ABSTRACT
We present observations of SN 2020fqv, a Virgo-cluster type II core-collapse supernova (CCSN) with a high temporal resolution light curve from the Transiting Exoplanet Survey Satellite ...(TESS) covering the time of explosion; ultraviolet (UV) spectroscopy from the Hubble Space Telescope (HST) starting 3.3 d post-explosion; ground-based spectroscopic observations starting 1.1 d post-explosion; along with extensive photometric observations. Massive stars have complicated mass-loss histories leading up to their death as CCSNe, creating circumstellar medium (CSM) with which the SNe interact. Observations during the first few days post-explosion can provide important information about the mass-loss rate during the late stages of stellar evolution. Model fits to the quasi-bolometric light curve of SN 2020fqv reveal 0.23 M⊙ of CSM confined within 1450 R⊙ (1014 cm) from its progenitor star. Early spectra (<4 d post-explosion), both from HST and ground-based observatories, show emission features from high-ionization metal species from the outer, optically thin part of this CSM. We find that the CSM is consistent with an eruption caused by the injection of ∼5 × 1046 erg into the stellar envelope ∼300 d pre-explosion, potentially from a nuclear burning instability at the onset of oxygen burning. Light-curve fitting, nebular spectroscopy, and pre-explosion HST imaging consistently point to a red supergiant (RSG) progenitor with $M_{\rm ZAMS}\approx 13.5\!-\!15 \, \mathrm{M}_{\odot }$, typical for SN II progenitor stars. This finding demonstrates that a typical RSG, like the progenitor of SN 2020fqv, has a complicated mass-loss history immediately before core collapse.
Spatially distributed potential and net recharge rates were assessed in the paddy dominated Hirakud command area (Eastern India) at 100 m grid resolution using surface water balance and Water Table ...Fluctuation (WTF) methods, respectively, for the period 2001–05. Net recharge estimated using the WTF method corresponding to observation well locations was further interpolated using kriging technique available in the ArcGIS software. Net recharge to potential recharge ratios (%) were also assessed spatially. Water balance components (i) runoff was estimated using the Natural Resources Conservation Service-Curve Number (NRCS-CN) method (ii) reference evapotranspiration by (Hargreaves and Samani, Applied Engineering Agriculture ASABE 1:96–99, 1985)), crop evapotranspiration by (Allen et al., Crop evapotranspiration: Guidelines for computing crop water requirements, FAO Irrigation and Drainage, Food and Agriculture Organization, Rome, Italy, 1998) and evaporation from uncultivated lands by Ritchie (1972) approaches, and (iii) canal seepage using simple canal flow model. Annual groundwater draft during
Kharif
and
Rabi
was found to be 144.41 and 112.49 ha-m, respectively. Nearly, 90% of the study area contributed runoff in the range of 200–400 mm during the years 2002–03, 2003–04, and 2004–05. The estimated seepage losses vary between 5 and 15% of irrigation depth for all distributaries. Potential groundwater recharge during wet, normal, and dry years ranges between 650 and 1033 mm, and equivalent to 67%, 78%, and 60% of annual rainfall, respectively. Net recharge ranges between 8 and 11% of the annual rainfall. Mean ratio between net recharge to potential recharge is nearly 30%, indicating that nearly 70% of potential recharge is accounted as outflow from the study area. Parmanpur distributary canal located at the centre of the study area that exhibited higher potential recharge can be scheduled at the end to avoid water logging problem. Further, extraction of groundwater during non-monsoon period for irrigation purpose not only helps in controlling waterlogging but also helps in maintaining stable groundwater level. Overall, spatio-temporal distribution of recharge in the command area indicated that the irrigation demands during non-monsoon season can be met through sustainable management of underexploited groundwater resources. Such an integrated management of surface and groundwater can help in improving water use efficiencies as well as agricultural productivity.
ABSTRACT
A better understanding of genetic influences on early white matter development could significantly advance our understanding of neurological and psychiatric conditions characterized by ...altered integrity of axonal pathways. We conducted a genome-wide association study (GWAS) of diffusion tensor imaging (DTI) phenotypes in 471 neonates. We used a hierarchical functional principal regression model (HFPRM) to perform joint analysis of 44 fiber bundles. HFPRM revealed a latent measure of white matter microstructure that explained approximately 50% of variation in our tractography-based measures and accounted for a large proportion of heritable variation in each individual bundle. An intronic SNP in PSMF1 on chromosome 20 exceeded the conventional GWAS threshold of 5 x 10−8 (p = 4.61 x 10−8). Additional loci nearing genome-wide significance were located near genes with known roles in axon growth and guidance, fasciculation, and myelination.
Forest fire is considered as one of the major threats to global biodiversity and a significant source of greenhouse gas emissions. Rising temperatures, weather conditions, and topography promote the ...incidences of fire due to human ignition in South Asia. Because of its synoptic, multi-spectral, and multi-temporal nature, remote sensing data can be a state of art technology for forest fire management. This study focuses on the spatio-temporal patterns of forest fires and identifying hotspots using the novel geospatial technique “emerging hotspot analysis tool” in South Asia. Daily MODIS active fire locations data of 15 years (2003–2017) has been aggregated in order to characterize fire frequency, fire density, and hotspots. A total of 522,348 active fire points have been used to analyze risk of fires across the forest types. Maximum number of forest fires in South Asia was occurring during the January to May. Spatial analysis identified areas of frequent burning and high fire density in South Asian countries. In South Asia, 51% of forest grid cells were affected by fires in 15 years. Highest number of fire incidences was recorded in tropical moist deciduous forest and tropical dry deciduous forest. The emerging hotspots analysis indicates prevalence of sporadic hotspots, followed by historical hotspots, consecutive hotspots, and persistent hotspots in South Asia. Of the seven South Asian countries, Bangladesh has highest emerging hotspot area (34.2%) in forests, followed by 32.2% in India and 29.5% in Nepal. Study results offer critical insights in delineation of fire vulnerable forest landscapes which will stand as a valuable input for strengthening management of fires in South Asia.
Spectral unmixing-based estimation of material abundances in hyperspectral imagery has a variety of applications in mineralogy, environmental monitoring, agriculture, food processing, pharmacy, etc. ...A substantial body of literature is available on different inversion algorithms, optional pre-processing such as dimensionality reduction, and algorithms for endmembers extraction. The quality of abundance estimation depends on the number of materials, size, the geometrical orientation of materials, the source of endmembers, and the inversion algorithm used. However, there is a lack of studies on one-to-one assessment of the retrieval of abundances under various scenarios of spectral material distributions, the spatial resolution of the imagery, and the potential of in-situ reflectance measurements as candidate endmembers. The unavailability of comprehensive benchmark data coupled with pixel-to-pixel ground truth data has impeded comprehensive assessment of the first principles of spectral unmixing from a verifiable experimental perspective. The objective of this research is assessing the dynamics of material abundance as a function of the source of endmembers, spatial resolution, number of materials, and the size of materials. Linear and its sparse-based spectral unmixing algorithms were implemented on the datasets acquired for the estimation of abundances, considering the different scenarios of material distributions, spatial resolution, and the source of endmembers. We validated the results using pixel-to-pixel ground truth maps prepared for the different cases of spectral unmixing. The results provide answers to some critical open challenges in spectral unmixing, such as, (i) for an unambiguous detection, the fractional distribution of material has to be at least 1% of the pixel, (ii) endmembers from the in-situ spectra based on the external spectral library can offer reasonably good abundance estimates (an error of up to 20% compared to the image-based endmembers), and (iii) geometric orientations of materials in the ground sampling distance influence the abundance estimations. The benchmark dataset generated in this work is a valuable resource for addressing intriguing questions in spectral unmixing using hyperspectral imagery from a multi-resolution perspective.
Up-scaling the evaluation of threat status of biodiversity from species to ecosystem level has remained for long a research challenge in global conservation science. To meet this challenge, the ...present study makes an attempt toward actionable conservation prescription and assigning a threat category scheme for forest ecosystems. The scheme sets the quantitative criteria for evaluation of cumulative anthropogenic threats in grid cells, such as deforestation, degradation, fragmentation, forest fires and biological invasions. Adopting the convention of IUCN, five conservation status categories (i.e. Critically Endangered, Endangered, Vulnerable, Near Threatened, Least Concern) have been similarly adopted for the forest ecosystems facing these threats. The operational success of this scheme of threat categories at ecosystem level has been strengthened by remote sensing and field data generated for the forest ecosystems of Odisha, India. The threat category status of the forest ecosystems were identified by creating grids (5km×5km) in GIS and assigned the degree of the threats for each grid. The database on deforestation was generated using topographical maps of 1935 and remote sensing data of 1975 and 2010. The degradation in forest ecosystems have been assessed based on the change in forest canopy closure, fragmentation pattern, forest fire distribution and impact of biological invasions. The analysis for conservation priority hotspots complements an assessment of the threatened ecosystems undergoing remarkable level of multiple threats. Areas under the danger of cumulative anthropogenic threats would have a higher priority. 5.8% grids of existing forest had included under the category of conservation priority hotspot-I, followed by 12.4% in conservation priority hotspot-II, and 12.5% in conservation priority hotspot-III. An integrated approach involving the cumulative anthropogenic threat indicators have been found to be the most appropriate tool to empirically evaluate the threat status of the forest ecosystems. Finally, identification of ecosystems especially those facing increasing extinction risks, as attempted in the present study, can help in devising an appropriate policy and management agenda for the conservation and sustainable use of biodiversity.
DLR’s Earth Sensing Imaging Spectrometer (DESIS) is mounted on the International Space Station (ISS). DESIS records data in the spectral range from 400 to 1000 nm with a spectral and spatial ...resolution of 2.55 nm and 30 m respectively. The high spectral resolution enables in detecting a target object distinctly in remotely sensed imagery which has many useful applications in different fields of surveillance and monitoring. In present work two different case studies have been carried out that use DESIS data for target detection. In the first case study brick kilns are detected in DESIS data using Adaptive Coherence Estimator (ACE) algorithm. In the second case study Photovoltaic (PV) panels are considered as target object and linear spectral unmixing is employed to distinctly detect them in the image. From experimental results it is observed that the first target which were sparsely located in the image is detected very precisely with F1 score value of 0.97. The accuracy of the output of PV panel detection is observed to be more than 98%. Both the case studies show the potential of DESIS data in target detection which is a very important application of hyperspectral remote sensing.
Randomized controlled trial with single-blinded primary outcome assessment.
To determine the efficacy and safety of autologous incubated macrophage treatment for improving neurological outcome in ...patients with acute, complete spinal cord injury (SCI).
Six SCI treatment centers in the United States and Israel.
Participants with traumatic complete SCI between C5 motor and T11 neurological levels who could receive macrophage therapy within 14 days of injury were randomly assigned in a 2:1 ratio to the treatment (autologous incubated macrophages) or control (standard of care) groups. Treatment group participants underwent macrophage injection into the caudal boundary of the SCI. The primary outcome measure was American Spinal Injury Association (ASIA) Impairment Scale (AIS) A-B or better at ≥6 months. Safety was assessed by analysis of adverse events (AEs).
Of 43 participants (26 treatment, 17 control) having sufficient data for efficacy analysis, AIS A to B or better conversion was experienced by 7 treatment and 10 control participants; AIS A to C conversion was experienced by 2 treatment and 2 control participants. The primary outcome analysis for subjects with at least 6 months follow-up showed a trend favoring the control group that did not achieve statistical significance (P=0.053). The mean number of AEs reported per participant was not significantly different between the groups (P=0.942).
The analysis failed to show a significant difference in primary outcome between the two groups. The study results do not support treatment of acute complete SCI with autologous incubated macrophage therapy as specified in this protocol.
Denoising of hyperspectral images (HSIs) is an important preprocessing step to enhance the performance of its analysis and interpretation. In reality, a remotely sensed HSI experiences disturbance ...from different sources and therefore gets affected by multiple noise types. However, most of the existing denoising methods concentrates in removal of a single noise type ignoring their mixed effect. Therefore, a method developed for a particular noise type doesn’t perform satisfactorily for other noise types. To address this limitation, a denoising method is proposed here, that effectively removes multiple frequently encountered noise patterns from HSI including their combinations. The proposed dual branch deep neural network based architecture works on wavelet transformed bands. The first branch of the network uses deep convolutional skip connected layers with residual learning for extracting local and global noise features. The second branch includes layered autoencoder together with subpixel upsampling that performs repeated convolution in each layer to extract prominent noise features from the image. Two hyperspectral datasets are used in the experiment to evaluate the performance of the proposed method for denoising of Gaussian, stripe and mixed noises. Experimental results demonstrate the superior performance of the proposed network compared to other state-of-the-art denoising methods with PSNR 36.74, SSIM 0.97 and overall accuracy 94.03 %.
Dependence of structural parameters on the size of nanoparticles is a topic of general interest where the effect of shape is often neglected. We report a comprehensive study on size-dependent ...structural parameters of ZnO nanostructures (NSs) having a wide range of aspect ratios (AR: length/diameter). With increase in size, ZnO NSs undergo a shape transition from spherical to rod-like morphology that induces a sudden change in the internal parameter (u) which represents the relative position of two hexagonal close-packed sublattices. The change in u is associated with the changes in anion–cation (Zn–O) bond lengths as well as bond angles and thereby bears a linear dependence on the AR. Further, the unit cell volume and microstrain decrease with increase in particle size and show a drastic reduction when flat crystal faces begin to appear at the spherical surface (AR~1.3). The significant change in structural parameters associated with the shape transition arises due to surface dipole-induced electrostatic relaxation that may be further influenced by interaction with the ambient gases as evidenced from the extended X-ray absorption fine structure (EXAFS) measurement. The present study addresses the underlying reasons for shape-induced change in structural and electronic properties of ZnO NSs.
Display omitted
•Size- and shape-dependent structural parameters of ZnO nanostructures have been investigated experimentally as well as by theoretical modelling.•Flat faces appear as the size of the nanosphere gradually increases due to relaxation of built-up strain.•The apparent change in morphology of nanostructures is linked with specific changes in various structural parameters.•Cell volume contracts for smaller nanoparticles but it remains almost constant for bigger nanorods.•The size- and shape-dependent structural properties of ZnO nanostructures are explained on the basis of dipolar interaction with the ambient gases.
PDF
