Hotspot and accumulated hotspot analysis for assessment of groundwater quality and pollution indices using GIS in the arid region of Iran.

Because groundwater quality representatives for drinking usage (i.e., Schuler method, Nitrate and Groundwater Quality Index) have been abruptly changing due to extreme events induced by global climate change and over-abstracting, applying an efficient tool for their assessments is vitally important. While hotspot analysis is introduced as an efficient tool concentrating on sharp changes in groundwater quality, it has not been closely examined. Accordingly, this study is an attempt to determine the groundwater quality proxies and assess them through hotspot and accumulated hotspot analyses. To this end, a GIS-based hotspot analysis (HA) applying Getis-Ord Gi* statistics was used. The accumulated hotspot analysis was launched to identify the Groundwater Quality Index (AHA-GQI). Moreover, Schuler method (AHA-SM) was utilized to determine the maximum levels (ML) for the hottest hotspot and the lowest levels (LL) for the coldest cold-spot, and compound levels (CL). The results revealed that a significant correlation (r = 0.8) between GQI and SM was observed. However, the correlation between GQI and nitrate was not significant and the correlation between SM and nitrate was so low (r = 0.298, sig > 0.05). The results also demonstrated that using hotspot analysis on only GQI, the correlation between GQI and SM increased from 0.8 to 0.856, while using hotspot analysis on both GQI and SM increased the correlation to 0.945. Likewise, when GQI was subjected to hotspot analysis and SM underwent accumulated hotspot analysis (i.e., AHA-SM (ML)), the correlation degree increased to the highest extent (i.e., 0.958), indicating the usefulness of including the hotspot analysis and accumulated hotspot analysis in the evaluation of groundwater quality.

Does adding the drug-drug similarity to drug-target interaction prediction methods make a noticeable improvement in their efficiency?

Predicting drug-target interactions (DTIs) has become an important bioinformatics issue because it is one of the critical and preliminary stages of drug repositioning. Therefore, scientists are trying to develop more accurate computational methods for predicting drug-target interactions. These methods are usually based on machine learning or recommender systems and use biological and chemical information to improve the accuracy of predictions. In the background of these methods, there is a hypothesis that drugs with similar chemical structures have similar targets. So, the similarity between drugs as chemical information is added to the computational methods to improve the prediction results. The question that arises here is whether this claim is actually true? If so, what method should be used to calculate drug-drug chemical structure similarities? Will we obtain the same improvement from any DTI prediction method we use? Here, we investigated the amount of improvement that can be achieved by adding the drug-drug chemical structure similarities to the problem. For this purpose, we considered different types of real chemical similarities, random drug-drug similarities, four gold standard datasets and four state-of-the-art methods. Our results show that the type and size of data, the method which is used to predict the interactions, and the algorithm used to calculate the chemical similarities between drugs are all important, and it cannot be easily stated that adding drug-drug similarities can significantly improve the results. Therefore, our results could suggest a checklist for scientists who want to improve their machine learning methods.

How tech-savvy employees make the difference in core facilities: Recognizing core facility expertise with dedicated career tracks: Recognizing core facility expertise with dedicated career tracks.

Core facilities have a different mission than academic research labs. Accordingly, they require different career paths and structures.

Garlic essential oil-based nanoemulsion carrier: Release and stability kinetics of volatile components.

An O/W nanoemulsion of garlic essential oil (GEO) at different oil-to-emulsion (O/E) ratios (5%, 10%, 15%, and 25%) was formulated to protect the volatile components of GEO. The effects of O/E ratios on the encapsulation efficiency (EE%) of volatile compounds and droplet size of nanoemulsions were studied. The results showed that with increasing in E/O ratio, droplet size increased while EE% decreased so that the droplet size was below 100 nm for all samples and the EE% was almost above 80% for most samples. The effects of various factors such as temperature (5°C-45°C), pH values (3-7), ionic strength (0-500 mM), and O/E ratios (5%-25%) on kinetic of nanoemulsions stability were studied. Reducing pH values and raising the temperature, ionic strength, and O/E ratios intensified the instability process and constant rate of instability in all nanoemulsions. The effects of temperature and O/E ratios on the release kinetics of volatile components were evaluated over time, and kinetic parameters such as release rate constant (k), Q10, and activation energy (Ea) were calculated in which results showed a zero-degree model to describe the release kinetic behavior of most nanoemulsions. Both temperature and O/E ratios factors as well as their interaction (which had a synergistic effect) had a significant effect on increasing the release rate of volatiles so that the degree of reaction rate was changed from zero to the first order at simultaneous high levels of both factors. FT-IR spectroscopy was carried out to study interactions among nanoemulsion ingredients. The presence of sulfur-containing functional groups of garlic oil (thiosulphate, diallyl trisulfide, etc.) in nanoemulsions was confirmed by FT-IR.

Evolutionary and Ecological Drivers Shape the Emergence and Extinction of Foot-and-Mouth Disease Virus Lineages.

Livestock farming across the world is constantly threatened by the evolutionary turnover of foot-and-mouth disease virus (FMDV) strains in endemic systems, the underlying dynamics of which remain to be elucidated. Here, we map the eco-evolutionary landscape of cocirculating FMDV lineages within an important endemic virus pool encompassing Western, Central, and parts of Southern Asia, reconstructing the evolutionary history and spatial dynamics over the last 20 years that shape the current epidemiological situation. We demonstrate that new FMDV variants periodically emerge from Southern Asia, precipitating waves of virus incursions that systematically travel in a westerly direction. We evidence how metapopulation dynamics drive the emergence and extinction of spatially structured virus populations, and how transmission in different host species regulates the evolutionary space of virus serotypes. Our work provides the first integrative framework that defines coevolutionary signatures of FMDV in regional contexts to help understand the complex interplay between virus phenotypes, host characteristics, and key epidemiological determinants of transmission that drive FMDV evolution in endemic settings.

Foot-and-mouth disease virus infection in the domestic dog (Canis lupus familiaris), Iran.

BACKGROUND: Foot-and-mouth disease (FMD) is a highly infectious viral disease, recognised to affect animals in the order Artiodactyla. The disease is rarely fatal in adult animals, however high mortality is associated with neonatal and juvenile infection. CASE PRESENTATION: Five puppies died after being fed lamb carcases, the lambs having died during an outbreak of FMD in Iran. Following a post-mortem examination, cardiac tissue from one of the dead puppies was subjected to virus isolation, antigen ELISA, real-time RT-PCR, sequencing and confocal microscopy to assess the presence and characteristics of any FMD virus. The virological and microscopic examination of the cardiac tissue provided evidence of FMD virus replication in the canine heart. CONCLUSIONS: The data generated in this study demonstrate for the first time that FMD virus can internalise and replicate in dogs and may represent an epidemiologically significant event in FMD transmission, highlighting the dangers of feeding diseased animal carcases to other species. The reporting of this finding may also focus attention on similar disease presentations in dogs in FMD endemic countries allowing a better understanding of the prevalence of such events.

Oral immunization of trout fry with recombinant Lactococcus lactis NZ3900 expressing G gene of viral hemorrhagic septicaemia virus (VHSV).

This study has investigated the ability of Lactococcus lactic (NZ3900) carried G gene of viral haemorrhagic septicaemia virus (VHSV) under nisin-controlled gene expression (NICE) system in rainbow trout (O.Mykiss). Two groups of trout fry (7 ± 0.65 g) were immunized with 1 × 1010 cfu/g and 1 × 108 cfu/g recombinant L. lactis NZ3900, two groups of fish were fed 1 × 1010 cfu/g and 1 × 108 cfu/g L. lactis vector free, and one group was fed by the basal diet as a control. Oral immunization was done on days 1-7 and boosting was performed on days 15-21. The relative expression of IFN-1 and MX-1 genes significantly increased in head kidney of vaccinated fish depend on vaccine dosage compared to the control group. Fish in vaccinated group also showed elevated VHSV-specific antibody levels compared to the control groups. Relative percent survival (RPS), under virulent isolate VHSV challenge were estimated 62%, 78% for 108 cfu/g 1010 cfu/g feed vaccinated groups 21 days post-vaccination, while groups fed similar doses of L. lactis vector free illustrated 22% and 27% RPSs, respectively. The significant reduction of viral loads (transcript levels of N gene) were detected in the immunized groups. Increased weight gain and decreased feed consumption in vaccinated group attributed to the probiotic effect were also observed. In conclusion, our results demonstrate the ability of recombinant L. lactis as oral vaccine against VHS in rainbow trout, which can be considered as effective method against different fish pathogens.

PMLPR: A novel method for predicting subcellular localization based on recommender systems.

The importance of protein subcellular localization problem is due to the importance of protein's functions in different cell parts. Moreover, prediction of subcellular locations helps to identify the potential molecular targets for drugs and has an important role in genome annotation. Most of the existing prediction methods assign only one location for each protein. But, since some proteins move between different subcellular locations, they can have multiple locations. In recent years, some multiple location predictors have been introduced. However, their performances are not accurate enough and there is much room for improvement. In this paper, we introduced a method, PMLPR, to predict locations for a protein. PMLPR predicts a list of locations for each protein based on recommender systems and it can properly overcome the multiple location prediction problem. For evaluating the performance of PMLPR, we considered six datasets RAT, FLY, HUMAN, Du et al., DBMLoc and Höglund. The performance of this algorithm is compared with six state-of-the-art algorithms, YLoc, WOLF-PSORT, prediction channel, MDLoc, Du et al. and MultiLoc2-HighRes. The results indicate that our proposed method is significantly superior on RAT and Fly proteins, and decent on HUMAN proteins. Moreover, on the datasets introduced by Du et al., DBMLoc and Höglund, PMLPR has comparable results. For the case study, we applied the algorithms on 8 proteins which are important in cancer research. The results of comparison with other methods indicate the efficiency of PMLPR.

VP2 (PTA motif) encoding DNA vaccine confers protection against lethal challenge with infectious pancreatic necrosis virus (IPNV) in trout.

IPNV in Atlantic salmon is represented by various strains with different virulence and immunogenicity linked to various motifs of the VP2 capsid. IPNV variant with P217, T221, A247 (PTA) motif is found to be avirulent in Atlantic salmon, but virulent in rainbow trout, and other salmonid species. This study describes a DNA vaccine delivered intramuscularly encoding the VP2 protein of infectious pancreatic necrosis virus (IPNV) with PTA motif that confers high protection in rainbow trout (Oncorhynchus mykiss). Intramuscular injection of 2, 5 and 10 µg of DNA (pcDNA3.1-VP2) in rainbow trout fry (4-5 g), confers relative protection of 75-83% in the different vaccine groups at 30 days post vaccination (450° days). The VP2 gene is expressed in spleen, kidney, muscle and liver at day 30 post-vaccination (RT-PCR), and IFN-1 and Mx-1 mRNA are upregulated at early time post vaccination, and so also for IgM, IgT, CD4 and CD8 in the head kidney of vaccinated fish compared to controls, 15 and 30 days post vaccination. Significant increase of serum anti-IPNV antibodies was found 30-90 days post-vaccination that was correlated with protection levels. Mortality corresponded with viral VP4 gene expression were significantly decreased in vaccinated and challenged fish. This shows for the first time that a VP2-encoding DNA vaccine delivered intramuscularly elicits a high level of protection alongside with high levels of circulating antibodies in rainbow trout and a lowered viral replication.

Oral DNA vaccines based on CS-TPP nanoparticles and alginate microparticles confer high protection against infectious pancreatic necrosis virus (IPNV) infection in trout.

Infectious pancreatic necrosis virus (IPNV) is the etiological agent of a contagious viral disease causing remarkable mortalities in different fish species. Despite the availability of commercial vaccines against IPN, the disease still constitutes one of the main threats to the aquaculture industry worldwide. In this study, we developed a DNA vaccine encoding the VP2 gene of IPNV and evaluated its ability to induce protective immunity in rainbow trout fry (3 g) at doses of 10 and 25 µg/fish and boosting with the same doses two weeks later through the oral route using chitosan/tripolyphosphate (CS-TPP) nanoparticles and alginate microparticles incorporated into fish feed. The distribution of the administered vaccines in different organs and transcription of VP2 gene were confirmed by RT-PCR assay at day 30 post boost-vaccination. Transcript levels of IFN-1, Mx-1, IgM, IgT and CD4 genes was dependent on vaccine dose and was significantly up-regulated in head kidney of all orally vaccinated fish groups compared to controls (pcDNA3.1). Cumulative mortalities post-challenge with virulent isolate of the virus were lower in the vaccinated fish and a relative percentage survival (RPS) of 59% and 82% were obtained for the 10 and 25 µg/fish pcDNA3.1-VP2 groups, respectively. Vaccination with the same amount of pcDNA3.1-VP2 encapsulated with CS-TPP nanoparticles resulted in RPS of 47 %and 70%, respectively. Detectable anti-IPNV antibodies were shown until 90 days postvaccination. The orally administrated vaccines significantly decreased VP4 transcripts thus contributing to reducing viral load in surviving fish on day 45 post-challenge. In conclusion, these results show good to high protection post-vaccination alongside with significant up-regulation of key immune genes and detectable levels of circulating antibodies after oral administration of the DNA vaccine formulated in CS-TPP nanoparticles and alginate microparticles in fish feed.

Infectious hematopoietic necrosis virus (IHNV) outbreak in farmed rainbow trout in Iran: Viral isolation, pathological findings, molecular confirmation, and genetic analysis.

Infectious hematopoietic necrosis virus (IHNV) is the etiological agent of a contagious disease (IHN) mainly in salmonid fish. In the present study, we isolated and identified IHNV in trout fry from Iranian trout farms with unexplained high mortality in 2016. The affected fry showed cumulative mortality of 90% with the gross pathological signs including exophthalmia and hemorrhage of the eye, skin darkening, abdominal distension, ulceration of the snout, and the visceral pallor and yellowish fluid in the intestine. Histopathological examination revealed marked necrosis in the anterior kidney, liver and spleen with the intracytoplasmic inclusion bodies in the liver sections. Also, intranuclear inclusion body and marginated chromatin were observable in the hematopoietic cells of the kidney. The homogenates tissues of infected fry induced IHNV-positive cytopathic effects (CPE) in EPC cells and confirmed by RT-PCR reactions and sequencing. Phylogenetic analysis revealed the Iranian IHNV isolates belonged to the European (E) genogroup with 100% identity to some Italian isolates. This is the first report of IHNV infection in farmed trout fry in Iran describing the viral isolation, clinical symptoms, histopathological findings, molecular confirmation, and genetic analysis suggestion of the specific country of origin.

Do triplets have enough information to construct the multi-labeled phylogenetic tree?

The evolutionary history of certain species such as polyploids are modeled by a generalization of phylogenetic trees called multi-labeled phylogenetic trees, or MUL trees for short. One problem that relates to inferring a MUL tree is how to construct the smallest possible MUL tree that is consistent with a given set of rooted triplets, or SMRT problem for short. This problem is NP-hard. There is one algorithm for the SMRT problem which is exact and runs in O(7n) time, where n is the number of taxa. In this paper, we show that the SMRT does not seem to be an appropriate solution from the biological point of view. Indeed, we present a heuristic algorithm named MTRT for this problem and execute it on some real and simulated datasets. The results of MTRT show that triplets alone cannot provide enough information to infer the true MUL tree. So, it is inappropriate to infer a MUL tree using triplet information alone and considering the minimum number of duplications. Finally, we introduce some new problems which are more suitable from the biological point of view.

Constructing phylogenetic supernetworks based on simulated annealing.

Different partial phylogenetic trees can be derived from different sources of evidence and different methods. One important problem is to summarize these partial phylogenetic trees using a supernetwork. We propose a novel simulated annealing based method called SNSA which uses an optimization function to produce a simple network that still retains a great deal of phylogenetic information. We report the performance of this new method on real and simulated datasets.

Constructing circular phylogenetic networks from weighted quartets using simulated annealing.

In this paper, we present a heuristic algorithm based on the simulated annealing, SAQ-Net, as a method for constructing phylogenetic networks from weighted quartets. Similar to QNet algorithm, SAQ-Net constructs a collection of circular weighted splits of the taxa set. This collection is represented by a split network. In order to show that SAQ-Net performs better than QNet, we apply these algorithm to both the simulated and actual data sets containing salmonella, Bees, Primates and Rubber data sets. Then we draw phylogenetic networks corresponding to outputs of these algorithms using SplitsTree4 and compare the results. We find that SAQ-Net produces a better circular ordering and phylogenetic networks than QNet in most cases. SAQ-Net has been implemented in Matlab and is available for download at http://bioinf.cs.ipm.ac.ir/softwares/saq.net.

MC-Net: a method for the construction of phylogenetic networks based on the Monte-Carlo method.

BACKGROUND: A phylogenetic network is a generalization of phylogenetic trees that allows the representation of conflicting signals or alternative evolutionary histories in a single diagram. There are several methods for constructing these networks. Some of these methods are based on distances among taxa. In practice, the methods which are based on distance perform faster in comparison with other methods. The Neighbor-Net (N-Net) is a distance-based method. The N-Net produces a circular ordering from a distance matrix, then constructs a collection of weighted splits using circular ordering. The SplitsTree which is a program using these weighted splits makes a phylogenetic network. In general, finding an optimal circular ordering is an NP-hard problem. The N-Net is a heuristic algorithm to find the optimal circular ordering which is based on neighbor-joining algorithm. RESULTS: In this paper, we present a heuristic algorithm to find an optimal circular ordering based on the Monte-Carlo method, called MC-Net algorithm. In order to show that MC-Net performs better than N-Net, we apply both algorithms on different data sets. Then we draw phylogenetic networks corresponding to outputs of these algorithms using SplitsTree and compare the results. CONCLUSIONS: We find that the circular ordering produced by the MC-Net is closer to optimal circular ordering than the N-Net. Furthermore, the networks corresponding to outputs of MC-Net made by SplitsTree are simpler than N-Net.