bluuebunny/crossref_metadata_2025_split huggingface dataset:
Observational study of the influences of thermal and dynamic boundary layer on the vertical distribution of black carbon aerosol in ShouXian county in wintertime, 2016
Authors: Cheng-gang Wang , Xia-lu Wei , Jia-de Yang , Ting Ni 
Published: September 2022

Using vertical observation data of black carbon aerosol and meteorological parameters in the ShouXian area of Anhui Province from 14 December 2016 to 3 January 2017, the thermal and dynamic effects of the boundary layer on the vertical distribution structure of black carbon were studied. The results show that 1) of 82 vertical sounding profiles obtained during the observation period, there were 72 boundary layer sounding profiles dominated by heat and 10 profiles dominated by dynamics. 2) When thermal effects were dominant, the concentration of black carbon aerosol was significantly affected by diurnal changes in the boundary layer. In the unstable boundary layer, black carbon aerosol was uniformly distributed in the vertical direction; during the transition from an unstable to a stable boundary layer, the concentrations of black carbon aerosol were significantly higher in the lower layer than in the upper layer; in the stable boundary layer, the concentrations of black carbon aerosol decreased continuously with height; and during the transition from a stable to an unstable boundary layer, the black carbon aerosol concentrations exhibited high values in the upper layer, with the concentration difference reaching 4 μg m−3. 3) When the dynamic effect was dominant, the structure of the vertical distribution of black carbon aerosol was affected by wind and by diurnal changes in the boundary layer simultaneously. The high winds (&gt;4 m/s) removed the black carbon aerosol. In the unstable boundary layer, the black carbon aerosol uniformly distributed in the vertical direction had significantly lower concentrations in the high-wind range; when stable boundary layers occurred and during transitions from stable to unstable boundary layers, the black carbon aerosol concentrations were higher in the lower layer and lower in the upper layer. The stratification was more obvious than that observed under thermal control.
Sand-ratio distribution in an unconventional tight sandstone reservoir of Hangjinqi area, Ordos Basin: Acoustic impedance inversion-based reservoir quality prediction
Authors: Anees Aqsa , Zhang Hucai , Ashraf Umar , Wang Ren , Thanh Hung Vo , Radwan Ahmed E. , Ullah Jar , Abbasi Ghazanfer Raza , Iqbal Ibrar , Ali Nafees , Zhang Xiaonan , Tan Shucheng , Shi Wanzhong 
Published: September 2022

Future gas field development and prospect assessment depend on accurate reservoir parameter characterization. The understanding of the tight sand distribution of the Shanxi and Taiyuan Formations within the Hangjinqi area is ambiguous due to the presence of coal and mudstone lithofacies, high heterogeneity, and poor resolution of the seismic data. Thus, it is difficult to determine the reservoir’s thickness. To cope with this challenge, we have employed the advanced method of constrained sparse spike inversion (CSSI) utilizing 3D seismic and nine wells for the distinction of tight sandstone facies from the coal and mudstone facies. Results of petrophysical analysis of studied well J54 show that the coal and mudstone facies are dominant towards the T9c horizon, whereas tight sandstone facies are present towards the T9d horizon. The obtained findings show that the CSSI accurately identified the spatial distribution of sand-ratio in the zone of interest (ZOI) that lies between the T9c and T9d horizons. The acoustic impedance (AI) of coal shows the lowest AI values, whereas the tight sandstone shows the highest AI values. The tight sandstone facies shows moderate values of AI in the range of 8.5 × 106 kg/m2s to 1.20 × 107 kg/m2s. The impedance map of T9c suggested the presence of coal and mud facies, whereas the T9d impedance map suggest the presence of maximum tight sandstone facies. The sand-ratio map of T9d showed maximum reservoir thickness that ranges from 0.65-0-95, whereas the sand-ratio value mostly ranges from 0 to 0.5 on the T9c map. The maximum sand-ratio values on T9d show that the lower Shanxi Formation has good reservoir characteristics. Whereas, due to the presence of coal and mudstone in the Taiyuan Formation, the T9c map shows low values of sand-ratio. The maximum sand-ratio regions within the targeted T9c-T9d layers should be exploited for future gas explorations.
On the feasibility and usefulness of high-performance computing in probabilistic volcanic hazard assessment: An application to tephra hazard from Campi Flegrei
Authors: Montesinos Beatriz Martínez , Luzón Manuel Titos , Sandri Laura , Rudyy Oleksandr , Cheptsov Alexey , Macedonio Giovanni , Folch Arnau , Barsotti Sara , Selva Jacopo , Costa Antonio 
Published: September 2022

For active volcanoes, knowledge about probabilities of eruption and impacted areas becomes valuable information for decision-makers to develop short- and long-term emergency plans, for which probabilistic volcanic hazard assessment (PVHA) is needed. High-resolution or spatially extended PVHA requires extreme-scale high-performance computing systems. Within the framework of ChEESE (Center of Excellence for Exascale in Solid Earth; www.cheese-coe.eu), an effort was made to generate exascale-suitable codes and workflows to collect and process in some hours the large amount of data that a quality PVHA requires. To this end, we created an optimized HPC-based workflow coined PVHA_HPC-WF to develop PVHA for a volcano. This tool uses the Bayesian event tree methodology to calculate eruption probabilities, vent-opening location(s), and eruptive source parameters (ESPs) based on volcano history, monitoring system data, and meteorological conditions. Then, the tool interacts with the chosen hazard model, performing a simulation for each ESP set or volcanic scenario (VS). Finally, the resulting information is processed by proof-of-concept-subjected high-performance data analytics (HPDA) scripts, producing the hazard maps which describe the probability over time of exceeding critical thresholds at each location in the investigated geographical domain. Although PVHA_HPC-WF can be adapted to other hazards, we focus here on tephra (i.e., lapilli and ash) transport and deposition. As an application, we performed PVHA for Campi Flegrei (CF), Italy, an active volcano located in one of the most densely inhabited areas in Europe and under busy air traffic routes. CF is currently in unrest, classified as being in an attention level by the Italian Civil Protection. We consider an approximate 2,000 × 2,000 × 40 km computational domain with 2 km grid resolution in the horizontal and 40 vertical levels, centered in CF. To explore the natural variability and uncertainty of the eruptive conditions, we consider a large number of VSs allowing us to include those of low probability but high impact, and simulations of tephra dispersal are performed for each of them using the FALL3D model. Results show the potential of HPC to timely execute a vast range of simulations of complex numerical models in large high-resolution computational domains and analyze great volumes of data to obtain quality hazard maps.
Different types of diabetes mellitus and risk of thyroid cancer: A meta-analysis of cohort studies
Authors: Dong Wen-wu , Zhang Da-Lin , Wang Zhi-Hong , Lv Cheng-Zhou , Zhang Ping , Zhang Hao 
Published: September 2022

ObjectiveSex-specific thyroid cancer risk exists in patients diagnosed with diabetes mellitus (DM). However, thyroid cancer risk in different types of DM is still unclear. This meta-analysis aims to identify the real correlation between different types of DM and thyroid cancer risk in both sexes.MethodsStudies were identified by an electronic search of PubMed, EMBASE, and Cochrane Library on 16 January 2022. A random-effects model was used to estimate the relative risks (RRs). The Cochran’s Q and I2 statistics were computed to detect heterogeneity between studies.ResultsIn comparison with non-DM counterparts, patients with DM had a 1.32-fold higher risk of thyroid cancer (95% CI, 1.22–1.44) with 1.26-fold (95% CI, 1.12–1.41) in men and 1.36-fold (95% CI, 1.22–1.52) in women, respectively. Subgroup analysis by the type of DM showed that the RR of thyroid cancer in patients with type 2 diabetes was 1.34 (95% CI, 1.17–1.53) in the study population with 1.32 (95% CI, 1.12–1.54) in men and 1.37 (95% CI, 1.12–1.68) in women, respectively; the RR of thyroid cancer was 1.30 (95% CI, 1.17–1.43) in patients with gestational diabetes; the risk of thyroid cancer in patients with type 1 diabetes was 1.51-fold in women but not in men. Although there were some heterogeneities, it did not affect the above results of this study.ConclusionThis study indicates that, compared with non-DM individuals, patients with any type of DM have an elevated thyroid cancer risk. This positive correlation between type 2 diabetes and thyroid cancer risk exists in both men and women.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/, CRD42022304028.
Drivers of nutrient content and spatial variability of soil multifunctionality in the topsoil of Kyrgyzstan
Authors: Chen Yusen , Zhang Shihang , Li Huiliang , Wang Yongdong 
Published: September 2022

Soil nutrient status is a crucial measure of soil fertility, which not only coordinates and provides the nutrients necessary for plant growth, but also stimulates the decomposition of soil humus and biogeochemical cycles. Therefore, understanding the distribution characteristics of soil nutrients in the Kyrgyzstan, while exploring the drivers of their variability, is important for understanding ecological processes and the distribution of soil resources. Simultaneously, the study of the spatial distribution of soil multifunctionality and its drivers across Kyrgyzstan can better elucidate the functions of the entire ecosystem, assist in optimizing the allocation of resources and serve as a reference material for the scientific and rational management of the country’s soil ecosystems. In this paper, We have used field sampling data from Kyrgyzstan, to examined the geographic variability and distribution of soil nutrients and soil multifunctionality within different soil depths, as well as the drivers in the country, using a combination of factor analysis and structural equation modeling (SEM). Findings indicated: (1)The topsoil of Kyrgyzstan (0–30 cm) stores 4.24 ± 0.59 Pg of SOC, 0.33 ± 0.06 Pg of total nitrogen (TN), 0.31 ± 0.03 Pg of total phosphorus (TP), and 5.12 ± 1.84 Pg of total potassium (TK). The nutrient reserves of various land-cover categories vary greatly across the nation, with grasslands accounting for the largest reserves (40%) but urban areas allocating the smallest proportion (0.3%). (2): the analysis revealed that using the linear or nonlinear fitting equations, the majority of soil nutrients exhibited a strong correlation with each other and also environmental parameters; (3): in Kyrgyzstan, soil multifunctionality varied substantially from –0.63 to 2.30 across regions. (4): according to the SEM results, NPP, wind speed (VS), MAP, longitude (Lon), SWC, and pH directly affected soil multifunctionality. In addition, several indirect effects of ST, MAT, Sard, and latitude (Lat) on soil multifunctionality were observed. Lon, map, SWC, and pH also had various direct and indirect impacts on soil multifunctionality. The findings of this study shed light on the functions of the entire soil ecosystem in the Kyrgyz Republic that help better predict the effects of environmental changes on ecosystem multifunctionality in drylands and provide a scientific basis for rational utilization of soil resources, efficient management of dryland soils, and prevention of land productivity decline in the country.
The Temporal-Based Forest Disturbance Monitoring Analysis: A Case Study of Nature Reserves of Hainan Island of China From 1987 to 2020
Authors: Xiao Han , Zhang Xiaoqian , Yan Min , Zhang Li , Wang Hua , Ma Yan , Liu Jianbo 
Published: September 2022

Forest disturbance monitoring can provide scientific data for the decision making and management of nature reserves. LandTrendr algorithm has been applied to identify forest disturbances on a long-time scale through appropriate segmentation and linear fitting. In this study, 23 nature reserves were detected using LandTrendr during 1987–2020, and the vegetation loss was quantified by years and pixel numbers. The results illustrated that (1) most disturbances occurred in the 1990s and early 21st century. (2) From the spatial distribution of forest loss, the area of forest vegetation disturbance in the coastal zone was larger than the protected area in the internal Hainan Island, the area disturbed in the coastal zone protected area was 97.12 km2, and the area disturbed in the internal area of Hainan Island protected area was 63.02 km2. (3) In terms of different levels of nature reserves, the disturbed area of national nature reserves was 28.39 km2 and the total disturbed area of provincial nature reserves was 131.75 km2. (4) In terms of different types of nature reserves, forest ecological nature reserves had the largest disturbed area of 102.96 km2, followed by marine coastal nature reserves with a disturbed area of 36.99 km2, wildlife nature reserves with a disturbed area of 10.22 km2, and wild plant nature reserves with the smallest disturbed area of 9.96 km2. The results are hoped to provide scientific support and data for the management and planning of nature reserves in Hainan Island.
Characterization of cancer-related fibroblasts (CAF) in hepatocellular carcinoma and construction of CAF-based risk signature based on single-cell RNA-seq and bulk RNA-seq data
Authors: Yu Lianghe , Shen Ningjia , Shi Yan , Shi Xintong , Fu Xiaohui , Li Shuang , Zhu Bin , Yu Wenlong , Zhang Yongjie 
Published: September 2022

BackgroundCancer-associated fibroblasts (CAFs) are involved in tumor growth, angiogenesis, metastasis, and resistance to therapy. We sought to explore the CAFs characteristics in hepatocellular carcinoma (HCC) and establish a CAF-based risk signature for predicting the prognosis of HCC patients.MethodsThe signal-cell RNA sequencing (scRNA-seq) data was obtained from the GEO database. Bulk RNA-seq data and microarray data of HCC were obtained from the TCGA and GEO databases respectively. Seurat R package was applied to process scRNA-seq data and identify CAF clusters according to the CAF markers. Differential expression analysis was performed to screen differentially expressed genes (DEGs) between normal and tumor samples in TCGA dataset. Then Pearson correlation analysis was used to determine the DEGs associated with CAF clusters, followed by the univariate Cox regression analysis to identify CAF-related prognostic genes. Lasso regression was implemented to construct a risk signature based on CAF-related prognostic genes. Finally, a nomogram model based on the risk signature and clinicopathological characteristics was developed.ResultsBased on scRNA-seq data, we identified 4 CAF clusters in HCC, 3 of which were associated with prognosis in HCC. A total of 423 genes were identified from 2811 DEGs to be significantly correlated with CAF clusters, and were narrowed down to generate a risk signature with 6 genes. These six genes were primarily connected with 39 pathways, such as angiogenesis, apoptosis, and hypoxia. Meanwhile, the risk signature was significantly associated with stromal and immune scores, as well as some immune cells. Multivariate analysis revealed that risk signature was an independent prognostic factor for HCC, and its value in predicting immunotherapeutic outcomes was confirmed. A novel nomogram integrating the stage and CAF-based risk signature was constructed, which exhibited favorable predictability and reliability in the prognosis prediction of HCC.ConclusionCAF-based risk signatures can effectively predict the prognosis of HCC, and comprehensive characterization of the CAF signature of HCC may help to interpret the response of HCC to immunotherapy and provide new strategies for cancer treatment.
Filtering out false Sargassum detections using context features
Authors: Podlejski Witold , Descloitres Jacques , Chevalier Cristèle , Minghelli Audrey , Lett Christophe , Berline Léo 
Published: September 2022

Since 2011, the distribution extent of pelagic Sargassum algae has substantially increased and now covers the whole Tropical North Atlantic Ocean, with significant inter-annual variability. The ocean colour imagery has been used as the only way to monitor regularly such a vast area. However, the detection is hampered by cloud masking, sunglint, coastal contamination and other phenomena. All together, they lead to false detections that can hardly be discriminated by classic radiometric analysis, but may be overcome by considering the shape and the context of the detections. Here, we built a machine learning model base exclusively on spatial features to filter out false detections after the detection process. Moderate-Resolution Imaging Spectroradiometer (MODIS, 1 km) data from Aqua and Terra satellites were used to generate daily map of Alternative Floating Algae Index (AFAI). Based on this radiometric index, Sargassum presence in the Tropical Atlantic North Ocean was inferred. For every Sargassum aggregations, five contextual indices were extracted (number of neighbours, surface of neighbours, temporal persistence, distance to the coast and aggregation texture) then used by a random forest binary classifier. Contextual features at large-scale were most important in the classifier. Trained with a multi-annual (2016-2020) learning set, the model performs the filtering of daily false detections with an accuracy of ~ 90%. This leads to a reduction of detected Sargassum pixels of ~ 50% over the domain. The method provides reliable data while preserving high spatial and temporal resolutions (1 km, daily). The resulting distribution is consistent with the literature for seasonal and inter-annual fluctuations, with maximum coverage in 2018 and minimum in 2016. This dataset will be useful for understanding the drivers of Sargassum dynamics at fine and large scale and validate future models. The methodology used here demonstrates the usefulness of contextual features for complementing classical remote sensing approaches. Our model could easily be adapted to other datasets containing erroneous detections.
Laser-induced breakdown spectroscopy of aqueous silicone rubber in an argon environment
Authors: Deng Yu , Peng Bo , Hao Yuguo , Du Yijun , Zhou Songsong , Gu Chen , Zhou Jun 
Published: September 2022

In a high humidity environment, the absorption of water in silicone rubber results in a significant decline or even loss in insulation performance, and the detection of the moisture content of silicone rubber is conducive to timely measuring the water absorption of silicone rubbers. Currently, the moisture content of silicone rubber can only be detected through manual sampling and laboratory analysis, which lack electric field analysis methods. Therefore, this paper proposes using laser-induced breakdown spectroscopy for the rapid qualitative and quantitative analysis of the moisture content of silicone rubber. Experiments were conducted on 50 silicone rubber samples with different moisture contents, with argon gas sprayed onto the sample surfaces to eliminate the interference of H and O elements in the air; a better argon gas flow rate of 3 L/min was determined. The information comprising the intensity and intensity ratio of the elemental spectral lines in the LIBS spectra of silicone rubber with moisture content was analyzed, with the results indicating that H elemental intensity increased as moisture content increased. Compared with the dry silicone rubber, the variation range of H/O and Al/H elements in the samples with different moisture contents was larger. Multivariate analysis was performed using a partial least squares regression model for the aqueous silicone rubber samples, and the model fit was obtained as 0.8088. The model prediction was better for the samples with moisture contents greater than 0.4%. The results indicated that the LIBS technique quantitatively analyzes the moisture content of silicone rubber, which is significant for improving the operation and maintenance of electric transmission lines.
Case report: Treatment of long COVID with a SARS-CoV-2 antiviral and IL-6 blockade in a patient with rheumatoid arthritis and SARS-CoV-2 antigen persistence
Authors: Visvabharathy Lavanya , Orban Zachary S. , Koralnik Igor J. 
Published: September 2022

IntroductionLong COVID, or post-acute sequelae of SARS-CoV-2 infection (PASC) in ∼30% of all infected individuals. Here, we present a case of PASC in a patient with rheumatoid arthritis characterized by viral persistence in the nasopharynx for 6 months after acute infection. We demonstrate transient disappearance of antigen persistence and decreased antiviral and autoimmune T cell responses after nirmatrelvir/ritonavir and tocilizumab treatment.Case presentationA 37-year-old female with a 7-year history of rheumatoid arthritis enrolled in a COVID-19 research study was found to continuously test SARS-CoV-2 antigen positive in the nasopharynx for 6 months after acute infection. She simultaneously presented with new-onset PASC symptoms including chronic occipital headache and periods of intense fatigue 8 weeks after acute infection. The patient was prescribed nirmatrelvir/ritonavir to treat SARS-CoV-2 persistence at 3.5 months post-acute infection and observed a reduction in PASC symptoms 3 weeks after completing antiviral treatment. After resurgence of PASC symptoms, she stopped treatment with tocilizumab for rheumatoid arthritis to attempt complete SARS-CoV-2 viral clearance. The severity of the patient’s PASC symptoms subsequently increased, and she developed new-onset brain fog in addition to previous symptoms, which resolved after resumption of tocilizumab treatment. Assessment of adaptive immune responses demonstrated that nirmatrelvir/ritonavir and tocilizumab treatment decreased antiviral and autoreactive T cell activation. After resuming tocilizumab treatment, the patient’s PASC symptoms were significantly reduced, but nasopharyngeal antigen positivity remained.ConclusionThese data suggest that nirmatrelvir/ritonavir should be considered in the treatment of PASC in patients who have SARS-CoV-2 antigen persistence, though care must be taken to monitor the patient for symptom resurgence or viral reactivation. In addition, the IL-6 inhibitor tocilizumab may ameliorate PASC symptoms in patients with persistent headache, fatigue, and brain fog.