Helicobacter zhangjianzhongii sp. nov., isolated from dog feces

In 2019, two distinct bacterial isolates were independently isolated from the fecal samples of separate dogs in Beijing, China. These cells exhibit microaerobic, are Gram-negative, motile, and possess a characteristic spiral shape with bipolar single flagellum. They display positive results for the oxidase test while being negative for both catalase and urease. These organisms measure approximately 0.2–0.3 μm in width and 4.5–6 μm in length. The colonies are wet, flat, grey, circular, and smooth with sizes ranging from 1 to 2 mm in diameter after 2 days of growth. However, strains may exhibit variations in size and morphology following extended incubation. Phylogenetic analyses based on the 16S rRNA gene and core genome indicated that these two isolates belong to the genus Helicobacter and formed a robust clade that was remains distinctly separate from currently recognized species. These two isolates shared low dDDH relatedness and ANI values with their closest species Helicobacter canis CCUG 32756T, with these values falling below the commonly cutoff values for strains of the same species. The genomic DNA G + C contents of strain XJK30-2 were 44.93 mol%. Comparing the phenotypic and phylogenetic features between these two isolates and their closely related species, XJK30-2 represents a novel species within the genus Helicobacter, for which the name Helicobacter zhangjianzhongii sp. nov. (Type strain XJK30-2T = GDMCC 1.3695T) is proposed

KDM6A Regulates Cell Plasticity and Pancreatic Cancer Progression by Non-Canonical Activin Pathway

BACKGROUND & AIMS: Inactivating mutations of KDM6A, a histone demethylase, were frequently found in pancreatic ductal adenocarcinoma (PDAC). We investigated the role of KDM6A in PDAC development. METHODS: We performed a pancreatic tissue microarray analysis of KDM6A protein levels. We used human PDAC cell lines for KDM6A knockout and knockdown experiments. We performed Bru-seq analysis to elucidate the effects of KDM6A loss on global transcription. We performed studies with Ptf1a(Cre); LSL-Kras(G12D); Trp53(R172H/+); Kdm6a(fl/fl or fl/Y), Ptf1a(Cre); Kdm6a(fl/fl or fl/Y), and orthotopic xenograft mice to investigate the impacts of Kdm6a deficiency on pancreatic tumorigenesis and pancreatitis. RESULTS: Loss of KDM6A was associated with metastasis in PDAC patients. Bru-seq analysis revealed upregulation of the epithelial-mesenchymal transition pathway in PDAC cells deficient of KDM6A. Loss of KDM6A promoted mesenchymal morphology, migration, and invasion in PDAC cells in vitro. Mechanistically, activin A and subsequent p38 activation likely mediated the role of KDM6A loss. Inhibiting either activin A or p38 reversed the effect. Pancreas-specific Kdm6a-knockout mice pancreata demonstrated accelerated PDAC progression, developed a more aggressive undifferentiated type PDAC, and increased metastases in the background of Kras and p53 mutations. Kdm6a-deficient pancreata in a pancreatitis model had a delayed recovery with increased PDAC precursor lesions compared to wild-type pancreata. CONCLUSIONS: Loss of KDM6A accelerates PDAC progression and metastasis, most likely by a non-canonical p38-dependant activin A pathway. KDM6A also promotes pancreatic tissue recovery from pancreatitis. Activin A might be utilized as a therapeutic target for KDM6A-deficient PDACs

Home country supportiveness/unfavorableness and outward foreign direct investment from China

What drives the outward foreign direct investments (OFDIs) by emerging market firms (EMFs)? Drawing on a strategy tripod framework, this article proposes a theoretical model to predict OFDI by EMFs from China. Specifically, we use institution- and industry-based views to examine two facets of home country environment, namely the supportiveness from home government and unfavorableness from home industry, as important determinants of OFDI, and compare the relative strength of these effects. Further, we use resource-based view to argue that the effect of the home country environment is contingent on the international experience portfolios of EMFs

Angel investor's selection criteria: a comparative institutional perspective

Despite the important role of angel investors as critical financial providers for new ventures, little is known regarding how institutions make their investment decisions. While angels make decisions based on selection criteria during the first stage, they are also embedded within and affected by different institutional settings and as a result weight these criteria differently than other investors. We compare angel investors' selection criteria in China and Denmark using the comparative institutional perspective. We use a policy capturing approach and hierarchy linear modeling, revealing that since Chinese angels are embedded within relationship-based institutional settings they tend to reply more on strong ties such as family and friends in management team, as well as weighting risks less compared to Danish angels operating within more rule-based institutional contexts

The Influence of Time Scale on the Quantitative Study of Soil and Water Conservation Effect of Grassland

Quantitative analysis of time scale effects is conducive to further understanding of vegetation water and soil conservation mechanism. Based on the observation data of the grass covered and bare soil (control) experimental plots located in Hetian Town, Changting County of Fujian Province from 2007 to 2010, the characteristics of 4 parameters (precipitation, vegetation, RE and SE) were analyzed at precipitation event, month, season, and annual scales, and then the linear regression models were established to describe the relationships between RE(SE) and its influencing factors of precipitation and vegetation. RE (SE) means the ratio of runoff depth (soil loss) of grass covered plot to that of the control plot. Results show that these 4 parameters presented different magnitude and variation on different time scales. RE and SE were relatively stable either within or among different time scales due to their ratios reducing the influence of other factors. The coupling of precipitation and vegetation led to better water conservation effect at lower RE (0.7) REs at precipitation event scale as well as at annual scale (R2 > 0.78). For the soil conservation effect, precipitation or/and vegetation was/were the dominated influence factor(s) at precipitation event and annual scales, and the grass LAI could basically describe the positive conservation effect (SE0.55), while the maximum 30 min intensity (I30) could describe the negative conservation effect more accurately (SE>1, R2>0.79). More uncertainties (R2≈0.4) exist in the models of both RE and SE at two moderate time scales (month and season). Consequently, factors influencing water and soil conservation effect of grass present different variation and coupling characteristics on different time scales, indicating the importance of time scale at the study on water and soil conservation

Comparison of Water and Soil Conservation Effect of Trees, Shrubs and Grass in the Red Soil Area of Southern China

Assessing the effects of vegetation on water and soil conversation is the key basis for research and management of ecological restoration on water-eroded areas. In this study, the runoff depth, soil loss and corresponding precipitation of five plots planted respectively with Pueraria lobata, Lespedeza bicolor Turcz, Manglietia yuyuanensis Law, Paspalum natatu Fliigge, Paspalum wettsteinii Hackel and one control plot were observed monthly from 2003 to 2010 in Hetian town of Changting County, Fujian Province, a typical water-eroded area in southern China. Then the effects of different vegetation on water/soil conversation (RE/SE) were determined using the ratios of runoff depth/soil loss between vegetated plots to the control plot. Meanwhile, the effect of rainfall on the water and soil loss was also analyzed. The results showed that, both the water and soil conservation effects of Pueraria lobata and Manglietia yuyuanensis Law are better than Lespedeza bicolor Turcz and Paspalum natatu, while Paspalum wettsteinii Hackel are the worst. The differences of effects of water conservation are more significantly than those of soil conversation between five kinds of vegetations. The runoff depth is mainly affected by rainfall, the determination coefficients (R2) of linear regression models between rainfall and runoff depth of all planted plots are all greater than 0.9, whereas the determination coefficients of the linear regression models between rainfall and soil loss vary form 0.3 to 0.8 for different vegetated plots. These results provide a reference for vegetation reconstruction in the current and similar areas

Bidirectional Flow Decision Tree for Reliable Remote Sensing Image Scene Classification

Remote sensing image scene classification (RSISC), which aims to classify scene categories for remote sensing imagery, has broad applications in various fields. Recent deep learning (DL) successes have led to a new wave of RSISC applications; however, they lack explainability and trustworthiness. Here, we propose a bidirectional flow decision tree (BFDT) module to create a reliable RS scene classification framework. Our algorithm combines BFDT and Convolutional Neural Networks (CNNs) to make the decision process easily interpretable. First, we extract multilevel feature information from the pretrained CNN model, which provides the basis for constructing the subsequent hierarchical structure. Then the model uses the discriminative nature of scene features at different levels to gradually refine similar subsets and learn the interclass hierarchy. Meanwhile, the last fully connected layer embeds decision rules for the decision tree from the bottom up. Finally, the cascading softmax loss is used to train and learn the depth features based on the hierarchical structure formed by the tree structure that contains rich remote sensing information. We also discovered that superclass results can be obtained well for unseen classes due to its unique tree structure hierarchical property, which results in our model having a good generalization effect. The experimental results align with theoretical predictions using three popular datasets. Our proposed framework provides explainable results, leading to correctable and trustworthy approaches

MDSNet: a multiscale decoupled supervision network for semantic segmentation of remote sensing images

Recent deep-learning successes have led to a new wave of semantic segmentation in remote sensing (RS) applications. However, most approaches rarely distinguish the role of the body and edge of RS ground objects; thus, our understanding of these semantic parts has been frustrated by the lack of detailed geometry and appearance. Here we present a multiscale decoupled supervision network for RS semantic segmentation. Our proposed framework extends a densely supervised encoder-decoder network with a feature decoupling module that can decouple semantic features with different scales into distinct body and edge components. We further conduct multiscale supervision of the original and decoupled body and edge features to enhance inner consistency and spatial boundaries in remote sensing image (RSI) ground objects, enabling new segmentation designs and semantic components that can learn to perform multiscale geometry and appearance. Our results outperform the previous algorithm and are robust to different datasets. These results demonstrate that decoupled supervision is an effective solution to semantic segmentation tasks of RS images