Evaluation of watershed health using Fuzzy-ANP approach considering geo-environmental and topo-hydrological criteria

Assessment of watershed health and prioritization of sub-watersheds are needed to allocate natural resources and efficiently manage watersheds. Characterization of health and spatial prioritization of sub-watersheds in data scarce regions helps better comprehend real watershed conditions and design and implement management strategies. Previous studies on the assessment of health and prioritization of sub-watersheds in ungauged regions have not considered environmental factors and their inter-relationship. In this regard, fuzzy logic theory can be employed to improve the assessment of watershed health. The present study considered a combination of climate vulnerability (Climate Water Balance), relative erosion rate of surficial rocks, slope weighted K-factor, topographic indices, thirteen morphometric characteristics (linear, areal, and relief aspects), and potential non-point source pollution to assess watershed health, using a new framework which considers the complex linkage between human activities and natural resources. The new framework, focusing on watershed health score (WHS), was employed for the spatial prioritization of 31 sub-watersheds in the Khoy watershed, West Azerbaijan Province, Iran. In this framework, an analytical network process (ANP) and fuzzy theory were used to investigate the inter-relationships between the above mentioned geo-environmental factors and to classify and rank the health of each sub-watershed in four classes. Results demonstrated that only one sub-watershed (C15) fell into the class that was defined as 'a potentially critical zone'. This article provides a new framework and practical recommendations for watershed management agencies with a high level of assurance when there is a lack of reliable hydrometric gauge data.

An optimal modelling approach for managing agricultural water-energy-food nexus under uncertainty

Agriculture is the largest user of freshwater which is essential for food production. Water, energy and food are closely intertwined, as both water and energy are critical inputs for food production. Increasing pressure from shortages of resources and increasing demand for food reinforce the need for optimal management of the water, energy, and food nexus. Uncertainties caused by natural conditions and human activities complicate the optimal allocation. An integrated model, called AWEFSM, was developed for the sustainable management of limited water-energy-food resource in an agricultural system by incorporating multi-objective programming, nonlinear programming, and intuitionistic fuzzy numbers into a general framework. The AWEFSM model is capable of identifying the tradeoffs of water, energy and land resources among various subareas and crops, generating high-profile and environment-friendly strategies and policies, and addressing parameter uncertainties associated with the fluctuations of natural resources and variation of socioeconomic activities. The AWEFSM model was solved, considering nonlinear membership and non-membership functions with both optimistic and pessimistic views of decision makers, and was demonstrated for a real-world case study in northwest China. The interrelationships and trade-offs among system components, including water supply-demand, energy supply-demand, land demand, food production, as well as water and energy footprints, were quantitatively analyzed under different scenarios. The AWEFSM model is applicable for similar regions dominated by agriculture with limited resource supplies to determine water-energy-food strategies under uncertainty.

Entropy Analysis and Neural Network-Based Adaptive Control of a Non-Equilibrium Four-Dimensional Chaotic System with Hidden Attractors

Today, four-dimensional chaotic systems are attracting considerable attention because of their special characteristics. This paper presents a non-equilibrium four-dimensional chaotic system with hidden attractors and investigates its dynamical behavior using a bifurcation diagram, as well as three well-known entropy measures, such as approximate entropy, sample entropy, and Fuzzy entropy. In order to stabilize the proposed chaotic system, an adaptive radial-basis function neural network (RBF-NN)–based control method is proposed to represent the model of the uncertain nonlinear dynamics of the system. The Lyapunov direct method-based stability analysis of the proposed approach guarantees that all of the closed-loop signals are semi-globally uniformly ultimately bounded. Also, adaptive learning laws are proposed to tune the weight coefficients of the RBF-NN. The proposed adaptive control approach requires neither the prior information about the uncertain dynamics nor the parameters value of the considered system. Results of simulation validate the performance of the proposed control method.

Multimodal Transgastric Local Pancreatic Hypothermia Reduces Severity of Acute Pancreatitis in Rats and Increases Survival

BACKGROUND & AIMS

Acute pancreatitis (AP) of different etiologies is associated with the activation of different signaling pathways in pancreatic cells, posing challenges to the development of targeted therapies. We investigated whether local pancreatic hypothermia, without systemic hypothermia, could lessen the severity of AP induced by different methods in rats.

METHODS

A urethane balloon with 2 polyurethane tubes was placed inside the stomach of rats. AP was induced in Wistar rats by the administration of cerulein or glyceryl tri-linoleate (GTL). Then, cold water was infused into the balloon to cool the pancreas. Pancreatic temperatures were selected based on those found to decrease acinar cell injury. An un-perfused balloon was used as a control. Pancreatic and rectal temperatures were monitored, and an infrared lamp or heating pad was used to avoid generalized hypothermia. We collected blood, pancreas, kidney, and lung tissues and analyzed them by histology, immunofluorescence, immunoblot, cytokine and chemokine magnetic bead, and DNA damage assays. The effect of hypothermia on signaling pathways initiated by cerulein and GTL was studied in acinar cells.

RESULTS

Rats with pancreatic cooling developed less severe GTL-induced AP compared with rats that received the control balloon. In acinar cells, cooling decreased the lipolysis induced by GTL, increased the micellar form of its fatty acid, lowered the increase in cytosolic calcium, prevented the loss of mitochondrial membrane potential (by 70%-80%), and resulted in a 40%-50% decrease in the uptake of a fatty acid tracer. In rats with AP, cooling decreased pancreatic necrosis by 48%, decreased serum levels of cytokines and markers of cell damage, and decreased markers of lung and renal damage. Pancreatic cooling increased the proportions of rats surviving 6 hours after induction of AP (to 90%, from <10% of rats that received the control balloon). In rats with cerulein-induced AP, pancreatic cooling decreased pancreatic markers of apoptosis and inflammation.

CONCLUSIONS

In rats with AP, transgastric local pancreatic hypothermia decreases pancreatic necrosis, apoptosis, inflammation, and markers of pancreatitis severity and increases survival.

Spatiotemporal impact of soil moisture on air temperature across the Tibet Plateau

The Himalayan Tibet Plateau (HTP) is regarded as the third pole of the globe and is highly sensitive to global climate change. The hydrothermal properties of HTP greatly impacts the water cycle of the HTP and climate change in its surrounding regions. Using the NCEP-CFSR dataset, this study investigated the spatiotemporal pattern of soil moisture (SM) during different seasons considering vegetation types. The response of the evaporation fraction (EF) to SM and the impact of SM on air temperature through evapotranspiration were analyzed. Results showed that the spatial distribution of SM across the HTP was persistent during different seasons. A decreasing SM trend was observed from southeastern to northwestern HTP. Further, results of this study indicated a wetting tendency in past thirty years, espcially in desert region. In addition, the majority of the HTP regions were dominated by persistent transitional SM conditions which could be identified in the Himalayas and the southeastern HTP, whereas a persistent SM deficit in the Qaidam basin. The sensitivity of temperature response to EF was the strongest during spring and summer. Moreover, the spatial distribution of sensitivity was highly consistent with the vegetation regionalization, indicating the remarkable impact of vegetation type on the sensitivity of temperature to EF changes in summer.

On the mechanisms of two composite methods for construction of multivariate drought indices

Droughts are comprehensive and complex issues that need to be characterized from a multivariate perspective. In recent years, a number of composite indices have been proposed for drought characterization. However, rare studies have systematically compared similarities and dissimilarities of these indices, and they have provided little insights into the combination mechanisms. To address this issue, two widely used combination approaches, namely the principal component analysis (PCA) and copula based joint probability distribution were employed, with the corresponding integrated product denoted as the Aggregate Drought Index (ADI) and Joint Drought Deficit Index (JDI). Five constituents for constructing ADI and JDI were derived from the variable infiltration capacity model (VIC) monthly simulations over the Yellow River basin (YRB), China, including precipitation (P), actual evapotranspiration (ET), soil moisture of top two layers, and runoff (during 1961-2012). Results showed that the behavioral patterns of ADI and JDI may not be easily influenced by the variation of one single element, and they represented comprehensive moisture status well. A further comparison between these two composite indices suggested that ADI and JDI behaved similarly in most areas of YRB, with some dissimilarities in the source region. The particular behavior of ET was responsible for the inconsistency. Comparing to other regions, an enhanced role of potential evapotranspiration (PET) was imposed on ET in the source region, leading to a poor relationship of ET with P and other hydrological variables. Accordingly, when constructing composite drought indices, the drought information indicated by ET was more easily abandoned by ADI but reserved in JDI. This study clearly demonstrates the mechanisms of two common integrated approaches in blending different drought information, which has significant implications for composite drought indices construction and application, and potentially provides some valuable references for the improvement of monitoring techniques in future drought related researches.

Bed and suspended sediment-associated rare earth element concentrations and fluxes in a polluted Brazilian river system

Rare earth elements (REEs) have been recently recognized as emergent pollutants in rivers. However, data regarding REE fluxes in association with either bed or suspended are scarce. To address this knowledge gap, we determined the concentrations and fluxes of La, Ce, Pr, Nd, Sm, Eu, Gd, Yb, Lu, Dy, Er, Ho, Tb, and Tm in bed and suspended sediment samples of a representative polluted Brazilian River. Sediment-associated data on REEs were placed in the context of corresponding background concentrations in soils under natural conditions along the Ipojuca watershed. Light rare earth elements (LREEs) comprised more than 94% of the total REEs associated with bed and suspended sediments. Suspended sediments accounted for more than 95% of the total REE flux. The Ce and Nd fluxes of about 7 t year^-1 underscore the importance of including REEs in future estimations of global suspended sediment-associated element fluxes. In contrast, bedload often transported less than 0.0007 t year^-1 of each REE. The main sources of pollution in the Ipojuca River are anthropogenic, likely due to domestic effluent and waste water from industrial and agricultural operations-major causes of sediment-associated Gd transport in polluted streams.

A comparison study of DRASTIC methods with various objective methods for groundwater vulnerability assessment

Groundwater vulnerability assessment is a measure of potential groundwater contamination for areas of interest. The main objective of this study is to modify original DRASTIC model using four objective methods, Weights-of-Evidence (WOE), Shannon Entropy (SE), Logistic Model Tree (LMT), and Bootstrap Aggregating (BA) to create a map of groundwater vulnerability for the Sari-Behshahr plain, Iran. The study also investigated impact of addition of eight additional factors (distance to fault, fault density, distance to river, river density, land-use, soil order, geological time scale, and altitude) to improve groundwater vulnerability assessment. A total of 109 nitrate concentration data points were used for modeling and validation purposes. The efficacy of the four methods was evaluated quantitatively using the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC). AUC value for original DRASTIC model without any modification of weights and rates was 0.50. Modification of weights and rates resulted in better performance with AUC values of 0.64, 0.65, 0.75, and 0.81 for BA, SE, LMT, and WOE methods, respectively. This indicates that performance of WOE is the best in assessing groundwater vulnerability for DRASTIC model with 7 factors. The results also show more improvement in predictability of the WOE model by introducing 8 additional factors to the DRASTIC as AUC value increased to 0.91. The most effective contributing factor for ground water vulnerability in the study area is the net recharge. The least effective factors are the impact of vadose zone and hydraulic conductivity.

Novel Hybrid Evolutionary Algorithms for Spatial Prediction of Floods

Adaptive neuro-fuzzy inference system (ANFIS) includes two novel GIS-based ensemble artificial intelligence approaches called imperialistic competitive algorithm (ICA) and firefly algorithm (FA). This combination could result in ANFIS-ICA and ANFIS-FA models, which were applied to flood spatial modelling and its mapping in the Haraz watershed in Northern Province of Mazandaran, Iran. Ten influential factors including slope angle, elevation, stream power index (SPI), curvature, topographic wetness index (TWI), lithology, rainfall, land use, stream density, and the distance to river were selected for flood modelling. The validity of the models was assessed using statistical error-indices (RMSE and MSE), statistical tests (Friedman and Wilcoxon signed-rank tests), and the area under the curve (AUC) of success. The prediction accuracy of the models was compared to some new state-of-the-art sophisticated machine learning techniques that had previously been successfully tested in the study area. The results confirmed the goodness of fit and appropriate prediction accuracy of the two ensemble models. However, the ANFIS-ICA model (AUC = 0.947) had a better performance in comparison to the Bagging-LMT (AUC = 0.940), BLR (AUC = 0.936), LMT (AUC = 0.934), ANFIS-FA (AUC = 0.917), LR (AUC = 0.885) and RF (AUC = 0.806) models. Therefore, the ANFIS-ICA model can be introduced as a promising method for the sustainable management of flood-prone areas.

c-Src regulates cargo transit via the Golgi in pancreatic acinar cells

The exocrine pancreatic acinar cell is unique for its rapid protein synthesis and packaging in zymogen granules (ZGs). However, while crucial to the pathogenesis of pancreatitis, the signaling involved in the transit of proteins via the Golgi is poorly understood in these cells. Noting the evidence of c-Src in regulating transit of cargo via the Golgi in other systems, we explored this in acinar cells. Stimulation of ZG formation with dexamethasone activated Src and increased the Golgi area in acinar cells. c-Src localized to the microsomes of acinar cells on immunofluorescence and subcellular fractionation. While other Src family members had no effect on the Golgi markers P115 and GM130, active c-Src increased the Golgi area these stained, extending them into the ER. Src inhibition reduced amylase staining outside the Golgi and increased it in a stack like Golgi morphology. In vivo pharmacologic inhibition or acinar specific genetic deletion of c-Src reduced ZG number and staining of amylase in ZGs along with increasing amylase retention in the microsomal fraction. Morphologically this was associated with smaller Golgi stacks, and dilation of the endoplasmic reticulum. Therefore the role c-Src regulated Golgi function, ZG formation and microsomal zymogen transit in acinar cells needs to be explored in pancreatitis.

Comparison of different heuristic and decomposition techniques for river stage modeling

This paper proposes hybrid soft computing models for daily river stage modeling. The models combine variational mode decomposition (VMD) with different soft computing models, including artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and random forest (RF). The performances of VMD-based models (VMD-ANN, VMD-ANFIS, and VMD-RF) are assessed by model efficiency indices and graphical comparison, and compared with those of single models (ANN, ANFIS, and RF) and ensemble empirical mode decomposition (EEMD)-based models (EEMD-ANN, EEMD-ANFIS, and EEMD-RF). Results show that VMD-ANN, VMD-ANFIS, and VMD-RF models are more efficient and accurate than ANN, ANFIS, and RF models, respectively, and slightly better than EEMD-ANN, EEMD-ANFIS, and EEMD-RF models, respectively. In terms of model efficiency and accuracy, the top five models are VMD-ANFIS, EEMD-ANFIS, VMD-ANN, VMD-RF, and ANFIS and the VMD-ANFIS model is the best. It is found that VMD can enhance the performance of conventional single soft computing models; VMD is more effective than EEMD for hybrid model development; and the ANFIS model combined with VMD and EEMD can yield better efficiency and accuracy than other models. Therefore, VMD-based hybrid modeling is a more effective method for reliable daily river stage modeling.

Pathogenic characterization of porcine reproductive and respiratory syndrome virus of Indian origin in experimentally infected piglets

Porcine reproductive and respiratory syndrome (PRRS) is an economically important transboundary viral disease of pigs confronting the swine industry worldwide. This study was aimed to assess the pathogenic potential of PRRS virus belonging to genotype 2 that emerged in India in 2013. Nine 6-week-old piglets were inoculated intranasally with 2 × 10^5.75  TCID₅₀ /ml of PRRSV (Ind-297221/2013). Three piglets were kept as uninfected controls. Blood and nasal swabs were collected daily up to 7 days post-infection (dpi) and on alternate days subsequently. Piglets were necropsied for tissue sample collection either on death or after euthanasia on 7, 14 or 21 dpi (one uninfected control and three PRRSV-infected piglets per interval). The virus caused high fever, typical blue ear, weight loss, respiratory distress, diarrhoea and leucopenia between 2 and 8 dpi. Two infected piglets died (on 3 and 17 dpi) during the course of study. The presence of virus in serum and nasal secretion was observed up to 19 and 17 dpi, respectively, with the maximum load between 4 and 7 dpi. Seroconversion started 6 dpi and the mean PRRSV antibody titre reached up to 640 by 21 dpi. Virus load was highest in tonsils at all the intervals, whereas in spleen and lymph nodes load was higher in later intervals. Major microscopic lesions in PRRSV-infected piglets included moderate to severe interstitial pneumonia, lymphoid depletion in tonsils and lymph nodes (cystic), thymic atrophy, reactive hyperplasia followed by lymphoid depletion in spleen. PRRSV antigen was consistently demonstrated by immunoperoxidase test in the lungs, spleen, tonsils and lymph nodes. Antigen distribution was more widespread on 7 and 14 dpi than on 21 dpi. The findings establish that the Indian PRRSV is highly pathogenic to piglets.

Sinonasal Mucormycosis: A to Z

Mucormycosis caused by one of the members of Mucoraceae family, is one of the most rapidly spreading and fatal fungal infection occurring mostly in Diabetic or Immunocompromised patients especially in developing countries. 26 patients suffering from sinonasal mucormycosis admitted in SGRRIM&HS, Dehradun from January 2013 to January 2017 are discussed. Diagnosis of mucormycosis was established on strong clinical suspicion with presence of grayish black crusting on nasal endoscopy which is confirmed by histopathology examination. Immediate correction of underlying immunocompromised status with debridement with intravenous liposomal amphotericin B was done in all the 26 cases out of which 10 patients were cured. Early detection and aggressive multidisciplinary management is must for the successful treatment of mucormycosis.

A framework for uncertainty and risk analysis in Total Maximum Daily Load applications

In the United States, the computation of Total Maximum Daily Loads (TMDL) must include a Margin of Safety (MOS) to account for different sources of uncertainty. In practice however, TMDL studies rarely include an explicit uncertainty analysis and the estimation of the MOS is often subjective and even arbitrary. Such approaches are difficult to replicate and preclude the comparison of results between studies. To overcome these limitations, a Bayesian framework to compute TMDLs and MOSs including an explicit evaluation of uncertainty and risk is proposed in this investigation. The proposed framework uses the concept of Predictive Uncertainty to calculate a TMDL from an equation of allowable risk of non-compliance of a target water quality standard. The framework is illustrated in a synthetic example and in a real TMDL study for nutrients in Sawgrass Lake, Florida.

A kriging and entropy-based approach to raingauge network design

Hydrological data, such as precipitation, is fundamental for planning, designing, developing, and managing water resource projects as well as for hydrologic research. An optimal raingauge network leads to more accurate estimates of mean or point precipitation at any site over the watershed. Some studies in the past have suggested increasing gauge network density for reducing the estimation error. However, more stations mean more cost of installation and monitoring. This study proposes an approach on the basis of kriging and entropy theory to determine an optimal network design in the city of Shanghai, China. Unlike the past studies using kriging interpolation and entropy theory for network design, the approach developed in the current study not only used the kriging method as an interpolator to determine rainfall data at ungauged locations but also incorporated the minimum kriging standard error (KSE) and maximum net information (NI) content. The approach would thus lead to an optimal network and would enable the reduction of kriging standard error of precipitation estimates throughout the watershed and achieve an optimum rainfall information. This study also proposed an NI-KSE-based criterion which is dependent on a single-objective optimization. To evaluate the final optimal gauge network, areal average rainfall was estimated and its accuracy was compared with that obtained with the existing rain gauge network.

Differential Phytotoxic Impact of Plant Mediated Silver Nanoparticles (AgNPs) and Silver Nitrate (AgNO3) on Brassica sp

Continuous formation and utilization of nanoparticles (NPs) have resulted into significant discharge of nanosized particles into the environment. NPs find applications in numerous products and agriculture sector, and gaining importance in recent years. In the present study, silver nanoparticles (AgNPs) were biosynthesized from silver nitrate (AgNO3) by green synthesis approach using Aloe vera extract. Mustard (Brassica sp.) seedlings were grown hydroponically and toxicity of both AgNP and AgNO3 (as ionic Ag+) was assessed at various concentrations (1 and 3 mM) by analyzing shoot and root length, fresh mass, protein content, photosynthetic pigments and performance, cell viability, oxidative damage, DNA degradation and enzyme activities. The results revealed that both AgNPs and AgNO3 declined growth of Brassica seedlings due to enhanced accumulation of AgNPs and AgNO3 that subsequently caused severe inhibition in photosynthesis. Further, the results showed that both AgNPs and AgNO3 induced oxidative stress as indicated by histochemical staining of superoxide radical and hydrogen peroxide that was manifested in terms of DNA degradation and cell death. Activities of antioxidants, i.e., ascorbate peroxidase (APX) and catalase (CAT) were inhibited by AgNPs and AgNO3. Interestingly, damaging impact of AgNPs was lesser than AgNO3 on Brassica seedlings which was due to lesser accumulation of AgNPs and better activities of APX and CAT, which resulted in lesser oxidative stress, DNA degradation and cell death. The results of the present study showed differential impact of AgNPs and AgNO3 on Brassica seedlings, their mode of action, and reasons for their differential impact. The results of the present study could be implied in toxicological research for designing strategies to reduce adverse impact of AgNPs and AgNO3 on crop plants.

Chain-Breaking Phenolic 2,3-Dihydrobenzo[b]selenophene Antioxidants: Proximity Effects and Regeneration Studies

Phenolic 2,3-dihydrobenzo[b]selenophene antioxidants bearing an OH-group ortho (9), meta (10, 11) and para (8) to the Se were prepared by seleno-Claisen rearrangement/intramolecular hydroselenation. meta-Isomer (11) was studied by X-ray crystallography. The radical-trapping activity and regenerability of compounds 8-11 were evaluated using a two-phase system in which linoleic acid was undergoing peroxidation in the lipid phase while regeneration of the antioxidant by co-antioxidants (N-acetylcysteine, glutathione, dithiothreitol, ascorbic acid, tris(carboxyethyl)phosphine hydrochloride) was ongoing in the aqueous layer. Compound 9 quenched peroxyl radicals more efficiently than α-tocopherol. It also provided the most long-lasting antioxidant protection. With thiol co-antioxidants it could inhibit peroxidation for more than five-fold longer than the natural product. Regeneration was more efficient when the aqueous phase pH was slightly acidic. Since calculated O-H bond dissociation energies for 8-11 were substantially larger than for α-tocopherol, an antioxidant mechanism involving O-atom transfer from peroxyl to selenium was proposed. The resulting phenolic selenoxide/alkoxyl radical would then exchange a hydrogen atom in a solvent cage before antioxidant regeneration at the aqueous lipid interphase.

Efficacy and evaluation of environmental management system to control sandfly vector of Kala-azar

The established vector for visceral leishmaniasis, Phlebotomus argentipes (Diptera: Psychodidae) breeds inside the human dwellings and cattle shed under crevices at the base of the wall. P. argentipes was controlled by plastering the base of wall (9″height × 9″base). The study was conducted in two phases: (i) Screening of plastering materials (ii) validation of the most suitable material. During the first phase (2014); four intervention materials were evaluated in four different arms: (i) cement (ii) brick chimney fly ash (BCFA i.e. waste material from an oven for backing raw earthen brick in charcoal) mixed with lime (95:5) (iii) wire mesh (25 holes/cm²) and (iv) glazed tiles. Ten houses were selected as test and same as control in four different villages for each arm having similar ecotype and similar density of sandflies. The pre and post intervention density of sandflies were evaluated. Significant reduction in sandfly density was found with cement (46.2%) and BCFA (29.6%) plastering (P < 0.05). In the second phase of the study (2015); the two most effective interventions were validated at village level with one control. A significant reduction in the density of P. argentipes was found with cement; 60.2% (Mean ± S.D. = 2.48 ± 2.78, 95% CI = 1.93-3.02) and BCFA; 48.2% (Mean ± S.D. = 1.98 ± 2.20, 95% CI = 1.55-2.41) (P < 0.05). BCFA was found easily accessible, acceptable and cost effective that can be used in any type of wall materials at own cost. This can be implemented as one of the integrated vector control approach in the programme.

Experimental and AI-based numerical modeling of contaminant transport in porous media

This study developed a new hybrid artificial intelligence (AI)-meshless approach for modeling contaminant transport in porous media. The key innovation of the proposed approach is that both black box and physically-based models are combined for modeling contaminant transport. The effectiveness of the approach was evaluated using experimental and real world data. Artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) were calibrated to predict temporal contaminant concentrations (CCs), and the effect of noisy and de-noised data on the model performance was evaluated. Then, considering the predicted CCs at test points (TPs, in experimental study) and piezometers (in Myandoab plain) as interior conditions, the multiquadric radial basis function (MQ-RBF), as a meshless approach which solves partial differential equation (PDE) of contaminant transport in porous media, was employed to estimate the CC values at any point within the study area where there was no TP or piezometer. Optimal values of the dispersion coefficient in the advection-dispersion PDE and shape coefficient of MQ-RBF were determined using the imperialist competitive algorithm. In temporal contaminant transport modeling, de-noised data enhanced the performance of ANN and ANFIS methods in terms of the determination coefficient, up to 6 and 5%, respectively, in the experimental study and up to 39 and 18%, respectively, in the field study. Results showed that the efficiency of ANFIS-meshless model was more than ANN-meshless model up to 2 and 13% in the experimental and field studies, respectively.

Non-point source contribution and dynamics of soluble and particulate phosphorus from main tributaries of the Zarivar Lake watershed, Iran

The temporal variability of phosphorus (P) transport and the relationships between discharge, suspended sediment concentration and particulate (PP), and soluble (SP) phosphorus were examined. The study was conducted at the event scale in seven tributaries of the Zarivar Lake watershed in Kurdistan Province (Iran) from March 2011 to April 2012. Based on eight runoff events, 82% of the total P was the PP carried out by suspended sediment. Results showed a high variability of P transport during different runoff events. It was found that soil erosion was the source of the high P load. For all tributaries, PP was linearly related to both discharge and suspended sediment concentration. However, the relationships of SP and PP with discharge and suspended sediment concentration showed different hysteresis patterns. The relationship between PP and discharge was generally characterized by a clockwise pattern (i.e., lower part contribution of the sub-watersheds) but the patterns between SP and discharge were mainly anticlockwise (i.e., upper part contribution of the sub-watersheds or perhaps due to a subsurface flow contribution).

Active versus passive management: issues for sustainable development of community forestry in mid hills of Nepal

Abstract available on the PDFBanko Janakari Vol.12(1) 2002: 62-70

Uncovering Potential Applications of Cyanobacteria and Algal Metabolites in Biology, Agriculture and Medicine: Current Status and Future Prospects

Cyanobacteria and algae having complex photosynthetic systems can channelize absorbed solar energy into other forms of energy for production of food and metabolites. In addition, they are promising biocatalysts and can be used in the field of "white biotechnology" for enhancing the sustainable production of food, metabolites, and green energy sources such as biodiesel. In this review, an endeavor has been made to uncover the significance of various metabolites like phenolics, phytoene/terpenoids, phytols, sterols, free fatty acids, photoprotective compounds (MAAs, scytonemin, carotenoids, polysaccharides, halogenated compounds, etc.), phytohormones, cyanotoxins, biocides (algaecides, herbicides, and insecticides) etc. Apart from this, the importance of these metabolites as antibiotics, immunosuppressant, anticancer, antiviral, anti-inflammatory agent has also been discussed. Metabolites obtained from cyanobacteria and algae have several biotechnological, industrial, pharmaceutical, and cosmetic uses which have also been discussed in this review along with the emerging technology of their harvesting for enhancing the production of compounds like bioethanol, biofuel etc. at commercial level. In later sections, we have discussed genetically modified organisms and metabolite production from them. We have also briefly discussed the concept of bioprocessing highlighting the functioning of companies engaged in metabolites production as well as their cost effectiveness and challenges that are being addressed by these companies.

An anoxic-aerobic system for simultaneous biodegradation of phenol and ammonia in a sequencing batch reactor

A laboratory-scale sequencing batch reactor (SBR) was investigated to treat artificial pretreated coal gasification wastewater that was mainly contained of ammonia and phenol. The efficiency of SBR fed with increasing phenol concentrations (from 150 to 300 mg l^-1) and the relationship among phenol, nitrogen removal, and the microbial community structure were evaluated. When the phenol feeding concentration was increased to about 300 mg l^-1, the removal efficiency was above 99.0%, demonstrating the robustness of phenol removal capacity. The study showed that most phenol was degraded in anoxic stage. The average removal efficiencies of ammonia and total nitrogen were 98.4 and 81.9%, respectively, with average NH₄⁺-N concentration of 107.5 mg l^-1 and COD/N 7.5. Low temperature caused sludge loss that led to the decreased performance. Increasing the temperature could not recover the performance effectively. The data from bacterial analysis revealed that Delftia, Hydrogenophaga, and unclassified Xanthomonadaceae played a significant role in phenol degradation before the temperature increase, while uncultured Syntrophococcus sp. and unclassified Rhodocyclaceae were responsible for phenol degradation after the temperature increase. These results imply that the SBR holds potential for the simultaneous removal of phenolic compounds and nitrogen through aerobic ammonia oxidation and anoxic denitrification with phenol as the co-organic carbon source.

Retraction notice to: Validity of existing CD 4+ classification in North Indians, in predicting immune status [J Infect 51 (2005) 41-46]

This article has been retracted: please see Elsevier Policy on Article Withdrawal (http://www.elsevier.com/locate/withdrawalpolicy). It was brought to the attention of the Editor-in-Chief that large sections of the abstract, methods and results and discussion of the paper are identical to a paper published in The National Medical Journal of India, Volume 14, Issue 6, November/December 2001, Pages 335-339. http://archive.nmji.in/archives/Volume-14/issue-6/original-articles-2.pdf We apologise to the readers of the journal that this plagiarism was not detected during the submission process. To verify originality, all articles submitted to the Journal of Infection are now checked by the originality detection service CrossCheck.

Traffic noise mapping of Indian roads through smartphone user community participation

Road traffic noise pollution is a global hazard, and rapid urbanization has aggravated the problem. This paper explores a novel approach which involves a smartphone user community to monitor the prevalent noise. The system involves a client application on smartphones that records noise, processes the information and communicates to a server and shares the information as visual noise levels on Google® Maps. A fuzzy logic-based classification of noise is proposed. Results from residential, commercial, and industrial areas of the northern region of India are demonstrated. The noise levels are generally found to be higher than the prescribed standards. The experiment demonstrates the huge potential of user community participation in monitoring noise pollution.

Obturator Nerve Block in Transurethral Resection of Bladder Tumor: A Comparative Study by two Techniques

CONTEXT

Sparing of obturator nerve is a common problem encountered during transurethral resection of bladder tumor (TURBT) under spinal anesthesia.

AIMS

To evaluate and compare obturator nerve block (ONB) by two different techniques during TURBT.

SETTINGS AND DESIGN

This is prospective observational study.

SUBJECTS AND METHODS

Forty adult male patients from the American Society of Anesthesiologists Class I-IV planned to undergo TURBT under spinal anesthesia were divided into two groups of twenty each. In one group, ONB was performed with nerve locator. In other group, transvesical nerve block was performed with a cystoscope. The primary endpoints of this study were the occurrence of adductor reflex, ability to resect the tumor, and number of surgical interruptions. A number of transfusions required and bladder perforation were the secondary endpoints.

RESULTS

There was statistically significant difference between the groups for resection without adductor jerk, resection with a minimal jerk, and unresectable with high-intensity adductor jerk. Bleeding was observed in both groups and one bladder perforation was encountered.

CONCLUSIONS

We conclude that ONB, when administered along with spinal anesthesia for TURBT, is extremely safe and effective method of anesthesia to overcome adductor contraction. ONB with nerve locator appears to be more effective method compared to the transvesical nerve block.

Nitric Oxide Ameliorates Zinc Oxide Nanoparticles Phytotoxicity in Wheat Seedlings: Implication of the Ascorbate-Glutathione Cycle

The present study investigates ameliorative effects of nitric oxide (NO) against zinc oxide nanoparticles (ZnONPs) phytotoxicity in wheat seedlings. ZnONPs exposure hampered growth of wheat seedlings, which coincided with reduced photosynthetic efficiency (F_v/F_m and qP), due to increased accumulation of zinc (Zn) in xylem and phloem saps. However, SNP supplementation partially mitigated the ZnONPs-mediated toxicity through the modulation of photosynthetic activity and Zn accumulation in xylem and phloem saps. Further, the results reveal that ZnONPs treatments enhanced levels of hydrogen peroxide and lipid peroxidation (as malondialdehyde; MDA) due to severely inhibited activities of the following ascorbate-glutatione cycle (AsA-GSH) enzymes: ascorbate peroxidase, glutathione reductase, monodehydroascorbate reductase and dehydroascorbate reductase, and its associated metabolites ascorbate and glutathione. In contrast to this, the addition of SNP together with ZnONPs maintained the cellular functioning of the AsA-GSH cycle properly, hence lesser damage was noticed in comparison to ZnONPs treatments alone. The protective effect of SNP against ZnONPs toxicity on fresh weight (growth) can be reversed by 2-(4carboxy-2-phenyl)-4,4,5,5-tetramethyl- imidazoline-1-oxyl-3-oxide, a NO scavenger, and thus suggesting that NO released from SNP ameliorates ZnONPs toxicity. Overall, the results of the present study have shown the role of NO in the reducing of ZnONPs toxicity through the regulation of accumulation of Zn as well as the functioning of the AsA-GSH cycle.

Nitro-, Azo-, and Amino Derivatives of Ebselen: Synthesis, Structure, and Cytoprotective Effects

Novel azo-bis-ebselen compounds 7 were prepared by reduction of 7-nitro-2-aryl-1,2-benzisoselenazol-3(2H)-ones 3 and 6 with sodium benzenetellurolate, NaTeC₆H₅, and by reaction of 2-bromo-3-nitrobenzamides with Na₂Se₂. The X-ray structure of 7b showed that the molecule, due to strong intramolecular secondary Se···N interactions, is completely planar. Azo-compounds 7 upon further reaction with NaTeC₆H₅ were reductively cleaved to provide 2 equiv of the corresponding aromatic amine. The weak Se-N bond was not stable enough to survive the reaction conditions, and diselenides 8 were isolated after workup. Whereas azo-bis-ebselens 7 were poor mimics of the glutathione peroxidase (GPx)-enzymes, nitroebselens 3, 6, and 11b and diselenides 8 were 3-6-fold more active than ebselen. Based on ⁷⁷Se NMR spectroscopy, a catalytic cycle for diselenide 8b, involving aminoebselen 14, was proposed. As assessed by chemiluminescence measurements, the good GPx-mimics could reduce production of reactive oxygen species (ROS) in stimulated human mononuclear cells more efficiently than Trolox. No toxic effects of the compounds were seen in MC3T3-cells at 25 μM.

Flexible anodized aluminum oxide membranes with customizable back contact materials

Anodized aluminum oxide (AAO) membranes were fabricated using flexible substrate/carrier material. This method facilitates the use of AAO templates with many different materials as substrates that are otherwise incompatible with most anodization techniques. Thin titanium (Ti) and tungsten (W) layers were employed as interlayer materials. Titanium enhances adhesion. Tungsten not only helps eliminate the barrier layer but also plays a critical role in enabling the use of flexible substrates. The resulting flexible templates provide new, exciting opportunities in photovoltaic and other device applications. CuInSe₂ nanowires were electrochemically deposited into porous AAO templates with molybdenum (Mo) as the back contact material. The feasibility of using any material to form a contact with semiconductor nanowires has been demonstrated for the first time enabling new avenues in photovoltaic applications.

Combined use of meteorological drought indices at multi-time scales for improving hydrological drought detection

Prediction of hydrological drought in the absence of hydrological records is of great significance for water resources management and risk assessment. In this study, two meteorological drought indices, including standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI) calculated at different time scales (1 to 12months), were analyzed for their capabilities in detecting hydrological droughts. The predictive skills of meteorological drought indices were assessed through correlation analysis, and two skill scores, i.e. probability of detection (POD) and false alarm rate (FAR). When used independently, indices of short time scales generally performed better than did those of long time scales. However, at least 31% of hydrological droughts were still missed in view of the peak POD score (0.69) of a single meteorological drought index. Considering the distinguished roles of different time scales in explaining hydrological droughts with disparate features, an optimization approach of blending SPI/SPEI at multiple time scales was proposed. To examine the robustness of the proposed method, data of 1964-1990 was used to establish the multiscalar index, then validate during 2000-2010. Results showed that POD exhibited a significant increase when more than two time scales were used, and the best performances were found when blending 8 time scales of SPI and 9 for SPEI, with the corresponding values of 0.82 and 0.85 for POD, 0.205 and 0.21 for FAR, in the calibration period, and even better performance in the validation period. These results far exceeded the performance of any single meteorological drought index. This suggests that when there is lack of streamflow measurements, blending climatic information of multiple time scales to jointly monitor hydrological droughts could be an alternative solution.

A multidimension cloud model-based approach for water quality assessment

Lakes are vitally important, because they perform a multitude of functions, such as water supply, recreation, fishing, and habitat. However, eutrophication limits the ability of lakes to perform these functions. In order to reduce eutrophication, the first step is its evaluation. The process of evaluation entails randomness and fuzziness which must therefore be incorporated. This study proposes an eutrophication evaluation method, named Multidimension Normal Cloud Model (MNCM). The model regards each evaluation factor as a one-dimension attribute of MNCM, chooses reasonable parameters and determines the weights of evaluation factors by entropy. Thus, all factors of MNCM belonging to each eutrophication level are generated and the final eutrophication level is determined by the certainty degree. MNCM is then used to evaluate eutrophication of 12 typical lakes and reservoirs in China and its results are compared with those of the reference method, one-dimension normal cloud model, related weighted nutrition state index method, scoring method, and fuzzy comprehensive evaluation method. Results of MNCM are found to be consistent with the actual water status; hence, MNCM can be an effective evaluation tool. With respect to the former one-dimension normal cloud model, parameters of MNCM are improved without increasing its complexity. MNCM can directly determine the eutrophication level according to the degree of certainty and can determine the final degree of eutrophication; thus, it is more consistent with the complexity of water eutrophication evaluation.

A cloud model-based approach for water quality assessment

Water quality assessment entails essentially a multi-criteria decision-making process accounting for qualitative and quantitative uncertainties and their transformation. Considering uncertainties of randomness and fuzziness in water quality evaluation, a cloud model-based assessment approach is proposed. The cognitive cloud model, derived from information science, can realize the transformation between qualitative concept and quantitative data, based on probability and statistics and fuzzy set theory. When applying the cloud model to practical assessment, three technical issues are considered before the development of a complete cloud model-based approach: (1) bilateral boundary formula with nonlinear boundary regression for parameter estimation, (2) hybrid entropy-analytic hierarchy process technique for calculation of weights, and (3) mean of repeated simulations for determining the degree of final certainty. The cloud model-based approach is tested by evaluating the eutrophication status of 12 typical lakes and reservoirs in China and comparing with other four methods, which are Scoring Index method, Variable Fuzzy Sets method, Hybrid Fuzzy and Optimal model, and Neural Networks method. The proposed approach yields information concerning membership for each water quality status which leads to the final status. The approach is found to be representative of other alternative methods and accurate.

Mid- and long-term runoff predictions by an improved phase-space reconstruction model

In recent years, the phase-space reconstruction method has usually been used for mid- and long-term runoff predictions. However, the traditional phase-space reconstruction method is still needs to be improved. Using the genetic algorithm to improve the phase-space reconstruction method, a new nonlinear model of monthly runoff is constructed. The new model does not rely heavily on embedding dimensions. Recognizing that the rainfall-runoff process is complex, affected by a number of factors, more variables (e.g. temperature and rainfall) are incorporated in the model. In order to detect the possible presence of chaos in the runoff dynamics, chaotic characteristics of the model are also analyzed, which shows the model can represent the nonlinear and chaotic characteristics of the runoff. The model is tested for its forecasting performance in four types of experiments using data from six hydrological stations on the Yellow River and the Yangtze River. Results show that the medium-and long-term runoff is satisfactorily forecasted at the hydrological stations. Not only is the forecasting trend accurate, but also the mean absolute percentage error is no more than 15%. Moreover, the forecast results of wet years and dry years are both good, which means that the improved model can overcome the traditional ''wet years and dry years predictability barrier,'' to some extent. The model forecasts for different regions are all good, showing the universality of the approach. Compared with selected conceptual and empirical methods, the model exhibits greater reliability and stability in the long-term runoff prediction. Our study provides a new thinking for research on the association between the monthly runoff and other hydrological factors, and also provides a new method for the prediction of the monthly runoff.

A review of atmospheric and land surface processes with emphasis on flood generation in the Southern Himalayan rivers

Floods in the southern rim of the Indian Himalayas are a major cause of loss of life, property, crops, infrastructure, etc. They have long term socio-economic impacts on the habitat living along/across the Himalayas. In the recent decade extreme precipitation events have led to numerous flash floods in and around the Himalayan region. Sporadic case-based studies have tried to explain the mechanisms causing the floods. However, in some of the cases, the causative mechanisms have been elusive. Various types of flood events have been debated at different spatial and temporal scales. The present study provides an overview of mechanisms that lead to floods in and around the southern rim of the Indian Himalayas. Atmospheric processes, landuse interaction, and glacier-related outbreaks are considered in the overview.

Koch's postulates, microbial dysbiosis and inflammatory bowel disease

Over the past 20 years, a growing amount of evidence supports the role of microbes and an imbalanced microbiota in inflammatory bowel disease (IBD). While many reviews have been written on the microbiota in IBD, few have considered how they fulfil the Koch's postulates. In this review, we consider how the Koch's postulates might be modified so that they can be fulfilled for polymicrobial diseases, and we discuss the progress made to date in fulfilling them.

Towards quantification of vibronic coupling in photosynthetic antenna complexes

Photosynthetic antenna complexes harvest sunlight and efficiently transport energy to the reaction center where charge separation powers biochemical energy storage. The discovery of existence of long lived quantum coherence during energy transfer has sparked the discussion on the role of quantum coherence on the energy transfer efficiency. Early works assigned observed coherences to electronic states, and theoretical studies showed that electronic coherences could affect energy transfer efficiency--by either enhancing or suppressing transfer. However, the nature of coherences has been fiercely debated as coherences only report the energy gap between the states that generate coherence signals. Recent works have suggested that either the coherences observed in photosynthetic antenna complexes arise from vibrational wave packets on the ground state or, alternatively, coherences arise from mixed electronic and vibrational states. Understanding origin of coherences is important for designing molecules for efficient light harvesting. Here, we give a direct experimental observation from a mutant of LH2, which does not have B800 chromophores, to distinguish between electronic, vibrational, and vibronic coherence. We also present a minimal theoretical model to characterize the coherences both in the two limiting cases of purely vibrational and purely electronic coherence as well as in the intermediate, vibronic regime.

Characterization and Predictive Value of Near Infrared 2-Deoxyglucose Optical Imaging in Severe Acute Pancreatitis

Background

Studying the uptake of 2-deoxy glucose (2-DG) analogs such as 2-Deoxy-2-[18F] fluoroglucose (FDG) is a common approach to identify and monitor malignancies and more recently chronic inflammation. While pancreatitis is a common cause for false positive results in human studies on pancreatic cancer using FDG, the relevance of these findings to acute pancreatitis (AP) is unknown. FDG has a short half-life. Thus, with an aim to accurately characterize the metabolic demand of the pancreas during AP in real-time, we studied the uptake of the non-radioactive, near infrared fluorescence labelled 2-deoxyglucose analog, IRDye^® 800CW 2-DG probe (NIR 2-DG; Li-Cor) during mild and severe biliary AP.

Methods

Wistar rats (300 g; 8–12/group) were administered NIR 2-DG (10 nM; I.V.). Mild and severe biliary AP were respectively induced by biliopancreatic duct ligation (DL) alone or along with infusing glyceryl trilinoleate (GTL; 50 μL/100 g) within 10 minutes of giving NIR 2-DG. Controls (CON) only received NIR 2-DG. Imaging was done every 5–10 minutes over 3 hrs. Average Radiant Efficiency [p/s/cm²/sr]/[μW/cm²] was measured over the pancreas using the IVIS 200 in-vivo imaging system (PerkinElmer) using the Living Image^® software and verified in ex vivo pancreata. Blood amylase, lipase and pancreatic edema, necrosis were measured over the course of AP.

Results

NIR 2-DG uptake over the first hour was not influenced by AP induction. However, while the signal declined in controls and rats with mild AP, there was significantly higher retention of NIR 2-DG in the pancreas after 1 hour in those with GTL pancreatitis. The increase was > 3 fold over controls in the GTL group and was verified to be in the pancreas ex vivo. In vitro, pancreatic acini exposed to GTL had a similar increase in NIR 2-DG uptake which was followed by progressively worse acinar necrosis. Greater retention of NIR 2-DG in vivo was associated with worse pancreatic necrosis, reduced ATP concentrations and mortality, which were not predicted by the blood parameters.

Conclusion

In-vivo fluorescent imaging of a non-radioactive near infrared 2-DG optical probe can predict the AP severity early during the disease.

Fatty Acid Ethyl Esters Are Less Toxic Than Their Parent Fatty Acids Generated during Acute Pancreatitis

Although ethanol causes acute pancreatitis (AP) and lipolytic fatty acid (FA) generation worsens AP, the contribution of ethanol metabolites of FAs, ie, FA ethyl esters (FAEEs), to AP outcomes is unclear. Previously, pancreata of dying alcoholics and pancreatic necrosis in severe AP, respectively, showed high FAEEs and FAs, with oleic acid (OA) and its ethyl esters being the most abundant. We thus compared the toxicities of FAEEs and their parent FAs in severe AP. Pancreatic acini and peripheral blood mononuclear cells were exposed to FAs or FAEEs in vitro. The triglyceride of OA (i.e., glyceryl tri-oleate) or OAEE was injected into the pancreatic ducts of rats, and local and systemic severities were studied. Unsaturated FAs at equimolar concentrations to FAEEs induced a larger increase in cytosolic calcium, mitochondrial depolarization, and necro-apoptotic cell death. Glyceryl tri-oleate but not OAEE resulted in 70% mortality with increased serum OA, a severe inflammatory response, worse pancreatic necrosis, and multisystem organ failure. Our data show that FAs are more likely to worsen AP than FAEEs. Our observations correlate well with the high pancreatic FAEE concentrations in alcoholics without pancreatitis and high FA concentrations in pancreatic necrosis. Thus, conversion of FAs to FAEE may ameliorate AP in alcoholics.

Heavy Metal Tolerance in Plants: Role of Transcriptomics, Proteomics, Metabolomics, and Ionomics

Heavy metal contamination of soil and water causing toxicity/stress has become one important constraint to crop productivity and quality. This situation has further worsened by the increasing population growth and inherent food demand. It has been reported in several studies that counterbalancing toxicity due to heavy metal requires complex mechanisms at molecular, biochemical, physiological, cellular, tissue, and whole plant level, which might manifest in terms of improved crop productivity. Recent advances in various disciplines of biological sciences such as metabolomics, transcriptomics, proteomics, etc., have assisted in the characterization of metabolites, transcription factors, and stress-inducible proteins involved in heavy metal tolerance, which in turn can be utilized for generating heavy metal-tolerant crops. This review summarizes various tolerance strategies of plants under heavy metal toxicity covering the role of metabolites (metabolomics), trace elements (ionomics), transcription factors (transcriptomics), various stress-inducible proteins (proteomics) as well as the role of plant hormones. We also provide a glance of some strategies adopted by metal-accumulating plants, also known as "metallophytes."