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Cone snails of the genus Conus are highly regarded for their medicinal compounds derived from their toxins. In order to examine the venom apparatus structure, 12 specimens of C. textile were collected from the coastal zone of Gheshm Island and fixed in Bouin's for 48 hours and transferred to laboratory in ethanol. After breaking of shellfish, the venom apparatus were isolated and their different parts (after molding and cutting) were stained by HE and HEG and photographed by Nikon microscope. The stereomicroscope observation showed that the venom apparatus consisted of  (1) toxin production part (venom duct), (2) toxin transmission part (venom bulb), and (3) injection part (radula and proposcis). Photographs of sections showed that the venom bulb was completely muscular, consisting of longitudinal and transverse muscle fibers, and in their middle part a channel with epithelial cells was observed. Venom duct walls composed of 3 parts including the outer layer of muscle an inner layer of columnar epithelial cells with basal nucleus and the inner lumens which filled by the granules. HEG stained slides showed a much sharper cytoplasmic and nuclear implementation, particularly granules containing toxins were easily countable and measurable. Although the onventional HE staining method clearly showed different parts of the gland, but HEG method in addition to distinguishing different sections of tissue, seemed to be a suitable technique for studying the role of different parts of the organ in producing conotoxin in the form of secretory granules.

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Abstract The present study has done on 20 pieces L. vannamei shrimp, with average weight 5.53±0.02 g, in 500-liter tanks with three replicates during 8 weeks. At the end, the shrimp intestine were fixed in Bouin's solution for classic histology by Hematoxylin-Eosin (H & E) and green light (GL) and for immunolocalization of the Na+,K+-ATPase enzyme (sodium - potassium pump) by Immunohistochemistry methods. The results showed that the midgut epithelium was covered by simple columnar cells and nucleus position was in basal region of cells and Na+,K+-ATPase enzyme observed in basal region of cells. Evaluation of rectal pad large lobes deep infoldings and distance between the lobes were observed. Lumen was observed in the middle of rectal pad and apical lobes were evident and columnar cells in the marginal infoldings with basal nucleus were observed. Small appendage that called posterior diverticulum was located in primary and in upper region of rectal pad. This region was tubular and it cells with basal nucleus were observed. Rectum was located in distal region of hindgut. This region was a short muscular tube and lined with a simple columnar epithelium with low infoldings and apical nucleus in these cells were observed. The Na+, K+-ATPase enzyme (sodium - potassium pump) was located in baso-latral area in all regions of hindgut cells. We concluded that the use of H&E and GL is an appropriate method to separate different parts of intestine in vannamei shrimp and also immunohistochemistry is a suitable method for Na+,K+-ATPase enzyme localization.

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Aims: Molecular insights into the analyte-bioreceptor interactions play a vital role in the efficacy of designing biosensors. Biosensors that utilize aptamers as bioreceptors are highly efficient with high specificity and reusability. Aptasensors can be used in a variety of conditions of in vivo or in vitro. The aim of this study was to study the changes in the solvent conditions of the binding of MUC1-G peptide and the anti-MUC1 aptamer.
Materials and Methods: The molecular dynamics simulation method has been used to investigate the change of molecular interactions due to selective variations in solvent conditions. The results can be used to reflect a variety of environments, in which the aptasensor utilizes anti-MUC1 S2.2 aptamer as a bioreceptor and MUC1–G peptide as a biomarker.
Findings: Based on the calculated binding energies, the medium containing 0.10M NaCl and anti-MUC1 S2.2 aptamer demonstrates the highest affinity toward the MUC1-G peptide among the studied concentrations of NaCl, and the arginine amino acid has a key role in the aptamer–peptide binding. Conclusion: The results of MD simulation indicated that the increase in the concentration of NaCl in the interaction environment leads to a decrease in binding energies; therefore, the binding affinity of the anti-MUC1 aptamer to MUC1-G peptide decreases. Insights from present modeling demonstrate the selectiveness and sensitivity to solvent conditions, which should be considered in the development of biosensors.

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The effect of tillage (conventional and zero tillage), crop establishment (raised-bed and flat-bed), and weed management practices (herbicide alone and integrated approach) was studied on productivity, profitability, nutrient uptake and physico-chemical properties of soil in soybean–wheat cropping system, at New Delhi during 2010–2012. Sixteen treatment combinations consisting of four tillage and crop establishment practices, viz. Conventional Tillage–raised-bed (CT–bed), CT–flat-bed, Zero Tillage–raised-bed (ZT–bed) and ZT–flat-bed; and four weed management practices, viz. unweeded control, herbicide+Hand Weeding (HW), herbicide combination, and crop residue + herbicide were laid out in a split-plot design. Soybean produced higher seed yield (+7.6%) under raised-bed and wheat under flat-bed (+6.2%), but the system productivity was highest under CT–flat-bed. Pre-emergence application of pendimethalin followed by HW gave higher yield of soybean, while all weed control treatments were found equally good for wheat. Conventional tillage resulted in higher uptake by soybean of N (+5.0%), P (+4.4%) and K (+3.1%) than ZT, particularly under raised-bed conditions. In wheat, CT and ZT resulted in almost similar nutrient uptake under flat-bed. Total nutrient uptake of the system was similar for N and P under all tillage and crop establishment practices, while herbicide+HW gave 4.9–7.2% higher uptake than herbicide combination or crop residue+herbicide. Net benefit: cost ratio of the system was the highest (> 2.0) under ZT–flat-bed and herbicide combination. There was beneficial effect on physico-chemical properties of soil under ZT and residue application, but it is necessary to run the experiment in long-term to see the cumulative effect over time.

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In recent decades, due to the high cost of running a water supply network, designers have been trying to design networks with the least cost and maximum reliability. Networks that are able to provide good services in the face of demand changes or failure of the pipeline. Several indexes for reliability measurements were introduced and used as one of the objective functions along with cost in water distribution systems design problem. One of the issues that has been highlighted in recent years is which of these indicators are more successful in measuring the reliability of a water supply network. In this study, six famous reliability indicator entitled Minimum surplus head (MSH), total surplus head (TSH), resilience index (RI), network resilience index (NRI) and modified resilience index (MRI), entropy reliability indicator (ERI) and a new presented reliability indicator entitled Ratio of surplus head (MSH) described and used as one of the objective functions of a water distribution system design optimization problem. For this purpose, a multi-objective differential evolution algorithm has been developed in Matlab software and linked to the Epanet as the hydraulic solver. The generated algorithm applied on two different sample networks with different nature (gravitational feeding and feeding with pumping stations). To analysis of real hydraulic and mechanical reliability of obtained networks in optimization processes, a large number of abnormal operating conditions such as water demand uncertainty or pipes burst scenarios have been generated and applied on obtained Pareto Fronts of each optimization process. Then, the percentage of scenarios that each network could not satisfy the design’s constraints or failed in response to them has been calculated. The over demand’s scenarios were sampled using the general normal distribution method. The percentage of scenarios that each answer (water network in Pareto Front) cannot satisfy the design constraint has been measured and called Hydraulic Failure Percentage (HFP). Also, for modeling the abnormal mechanical conditions, lot of scenarios were produced with broken pipes. In each of these scenarios, there is a possibility of one to ten different pipes break. The locations of burst pipes are selected randomly. The percentage of scenarios that each case cannot satisfy the design constraint has been measured and called Mechanical Failure Percentage (MFP). These scenarios would remain constant for all of members of Pareto Fronts. The lower value of HFP and MFP demonstrate the greater ability of the network to deal with changes in nodal demand and the pipe bursts respectively. For deeper analysis, the conditions of failing (Not satisfying the constraints) divides into three sub-state as flowing: State A: Pressure of all nodes is more than the minimum acceptable pressure in all time. State B: Pressure of all nodes is more than 95% of the minimum acceptable pressure in all time. State C: Pressure of 95% of nodes is more than the minimum acceptable pressure in all time. The results of calculations summarized and have been shown in the diagrams. Results show that MSH and RI are the best indicators for optimal design of water supply networks without pumping station and include it.

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Given the varying water demand for various hours of the day from different seasons, the pressure of the water distribution network (WDN) will vary at different times. In the event of a decrease in demand, the network pressure increases, and the excess pressure leads to increase the leakage from old connections and small fractures. One of the ways to reduce leakage is the network pressure management and reduce excess pressure, which it can be achieved by the pressure reducing valves.‏ But the question is, how many pressure reducing valves and at which points of the water network should be installed. In the first part of this study, the optimal location of the pressure reducing valves (PRV's) was found by the combination of the binary genetic optimization algorithm (GA) and real differential evolution (DE) optimization algorithm. For this purpose, the GA proposes the potential locations (pipes) for the valve installation to the DE algorithm, and it attempts to eliminate surplus head in the WDN by making changes in the Hazen-William coefficient of proposed pipes and creating a head loss on the pipes. These changes should be in such a way that the WDN's constraints like the minimum allowable pressure to be respected. The related hybrid algorithm was coded in MATLAB software and connected to the Epanet software as a hydraulic solver. After determining the hydraulic model of the water network and the number of PRVs by the designer, the proposed code determines the optimal installation location of the PRVs in order to the reduction of network background leakage. In the next part of the study, after determining the optimal location of the PRVs, the optimal set-point of each PRVs has been determined. To this end, a single objective differential evolution algorithm is used. The design variable of the optimization algorithm is the outlet pressure of the installed PRVs and the permissible pressure ratio on both sides of PRVs considered as a new network constraint alongside the minimum allowable pressure. The objective function of this optimization problem is minimizing of WDN's background leakage. After validating of presented codes, they applied on a local WDN in the north of IRAN, Guilan, entitled Mehr Water Network. The covered area of this network is 144 acres and its daily average demand is 366 m3 per hour. The altitude difference of Mehr WDS is about 4 meters and it has 371 pipes with the length of 33 kilometers, 366 junctions, one reservoir and a pump station with 3 pumps. Results show that installing two pressure reducing valves in determined locations and control them with DE optimization algorithm can reduce the surplus head and background water leakage from 21.9% to 12.3% (about 41.3%) On a full day. It is noteworthy that this method can be used in Supervisory control and data acquisition (SCADA) in order to pressure management of WDNs and leakage reduction. A calibrated hydraulic model of WDN, current state of valves and pumps and demand multiplier (obtained from installed flow meters or estimated demand profile) are required as the input of the optimization code to determine the optimum output pressure (set-point) of PRVs in SCADA telecontrol system.