Seroprevalence of Neospora caninum Infection in Dairy Cattle in Tabriz, Northwest Iran

Background: The aim of this study was to determine the seroprevalence of antibody to Neospora can­inum in healthy and aborted dairy cattle in Tabriz, capital of East-Azarbaijan in northwest of Iran.Methods: In this cross-sectional study serum samples were collected from 266 healthy and ab­orted Holestein-Feriesisnc cows from September 2008 to August 2009. The sera were analyzed to de­tect of antibody against N. caninum using the commercially ELISA kit.Results: Seroprevalence of antibody to N. caninum was 10.5% in Tabriz dairy cattle. Also the abortion rate in all cattle sampled was 33.6% but percentage of seropositive aborted cattle was 18.4%.Conclusion: Neosporosis could be one of the possible causes of abortion in dairy cattle in Tabriz and regarding the distribution in dogs as definitive host for the parasite, further studies in dog and cat­tle are recommended

Fire Scenarios Inside a Room-and-Pillar Underground Quarry Using Numerical Modeling to Define Emergency Plans

Underground fires are still one of the most significant risks in mines today. In order to manage this risk, it is necessary to know the potential evolution of a fire and the effects it can have on people and other objects. Ventilation plays an essential role in the development of a fire; it also influences the propagation of toxic fumes and the variation of temperatures in all other areas of a mine. Currently, it is possible to jointly analyze, through numerical modeling, the ventilation circuit and a fire for different possible scenarios in order to define, in detail, the emergency plans that need to be adopted. In this paper, a numerical study was conducted via the use of Ventsim Software (an integrated mine and tunnel ventilation numerical package that is able to analyze airflows, pressures, heat, gases, and fires along all of a defined circuit over time using an iterative procedure to solve Kirchhoff’s current law). Furthermore, in this study, it is illustrated how the joint numerical modeling of the ventilation circuit and fire, when applied to an underground gypsum mine in the northwest of Italy, provides all the elements necessary to define the safety procedures that should be adopted in standard conditions as well as during an emergency due to a fire. More specifically, it was possible to identify suitable escape routes depending on the location of the possible fire and the time available for the staff to be able to evacuate safely

Inference on Selected Population under Generalized Stein Loss Function

Inference on selected population is concerned with the problem of selecting the best population among the given k populations, and then doing inference on the parameter of selected population. Suppose independent random samples (Xi1,…,Xin), i=1,…,k are drawn from U(0,ϴi) population, respectively. Let Xi= max(Xi1,…,Xin) and X(1)≤X(2)≤…≤X(k) be the order statistics of X1,…,Xk. The population corresponding to largest X(k) (or the smallest X(1)) is selected and the problem of estimation the parameter ϴM (or ϴJ) of the selected population under generalized Stein loss function is considered. We obtain the Uniformly Minimum Risk Unbiased (UMRU) estimator of ϴM (and ϴJ) and show that the UMRU estimator of ϴM is inadmissible. For k=2, we derive the class of all linear admissible estimators of ϴM  and ϴJ, respectively

The prediction model for additively manufacturing of NiTiHf high-temperature shape memory alloy

NiTi-based alloys are one of the most well-known alloys among shape memory alloys having a wide range of applications from biomedical to aerospace areas. Adding a third element to the binary alloys of NiTi changes the thermomechanical properties of the material remarkably. Two unique features of stability and high transformation temperature have turned NiTiHf as a suitable ternary shape memory alloys in various applications. Selective laser melting (SLM) as a layer-based fabrication method addresses the difficulties and limitations of conventional methods. Process parameters of SLM play a prominent role in the properties of the final parts so that by using the different sets of process parameters, different thermomechanical responses can be achieved. In this study, different sets of process parameters (PPs) including laser power, hatch space, and scanning speed were defined to fabricate the NiTiHf samples. Changing the PPs is a powerful tool for tailoring the thermomechanical response of the fabricated parts such as transformation temperature (TTs), density, and mechanical response. In this work, an artificial neural network (ANN) was developed to achieve a prediction tool for finding the effect of the PPs on the TTs and the size deviation of the printed parts

Differential toxicological effects of natural and synthetic sources and enantiomeric forms of limonene on mosquito larvae

Common fragranced consumer products, such as cleaning supplies and personal care products, emit chiral compounds such as limonene that have been associated with adverse effects on human health. However, those same compounds abound in nature, and at similar concentrations as in products, but without the same apparent adverse human health effects. We investigated whether different types of limonene may elicit different biological effects. In this study, we investigated the mortality rate of mosquito larvae in response to changes in their environment. Specifically, we tested different sources of naturally occurring R-limonene and chemically synthetized limonene, containing one of its enantiomeric forms (R-, S-) in mortality bioassays with Aedes aegypti mosquito larvae. We found that a natural source of limonene extracted from oranges induced lower mortality of mosquito larvae compared to synthetic sources at the same concentration. However, enantiomeric forms did not differ in their effects on mortality. Our results provide novel evidence that natural sources of a chemical can cause lower rates of mortality than synthetic sources

Advanced data mining in field ion microscopy

Field ion microscopy (FIM) allows to image individual surface atoms by exploiting the effect of an intense electric field. Widespread use of atomic resolution imaging by FIM has been hampered by a lack of efficient image processing/data extraction tools. Recent advances in imaging and data mining techniques have renewed the interest in using FIM in conjunction with automated detection of atoms and lattice defects for materials characterization. After a brief overview of existing routines, we review the use of machine learning (ML) approaches for data extraction with the aim to catalyze new data-driven insights into high electrical field physics. Apart from exploring various supervised and unsupervised ML algorithms in this context, we also employ advanced image processing routines for data extraction from large sets of FIM images. The outcomes and limitations of such routines are discussed, and we conclude with the possible application of energy minimization schemes to the extracted point clouds as a way of improving the spatial resolution of FIM

Spectral Analysis of Multi-dimensional Self-similar Markov Processes

In this paper we consider a discrete scale invariant (DSI) process $\{X(t), t\in {\bf R^+}\}$ with scale $l>1$. We consider to have some fix number of observations in every scale, say $T$, and to get our samples at discrete points $\alpha^k, k\in {\bf W}$ where $\alpha$ is obtained by the equality $l=\alpha^T$ and ${\bf W}=\{0, 1,...\}$. So we provide a discrete time scale invariant (DT-SI) process $X(\cdot)$ with parameter space $\{\alpha^k, k\in {\bf W}\}$. We find the spectral representation of the covariance function of such DT-SI process. By providing harmonic like representation of multi-dimensional self-similar processes, spectral density function of them are presented. We assume that the process $\{X(t), t\in {\bf R^+}\}$ is also Markov in the wide sense and provide a discrete time scale invariant Markov (DT-SIM) process with the above scheme of sampling. We present an example of DT-SIM process, simple Brownian motion, by the above sampling scheme and verify our results. Finally we find the spectral density matrix of such DT-SIM process and show that its associated $T$-dimensional self-similar Markov process is fully specified by $\{R_{j}^H(1),R_{j}^H(0),j=0, 1,..., T-1\}$ where $R_j^H(\tau)$ is the covariance function of $j$th and $(j+\tau)$th observations of the process.Comment: 16 page

A comprehensive integrated drug similarity resource for in-silico drug repositioning and beyond.

Drug similarity studies are driven by the hypothesis that similar drugs should display similar therapeutic actions and thus can potentially treat a similar constellation of diseases. Drug-drug similarity has been derived by variety of direct and indirect sources of evidence and frequently shown high predictive power in discovering validated repositioning candidates as well as other in-silico drug development applications. Yet, existing resources either have limited coverage or rely on an individual source of evidence, overlooking the wealth and diversity of drug-related data sources. Hence, there has been an unmet need for a comprehensive resource integrating diverse drug-related information to derive multi-evidenced drug-drug similarities. We addressed this resource gap by compiling heterogenous information for an exhaustive set of small-molecule drugs (total of 10 367 in the current version) and systematically integrated multiple sources of evidence to derive a multi-modal drug-drug similarity network. The resulting database, 'DrugSimDB' currently includes 238 635 drug pairs with significant aggregated similarity, complemented with an interactive user-friendly web interface (http://vafaeelab.com/drugSimDB.html), which not only enables database ease of access, search, filtration and export, but also provides a variety of complementary information on queried drugs and interactions. The integration approach can flexibly incorporate further drug information into the similarity network, providing an easily extendable platform. The database compilation and construction source-code has been well-documented and semi-automated for any-time upgrade to account for new drugs and up-to-date drug information

Online learning for infectious disease fellows-A needs assessment

BACKGROUND: Online resources and social media have become increasingly ubiquitous in medical education. Little is known about the need for educational resources aimed at infectious disease (ID) fellows. METHODS: We conducted an educational needs assessment through a survey that aimed to describe ID fellows\u27 current use of online and social media tools, assess the value of online learning, and identify the educational content preferred by ID fellows. We subsequently convened focus groups with ID fellows to explore how digital tools contribute to fellow learning. RESULTS: A total of 110 ID fellows responded to the survey. Over half were second-year fellows (61, 55%). Although many respondents were satisfied with the educational resources provided by their fellowship program (70, 64%), the majority were interested in an online collaborative educational resource (97, 88%). Twitter was the most popular social media platform for education and the most valued online resource for learning. Focus groups identified several themes regarding social medial learning: broadened community, low barrier to learning, technology-enhanced learning, and limitations of current tools. Overall, the focus groups suggest that fellows value social media and online learning. CONCLUSIONS: ID fellows are currently using online and social media resources, which they view as valuable educational tools. Fellowship programs should consider these resources as complementary to traditional teaching and as a means to augment ID fellow education