Fast and motion-robust saturation transfer MRI with inherent B0 correction using rosette trajectories and compressed sensing.

PURPOSE: To implement rosette readout trajectories with compressed sensing reconstruction for fast and motion-robust CEST and magnetization transfer contrast imaging with inherent correction of B0 inhomogeneity. METHODS: A pulse sequence was developed for fast saturation transfer imaging using a stack of rosette trajectories with a higher sampling density near the k-space center. Each rosette lobe was segmented into two halves to generate dual-echo images. B0 inhomogeneities were estimated using the phase difference between the images and corrected subsequently. The rosette-based imaging was evaluated in comparison to a fully sampled Cartesian trajectory and demonstrated on CEST phantoms (creatine solutions and egg white) and healthy volunteers at 3 T. RESULTS: Compared with the conventional Cartesian acquisition, compressed sensing reconstructed rosette images provided image quality with overall higher contrast-to-noise ratio and significantly faster readout time. Accurate B0 map estimation was achieved from the rosette acquisition with a negligible bias of 0.01 Hz between the rosette and dual-echo Cartesian gradient echo B0 maps, using the latter as ground truth. The water-saturation spectra (Z-spectra) and amide proton transfer weighted signals obtained from the rosette-based sequence were well preserved compared with the fully sampled data, both in the phantom and human studies. CONCLUSIONS: Fast, motion-robust, and inherent B0-corrected CEST and magnetization transfer contrast imaging using rosette trajectories could improve subject comfort and compliance, contrast-to-noise ratio, and provide inherent B0 homogeneity information. This work is expected to significantly accelerate the translation of CEST-MRI into a robust, clinically viable approach.

Unraveling contributions to the Z-spectrum signal at 3.5 ppm of human brain tumors.

PURPOSE: To evaluate the influence of the confounding factors, direct water saturation (DWS), and magnetization transfer contrast (MTC) effects on measured Z-spectra and amide proton transfer (APT) contrast in brain tumors. METHODS: High-grade glioma patients were scanned using an RF saturation-encoded 3D MR fingerprinting (MRF) sequence at 3 T. For MRF reconstruction, a recurrent neural network was designed to learn free water and semisolid macromolecule parameter mappings of the underlying multiple tissue properties from saturation-transfer MRF signals. The DWS spectra and MTC spectra were synthesized by solving Bloch-McConnell equations and evaluated in brain tumors. RESULTS: The dominant contribution to the saturation effect at 3.5 ppm was from DWS and MTC effects, but 25%-33% of the saturated signal in the gadolinium-enhancing tumor (13%-20% for normal tissue) was due to the APT effect. The APT# signal of the gadolinium-enhancing tumor was significantly higher than that of the normal-appearing white matter (10.1% vs. 8.3% at 1 µT and 11.2% vs. 7.8% at 1.5 µT). CONCLUSION: The RF saturation-encoded MRF allowed us to separate contributions to the saturation signal at 3.5 ppm in the Z-spectrum. Although free water and semisolid MTC are the main contributors, significant APT contrast between tumor and normal tissues was observed.

CEST and nuclear Overhauser enhancement imaging with deep learning-extrapolated semisolid magnetization transfer reference: Scan-rescan reproducibility and reliability studies.

PURPOSE: To develop a novel MR physics-driven, deep-learning, extrapolated semisolid magnetization transfer reference (DeepEMR) framework to provide fast, reliable magnetization transfer contrast (MTC) and CEST signal estimations, and to determine the reproducibility and reliability of the estimates from the DeepEMR. METHODS: A neural network was designed to predict a direct water saturation and MTC-dominated signal at a certain CEST frequency offset using a few high-frequency offset features in the Z-spectrum. The accuracy, scan-rescan reproducibility, and reliability of MTC, CEST, and relayed nuclear Overhauser enhancement (rNOE) signals estimated from the DeepEMR were evaluated on numerical phantoms and in heathy volunteers at 3 T. In addition, we applied the DeepEMR method to brain tumor patients and compared tissue contrast with other CEST calculation metrics. RESULTS: The DeepEMR method demonstrated a high degree of accuracy in the estimation of reference MTC signals at ±3.5 ppm for APT and rNOE imaging, and computational efficiency (˜190-fold) compared with a conventional fitting approach. In addition, the DeepEMR method achieved high reproducibility and reliability (intraclass correlation coefficient = 0.97, intersubject coefficient of variation = 3.5%, and intrasubject coefficient of variation = 1.3%) of the estimation of MTC signals at ±3.5 ppm. In tumor patients, DeepEMR-based amide proton transfer images provided higher tumor contrast than a conventional MT ratio asymmetry image, particularly at higher B1 strengths (>1.5 µT), with a distinct delineation of the tumor core from normal tissue or peritumoral edema. CONCLUSION: The DeepEMR approach is feasible for measuring clean APT and rNOE effects in longitudinal and cross-sectional studies with low scan-rescan variability.

Bloch simulator-driven deep recurrent neural network for magnetization transfer contrast MR fingerprinting and CEST imaging.

PURPOSE: To develop a unified deep-learning framework by combining an ultrafast Bloch simulator and a semisolid macromolecular magnetization transfer contrast (MTC) MR fingerprinting (MRF) reconstruction for estimation of MTC effects. METHODS: The Bloch simulator and MRF reconstruction architectures were designed with recurrent neural networks and convolutional neural networks, evaluated with numerical phantoms with known ground truths and cross-linked bovine serum albumin phantoms, and demonstrated in the brain of healthy volunteers at 3 T. In addition, the inherent magnetization-transfer ratio asymmetry effect was evaluated in MTC-MRF, CEST, and relayed nuclear Overhauser enhancement imaging. A test-retest study was performed to evaluate the repeatability of MTC parameters, CEST, and relayed nuclear Overhauser enhancement signals estimated by the unified deep-learning framework. RESULTS: Compared with a conventional Bloch simulation, the deep Bloch simulator for generation of the MTC-MRF dictionary or a training data set reduced the computation time by 181-fold, without compromising MRF profile accuracy. The recurrent neural network-based MRF reconstruction outperformed existing methods in terms of reconstruction accuracy and noise robustness. Using the proposed MTC-MRF framework for tissue-parameter quantification, the test-retest study showed a high degree of repeatability in which the coefficients of variance were less than 7% for all tissue parameters. CONCLUSION: Bloch simulator-driven, deep-learning MTC-MRF can provide robust and repeatable multiple-tissue parameter quantification in a clinically feasible scan time on a 3T scanner.

Deep learning-based Lorentzian fitting of water saturation shift referencing spectra in MRI.

PURPOSE: Water saturation shift referencing (WASSR) Z-spectra are used commonly for field referencing in chemical exchange saturation transfer (CEST) MRI. However, their analysis using least-squares (LS) Lorentzian fitting is time-consuming and prone to errors because of the unavoidable noise in vivo. A deep learning-based single Lorentzian Fitting Network (sLoFNet) is proposed to overcome these shortcomings. METHODS: A neural network architecture was constructed and its hyperparameters optimized. Training was conducted on a simulated and in vivo-paired data sets of discrete signal values and their corresponding Lorentzian shape parameters. The sLoFNet performance was compared with LS on several WASSR data sets (both simulated and in vivo 3T brain scans). Prediction errors, robustness against noise, effects of sampling density, and time consumption were compared. RESULTS: LS and sLoFNet performed comparably in terms of RMS error and mean absolute error on all in vivo data with no statistically significant difference. Although the LS method fitted well on samples with low noise, its error increased rapidly when increasing sample noise up to 4.5%, whereas the error of sLoFNet increased only marginally. With the reduction of Z-spectral sampling density, prediction errors increased for both methods, but the increase occurred earlier (at 25 vs. 15 frequency points) and was more pronounced for LS. Furthermore, sLoFNet performed, on average, 70 times faster than the LS-method. CONCLUSION: Comparisons between LS and sLoFNet on simulated and in vivo WASSR MRI Z-spectra in terms of robustness against noise and decreased sample resolution, as well as time consumption, showed significant advantages for sLoFNet.

Only-train-once MR fingerprinting for B0 and B1 inhomogeneity correction in quantitative magnetization-transfer contrast.

PURPOSE: To develop a fast, deep-learning approach for quantitative magnetization-transfer contrast (MTC)-MR fingerprinting (MRF) that simultaneously estimates multiple tissue parameters and corrects the effects of B0 and B1 variations. METHODS: An only-train-once recurrent neural network was designed to perform the fast tissue-parameter quantification for a large range of different MRF acquisition schedules. It enabled a dynamic scan-wise linear calibration of the scan parameters using the measured B0 and B1 maps, which allowed accurate, multiple-tissue parameter mapping. MRF images were acquired from 8 healthy volunteers at 3 T. Estimated parameter maps from the MRF images were used to synthesize the MTC reference signal (Zref ) through Bloch equations at multiple saturation power levels. RESULTS: The B0 and B1 errors in MR fingerprints, if not corrected, would impair the tissue quantification and subsequently corrupt the synthesized MTC reference images. Bloch equation-based numerical phantom studies and synthetic MRI analysis demonstrated that the proposed approach could correctly estimate water and semisolid macromolecule parameters, even with severe B0 and B1 inhomogeneities. CONCLUSION: The only-train-once deep-learning framework can improve the reconstruction accuracy of brain-tissue parameter maps and be further combined with any conventional MRF or CEST-MRF method.

Prediction of response to radiotherapy in locally advanced carcinoma cervix using multiparametric MRI: A prospective, single-center, longitudinal study.

PURPOSE: To evaluate the possible role of a multiparametric magnetic resonance imaging (MRI) and semiquantitative fusion map for the prediction of response to radiotherapy in carcinoma cervix. METHODS: This was a prospective, single-center, longitudinal observational study performed on patients with locally advanced carcinoma cervix. Relative blood flow (rBF), relative blood volume (rBV), and apparent diffusion coefficient (ADC) values were obtained before and after the onset of radiotherapy. rBV, rBF, and ADC values were used to generate a semiquantitative pharmacokinetic model map to identify any hypoxic component of the tumor. The subjects were retrospectively classified as responders and nonresponders based on response to treatment. Prospective prediction of response status was done using pretreatment multiparametric MRI parameters (rBF, rBV, and ADC) and semiquantitative parametric map. RESULTS: In 32 patients (29 with parametrial involvement and 15 with lymphadenopathy), pretreatment ADC of the primary tumor was the most accurate measure for predicting response to treatment as well as for treatment-induced fractional volume reduction. Although rBV and rBF were insignificant metrics in isolation for predicting response status, a combination with ADC in the form of parametric map had a sensitivity of 86.4% and 77.2%, specificity of 70% and 70%, positive predictive value of 86.4% and 85%, and negative predictive value 70% and 59% respectively by two independent observers. CONCLUSION: ADC is the most accurate measure for predicting the response to treatment. A manual parametric map generated by an off-line fusion of the above map with those generated by pharmacokinetic modeling of perfusion-weighted MRI may be a useful tool for the prediction of response to radiotherapy.

Assessment of Surface and Build-up Doses for a 6 MV Photon Beam using Parallel Plate Chamber, EBT3 Gafchromic Films, and PRIMO Monte Carlo Simulation Code.

Background: Accurate assessment of surface and build-up doses has a key role in radiotherapy, especially for the superficial lesions with uncertainties involved while performing measurements in the build-up region. Objective: This study aimed to assess surface and build-up doses for 6 MV photon beam from linear accelerator using parallel plate ionization chamber, EBT3 Gafchromic films, and PRIMO Monte Carlo (MC) simulation code. Material and Methods: In this experimental study, parallel plate chamber (PPC05) and EBT3 Gafchromic films were used to measure doses in a build-up region for 6 MV beam from the linear accelerator for different field sizes at various depths ranging from 0 to 2 cm from the surface with 100 cm source to surface distance (SSD) in a solid water phantom. Measured results were compared with Monte Carlo simulated results using PENELOPE-based PRIMO simulation code for the same setup conditions. Effect of gantry angle incidence and SSD were also analyzed for depth doses at the surface and build-up regions using PPC05 ion chamber and EBT3 Gafchromic films. Results: Doses measured at the surface were 14.78%, 19.87%, 25.83%, and 31.54% for field sizes of 5×5, 10×10, 15×15, and 20×20 cm2, respectively for a 6 MV photon beam with a parallel plate chamber and 14.20%, 19.14%, 25.149%, and 30.90%, respectively for EBT3 Gafchromic films. Both measurement sets were in good agreement with corresponding simulated results from the PRIMO MC simulation code; doses increase with the increase in field sizes. Conclusion: Good agreement was observed between the measured depth doses using parallel plate ionization chamber, EBT3 Gafchromic films, and the simulated depth doses using PRIMO Monte Carlo simulation code.

Biotite as a geoindicator of rare earth element contamination in Gomati River Basin, Ganga Alluvial Plain, northern India.

Rare earth elements (REE) are emerging as modern high-technology-related novel micro-contaminants in freshwater aquatic systems and are therefore attracting global attention due to their potential human health risks. The Gomati River (a tributary of the Ganga River) sediments were analyzed for REE concentrations to establish REE contamination and to identify biotite mica mineral as a geoindicator. Chondrite-normalized REE pattern of the river sediments and biotite mica mineral were similar and depict a strong light REE (LREE) enrichment and relatively flatter heavy REE (HREE). The maximum total REE (∑REE) concentration increased from 323 µg/g in 2012 to 673 µg/g in 2019. In the ∑REE, LREE contribution was > 80%, because of anthropogenic inputs, mainly petroleum-cracking catalysts and other high-technology-based products. The XRD analysis and the geochemical signature of the Gomati River sediments reveal the meaningful existence of biotite mica mineral. A distinct downstream REE enrichment pattern was identified in biotite from the mica-rich bedload sediments. The scanning electron microscopy-energy dispersive X-ray (SEM-EDX) mapping images of biotite also revealed the precipitation of Lanthanum, at the weathered edges, during the early stage of mineral weathering. Biotite mica was identified as a geoindicator for the assessment of REE contamination in the Gomati River and the Hindon River Basin of the Ganga Alluvial Plain. Future research is needed for the application of biotite mica mineral as a geoindicator that can help the environmental scientists to contribute more effectively to the interdisciplinary efforts in River Science.

Changes in the proportions of an active pharmaceutical through cocrystals.

In this study, the modulation of amounts sulfathiazolium cations in different 2,6-pyridinedicarboxylates is demonstrated. An uncommon monoionic sulfathiazolium zinc 2,6-pyridinedicarboxylate (1:1 electrolyte) complex was characterized. Conventional sulfathiazolium zinc-bis-2,6-pyridinedicarboxylate dianionic complexes (2:1 electrolyte) were formed when hydroxyaromatic compounds such as 1,3-dihydroxybenzene or 3-nitrophenol were used as guest components. Thus, with the aid of the hydroxyaromatic molecules the zinc-bis-2,6-pyridinedicarboxylate complexes were stabilized with the relatively large sized sulfathiazolium cations. It was a consequence of domain expansion by the phenolic compounds. Sandwiched aromatic guests between the 2,6-pyridinedicarboxylates provided appropriate packing to accommodate the two large cations in the self-assemblies, which helped to modulate the amounts of sulfathiazole in different formulations. Antibacterial activities with E. coli DH5α have shown that the salt and the complexes have lower g/ml antibacterial activity than the parent drug.

Intensity inhomogeneity correction of MRI images using InhomoNet.

Intensity inhomogeneity is one of the major artifacts in magnetic resonance imaging (MRI). Bias field present in MRI images alters true pixel value and produces spurious varying pixel intensities. This artifact affects the diagnosis by radiologists in a detrimental manner and also degrades the performance of computer-aided diagnosis algorithms such as segmentation. The present work proposes a novel network called InhomoNet for intensity inhomogeneity correction of MRI image. The generator architecture of InhomoNet consists of a new multi-scale local information module at each encoder block that helps to capture features at multiple scales. The horizontal and vertical kernels help to reduce the problems like loss of neighborhood information, gridding issues caused due to large dilated convolution operations. The attention-driven skip connections in the generator network are utilized to transfer optimal semantic and spatial localization information from the encoder to decoder blocks. Further, the present work proposes two new losses functions, i.e. histogram correlation and 3D pixel loss. These losses help to realize pixel consistency across different regions of brain MRI. The inculcation of the L1 loss provides guidance to the upsampling process as it compares the prediction from each decoder block with the ground truth. The proposed method is evaluated on simulated and real MRI data. The comparative analysis with popular state-of-the-art methods depicts the ability of the proposed method to perform intensity inhomogeneity correction accurately.

Unsupervised smoke to desmoked laparoscopic surgery images using contrast driven Cyclic-DesmokeGAN.

In laparoscopic surgery, energized dissecting devices and laser ablation causes smoke, which degrades the visual quality of the operative field. This paper proposes an unsupervised approach to desmoke laparoscopic images called Cyclic-DesmokeGAN. In the generator, multi-scale residual blocks help to alleviate the smoke component at multiple scales, while refinement module helps to obtain desmoked images with sharper boundaries. As the presence of smoke degrades contrast and fine structure, the proposed method utilizes high boost filtered image at each encoder layer. The contrast loss improves overall contrast, thereby reducing the smoke, while Unsharp Regularization loss helps to stabilize the network. The proposed Cyclic-DesmokeGAN is tested on 200 smoke images obtained from Cholec80 dataset consisting of videos of cholecystectomy surgeries. The results depict effectiveness, as proposed approach achieved 3.47±0.09 Contrast-Distorted Images Quality, 4.15±0.74 Naturalness Image Quality Evaluator, and 0.23±0.00 Fog Aware Density Evaluator, these indexes are best in comparison to other state-of-the-art methods.

Combinations of Tautomeric Forms and Neutral-Cationic Forms in the Cocrystals of Sulfamethazine with Carboxylic Acids.

The cocrystals of sulfamethazine with different acids, namely, 2-mercaptophenylcarboxylic acid, 2,6-pyridinedicarboxylic acid, 4-(4-hydroxyphenylazo)phenylcarboxylic acid, 3-(4-hydroxyphenyl)propanoic acid, and 4-(phenyl)phenylcarboxylic acid, are studied here. Each has distinct notable supramolecular features. The pyrimidin-2-amine unit of the sulfamethazine provided unique examples of cocrystals in which amidine and imidine forms or neutral and protonated forms of sulfamethazine are observed in 2:2 ratios. Hence, this study provides avenues to explore cocrystals with tautomeric forms together in a cocrystal and also neutral and protonated cocrystal partners as apparent multicomponents in cocrystals. Among the cocrystals, three of them have the amidine form of the sulfamethazine in respective self-assembly. The cocrystal of 2-mercapto-phenylcarboxylic acid with sulfamethazine has the amidine form and it has the distinction of having S-H···π interactions. The cocrystal of sulfamethazine with 2,6-pyridinecarboxylic acid is a rare example of a 1:1 cocrystal of sulfmethazine with dicarboxylic acid. It has methanol molecules as a solvent of crystallization. Sulfamethazine forms a hydrated cocrystal with 4-(4-hydroxyphenylazo)-phenylcarboxylic acid that has conventional R 2 2(8) synthons of amidine hydrogen-bonding with carboxylic acid. The phenolic part of the acid component is anchored to the water molecule and provides a robust self-assembly. The hydrated cocrystal of sulfamethazine with 3-(4-hydroxyphenyl)propanoic acid (2:2 cocrystal) has two independent molecules of sulfamethazine, one in amidine form and the other in imidine form. It has two neutral carboxylic acids anchored through complementary hydrogen bonds and also has two water molecules of crystallization. The cocrystal of sulfamethazine with 4-(phenyl)phenylcarboxylic acid is also a 2:2 cocrystal. It is a di-hydrate, which has a neutral and protonated form of sulfamethazine. The neutral form is hydrogen-bonded to a neutral carboxylic acid, whereas the protonated form is charge-assisted hydrogen-bonded to the corresponding carboxylate anion.

Mapping human health risk by geostatistical method: a case study of mercury in drinking groundwater resource of the central ganga alluvial plain, northern India.

Human health is "at risk" from exposure to sub-lethal elemental occurrences at a local and or regional scale. This is of global concern as good-quality drinking water is a basic need for our wellbeing. In the present study, the "probability kriging," a geostatistical method that has been used to predict the risk magnitude of the areas where the probability of dissolved mercury concentration (dHg) is higher than the World Health Organization (WHO) permissible limit. The method was applied to geochemical data of dHg concentration in 100 drinking groundwater samples of Lucknow monitoring area (1222 km2) located within the Ganga Alluvial Plain, India. Threefold (high to extreme risk) and twofold (moderate risk) higher dHg concentration values than the WHO permissible limit were observed in all of the groundwater samples. The generated prediction map using the probability kriging method shows that the probability of exceedance of dHg is the highest in the northwestern part of the Lucknow monitoring area due to anthropogenic interferences. The hotspots with high to very high probability are potentially alarming in the urban sector where 32.4% of the total population is residing in 6.8% of the total area. Interpolation of local estimates results in an easily readable and communicable human health risk map. It may help to consider substantial remediation measures for managing drinking water resources of the Ganga Alluvial Plain, which is among the anthropogenic mercury emission-dominated regions of the world.

Stacking among the clips of the poly-aromatic rings of phenazine with hydroxy-aromatics and photophysical properties.

Clip-like arrangements of molecules in the cocrystals of phenazine with hydroxy-aromatics in their respective self-assemblies and photophysical properties were presented. Phenazine cocrystals with 1,2-dihydroxybenzene provided assembly with butterfly-like arrangements. In these cocrystals, the phenazine molecules occurred in parallel pairs having extensive π-stacking. The clip-like cocrystals with 1,3-dihydroxybenzene also exhibited parallel pairs of phenazine molecules that were parallel cofacial π-stacked. The hydrated cocrystals of phenazine with 1,2,3-trihydroxybenzene had chains of parallel cofacial phenazine rings having three distinguishable π-separation distances among the centroids of the phenazine rings. Also, 2,7-dihydroxynaphthalene formed a clip-like cocrystal with phenazine, which encapsulated an additional molecule of phenazine. This cocrystal also provided chain-like parallel arrangements of the phenazine molecules. The emission and quantum yields of the cocrystals were determined by the integrating sphere method, which indicated that only the cocrystal of phenazine with 2,7-dihydroxynaphthalene showed monomer-like emission of phenazine and the rest of the cocrystals were in a quenched state. In the solution phase, quenching of the emission of hydroxynaphthalene was observed when phenazine was added to an independent solution of 2,7-dihydroxynaphthalene or another hydroxynaphthalene. However, when hydroxybenzenes were added to a solution of phenazine, fluorescence enhancements of phenazine occurred due to photo-electron transfer.

Optimized Multistable Stochastic Resonance for the Enhancement of Pituitary Microadenoma in MRI.

Magnetic resonance imaging (MRI) is the modality of choice as far as imaging diagnosis of pathologies in the pituitary gland is concerned. Furthermore, the advent of dynamic contrast enhanced (DCE) has enhanced the capability of this modality in detecting minute benign but endocrinologically significant tumors called microadenoma. These lesions are visible with difficulty and a low confidence level in routine MRI sequences, even after administration of intravenous gadolinium. Techniques to enhance the visualization of such foci would be an asset in improving the overall accuracy of DCE-MRI for detection of pituitary microadenomas. The present study proposes an algorithm for postprocessing DCE-MRI data using multistable stochastic resonance (MSSR) technique. Multiobjective ant lion optimization optimizes the contrast enhancement factor (CEF) and anisotropy of an image by varying the parameters associated with the dynamics of MSSR. The marked regions of interest (ROIs) are labeled as normal and microadenoma of pituitary obtained with increased level of accuracy and confidence using proposed algorithm. The increased difference between the mean intensity curves obtained using these ROIs validated the obtained subjective results. Furthermore, the proposed MSSR-based algorithm has been evaluated on standard T1 and T2 weighted BrainWeb dataset images and quantified in terms of CEF, peak signal to noise ratio (PSNR), structure similarity index measure (SSIM), and universal quality index (UQI). The obtained mean values of CEF 1.22, PSNR 27.68, SSIM 0.75, UQI 0.83 for twenty dataset images were highest among considered contrast enhancement algorithms for the comparison.

Dual Modes and Dual Emissions of an Amino-Naphthoquinone Derivative.

3-(1,4-Dioxo-1,4-dihydronaphthalen-2-yl-amino)benzoic acid shows multiple tunable fluorescence emissions depending on solvent, pH and wavelength of excitation. Independent dual fluorescence emissions are observed while exciting compound 3-(1,4-dioxo-1,4-dihydronaphthalen-2-yl-amino)benzoic acid in UV-region or in visible region. In methanol at low concentration it shows both S1-S0 emission and ESIPT emission at 307 nm and 480 nm. Whereas in concentrated solution S1-S0 emission as well as dimer-like emission at 307 nm and 425 nm respectively are observed. Upon excitation in visible region, it shows two emission bands in visible region which are highly dependent on concentration and are attributed to charge transfer emission and emissions of anionic species. Modulations of these emissions by varying conditions are established.

Bibliography of environmental studies in natural characteristics and anthropogenic influences on the Ganga River.

During the second half of the 20th century, the Ganga River ecosystem has been continuously altered by several ongoing anthropogenic processes, accommodating multi-dimensional pressure due to increase of nearly four-fold human population. For solution of any environmental issues of the river, the Earth System Science approach is required to have maximum socio-economic benefits to millions of people living in Indian and Bangladesh. A bibliography containing more than 250 references on environmental studies of the Ganga River was prepared to preserve its ecosystem by providing the baseline support in this regard.

Lead decline in the Indian environment resulting from the petrol-lead phase-out programme.

Recently, the lead content of various environmental components has decreased in response to replacement of leaded petrol by unleaded petrol. In India, 15 research studies are here assessed with respect to lead concentrations in various environmental components during the leaded petrol phase (before 1996), the transitional phase (1996-2000) and the unleaded petrol phase (2000 onwards). The Ganga River Water exhibited a decrease in lead concentration from 18.0 microg/l in 1988 to 3.1 microg/l in 2001. In Lucknow urban centre, mean lead concentrations in the urban air decreased from 1.6 microg/m(3) in 1994 to 0.2 microg/m(3) in 2002. Lead concentrations in Dalbergia sissoo tree leaves also decreased from 18.7 microg/g dry wt. in 1994 to 8.3 microg/g dry wt. in 2004. Mean blood-lead levels of children from Mumbai, Chennai, Bangalore, Amritsar and Lucknow urban centres have fallen from 18.1 microg/dl in the leaded petrol phase to 12.1 microg/dl in the unleaded petrol phase. The petrol-lead phase-out effort in India has reduced lead concentrations in the various environmental components after 2000. It will help to reduce the exposure of millions of people to environmental lead.

Biomonitoring of lead in atmospheric environment of an urban center of the Ganga Plain, India.

Monitoring of atmospheric lead from the Dalbergia sissoo tree was undertaken at Lucknow urban centre of the Ganga Plain, India. A total of 26 leaf samples were collected in spring, monsoon and winter seasons from 16 sampling sites and was analysed by Atomic Absorption Spectrophotometry method. Lead concentrations were low in spring season, increased in monsoon to winter seasons and range from 2.1 to 28.2 microg/g (dry wt.). This accumulative response of lead in the tree leaves is directly linked with the exposure time of automobile emission that is considered to be the predominant source for it. Highway localities show higher lead concentrations by a factor of 2 as compared to urban localities. Highest concentration was recorded at Sitapur Road (National Highway No. 24) in winter season. A linear quantitative relationship between urban air-lead levels and lead-in-the Dalbergia sissoo leaves is used to infer the qualitative assessment of present day atmospheric lead pollution. Reported results suggest a drastic reduction in mean lead concentration in Lucknow urban air from 1.32 microg/m3 in 1994 to 0.19 microg/m3 in 2002. Similarly, mean lead concentration in the tree leaves during winter season also dropped from 17.9 microg/g in 1994 to 8.1 microg/g in 2004. Despite of increasing urban population, urban area, vehicle population and traffic density, the introduction of unleaded-petrol (vehicular fuel) keeps lead level in the urban environment of Lucknow much lower than the past. Like Lucknow, other urban centres of the Ganga Plain are also on way to the exponential increase in pressure of urbanization. An appropriate urban public transport planning is required to provide healthy atmospheric environment for millions of people especially future young generation.