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Various SARS-CoV-2-related coronaviruses have been increasingly identified in pangolins, showing a potential threat to humans. Here we report the infectivity and pathogenicity of the SARS-CoV-2-related virus, PCoV-GX/P2V, which was isolated from a Malayan pangolin (Manis javanica). PCoV-GX/P2V could grow in human hepatoma, colorectal adenocarcinoma cells, and human primary nasal epithelial cells. It replicated more efficiently in cells expressing human angiotensin-converting enzyme 2 (hACE2) as SARS-CoV-2 did. After intranasal inoculation to the hACE2-transgenic mice, PCoV-GX/P2V not only replicated in nasal turbinate and lungs, but also caused interstitial pneumonia, characterized by infiltration of mixed inflammatory cells and multifocal alveolar hemorrhage. Existing population immunity established by SARS-CoV-2 infection and vaccination may not protect people from PCoV-GX/P2V infection. These findings further verify the hACE2 utility of PCoV-GX/P2V by in vivo experiments using authentic viruses and highlight the importance for intensive surveillance to prevent possible cross-species transmission. [ABSTRACT FROM AUTHOR]

Septic cardiomyopathy (SCM) is one of the most serious complications of sepsis. The present study investigated the role and mechanism of upstream stimulatory factor 2 (USF2) in SCM. Serum samples were extracted from SCM patients and healthy individuals. A murine model of sepsis was induced by caecal ligation and puncture (CLP) surgery. Myocardial injury was examined by echocardiography and HE staining. ELISA assay evaluated myocardial markers (CK-MB, cTnI) and inflammatory cytokines (TNF-α, IL-1β, IL-18). Primary mouse cardiomyocytes were treated with lipopolysaccharide (LPS) to simulate sepsis in vitro. RT-qPCR and Western blot were used for analyzing gene and protein levels. CCK-8 assay assessed cell viability. NLRP3 was detected by immunofluorescence. ChIP, RIP and dual luciferase reporter assays were conducted to validate the molecular associations. USF2 was increased in serum from SCM patients, septic mice and primary cardiomyocytes. USF2 silencing improved the survival of septic mice and attenuated sepsis-induced myocardial pyroptosis and inflammation in vitro and in vivo. Mechanistically, USF2 could directly bind to the promoter of miR-206 to transcriptionally inhibit its expression. Moreover, RhoB was confirmed as a target of miR-206 and could promote ROCK activation and NLRP3 inflammasome formation. Moreover, overexpression of RhoB remarkably reversed the protection against LPS-induced inflammation and pyroptosis mediated by USF2 deletion or miR-206 overexpression in cardiomyocytes. The above findings elucidated that USF2 knockdown exerted a cardioprotective effect on sepsis by decreasing pyroptosis and inflammation via miR-206/RhoB/ROCK pathway, suggesting that USF2 may be a novel drug target in SCM. [ABSTRACT FROM AUTHOR]

Previous reports have confirmed that miR-206 participates in inflammatory cardiomyopathy, but its definite mechanism remains elusive. This study aims to elucidate the potential mechanism of miR-206 in septic cardiomyopathy (SCM). The primary mouse cardiomyocytes were isolated and exposed to lipopolysaccharides (LPS) to construct a septic injury model in vitro. Then, the gene transcripts and protein levels were detected by RT-qPCR and/or Western blot assay. Cell proliferation, apoptosis, and inflammatory responses were evaluated by CCK-8/EdU, flow cytometry, and ELISA assays, respectively. Dual luciferase assay, Co-IP, and ubiquitination experiments were carried out to validate the molecular interactions among miR-206, USP33, and JAK2/STAT3 signaling. miR-206 was significantly downregulated, but USP33 was upregulated in LPS-induced cardiomyocytes. Gain-of-function of miR-206 elevated the proliferation but suppressed the inflammatory responses and apoptosis in LPS-induced cardiomyocytes. USP33, as a member of the USP protein family, was confirmed to be a direct target of miR-206 and could catalyze deubiquitination of JAK2 to activate JAK2/STAT3 signaling. Rescue experiments presented that neither upregulation of USP33 nor JAK2/STAT3 signaling activation considerably reversed the protective effects of miR-206 upregulation in LPS-induced cardiomyocytes. The above data showed that miR-206 protected cardiomyocytes from LPS-induced inflammatory injuries by targeting the USP33/JAK2/STAT3 signaling pathway, which might be a novel target for SCM treatment. [ABSTRACT FROM AUTHOR]

Copyright of Carbon Research is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A recent Cyberball study has indicated that the experience of loss of control can affect how people process subsequent social exclusion. This "preexposure effect" supports the idea of a common cognitive system involved in the processing of different types of social threats. To test the validity of this assumption in the current study, we reversed the sequence of the preexposure setup. We measured the effects of social exclusion on the subsequent processing of loss of control utilizing event-related brain potentials (ERPs) and self-reports. In the control group (CG, n = 26), the transition to loss of control elicited significant increases in both the P3 amplitude and the self-reported negative mood. Replicating the results of the previous preexposure study, these effects were significantly reduced by the preexposure to an independent social threat (here: social exclusion). In contrast to previous findings, these effects were not modulated by the discontinuation (EG1disc, n = 25) or continuation (EG2cont, n = 24) of the preexposure threat. Given that the P3 effect is related to the violation of subjective expectations, these results support the notion that preexposure to a specific social threat has widespread effects on the individuals' expectancy of upcoming social participation and control. [ABSTRACT FROM AUTHOR]

In Socially Aware Networks (SANs), nodes exhibit some degree of individual selfishness and social selfishness because of their limited resources and the strength of social relationships, which affects network performance. Therefore, this paper proposes a Resource-Constrained and Socially Selfish-Based Incentive Algorithm (RSIA), which employs the payment of virtual currencies to intermediate nodes as rewards to incentivize them to forward messages. At the same time, nodes can have a preference for selfishness due to the high affluence of their wealth. For the selfish preference of highly affluent nodes that do not participate in message cooperative forwarding, the service level of the node will be reduced as a punishment. So, the nodes' resource status and social relationship, and service level are considered to price the message, and the message forwarding is done by consensus through a bargaining game. Secondly, this paper introduces a security module and an encryption mechanism to prevent false offers from the nodes. Finally, the results of simulation experiments indicate that the RSIA algorithm improves the delivery rate of messages with effect and improves in terms of average latency. [ABSTRACT FROM AUTHOR]

The temperature and equivalent strain distribution of SUS436L slabs were analyzed by a finite element (FE) model in the hot rolling processes considering different surface temperatures of the intermediate slab for finish rolling (IST). The practical rolling processes were operated and different ISTs were designed. With IST decreased, the deformation permeability and the deformation uniformity of the hot-rolled strips are improved due to the increased temperature difference between the surface and the center of the strips. After annealing at 980 °C for 180 s, the strips are dominated by substructure, followed by recrystallized grains. The fraction of the recrystallized grains, the grain homogeneity and the maximum of γ texture orientation density of the hot-rolled and annealed strips are increased with the IST decreased, and the average grain size decreased. As a result, the yield strength (RP0.2), the tensile strength (Rm), the percentage elongation after fracture (A) and the product of strength and elongation (U) of the hot-rolled and annealed strips are all increased with IST decreased and are 285, 441 MPa, 45.2% and 19.9 GPa·%, respectively, at 800 °C of IST. [ABSTRACT FROM AUTHOR]

Experiencing a social threat, such as social exclusion, is a painful event. In contrast to previous studies providing insight into the processing of a single short-termed threat, we exposed healthy individuals to the simultaneous onset of different social threats. This approach allowed us to track whether these threats are processed independently—or whether they interact in a common system. Using a virtual ball-throwing game (Cyberball), electrophysiological (event-related brain potentials, ERPs) and behavioral (self-reports) responses were collected. We assigned undergraduates to three experimental groups: single threat exclusion (n = 24), single threat loss of control (n = 26), and joint onset of both threats (dual-threat, n = 25). Self-reports indicated an increase in threats (i.e., in perceived exclusion and loss-of-control) in the latter group. The ERPs disentangled the neural responses to each threat: In the dual-threat group, the amplitudes of the P3 responses to exclusionary and intervention events were enhanced. This indicates that individuals are sensitized to each of the threats when the other threat is present simultaneously. Our findings support the theoretical notion of a common cognitive system responding to violations in subjective expectations. [ABSTRACT FROM AUTHOR]

Purpose: To develop and validate an automated magnetic resonance imaging (MRI)-based model to preoperatively differentiate pancreatic adenosquamous carcinoma (PASC) from pancreatic ductal adenocarcinoma (PDAC). Methods: This retrospective study included patients with surgically resected, histopathologically confirmed PASC or PDAC who underwent MRI between January 2011 and December 2020. According to time of treatment, they were divided into training and validation sets. Automated deep-learning-based artificial intelligence was used for pancreatic tumor segmentation. Linear discriminant analysis was performed with conventional MRI and radiomic features to develop clinical, radiomics, and mixed models in the training set. The models' performances were determined from their discrimination and clinical utility. Kaplan–Meier and log-rank tests were used for survival analysis. Results: Overall, 389 and 123 patients with PDAC (age, 61.37 ± 9.47 years; 251 men) and PASC (age, 61.99 ± 9.82 years; 78 men) were included, respectively; they were split into the training (n = 358) and validation (n = 154) sets. The mixed model showed good performance in the training and validation sets (area under the curve: 0.94 and 0.96, respectively). The sensitivity, specificity, and accuracy were 76.74%, 93.38%, and 89.39% for the training set, respectively, and 67.57%, 97.44%, and 90.26% for the validation set, respectively. The mixed model outperformed the clinical (p = 0.001) and radiomics (p = 0.04) models in the validation set. Log-rank test revealed significantly longer survival in the predicted PDAC group than in the predicted PASC group (p = 0.003), according to the mixed model. Conclusion: Our mixed model, which combined MRI and radiomic features, can be used to differentiate PASC from PDAC. [ABSTRACT FROM AUTHOR]

Objectives: To develop and validate a radiomics nomogram based on a fully automated pancreas segmentation to assess pancreatic exocrine function. Furthermore, we aimed to compare the performance of the radiomics nomogram with the pancreatic flow output rate (PFR) and conclude on the replacement of secretin-enhanced magnetic resonance cholangiopancreatography (S-MRCP) by the radiomics nomogram for pancreatic exocrine function assessment. Methods: All participants underwent S-MRCP between April 2011 and December 2014 in this retrospective study. PFR was quantified using S-MRCP. Participants were divided into normal and pancreatic exocrine insufficiency (PEI) groups using the cut-off of 200 µg/L of fecal elastase-1. Two prediction models were developed including the clinical and non-enhanced T1-weighted imaging radiomics model. A multivariate logistic regression analysis was conducted to develop the prediction models. The models' performances were determined based on their discrimination, calibration, and clinical utility. Results: A total of 159 participants (mean age ± standard deviation, 45 years ± 14;119 men) included 85 normal and 74 PEI. All the participants were divided into a training set comprising 119 consecutive patients and an independent validation set comprising 40 consecutive patients. The radiomics score was an independent risk factor for PEI (odds ratio = 11.69; p < 0.001). In the validation set, the radiomics nomogram exhibited the highest performance (AUC, 0.92) in PEI prediction, whereas the clinical nomogram and PFR had AUCs of 0.79 and 0.78, respectively. Conclusion: The radiomics nomogram accurately predicted pancreatic exocrine function and outperformed pancreatic flow output rate on S-MRCP in patients with chronic pancreatitis. Key Points: • The clinical nomogram exhibited moderate performance in diagnosing pancreatic exocrine insufficiency. • The radiomics score was an independent risk factor for pancreatic exocrine insufficiency, and every point rise in the rad-score was associated with an 11.69-fold increase in pancreatic exocrine insufficiency risk. • The radiomics nomogram accurately predicted pancreatic exocrine function and outperformed the clinical model and pancreatic flow output rate quantified by secretin-enhanced magnetic resonance cholangiopancreatography on MRI in patients with chronic pancreatitis. [ABSTRACT FROM AUTHOR]

Background: Unilateral biportal endoscopic (UBE) has been gradually applied in clinical practice. UBE has two channels, with good visual field and operating space, and has achieved good results in the treatment of lumbar spine diseases. Some scholars combine UBE with vertebral body fusion to replace traditional open fusion surgery and minimally invasive fusion surgery. The efficacy of biportal endoscopic transforaminal lumbar interbody fusion (BE-TLIF) is still controversial. In this systematic review and meta-analysis, BE-TLIF and minimally invasive transforaminal lumbar interbody fusion (MI-TLIF) are compared in the efficacy and complications of lumbar degenerative diseases. Methods: PubMed, Cochrane Library, Web of Science and China National Knowledge Infrastructure (CNKI) were used to search literatures related to BE-TLIF before January 2023, to identify relevant studies, and systematically review all literatures. Evaluation indicators mainly include operation time, hospital stay, estimated blood loss, visual analog scale (VAS), Oswestry Disability Index (ODI), and Macnab. Results: A total of 9 studies were included in this study; a total of 637 patients were collected, and 710 vertebral bodies were treated. Nine studies showed that there was no significant difference in VAS score, ODI, fusion rate, and complication rate between BE-TLIF and MI-TLIF at the final follow-up after surgery. Conclusion: This study suggests that BE-TLIF is a safe and effective surgical approach. BE-TLIF surgery has similar good efficacy to MI-TLIF in the treatment of lumbar degenerative diseases. And compared with MI-TLIF, it has the advantages of early postoperative relief of low-back pain, shorter hospital stay, and faster functional recovery. However, high-quality prospective studies are needed to validate this conclusion. [ABSTRACT FROM AUTHOR]

Today, semantic web services are rapidly evolving and updating. The discovery of semantic web services is an important concept for the comprehensiveness of individual web services in creating new intelligent systems that meet the complex needs of users and is an important technology in the domain of web services. One of its main goals is to reuse existing web services and combine them in a process that has attracted a lot of attention from different communities like the Internet of Things (IoT). Currently, the discovery of web services in the most common category includes four main methods and a set of sub-methods. Most semantic web service discovery methods are done using semantic descriptions of web services using ontology based on existing pattern recognition approaches. In this research, a new approach is presented that in the first step, the web services description language (WSDL) is scanned and the infrastructure is examined. In the second step, by adding the technique of extracting background information that can be received from the WSDL, the field limitations and existing patterns are considered and detected by semantic spacing on the discovering web services. Also, web services whose parameters contain synonymous synonyms, irregular composite fragments, and similar abbreviations with high accuracy in a cluster contract. The proposed approach is based on a semantic pattern recognition using data mining and finally, the output of the proposed method is single and combined services that have high accuracy and speed of the proposed algorithm in web service discussions. [ABSTRACT FROM AUTHOR]

In the big data environment, the social information of a large number of nodes cannot be reasonably analyzed and utilized, thus leading to the problem of uneven routing performance. Therefore, this paper proposes a Social Similarity Routing Algorithm (SRRA) based on socially aware networks in the big data environment. In the SRRA algorithm, two main parts in which the process of nodes forwarding messages are in-community and out-of-community. First, we defined three indexes for nodes and communities in which nodes are located by analyzing human social behavior: community connectedness between communities, the activity of nodes, and the social similarity of nodes. Then these three indexes are used to make up two measures: the in-community forwarding measure and the out-of-community forwarding measure. When messages are forwarded within a community, we choose nodes with high in-community forwarding measures as relay nodes so that messages can be delivered quickly in the same community. The relay node with the highest out-of-community forwarding measure is chosen to forward the message to the adjacent communities that are as near as possible to the destination community as much as possible when messages are forwarded outside the community, which ensures that messages can always be sent to the target community fast and accurately. The results of the simulation experiments compared with existing routing algorithms prove that the SRRA routing algorithm significantly improves the message delivery ratio while effectively reducing the network overhead and average latency. [ABSTRACT FROM AUTHOR]

In socially aware networks, data forwarding is usually performed based on opportunistic peer-to-peer connections and using a store-and-forward method. This method requires active collaboration from all nodes. However, due to resource limitations and other factors, several nodes exhibit selfishness and are unwilling to consume their resources to assist other nodes in forwarding data. To tackle the problems of insufficient participating nodes and low willingness to participate, this paper proposes a Service pricing-based Two-stage Incentive Algorithm (STIA). First, in view of the lack of rewards for high reputation nodes in reputation incentive and the indistinguishability of prices for different service levels in credit mechanism, leading to poor incentive effects, this paper uses the service quality of nodes and their own resources to design message pricing functions as rewards for service providers and prevents nodes from making fraudulent offers by disclosing the resource status of both parties. Secondly, this paper considers the case where relay nodes will discard accepted data, thus proposing a two-stage incentive mechanism that motivates nodes to receive and forward data. Finally, in response to nodes discarding received messages due to short-term currency shortages, nodes are permitted to overdraw their currency according to their forwarding capacity under the condition of setting restrictions to increase network cooperation. Experimental simulation results show that STIA offers significant improvements in delivery ratio and latency of messages over existing incentive schemes. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Central South University is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Pipe burst events pose threats to the safety and stability of water supply networks. Deep learning models have been studied for burst location analysis in recent years, but the applicability of the methods needs further research to cope with different size and hydraulic status of pipe networks. A novel framework of burst area identification based on an improved DenseNet model is proposed in this paper. First, according to regional characteristics of hydraulic state caused by pipe burst events, pipe network is divided into monitoring areas. Then, a new model by fully linear DenseNet with attention mechanism (FA-DenseNet) is developed for effective feature extraction and burst area identification. Thirdly, Bayesian optimization algorithm is introduced to automatically optimize the hyperparameters of the deep learning model for better accuracy. The proposed framework was tested in a real-life example network with synthetic burst monitoring data, and the accuracy of burst area identification reaches 0.918. The model is also compared with a traditional BP neural network and a DenseNet model without attention mechanism, and the results show that FA-DenseNet is more robust with the respects of different pipe burst conditions, monitoring noise and the number of pressure monitoring points. The proposed framework provides water companies an applicable and effective method of pipe burst identification and inspection. [ABSTRACT FROM AUTHOR]

Photocatalytic hydrogen production has been considered a promising approach to obtain green hydrogen energy. Crystalline porous materials have arisen as key photocatalysts for efficient hydrogen production. Here, we report a strategy to in situ photodeposit platinum clusters as cocatalyst on a covalent organic framework, which makes it an efficient photocatalyst for light-driven hydrogen evolution. Periodically dispersed adsorption sites of platinum species are constructed by introducing adjacent hydroxyl group and imine-N in the region of the covalent organic framework structural unit where photogenerated electrons converge, leading to the in situ reduction of the adsorbed platinum species into metal clusters by photogenerated electrons. The widespread platinum clusters on the covalent organic framework expose large active surface and greatly facilitate the electron transfer, finally contributing to a high photocatalytic hydrogen evolution rate of 42432 μmol g−1 h−1 at 1 wt% platinum loading. This work provides a direction for structural design on covalent organic frameworks to precisely manipulate cocatalyst morphologies and positions at the atomic level for developing efficient photocatalysts. Porous covalent organic frameworks have arisen as tunable photocatalysts for H2 production. Here, authors report frameworks engineered with co-catalyst binding sites to improve photocatalytic H2 evolution performances. [ABSTRACT FROM AUTHOR]

The cytosolic DNA sensor cyclic GMP-AMP synthase (cGAS) plays a critical role in antiviral immunity and autoimmunity. The activity and stability of cGAS are fine-tuned by post-translational modifications. Here, we show that ariadne RBR E3 ubiquitin protein ligase 1 (ARIH1) catalyzes the mono-ISGylation and induces the oligomerization of cGAS, thereby promoting antiviral immunity and autoimmunity. Knockdown or knockout of ARIH1 significantly inhibits herpes simplex virus 1 (HSV-1)- or cytoplasmic DNA-induced expression of type I interferons (IFNs) and proinflammatory cytokines. Consistently, tamoxifen-treated ER-Cre;Arih1fl/fl mice and Lyz2-Cre; Arih1fl/fl mice are hypersensitive to HSV-1 infection compared with the controls. In addition, deletion of ARIH1 in myeloid cells alleviates the autoimmune phenotypes and completely rescues the autoimmune lethality caused by TREX1 deficiency. Mechanistically, HSV-1- or cytosolic DNA-induced oligomerization and activation of cGAS are potentiated by ISGylation at its K187 residue, which is catalyzed by ARIH1. Our findings thus reveal an important role of ARIH1 in innate antiviral and autoimmune responses and provide insight into the post-translational regulation of cGAS.The activity and stability of the cytosolic DNA sensor cGAS, a key mediator of innate antiviral immunity and autoimmunity, is fine-tuned by post-translational modifications. Here, the authors demonstrate that the ubiquitin E3 ligase ARIH1 catalyzes the mono-ISGylation of cGAS and promotes its oligomerization in response to viral and self DNA. [ABSTRACT FROM AUTHOR]

Objectives: To develop and validate a CT nomogram and a radiomics nomogram to differentiate mass-forming chronic pancreatitis (MFCP) from pancreatic ductal adenocarcinoma (PDAC) in patients with chronic pancreatitis (CP). Methods: In this retrospective study, the data of 138 patients with histopathologically diagnosed MFCP or PDAC treated at our institution were retrospectively analyzed. Two radiologists analyzed the original cross-sectional CT images based on predefined criteria. Image segmentation, feature extraction, and feature reduction and selection were used to create the radiomics model. The CT and radiomics models were developed using data from a training cohort of 103 consecutive patients. The models were validated in 35 consecutive patients. Multivariable logistic regression analysis was conducted to develop a model for the differential diagnosis of MFCP and PDAC and visualized as a nomogram. The nomograms' performances were determined based on their differentiating ability and clinical utility. Results: The mean age of patients was 53.7 years, 75.4% were male. The CT nomogram showed good differentiation between the two entities in the training (area under the curve [AUC], 0.87) and validation (AUC, 0.94) cohorts. The radiomics nomogram showed good differentiation in the training (AUC, 0.91) and validation (AUC, 0.93) cohorts. Decision curve analysis showed that patients could benefit from the CT and radiomics nomograms, if the threshold probability was 0.05–0.85 and > 0.05, respectively. Conclusions: The two nomograms reasonably accurately differentiated MFCP from PDAC in patients with CP and hold potential for refining the management of pancreatic masses in CP patients. Key Points: • A CT nomogram and a computed tomography-based radiomics nomogram reasonably accurately differentiated mass-forming chronic pancreatitis from pancreatic ductal adenocarcinoma in patients with chronic pancreatitis (CP). • The two nomograms can monitor the cancer risk in patients with CP and hold promise to optimize the management of pancreatic masses in patients with CP. [ABSTRACT FROM AUTHOR]

Strengthening the oxide—metal interfacial synergistic interaction in nanocatalysts is identified as potential strategy to boost intrinsic activities and the availability of active sites by regulating the surface/interface environment of catalysts. Herein, the SnO2/PtNi concave nanocubes (CNCs) enclosed by high-index facets (HIFs) with tunable SnO2 composition are successfully fabricated through combining the hydrothermal and self-assembly method. The interfacial interaction between ultrafine SnO2 nanoparticles and PtNi with HIFs surface structure is characterized by analytical techniques. The as-prepared 0.20%SnO2/PtNi catalyst exhibits extraordinarily high catalytic performance for ethylene glycol electrooxidation (EGOR) in acidic conditions with specific activity of 3.06 mA/cm2, which represents 6.2-fold enhancement compared with the state-of-the-art Pt/C catalyst. Additionally, the kinetic study demonstrates that the strong interfacial interaction between SnO2 and PtNi not only degrades the activation energy barrier during the process of EGOR but also enhances the CO-resistance ability and long-term stability. This study provides a novel perspective to construct highly efficient and stable electrocatalysts for energy conversions. [ABSTRACT FROM AUTHOR]

Most intensive human activities occur in lowlands. However, sporadic reports indicate that human activities are expanding in some Asian highlands. Here we investigate the expansions of human activities in highlands and their effects over Asia from 2000 to 2020 by combining earth observation data and socioeconomic data. We find that ∼23% of human activity expansions occur in Asian highlands and ∼76% of these expansions in highlands comes from ecological lands, reaching 95% in Southeast Asia. The expansions of human activities in highlands intensify habitat fragmentation and result in large ecological costs in low and lower-middle income countries, and they also support Asian developments. We estimate that cultivated land net growth in the Asian highlands contributed approximately 54% in preventing the net loss of the total cultivated land. Moreover, the growth of highland artificial surfaces may provide living and working spaces for ∼40 million people. Our findings suggest that highland developments hold dual effects and provide new insight for regional sustainable developments. Most of the intensive human activities usually occur in lowlands. Here the authors report that human activity expansions also were widely distributed in Asian highlands in the 21st century and held dual effects, which provides new insights for regional human activity expansions. [ABSTRACT FROM AUTHOR]

Background: There is a relationship between autophagy and the occurrence, maintenance, and progression of several neurodegenerative diseases. The activation of microglia after ischemia contributes to neuronal injury via proinflammatory cytokines and neurotoxic elements. The purpose of this study was to evaluate the function of autophagy in the microglia-mediated death of neuronal cells. Methods and results: Microglial activation by oxygen/glucose deprivation induced both apoptosis and autophagy in neuron-like PC12 cells. Microglia-derived interleukin (IL)-6 induced PC12 cell apoptosis in vitro; however, this effect was inhibited by the autophagy inhibitor chloroquine. Further analysis demonstrated that miR-30d in PC12 cells suppressed microglia-induced PC12 apoptosis and autophagy by directly targeting autophagy protein 5. Moreover, microglia-derived IL-6 activated signal transducer and activator of transcription 3 (STAT3), which can then directly repress miR-30d genes via a conserved STAT3-binding site in its promoter, thereby promoting PC12 cell autophagy and apoptosis. Conclusions: Our study identified IL-6-dependent autophagy-related signaling between microglia and neurons, which contributed to neuronal apoptosis. Importantly, we also provided potential therapeutic targets for ischemic treatment via the interruption of proinflammatory signaling. [ABSTRACT FROM AUTHOR]

Purpose: To develop and validate a radiomics model to predict fibroblast activation protein (FAP) expression in patients with pancreatic ductal adenocarcinoma (PDAC). Methods: This retrospective study included consecutive 152 patients with PDAC who underwent MDCT scan and surgical resection from January 2017 to December 2017 (training set) and from January 2018 to April 2018 (validation set). In the training set, 1409 portal radiomic features were extracted from each patient's preoperative imaging. Optimal features were selected using the least absolute shrinkage and selection operator (LASSO) logistic regression algorithm, whereupon the extreme gradient boosting (XGBoost) was developed using the radiomics features. The performance of the XGBoost classifier performance was assessed by its calibration, discrimination, and clinical usefulness. Results: The patients were divided into FAP-low (n = 91; 59.87%) and FAP-high (n = 61; 40.13%) groups according to the optimal FAP cutoff (45.71%). Patients in the FAP-low group showed longer survival. The XGBoost classifier comprised 13 selected radiomics features and showed good discrimination in the training set [area under the curve (AUC), 0.97] and the validation set (AUC, 0.75). It also performed well in the calibration test and decision-curve analysis, demonstrating its potential clinical value. Conclusions: The XGBoost classifier based on CT radiomics in the portal venous phase can non-invasively predict FAP expression and may help to improve clinical decision-making in patients with PDAC. [ABSTRACT FROM AUTHOR]

Eucalyptus urophylla is an economically important tree species that widely planted in tropical and sub-tropical areas around the world, which suffers significant losses due to Ralstonia solanacearum. However, little is known about the molecular mechanism of pathogen-response of Eucalyptus. We collected the vascular tissues of a E. urophylla clone infected by R. solanacearum in the laboratory, and combined transcriptome and metabolome analysis to investigate the defense responses of Eucalyptus. A total of 11 flavonoids that differentially accumulated at the first stage or a later stage after infection. The phenylpropanoid of p-coumaraldehyde, the two alkaloids trigonelline and dl-ephedrine, two types of traditional Chinese medicine with patchouli alcohol and 3-dihydrocadambine, and the amino acid phenylalanine were differentially accumulated after infection, which could be biomarkers indicating a response to R. solanacearum. Differentially expressed genes involved in plant hormone signal transduction, phenylpropanoids, flavonoids, mitogen-activated protein kinase (MAPK) signaling, and amino acid metabolism were activated at the first stage of infection or a later stage, indicating that they may participate in the defense against infection. This study is expected to deliver several insights into the molecular mechanism in response to pathogens in E. urophylla, and the findings have far-reaching implications in the control of E. urophylla pathogens. [ABSTRACT FROM AUTHOR]

Paeonol, derived from natural plants (Moutan Cortex), has a wide range of biological effects, including anti-inflammatory and antitumor effects as well as favorable effects against cardiovascular and neurodegenerative diseases. The anti-inflammatory action is the main pharmacological activity of paeonol and has the greatest clinical relevance. However, the anti-inflammatory mechanism of paeonol has not been reported in sufficient detail. We systematically analyzed the anti-inflammatory mechanism of paeonol using network pharmacological databases and platforms, including TCMSP, Swiss TargetPrediction, OMIM, DrugBank, TTD, Jevnn, STRING11.0, and Metascape. Furthermore, we used high-throughput molecular docking method to prove the results of the above analyses, providing a reference for exploring the mechanism of paeonol and developing targeted drugs. [ABSTRACT FROM AUTHOR]

It is generally accepted that the interface effect and surface electronic structure of catalysts have vital impact on catalytic properties. Understanding and tailoring the atomic arrangement of interface structure are of great importance for electrocatalysis. Herein, we proposed a simple method to synthesize etching-PtNiCu nanowires (e-PtNiCu NWs) enclosed by both (110) and (100) facets evolving from PtNiCu nanowires (PtNiCu NWs) mainly with (111) facets by selectively etching process. After acetic acid etching treatment, the e-PtNiCu NWs possess high total proportions (88.3%) of (110) and (100) facets, whereas the (111) facet is dominant in PtNiCu NWs (64%) by qualitatively and quantitatively evaluation. Combining the structure characterizations and performance tests of ethanol electrooxidation reaction (EOR), we find that the e-PtNiCu NWs display remarkably performance for EOR, which is nearly 4.5 times and 1.5 times enhancement compared with the state-of-the-art Pt/C catalyst, as well as 2.2 and 1.4 times of PtNiCu NWs, in specific activity and mass activity, respectively. The improved performance of e-PtNiCu NWs is attributed to synergistic catalytic effect between (110) and (100) facets that not only significantly decreases the onset potentials of adsorbed CO (COads) but also favors the oxidation of COads on the surface of catalyst. Furthermore, thermodynamics and kinetic studies indicate that the synergistic effect of both (110) and (100) facets in e-PtNiCu NWs can decrease the activation energy barrier and facilitate the charge transfer during the reaction. This work provides a promising approach to construct catalysts with tunable surface electronic structure towards efficient electrocatalysis. [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "miR-206 alleviates LPS-induced inflammatory injury in cardiomyocytes via directly targeting USP33 to inhibit the JAK2/STAT3 signaling pathway" published in the journal Molecular & Cellular Biochemistry. The correction pertains to the funding information, which has been updated from "The Natural Science Foundation of Jiangxi Province (S2021QNJJL0757)" to "The Natural Science Foundation of Jiangxi Province (20212BAB216039)." The original article has been revised accordingly. The publisher, Springer Nature, maintains a neutral stance on jurisdictional claims and institutional affiliations. The authors of the article are Wei Dong, Jin Chen, Yadong Wang, Junfei Weng, Xingxiang Du, Xu Fang, Wenyu Liu, Tao Long, Jiaxiang You, Wensheng Wang, and Xiaoping Peng. [Extracted from the article]

Purpose: To develop and validate a machine-learning classifier based on contrast-enhanced computed tomography (CT) for the preoperative prediction of CD20+ B lymphocyte expression in patients with pancreatic ductal adenocarcinoma (PDAC). Methods: Overall, 189 patients with PDAC (n = 132 and n = 57 in the training and validation sets, respectively) underwent immunohistochemistry and radiomics feature extraction. The X-tile software was used to stratify them into groups with 'high' and 'low' CD20+ B lymphocyte expression levels. For each patient, 1409 radiomic features were extracted from volumes of interest and reduced using variance analysis and Spearman correlation analysis. A multilayer perceptron (MLP) network classifier was developed using the training and validation set. Model performance was determined by its discriminative ability, calibration, and clinical utility. Results: A log-rank test showed that the patients with high CD20+ B expression had significantly longer survival than those with low CD20+ B expression. The prediction model showed good discrimination in both the training and validation sets. For the training set, the area under the curve (AUC), sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 0.82 (95% CI 0.74–0.89), 92.42%, 57.58%, 0.75, 0.69, and 0.88, respectively; whereas these values for the validation set were 0.84 (95% CI 0.72–0.93), 86.21%, 78.57%, 0.83, 0.81, and 0.85, respectively. Conclusion: The MLP network classifier based on contrast-enhanced CT can accurately predict CD20+ B expression in patients with PDAC. [ABSTRACT FROM AUTHOR]

Objective: The intraductal papillary mucinous neoplasm (IPMN) of the pancreas is regarded as a precursor to pancreatic cancer; this study aimed to develop and validate a model based on CT characteristics for the non-invasive prediction of the high-risk IPMN of the pancreas. Materials and methods: In this retrospective study, all patients underwent multidetector CT and surgical resection. A prediction model was developed based on a training set consisting of 136 patients with low-risk IPMN and 85 patients with high-risk IPMN between October 2012 and April 2019, and a multivariable logistic regression model was adopted to establish a nomogram. The nomogram performance was determined by its discrimination, calibration, and clinical usefulness. The model was validated in 80 consecutive patients between May 2019 and April 2020, of which 47 and 33 patients had low-risk and high-risk IPMNs, respectively. Results: The multivariable logistic regression model of CT characteristics included the enhancing mural nodule size, the main pancreatic duct (MPD) diameter, the abrupt change in caliber of the MPD with distal pancreatic atrophy, cyst size, thickened enhancing cyst wall, and the presence of lymphadenopathy. The individualized prediction nomogram using these predictors of the high-risk IPMN achieved an area under the curve (AUC) of 0.92 (95% CI 0.88–0.95) in the training set and 0.87 (95% CI 0.79–0.95) in the validation set. The decision curve analysis demonstrated that the nomogram was clinically useful. Conclusion: The CT nomogram, which is a non-invasive predictive tool, can preoperatively predict the risk of malignant IPMN and help identify patients who require a surgical procedure. [ABSTRACT FROM AUTHOR]

Objectives: To investigate the association between longitudinal total pulmonary infection volume and volume ratio over time and clinical types in COVID-19 pneumonia patients. Methods: This retrospective review included 367 patients with COVID-19 pneumonia. All patients underwent CT examination at baseline and/or one or more follow-up CT. Patients were categorized into two clinical types (moderate and severe groups). The severe patients were matched to the moderate patients via propensity scores (PS). The association between total pulmonary infection volume and volume ratio and clinical types was analyzed using a generalized additive mixed model (GAMM). Results: Two hundred and seven moderate patients and 160 severe patients were enrolled. The baseline clinical and imaging variables were balanced using PS analysis to avoid patient selection bias. After PS analysis, 172 pairs of moderate patients were allocated to the groups; there was no difference in the clinical and CT characteristics between the two groups (p > 0.05). A total of 332 patients, including 396 CT scans, were assessed. The impact of total pulmonary infection volume and volume ratio with time was significantly affected by clinical types (p for interaction = 0.01 and 0.01, respectively) using GAMM. Total pulmonary infection volume and volume ratio of the severe group increased by 14.66 cm3 (95% confidence interval [CI]: 3.92 to 25.40) and 0.45% (95% CI: 0.13 to 0.77) every day, respectively, compared to that of the moderate group. Conclusions: Longitudinal total pulmonary infection volume and volume ratio are independently associated with the clinical types of COVID-19 pneumonia. Key Points: • The impact of total pulmonary infection volume and volume ratio over time was significantly affected by the clinical types (p for interaction = 0.01 and 0.01, respectively) using the GAMM. • Total pulmonary infection volume and volume ratio of the severe group increased by 14.66 cm3(95% CI: 3.92 to 25.40) and 0.45% (95% CI: 0.13 to 0.77) every day, respectively, compared to that of the moderate group. [ABSTRACT FROM AUTHOR]

Objective: To develop and validate a machine learning classifier based on magnetic resonance imaging (MRI), for the preoperative prediction of tumor-infiltrating lymphocytes (TILs) in patients with pancreatic ductal adenocarcinoma (PDAC). Materials and methods: In this retrospective study, 156 patients with PDAC underwent MR scan and surgical resection. The expression of CD4, CD8 and CD20 was detected and quantified using immunohistochemistry, and TILs score was achieved by Cox regression model. All patients were divided into TILs score-low and TILs score-high groups. The least absolute shrinkage and selection operator method and the extreme gradient boosting (XGBoost) were used to select the features and to construct a prediction model. The performance of the models was assessed using the training cohort (116 patients) and the validation cohort (40 patients), and decision curve analysis (DCA) was applied for clinical use. Results: The XGBoost prediction model showed good discrimination in the training (AUC 0.86; 95% CI 0.79–0.93) and validation sets (AUC 0.79; 95% CI 0.64–0.93). The sensitivity, specificity, and accuracy for the training set were 86.67%, 75.00%, and 0.81, respectively, whereas those for the validation set were 84.21%, 66.67%, and 0.75, respectively. Decision curve analysis indicated the clinical usefulness of the XGBoost classifier. Conclusion: The model constructed by XGBoost could predict PDAC TILs and may aid clinical decision making for immune therapy. [ABSTRACT FROM AUTHOR]

Objectives: To develop and validate a nomogram for the preoperative prediction of pancreatic serous cystic neoplasm (SCN) and mucinous cystic neoplasm (MCN) based on multidetector computed tomography (MDCT). Materials and methods: In this retrospective study, the data of 227 patients with SCN and MCN were analyzed. Each patient underwent MDCT and surgical resection. A multivariable logistic regression model was developed using a training set consisting of 129 patients with SCN and 38 patients with MCN who were admitted between October 2012 and April 2019. The model was validated in 60 consecutive patients, 44 of whom had SCN and 16 of whom had MCN, admitted between May 2019 and April 2020. The regression model was adopted to establish a nomogram. Nomogram performance was determined by its discriminative ability and clinical utility. Result: The multivariable logistic regression model included sex, size, location, shape, cyst characteristic, and cystic wall thickening. The individualized prediction nomogram showed good discrimination in the training sample (AUC 0.89; 95% CI 0.83–0.95) and in the validation sample (AUC 0.81; 95% CI 0.70–0.94). If the threshold probability is between 0.03 and 0.9, and > 0.93 in the prediction model, using the nomogram to predict SCN and MCN is more beneficial than the treat-all-patients as SCN scheme or the treat-all-patients as MCN scheme. The prediction model showed better discrimination than the radiologists' diagnosis (AUC = 0.68). Conclusion: The nomogram could predict SCN and MCN preoperatively and may aid clinical decision-making. [ABSTRACT FROM AUTHOR]

Reference 1 Niu K, Liu Z, Feng Y, Gao T, Wang Z, Zhang P, Du Z, Gao D, Fang X. A novel strategy for efficient disaccharides synthesis from glucose by -glucosidase. Correction to: A novel strategy for efficient disaccharides synthesis from glucose by -glucosidase Correction to: Bioresour Bioprocess (2020) 7:45 https://doi.org/10.1186/s40643-020-00334-6 In the original publication of the article (Niu et al. [1]), the statement in the Funding information section was incorrectly given as "the National Key R&D Program of China (No. 2018YFA090010)" and should have read "the National Key R&D Program of China (No. 2018YFA0901700)". [Extracted from the article]

Purpose: To develop and validate a radiomics nomogram for the preoperative prediction of lymph node (LN) metastasis in pancreatic ductal adenocarcinoma (PDAC)., Materials and Methods: In this retrospective study, 225 patients with surgically resected, pathologically confirmed PDAC underwent multislice computed tomography (MSCT) between January 2014 and January 2017. Radiomics features were extracted from arterial CT scans. The least absolute shrinkage and selection operator method was used to select the features. Multivariable logistic regression analysis was used to develop the predictive model, and a radiomics nomogram was built and internally validated in 45 consecutive patients with PDAC between February 2017 and December 2017. The performance of the nomogram was assessed in the training and validation cohort. Finally, the clinical usefulness of the nomogram was estimated using decision curve analysis (DCA)., Results: The radiomics signature, which consisted of 13 selected features of the arterial phase, was significantly associated with LN status (p < 0.05) in both the training and validation cohorts. The multivariable logistic regression model included the radiomics signature and CT-reported LN status. The individualized prediction nomogram showed good discrimination in the training cohort [area under the curve (AUC), 0.75; 95% confidence interval (CI), 0.68-0.82] and in the validation cohort (AUC, 0.81; 95% CI, 0.69-0.94) and good calibration. DCA demonstrated that the radiomics nomogram was clinically useful., Conclusions: The presented radiomics nomogram that incorporates the radiomics signature and CT-reported LN status is a noninvasive, preoperative prediction tool with favorable predictive accuracy for LN metastasis in patients with PDAC., (© 2022. The Author(s).)