of quantitative CT (QCT) into a multicenter trial with a variety of scanner makes and models along with the variety of pressures within a clinical radiology setting has proven challenging, especially in the context of a longitudinal study. SPIROMICS (Subpopulations and Intermediate Outcome Measures in COPD Study), sponsored by the National Institutes of Health, has established a QCT lung assessment system (QCT-LAS), which includes scanner-specific imaging protocols for lung assessment at total lung capacity and residual volume. Also included are monthly scanning of a standardized test object and web-based tools for subject registration, protocol assignment, and data transmission coupled with automated image interrogation to assure protocol adherence. The SPIROMICS QCT-LAS has been adopted and contributed to by a growing number of other multicenter studies in which imaging is embedded. The key components of the SPIROMICS QCT-LAS along with evidence of implementation success are described herein. While imaging technologies continue to evolve, the required components of a QCT-LAS provide the framework for future studies, and the QCT results emanating from SPIROMICS and the growing number of other studies using the SPIROMICS QCT-LAS will provide a shared resource of image-derived pulmonary metrics.

Under the framework of computational integral imaging, a multi-image encryption scheme based on the DNA-chaos algorithm is proposed. In this scheme, multiple images are merged to one image by a computational integral imaging algorithm, which significantly improves the efficiency of image encryption. Meanwhile, the computational integral imaging algorithm can merge images at different depth distances, thereby the different depth distances of multiple images can also be used as keys to increase the security of the encryption method. In addition, the high randomness of the chaos algorithm is combined to address the outline effect caused by the DNA encryption algorithm. We have experimentally verified the proposed multi-image encryption scheme. The entropy value of the encrypted image is 7.6227, whereas the entropy value of the merge image with two input images is 3.2886, which greatly reduces the relevance of the image. The simulation results also confirm that the proposed encryption scheme has high key security and can protect against various attacks.

Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive fibrotic disease with an unclear etiology and no effective treatment. This study aims to elucidate the pathogenic mechanism networks involving multiple targets and pathways in IPF. Extracts and metabolites of Astragalus membranaceus (AM) and Radix paeoniae rubra (RPR), two well-known traditional Chinese medicines, have demonstrated therapeutic effects on IPF. However, the underlying mechanisms of AM and RPR remain unclear. Utilizing network pharmacology analysis, differentially expressed genes (DEGs) associated with IPF were obtained from the GEO database. Targets of AM and RPR were identified using the TCM Systems Pharmacology Database and Analysis Platform and SwissTargetPrediction. A protein–protein interaction (PPI) network was subsequently constructed and analyzed using the STRING database and Cytoscape software. Gene ontology enrichment analysis and kyoto encyclopedia of genes and genomes analysis were conducted using Metascape. Additionally, a component-target-pathway network and a Sankey diagram were employed to identify the main active components, and molecular docking was performed between these components and proteins encoded by key targets. Finally, in vivo studies were conducted based on network pharmacology. A total of 117 common targets between DEGs of IPF and drug targets were identified and included in the PPI network, in which AKT1 , MAPK3 , HSP90AA1 , VEGFA , CASP3 , JUN , HIF1A , CCND1 , PTGS2 , and MDM2 were predicted as key targets. These 117 targets were enriched in the PI3K-AKT pathway, HIF-1 signaling pathway, apoptosis, and microRNAs in cancer. Astragaloside III, (R)-Isomucronulatol, Astragaloside I, Paeoniflorin, and β-sitosterol were selected as the main active components. Docking scores ranged from − 4.7 to − 10.7 kcal/mol, indicating a strong binding affinity between the main active compounds and key targets. In vivo studies have indeed shown that AM and RPR can alleviate the pathological lung fibrotic damage caused by bleomycin treatment. The treatment with AM and RPR resulted in a reduction of mRNA levels for key targets AKT1 , HSP90AA1 , CASP3 , MAPK3 , and VEGFA . Additionally, the protein expression levels of AKT1 , HSP90AA1 , and VEGFA were also reduced. These results support the therapeutic potential of AM and RPR in ameliorating pulmonary fibrosis and provide insight into the molecular mechanisms involved in their therapeutic effects.