Forest Road Detection Using LiDAR Data

We developed a three-step classification approach for forest road extraction utilizing LiDAR data. The first step employed the IDW method to interpolate LiDAR point data(first and last pulses) to achieve DSM, DTM and DNTM layers(at 1 m resolution). For this interpolation RMSE was 0.19 m. In the second step, the Support Vector Machine(SVM) was employed to classify the LiDAR data into two classes, road and non-road. For this classification, SVM indicated the merged distance layer with intensity data and yielded better identification of the road position. Assessments of the obtained results showed 63% correctness, 75% completeness and 52% quality of classification. In the next step, road edges were defined in the LiDAR-extracted layers, enabling accurate digitizing of the centerline location. More than 95% of the LiDAR-derived road was digitized within 1.3 m to the field surveyed normal. The proposed approach can provide thorough and accurate road inventory data to support forest management.

Canopy cover or remotely sensed vegetation index,explanatory variables of above-ground biomass in an arid rangeland,Iran

Estimation of above-ground biomass is vital for understanding ecological processes. Since direct measurement of above-ground biomass is destructive, time consuming and labor intensive, canopy cover can be considered as a predictor if a significant correlation between the two variables exists. In this study, relationship between canopy cover and above-ground biomass was investigated by a general linear regression model. To do so, canopy cover and above-ground biomass were measured at 5 sub-life forms(defined as life forms grouped in the same height classes) using 380 quadrats, which is systematic-randomly laid out along a 10-km transect, during four sampling periods(May, June, August, and September) in an arid rangeland of Marjan, Iran. To reveal whether obtained canopy cover and above-ground biomass of different sampling periods can be lumped together or not, we applied a general linear model(GLM). In this model, above-ground biomass was considered as a dependent or response variable, canopy cover as an independent covariate or predictor factor and sub-life forms as well as sampling periods as fixed factors. Moreover, we compared the estimated above-ground biomass derived from remotely sensed images of Landsat-8 using NDVI(normalized difference vegetation index), after finding the best regression line between predictor(measured canopy cover in the field) and response variable(above-ground biomass) to test the robustness of the induced model. Results show that above-ground biomass(response variable) of all vegetative forms and periods can be accurately predicted by canopy cover(predictor), although sub-life forms and sampling periods significantly affect the results. The best regression fit was found for short forbs in September and shrubs in May, June and August with R^2 values of 0.96, 0.93 and 0.91, respectively, whilst the least significant was found for short grasses in June, tall grasses in August and tall forbs in June with R^2 values of 0.71, 0.73 and 0.75, respectively. Even though the estimated above-ground biomass by NDVI is also convincing(R^2=0.57), the canopy cover is a more reliable predictor of above-ground biomass due to the higher R^2 values(from 0.75 to 0.96). We conclude that canopy cover can be regarded as a reliable predictor of above-ground biomass if sub-life forms and sampling periods(during growing season) are taken into account. Since,(1) plant canopy cover is not distinguishable by remotely sensed images at the sub-life form level, especially in sparse vegetation of arid and semi-arid regions, and(2) remotely sensed-based prediction of above-ground biomass shows a less significant relationship(R^2=0.57) than that of canopy cover(R^2 ranging from 0.75 to 0.96), which suggests estimating of plant biomass by canopy cover instead of cut and weighting method is highly recommended. Furthermore, this fast, nondestructive and robust method that does not endanger rare species, gives a trustworthy prediction of above-ground biomass in arid rangelands.

Identification of lncRNAs associated with the progression of acute lymphoblastic leukemia using a competing endogenous RNAs network

Acute lymphoblastic leukemia(ALL)is a malignancy of bone marrow lymphoid precursors.Despite effective treatments,the causes of its progression or recurrence are still unknown.Finding prognostic biomarkers is needed for early diagnosis and more effective treatment.This study was performed to identify long non-coding RNAs(lncRNAs)involved in ALL progression by constructing a competitive endogenous RNA(ceRNA)network.These lncRNAs may serve as potential new biomarkers in the development of ALL.The GSE67684 dataset identified changes in lncRNAs and mRNAs involved in ALL progression.Data from this study were re-analyzed,and probes related to lncRNAs were retrieved.Targetscan,miRTarBase,and miRcode databases were used to identify microRNAs(miRNAs)related to the discovered genes and lncRNAs.The ceRNA network was constructed,and the candidate lncRNAs were selected.Finally,the results were validated with reverse transcription quantitative real-time PCR(RT-qPCR).The ceRNA network outcomes demonstrated that the top lncRNAs associated with altered mRNAs in ALL are IRF1-AS1,MCM3AP-AS1,TRAF3IP2-AS1,HOTAIRM1,CRNDE,and TUG1.Investigations of the subnets linked to MCM3AP-AS1,TRAF3IP2-AS1,and IRF1-AS1 indicated that these lncRNAs were considerably related to pathways associated with inflammation,metastasis,and proliferation.Higher expression levels of IRF1-AS1,MCM3AP-AS1,TRAF3IP2-AS1,CRNDE,and TUG1 were found in ALL samples compared to controls.The expression of MCM3AP-AS1,TRAF3IP2-AS1,and IRF1-AS1 is significantly elevated during the progression of ALL,playing an oncogenic role.Due to their role in the main cancer pathways,lncRNAs could be suitable therapeutic and diagnostic targets in ALL.

Targeting glucose metabolism for treatment of COVID-19

A recent study published in Cell Metabolism by Codo et al.1 shows that metabolic rewiring of human monocytes by SARS-CoV-2 infection cultured under high glucose highly induces viral replication and cytokine production compromising T-cell response and function.Ultimately,this triggers lung epithelial cell death and,mechanistically,provides an explanation why people with diabetes might be more susceptible to develop severe COVID-19.