Dissection of QTLs conferring drought tolerance in B. carinata derived B. juncea introgression lines.

BACKGROUND: Drought is one of the important abiotic stresses that can significantly reduce crop yields. In India, about 24% of Brassica juncea (Indian mustard) cultivation is taken up under rainfed conditions, leading to low yields due to moisture deficit stress. Hence, there is an urgent need to improve the productivity of mustard under drought conditions. In the present study, a set of 87 B. carinata-derived B. juncea introgression lines (ILs) was developed with the goal of creating drought-tolerant genotypes. METHOD: The experiment followed the augmented randomized complete block design with four blocks and three checks. ILs were evaluated for seed yield and its contributing traits under both rainfed and irrigated conditions in three different environments created by manipulating locations and years. To identify novel genes and alleles imparting drought tolerance, Quantitative Trait Loci (QTL) analysis was carried out. Genotyping-by-Sequencing (GBS) approach was used to construct the linkage map. RESULTS: The linkage map consisted of 5,165 SNP markers distributed across 18 chromosomes and spanning a distance of 1,671.87 cM. On average, there was a 3.09 cM gap between adjoining markers. A total of 29 additive QTLs were identified for drought tolerance; among these, 17 (58.6% of total QTLs detected) were contributed by B. carinata (BC 4), suggesting a greater contribution of B. carinata towards improving drought tolerance in the ILs. Out of 17 QTLs, 11 (64.7%) were located on the B genome, indicating more introgression segments on the B genome of B. juncea. Eight QTL hotspots, containing two or more QTLs, governing seed yield contributing traits, water use efficiency, and drought tolerance under moisture deficit stress conditions were identified. Seventeen candidate genes related to biotic and abiotic stresses, viz., SOS2, SOS2 like, NPR1, FAE1-KCS, HOT5, DNAJA1, NIA1, BRI1, RF21, ycf2, WRKY33, PAL, SAMS2, orf147, MAPK3, WRR1 and SUS, were reported in the genomic regions of identified QTLs. CONCLUSIONS: The significance of B. carinata in improving drought tolerance and WUE by introducing genomic segments in Indian mustard is well demonstrated. The findings also provide valuable insights into the genetic basis of drought tolerance in mustard and pave the way for the development of drought-tolerant varieties.

The expression profiles of chemokines, innate immune and apoptotic genes in tumors caused by Rous Sarcoma Virus (RSV-A) in chickens.

Innate immune genes play an important role in the immune responses to Rous sarcoma virus (RSV)-induced tumor formation and metastasis. Here, we determined in vivo expression of chemokines, innate immune and apoptotic genes in Synthetic Broiler Dam Line (SDL) chickens following RSV-A infection. The mRNA expression of genes was determined at the primary site of infection and in different organs of progressor, regressor and non-responder chicks, using RT-qPCR. Our results indicated a significant upregulation of: (1) chemokines, such as MIP1ß and RANTES, (2) the innate immune gene TLR4, and (3) p53, a tumor-suppressor gene, at the site of primary infection in progressor chickens. In contrast, inducible nitric oxide synthase (iNOS) gene expression was significantly downregulated in progressor chicks compared to uninfected, control chicks. All of the innate immune genes were significantly upregulated in the lungs and liver of the progressor and regressor chicks compared to control chicks. In the spleen of progressor chicks, RANTES, iNOS and p53 gene expression were significantly increased, whereas MIP1ß and TLR4 gene expression was significantly downregulated, compared to control chicks. The lungs and livers of non-responder chicks expressed a low level of iNOS and MIP1ß, whereas RANTES, TLR4, and p53 gene expression were significantly upregulated compared to uninfected control chicks. In addition, there was a significant downregulation of RANTES, MIP1ß, and TLR4 gene expression in non-responder chicks. These results suggest the different response to infection of chicks with RSV-A is due to differential changes in the expression of innate immune genes in different organs.

Pathology updates and diagnostic approaches to haemophagocytic lymphohistiocytosis.

Haemophagocytic lymphohistiocytosis (HLH) is a complex, often under-recognised hyperinflammatory immune dysregulation syndrome arising in a diverse range of clinical scenarios and conditions. The accurate and timely diagnosis of HLH is crucial for patient survival, and usually requires a high level of clinical suspicion. The histological corollary to clinical HLH-haemophagocytosis-is neither necessary nor sufficient for the diagnosis of HLH, as it may be seen in a variety of reactive conditions and may be absent in true HLH. Nevertheless, the finding of haemophagocytosis in specific clinical situations should prompt consideration of HLH and further testing to exclude the condition. Although haemophagocytosis is traditionally described in bone marrow, identification of it in other tissues, including lymphoid, splenic, liver or neural tissue, can contribute importantly to the overall recognition of HLH. In this review we discuss the underlying pathophysiology and aetiologies of HLH, and the morphological aspects of haemophagocytosis and its associated histological findings in different tissues, and give a brief overview of diagnostic criteria and clinical evaluation.

Suspended Particulate Matter Analysis of Pre and During Covid Lockdown Using Google Earth Engine Cloud Computing: A Case Study of Ukai Reservoir.

Presence of suspended particulate matter (SPM) in a waterbody or a river can be caused by multiple parameters such as other pollutants by the discharge of poorly maintained sewage, siltation, sedimentation, flood and even bacteria. In this study, remote sensing techniques were used to understand the effects of pandemic-induced lockdown on the SPM concentration in the lower Tapi reservoir or Ukai reservoir. The estimation was done using Landsat-8 OLI (Operational Land Imager) having radiometric resolution (12-bit) and a spatial resolution of 30 m. The Google Earth Engine (GEE) cloud computing platform was used in this study to generate the products. The GEE is a semi-automated workflow system using a robust approach designed for scientific analysis and visualization of geospatial datasets. An algorithm was deployed, and a time-series (2013-2020) analysis was done for the study area. It was found that the average mean value of SPM in Tapi River during 2020 is lowest than the last seven years at the same time.

Rare EBV-associated B cell neoplasms of the gastrointestinal tract.

EBV-driven B cell neoplasms can rarely present as an extranodal mass in the gastrointestinal tract and can be missed, even by experienced pathologists, because of this uncommon presentation. A selection of these neoplasms, namely EBV-positive diffuse large B cell lymphoma, not otherwise specified (DLBCL NOS), EBV-positive mucocutaneous ulcer (EBV MCU), extracavitary primary effusion lymphoma (EPEL), and EBV-positive Burkitt lymphoma, will be discussed in the present review. Besides the common thread of EBV positivity, these lymphoproliferative disorders arise in unique clinical settings that are often associated with immunodeficiency, immunosuppression or immunosenescence and can present as solitary masses albeit rarely, within the gastrointestinal tract.

SSR marker variations in Brassica species provide insight into the origin and evolution of Brassica amphidiploids.

BACKGROUND: Oilseed Brassica represents an important group of oilseed crops with a long history of evolution and cultivation. To understand the origin and evolution of Brassica amphidiploids, simple sequence repeat (SSR) markers were used to unravel genetic variations in three diploids and three amphidiploid Brassica species of U's triangle along with Eruca sativa as an outlier. RESULTS: Of 124 Brassica-derived SSR loci assayed, 100% cross-transferability was obtained for B. juncea and three subspecies of B. rapa, while lowest cross-transferability (91.93%) was obtained for Eruca sativa. The average % age of cross-transferability across all the seven species was 98.15%. The number of alleles detected at each locus ranged from one to six with an average of 3.41 alleles per primer pair. Neighbor-Joining-based dendrogram divided all the 40 accessions into two main groups composed of B. juncea/B. nigra/B. rapa and B. carinata/B. napus/B. oleracea. C-genome of oilseed Brassica species remained relatively more conserved than A- and B-genome. A- genome present in B. juncea and B. napus seems distinct from each other and hence provides great opportunity for generating diversity through synthesizing amphidiploids from different sources of A- genome. B. juncea had least intra-specific distance indicating narrow genetic base. B. rapa appears to be more primitive species from which other two diploid species might have evolved. CONCLUSION: The SSR marker set developed in this study will assist in DNA fingerprinting of various Brassica species cultivars, evaluating the genetic diversity in Brassica germplasm, genome mapping and construction of linkage maps, gene tagging and various other genomics-related studies in Brassica species. Further, the evolutionary relationship established among various Brassica species would assist in formulating suitable breeding strategies for widening the genetic base of Brassica amphidiploids by exploiting the genetic diversity present in diploid progenitor gene pools.

Valorization of Poly (ethylene) terephthalate (PET) wastes into magnetic carbon for adsorption of antibiotic from water: Characterization and application.

Waste Polyethylene terephthalate (PET) bottles were pyrolyzed in the presence of nitrogen and converted into activated carbon (PETAC) by physical activation in carbon dioxide flow. An ex-situ precipitation and external reduction method were applied for the intercalation of ferromagnetic iron oxides onto the PETAC matrix. The characteristic structural and chemical properties of PETAC and magnetic PETAC (M-PETAC) were studied by Brunauer Emmett Teller (BET) surface area analysis, Scanning Electron Microscopy (SEM), Transmission Electron Microscopy (TEM), Fourier Transform Infrared (FTIR) analysis, Raman spectroscopy, X-Ray Diffraction (XRD) analysis, Energy Dispersive analysis of X-rays (EDAX), Vibrating Sample Magnetometer (VSM), Thermal gravimetric analysis (TGA) and elemental analysis. Characterization results indicated that PETAC exhibited a relatively smooth and microporous texture with a surface area of 659.6 m2g-1 while M-PETAC displayed a rugged morphology with a diminished surface area of 288.8 m2g-1. XRD measurements confirmed the formation of iron oxide nanocrystallites with an average Scherrer crystallite size of 19.2 nm. M-PETAC delivered a quick response to an external magnet and exhibited saturation magnetization value of 35.4 emu g-1. PETAC and M-PETAC were explored as potential adsorbents for the adsorption of a pharmaceutical (cephalexin) from water. Isotherm analysis revealed that M-PETAC exhibited a superior adsorption capacity (71.42 mg g-1) compared to PETAC (21.27 mg g-1). FTIR analysis of the adsorbents after CEX adsorption revealed the role of FeO as the nucleation site for enhanced adsorption of cephalexin by M-PETAC.

Current-voltage characteristics influenced by the nanochannel diameter and surface charge density in a fluidic field-effect-transistor.

The nanochannel diameter and surface charge density have a significant impact on current-voltage characteristics in a nanofluidic transistor. We have simulated the effect of the channel diameter and surface charge density on current-voltage characteristics of a fluidic nanochannel with positive surface charge on its walls and a gate electrode on its surface. Anion depletion/enrichment leads to a decrease/increase in ion current with gate potential. The ion current tends to increase linearly with gate potential for narrow channels at high surface charge densities and narrow channels are more effective to control the ion current at high surface charge densities. The current-voltage characteristics are highly nonlinear for wide channels at low surface charge densities and they show different regions of current change with gate potential. The ion current decreases with gate potential after attaining a peak value for wide channels at low values of surface charge densities. At low surface charge densities, the ion current can be controlled by a narrow range of gate potentials for wide channels. The current change with source drain voltage shows ohmic, limiting and overlimiting regions.

Uncertainties in mapping forest carbon in urban ecosystems.

Spatially explicit urban forest carbon estimation provides a baseline map for understanding the variation in forest vertical structure, informing sustainable forest management and urban planning. While high-resolution remote sensing has proven promising for carbon mapping in highly fragmented urban landscapes, data cost and availability are the major obstacle prohibiting accurate, consistent, and repeated measurement of forest carbon pools in cities. This study aims to evaluate the uncertainties of forest carbon estimation in response to the combined impacts of remote sensing data resolution and neighborhood spatial patterns in Charlotte, North Carolina. The remote sensing data for carbon mapping were resampled to a range of resolutions, i.e., LiDAR point cloud density - 5.8, 4.6, 2.3, and 1.2 pt s/m2, aerial optical NAIP (National Agricultural Imagery Program) imagery - 1, 5, 10, and 20 m. Urban spatial patterns were extracted to represent area, shape complexity, dispersion/interspersion, diversity, and connectivity of landscape patches across the residential neighborhoods with built-up densities from low, medium-low, medium-high, to high. Through statistical analyses, we found that changing remote sensing data resolution introduced noticeable uncertainties (variation) in forest carbon estimation at the neighborhood level. Higher uncertainties were caused by the change of LiDAR point density (causing 8.7-11.0% of variation) than changing NAIP image resolution (causing 6.2-8.6% of variation). For both LiDAR and NAIP, urban neighborhoods with a higher degree of anthropogenic disturbance unveiled a higher level of uncertainty in carbon mapping. However, LiDAR-based results were more likely to be affected by landscape patch connectivity, and the NAIP-based estimation was found to be significantly influenced by the complexity of patch shape.

Maximizing ion current rectification in a bipolar conical nanopore fluidic diode using optimum junction location.

The ion current rectification has been obtained as a function of the location of a heterojunction in a bipolar conical nanopore fluidic diode for different parameters to determine the junction location for maximum ion current rectification using numerical simulations. Forward current peaks for a specific location of the junction and reverse current decreases with the junction location due to a change in ion enrichment/depletion in the pore. The optimum location of the heterojunction shifts towards the tip with base/tip diameter and surface charge density, and towards the base with the electrolyte concentration. The optimum location of the heterojunction has been approximated by an equation as a function of pore length, base/tip diameter, surface charge density and electrolyte concentration. The study is useful to design a rectifier with maximum ion current rectification for practical purposes.

A three-tier QSAR modeling strategy for estimating eye irritation potential of diverse chemicals in rabbit for regulatory purposes.

Experimental determination of the eye irritation potential (EIP) of chemicals is not only tedious, time and resource intensive, it involves cruelty to test animals. In this study, we have established a three-tier QSAR modeling strategy for estimating the EIP of chemicals for the use of pharmaceutical industry and regulatory agencies. Accordingly, a qualitative (binary classification: irritating, non-irritating), semi-quantitative (four-category classification), and quantitative (regression) QSAR models employing the SDT, DTF, and DTB methods were developed for predicting the EIP of chemicals in accordance with the OECD guidelines. Structural features of chemicals responsible for eye irritation were extracted and used in QSAR analysis. The external predictive power of the developed QSAR models were evaluated through the internal and external validation procedures recommended in QSAR literature. In test data, the two and four category classification QSAR models (DTF, DTB) rendered accuracy of >93%, while the regression QSAR models (DTF, DTB) yielded correlation (R(2)) of >0.92 between the measured and predicted EIPs. Values of various statistical validation coefficients derived for the test data were above their respective threshold limits (except rm(2) in DTF), thus put a high confidence in this analysis. The applicability domain of the constructed QSAR models were defined using the descriptors range and leverage approaches. The QSAR models in this study performed better than any of the previous studies. The results suggest that the developed QSAR models can reliably predict the EIP of diverse chemicals and can be useful tools for screening of candidate molecules in the drug development process.

Predicting Toxicities of Diverse Chemical Pesticides in Multiple Avian Species Using Tree-Based QSAR Approaches for Regulatory Purposes.

A comprehensive safety evaluation of chemicals should require toxicity assessment in both the aquatic and terrestrial test species. Due to the application practices and nature of chemical pesticides, the avian toxicity testing is considered as an essential requirement in the risk assessment process. In this study, tree-based multispecies QSAR (quantitative-structure activity relationship) models were constructed for predicting the avian toxicity of pesticides using a set of nine descriptors derived directly from the chemical structures and following the OECD guidelines. Accordingly, the Bobwhite quail toxicity data was used to construct the QSAR models (SDT, DTF, DTB) and were externally validated using the toxicity data in four other test species (Mallard duck, Ring-necked pheasant, Japanese quail, House sparrow). Prior to the model development, the diversity in the chemical structures and end-point were verified. The external predictive power of the QSAR models was tested through rigorous validation deriving a wide series of statistical checks. Intercorrelation analysis and PCA methods provided information on the association of the molecular descriptors related to MW and topology. The S36 and MW were the most influential descriptors identified by DTF and DTB models. The DTF and DTB performed better than the SDT model and yielded a correlation (R(2)) of 0.945 and 0.966 between the measured and predicted toxicity values in test data array. Both these models also performed well in four other test species (R(2) > 0.918). ChemoTyper was used to identify the substructure alerts responsible for the avian toxicity. The results suggest for the appropriateness of the developed QSAR models to reliably predict the toxicity of pesticides in multiple avian test species and can be useful tools in screening the new chemical pesticides for regulatory purposes.

Estimating sensory irritation potency of volatile organic chemicals using QSARs based on decision tree methods for regulatory purpose.

Volatile organic compounds (VOCs) are among the priority atmospheric pollutants that have high indoor and outdoor exposure potential. The toxicity assessment of VOCs to living ecosystems has received considerable attention in recent years. Development of computational methods for safety assessment of chemicals has been advocated by various regulatory agencies. The paper proposes robust and reliable quantitative structure-activity relationships (QSARs) for estimating the sensory irritation potency and screening of the VOCs. Here, decision tree (DT) based classification and regression QSARs models, such as single DT, decision tree forest (DTF), and decision tree boost (DTB) were developed using the sensory irritation data on VOCs in mice following the OECD principles. Structural diversity and nonlinearity in the data were evaluated through the Euclidean distance and Brock-Dechert-Scheinkman statistics. The constructed QSAR models were validated with external test data and the predictive performance of these models was established through a set of coefficients recommended in QSAR literature. The performance of all three classification and regression QSAR models was satisfactory, but DTF and DTB performed relatively better. The classification and regression QSAR models (DTF, DTB) rendered classification accuracies of 98.59 and 100 %, and yielded correlations (R(2)) of 0.950 and 0.971, respectively in complete data. The lipoaffinity index and SwHBa were identified as the most influential descriptors in proposed QSARs. The developed QSARs performed better than the previous studies. The developed models exhibited high statistical confidence and identified the structural properties of the VOCs responsible for their sensory irritation, and hence could be useful tools in screening of chemicals for regulatory purpose.

Intravenous autologous bone marrow mononuclear stem cell therapy for ischemic stroke: a multicentric, randomized trial.

BACKGROUND AND PURPOSE: Pilot studies have suggested benefit from intravenous administration of bone marrow mononuclear stem cells (BMSCs) in stroke. We explored the efficacy and safety of autologous BMSCs in subacute ischemic stroke. METHODS: This was a phase II, multicenter, parallel group, randomized trial with blinded outcome assessment that included 120 patients. Patients with subacute ischemic stroke were randomly assigned to the arm that received intravenous infusion of autologous BMSCs or to control arm. Coprimary clinical efficacy outcomes were Barthel Index score and modified Rankin scale at day 180. Secondary outcomes were change in infarct volume, National Institute of Health Stroke Scale (NIHSS) at day 90 and 180. Main safety outcomes were adverse events, any new area of (18)fluorodeoxyglucose positron emission tomography uptake in any body part over 365 days. RESULTS: Fifty-eight patients received a mean of 280.75 million BMSCs at median of 18.5 days after stroke onset. There was no significant difference between BMSCs arm and control arm in the Barthel Index score (63.1 versus 63.6; P=0.92), modified Rankin scale shift analysis (P=0.53) or score >3 (47.5% versus 49.2%; P=0.85), NIHSS score (6.3 versus 7.0; P=0.53), change in infarct volume (-11.1 versus -7.36; P=0.63) at day 180. Adverse events were also similar in the 2 arms, and no patient showed any new area of (18)fluorodeoxyglucose uptake. CONCLUSIONS: With the methods used, results of this hitherto first randomized controlled trial indicate that intravenous infusion of BMSCs is safe, but there is no beneficial effect of treatment on stroke outcome. CLINICAL TRIAL REGISTRATION: URLs: http://ctri.nic.in/Clinicaltrials and http://www.clinicaltrials.gov. Unique identifiers: CTRI-ROVCTRI/2008/091/0004 and NCT0150177.

In silico prediction of toxicity of non-congeneric industrial chemicals using ensemble learning based modeling approaches.

Ensemble learning approach based decision treeboost (DTB) and decision tree forest (DTF) models are introduced in order to establish quantitative structure-toxicity relationship (QSTR) for the prediction of toxicity of 1450 diverse chemicals. Eight non-quantum mechanical molecular descriptors were derived. Structural diversity of the chemicals was evaluated using Tanimoto similarity index. Stochastic gradient boosting and bagging algorithms supplemented DTB and DTF models were constructed for classification and function optimization problems using the toxicity end-point in T. pyriformis. Special attention was drawn to prediction ability and robustness of the models, investigated both in external and 10-fold cross validation processes. In complete data, optimal DTB and DTF models rendered accuracies of 98.90%, 98.83% in two-category and 98.14%, 98.14% in four-category toxicity classifications. Both the models further yielded classification accuracies of 100% in external toxicity data of T. pyriformis. The constructed regression models (DTB and DTF) using five descriptors yielded correlation coefficients (R(2)) of 0.945, 0.944 between the measured and predicted toxicities with mean squared errors (MSEs) of 0.059, and 0.064 in complete T. pyriformis data. The T. pyriformis regression models (DTB and DTF) applied to the external toxicity data sets yielded R(2) and MSE values of 0.637, 0.655; 0.534, 0.507 (marine bacteria) and 0.741, 0.691; 0.155, 0.173 (algae). The results suggest for wide applicability of the inter-species models in predicting toxicity of new chemicals for regulatory purposes. These approaches provide useful strategy and robust tools in the screening of ecotoxicological risk or environmental hazard potential of chemicals.

QSTR modeling for qualitative and quantitative toxicity predictions of diverse chemical pesticides in honey bee for regulatory purposes.

Pesticides are designed toxic chemicals for specific purposes and can harm nontarget species as well. The honey bee is considered a nontarget test species for toxicity evaluation of chemicals. Global QSTR (quantitative structure-toxicity relationship) models were established for qualitative and quantitative toxicity prediction of pesticides in honey bee (Apis mellifera) based on the experimental toxicity data of 237 structurally diverse pesticides. Structural diversity of the chemical pesticides and nonlinear dependence in the toxicity data were evaluated using the Tanimoto similarity index and Brock-Dechert-Scheinkman statistics. Probabilistic neural network (PNN) and generalized regression neural network (GRNN) QSTR models were constructed for classification (two and four categories) and function optimization problems using the toxicity end point in honey bees. The predictive power of the QSTR models was tested through rigorous validation performed using the internal and external procedures employing a wide series of statistical checks. In complete data, the PNN-QSTR model rendered a classification accuracy of 96.62% (two-category) and 95.57% (four-category), while the GRNN-QSTR model yielded a correlation (R(2)) of 0.841 between the measured and predicted toxicity values with a mean squared error (MSE) of 0.22. The results suggest the appropriateness of the developed QSTR models for reliably predicting qualitative and quantitative toxicities of pesticides in honey bee. Both the PNN and GRNN based QSTR models constructed here can be useful tools in predicting the qualitative and quantitative toxicities of the new chemical pesticides for regulatory purposes.

Multispecies QSAR modeling for predicting the aquatic toxicity of diverse organic chemicals for regulatory toxicology.

The research aims to develop multispecies quantitative structure-activity relationships (QSARs) modeling tools capable of predicting the acute toxicity of diverse chemicals in various Organization for Economic Co-operation and Development (OECD) recommended test species of different trophic levels for regulatory toxicology. Accordingly, the ensemble learning (EL) approach based classification and regression QSAR models, such as decision treeboost (DTB) and decision tree forest (DTF) implementing stochastic gradient boosting and bagging algorithms were developed using the algae (P. subcapitata) experimental toxicity data for chemicals. The EL-QSAR models were successfully applied to predict toxicities of wide groups of chemicals in other test species including algae (S. obliguue), daphnia, fish, and bacteria. Structural diversity of the selected chemicals and those of the end-point toxicity data of five different test species were tested using the Tanimoto similarity index and Kruskal-Wallis (K-W) statistics. Predictive and generalization abilities of the constructed QSAR models were compared using statistical parameters. The developed QSAR models (DTB and DTF) yielded a considerably high classification accuracy in complete data of model building (algae) species (97.82%, 99.01%) and ranged between 92.50%-94.26% and 92.14%-94.12% in four test species, respectively, whereas regression QSAR models (DTB and DTF) rendered high correlation (R(2)) between the measured and model predicted toxicity end-point values and low mean-squared error in model building (algae) species (0.918, 0.15; 0.905, 0.21) and ranged between 0.575 and 0.672, 0.18-0.51 and 0.605-0.689 and 0.20-0.45 in four different test species. The developed QSAR models exhibited good predictive and generalization abilities in different test species of varied trophic levels and can be used for predicting the toxicities of new chemicals for screening and prioritization of chemicals for regulation.

A modified implementation of tristate inverter based static master-slave flip-flop with improved power-delay-area product.

The paper introduces novel architectures for implementation of fully static master-slave flip-flops for low power, high performance, and high density. Based on the proposed structure, traditional C(2)MOS latch (tristate inverter/clocked inverter) based flip-flop is implemented with fewer transistors. The modified C(2)MOS based flip-flop designs mC(2)MOSff1 and mC(2)MOSff2 are realized using only sixteen transistors each while the number of clocked transistors is also reduced in case of mC(2)MOSff1. Postlayout simulations indicate that mC(2)MOSff1 flip-flop shows 12.4% improvement in PDAP (power-delay-area product) when compared with transmission gate flip-flop (TGFF) at 16X capacitive load which is considered to be the best design alternative among the conventional master-slave flip-flops. To validate the correct behaviour of the proposed design, an eight bit asynchronous counter is designed to layout level. LVS and parasitic extraction were carried out on Calibre, whereas layouts were implemented using IC station (Mentor Graphics). HSPICE simulations were used to characterize the transient response of the flip-flop designs in a 180 nm/1.8 V CMOS technology. Simulations were also performed at 130 nm, 90 nm, and 65 nm to reveal the scalability of both the designs at modern process nodes.

Predicting dissolved oxygen concentration using kernel regression modeling approaches with nonlinear hydro-chemical data.

Kernel function-based regression models were constructed and applied to a nonlinear hydro-chemical dataset pertaining to surface water for predicting the dissolved oxygen levels. Initial features were selected using nonlinear approach. Nonlinearity in the data was tested using BDS statistics, which revealed the data with nonlinear structure. Kernel ridge regression, kernel principal component regression, kernel partial least squares regression, and support vector regression models were developed using the Gaussian kernel function and their generalization and predictive abilities were compared in terms of several statistical parameters. Model parameters were optimized using the cross-validation procedure. The proposed kernel regression methods successfully captured the nonlinear features of the original data by transforming it to a high dimensional feature space using the kernel function. Performance of all the kernel-based modeling methods used here were comparable both in terms of predictive and generalization abilities. Values of the performance criteria parameters suggested for the adequacy of the constructed models to fit the nonlinear data and their good predictive capabilities.

Occurrence of pharmaceuticals in urban wastewater of north Indian cities and risk assessment.

Six pharmaceuticals of different categories, such as nonsteroidal anti-inflammatory drugs (ibuprofen, ketoprofen, naproxen, diclofenac), anti-epileptic (carbamazepine), and anti-microbial (trimethoprim), were investigated in wastewater of the urban areas of Ghaziabad and Lucknow, India. Samples were concentrated by solid phase extraction (SPE) and determined by high-performance liquid chromatography (HPLC) methods. The SPE-HPLC method was validated according to the International Conference on Harmonization guidelines. All the six drugs were detected in wastewater of Ghaziabad, whereas naproxen was not detected in Lucknow wastewater. Results suggest that levels of these detected drugs were relatively higher in Ghaziabad as compared to those in Lucknow, and diclofenac was the most frequently detected drug in both the study areas. Detection of these drugs in wastewater reflects the importance of wastewater inputs as a source of pharmaceuticals. In terms of the regional distribution of compounds in wastewater of two cities, higher spatial variations (coefficient of variation 112.90-459.44%) were found in the Lucknow wastewater due to poor water exchange ability. In contrast, lower spatial variation (162.38-303.77%) was observed in Ghaziabad. Statistical analysis results suggest that both data were highly skewed, and populations in two study areas were significantly different (p < 0.05). A risk assessment based on the calculated risk quotient (RQ) in six different bioassays (bacteria, duckweed, algae, daphnia, rotifers, and fish) showed that the nonsteroidal anti-inflammatory drugs (NSAIDs) posed high (RQ >1) risk to all the test species. The present study would contribute to the formulation of guidelines for regulation of such emerging pharmaceutical contaminants in the environment.