Reconstructing Fire Severity and Post-Fire Recovery in a Southern California Watershed Using Hyperspectral Imagery and LiDAR

Wildfire is a serious threat to millions of people living in the Western United States, yet also an integral part of Southern California ecosystems. Therefore, it is important to quantify fire impacts and patterns of post-fire landscape recovery in order to understand the links between fire events and ecosystems. This research combined Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) remote sensing imagery and Light Detection and Ranging (LiDAR) data to produce a comprehensive, multi-year analysis of the May 2009 Jesusita Fire landscape within the Mission Creek Canyon watershed in Santa Barbara, California, USA. Combining passive and active remote sensing datasets allowed for a more detailed analysis of fire severity and the post-fire landscape recovery. Passive hyperspectral data provided information for a spectrally based assessment of fire severity and for mapping land cover types, while LiDAR provided geometric information such as topography and above ground vegetation structure. The study proposed a new fire severity definition based on multiple hyperspectral and LiDAR metrics: Multiple Endmember Spectral Mixture Analysis (MESMA) fractions and differenced Normalized Burn Ratio (dNBR) from AVIRIS; and a Canopy Height Model (CHM) from LiDAR. The study also examined the topographic effects on fire severity and post-fire recovery, using a LiDAR derived Topographic Wetness Index (TWI) and riparian areas defined from river locations collected from fieldwork. The result showed that the dNBR-MESMA-CHM based severity definition depicted a more detailed severity distribution in the Jesusita fire scar compared to the traditional spectral fire indices, especially for those areas with significant amounts of dead trunks. The riparian zone or areas with high soil water content were less affected by the fire, and the level of green vegetation cover returned to pre-fire status earlier compared to the fire scar average

Surface electrocardiographic characteristics in coronavirus disease 2019: repolarization abnormalities associated with cardiac involvement

AIMS The coronavirus disease 2019 (COVID-19) has spread rapidly around the globe, causing significant morbidity and mortality. This study aims to describe electrocardiographic (ECG) characteristics of COVID-19 patients and to identify ECG parameters that are associated with cardiac involvement. METHODS AND RESULTS The study included patients who were hospitalized with COVID-19 diagnosis and had cardiac biomarker assessments and simultaneous 12-lead surface ECGs. Sixty-three hospitalized patients (median 53 [inter-quartile range, 43-65] years, 76.2% male) were enrolled, including patients with (n = 23) and without (n = 40) cardiac injury. Patients with cardiac injury were older, had more pre-existing co-morbidities, and had higher mortality than those without cardiac injury. They also had prolonged QTc intervals and more T wave changes. Logistic regression model identified that the number of abnormal T waves (odds ratio (OR), 2.36 [95% confidence interval (CI), 1.38-4.04], P = 0.002) and QTc interval (OR, 1.31 [95% CI, 1.03-1.66], P = 0.027) were independent indicators for cardiac injury. The combination model of these two parameters along with age could well discriminate cardiac injury (area the under curve 0.881, P < 0.001) by receiver operating characteristic analysis. Cox regression model identified that the presence of T wave changes was an independent predictor of mortality (hazard ratio, 3.57 [1.40, 9.11], P = 0.008) after adjustment for age. CONCLUSIONS In COVID-19 patients, presence of cardiac injury at admission is associated with poor clinical outcomes. Repolarization abnormalities on surface ECG such as abnormal T waves and prolonged QTc intervals are more common in patients with cardiac involvement and can help in further risk stratification

Angle dependent field-driven reorientation transitions in uniaxial antiferromagnet MnBi2_2Te4_4 single crystal

MnBi$_2$Te$_4$, a two-dimensional magnetic topological insulator with a uniaxial antiferromagnetic structure, is an ideal platform to realize quantum anomalous Hall effect. However, the strength of magnetic interactions is not clear yet. We performed systematic studies on the magnetization and angle dependent magnetotransport of MnBi$_2$Te$_4$ single crystal. The results show that the direction of the magnetic field has significant effects on the critical field values and magnetic structure of this compound, which leads to different magnetotransport behaviors. The field-driven reorientation transitions can be utilized to estimate the AFM interlayer exchange interaction coupling and uniaxial magnetic anisotropy D. The obtained Hamiltonian can well explain the experimental data by Monte Carlo simulations. Our comprehensive studies on the field-driven magnetic transitions phenomenon in MnBi$_2$Te$_4$ provide a general approach for other topological systems with antiferromagnetism.Comment: 6 figure

Correlation between systemic immune inflammatory index and prognosis of patients with hepatic alveolar hydatid disease and establishment of a nomogram prediction model

BackgroundTo explore the evaluation value of systemic immune inflammation index (SII) in the prognosis of patients with alveolar hydatid disease, and establish a nomogram prediction model.MethodsCollect the clinical data of 351 patients undergoing hepatic alveolar hydatid surgery admitted to the Department of Hepatobiliary and Pancreatic Surgery, Affiliated Hospital of Qinghai University from January 2015 to December 2020, calculate the SII value, and use the receiver operating characteristic curve (ROC curve) to determine According to the optimal clinical cut-off value of SII, patients were divided into two groups with high SII and low SII, and the relationship between SII and clinicopathological factors and prognosis of patients with alveolar echinococcosis was analyzed. Establish a nomogram prediction model based on independent risk factors for patient prognosis, and evaluate the prediction accuracy and discrimination ability of the nomogram through the consistency index (C-index) and calibration curve. The result is through the use of bootstrapping validation with 1,000 re-sampling Method for internal verification.ResultsThe ROC curve was used to determine the optimal cut-off value of SII before operation 761.192, and patients were divided into low SII group (n = 184) cases and high SII group (n = 167) cases. The 1, 3, and 5-year survival rates of patients with hepatic alveolar hydatid in the low SII group and the high SII group were 98.90%, 96.90%, 86.50% and 98.20%, 72.50%, 40.30%, respectively. The survival rate of worm disease patients was significantly better than that of the high SII group, and the overall survival rate difference between the two groups was statistically significant (P &lt; 0.001). Multivariate Cox regression model analysis results showed that intraoperative blood loss (HR = 1.810, 95%CI: 1.227–2.668, P = 0.003), SII (HR = 5.011, 95%CI: 3.052–8.228, P &lt; 0.001), Complications (HR = 1.720, 95%CI: 1.162–2.545, P = 0.007) are independent risk factors for the prognosis of patients with alveolar hydatid disease. Draw a nomogram and include statistically significant factors in the multivariate Cox regression model to predict the overall survival rate of patients with alveolar hydatid disease at 1, 3, and 5 years. The survival probability calibration curve is displayed. The nomogram is compared with The actual results have a high degree of agreement. The concordance index (C-index) of the nomogram model in the modeling sample is 0.777, and the C-index in the verification sample is 0.797, indicating that the nomogram model of this study has good accuracy and discrimination.ConclusionsSII has a clear correlation to the prognosis of patients with alveolar echinococcosis. The nomogram prediction model constructed on this basis is beneficial to the clinically individualized analysis of the patient's prognosis

16S rRNA gene sequencing reveals the correlation between the gut microbiota and the susceptibility to pathological scars

The gut microbiome profile in patients with pathological scars remains rarely known, especially those patients who are susceptible to pathological scars. Previous studies demonstrated that gut microbial dysbiosis can promote the development of a series of diseases via the interaction between gut microbiota and host. The current study aimed to explore the gut microbiota of patients who are prone to suffer from pathological scars. 35 patients with pathological scars (PS group) and 40 patients with normal scars (NS group) were recruited for collection of fecal samples to sequence the 16S ribosomal RNA (16S rRNA) V3-V4 region of gut microbiota. Alpha diversity of gut microbiota showed a significant difference between NS group and PS group, and beta diversity indicated that the composition of gut microbiota in NS and PS participants was different, which implied that dysbiosis exhibits in patients who are susceptible to pathological scars. Based on phylum, genus, species levels, we demonstrated that the changing in some gut microbiota (Firmicutes; Bacteroides; Escherichia coli, etc.) may contribute to the occurrence or development of pathological scars. Moreover, the interaction network of gut microbiota in NS and PS group clearly revealed the different interaction model of each group. Our study has preliminary confirmed that dysbiosis exhibits in patients who are susceptible to pathological scars, and provide a new insight regarding the role of the gut microbiome in PS development and progression

Spin-phonon scattering-induced low thermal conductivity in a van der Waals layered ferromagnet Cr2_2Si2_2Te6_6

Layered van der Waals (vdW) magnets are prominent playgrounds for developing magnetoelectric, magneto-optic and spintronic devices. In spintronics, particularly in spincaloritronic applications, low thermal conductivity ($\kappa$) is highly desired. Here, by combining thermal transport measurements with density functional theory calculations, we demonstrate low $\kappa$ down to 1 W m$^{-1}$ K$^{-1}$ in a typical vdW ferromagnet Cr$_2$Si$_2$Te$_6$. In the paramagnetic state, development of magnetic fluctuations way above $T_\mathrm{c}=$ 33 K strongly reduces $\kappa$ via spin-phonon scattering, leading to low $\kappa \sim$ 1 W m$^{-1}$ K$^{-1}$ over a wide temperature range, in comparable to that of amorphous silica. In the magnetically ordered state, emergence of resonant magnon-phonon scattering limits $\kappa$ below $\sim$ 2 W m$^{-1}$ K$^{-1}$, which would be three times larger if magnetic scatterings were absent. Application of magnetic fields strongly suppresses the spin-phonon scattering, giving rise to large enhancements of $\kappa$. Our calculations well capture these complex behaviours of $\kappa$ by taking the temperature- and magnetic-field-dependent spin-phonon scattering into account. Realization of low $\kappa$ which is easily tunable by magnetic fields in Cr$_2$Si$_2$Te$_6$, may further promote spincaloritronic applications of vdW magnets. Our theoretical approach may also provide a generic understanding of spin-phonon scattering, which appears to play important roles in various systems.Comment: 14 pages, 6 figures, accepted for publication in Advanced Functional Material

Global fire emissions estimates during 1997-2016

Climate, land use, and other anthropogenic and natural drivers have the potential to influence fire dynamics in many regions. To develop a mechanistic understanding of the changing role of these drivers and their impact on atmospheric composition, long-term fire records are needed that fuse information from different satellite and in situ data streams. Here we describe the fourth version of the Global Fire Emissions Database (GFED) and quantify global fire emissions patterns during 1997-2016. The modeling system, based on the Carnegie-Ames-Stanford Approach (CASA) biogeochemical model, has several modifications from the previous version and uses higher quality input datasets. Significant upgrades include (1) new burned area estimates with contributions from small fires, (2) a revised fuel consumption parameterization optimized using field observations, (3) modifications that improve the representation of fuel consumption in frequently burning landscapes, and (4) fire severity estimates that better represent continental differences in burning processes across boreal regions of North America and Eurasia. The new version has a higher spatial resolution (0.25) and uses a different set of emission factors that separately resolves trace gas and aerosol emissions from temperate and boreal forest ecosystems. Global mean carbon emissions using the burned area dataset with small fires (GFED4s) were 2.21015 grams of carbon per year (Pg Cyr-1) during 1997-2016, with a maximum in 1997 (3.0 Pg C yr-1) and minimum in 2013 (1.8 Pg C yr-1). These estimates were 11% higher than our previous estimates (GFED3) during 1997-2011, when the two datasets overlapped. This net increase was the result of a substantial increase in burned area (37 %), mostly due to the inclusion of small fires, and a modest decrease in mean fuel consumption (-19 %) to better match estimates from field studies, primarily in savannas and grasslands. For trace gas and aerosol emissions, differences between GFED4s and GFED3 were often larger due to the use of revised emission factors. If small fire burned area was excluded (GFED4 without the s for small fires), average emissions were 1.5 Pg C yr-1. The addition of small fires had the largest impact on emissions in temperate North America, Central America, Europe, and temperate Asia. This small fire layer carries substantial uncertainties; improving these estimates will require use of new burned area products derived from high-resolution satellite imagery. Our revised dataset provides an internally consistent set of burned area and emissions that may contribute to a better understanding of multi-decadal changes in fire dynamics and their impact on the Earth system. GFED data are available from http://www.globalfiredata.org

Promoter Hypermethylation Mediated Downregulation of FBP1 in Human Hepatocellular Carcinoma and Colon Cancer

FBP1, fructose-1,6-bisphosphatase-1, a gluconeogenesis regulatory enzyme, catalyzes the hydrolysis of fructose 1,6-bisphosphate to fructose 6-phosphate and inorganic phosphate. The mechanism that it functions to antagonize glycolysis and was epigenetically inactivated through NF-kappaB pathway in gastric cancer has been reported. However, its role in the liver carcinogenesis still remains unknown. Here, we investigated the expression and DNA methylation of FBP1 in primary HCC and colon tumor. FBP1 was lowly expressed in 80% (8/10) human hepatocellular carcinoma, 66.7% (6/9) liver cancer cell lines and 100% (6/6) colon cancer cell lines, but was higher in paired adjacent non-tumor tissues and immortalized normal cell lines, which was well correlated with its promoter methylation status. Methylation was further detected in primary HCCs, gastric and colon tumor tissues, but none or occasionally in paired adjacent non-tumor tissues. Detailed methylation analysis of 29 CpG sites at a 327-bp promoter region by bisulfite genomic sequencing confirmed its methylation. FBP1 silencing could be reversed by chemical demethylation treatment with 5-aza-2′-deoxycytidine (Aza), indicating direct epigenetic silencing. Restoring FBP1 expression in low expressed cells significantly inhibited cell growth and colony formation ability through the induction of G2-M phase cell cycle arrest. Moreover, the observed effects coincided with an increase in reactive oxygen species (ROS) generation. In summary, epigenetic inactivation of FBP1 is also common in human liver and colon cancer. FBP1 appears to be a functional tumor suppressor involved in the liver and colon carcinogenesis