Evaluation of Continuous Monitoring as a Tool for Municipal Stormwater Management Programs

The purpose of this study is to evaluate the uncertainty attributable to inadequate temporal sampling of stormwater discharge and water quality, and understand its implications for meeting monitoring objectives relevant to municipal separate storm sewer systems (MS4s). A methodology is presented to evaluate uncertainty attributable to inadequate temporal sampling of continuous stormflow and water quality, and a case study demonstrates the application of the methodology to six small urban watersheds (0.8-6.8 km2) and six large rural watersheds (30-16,192 km2) in Virginia. Results indicate the necessity of high-frequency continuous monitoring for accurately capturing multiple monitoring objectives, including illicit discharges, acute toxicity events, and stormflow pollutant concentrations and loads, as compared to traditional methods of sampling. For example, 1-h sampling in small urban watersheds and daily sampling in large rural watersheds would introduce uncertainty in capturing pollutant loads of 3–46% and 10–28%, respectively. Overall, the outcomes from this study highlight how MS4s can leverage continuous monitoring to meet multiple objectives under current and future regulatory environments

Inhibition of Formalin Induced Paw Edema in Rats by Various Fractions/Extracts of Bryophyllum pinnatum

Traditionally, Bryophyllum pinnatum is used in the management of arthritis and inflammatory diseases. However, B. pinnatum has not been analysed previously for anti-inflammatory activity. Hence, this study is designed to determine the anti-inflammatory effects of various fractions of B. pinnatum leaf extract using rat model of formalin-induced paw edema. Treatment with various fractions showed marked decrease in formalin-induced paw volume and edema in rats. The results of BPAAF treatment were comparable to standard drug, diclofenac. These results indicate that B. pinnatum could be developed as ant-inflammatory drug after further studies

LiDAR mapping of tidal marshes for ecogeomorphological modelling in the TIDE project

The European research project TIDE (Tidal Inlets Dynamics and Environment) is developing and validating coupled models describing the morphological, biological and ecological evolution of tidal environments. The interactions between the physical and biological processes occurring in these regions requires that the system be studied as a whole rather than as separate parts. Extensive use of remote sensing including LiDAR is being made to provide validation data for the modelling. This paper describes the different uses of LiDAR within the project and their relevance to the TIDE science objectives. LiDAR data have been acquired from three different environments, the Venice Lagoon in Italy, Morecambe Bay in England, and the Eden estuary in Scotland. LiDAR accuracy at each site has been evaluated using ground reference data acquired with differential GPS. A semi-automatic technique has been developed to extract tidal channel networks from LiDAR data either used alone or fused with aerial photography. While the resulting networks may require some correction, the procedure does allow network extraction over large areas using objective criteria and reduces fieldwork requirements. The networks extracted may subsequently be used in geomorphological analyses, for example to describe the drainage patterns induced by networks and to examine the rate of change of networks. Estimation of the heights of the low and sparse vegetation on marshes is being investigated by analysis of the statistical distribution of the measured LiDAR heights. Species having different mean heights may be separated using the first-order moments of the height distribution

Molecular Mechanism of Cancer Susceptibility Associated with Fok1 Single Nucleotide Polymorphism of VDR in Relation to Breast Cancer

Breast cancer is the leading cause of death among women worldwide. It is a multi-factorial disease caused by genetic and environmental factors. Vitamin D has been hypothesized to lower the risk of breast cancer via the nuclear vitamin D receptor (VDR). Genetic variants of these vitamin D metabolizing genes may alter the bioavailability of vitamin D, and hence modulate the risk of breast cancer. Materials and Methods: The distribution of Fok1 VDR gene (rs2228570) polymorphism and its association with breast cancer was analysed in a case–control study based on 125 breast cancer patients and 125 healthy females from North Indian population, using PCR-RFLP. An In silico exploration of the probable mechanism of increased risk of breast cancer was performed to investigate the role of single nucleotide polymorphisms (SNPs) in cancer susceptibility. Results: The Fok1 ff genotype was significantly associated with an increased risk of breast cancer (p=0.001; χ2=13.09; OR=16.909; %95 CI=2.20 - 130.11). In silico analysis indicated that SNPs may lead to a loss in affinity of VDR to calcitriol, and may also cause the impairment of normal interaction of liganded VDR with its heterodimeric partner, the retinoid X receptor (RXR), at protein level, thereby affecting target gene transcription. Conclusion: Breast cancer risk and pathogenesis in females can be influenced by SNPs. SNPs in VDR may cause alterations in the major molecular actions of VDR, namely ligand binding, heterodimerization and transactivation. VDRE binding and co-activator recruitment by VDR appear to be functionally inseparable events that affect vitamin D-elicited gene transcription. This indicates that breast cancer risk and pathogenesis in females may be influenced by SNPs

Molecular Docking of Known Carcinogen 4- (Methyl-nitrosamino)-1-(3-pyridyl)-1-butanone (NNK) with Cyclin Dependent Kinases towards Its Potential Role in Cell Cycle Perturbation

Cell cycle is maintained almost all the times and is controlled by various regulatory proteins and their complexes (Cdk+Cyclin) in different phases of interphase (G1, S and G2) and mitosis of cell cycle. A number of mechanisms have been proposed for the initiation and progression of carcinogenesis by abruption in cell cycle process. One of the important features of cancer/carcinogenesis is functional loss of these cell cycle regulatory proteins particularly in CDKs and cyclins. We hypothesize that there is a direct involvement of these cell cycle regulatory proteins not only at the genetic level but also proteins level, during the initiation of carcinogenesis. Therefore, it becomes significant to determine inconsistency in the functioning of regulatory proteins due to interaction with carcinogen 4-(Methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK). Hence, we investigated the interaction efficiency of NNK, against cell cycle regulatory proteins. We found a different value of ΔG (free energy of binding) among the studied proteins ranging between -3.29 to -7.25 kcal/mol was observed. To validate the results, we considered Human Oxy-Hemoglobin at 1.25 Å Resolution, [PDB_ID:1HHO] as a +ve control, (binding energy -6.06 kcal/mol). Finally, the CDK8 (PDB_ID:3RGF) and CDK2 (PDB_ID:3DDP) regulatory proteins showing significantly strong molecular interaction with NNK -7.25 kcal/mol, -6.19 kcal/mol respectively were analyzed in details. In this study we predicted that CDK8 protein fails to form functional complex with its complementary partner cyclin C in presence of NNK. Consequently, inconsistency of functioning in regulatory proteins might lead to the abruption in cell cycle progression; contribute to the loss of cell cycle control and subsequently increasing the possibility of carcinogenesis

Spatial Mode Correction of Single Photons using Machine Learning

Spatial modes of light constitute valuable resources for a variety of quantum technologies ranging from quantum communication and quantum imaging to remote sensing. Nevertheless, their vulnerabilities to phase distortions, induced by random media, impose significant limitations on the realistic implementation of numerous quantum-photonic technologies. Unfortunately, this problem is exacerbated at the single-photon level. Over the last two decades, this challenging problem has been tackled through conventional schemes that utilize optical nonlinearities, quantum correlations, and adaptive optics. In this article, we exploit the self-learning and self-evolving features of artificial neural networks to correct the complex spatial profile of distorted Laguerre-Gaussian modes at the single-photon level. Furthermore, we demonstrate the possibility of boosting the performance of an optical communication protocol through the spatial mode correction of single photons using machine learning. Our results have important implications for real-time turbulence correction of structured photons and single-photon images.Comment: 7 pages, 4 figure