scholarly journals Cryptosporidium and Giardia as water contaminant pathogens in Hungary

2013 ◽  
Vol 154 (46) ◽  
pp. 1836-1842 ◽  
Author(s):  
Judit Plutzer
Keyword(s):  
Epidemiological Study ◽  
Simple Procedure ◽  
Sewage Treatment ◽  
Giardia Duodenalis ◽  
Cost Effective ◽  
Control Measures ◽  
Detection Methods ◽  
Forest Environment ◽  
Detection Technology ◽  
Wide Range

Introduction: Many species of Cryptosporidium, and two assemlages of Giardia duodenalis cause typically acute diaorrhoea in human. The oocysts and cysts of these parasites excreted in faeces are capable of infecting other hosts and those are environmentally stable. Aim: The aims of the study were to evaluate the prevalence and genotypes of Cryptosporidium and Giardia species from different water sources as well as to monitor and characterize the (oo)cyst contamination sources in watersheds. In addition, an epidemiological study was performed in three selected settlements. Method: Wide range of modern epidemiological and molecular detection methods have been applied. Results: (Oo)cysts densities were associated with water receiving effluents of sewage treatment plants or originating from a forest environment. It was confirmed, that cattle can be a source of Cryptosporidium oocysts at watersheds and aquatic birds can play a role in the environmental dissemination of these protozoa. The epidemiological study demonstrated a specific epidemiological situation, giving essential evidence about giardiasis in asymptomatic carriers. The applied novel detection technology was found to be cost effective and simple procedure for screening catchments to identify those that require further treatment and more detailed microscopic counts. Conclusions: The presented results contribute to a better understanding the epidemiology and relevance of waterborne parasites, their surveillance and performance of future control measures to prevent waterborne infections in Hungary. Orv. Hetil., 154(46), 1836–1842.

scholarly journals Deep Learning for Image Based Mango Leaf Disease Detection

2019 ◽  
Vol 8 (3S3) ◽  
pp. 54-56
Keyword(s):  
Deep Learning ◽  
Cost Effective ◽  
Control Measures ◽  
Disease Detection ◽  
New Techniques ◽  
Biennial Bearing ◽  
Detection Technology ◽  
Wide Range ◽  
And Control ◽  
Effective Case

Among world’s mango producing countries, India ranks first and account 50% of the world’s mango production. The mango fruit is popular because of its wide range of adaptability, high nutritional value, different variety, delicious taste and excellent flavor. The fruit contains vitamin A and vitamin C in a rich extent. The crop is prone to diseases like powdery mildew, anthracnose, die back, blight, red rust, sooty mould, etc. Disorders may also impact the plant in the absence of effective case and control measures. These include change of form, biennial bearing, fall of fruit, black top, clustering, etc. The farmer must consult and take professional support for the prevention / control of diseases and crop disorder. New techniques of detecting mango disease are required to promote better control to avoid this crisis. By considering this, paper describes image recognition which provides cost effective and scalable disease detection technology. Paper further describes new deep learning models which give an opportunity for easy deployment of this technology. By considering a dataset of mango disease, pictures are taken from Konkan area in India. Transfer learning technique is used to train a profound Convolutionary Neural Network (CNN) to recognize 91% accuracy.

Intestinal parasites of gray wolves (Canis lupus) in northern and western Canada

2016 ◽  
Vol 94 (9) ◽  
pp. 643-650 ◽  
Author(s):  
Janna M. Schurer ◽  
Michael Pawlik ◽  
Anna Huber ◽  
Brett Elkin ◽  
H. Dean Cluff ◽  
...  
Keyword(s):  
Canis Lupus ◽  
Intestinal Parasites ◽  
Giardia Duodenalis ◽  
Parasite Fauna ◽  
Detection Methods ◽  
Taenia Hydatigena ◽  
Gray Wolves ◽  
Wide Range ◽  
Parasite Spillover ◽  
Diphyllobothrium Latum

Gray wolves (Canis lupus L., 1758) are mobile opportunistic predators that can be infected by a wide range of parasites, with many acquired via predator–prey relationships. Historically, many of these parasites were identified only to genus or family, but genetic tools now enable identification of parasite fauna to species and beyond. We examined 191 intestines from wolves harvested for other purposes from regions in the Northwest Territories, British Columbia, Saskatchewan, and Manitoba. Adult helminths were collected from intestinal contents for morphological and molecular identification, and for a subset of wolves, fecal samples were also analyzed to detect helminth eggs and protozoan (oo)cysts. Using both detection methods, we found that 83% of 191 intestines contained one or more parasite species, including cestodes (Taenia spp., Echinococcus spp., and Diphyllobothrium sp.), nematodes (Uncinaria stenocephala Railliet, 1884, Trichuris spp., Physaloptera spp., and Toxascaris leonina (von Linstow, 1902)), a trematode (Alaria sp.), and protozoa (Sarcocystis spp., Giardia sp., and Cryptosporidium spp.). Molecular characterization identified one species of Diphyllobothrium (Diphyllobothrium latum (L., 1758) Cobbold, 1858), three species of Taenia (Taenia krabbei Moniez, 1879, Taenia hydatigena Pallas, 1766, and Taenia multiceps Leske, 1786), and two Giardia duodenalis (Davaine) Deschiens, 1921 assemblages (B and C). These results demonstrate the diverse diet of wolves and illustrate the possibility of parasite spillover among wildlife, domestic animals, and people.

Water Quality Soft-Sensing Model of Sewage Treatment Process Combined with the Mechanism and RBF

2014 ◽  
Vol 543-547 ◽  
pp. 1215-1218 ◽  
Author(s):  
Jin Xiang Pian ◽  
Zhen Wang ◽  
Jie Jia Li ◽  
Rui Zhang
Keyword(s):  
Water Quality ◽  
Treatment Process ◽  
Sewage Treatment ◽  
Present Situation ◽  
Time Varying ◽  
Soft Sensing ◽  
Detection Technology ◽  
Wide Range ◽  
Sensing Model ◽  
Mechanism Modeling

As the activated sludge sewage treatment process has strong non-linearity, uncertainty, time-varying and other complex characteristics, it is difficult to establish water quality soft-sensing model of sewage treatment process. This paper studies the present situation of detection technology of sewage quality COD, and summarizes the problems of the existing water quality soft-sensing model. Adopting the technology of mechanism modeling and RBF, establishes water quality soft-sensing model to adapt changes in a wide range of conditions, and has high precision structure.

scholarly journals Implementation of novel and conventional outbreak control measures in managing a COVID-19 outbreak in a large UK prison

2020 ◽  
Author(s):  
Paul Coleman ◽  
Roger Gajraj ◽  
Joht Singh Chandan ◽  
Anjana Roy ◽  
Victoria Lumby ◽  
...  
Keyword(s):  
Public Health ◽  
Cost Effective ◽  
Control Measures ◽  
Correctional Facilities ◽  
Outbreak Control ◽  
Wide Range ◽  
Rapid Transmission ◽  
Uk National Health Service ◽  
The Uk ◽  
Health Experts

Background: SARS-CoV-2 can spread rapidly within correctional facilities. On 22nd March 2020, following identification of a confirmed COVID-19 case in a prisoner in Prison A (UK), an Outbreak Control Team was convened consisting of prison staff and public health experts from Public Health England and the UK National Health Service. Methods: At the start of the outbreak, four prisoners and 40 staff were isolating with COVID-19 symptoms. An outbreak was declared and full prison lockdown implemented. Prompt implementation of novel outbreak control measures prevented an explosive prison outbreak, specifically establishment of dedicated isolation and cohorting units, including (i) Reverse Cohorting Units (RCUs) for accommodating new detainees; (ii) Protective Isolation Units (PIUs) for isolating symptomatic prisoners (new detainees and existing residents), and (iii) Shielding Units (SUs) to protect medically vulnerable prisoners. Findings: In total, 120 probable and 25 confirmed cases among prisoners and staff were recorded between March and June 2020. Among prisoners, there were six possible, 79 probable, and three confirmed cases. Among staff, there were 83 possible, 79 probable, and 22 confirmed cases. Testing of symptomatic prisoners was limited for most of the outbreak, with only 33% of probable cases tested. This explains the low number of confirmed cases (three) among prisoners despite the large number of probable cases (n=81; 92%). Over 50% of the initial cases among prisoners were on the two wings associated with the index case. Interpretation: Rapid transmission of SARS-COV-2 was prevented through proactive steps in identifying and isolating infected prisoners (and staff), cohorting new admissions and shielding vulnerable individuals. These novel and cost-effective approaches can be implemented in a wide range of correctional facilities globally and proved effective even in the absence of mass testing.

2D Color-Intensity Representation of Ultrasonic Thickness Gauge Measurements

2020 ◽  
pp. 1192-1198
Author(s):  
M.S. Mohammad ◽  
Tibebe Tesfaye ◽  
Kim Ki-Seong
Keyword(s):  
Cost Effective ◽  
The Other ◽  
Thickness Gauge ◽  
Color Intensity ◽  
Digital Readout ◽  
Flat Surfaces ◽  
Video Cameras ◽  
Wide Range ◽  
Cost Effective Alternative ◽  
Ultrasonic Thickness

Ultrasonic thickness gauges are easy to operate and reliable, and can be used to measure a wide range of thicknesses and inspect all engineering materials. Supplementing the simple ultrasonic thickness gauges that present results in either a digital readout or as an A-scan with systems that enable correlating the measured values to their positions on the inspected surface to produce a two-dimensional (2D) thickness representation can extend their benefits and provide a cost-effective alternative to expensive advanced C-scan machines. In previous work, the authors introduced a system for the positioning and mapping of the values measured by the ultrasonic thickness gauges and flaw detectors (Tesfaye et al. 2019). The system is an alternative to the systems that use mechanical scanners, encoders, and sophisticated UT machines. It used a camera to record the probe’s movement and a projected laser grid obtained by a laser pattern generator to locate the probe on the inspected surface. In this paper, a novel system is proposed to be applied to flat surfaces, in addition to overcoming the other limitations posed due to the use of the laser projection. The proposed system uses two video cameras, one to monitor the probe’s movement on the inspected surface and the other to capture the corresponding digital readout of the thickness gauge. The acquired images of the probe’s position and thickness gauge readout are processed to plot the measured data in a 2D color-coded map. The system is meant to be simpler and more effective than the previous development.

COVID-19 Outbreak Prediction with Machine Learning

2020 ◽  
Author(s):  
Sina Faizollahzadeh Ardabili ◽  
Amir Mosavi ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
Annamaria R. Varkonyi-Koczy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Control Measures ◽  
Future Research ◽  
Complex Nature ◽  
Inference System ◽  
Wide Range ◽  
Standard Models ◽  
High Level

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to SIR and SEIR models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. Paper further suggests that real novelty in outbreak prediction can be realized through integrating machine learning and SEIR models.

Urease as a Possible Tracer for Sewage Effluent Plumes

1989 ◽  
Vol 21 (2) ◽  
pp. 93-97 ◽  
Author(s):  
R. Sadler
Keyword(s):  
Urease Activity ◽  
Simple Procedure ◽  
Sewage Treatment ◽  
Sewage Treatment Plant ◽  
Treatment Plant ◽  
Particulate Phase ◽  
Untreated Sewage ◽  
Potential Alternative ◽  
Two Parameters ◽  
Point Source Discharges

The suitability of sedimentary urease activity as a potential tracer for sewage outfall plumes has been examined. Enzyme activity is readily measured in the sediments by a relatively simple procedure and results may be obtained within a few hours of sampling. The results of urease measurements in areas around point source discharges were compared with bacteriological data for the same areas. Three areas were selected for study: a discharge of untreated sewage into a harbour, a discharge from a sewage treatment plant to a river and a discharge from a contaminated drain to a small beach. In all cases, positive correlation between the distribution of the two parameters was observed. Urease activity probably reflects the movement of soluble products from the outfall whereas E.coli represents the particulate phase of the discharge. Although further work will be required, urease activity does offer a potential alternative to E.coli for tracing plumes of faecal pollution.

Bioavailability of phosphorus as a tool for efficient P reduction schemes

1998 ◽  
Vol 37 (3) ◽  
pp. 241-247 ◽  
Author(s):  
Peter Gerdes ◽  
Sabine Kunst
Keyword(s):  
Sewage Treatment ◽  
Cost Effective ◽  
Effective Management ◽  
Lower Saxony ◽  
Effective Strategies ◽  
P Balance ◽  
Different Sources ◽  
Total P ◽  
P Sources ◽  
P Bioavailability

The bioavailability of phosphorus from different sources has been evaluated in the catchment area of the River Ilmenau (Lower-Saxony, Germany) by using algal assays. The P bioavailability describes the different potential of P from various sources of supporting eutrophication. Effluents from sewage treatment plants were highly bioavailable (72% of TP) whereas rainwater (26%) and erosion effluents (30%) showed a low bioavailability. In order to develop effective strategies to minimize P inputs into the river, source specific P bioavailability indices were determined and combined with a P balance to calculate inputs of vioavailable P (BAP) instead of total P (TP). It could be shown that the relative importance of the different P sources changes when applying BAP. Measures to reduce P inputs into the River Ilmenau will take P bioavailability into consideration and therefore lead to a more cost-effective management.

The Future of Plasma-Based Surface Engineering Techniques in Tribology (Keynote)

2005 ◽  
Author(s):  
Allan Matthews ◽  
Adrian Leyland
Keyword(s):  
Surface Engineering ◽  
Performance Enhancement ◽  
Bulk Material ◽  
Cost Savings ◽  
Cost Effective ◽  
Ceramic Coatings ◽  
Performance Improvements ◽  
Treatment Techniques ◽  
Wide Range ◽  
Macro Scale

Over the past twenty years or so, there have been major steps forward both in the understanding of tribological mechanisms and in the development of new coating and treatment techniques to better “engineer” surfaces to achieve reductions in wear and friction. Particularly in the coatings tribology field, improved techniques and theories which enable us to study and understand the mechanisms occurring at the “nano”, “micro” and “macro” scale have allowed considerable progress to be made in (for example) understanding contact mechanisms and the influence of “third bodies” [1–5]. Over the same period, we have seen the emergence of the discipline which we now call “Surface Engineering”, by which, ideally, a bulk material (the ‘substrate’) and a coating are combined in a way that provides a cost-effective performance enhancement of which neither would be capable without the presence of the other. It is probably fair to say that the emergence and recognition of Surface Engineering as a field in its own right has been driven largely by the availability of “plasma”-based coating and treatment processes, which can provide surface properties which were previously unachievable. In particular, plasma-assisted (PA) physical vapour deposition (PVD) techniques, allowing wear-resistant ceramic thin films such as titanium nitride (TiN) to be deposited on a wide range of industrial tooling, gave a step-change in industrial productivity and manufactured product quality, and caught the attention of engineers due to the remarkable cost savings and performance improvements obtained. Subsequently, so-called 2nd- and 3rd-generation ceramic coatings (with multilayered or nanocomposite structures) have recently been developed [6–9], to further extend tool performance — the objective typically being to increase coating hardness further, or extend hardness capabilities to higher temperatures.

scholarly journals Gamma models for estimating the odds ratio for a skewed biomarker measured in pools and subject to errors

Biostatistics ◽  
2019 ◽  
Author(s):  
Dane R Van Domelen ◽  
Emily M Mitchell ◽  
Neil J Perkins ◽  
Enrique F Schisterman ◽  
Amita K Manatunga ◽  
...  
Keyword(s):  
Odds Ratio ◽  
Adjusted Odds Ratio ◽  
Lower Cost ◽  
Cost Effective ◽  
Ratio Estimation ◽  
Statistical Efficiency ◽  
Function Model ◽  
Processing Error ◽  
Wide Range ◽  
Pooled Samples

SUMMARYMeasuring a biomarker in pooled samples from multiple cases or controls can lead to cost-effective estimation of a covariate-adjusted odds ratio, particularly for expensive assays. But pooled measurements may be affected by assay-related measurement error (ME) and/or pooling-related processing error (PE), which can induce bias if ignored. Building on recently developed methods for a normal biomarker subject to additive errors, we present two related estimators for a right-skewed biomarker subject to multiplicative errors: one based on logistic regression and the other based on a Gamma discriminant function model. Applied to a reproductive health dataset with a right-skewed cytokine measured in pools of size 1 and 2, both methods suggest no association with spontaneous abortion. The fitted models indicate little ME but fairly severe PE, the latter of which is much too large to ignore. Simulations mimicking these data with a non-unity odds ratio confirm validity of the estimators and illustrate how PE can detract from pooling-related gains in statistical efficiency. These methods address a key issue associated with the homogeneous pools study design and should facilitate valid odds ratio estimation at a lower cost in a wide range of scenarios.

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