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This study introduces a novel SI2HR model, where “I2” denotes two infectious classes representing asymptomatic and symptomatic infections, aiming to investigate and analyze the cost-effective optimal control measures for managing COVID-19. The model incorporates a novel concept of infectious density-induced additional screening (IDIAS) and accounts for treatment saturation. Furthermore, the model considers the possibility of reinfection and the loss of immunity in individuals who have previously recovered. To validate and calibrate the proposed model, real data from November–December 2022 in Hong Kong are utilized. The estimated parameters obtained from this calibration process are valuable for prediction purposes and facilitate further numerical simulations. An analysis of the model reveals that delays in screening, treatment, and quarantine contribute to an increase in the basic reproduction number R0, indicating a tendency towards endemicity. In particular, from the elasticity of R0, we deduce that normalized sensitivity indices of baseline screening rate (θ), quarantine rates (γ, αs), and treatment rate (α) are negative, which shows that delaying any of these may cause huge surge in R0, ultimately increases the disease burden. Further, by the contour plots, we note the two-parameter behavior of the infectives (both symptomatic and asymptomatic). Expanding upon the model analysis, an optimal control problem (OCP) is formulated, incorporating three control measures: precautionary interventions, boosted IDIAS, and boosted treatment. The Pontryagin's maximum principle and the forward-backward sweep method are employed to solve the OCP. The numerical simulations highlight that enhanced screening and treatment, coupled with preventive interventions, can effectively contribute to sustainable disease control. However, the cost-effectiveness analysis (CEA) conducted in this study suggests that boosting IDIAS alone is the most economically efficient and cost-effective approach compared to other strategies. The CEA results provide valuable insights into identifying specific strategies based on their cost-efficacy ranking, which can be implemented to maximize impact while minimizing costs. Overall, this research offers significant insights for policymakers and healthcare professionals, providing a framework to optimize control efforts for COVID-19 or similar epidemics in the future.

Precise spatial mapping of individual species using hyperspectral data is crucial for effective forest management and policy-making. This study focuses on Rhododendron arboreum, known for its medicinal properties attributed to the flavonoid Quercitrin. Sample data and spectroradiometer data were collected from the complex terrain of the Kumaon region in the Himalayas. Hyperspectral data, which includes signal variations based on biophysical and biochemical properties along with noise, were preprocessed using filtering techniques to enhance signal clarity by removing noise. Smoothing techniques were applied to remove noisy bands from the spectra, such as the Savitzky-Golay filter for reduced least square fit complexity and the Average Mean filter for taking mean spectral values. Subsequently, Spectral Analysis (SA) techniques, including first derivative, second derivative, and continuum removal, were employed. These mathematical transformations highlighted absorption troughs and determined the effect of Quercitrin on spectral wavelengths. Principal Component Analysis (PCA) was used to identify the most relevant bands related to Quercitrin. Additionally, regression analysis was applied on resampled spectral data, selected significant wavelengths based on variable importance values, pinpointing the most prominent wavelengths: 1196, 1229, 1328, 1383, 1425, 1636, 1661, 1699, 1785, and 1715 nm. Over 50 two-band combination indices were tested, and those with p-values less than 0.05 were deemed significant. For the development of prediction model, Machine Learning (ML) algorithms, including Support Vector Machine (SVM), Relevance Vector Machine (RVM), Random Forest (RF), and Artificial Neural Network (ANN), were applied. The Random Forest model, which splits input data into trees to simulate the best model based on observed values, demonstrated high effectiveness in predicting Quercitrin levels, achieving a training correlation of 0.864 and a testing correlation of 0.570. Hence RF proved to be a best technique of band selection as well as robust for Quercitrin prediction. This methodological approach highlights the importance of advanced data processing and analysis techniques in remote sensing applications for forest phytochemical prediction.

Hydrological Ecosystem Services (HES) are crucial components of environmental sustainability and provide indispensable benefits. The present study identifies critical hot and cold spots areas of HES in the Aglar watershed of the Indian Himalayan Region using six HES descriptors, namely water yield (WYLD), crop yield factor (CYF), sediment yield (SYLD), base flow (LATQ), surface runoff (SURFQ), and total water retention (TWR). The analysis was conducted using weightage-based approaches under two methods: (1) evaluating six HES descriptors individually and (2) grouping them into broad ecosystem service categories. Furthermore, the study assessed pixel-level uncertainties that arose because of the distinctive methods used in the identification of hot and cold spots. The associated synergies and trade-offs among HES descriptors were examined too. From method 1, 0.26% area of the watershed was classified as cold spots and 3.18% as hot spots, whereas method 2 classified 2.42% area as cold spots and 2.36% as hot spots. Pixel-level uncertainties showed that 0.57 km2 and 6.86 km2 of the watershed were consistently under cold and hot spots, respectively, using method 1, whereas method 2 identified 2.30 km2 and 6.97 km2 as cold spots and hot spots, respectively. The spatial analysis of hot spots showed consistent patterns in certain parts of the watershed, primarily in the south to southwest region, while cold spots were mainly found on the eastern side. Upon analyzing HES descriptors within broad ecosystem service categories, hot spots were mainly in the southern part, and cold spots were scattered throughout the watershed, especially in agricultural and scrubland areas. The significant synergistic relation between LATQ and WYLD, and sediment retention and WYLD and trade-offs between SURFQ and HES descriptors like WYLD, LATQ, sediment retention, and TWR was attributed to varying factors such as land use and topography impacting the water balance components in the watershed. The findings underscore the critical need for targeted conservation efforts to maintain the ecologically sensitive regions at watershed scale.

RNA editing, a common and potentially highly functional form of RNA modification, encompasses two different RNA modifications, namely adenosine to inosine (A-to-I) and cytidine to uridine (C-to-U) editing. As inosines are interpreted as guanosines by the cellular machinery, both A-to-I and C-to-U editing change the nucleotide sequence of the RNA. Editing events in coding sequences have the potential to change the amino acid sequence of proteins, whereas editing events in noncoding RNAs can, for example, affect microRNA target binding. With advancing RNA sequencing technology, more RNA editing events are being discovered, studied, and reported. However, RNA editing events are still often overlooked or discarded as sequence read quality defects. With this position paper, we aim to provide guidelines and recommendations for the detection, validation, and follow-up experiments to study RNA editing, taking examples from the fields of cardiovascular and brain disease. We discuss all steps, from sample collection, storage, and preparation, to different strategies for RNA sequencing and editing-sensitive data analysis strategies, to validation and follow-up experiments, as well as potential pitfalls and gaps in the available technologies. This paper may be used as an experimental guideline for RNA editing studies in any disease context.

Himalayan species conservation faces major challenges due to unprecedented climate change. Alpine Rhododendrons are crucial components of Himalaya, yet their vulnerability to climate change remains poorly understood. This study examines niche shifting of Rhododendron campanulatum, a keystone species of alpine treeline, under different climate change scenarios using ensemble models. The study presents extensive use of four machine learning models and three global circulation models for niche modelling. Models achieved True Skill Statistic ≥0.8, Area Under Curve ≥0.9, Cohen’s Kappa ≥0.7, and overall accuracy of ≥0.9. Results showed distribution of R. campanulatum is governed by annual temperature range, minimum temperature of coldest month and precipitation of warmest quarter. Analyses revealed niche contraction and expansion of a 3–5%. Contractions are particularly evident at lower treeline boundaries. Both upward and downward shifts are anticipated under future climatic scenarios.

AbstractThe use of drones and remote sensing in combination with geospatial analysis is a cost-efficient way to monitor energy distribution networks, especially those in fire-prone areas. This study investigated the use of image and photogrammetric analysis together with segmentation algorithms to assess vegetation height and volume in power line corridors in Southeast Queensland, Australia. Various fuel reduction techniques, including mega-mulching, spot sprays and cool mosaic burns, were implemented, and drone-generated models were employed to evaluate their effectiveness. The fuel hazard reduction and regrowth in terms of vegetation height and volume were recorded and analysed. Importantly, the study demonstrates a robust correlation (R2 = 0.9073; df = 1,16; F = 156; p

Leaf chlorophyll is an important dynamic biochemical parameter to assess crop phenology, health stress, and yield. Optical remote sensing data is a widely used technique for the estimation of vegetation leaf chlorophyll. Drone technology is a promising solution for high-resolution monitoring of the leaf chlorophyll content and employed in this study. Image fusion was also accomplished to provide high-resolution multi-spectral images by combining Sentinel-2A and drone imagery. Support vector machine regression was adopted to determine the best vegetation indices for the retrieval of leaf chlorophyll content. Random forest regression and support vector machine regression algorithms were adopted to develop the best models for chlorophyll retrieval. The 34 models derived from drone data and the 46 models derived from fusion data were evaluated for chlorophyll retrieval. It was found that the best support vector machine model, based on anthocyanin content index and enhanced vegetation index (M31), had the best correlation coefficient (r = 0.732) and root mean square error (1.93). The random forest regression models M32 (based on chlorophyll vegetation index and visible atmospherically resistant index) and M16 (based on canopy chlorophyll content index and visible atmospherically resistant index) for drone data were found to be the best, which performed equally well in terms of correlation coefficients (r = 0.77) and root mean square error (1.51). The multiple vegetation indices of drone-based leaf chlorophyll content random forest regression models (M16 and M32) provided higher performance than single vegetation indices-based linear (simple linear fit) and nonlinear leaf chlorophyll content models. Out of 46 models of fusion products, the random forest regression M14 (green normalized difference vegetation index with narrow near infrared band and visible atmospherically resistant index) offered the best performance for leaf chlorophyll content retrieval, as can be observed through a comparison of correlation (r = 0.77) and RMSE (=1.71) values. It was also investigated that inversion performances (r ∼ 0.8 and RMSD ∼ 1.4 with standard deviation ∼ 2.6) of fusion data-based models in cross-applicability and transferability were found to be suitable for large-scale inversion of Sentinel-2A data products.

This study involved an investigation of the long-term seasonal rainfall patterns in central India at the district level during the period from 1991 to 2020, including various aspects such as the spatiotemporal seasonal trend of rainfall patterns, rainfall variability, trends of rainy days with different intensities, decadal percentage deviation in long-term rainfall patterns, and decadal percentage deviation in rainfall events along with their respective intensities. The central region of India was meticulously divided into distinct subparts, namely, Gujarat, Daman and Diu, Maharashtra, Goa, Dadra and Nagar Haveli, Madhya Pradesh, Chhattisgarh, and Odisha. The experimental outcomes represented the disparities in rainfall distribution across different districts of central India with the spatial distribution of mean rainfall ranges during winter (2.08 mm over Dadra and Nagar Haveli with an average of 24.19 mm over Odisha), premonsoon (6.65 mm over Gujarat to 132.89 mm over Odisha), monsoon (845.46 mm over Gujarat to 3188.21 mm over Goa), and post-monsoon (30.35 mm over Gujarat to 213.87 mm over Goa), respectively. Almost all the districts of central India displayed an uneven pattern in the percentage deviation of seasonal rainfall in all three decades for all seasons, which indicates the seasonal rainfall variability over the last 30 years. A noticeable variation in the percentage deviation of seasonal rainfall patterns has been observed in the following districts: Rewa, Puri, Anuppur, Ahmadabad, Navsari, Chhindwara, Devbhumi Dwarka, Amreli, Panch Mahals, Kolhapur, Kandhamal, Ratnagiri, Porbandar, Bametara, and Sabar Kantha. In addition, a larger number of rainy days of various categories occurred in the monsoon season in comparison to other seasons. A higher contribution of trace rainfall events was found in the winter season. The highest contributions of very light, light rainfall, moderate, rather high, and high events were found in the monsoon season in central India. The percentage of various categories of rainfall events has decreased over the last two decades (2001–2020) in comparison to the third decade (1991–2000), according to the mean number of rainfall events in the last 30 years. This spatiotemporal analysis provides valuable insights into the rainfall trends in central India, which represent regional disparities and the potential challenges impacted by climate patterns. This study contributes to our understanding of the changing rainfall dynamics and offers crucial information for effective water resource management in the region.

Deriving the thematic accuracy of models is a fundamental part of image classification analyses. K-fold cross-validation (KCV), as an accuracy assessment technique, can be biased because existing built-in algorithms of software solutions do not handle the high autocorrelation of remotely sensed images, leading to overestimation of accuracies. We aimed to quantify the magnitude of the overestimation of KCV-based accuracies and propose a method to overcome this problem with the example of rooftops using a WorldView-2 (WV2) satellite image, and two orthophotos. Random split to training/testing subsets, independent testing and different types of repeated KCV sampling strategies were used to generate input datasets for classification. Results revealed that applying the random splitting of reference data to training/testing subsets and KCV methods had significantly biased the accuracies by up to 17%; overall accuracies (OAs) can incorrectly reach >99%. We found that repeated KCV can provide similar results to independent testing when spatial sampling is applied with a sufficiently large distance threshold (in our case 10 m). Coarser resolution of WV2 ensured more reliable results (up to a 5–9% increase in OA) than orthophotos. Object-based pixel purity of buildings showed that when using a majority filter for at least of 50% of objects the final accuracy approached 100% with each sampling method. The final conclusion is that KCV-based modelling ensures better accuracy than single models (with better pixel purity on the object level), but the accuracy metrics without spatially filtered sampling are not reliable.

Satellite images provide consistent and frequent information that can be used to estimate mineral resources over a large spatial extent. Advances in spaceborne hyperspectral remote sensing (HRS) and machine learning can help to support various remote-sensing-based applications, including mineral exploration. Leveraging these advances, the present study evaluates recently launched PRISMA spaceborne satellite images to map hydrothermally altered and weathered minerals using various machine-learning-based classification algorithms. The study was performed for the town of Jahazpur in Rajasthan, India (75°06′23.17″E, 25°25′23.37″N). The distribution map for minerals such as kaolinite, talc, and montmorillonite was generated using the spectral angle mapper technique. The resultant mineral distribution map was verified through an intensive field validation survey on surface exposures of the minerals. Furthermore, the obtained pixels of the end-members were used to develop the machine-learning-based classification models. Measures such as accuracy, kappa coefficient, F1 score, precision, recall, and ROC curve were employed to evaluate the performance of developed models. The results show that the stochastic gradient descent and artificial-neural-network-based multilayer perceptron classifiers were more accurate than other algorithms. Results confirm that the PRISMA dataset has enormous potential for mineral mapping in mountainous regions utilizing a machine-learning-based classification framework.

Shallow landslides due to hydro-meteorological factors are one of the most common destructive geological processes, which have become more frequent in recent years due to changes in rainfall frequency and intensity. The present study assessed a dynamic, physically based slope stability model, Transient Rainfall Infiltration and Grid-Based Slope Stability Model (TRIGRS), in Idukki district, Kerala, Western Ghats. The study compared the impact of hydrogeomechanical parameters derived from two different data sets, FAO soil texture and regionally available soil texture, on the simulation of the distribution and timing of shallow landslides. For assessing the landslide distribution, 1913 landslides were compared and true positive rates (TPRs) of 68% and 60% were obtained with a nine-day rainfall period for the FAO- and regional-based data sets, respectively. However, a false positive rate (FPR) of 36% and 31% was also seen, respectively. The timing of occurrence of nine landslide events was assessed, which were triggered in the second week of June 2018. Even though the distribution of eight landslides was accurately simulated, the timing of only three events was found to be accurate. The study concludes that the model simulations using parameters derived from either of the soil texture data sets are able to identify the location of the event. However, there is a need for including a high-spatial-resolution hydrogeomechanical parameter data set to improve the timing of landslide event modeling.

Regional quantification of energy and water balance fluxes depends inevitably on the estimation of surface and rootzone soil moisture. The simulation of soil moisture depends on the soil retention characteristics, which are difficult to estimate at a regional scale. Thus, the present study proposes a new method to estimate high-resolution Soil Hydraulic Parameters (SHPs) which in turn help to provide high-resolution (spatial and temporal) rootzone soil moisture (RZSM) products. The study is divided into three phases—(I) involves the estimation of finer surface soil moisture (1 km) from the coarse resolution satellite soil moisture. The algorithm utilizes MODIS 1 km Land Surface Temperature (LST) and 1 km Normalized difference vegetation Index (NDVI) for downscaling 25 km C-band derived soil moisture from AMSR-2 to 1 km surface soil moisture product. At one of the test sites, soil moisture is continuously monitored at 5, 20, and 50 cm depth, while at 44 test sites data were collected randomly for validation. The temporal and spatial correlation for the downscaled product was 70% and 83%, respectively. (II) In the second phase, downscaled soil moisture product is utilized to inversely estimate the SHPs for the van Genuchten model (1980) at 1 km resolution. The numerical experiments were conducted to understand the impact of homogeneous SHPs as compared to the three-layered parameterization of the soil profile. It was seen that the SHPs estimated using the downscaled soil moisture (I-d experiment) performed with similar efficiency as compared to SHPs estimated from the in-situ soil moisture data (I-b experiment) in simulating the soil moisture. The normalized root mean square error (nRMSE) for the two treatments was 0.37 and 0.34, respectively. It was also noted that nRMSE for the treatment with the utilization of default SHPs (I-a) and AMSR-2 soil moisture (I-c) were found to be 0.50 and 0.43, respectively. (III) Finally, the derived SHPs were used to simulate both surface soil moisture and RZSM. The final product, RZSM which is the daily 1 km product also showed a nearly 80% correlation at the test site. The estimated SHPs are seen to improve the mean NSE from 0.10 (I-a experiment) to 0.50 (I-d experiment) for the surface soil moisture simulation. The mean nRMSE for the same was found to improve from 0.50 to 0.31.

Forest canopy height estimates, at a regional scale, help understand the forest carbon storage, ecosystem processes, the development of forest management and the restoration policies to mitigate global climate change, etc. The recent availability of the NASA’s Global Ecosystem Dynamics Investigation (GEDI) LiDAR data has opened up new avenues to assess the plant canopy height at a footprint level. Here, we present a novel approach using the random forest (RF) for the wall-to-wall canopy height estimation over India’s forests (i.e., evergreen forest, deciduous forest, mixed forest, plantation, and shrubland) by employing the high-resolution top-of-the-atmosphere (TOA) reflectance and vegetation indices, the synthetic aperture radar (SAR) backscatters, the topography and tree canopy density, as the proxy variables. The variable importance plot indicated that the SAR backscatters, tree canopy density and the topography are the most influential height predictors. 33.15% of India’s forest cover demonstrated the canopy height 20 m). This study advocates the importance and use of GEDI data for estimating the canopy height, preferably in data-deficit mountainous regions, where most of India’s natural forest vegetation exists.

Abstract Objective To identify a convergent, multitarget proliferation characteristic for astrocytoma transformation that could be targeted for therapy discovery. Methods Using an integrated functional genomics approach, we prioritized networks associated with astrocytoma progression using the following criteria: differential co‐expression between grade II and grade III IDH1‐mutated and 1p/19q euploid astrocytomas, preferential enrichment for genetic risk to cancer, association with patient survival and sample‐level genomic features. Drugs targeting the identified multitarget network characteristic for astrocytoma transformation were computationally predicted using drug transcriptional perturbation data and validated using primary human astrocytoma cells. Results A single network, M2, consisting of 177 genes, was associated with glioma progression on the basis of the above criteria. Functionally, M2 encoded physically interacting proteins regulating cell cycle processes and analysis of genome‐wide gene‐regulatory interactions using mutual information and DNA–protein interactions revealed the known regulators of cell cycle processes FoxM1, B‐Myb, and E2F2 as key regulators of M2. These results suggest functional disruption of M2 via gene mutation or altered expression as a convergent pathway regulating astrocytoma transformation. By considering M2 as a multitarget drug target regulating astrocytoma transformation, we identified several drugs that are predicted to restore M2 expression in anaplastic astrocytoma toward its low‐grade profile and of these, we validated the known antiproliferative drug resveratrol as down‐regulating multiple nodes of M2 including at nanomolar concentrations achievable in human cerebrospinal fluid by oral dosing. Interpretation Our results identify M2 as a multitarget network characteristic for astrocytoma progression and encourage M2‐based drug screening to identify new compounds for preventing glioma transformation.

Pathological cardiac fibrosis is a hallmark of diseases leading to heart failure. Here, the authors used systems genetics to identify a pro-fibrotic gene network regulated by WWP2, a E3 ubiquitin ligase, which orchestrates the nucleocytoplasmic shuttling and transcriptional activity of SMAD2 in the diseased heart.

The leaf chlorophyll content (LCC) is a vital parameter that indicates plant production, stress, and nutrient availability. It is critically needed for precision farming. There are several multispectral images available freely, but their applicability is restricted due to their low spectral resolution, whereas hyperspectral images which have high spectral resolution are very limited in availability. In this work, hyperspectral imagery (AVIRIS-NG) is simulated using a multispectral image (Sentinel-2) and a spectral reconstruction method, namely, the universal pattern decomposition method (UPDM). UPDM is a linear unmixing technique, which assumes that every pixel of an image can be decomposed as a linear composition of different classes present in that pixel. The simulated AVIRIS-NG was very similar to the original image, and its applicability in estimating LCC was further verified by using the ground based measurements, which showed a good correlation value (R = 0.65). The simulated image was further classified using a spectral angle mapper (SAM), and an accuracy of 87.4% was obtained, moreover a receiver operating characteristic (ROC) curve for the classifier was also plotted, and the area under the curve (AUC) was calculated with values greater than 0.9. The obtained results suggest that simulated AVIRIS-NG is quite useful and could be used for vegetation parameter retrieval.

The problems of atmospheric pollutants are causing significant concern across the globe and in India. The aggravated level of atmospheric pollutants in the surrounding environment poses serious threats to normal living conditions by deteriorating air quality and causing adverse health impacts. Pollutant concentration increases during harvesting seasons of Kharif/Rabi due to stubble burning and is aggravated by other points or mobile sources. The present study is intended to monitor the spatio-temporal variation of the major atmospheric pollutants using Sentinel-5P TROPOMI data through cloud computing. Land Use/Land Cover (LULC-categorization or classification of human activities and natural coverage on the landscape) was utilised to extract the agricultural area in the study site. It involves the cloud computing of MOD64A1 (MODIS Burned monthly gridded data) and Sentinel-5P TROPOMI (S5P Tropomi) data for major atmospheric pollutants, such as CH4, NO2, SOX, CO, aerosol, and HCHO. The burned area output provided information regarding the stubble burning period, which has seen post-harvesting agricultural residue burning after Kharif crop harvesting (i.e., rice from April to June) and Rabi crop harvesting (i.e., wheat from September to November). The long duration of stubble burning is due to variation in farmers’ harvesting and burning stubble/biomass remains in the field for successive crops. This period was used as criteria for considering the cloud computing of the Sentinel-5P TROPOMI data for atmospheric pollutants concentration in the study site. The results showed a significant increase in CH4, SO2, SOX, CO, and aerosol concentration during the AMJ months (stubble burning of Rabi crops) and OND months (stubble burning of Kharif crops) of each year. The results are validated with the ground control station data for PM2.5/PM10. and patterns of precipitation and temperature-gridded datasets. The trajectory frequency for air mass movement using the HYSPLIT model showed that the highest frequency and concentration were observed during OND months, followed by the AMJ months of each year (2018, 2019, 2020, and 2021). This study supports the role and robustness of Earth observation Sentinel-5P TROPOMI to monitor and evaluate air quality and pollutants distribution.

Epilepsy is a complex network phenomenon that, as yet, cannot be prevented or cured. We recently proposed network-based approaches to prevent epileptogenesis. For proof of concept we combined two drugs (levetiracetam and topiramate) for which in silico analysis of drug-protein interaction networks indicated a synergistic effect on a large functional network of epilepsy-relevant proteins. Using the intrahippocampal kainate mouse model of temporal lobe epilepsy, the drug combination was administered during the latent period before onset of spontaneous recurrent seizures (SRS). When SRS were periodically recorded by video-EEG monitoring after termination of treatment, a significant decrease in incidence and frequency of SRS was determined, indicating antiepileptogenic efficacy. Such efficacy was not observed following single drug treatment. Furthermore, a combination of levetiracetam and phenobarbital, for which in silico analysis of drug-protein interaction networks did not indicate any significant drug-drug interaction, was not effective to modify development of epilepsy. Surprisingly, the promising antiepileptogenic effect of the levetiracetam/topiramate combination was obtained in the absence of any significant neuroprotective or anti-inflammatory effects as indicated by multimodal brain imaging and histopathology. High throughput RNA-sequencing (RNA-seq) of the ipsilateral hippocampus of mice treated with the levetiracetam/topiramate combination showed that several genes that have been linked previously to epileptogenesis, were significantly differentially expressed, providing interesting entry points for future mechanistic studies. Overall, we have discovered a novel combination treatment with promise for prevention of epilepsy.

Vegetation cover and soil surface roughness are vital parameters in the soil moisture retrieval algorithms. Due to the high sensitivity of passive microwave and optical observations to Vegetation Water Content (VWC), this study assesses the integration of these two types of data to approximate the effect of vegetation on passive microwave Brightness Temperature (BT) to obtain the vegetation transmissivity parameter. For this purpose, a newly introduced index named Passive microwave and Optical Vegetation Index (POVI) was developed to improve the representativeness of VWC and converted into vegetation transmissivity through linear and nonlinear modelling approaches. The modified vegetation transmissivity is then applied in the Simultaneous Land Parameters Retrieval Model (SLPRM), which is an error minimization method for better retrieval of BT. Afterwards, the Volumetric Soil Moisture (VSM), Land Surface Temperature (LST) as well as canopy temperature (TC) were retrieved through this method in a central region of Iran (300 × 130 km2) from November 2015 to August 2016. The algorithm validation returned promising results, with a 20% improvement in soil moisture retrieval.

The identification of new drug targets is highly challenging, particularly for diseases of the brain. This study describes a general computational gene regulatory framework called CRAFT for drug target discovery, and the authors use CRAFT to identify the microglial membrane receptor Csf1R as a potential therapeutic target for epilepsy.

The integration of ecological and atmospheric characteristics for biodiversity management is fundamental for long-term ecosystem conservation and drafting forest management strategies, especially in the current era of climate change. The explicit modelling of regional ecological responses and their impact on individual species is a significant prerequisite for any adaptation strategy. The present study focuses on predicting the regional distribution of Rhododendron arboreum, a medicinal plant species found in the Himalayan region. Advanced Species Distribution Models (SDM) based on the principle of predefined hypothesis, namely BIOCLIM, was used to model the potential distribution of Rhododendron arboreum. This hypothesis tends to vary with the change in locations, and thus, robust models are required to establish nonlinear complex relations between the input parameters. To address this nonlinear relation, a class of deep neural networks, Convolutional Neural Network (CNN) architecture is proposed, designed, and tested, which eventually gave much better accuracy than the BIOCLIM model. Both of the models were given 16 input parameters, including ecological and atmospheric variables, which were statistically resampled and were then utilized in establishing the linear and nonlinear relationship to better fit the occurrence scenarios of the species. The input parameters were mostly acquired from the recent satellite missions, including MODIS, Sentinel-2, Sentinel-5p, the Shuttle Radar Topography Mission (SRTM), and ECOSTRESS. The performance across all the thresholds was evaluated using the value of the Area Under Curve (AUC) evaluation metrics. The AUC value was found to be 0.917 with CNN, whereas it was 0.68 with BIOCLIM, respectively. The performance evaluation metrics indicate the superiority of CNN for species distribution over BIOCLIM.

Soil Moisture Deficit (SMD) is a key indicator of soil water content changes and is valuable to a variety of applications, such as weather and climate, natural disasters, agricultural water management, etc. Soil Moisture and Ocean Salinity (SMOS) is a dedicated mission focused on soil moisture retrieval and can be utilized for SMD estimation. In this study, the use of soil moisture derived from SMOS has been provided for the estimation of SMD at a catchment scale. Several approaches for the estimation of SMD are implemented herein, using algorithms such as Random Forests (RF) and Genetic Algorithms coupled with Least Trimmed Squares (GALTS) regression. The results show that for SMD estimation, the RF algorithm performed best as compared to the GALTS, with Root Mean Square Errors (RMSEs) of 0.021 and 0.024, respectively. All in all, our study findings can provide important assistance towards developing the accuracy and applicability of remote sensing-based products for operational use.

BCATs catabolize leucine and other BCAAs. Here the authors show that BCAA metabolism affects the broken Krebs cycle, reprogramming macrophages to be less proinflammatory, and show that BCAT1 inhibitor ERG240 can treat arthritis in mice and glomerulonephritis in rats.

Spaceborne and airborne polarimetric synthetic-aperture radar interferometry (PolInSAR) data have been extensively used for forest parameter retrieval. The PolInSAR models have proven their potential in the accurate measurement of forest vegetation height. Spaceborne monostatic multifrequency data of different SAR missions and the Global Ecosystem Dynamics Investigation (GEDI)-derived forest canopy height map were used in this study for vegetation height retrieval. This study tested the performance of PolInSAR complex coherence-based inversion models for estimating the vegetation height of the forest ranges of Doon Valley, Uttarakhand, India. The inversion-based forest height obtained from the three-stage inversion (TSI) model had higher accuracy than the coherence amplitude inversion (CAI) model-based estimates. The vegetation height values of GEDI-derived canopy height map did not show good relation with field-measured forest height values. It was found that, at several locations, GEDI-derived forest height values underestimated the vegetation height. The statistical analysis of the GEDI-derived estimates with field-measured height showed a high root mean square error (RMSE; 5.82 m) and standard error (SE; 5.33 m) with a very low coefficient of determination (R2; 0.0022). An analysis of the spaceborne-mission-based forest height values suggested that the L-band SAR has great potential in forest height retrieval. TSI-model-based forest height values showed lower p-values, which indicates the significant relation between modelled and field-measured forest height values. A comparison of the results obtained from different SAR systems is discussed, and it is observed that the L-band-based PolInSAR inversion gives the most reliable result with low RMSE (2.87 m) and relatively higher R2 (0.53) for the linear regression analysis between the modelled tree height and the field data. These results indicate that higher wavelength PolInSAR datasets are more suitable for tree canopy height estimation using the PolInSAR inversion technique.

Forest degradation is considered to be one of the major threats to forests over the globe, which has considerably increased in recent decades. Forests are gradually getting fragmented and facing biodiversity losses because of climate change and anthropogenic activities. Future prediction of forest degradation spatiotemporal dynamics and fragmentation is imperative for generating a framework that can aid in prioritizing forest conservation and sustainable management practices. In this study, a random forest algorithm was developed and applied to a series of Landsat images of 1998, 2008, and 2018, to delineate spatiotemporal forest cover status in the sanctuary, along with the predictive model viz. the Cellular Automata Markov Chain for simulating a 2028 forest cover scenario in Shoolpaneshwar Wildlife Sanctuary (SWS), Gujarat, India. The model’s predicting ability was assessed using a series of accuracy indices. Moreover, spatial pattern analysis—with the use of FRAGSTATS 4.2 software—was applied to the generated and predicted forest cover classes, to determine forest fragmentation in SWS. Change detection analysis showed an overall decrease in dense forest and a subsequent increase in the open and degraded forests. Several fragmentation metrics were quantified at patch, class, and landscape level, which showed trends reflecting a decrease in fragmentation in forest areas of SWS for the period 1998 to 2028. The improvement in SWS can be attributed to the enhanced forest management activities led by the government, for the protection and conservation of the sanctuary. To our knowledge, the present study is one of the few focusing on exploring and demonstrating the added value of the synergistic use of the Cellular Automata Markov Chain Model Coupled with Fragmentation Statistics in forest degradation analysis and prediction.

Space-borne soil moisture (SM) satellite products such as those available from Soil Moisture Active Passive (SMAP) offer unique opportunities for global and frequent monitoring of SM and also to understand its spatiotemporal variability. The present study investigates the performance of the SMAP L4 SM product at selected experimental sites across four continents, namely North America, Europe, Asia and Australia. This product provides global scale SM estimates at 9 km × 9 km spatial resolution at daily intervals. For the product evaluation, co-orbital in situ SM measurements were used, acquired at 14 test sites in North America, Europe, and Australia belonging to the International Soil Moisture Network (ISMN) and local networks in India. The satellite SM estimates of up to 0–5 cm soil layer were compared against collocated ground measurements using a series of statistical scores. Overall, the best performance of the SMAP product was found in North America (RMSE = 0.05 m3/m3) followed by Australia (RMSE = 0.08 m3/m3), Asia (RMSE = 0.09 m3/m3) and Europe (RMSE = 0.14 m3/m3). Our findings provide important insights into the spatiotemporal variability of the specific operational SM product in different ecosystems and environments. This study also furnishes an independent verification of this global product, which is of international interest given its suitability for a wide range of practical and research applications.

Mangrove forest coastal ecosystems contain significant amount of carbon stocks and contribute to approximately 15% of the total carbon sequestered in ocean sediments. The present study aims at exploring the ability of Earth Observation EO-1 Hyperion hyperspectral sensor in estimating aboveground carbon stocks in mangrove forests. Bhitarkanika mangrove forest has been used as case study, where field measurements of the biomass and carbon were acquired simultaneously with the satellite data. The spatial distribution of most dominant mangrove species was identified using the Spectral Angle Mapper (SAM) classifier, which was implemented using the spectral profiles extracted from the hyperspectral data. SAM performed well, identifying the total area that each of the major species covers (overall kappa = 0.81). From the hyperspectral images, the NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index) were applied to assess the carbon stocks of the various species using machine learning (Linear, Polynomial, Logarithmic, Radial Basis Function (RBF), and Sigmoidal Function) models. NDVI and EVI is generated using covariance matrix based band selection algorithm. All the five machine learning models were tested between the carbon measured in the field sampling and the carbon estimated by the vegetation indices NDVI and EVI was satisfactory (Pearson correlation coefficient, R, of 86.98% for EVI and of 84.1% for NDVI), with the RBF model showing the best results in comparison to other models. As such, the aboveground carbon stocks for species-wise mangrove for the study area was estimated. Our study findings confirm that hyperspectral images such as those from Hyperion can be used to perform species-wise mangrove analysis and assess the carbon stocks with satisfactory accuracy.

Macrophages can fuse to form osteoclasts in bone or multinucleate giant cells (MGCs) as part of the immune response. We use a systems genetics approach in rat macrophages to unravel their genetic determinants of multinucleation and investigate their role in both bone homeostasis and inflammatory disease. We identify a trans-regulated gene network associated with macrophage multinucleation and Kcnn4 as being the most significantly trans-regulated gene in the network and induced at the onset of fusion. Kcnn4 is required for osteoclast and MGC formation in rodents and humans. Genetic deletion of Kcnn4 reduces macrophage multinucleation through modulation of Ca2+ signaling, increases bone mass, and improves clinical outcome in arthritis. Pharmacological blockade of Kcnn4 reduces experimental glomerulonephritis. Our data implicate Kcnn4 in macrophage multinucleation, identifying it as a potential therapeutic target for inhibition of bone resorption and chronic inflammation.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Soil moisture represents a vital component of the ecosystem, sustaining life-supporting activities at micro and mega scales. It is a highly required parameter that may vary significantly both spatially and temporally. Due to this fact, its estimation is challenging and often hard to obtain especially over large, heterogeneous surfaces. This study aimed at comparing the performance of four widely used interpolation methods in estimating soil moisture using GPS-aided information and remote sensing. The Distance Weighting (IDW), Spline, Ordinary Kriging models and Kriging with External Drift (KED) interpolation techniques were employed to estimate soil moisture using 82 soil moisture field-measured values. Of those measurements, data from 54 soil moisture locations were used for calibration and the remaining data for validation purposes. The study area selected was Varanasi City, India covering an area of 1535 km2. The soil moisture distribution results demonstrate the lowest RMSE (root mean square error, 8.69%) for KED, in comparison to the other approaches. For KED, the soil organic carbon information was incorporated as a secondary variable. The study results contribute towards efforts to overcome the issue of scarcity of soil moisture information at local and regional scales. It also provides an understandable method to generate and produce reliable spatial continuous datasets of this parameter, demonstrating the added value of geospatial analysis techniques for this purpose.

This study provides the results of an extensive investigation of the Advanced Scaterometter (ASCAT) surface soil moisture global operational product accuracy across three continents (United States of America (USA), Europe, and Australia). ASCAT predictions of surface soil moisture were compared against near concurrent in situ measurements from the FLUXNET observational network. A total of nine experimental sites were used to assess the accuracy of ASCAT Surface Soil Moisture (ASCAT SSM) predictions for two complete years of observations (2010, 2011). Results showed a generally reasonable agreement between the ASCAT product and the in situ soil moisture measurements in the 0–5 cm soil moisture layer. The Root Mean Square Error (RMSE) was below 0.135 m3 m−3 at all of the sites. With a few exceptions, Pearson’s correlation coefficient was above 45%. Grassland, shrublands, and woody savanna land cover types exhibited satisfactory agreement in all the sites analyzed (RMSE ranging from 0.05 to 0.13 m3 m−3). Seasonal performance was tested, but no definite conclusion can be made with statistical significance at this time, as the seasonal results varied from continent to continent and from year to year. However, the satellite and in situ measurements for Needleleaf forests were practically uncorrelated (R = −0.11 and −0.04). ASCAT predictions overestimated the observed values at all of the sites in Australia. A positive bias of approximately 0.05 m3 m−3 was found with respect to the observed values that were in the range 0–0.3 m3 m−3. Better agreement was observed for the grassland sites in most cases (RMSE ranging from 0.09 to 0.10 m3 m−3 and R from 0.46 to 0.90). Our results provide supportive evidence regarding the potential value of the ASCAT global operational product for meso-scale studies and the relevant practical applications. A key contribution of this study is a comprehensive evaluation of ASCAT product soil moisture estimates at different sites around the globe. These sites represent a variety of climatic, environmental, biome, and topographical conditions.

Drought is an intricate phenomenon assessed by analyzing several hydro-meteorological factors such as rainfall, soil moisture, temperature, evapotranspiration, vegetation cover, etc. For effective drought hazard management and preparedness, the monitoring of drought requires the evaluation of influencing factors via the Drought Hazard Inventory (DHI). The main objective of this study is to compare spatial occurrences of drought hazard with the help of microwave and Optical/Infrared datasets obtained from multiple satellites. The long-term climatology of the Tropical Rainfall Measuring Mission (TRMM) Rainfall, Climate Change Initiative soil moisture (CCI-SM) and Moderate Resolution Imaging Spectroradiometer (MODIS) derived Land Surface Temperature (LST), Evapotranspiration (ET) and Normalized Difference Vegetation Index (NDVI) were used in this study for drought hazard assessment. This study was carried out in the Bundelkhand region of Uttar Pradesh, considered as one of the most frequent and dominant drought-prone areas of India. The current study includes the Analytical Hierarchy Process (AHP) technique based on Multi-Criteria Decision Making Analysis (MCDM) for weighting assignment and decision making, while the geospatial platform was used for data layer standardization, integration, and drought assessment. The results indicate that a large percentage of area (38.05% and 27.54%, respectively) lying in the central part of Bundelkhand region is under high to extreme drought conditions, where precautionary measures are needed. To demonstrate the robustness of our results, we compare them with the long-term in-situ ground water depletion as a proxy. Finally, based on the findings of this study, we recommend the methodology for drought assessment at a larger scale, as well as in the remote areas where ground based measurements are limited.

Technological advances in hyperspectral remote sensing have been widely applied in heavy metal soil contamination studies, as they are able to provide assessments in a rapid and cost-effective way. The present work investigates the potential role of combining field and laboratory spectroradiometry with geochemical data of lead (Pb), zinc (Zn), copper (Cu) and cadmium (Cd) in quantifying and modelling heavy metal soil contamination (HMSC) for a floodplain site located in Wales, United Kingdom. The study objectives were to: (i) collect field- and lab-based spectra from contaminated soils by using ASD FieldSpec® 3, where the spectrum varies between 350 and 2500 nm; (ii) build field- and lab-based spectral libraries; (iii) conduct geochemical analyses of Pb, Zn, Cu and Cd using atomic absorption spectrometer; (iv) identify the specific spectral regions associated to the modelling of HMSC; and (v) develop and validate heavy metal prediction models (HMPM) for the aforementioned contaminants, by considering their spectral features and concentrations in the soil. Herein, the field- and lab-based spectral features derived from 85 soil samples were used successfully to develop two spectral libraries, which along with the concentrations of Pb, Zn, Cu and Cd were combined to build eight HMPMs using stepwise multiple linear regression. The results showed, for the first time, the feasibility to predict HMSC in a highly contaminated floodplain site by combining soil geochemistry analyses and field spectroradiometry. The generated models help for mapping heavy metal concentrations over a huge area by using space-borne hyperspectral sensors. The results further demonstrated the feasibility of combining geochemistry analyses with filed spectroradiometric data to generate models that can predict heavy metal concentrations.

The conservation and protection of painted rock shelters is an important issue. Throughout the world, if unprotected, they are vulnerable to vandalism or to industrial activities such as quarrying. This research explores the integrated use of a Geographic Information System (GIS) with a multi-criteria decision support system and fuzzy logic to identify possible rock art sites over the Vindhyan Plateau in the district of Mirzapur, Central India. The methodology proposed compares results obtained by spatial modelling with validation data derived from recent exhaustive field surveys of more than forty newly discovered rock-shelters in the Vindhyan region. The zones obtained by predictive modelling are in agreement with validation datasets and show that the method can be used for new site prospection. This method represents a potential tool for landscape planners and policy makers to employ when seeking protection from anthropogenic activities of potential areas of painted rock-shelter sites and archaeological deposits.