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Excessive growth of microalgae results in the eutrophication and degradation of water quality in lakes and ponds. Microalgae have proved to be very beneficial for sustainable development. Its applications, starting from growth to death, make them a valuable resource for food and energy production. However, presence of microalgae in surface water sources affects water quality and its beneficial uses. The excess nutrient-rich agriculture runoff also adds problems to the downstream systems. Nutrient removal and microalgae harvesting are considered the most problematic task for improving water quality of surface waters. The main objective of the present study was to improve the water quality of nutrient rich waters using attached growth systems. The use of batch and continuous flow baffled attached growth systems has successfully improved the water quality and produced algal biomass that can be utilized or transformed into other valuable products. The performance of continuous flow baffled attached growth system was better and it improved the quality of oligotrophic, mesotrophic, eutrophic and hypereutrophic waters by removing>95% of TN and TP in 35, 63, 63 and 4 days, with biomass productivity of 2.69 ± 0.21, 3.17 ± 0.06, 3.61 ± 0.16 and 4.64 ± 0.3 gDWm−2 day−1, respectively. [ABSTRACT FROM AUTHOR]

The polarimetric synthetic aperture radar (PolSAR) provides us with a two-by-two scattering matrix data set. The ensemble averaged coherency matrix in an imaging window derived using a scattering matrix has all non-zero elements in its three-by-three matrix. It is a full 3 × 3 matrix that bears nine real-valued and independent polarimetric parameters inside. In the proposed decomposition method, G5U, we preprocess observed coherency matrix [T] by using two consecutive unitary transformations to become an ideal form for five-component decomposition. The transformation reduces nine parameters to seven, which is the best fit for five-component scattering model expansion. We can retrieve five powers corresponding to surface scattering, double bounce scattering, volume scattering, oriented dipole scattering, and compound dipole scattering, directly. These powers can be calculated easily and used to display superb polarimetric RBG images as never before, and are further applicable for polarimetric calibration, classification, validation, etc. [ABSTRACT FROM AUTHOR]

Glacier velocity is an important parameter to characterize glacier dynamics and to derive ice thickness and mass balance. The 2-pass/3-pass Differential Synthetic Aperture Radar Interferometry (DInSAR) techniques are advantageous in estimating glacier movements. However, the 2-pass DInSAR requires an external Digital Elevation Model (DEM), whereas the 3-pass DInSAR does not. The 3-pass DInSAR technique is adopted with Sentinel-1A/1B to map the Svalbard glacier flow velocities. Furthermore, the effect of glacier surface elevation change on the 2-pass DInSAR results were revealed by comparing glacier velocity with the two time period topographic information. The coherence indicates that Sentinel-1A/1B has a high potential to infer operational glacier velocity using the 3-pass DInSAR method with an average atmospheric uncertainty of 0.24cm/day over the studied region. The precision of the 3-pass DInSAR is analyzed by comparing 2-pass derived line-of-sight (LOS) velocity on the same dates. The 3- pass DInSAR achieves high-resolution and detailed information. [ABSTRACT FROM AUTHOR]

• Importance of GEDI-Lidar data filtering before biomass model development. • Forest AGB model development based on relative height metrics and ground truth value. • Conversion of GEDI Lidar footprints into biomass footprints and computation of forest compartment-wise biomass. • Forest AGB compartment wise map and validation. Measurement of forest aboveground biomass is critical to account for carbon budgeting, carbon flux monitoring, biodiversity health monitoring, and climate change studies. Therefore, there is a crucial requirement to develop methods for improvement in forest biomass estimation. This study uses recently launched Global Ecosystem Dynamic Investigations (GEDI) mission LiDAR data combined with field-measured biomass and geospatial analysis to estimate forest aboveground woody biomass (AGB) in the managed forest. From a total of 300 predictor variables obtained from GEDI height metrics (100) and derived products (200), an exhaustive search is performed using regsubsets function in the leaps package, which uses Adjusted correlation coefficient (Adj. R 2 ) statistical measure to select suitable predictor variables for AGB estimation. We constrained the search algorithm with a maximum number of variables to be equal to or lower than four to avoid over-fitting. Therefore we selected the best set of variables with the least root mean squared error (RMSE) and high Adj. R 2 , and developed an AGB model using regression analysis. The developed model predicted AGB with R 2 = 0.75 , RMSE = 32.06 Mg/ha, and relative RMSE = 27.26%. The results obtained in this study illustrate the potential of GEDI LiDAR data to model forest AGB with improved accuracy. [ABSTRACT FROM AUTHOR]

With realizing the potential of algal biomass as a good natural resource for the harnessing of valuable bioproducts, algal biomass production has gained a lot of interest in recent years. However, due to some limitations such as low harvesting efficiency, higher nutrient supply, and high water requirement, the production of algal biomass is uneconomical. Over the past several years, researchers are continuously working on growing algae as a biofilm for easy microalgae harvesting, concentration of algal biomass to a great extent, and requiring less quantity of water as compared to other microalgae cultivation methods. Most of the documented studies have been carried out on either use of algal biomass for tertiary treatment of wastewater or cultivation and harvesting of algal biomass for biofuel production. Limited research studies have documented other applications of the algal biofilm system. The present review paper summarizes the current knowledge on various factors affecting microalgae growth, development of algal biofilm, and operation of algal biofilm systems to help properly understand and optimize these factors for better economics, more positive environmental impacts, and successful potential applications of the attached growth systems. The important factors include the structure of algal biofilms, EPS matrix, supporting materials, nutrient availability, environmental conditions, and biofilm thickness and harvesting frequency. The potential applications such as wastewater treatment, CO2 sequestration, microalgae–microbial fuel cell, large-scale biomass production, and water quality improvement are also discussed. [ABSTRACT FROM AUTHOR]

In this article, an algorithm for snow depth (SD) and snow water equivalent (SWE) retrieval is proposed based on a polarimetric synthetic aperture radar (SAR) decomposition model and field measured snow data. The field campaigns were conducted at the Dhundi observatory (in the Indian Himalaya) in January 2016 and 2018. The field-measured data are used here to build a linear regression between wetness (w) and the imaginary part of the snow permittivity (ε''), and the validation of retrieved SD and SWE. The snow density (ρs) and w are calculated with a generalized volume parameter derived using a theoretical model and SAR data (coherency matrix). These snow parameters and the field-based regression relating w and ε'' are eventually used for the SD and SWE retrieval. Three TerraSAR-X scenes of the quad-polarization X-band data acquired in January 2016 are used to study the effect of the snow conditions on the accuracy of the proposed algorithm. The mean absolute error (MAE), root-mean-square error (RMSE), and index of agreement (IOA) for SD are 4.84 cm, 5.12 cm, and 0.71, respectively. On the other hand, for SWE, it is 1.42 cm, 1.53 cm, and 0.71, respectively. [ABSTRACT FROM AUTHOR]

Excessive growth of microalgae due to use of fertilizers, detergents, and discharge of domestic waste results in the eutrophication and degradation of the water quality of lakes and ponds. The present study aimed to improve the quality of pond water by harvesting mixed cultures of microalgae grown in a pond using Moringa oleifera (MO) extract as a coagulant. The study comprises evaluating the effects of coagulant dose, pH, mixing time, mixing rate, settling time, temperature, and algal biomass concentration on nutrient and microalgae cell removal. The pond water quality restoration was assessed by characterization of raw and MO‐treated water. The MO was found to be very effective for mixed culture microalgae harvesting with flocculation efficiency of 92.97% at dose of 15 ml/L of MO extract, pH 8, mixing time 5 min, mixing rate 30 rpm, and 20‐min settling time. The results have shown significant improvement in pond water quality with reduction in turbidity from 388.16 ± 48.23 to 8.39 ± 1.0 NTU, chlorophyll‐a from 635.01 ± 86.20 to 15.03 ± 1.5 mg/m3, total nitrogen (TN) from 20.37 ± 4.64 to 12.10 ± 1.19 mg/L, and total phosphorus (TP) from 2.05 ± 0.10 to 1.61 ± 0.24 mg/L. Practitioner points: Production of biomass and water quality improvement of the pond at the same timeMO extract shows better removal efficiency at a lower dose with fast kineticsCost‐effective flocculant saves time and energy during flocculationCommon bioflocculant to remove both microalgae and nutrients. [ABSTRACT FROM AUTHOR]

The $2\times 2$ relative scattering matrix $[S]$ is characterized by five elements (three amplitudes and two relative phases). A more suitable representation of the data in terms of power expression for target identification exists in the $3\times 3$ Pauli-based polarimetric coherency matrix $[T]$. The aim of this work is to expand the physical interpretation of $[T]$ elements in terms of a physical scattering mechanism, which has not yet been achieved completely. Compound scattering matrix nature is considered to interpret the elements of $[T]$. Compound scattering phenomenon, and thereby generated matrices, are verified with measured compound scattering matrices in an anechoic chamber by comparing measured and theoretical polarization signature responses. The behavior of measured polarization signature(s) is consistent with the theoretical/hypothetical polarization signature(s). It is also observed that the combination of two or more dipole-type targets formed the scattering generators and helped to reveal the unknown scattering mechanisms in the coherency matrix. Furthermore, the existence of compound scattering phenomenon is also justified with airborne and spaceborne fully polarimetric synthetic aperture radar (POLSAR) data by implementing recent physical model-based scattering power decomposition methods. [ABSTRACT FROM AUTHOR]

Applications of fully polarimetric synthetic aperture radar (POLSAR) have increased in the past few decades. The potential of model-based decompositions is coupled with polarimetric information extraction from the POLSAR data for target identification and classification. The coherency matrix $[T]$ with nine independent parameters, and associated with some physical scattering models, serves as input to these decompositions. This paper attempts to assign one such physical scattering model to the real part of $T_{23}$ ($\text {Re}\{{T}_{23}\}$) and develop a new scattering power decomposition model, called as the seven-component scattering decomposition (7SD). Previously developed scattering power models have eliminated $\text {Re}\{T_{23}\}$ , assuming the orientation angle compensation condition, to reduce the number of independent $[T]$ parameters. The proposed 7SD model has been tested on fully polarimetric SAR data sets acquired by the spaceborne Advanced Land Observing Satellite-2/Phased Array type L-band Synthetic Aperture Radar-2 (ALOS-2/PALSAR-2) and airborne F-SAR, and the results are compared with the existing scattering power decompositions. The physical scattering model for $\text {Re}\{T_{23}\}$ is derived from a particular configuration of dipoles (referred to as “mixed dipole” configuration), which gives rise to compound scattering. The mixed-dipole scattering occurs in urban areas that are highly oriented to the radar illumination direction as well as in vegetation areas. 7SD also delivers an additional mixed dipole scattering power compared to the previous six-component scattering model. The mixed-dipole scattering model reduces the contribution of volume scattering power in double-bounce predominant areas (such as oriented urban blocks), thereby imparting improved understanding of the polarimetric information contained in the coherency matrix. [ABSTRACT FROM AUTHOR]

Low-frequency synthetic aperture radar (SAR) sensors are prone to ionospheric irregularity structures that affect the amplitude and/or phase of the radar signal. Azimuthal striping caused by amplitude scintillation in SAR data is an initial observation of such effects. In the absence of any physical model and/or technique to mitigate scintillation stripes, we have applied a 2-D fast Fourier transform (FFT) approach for improved interpretation and target identification in fully polarimetric SAR (POLSAR) model-based decomposition scattering powers. Few scenes of fully polarimetric Advanced Land Observation Satellite-Phased Array type L-band Synthetic Aperture Radar (ALOS/PALSAR) and ALOS-2/PALSAR-2 under different ionospheric conditions are acquired and used in this study. As a reference, data sets over the same areas are also acquired on different dates with negligible ionospheric activity (ionospheric quiet day). All the data sets are corrected for the Faraday rotation angle ($\Omega$), which is estimated using the Bickel–Bates approach. Three correction strategies of scintillation-affected model-based scattering power decomposition images are presented. The strategies (zero masking, thresholding, and averaging-post-thresholding) are implemented on 2-D FFT of scattering power images. An intercomparison of corrected decomposition results from the correction strategies demonstrates the capability of 2-D-FFT-based correction strategies to improve the dominant scattering component by 3%–5% for homogeneous terrains. The correction method based on averaging-post-thresholding gives the best results that are further tested by performing a supervised classification. The overall accuracy and kappa coefficient (${OA}, \hat {k}$) of the averaging-post-thresholding technique (72.87%, 0.59) is comparable to those of the reference data (78.74%, 0.69). [ABSTRACT FROM AUTHOR]

Fully polarimetric model-based decompositions are developed by accounting for the physical scattering model and experimental polarimetric SAR data acquisition processes. These decompositions offer the promising straightforward interpretation and highly improved inversion models for visualizing images of scattering scenarios optimally. However, the attempts in existing decompositions to implement the split real and imaginary components of the $T_{13}$ element of the coherency matrix have been hampered by the absence of physical models to fit the coherency matrix. In this paper, two additional physical scattering submodels are derived. The real and imaginary parts of $T_{13}$ are accounted for by implementing two newly developed physical scattering models. (One is for oriented dipole scattering and the other is for oriented quarter-wave reflection.) Furthermore, this paper is extended by implementing these physical models into a six-component scattering power model-based decomposition. To this date, the developed novel decompositions account for the maximum elements of the coherency matrix in a physical manner compared to the existing model-based decompositions. The proposed novel decomposition is tested on L-band and X-band fully polarimetric SAR data sets of the Advanced Land Observing Satellite-2/Phased Array L-band Synthetic Aperture Radar-2 and the X-band TerraSAR-X, respectively. This new decomposition produces additional two scattering submatrix components. Such scattering components are prevalent in vegetation and urban areas and even dominant over highly oriented urban scenarios. The new method enhances the truly existing double-bounce scattering contributions and reduces the overrated volume scattering from double-bounce scatterers. By comparing the results, it is found that the proposed decomposition considerably enhances the SAR image quality and its more correct visualizing presentation compared to existing decompositions. It is also found to be more robust over the oriented urban areas than the existing decompositions, resulting from the utilization of both the real and imaginary components of $T_{13}$ polarimetric information in a physical scattering manner. [ABSTRACT FROM AUTHOR]

The regioselective benzoyloxylation process involves stirring a mixture of phenyl/benzyl-sulfanyl/selenylethanoate 1a-c with appropriate oxidant [benzoyl peroxide (BPO) 2a/ m-chloroperbenzoic acid (m-CPBA) 2b /bis(p-methoxybenzoyl peroxide) (BPM-BPO) 2c] under catalysis of hydrated copper acetate in toluene at 60 °C/RT. Regioselective C–H functionalization of esters 1a-c leads to benzoyloxy substituted phenyl/benzyl-sulfanyl/selenylbisesters 3a-g in good to excellent yields (80–95%). Variably substituted trans-3-phenylsulfanyl-β-lactams 4 employed for C3-H functionalization with BPO 2a generates diastereoisomeric mixture of trans- and cis-3-benzoyloxy-3-(phenylsulfanyl)-β-lactams 5 and 6 as major and minor isomers after efficient column chromatographic purification. The structural confirmation was done using IR, 1H NMR, 13C NMR, DEPT-90 NMR spectroscopic analysis and CHNS elemental analysis. In vitro antibacterial and antifungal evaluations on bisesters suggest benzylselenyl-bisester 3c as potent antimicrobial agent. Out of trans- and cis-3-benzoyloxy-β-lactams 5a and 6a, the trans isomer 5a was active against all microbial strains whereas the cis isomer 6a was completely inactive. [ABSTRACT FROM AUTHOR]

Microalgae with wide range of commercial applications have attracted a lot of attention of the researchers in the last few decades. However, microalgae utilization is not economically sustainable due to high cost of harvesting. A wide range of solid - liquid separation techniques are available for microalgae harvesting. The techniques include coagulation and flocculation, flotation, centrifugation and filtration or a combination of various techniques. Despite the importance of harvesting to the economics and energy balance, there is no universal harvesting technique for microalgae. Therefore, this review focuses on assessing technical, economical and application potential of various harvesting techniques so as to allow selection of an appropriate technology for cost effectively harvesting of microalgae from their culture medium. Various harvesting and concentrating techniques of microalgae were reviewed to suggest order of suitability of the techniques for four main microalgae applications i.e biofuel, human and animal food, high valued products, and water quality restoration. For deciding the order of suitability, a comparative analysis of various harvesting techniques based on the six common criterions (i.e biomass quality, cost, biomass quantity, processing time, species specific and toxicity) has been done. Based on the order of various techniques vis-a-vis various criteria and preferred order of criteria for various applications, order of suitability of harvesting techniques for various applications has been decided. Among various harvesting techniques, coagulation and flocculation, centrifugation and filtration were found to be most suitable for considered applications. These techniques may be used alone or in combination for increasing the harvesting efficiency. [ABSTRACT FROM AUTHOR]

In this study, mass balance for Chorabari glacier of the Central Himalaya, India has been estimated. This glacier has been considered for the case study as it feeds the Mandakini River and was one of the reasons for flash floods in Rudraprayag district, Uttarakhand in 2013. The observations are based on glacier area/length change and rate of melting in the multidecade (1976-2016) using Landsat data. The study estimates an overall decrease in area at 0.8% per year. Elevation change has been studied using geodetic method utilizing shuttle radar topography mission and TanDEM-X datasets, which have shown a decrease in elevation in 16 years (2000-16). From these remotely observed parameters, a negative mass balance for the decade 2000-11 indicates glacier retreat. This study highlights the applicability of optical and radar remote sensing for the Himalayan glaciers, for better disaster management and understanding glacier dynamics in response to climate change. [ABSTRACT FROM AUTHOR]

This paper focuses on the development of a novel algorithm for deriving snowpack density over the snow-covered region of the Himalayas. The analysis utilizes fully polarimetric TerraSAR-X synthetic aperture radar data sets, field observations, and other ancillary information for the retrieval of snowpack density. The algorithm involves the development of a new generalized hybrid decomposition model. The generalized volume scattering parameter from the decomposition model is inverted for snow density estimation. A few field data measurements’ campaigns were carried out, within near-real time of satellite passing over the area, to collect various parameters such as temperature, water content, and the density of the snowpack at varying depths. These field observations are further used for validation of the results obtained from the inversion algorithm. It is also found that the model-estimated snowpack density is highly congruent with the field-measured snowpack density. The mean absolute error of snowpack density, root-mean-square error, and index of agreement are found to be 9.9 kg/ \textm^3 , 10 kg/ \textm^3 , and 0.96, respectively, which are well within the acceptable range. [ABSTRACT FROM PUBLISHER]

TerraSAR-X/TanDEM-X form the first space-borne interferometer providing full-PolInSAR acquisitions with a near-zero temporal gap. This paper explores the potential of X-band SAR data for forest height estimation over tropical Indian forests. Unique TanDEM-X data sets with varied spatial baselines are acquired over a tropical, deciduous Indian forest. Multiple dual polarimetric and fully polarimetric acquisitions are analysed and the best performance is achieved using fully polarimetric data acquired over both high- and low-biomass forests ( r 2 = 0.86 and r 2 = 0.80 respectively). It is shown for the first time that, for deciduous Indian tropical forests, seasonal changes such as leaf-fall affect the PolInSAR based height inversion performance. For species exhibiting leaf-fall, acquisition during leaf-on season provides better height inversion accuracy ( r 2 = 0.75) than for leaf-fall season ( r 2 = 0.65). Results show consistent PolInSAR height inversion performance for both high- and low-biomass forests in both natural and managed forest tracts. Spatial baseline is a critical factor for selection of suitable acquisitions for PolInSAR based height estimation. The effect of spatial baseline on height inversion performance is also discussed. [ABSTRACT FROM AUTHOR]

Rice straw is a suitable alternative to a cheaper carbohydrate source for the production of ethanol. For pretreatment efficiency, different sodium hydroxide concentrations (0.5–2.5% w/v) were tested. When compared to other concentrations, rice straw processed with 2% NaOH (w/v) yielded more sugar (8.17 ± 0.01 mg/ml). An alkali treatment induces effective delignification and swelling of biomass. The pretreatment of rice straw with 2% sodium hydroxide (w/v) is able to achieve 55.34% delignification with 53.30% cellulose enrichment. The current study shows the effectiveness of crude cellulolytic preparation from Aspergillus niger resulting in 80.51 ± 0.4% cellulose hydrolysis. Rice straw hydrolysate was fermented using ethanologenic Saccharomyces cerevisiae (yeast) and Zymomonas mobilis (bacteria). Overall, superior efficiency of sugar conversion to ethanol 70.34 ± 0.3% was obtained with the yeast compared to bacterial strain 39.18 ± 0.5%. The current study showed that pretreatment with sodium hydroxide is an effective method for producing ethanol from rice straw and yeast strain S. cerevisiae having greater fermentative potential for bioethanol production than bacterial strain Z. mobilis. [ABSTRACT FROM AUTHOR]

This paper examines the capability assessment of fully polarimetric L-band data for the snow and nonsnow-area classifications. The data sets used are the fully polarimetric Advanced Land Observation Satellite-Phased Array-Type L-Band Synthetic Aperture Radar data, optical Advanced Land Observing Satellite (ALOS)-advanced visible and near-infrared radiometer-2 data close to the radar acquisition, and environmental satellite-advanced synthetic aperture radar data. Several parameters are used to discriminate the snow-covered areas from nonsnow-covered areas in the Indian Himalayan region, including backscattering coefficients, the ratio of cross/copolarized backscattering power and polarization fraction (PF) value. Supervised classification schemes are employed using polarimetric decomposition methods based on the complex Wishart classifier. The accuracy of the classification was found to be 97.95% for the Wishart-supervised classification. Among various parameters and methods, it was found that the alternative newly proposed PF scheme, based on the implementation of fully polarimetric synthetic aperture radar data, yielded the best classification result in the absence of the training samples. The PF value has been effective for discrimination of the snow-covered areas from nonsnow-covered areas, debris-covered glacier, and vegetation. The results of this investigation show that L-band fully polarimetric SAR data provide considerable improvement but may not possess the optimal capability to discriminate snow from other inherent natural and man-made scatterers in heavy snow-laden mountainous scenarios, which may require fully polarimetric S-band or C-band PolSAR measurements. [ABSTRACT FROM AUTHOR]

This paper presents a new general four-component scattering power decomposition method by implementing a set of unitary transformations for the polarimetric coherency matrix. There exist nine real independent observation parameters in the 3 \times 3 coherency matrix with respect to the second-order statistics of polarimetric information. The proposed method accounts for all observation parameters in the new scheme. It is known that the existing four-component decomposition method reduces the number of observation parameters from nine to eight by rotation of the coherency matrix and that it accounts for six parameters out of eight, leaving two parameters (i.e., the real and imaginary parts of T13 component) unaccounted for. By additional special unitary transformation to this rotated coherency matrix, it became possible to reduce the number of independent parameters from eight to seven. After the unitary transformation, the new four-component decomposition is carried out that accounts for all parameters in the coherency matrix, including the remaining T13 component. Therefore, the proposed method makes use of full utilization of polarimetric information in the decomposition. The decomposition also employs an extended volume scattering model, which discriminates volume scattering between dipole and dihedral scattering structures caused by the cross-polarized HV component. It is found that the new method enhances the double-bounce scattering contributions over the urban areas compared with those of the existing four-component decomposition, resulting from the full utilization of polarimetric information, which requires highly improved acquisitions of the cross-polarized HV component above the noise floor. [ABSTRACT FROM PUBLISHER]

The production of poly β-hydroxybutyrate (PHB) by Bacillus subtilis NG220 was observed utilizing the sugar industry waste water supplemented with various carbon and nitrogen sources. At a growth rate of 0.14 g h-1 L-1, using sugar industry waste water was supplemented with maltose (1% w/v) and ammonium sulphate (1% w/v); the isolate produced 5.297 g/L of poly β-hydroxybutyrate accumulating 51.8% (w/w) of biomass. The chemical nature of the polymer was confirmed with nuclear magnetic resonance, Fourier transform infrared, and GC-MS spectroscopy whereas thermal properties were monitored with differential scanning calorimetry. In biodegradability study, when PHB film of the polymer (made by traditional solvent casting technique) was subjected to degradation in various natural habitats like soil, compost, and industrial sludge, it was completely degraded after 30 days in the compost having 25% (w/w) moisture. So, the present study gives insight into dual benefits of conversion of a waste material into value added product, PHB, and waste management. [ABSTRACT FROM AUTHOR]

Background. Polyhydroxyalkanoates (PHAs) are storage materials that accumulate by various bacteria as energy and carbon reserve materials. They are biodegradable, environmentally friendly, and also biocompatible bioplastics. Unlike petrochemical-based plastics that take several decades to fully degrade, PHAs can be completely degraded within a year by variety of microorganisms into CO2 and water. In the present study, we aim to utilize pulp, paper, and cardboard industry sludge and waste water for the isolation and screening of polyhydroxyalkanoates (PHAs) accumulating bacteria and production of cost-effective PHB using cardboard industry waste water. Results. A total of 42 isolates showed black-blue coloration when stained with Sudan black B, a preliminary screening agent for lipophilic compounds, and a total of 15 isolates showed positive result with Nile blue A staining, a more specific dye for PHA granules. The isolates NAPll and NACl showed maximum PHA production 79.27% and 77.63% with polymer concentration of 5.236 g/L and 4.042 g/L with cardboard industry waste water. Both of the selected isolates, NAPll and NACl, were classified up to genus level by studying their morphological and biochemical characteristics and were found to be Enterococcus sp., Brevundimonas sp. and, respectively. Conclusion. The isolates Enterococcus sp. NAPll and Brevundimonas sp. NACl can be considered as good candidates for industrial production of PHB from cardboard industry waste water. We are reporting for the first time the use of cardboard industry waste water as a cultivation medium for the PHB production. [ABSTRACT FROM AUTHOR]

Snow cover is an important parameter for hydrological modelling and climate change modelling. Various methods are available only for wet snow-cover mapping using conventional synthetic aperture radar (SAR) data. Total snow (wet + dry) cover mapping with SAR data is still a topical research area. Therefore, incoherent target decomposition theorems have been implemented on fully polarimetric SAR data to characterize the scattering of various targets. Further classification techniques – both unsupervised and supervised – have been applied for accurate mapping of total snow cover. For this purpose, Advanced Land Observing Satellite – phased array-type L-band SAR (ALOS–PALSAR) data (12 May 2007) have been analysed for snow classification of glaciated terrain in and around Badrinath region in Himalaya. An ALOS-Advanced Visible and Near Infrared Radiometer (AVNIR)-2 image (6 May 2007) was also used to provide assistance in the selection of different training classes. It has been found that the application of incoherent target decomposition theorems such as H/A/α and four-component scattering mechanism models are good for extracting the desired information of snow cover from fully polarimetric PALSAR data. Finally, based on these target decomposition theorems and the Wishart classifier, PALSAR data have been classified into snow or non-snow cover, and the user accuracy of snow classes was found to be better than the user accuracy of other classes. Hence, the application of incoherent target decomposition theorems with full polarimetric ALOS-PALSAR data is useful for snow-cover mapping. [ABSTRACT FROM PUBLISHER]

In recent years, there has been increased utilization of fully polarimetric synthetic aperture radar (POLSAR) data to study glaciated terrain features for glaciological and climate change modelling. This article is concerned with more accurate results and appropriate analysis of POLSAR data over a highly rugged glaciated area in Himalayan region. For this purpose, the modified Yamaguchi four-component scattering power decomposition (4-CSPD) method with a rotation concept of 3 × 3 coherency matrix [T] about line of sight is evaluated. It has been found that the modified Yamaguchi 4-CSPD method significantly improved the decomposition results as compared with the original 4-CSPD by minimizing the cross-polarized Horizontal-Vertical (HV) components. This modified 4-CSPD leads to enhancement in the double bounce scattering and surface scattering components and also avoids the overestimation problem in the volume scattering component as compared with the original 4-CSPD from the sloped terrain. The significant reductions of the negative power occurrence in the surface scattering (3.9%) and the double bounce scattering (19.7%) components have also been noticed as compared with the original 4-CSPD method over the glaciated area in this part of the Indian Himalaya. [ABSTRACT FROM PUBLISHER]

Phytase producing strain of Klebsiella sp. DB-3 FJ711774.1 was isolated from poultry field soil. The bacteria produced extracellular phytase under submerged fermentation (Smf) using orange peel and other agro-residues as nutritive substrates. Among different substrates, orange peel flour and wheat bran (2% w/v) provide a maximal yield of production of phytase i.e., 3.15 and 2.41 U/ml respectively when Smf was carried out by using fermentative media supplemented with 0.2% sucrose, 0.1% NH4H2PO4; inoculated with 1.5% cell suspension 24 h age and then, incubated at 50oC for 72 h. As such, there is no report of phytase production by using orange peel as substrate. The phytate biosynthesis by bacteria cultivation on medium based on orange peel flour was accompanied by the liberation of soluble phosphate without any extra supplementation of metal ions. Based on these results the use agro residues in the form of orange peel flour could be one of the best and cost effective alternatives to the costly pure phytate for industrial production of phytase using selected strain of Klebsiella spp. [ABSTRACT FROM AUTHOR]

Statistical experimental designs were applied for the optimization of phytase production by a bacteria Klebsiella sp. DB3 in orange peel flour. Four factors i.e., orange peel flour, ammonium dihydrogen phosphate, temperature and pH were optimized using central composite design (CCD). The optimum levels of variables that supported maximum enzyme activity were orange peel bran 2.0%, sucrose 2.0% and ammonium dihydrogen phosphate (NH4H2PO4) 0.1% and pH 5.5. The validity of the model in optimized conditions was verified. An overall 5.25-fold enhancement in phytase activity (0.60 to 3.15 U) was attained due to the optimization. [ABSTRACT FROM AUTHOR]

Glaciated terrain classification is important for hydrological and climate change modelling. For this purpose, fully polarimetric Advanced Land Observation Satellite-Phase Array L-band Synthetic Aperture Radar (ALOS-PALSAR) data has been used over Indian Himalayan glaciated region. PALSAR data has been analyzed based on the three and four component scattering decomposition methods for glaciated terrain classification. These methods have been applied on multi-looked 3 × 3 coherency matrix of ALOS-PALSAR data. The analysis of these methods shows that the Freeman and Durden three-component scattering power decomposition (3-CSPD) method has over estimation problem in volume backscattering component as compared to the Yamaguchi four-component scattering power decomposition (4-CSPD) method. After finding suitability of 4-CSPD method over Himalayan glaciated terrain, it has been combined with complex Wishart distribution for supervised classification of ALOS-PALSAR image. By this way, an overall accuracy has been found to be 93.38%. Even this procedure shows very high accuracy but discrimination between vegetation and glacier snow/ice classes was not properly done. To overcome this ambiguity, the probability difference between surface backscattering and volume backscattering has been introduced as further steps in classification procedure. [ABSTRACT FROM AUTHOR]

The environmental satellite (ENVISAT) advanced synthetic aperture radar (ASAR) offers the opportunity for monitoring snow parameters with dual polarization and multi-incidence angle. Snow wetness is an important index for indicating snow avalanche, snowmelt runoff modelling, water supply for irrigation and hydropower stations, weather forecasts and understanding climate change. We used a first-order scattering model that includes both volume and air/snow surface scattering based on a developed inversion model to estimate snow dielectric constant, which can be further related for estimating snow wetness. Comparison with field measurement showed that the correlation coefficient for snow permittivity estimated from ASAR data was observed to be 0.8 at 95% confidence interval and model bias was observed as 2.42% by volume at 95% confidence interval. The comparison of ASAR-derived snow permittivity with ground measurements shows the average absolute error 2.5%. The snow wetness range varies from 0 to 15% by volume. [ABSTRACT FROM AUTHOR]

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

Glacier velocity is one of the important parameters to know about glacier dynamics, its mass and health. Understanding glacier velocity also assists in the analysis of ice thickness, ice flux and mass balance. Furthermore, basin/sub-basin wise studies are helpful to understand the climate change over that region instead of monitoring individual glaciers. In the current study, the spatial distribution of glacier velocity is generated for the entire Chandra and Bhaga sub-basins, in the Lahaul-Spiti district of Himachal Pradesh, India, using Advanced Land Observing Satellite-2/Phased Array type L-band Synthetic Aperture Radar-2 (ALOS-2/PALSAR-2) differential interferometry pair images with a 14-days temporal gap. The glaciers in Chandra sub-basin are found to have a high flow rate as compared to the glaciers in Bhaga sub-basin. Local slope and ice thickness, which decrease towards the terminus in general, control the movement of valley glaciers, and lower movement rate is observed for most of glaciers towards the terminus/frontal area. However, a few glaciers of Bhaga sub-basin demonstrate an accelerated movement in their frontal areas. This irregular behaviour is also studied in this work and it is discovered that terrain slope and influx of melting water from adjacent glaciers are the major causes for such behaviour of these glaciers. They are, therefore, prone to accelerated mass loss compared to other glaciers in the sub-basin. Another key finding of the anomalous glacier dynamics study is the disintegrating behaviour of glaciers, which is also identified by interpreting the responses of physical scattering mechanisms retrieved from glaciers using fully polarimetric ALOS-2/PALSAR-2. Warming temperatures are expected to cause glacier retreat and separation of tributary glaciers from the main glacier body. Melt water from tributary glaciers can further accelerate glacier flow and ice melt in lower part of glacier trunk. This can lead to formation of dead ice zones and disintegration of Himalayan glaciers, which are supported by numerous tributary glaciers. Therefore, we expect that the loss in glacier area due to warming will be larger than the present prediction, where mass loss is considered from conventional way. • DInSAR glacier velocity spatial distribution is generated for Chandra and Bhaga basins, Himalaya. • Anomalous glacier dynamics information is discovered using DInSAR. • Influx of melting water from adjacent glaciers is major cause for anomalous dynamics of glaciers. • Disintegrating behaviour in glaciers is also observed due to influx of melting water. • Melt water from tributary glaciers accelerates glacier flow and bottom ice melt of glacier trunk. [ABSTRACT FROM AUTHOR]

This paper deals with the retrieval of snow depth (SD) and snow water equivalent (SWE) using dual-polarization (HH-VV) synthetic aperture radar (SAR) data. The effect of different snowpack conditions on the SD and SWE inversion accuracy was demonstrated by using three TerraSAR-X acquisitions. The algorithm is based on the relationship between the SD, the co-polar phase difference (CPD), and particle anisotropy. The Dhundi observatory in the Indian Himalaya was selected as a validation test site where a field campaign was conducted for ground truth measurements in January 2016. Using the field measured values of the snow parameters, the particle anisotropy has been optimized and provided as an input to the SD retrieval algorithm. A spatially variable snow density ( ρ s ) was used for the estimation of the SWE, and a temporal resolution of 90 m was achieved in the inversion process. When the retrieval accuracy was tested for different snowpack conditions, it was found that the proposed algorithm shows good accuracy for recrystallized dry snowpack without distinct layering and low wetness (w). The statistical indices, namely, the root mean square error (RMSE), the mean absolute difference (MAD), and percentage error (PE), were used for the accuracy assessment. The algorithm was able to retrieve SD with an average MAE and RMSE of 6.83 cm and 7.88 cm, respectively. The average MAE and RMSE values for SWE were 17.32 mm and 21.41 mm, respectively. The best case PE in the SD and the SWE retrieval were 8.22 cm and 18.85 mm, respectively. [ABSTRACT FROM AUTHOR]

Himalayan glaciers have long been the focus of glaciologists across the world while trying to understand the contrasting patterns of elevation and mass changes. However, with limited number of ground observations, a comprehensive assessment of mass balance on a regional scale still remains elusive. Using the synoptic coverage of remote sensing data, we estimate a detailed spatial variation of glacier ice thickness change in the Central Himalaya of Uttarakhand using geodetic method, on a catchment scale. High resolution TerraSAR-X/TanDEM-X (12 m) and SRTM (30 m) digital elevation models (DEMs) have been utilized. The mean elevation change in the catchments is found to be −9.56 ± 0.2 m (mean annual elevation change rate is −0.68 ± 0.01 m a−1). To highlight the water potential of this region, the total ice mass loss has been estimated to be 16.0 ± 1.2 Gigatonne (Gt) from 2000–2014 from eight identified catchments namely Yamunotri, Bhagirathi, Mandakini, Alaknanda, Dhauliganga, Pindar, Goriganga and Kali/Sarda. The estimated mass balance has been validated using reported observations on five selective glaciers and the coefficient of determination is 0.93. This spatial variation of ice thickness estimated in the eight catchments is critical, as the melt-water from these glaciers contribute to the upper Ganga basin. [ABSTRACT FROM AUTHOR]

Glacier mass balance is a crucial parameter to understand the health of a glacier. The IndianHimalayas being one of the largest mountain glacier systems of the world, draws attention notonly in terms of future water potential, but also the effect it shall have on people livingdownstream upon accelerated melting. With the continuous deglaciation and retreat in mostparts of this glaciated terrain, our study focusses on the changes in mass balance over theIndian Himalayas from 2000-2014. We use geodetic method, utilizing the latestglobal digital elevation model (DEM) of TanDEM-X mission and SRTM datasetto analyze these changes. Ground observations being sparse, has been used onlyfor validation purpose. The coefficient of correlation between in-situ data and ourestimates is 0.93. It is observed that the mass balance largely varies, as we proceedfrom the extreme end of the Karakoram Himalayas (in the west) towards the eastin Arunachal Pradesh. This has been attributed to the variability in the regionalclimatic conditions and changing geomorphology of the glaciers. The total massbalance change in the Indian Himalayas has been estimated to be - 4.7 ± 1.2 Gt yr−1. This study not only highlights the water lost in the Indian Himalayas which potentiallyincreases our dependence on the seasonal precipitated water, but also portrays the potential ofthe newly released TanDEM-X DEM to monitor such dynamic systems like the mountainglaciers. [ABSTRACT FROM AUTHOR]

Hydrogen is consider as clean fuel with potential source of unlimited clean power. The global market for the hydrogen generation is expected to be $317.39 billion by 2030. The key factors responsible for hydrogen generation market includes high demand of conventional fuel in steel, cement and power generation industries and government initiatives for green and sustainable environment. It is of the utmost note that the major production of hydrogen is based on conversion from fossil fuels such as coal, petroleum etc. Among green hydrogen production method transformation of biomass to biohydrogen via anaerobic fermentation is considered as carbon neutral source of energy. However, the yield of hydrogen production is low and depends upon various factors such waste composition, bioreactor type, and microorganisms used its production, but production is limited due to shortage of raw materials. Since, the biomass such as agriculture waste residues are largely available as corn stover, sugar beet juice, beverage waste; water, wheat straw, rice straw, wood chips, sawdust, shrubs, and branches etc. The major hurdle in dark fermentation is lower biohydrogen yield due to inefficient conversion of carbohydrates into biohydrogen. Nanomaterials can help in valorization of agriculture residue for efficient biohydrogen production. Nano-technological application like increasing surface area, enzyme stability and saccharification efficiency of enzyme can contribute in biohydrogen production. Nanoparticles may result in a shift in the microbial community and the metabolic pathway for hydrogen production. This review explore the impending application of nanomaterial for valorization of agriculture biomass, biochemical pathways, and possible application of nanomaterial as individual or in composite to improve biohydrogen production. • Agriculture biomass is an inexpensive substrate that may be used to produce biohydrogen economically. • Comparing to traditional acid-based pretreatment, nanomaterial-mediated pretreatment is more environmentally friendly. • Availability of cellulose and hemicellulose for the microorganisms is increased by pretreatment of agricultural leftovers. • Nanomaterials can be used as nano-catalysts that increase the synthesis of biohydrogen. [ABSTRACT FROM AUTHOR]

Managed forests, dynamic with activities of harvest and afforestation, have become an area of imminent research opportunity to understand the effects of these disturbances and regrowth dynamics. As low frequency polarized radar signals have the ability to penetrate forest canopies, the fully polarized Synthetic Aperture Radar (SAR) data gives an ideal representation of conditions in the forest. However, this valuable information has not been optimally utilized in understanding the forest disturbances and regrowth dynamics. Several forest change monitoring studies have been performed using single or dual polarization SAR data, whereas limited studies utilized full polarimetric information. In this study, L-band ALOS/PALSAR and ALOS-2/PALSAR-2 fully polarimetric data is used to study and understand the behavioural dynamics of homogeneous and heterogeneous forest compartments having single and multiple species composition, respectively. Radar scattering pattern of different species, and its variation with respect to phenology, disturbances, harvesting and plantation regrowth are analysed to understand the trend in scattering components with respect to above said forest changes. Based on the scattering properties and their analyses, a two stage rule based classifier has been developed to categorize the forest based on its disturbance and regrowth status into disturbed, re-growth, stable/no change and non forest classes. The classifier performed with, considerably, high accuracy having an overall accuracy of 85.39% and a kappa coefficient of 0.8. The classifier developed in this work presents the potential of fully polarimetric data in understanding the level and extent of disturbances and regrowth dynamics of a forest. [ABSTRACT FROM AUTHOR]

Monitoring the dynamics of snow and glaciers in the Indian Himalaya has always attracted the attention of the remote sensing community. The snow water equivalent (SWE) represents the amount of water contained in the snowpack, and it is a product of snow depth (SD) and snow density ( ρ s ). The estimation of SD at high spatial and temporal resolution is still a challenge, especially in rugged mountainous terrain, like the Himalayas. In this paper, bistatic TerraSAR-X quad-polarization data is used to retrieve the SD using the Polarimetric SAR Interferometry (PolInSAR) coherences. The PolInSAR coherence at hv polarization shows a linear relationship with SD, which is verified with the field data. This relationship is utilized for the retrieval of SD using the proposed algorithm. Spatially variable ρ s is used to retrieve the SWE. The coefficient of determination ( R 2 ) values of the retrieved SD on 8 January 2016 and 19 January 2016 are 0.62 and 0.80, respectively. Likewise, it is 0.61 and 0.72 for retrieved SWE. The root mean square error (RMSE) in SD on 8 January 2016 and 19 January 2016 are 7.00 cm and 5.48 cm, respectively; for SWE, it is 2.02 cm and 1.48 cm, respectively. [ABSTRACT FROM AUTHOR]

Studies pertaining to small scale structures producing scintillations using synthetic aperture radars (SARs) have predominantly been conducted at low-latitude regions. The high-latitude region (auroral belt and polar caps) is highly dynamic and varies in response to stimuli from solar winds and the magnetosphere in complex ways. In this paper, the authors have shown the capability of SAR for scintillation observation in the auroral region. An attempt has been made to fit an irregularity anisotropy model to SAR measurements for characterizing the ionospheric irregularities in the auroral regions. The dependency of anisotropy irregularity model on parameters, such as irregularity structure (axial ratio), their orientation with respect to magnetic field lines, and the ionospheric plasma drift, is closely studied using Advanced Land Observing Satellite (ALOS)-2 datasets acquired over Alaska. Geomagnetic indices and total electron content data are consistent with the occurrence of the scintillation event under study. Drift velocity measurements from high-frequency radars in the super dual array radar network (SuperDARN) showed that the anisotropy is independent of the magnitude and the azimuth angle of the plasma drift. The typical range of orientation angle suitable for the high latitude regions probed by ALOS-2 is demonstrated to be between 120°–135°. This paper explores the idea of inferring irregularity anisotropy by comparing the amplitude scintillation (S4) index measured in SAR data pairs using two well-established techniques. The image contrast technique heavily relies on the accurate modeling of anisotropy, whereas the radar cross-sectional enhancement method is independent of it. This feature has been exploited in the $S_{4}$ comparison to finally fit the choice of irregularity axial ratio and conclude that the sheet-like structures best describe the ionospheric irregularity structure in the region under observation. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar (SAR) backscatter is the coherent combination of the scattering from multiple individual scatterers within the radar resolution cell, which results into a 2-D radar image. Tomographic SAR (TomoSAR) takes advantage of multiple SAR acquisitions to provide 3-D vertical structure of the imaged target. Over forests, parameters such as canopy structure, canopy density, leaf area and phenology contribute to different SAR scattering mechanisms along the vertical dimension of the trees. Tomography has been demonstrated in various earlier works for forest bio-physical parameter estimation using airborne data. In this research work, multi-polarimetric space-borne TanDEM-X tomograms are examined for the first time over a multi-species Indian tropical forest. Multiple TanDEM-X acquisitions are focused using Capon beamforming at five polarimetric channels — HH, HV, VV, HH − VV and HH + VV. Four distinct forest species compartments are selected representing different canopy structure and density. The tomograms obtained in different polarizations for these species are analyzed in detail to understand the scattering patterns across different forest species. Field surveys carried out in several forest locations provide in situ observations of forest height and vertical structure. It was observed that canopy gaps and leaf density play a crucial role for X-band SAR signal penetration through ground. For dense canopy species the backscatter contributions are spread through the canopy, while in case of sparse canopy species, the ground scattering is dominant. Vertical profiles obtained at surveyed plot locations are plotted in all polarizations, and provide a good agreement with field observations. Further, the obtained TomoSAR backscatter layers have a good correlation with field measured above-ground biomass (AGB). The AGB is modeled from TomoSAR with the HH-pol TomoSAR backscatter layer at 27 m canopy height leading to an AGB estimation with correlation r = 0.76 and RMSE of 50.4 t/ha. • First space-borne multi-polarimetric tomograms over Indian tropical forests • Analysis of effect of species on X-band TanDEM-X tomograms presented • SAR scattering along height of tropical forest visualized for different species • Potential of single-baseline space-borne SAR systems explored [ABSTRACT FROM AUTHOR]

For a long time, Polarimetric Synthetic Aperture Radar (PolSAR) data was not available free of cost, so the applications were limited. With the recent increase in availability of PolSAR data due to missions like ALOS-2, UAVSAR and Sentinel, the data can be acquired through these sensors periodically. Since the volume of data is large, applying traditional classifiers and conventional machine learning algorithms becomes confounding, as in that case, manual feature extraction must be done by the researcher for training the model. To automate this feature extraction step and accelerate the process, many researchers have used various deep learning algorithms. However, most studies fail to tap into the potential of PolSAR data by not utilizing the complete range of complex data of float-32 bit-depth. The work in this paper suggests the development of a generalized deep learning model for nine elements of coherency matrix [ T 3 ] in the float-32 data-space from ALOS/PALSAR-2 L-Band sensor. Semantic segmentation is used to classify land-covers into various classes like water, settlement, forest, open land, wetlands etc. This is done through modification on the UNet backbone. The deep learning model is trained using data on San Francisco and New Delhi regions and tested on data from Mumbai region. It is observed that the classification accuracy for Mumbai region is " 93.82 % ". This kind of system can assist the decision makers like urban planners to take informed decisions. [ABSTRACT FROM AUTHOR]

Classification of Polarimetric Synthetic Aperture Radar (PolSAR) images is an incredibly challenging task. In early days researchers have applied parametric classifiers like maximum likelihood and Wishart classifiers. Later it is found that each class may or may not follow the Gaussian distribution hence researchers have used nonparametric classifiers like ANN, Decision Tree and SVM etc. It is observed from their work that these classifiers give good classification accuracy, but a lot of time is consumed in extracting the features and training the model. To avoid this, in this paper, Semantic Segmentation with the U-net architecture is used. Compared to covariance or coherency matrix, target decomposition techniques provide more information about scattering mechanisms. Hence, the effect of Touzi and Gulab Singh's 4- (G4U), 7- component (7SD) target decomposition techniques is studied on Semantic Segmentation. Comparative results show that G4U and 7SD decomposition gives almost similar classification accuracy. It is also observed that out of many of Touzi parameters, the first and second components of α, Φ, λ and τ can effectively classify various features and the classification accuracy is comparable with G4U and 7 SD decomposition. Moreover, the classification accuracy using nine elements of the coherency matrix is less than all the above-mentioned decomposition techniques. [ABSTRACT FROM AUTHOR]

Chemical leukoderma, or chemical-based vitiligo, is a dermal disease triggered by exposure to chemicals and characterized by the emergence of depigmentation or hypopigmentation of the skin. The etiology of this condition is associated with exposure to various chemical substances present in both occupational and non-occupational settings. The precise mechanism that underlies chemical leukoderma remains elusive and is believed to result from the demise of melanocytes, which are responsible for producing skin pigments. This condition has gained particular prominence in developing countries like India. An interesting connection between chemical leukoderma and vitiligo has been identified; studies suggest that exposure to many household chemicals, which are derivatives of phenols and catechol, may serve as a primary etiological factor for the condition. Similar to autoimmune diseases, its pathogenesis involves contributions from both genetic and environmental factors. Furthermore, over the last few decades, various studies have demonstrated that exposure to chemicals plays a crucial role in initiating and progressing chemical leukoderma, including cases stemming from occupational exposure. [ABSTRACT FROM AUTHOR]

Background: Glutathione S-transferases (GSTs) are a class of important Phase II detoxification enzymes that catalyze the conjugation of glutathione and xenobiotic compounds (environmental carcinogens, pollutants and drugs) to protect against oxidative stress. GSTT1 and GSTM1 genetic polymorphisms have been extensively studied, and null genotypes or homozygous deletions have been reported in various populations. Previous studies have suggested that those who are homozygous null at the GSTM1 or GSTT1 loci are more susceptible and have a higher risk of cancers linked to environmental pollutants and drug-induced toxicity. Our study focused on GSTM1 and GSTT1 null allele frequency in the Doon population of Himachal Pradesh (India) with a comparison across other Inter and Intra-Indian ethnic groups to predict variation in the possible susceptible status. Material and methods: Genomic DNA samples were extracted from 297 healthy unrelated individuals by a ReliaPrep™ Blood gDNA Miniprep kit (Promega, USA), and genotyped for allelic variation in GSTM1 and GSTT1 genotypes by multiplex polymerase chain reaction. Fisher's exact test was applied using SPSS.20 to analyze the genotypic distribution of GSTM1 and GSTT1 null alleles in male and female of Doon region (Solan) Himachal Pradesh. Results: In our study, the frequency distribution of the homozygous null genotypes of GSTM1, GSTT1 individually as well as combined was found as 33.3%, 32% and 9%, respectively. Upon gender-wise comparison, a non-significant distribution (p > 0.05) for null genotypes of GSTM1 (32.8% and 35.4%, OR-0.77, 95% CI 0.42–1.41), GSTT1 (33.2% and 27.7%, OR-1.12, 95% CI 0.63–2.0) individually and combined GSTM1 and GSTT1 (10.8% and 3.7%, OR-0.31, 95% CI 0.07–1.42) were observed in studied population. Conclusions: In our studied population, the frequency of GSTM1 null genotypes was found deviated from Inter- and Intra-Indian ethnic groups. However, the frequency of homozygous null type of GSTT1 was not significantly different, when compared to previous Indian studies, comparison with global ethnic groups showed deviation. Thus, our study has highlighted possible susceptibility risk to various xenobiotics in the Doon population of Himachal Pradesh, India. [ABSTRACT FROM AUTHOR]

0.7Bi0.99Nd0.01Fe0.99(Co, Ti)0.01O3-0.3CaTiO3 (BNFCTO-CaTO) composite was prepared by using sol-gel method. The structural formation was investigated using the XRD pattern, which confirms the presence of distorted rhombohedral structure with space group R3c and the superlattice orthorhombic crystal structure with space group Pbmn. The dielectric behavior of BNFCTO-CaTO has been studied within the frequency range of 100 Hz to 1 MHz from 300 K to 773 K. The sample shows dielectric dispersion in low frequency region that can be explained by Maxwell-Wagner type interfacial polarization. The temperature-dependent dielectric plot shows an anomaly near to magnetic transition temperature (TN) of BiFeO3 and shows relaxor behavior. Furthermore, BNFCTO-CaTO shows a strong magneto-dielectric coupling effect at room temperature. [ABSTRACT FROM AUTHOR]

This study discusses the capability assessment of fully polarimetric L-band spaceborne synthetic aperture radar (SAR) for detection of seasonal snow covered areas. In this paper, ALOS PALSAR time-series data sets obtained in quad-pol modes have been investigated to evaluate the polarimetric signal scattered from a snow-covered mountainous ecosystem in Niigata, Japan. Results show that changes in the scattering mechanism across the various snow states can be identified from polarimetric parameters. In particular, different polarimetric parameters offer complementary information on the snow properties. Based on the characteristic seasonal changes of polarimetric parameters, a new method to map snow-covered areas is proposed in this study using an information fusion approach. Snow extent can be identified successfully by combining polarimetric indices with an overall accuracy of 74.4% as compared with in situ measurements and 77.0% as compared with optical images. [ABSTRACT FROM AUTHOR]

The 231 km long Ken-Betwa River Linking canal will transfer 1,020 hm of surplus water from the Ken River to the deficit Betwa River basin. The landslide susceptibility zonation map of the river link has been assessed using remote sensing data in GIS. Various thematic maps such as geology, land use/land cover, lineament, drainage, slope, aspect, normalized difference vegetation index and soil type were generated from the Landsat Thematic Mapper 5 satellite data of 2005, the Survey of India topographic sheets, Shuttle Radar Topographic Mission Digital Elevation (SRTM-DEM) data and other existing maps. Numerical rating schemes were used for ranking the thematic layers. The results were supported with the rainfall data, groundwater level data and a petrological study of rock thin sections. In addition to providing valuable information for project decision-makers, the results will assist in slope management and land use planning in the area. [ABSTRACT FROM AUTHOR]

The article discusses the evaluation of a Neighbourhood Renewal Fund health and wellbeing programme by the University of Lancaster in Lancashire, England. The programme encouraged action to reduce health inequalities in disadvantaged neighborhoods. The author explores the methods used for increasing awareness of health risks, community network linking, and the valuable role community nurses played in the success of the projects.

Introduction: The mutational changes in Polycystin-1(PC-1) encoded by PKD1 gene is the main cause of Autosomal Dominant Polycystic kidney disease (ADPKD). The pathological changes in renal epithelial cells and multiple cyst formation occur due to activation of cascade of signalling pathways and membrane renal transporters (RTs). Our study have focused on the identification, of different RTs, their interactions with Polycystin-1 and other selected target proteins to find out their role in pathogenesis.Methods: In this study, various RTs protein sequences were identified and retrieved from NCBI's GenBank and UniProt. RTs were categorized according to different nephronal segmenta as per their functional information retrieved from UniProt and Transpoter databases. Further, sequences were subjected for interaction network analysis in String database and Cytoscape 3.7.2. Different interactions including experimentally validated were identified and can be further validated through in vivo methods.Results: The cross talk between different RT, Polycystin-1 and other sequences were analysed. The various pathways of the interaction with PC-1 were categorised. The total number of 119 nodes and 769 edges interactions were generated. The results were visualized and cross verified with other databases in cytoscape.Conclusion: The cross signalling of PKD1 with SCNN1A, SCNN1G, SLC12A1, AVPR2 shows their importance in the cyst formation and in pathogenesis of ADPKD. [ABSTRACT FROM AUTHOR]

The studies on snow depth comprise a crucial area of research in the Indian Himalayas, where the seasonal snow cover primarily drives the rivers and significant water resources. In this paper, the initial estimates of the line of sight displacement obtained using differential interferometric phase in VV and VH polarizations using Sentinel-1 bi-temporal dual polarimetric SAR data corresponding to snow covered and snow free land cover, are improved by applying bias corrections for the snow phase and for residual errors in displacement derived from the corrected snow phase. The bias for the snow phase is computed from the observed phase in VV and VH polarizations for the snow free area and the bias for the residual errors is computed by observing the stationary pixels identified in the snow free area using a digital elevation model. The snow depth is computed as a weighted sum of the corrected displacements in the VV and VH polarization, with the weights derived using the local incidence angle. The snow depth results based on the proposed approach was evaluated with respect to field measurements and a coefficient of determination of 0.628 was observed with an improvement of ~0.4 as compared to the displacement observed in the VV and VH channel using the conventional method. [ABSTRACT FROM AUTHOR]