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Abstract Macrophages (Mφ) autophagy is a pivotal contributor to inflammation-related diseases. However, the mechanistic details of its direct role in acute kidney injury (AKI) were unclear. Here, we show that Mφ promote AKI progression via crosstalk with tubular epithelial cells (TECs), and autophagy of Mφ was activated and then inhibited in cisplatin-induced AKI mice. Mφ-specific depletion of ATG7 (Atg7Δmye) aggravated kidney injury in AKI mice, which was associated with tubulointerstitial inflammation. Moreover, Mφ-derived exosomes from Atg7Δmye mice impaired TEC mitochondria in vitro, which may be attributable to miR-195a-5p enrichment in exosomes and its interaction with SIRT3 in TECs. Consistently, either miR-195a-5p inhibition or SIRT3 overexpression improved mitochondrial bioenergetics and renal function in vivo. Finally, adoptive transfer of Mφ from AKI mice to Mφ-depleted mice promotes the kidney injury response to cisplatin, which is alleviated when Mφ autophagy is activated with trehalose. We conclude that exosomal miR-195a-5p mediate the communication between autophagy-deficient Mφ and TECs, leading to impaired mitochondrial biogenetic in TECs and subsequent exacerbation of kidney injury in AKI mice via miR-195a-5p-SIRT3 axis.

Lime is a widely used construction and building material. As an alkali activator, lime can reduce costs, improve safety of on-site construction, and ensure environmental benefits. In this study, the effects of different lime contents on the physicomechanical characteristics and the hydration properties of circulating fluidized bed fly ash-blast furnace slag-based cementitious materials (CBCM) were evaluated. The results revealed that flowability and setting time of CBCM decreased with the increasing lime content. The mechanical properties first showed an increasing trend and then declined, achieving their optimal levels at 20 % lime content. Similar to the mechanical properties, the density of CBCM increased first and then decreased with the increase of lime content, enriching its maximum at the same lime amount. The main hydration products of CBCM are ettringite and C-(A)-S-H gel. Ettringite is mainly generated in large quantities within 3 days, which is the main reason for the early strength of CBCM. The highest reaction degree of calcium hydroxide was obtained at the lime content of 20 %, and a significant amount of dense foil-like C-(A)-S-H gel was formed. The calcium hydroxide precipitated once the lime content exceeded 20 %. The hydration reaction was blocked, and the hydration products were relatively reduced.

Abstract Oxidative stress is an imbalance between the body’s reactive oxygen species and antioxidant defense mechanisms. Oxidative stress is involved in the development of several cardiovascular diseases, such as pulmonary hypertension, atherosclerosis, and diabetes mellitus. A growing number of studies have suggested the potential role of oxidative stress in the pathogenesis of pulmonary embolism. Biomarkers of oxidative stress in pulmonary embolism have also been explored, such as matrix metalloproteinases, asymmetric dimethylarginine, and neutrophil/lymphocyte ratio. Here, we comprehensively summarize some oxidative stress mechanisms and biomarkers in the development of acute pulmonary embolism and summarize related treatments based on antioxidant stress to explore effective treatment strategies for acute pulmonary embolism.

Abstract Background Salmonella enterica, serovar Enteritidis (SE) is a food-borne pathogen, which can cause great threat to human health through consumption of the contaminated poultry products. Chicken is the main host of SE. The mRNA and microRNA (miRNA) expression profiles were analyzed on cecum of Shouguang chicken via next-generation sequencing and bioinformatics approaches. The treated group was inoculated SE, and the control group was inoculated with phosphate buffer saline (PBS). Results There were 1760 differentially expressed mRNAs in the SE-infected group, of which 1046 were up-regulated mRNA, and 714 were down-regulated mRNA. In addition, a total of 821 miRNAs were identified, and 174 miRNAs were differentially expressed, of which 100 were up-regulated and 74 were down-regulated. Functional enrichment of differentially expressed mRNAs was similar to miRNA target genes. The functional analysis results of differentially expressed mRNAs and miRNAs were performed. Immune-related processes and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways were enriched by up-regulated mRNA. The down-regulated mRNAs were enriched in tissue development and metabolic-related KEGG pathways. The functional analysis of up-regulated miRNA target genes was similar to the down-regulated mRNAs. The down-regulated miRNA target genes were enriched in metabolic-related GO (Gene Ontology) -BP (Biological process) terms and KEGG pathways. The overlap of the up-regulated mRNA and the up-regulated miRNA target genes (class I) was 325, and the overlap of the down-regulated miRNA target genes (class II) was 169. The class I enriched in the immune-related GO-BP terms and KEGG pathways. The class II mainly enriched in metabolic-related GO-BP terms and KEGG pathways. Then we detected the expression of mRNA and miRNA through qRT-PCR. The results shown that the expression of HHIP, PGM1, HTR2B, ITGB5, RELN, SFRP1, TCF7L2, SCNN1A, NEK7, miR-20b-5p, miR-1662, miR-15a, miR-16-1-3p was significantly different between two groups. Dual-luciferase reporter assay was used to detect the relationship between miR-20b-5p and SCNN1A. The result indicated that miR-20b-5p regulate immune or metabolic responses after SE infection in Shouguang chickens by directly targeting SCNN1A. Conclusions The findings here contribute to the further analysis of the mechanism of mRNA and miRNA defense against SE infection, and provide a theoretical foundation for the molecular disease-resistant breeding of chickens.

Senescence of bone marrow mesenchymal stem cells (BMSCs) is one of the leading causes of osteoporosis. SIRT3, an essential NAD-dependent histone deacetylase, is highly correlated with BMSC senescence-mediated bone degradation and mitochondrial/heterochromatic disturbance. S-sulfhydration of cysteine residues favorably enhances SIRT3 activity by forming persulfides. Nevertheless, the underlying molecular mechanism of SIRT3 S-sulfhydration on mitochondrial/heterochromatic homeostasis involved in BMSC senescence remains unknown. Here, we demonstrated that CBS and CSE, endogenous hydrogen sulfide synthases, are downregulated with BMSC senescence. Exogenous H2S donor NaHS-mediated SIRT3 augmentation rescued the senescent phenotypes of BMSCs. Conversely, SIRT3 deletion accelerated oxidative stress-induced BMSC senescence through mitochondrial dysfunction and the detachment of the heterochromatic protein H3K9me3 from the nuclear envelope protein Lamin B1. H2S-mediated SIRT3 S-sulfhydration modification rescued the disorganized heterochromatin and fragmented mitochondria induced by the S-sulfhydration inhibitor dithiothreitol, thus leading to elevated osteogenic capacity and preventing BMSC senescence. The antisenescence effect of S-sulfhydration modification on BMSCs was abolished when the CXXC sites of the SIRT3 zinc finger motif were mutated. In vivo, aged mice-derived BMSCs pretreated with NaHS were orthotopically transplanted to the ovariectomy-induced osteoporotic mice, and we proved that SIRT3 ameliorates bone loss by inhibiting BMSC senescence. Overall, our study for the first time indicates a novel role of SIRT3 S-sulfhydration in stabilizing heterochromatin and mitochondrial homeostasis in counteracting BMSC senescence, providing a potential target for the treatment of degenerative bone diseases.

Introduction Tranexamic acid (TXA) has been proven to prevent thrombolysis and reduce bleeding and blood transfusion requirements in various surgical settings. However, the optimal dose of TXA that effectively reduce intraoperative bleeding and blood product infusion in patients undergoing neurosurgical resection of meningioma with a diameter ≥ 5 cm remains unclear. Methods This is a single-center, randomized, double-blinded, paralleled-group controlled trial. Patients scheduled to receive elective tumor resection with meningioma diameter ≥ 5 cm will be randomly assigned the high-dose TXA group, the low-dose group, and the placebo. Patients in the high-dose TXA group will be administered with a loading dose of 20 mg/kg TXA followed by continuous infusion TXA at a rate of 5 mg/kg/h. In the low-dose group, patients will receive the same loading dose of TXA followed by a continuous infusion of normal saline. In the control group, patients will receive an identical volume of normal saline. The primary outcome is the estimated intraoperative blood loss calculated using the following formula: collected blood volume in the suction canister (mL)–the volume of flushing (mL) + the volume from the gauze tampon (mL). Secondary outcomes include calculated intraoperative blood loss, intraoperative coagulation function assessed using thromboelastogram (TEG), intraoperative cell salvage use, blood product infusion, and other safety outcomes. Discussion Preclinical studies suggest that TXA could reduce intraoperative blood loss, yet the optimal dose was controversial. This study is one of the early studies to evaluate the impact of intraoperative different doses infusion of TXA on reducing blood loss in neurological meningioma patients. Trial registration ClinicalTrials.gov, NCT05230381. Registered on February 8, 2022.

We conducted an evaluation of the impact of meteorological factor forecasts on the prediction of fine particles in Chengdu, China, during autumn and winter, utilizing the European Cooperation in Science and Technology (COST)733 objective weather classification software and the Community Multiscale Air Quality model. This analysis was performed under four prevailing weather patterns. Fine particle pollution tended to occur under high-pressure rear, homogeneous-pressure, and low-pressure conditions; by contrast, fine particle concentrations were lower under high-pressure bottom conditions. The forecasts of fine particle concentrations were more accurate under high-pressure bottom conditions than under high-pressure rear and homogeneous-pressure conditions. Moreover, under all conditions, the 24 h forecast of fine particle concentrations were more accurate than the 48 and 72 h forecasts. Regarding meteorological factors, forecasts of 2 m relative humidity and 10 m wind speed were more accurate under high-pressure bottom conditions than high-pressure rear and homogeneous-pressure conditions. Moreover, 2 m relative humidity and 10 m wind speed were important factors for forecasting fine particles, whereas 2 m air temperature was not. Finally, the 24 h forecasts of meteorological factors were more accurate than the 48 and 72 h forecasts, consistent with the forecasting of fine particles.

Autophagy is a highly conserved cellular progress for the degradation of cytoplasmic contents including micromolecules, misfolded proteins, and damaged organelles that has recently captured attention in kidney diseases. Basal autophagy plays a pivotal role in maintaining cell survival and kidney homeostasis. Accordingly, dysregulation of autophagy has implicated in the pathologies of kidney diseases. In this review, we summarize the multifaceted role of autophagy in kidney aging, maladaptive repair, tubulointerstitial fibrosis and discuss autophagy-related drugs in kidney diseases. However, uncertainty still remains as to the precise mechanisms of autophagy in kidney diseases. Further research is needed to clarify the accurate molecular mechanism of autophagy in kidney diseases, which will facilitate the discovery of a promising strategy for the prevention and treatment of kidney diseases.

In 2019, COVID-19 quickly spread across the world, infecting billions of people and disrupting the normal lives of citizens in every country. Governments, organizations, and research institutions all over the world are dedicating vast resources to research effective strategies to fight this rapidly propagating virus. With virus testing, most countries publish the number of confirmed cases, dead cases, recovered cases, and locations routinely through various channels and forms. This important data source has enabled researchers worldwide to perform different COVID-19 scientific studies, such as modeling this virus’s spreading patterns, developing prevention strategies, and studying the impact of COVID-19 on other aspects of society. However, one major challenge is that there is no standardized, updated, and high-quality data product that covers COVID-19 cases data internationally. This is because different countries may publish their data in unique channels, formats, and time intervals, which hinders researchers from fetching necessary COVID-19 datasets effectively, especially for fine-scale studies. Although existing solutions such as John’s Hopkins COVID-19 Dashboard and 1point3acres COVID-19 tracker are widely used, it is difficult for users to access their original dataset and customize those data to meet specific requirements in categories, data structure, and data source selection. To address this challenge, we developed a toolset using cloud-based web scraping to extract, refine, unify, and store COVID-19 cases data at multiple scales for all available countries around the world automatically. The toolset then publishes the data for public access in an effective manner, which could offer users a real time COVID-19 dynamic dataset with a global view. Two case studies are presented about how to utilize the datasets. This toolset can also be easily extended to fulfill other purposes with its open-source nature.

The advancements of sensing technologies, including remote sensing, in situ sensing, social sensing, and health sensing, have tremendously improved our capability to observe and record natural and social phenomena, such as natural disasters, presidential elections, and infectious diseases. The observations have provided an unprecedented opportunity to better understand and respond to the spatiotemporal dynamics of the environment, urban settings, health and disease propagation, business decisions, and crisis and crime. Spatiotemporal event detection serves as a gateway to enable a better understanding by detecting events that represent the abnormal status of relevant phenomena. This paper reviews the literature for different sensing capabilities, spatiotemporal event extraction methods, and categories of applications for the detected events. The novelty of this review is to revisit the definition and requirements of event detection and to layout the overall workflow (from sensing and event extraction methods to the operations and decision-supporting processes based on the extracted events) as an agenda for future event detection research. Guidance is presented on the current challenges to this research agenda, and future directions are discussed for conducting spatiotemporal event detection in the era of big data, advanced sensing, and artificial intelligence.

Salmonella enterica serovar Enteritidis (S. Enteritidis) is a foodborne pathogen, which can cause great threats to human health through the consumption of contaminated poultry products. This research combines TMT labeling, HPLC and mass-spectrometry-based phosphoproteomics on cecum of the F1 cross of Guangxi Yao chicken and Jining Bairi chicken. The treated group was inoculated with 0.3 mL inoculum S. Enteritidis, and the control group was inoculated with 0.3 mL phosphate-buffered saline (PBS). A total of 338 differentially phosphorylated modification sites in 243 differentially phosphorylated proteins (DPPs) were chosen for downstream analyses. A total of 213 sites in 146 DPPs were up-regulated and 125 sites in 97 DPPs were down-regulated. Functional analysis was performed for DPPs based on gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and the protein domain. The DPPs were mainly enriched in immune- and metabolic-related GO-BP (biological process) and KEGG pathways. We predicted and classified the subcellular structure and COG/KOG of DPPs. Furthermore, protein–protein interaction network analyses were performed by using multiple algorithms. We identified 71 motifs of the phosphorylated modification sites and selected 18 sites randomly to detect the expression level through parallel reaction monitoring (PRM). S. Enteritidis inoculation caused phosphorylation alteration in immune- and metabolic-related proteins. The invasion of S. Enteritidis may be actualized by inducing cecum cell apoptosis through the endoplasmic reticulum pathway, and chickens could resist the invasion of S. Enteritidis by affecting the function of ECM receptors. The findings herein provide a crucial theoretical foundation to understand the molecular mechanism and epigenetic regulation in response to S. Enteritidis inoculation in chickens.

The developmental complexity of muscle arises from elaborate gene regulation. Long non-coding RNAs (lncRNAs) play critical roles in muscle development through the regulation of transcription and post-transcriptional gene expression. In chickens, previous studies have focused on the lncRNA profile during the embryonic periods, but there are no studies that explore the profile from the embryonic to post-hatching period. Here, we reconstructed 14,793 lncRNA transcripts and identified 2,858 differentially expressed lncRNA transcripts and 4,282 mRNAs from 12-day embryos (E12), 17-day embryos (E17), 1-day post-hatch chicks (D1), 14-day post-hatch chicks (D14), 56-day post-hatch chicks (D56), and 98-day post-hatch chicks (D98), based on our published RNA-seq datasets. We performed co-expression analysis for the differentially expressed lncRNAs and mRNAs, using STEM, and identified two profiles with opposite expression trends: profile 4 with a downregulated pattern and profile 21 with an upregulated pattern. The cis- and trans-regulatory interactions between the lncRNAs and mRNAs were predicted within each profile. Functional analysis of the lncRNA targets showed that lncRNAs in profile 4 contributed to the cell proliferation process, while lncRNAs in profile 21 were mainly involved in metabolism. Our work highlights the lncRNA profiles involved in the development of chicken breast muscle and provides a foundation for further experiments on the role of lncRNAs in the regulation of muscle development.

Sea ice acts as both an indicator and an amplifier of climate change. High spatial resolution (HSR) imagery is an important data source in Arctic sea ice research for extracting sea ice physical parameters, and calibrating/validating climate models. HSR images are difficult to process and manage due to their large data volume, heterogeneous data sources, and complex spatiotemporal distributions. In this paper, an Arctic Cyberinfrastructure (ArcCI) module is developed that allows a reliable and efficient on-demand image batch processing on the web. For this module, available associated datasets are collected and presented through an open data portal. The ArcCI module offers an architecture based on cloud computing and big data components for HSR sea ice images, including functionalities of (1) data acquisition through File Transfer Protocol (FTP) transfer, front-end uploading, and physical transfer; (2) data storage based on Hadoop distributed file system and matured operational relational database; (3) distributed image processing including object-based image classification and parameter extraction of sea ice features; (4) 3D visualization of dynamic spatiotemporal distribution of extracted parameters with flexible statistical charts. Arctic researchers can search and find arctic sea ice HSR image and relevant metadata in the open data portal, obtain extracted ice parameters, and conduct visual analytics interactively. Users with large number of images can leverage the service to process their image in high performance manner on cloud, and manage, analyze results in one place. The ArcCI module will assist domain scientists on investigating polar sea ice, and can be easily transferred to other HSR image processing research projects.

Semantic segmentation of remote sensing images is an important technique for spatial analysis and geocomputation. It has important applications in the fields of military reconnaissance, urban planning, resource utilization and environmental monitoring. In order to accurately perform semantic segmentation of remote sensing images, we proposed a novel multi-scale deep features fusion and cost-sensitive loss function based segmentation network, named MFCSNet. To acquire the information of different levels in remote sensing images, we design a multi-scale feature encoding and decoding structure, which can fuse the low-level and high-level semantic information. Then a max-pooling indices up-sampling structure is designed to improve the recognition rate of the object edge and location information in the remote sensing image. In addition, the cost-sensitive loss function is designed to improve the classification accuracy of objects with fewer samples. The penalty coefficient of misclassification is designed to improve the robustness of the network model, and the batch normalization layer is also added to make the network converge faster. The experimental results show that the classification performance of MFCSNet outperforms U-Net and SegNet in classification accuracy, object details and prediction consistency.

Planetary Defense (PD) has become a critical effort of protecting our home planet by discovering potentially hazardous objects (PHOs), simulating the potential impact, and mitigating the threats. Due to the lack of structured architecture and framework, pertinent information about detecting and mitigating near earth object (NEO) threats are still dispersed throughout numerous organizations. Scattered and unorganized information can have a significant impact at the time of crisis, resulting in inefficient processes, and decisions made on incomplete data. This PD Mitigation Gateway (pd.cloud.gmu.edu) is developed and embedded within a framework to integrate the dispersed, diverse information residing at different organizations across the world. The gateway offers a home to pertinent PD-related contents and knowledge produced by the NEO mitigation team and the community through (1) a state-of-the-art smart-search discovery engine based on PD knowledge base; (2) a document archiving and understanding mechanism for managing and utilizing the results produced by the PD science community; (3) an evolving PD knowledge base accumulated from existing literature, using natural language processing and machine learning; and (4) a 4D visualization tool that allows the viewers to analyze near-Earth approaches in a three-dimensional environment using dynamic, adjustable PHO parameters to mimic point-of-impact asteroid deflections via space vehicles and particle system simulations. Along with the benefit of accessing dispersed data from a single port, this framework is built to advance discovery, collaboration, innovation, and education across the PD field-of-study, and ultimately decision support.

Social media data have been used to improve geographic situation awareness in the past decade. Although they have free and openly availability advantages, only a small proportion is related to situation awareness, and reliability or trustworthiness is a challenge. A credibility framework is proposed for Twitter data in the context of disaster situation awareness. The framework is derived from crowdsourcing, which states that errors propagated in volunteered information decrease as the number of contributors increases. In the proposed framework, credibility is hierarchically assessed on two tweet levels. The framework was tested using Hurricane Harvey Twitter data, in which situation awareness related tweets were extracted using a set of predefined keywords including power, shelter, damage, casualty, and flood. For each tweet, text messages and associated URLs were integrated to enhance the information completeness. Events were identified by aggregating tweets based on their topics and spatiotemporal characteristics. Credibility for events was calculated and analyzed against the spatial, temporal, and social impacting scales. This framework has the potential to calculate the evolving credibility in real time, providing users insight on the most important and trustworthy events.

Keypoint matching is of fundamental importance in computer vision applications. Fish-eye lenses are convenient in such applications that involve a very wide angle of view. However, their use has been limited by the lack of an effective matching algorithm. The Scale Invariant Feature Transform (SIFT) algorithm is an important technique in computer vision to detect and describe local features in images. Thus, we present a Tri-SIFT algorithm, which has a set of modifications to the SIFT algorithm that improve the descriptor accuracy and matching performance for fish-eye images, while preserving its original robustness to scale and rotation. After the keypoint detection of the SIFT algorithm is completed, the points in and around the keypoints are back-projected to a unit sphere following a fish-eye camera model. To simplify the calculation in which the image is on the sphere, the form of descriptor is based on the modification of the Gradient Location and Orientation Histogram (GLOH). In addition, to improve the invariance to the scale and the rotation in fish-eye images, the gradient magnitudes are replaced by the area of the surface, and the orientation is calculated on the sphere. Extensive experiments demonstrate that the performance of our modified algorithms outweigh that of SIFT and other related algorithms for fish-eye images.

Big geospatial raster data pose a grand challenge to data management technologies for effective big data query and processing. To address these challenges, various big data container solutions have been developed or enhanced to facilitate data storage, retrieval, and analysis. Data containers were also developed or enhanced to handle geospatial data. For example, Rasdaman was developed to handle raster data and GeoSpark/SpatialHadoop were enhanced from Spark/Hadoop to handle vector data. However, there are few studies to systematically compare and evaluate the features and performances of these popular data containers. This paper provides a comprehensive evaluation of six popular data containers (i.e., Rasdaman, SciDB, Spark, ClimateSpark, Hive, and MongoDB) for handling multi-dimensional, array-based geospatial raster datasets. Their architectures, technologies, capabilities, and performance are compared and evaluated from two perspectives: (a) system design and architecture (distributed architecture, logical data model, physical data model, and data operations); and (b) practical use experience and performance (data preprocessing, data uploading, query speed, and resource consumption). Four major conclusions are offered: (1) no data containers, except ClimateSpark, have good support for the HDF data format used in this paper, requiring time- and resource-consuming data preprocessing to load data; (2) SciDB, Rasdaman, and MongoDB handle small/mediate volumes of data query well, whereas Spark and ClimateSpark can handle large volumes of data with stable resource consumption; (3) SciDB and Rasdaman provide mature array-based data operation and analytical functions, while the others lack these functions for users; and (4) SciDB, Spark, and Hive have better support of user defined functions (UDFs) to extend the system capability.