Broad Bean Stain Virus: Identification, Detectability with ELISA in Faba Bean Leaves and Seeds, Occurrence in West Asia and North Africa and Possible Wild Hosts

Abstract During a survey of faba bean viruses in West Asia and North Africa a virus was identified as broad bean stain virus (BBSV) based on host reactions, electron microscopy, physical properties and serology. An antiserum to a Syrian isolate was prepared. With this antiserum both the direct double antibody sandwich ELISA (DAS-ELISA) and dot-ELISA were very sensitive in detecting BBSV in leaf extracts, ground whole seeds and germi nated embryos. Sens it i vity was not reduced when the two-day procedure was replaced by a one-day procedure. us i ng ELISA the vi rus was detected in 73 out of 589 faba bean samples with virus-like symptoms collected from Egypt (4 out of 70 samples tested), Lebanon (6/44) , Morocco (017), Sudan (19/254), Syria (36/145) and Tunisia (8/69). This is the first report of BBSV infection of faba bean in Lebanon, Sudan, Syria and Tunisia. speci es i ndi genous to Syri a were Fourteen wild legume susceptible to BBSV infection, with only two producing obvious symptoms. The virus was found to be seed transmitted ~n Vicia palaestina.

Broad Bean Mottle Virus: Identification, Serology, Host Range, and Occurrence on Faba Bean (Vicia Faba) in West Asia and Norm Africa

One of the faba bean viruses found in West Asia and North Africa was identified as broad bean mottle virus (BBMV) by host reactions, particle morphology and size, serology, and granular, often vesiculated cytoplasmic inclusions. Detailed research on four isolates, one each from Morocco, Tunisia, Sudan and Syria, provided new information on the virus. The isolates, though indistinguishable in ELISA or gel-diffusion tests, differed slightly in host range and symptoms. Twenty-one species (12 legumes and 9 non-legumes) out of 27 tested were systemically infected, and 14 of these by all four isolates. Infection in several species was symptomless, but major legumes such as chickpea, lentil and especially pea, suffered severely from infection. All 23 genotypes of faba bean, 2 of chickpea, 4 of lentil, 11 out of 21 of Phaseolus bean, and 16 out of 17 of pea were systemically sensitive to the virus. Twelve plant species were found to be new potential hosts and cucumber a new local-lesion test plant of the virus. BBMV particles occurred in faba bean plants in very high concentrations and seed transmission in this species (1.37%) was confirmed. An isolate from Syria was purified and two antisera were produced, one of which was used in ELISA to detect BBMV in faba bean field samples. Two hundred and three out of the 789 samples with symptoms suggestive of virus infection collected in 1985, 1986 and 1987, were found infected with BBMV: 4 out of 70 (4/70) tested samples from Egypt, 0/44 from Lebanon, 1/15 from Morocco, 46/254 from Sudan, 72/269 from Syria and 80/137 from Tunisia. This is the first report on its occurrence in Egypt, Syria and Tunisia. The virus is a potential threat to crop improvement in the region.

Identification of novel markers for uncomplicated lower genital tract infections and upper genital tract pathology due to Chlamydia trachomatis.

Background: Untreated Chlamydia trachomatis infections in women can result in disease sequelae such as salpingitis and pelvic inflammatory disease (PID), ultimately culminating in tubal occlusion and infertility. Whilst nucleic acid amplification tests can effectively diagnose uncomplicated lower genital tract (LGT) infections, they are not suitable for diagnosing upper genital tract (UGT) pathological sequelae. As a consequence, this study aimed to identify serological markers that can, with a high degree of sensitivity and specificity, discriminate between LGT infections and UGT pathology. Methods: Plasma was collected from 73 women with a history of LGT infection, UGT pathology due to C. trachomatis or no serological evidence of C. trachomatis infection. Western blotting was used to analyse antibody reactivity against extracted chlamydial proteins. Sensitivity and specificity of differential markers were also calculated. Results: Four antigens (CT157, CT423, CT727 and CT396) were identified and found to be capable of discriminating between the infection and disease sequelae state. Sensitivity and specificity calculations showed that our assay for diagnosing LGT infection had a sensitivity and specificity of 75% and 76% respectively, whilst the assay for identifying UGT pathology demonstrated 80% sensitivity and 86% specificity. Conclusions: The use of these assays could potentially facilitate earlier diagnoses in women suffering UGT pathology due to C. trachomatis.

The identification of Malaysian contractor satisfaction dimensions : a strategy for continuous improvement

The unique characteristics of the construction industry - such as the fragmentation of its processes, varied scope of works and diversity of its participants - are contributory factors to poor project performance. Several issues are unresolved due to the lack of a comprehensive technique to measure project outcomes including: inefficient decision making, insufficient communication, uncertain site conditions, a continuously changing environment, inharmonious working relationships, mismatched objectives within the project team and a blame culture. One approach to overcoming these problems appears to be to measure performance by gauging contractor satisfaction (Co-S) levels, but this has not been widely investigated as yet. Additionally, the key Co-S dimensions at the project level are still not fully identified. ----- ----- This paper concerns a study of satisfaction dimensions, primarily by a postal questionnaire survey of construction contractors registered by the Malaysian Construction Industry Development Board (CIDB). Eight satisfaction dimensions are identified that are significantly and substantially relate to these contractors - comprising: project cost performance, schedule performance, product performance, design satisfaction, site safety, project profitability, business performance and relationships between participants. -Each of these dimensions is accorded different priority levels of satisfaction by different contractors. ----- ----- The output of this study will be useful in raising the awareness and understanding of project teams regarding contractors’ needs, mutual objectives and open communication to help to deliver a successful project.

Strategy content analysis for service identification : a case study on government agencies

In a resource constrained business world, strategic choices must be made on process improvement and service delivery. There are calls for more agile forms of enterprises and much effort is being directed at moving organizations from a complex landscape of disparate application systems to that of an integrated and flexible enterprise accessing complex systems landscapes through service oriented architecture (SOA). This paper describes the analysis of strategies to detect supporting business services. These services can then be delivered in a variety of ways: web-services, new application services or outsourced services. The focus of this paper is on strategy analysis to identify those strategies that are common to lines of business and thus can be supported through shared services. A case study of a state government is used to show the analytical method and the detection of shared strategies.

The influence of underreported crashes on hotspot identification

Hot spot identification (HSID) plays a significant role in improving the safety of transportation networks. Numerous HSID methods have been proposed, developed, and evaluated in the literature. The vast majority of HSID methods reported and evaluated in the literature assume that crash data are complete, reliable, and accurate. Crash under-reporting, however, has long been recognized as a threat to the accuracy and completeness of historical traffic crash records. As a natural continuation of prior studies, the paper evaluates the influence that under-reported crashes exert on HSID methods. To conduct the evaluation, five groups of data gathered from Arizona Department of Transportation (ADOT) over the course of three years are adjusted to account for fifteen different assumed levels of under-reporting. Three identification methods are evaluated: simple ranking (SR), empirical Bayes (EB) and full Bayes (FB). Various threshold levels for establishing hotspots are explored. Finally, two evaluation criteria are compared across HSID methods. The results illustrate that the identification bias—the ability to correctly identify at risk sites--under-reporting is influenced by the degree of under-reporting. Comparatively speaking, crash under-reporting has the largest influence on the FB method and the least influence on the SR method. Additionally, the impact is positively related to the percentage of the under-reported PDO crashes and inversely related to the percentage of the under-reported injury crashes. This finding is significant because it reveals that despite PDO crashes being least severe and costly, they have the most significant influence on the accuracy of HSID.

Genome-wide identification and expression analysis of the NF-Y family of transcription factors in Triticum aestivum

Nuclear Factor Y (NF-Y) is a trimeric complex that binds to the CCAAT box, a ubiquitous eukaryotic promoter element. The three subunits NF-YA, NF-YB and NF-YC are represented by single genes in yeast and mammals. However, in model plant species (Arabidopsis and rice) multiple genes encode each subunit providing the impetus for the investigation of the NF-Y transcription factor family in wheat. A total of 37 NF-Y and Dr1 genes (10 NF-YA, 11 NF-YB, 14 NF-YC and 2 Dr1) in Triticum aestivum were identified in the global DNA databases by computational analysis in this study. Each of the wheat NF-Y subunit families could be further divided into 4-5 clades based on their conserved core region sequences. Several conserved motifs outside of the NF-Y core regions were also identified by comparison of NF-Y members from wheat, rice and Arabidopsis. Quantitative RT-PCR analysis revealed that some of the wheat NF-Y genes were expressed ubiquitously, while others were expressed in an organ-specific manner. In particular, each TaNF-Y subunit family had members that were expressed predominantly in the endosperm. The expression of nine NF-Y and two Dr1 genes in wheat leaves appeared to be responsive to drought stress. Three of these genes were up-regulated under drought conditions, indicating that these members of the NF-Y and Dr1 families are potentially involved in plant drought adaptation. The combined expression and phylogenetic analyses revealed that members within the same phylogenetic clade generally shared a similar expression profile. Organ-specific expression and differential response to drought indicate a plant-specific biological role for various members of this transcription factor family.

A methodology for object identification and tracking in construction based on spatial modeling and image matching techniques

Understanding the motion characteristics of on-site objects is desirable for the analysis of construction work zones, especially in problems related to safety and productivity studies. This article presents a methodology for rapid object identification and tracking. The proposed methodology contains algorithms for spatial modeling and image matching. A high-frame-rate range sensor was utilized for spatial data acquisition. The experimental results indicated that an occupancy grid spatial modeling algorithm could quickly build a suitable work zone model from the acquired data. The results also showed that an image matching algorithm is able to find the most similar object from a model database and from spatial models obtained from previous scans. It is then possible to use the matched information to successfully identify and track objects.

Object identification based on 3D spatial models of construction sites

On obstacle-cluttered construction sites, understanding the motion characteristics of objects is important for anticipating collisions and preventing accidents. This study investigates algorithms for object identification applications that can be used by heavy equipment operators to effectively monitor congested local environment. The proposed framework contains algorithms for three-dimensional spatial modeling and image matching that are based on 3D images scanned by a high-frame rate range sensor. The preliminary results show that an occupancy grid spatial modeling algorithm can successfully build the most pertinent spatial information, and that an image matching algorithm is best able to identify which objects are in the scanned scene.

Modeling object identification and tracking errors on image-based safety assessment of earthmoving operations

Object identification and tracking have become critical for automated on-site construction safety assessment. The primary objective of this paper is to present the development of a testbed to analyze the impact of object identification and tracking errors caused by data collection devices and algorithms used for safety assessment. The testbed models workspaces for earthmoving operations and simulates safety-related violations, including speed limit violations, access violations to dangerous areas, and close proximity violations between heavy machinery. Three different cases were analyzed based on actual earthmoving operations conducted at a limestone quarry. Using the testbed, the impacts of device and algorithm errors were investigated for safety planning purposes.

Fast content-based file type identification

Digital forensic examiners often need to identify the type of a file or file fragment based only on the content of the file. Content-based file type identification schemes typically use a byte frequency distribution with statistical machine learning to classify file types. Most algorithms analyze the entire file content to obtain the byte frequency distribution, a technique that is inefficient and time consuming. This paper proposes two techniques for reducing the classification time. The first technique selects a subset of features based on the frequency of occurrence. The second speeds classification by sampling several blocks from the file. Experimental results demonstrate that up to a fifteen-fold reduction in file size analysis time can be achieved with limited impact on accuracy.

Automated object identification using optical video cameras on construction sites

Visual recording devices such as video cameras, CCTVs, or webcams have been broadly used to facilitate work progress or safety monitoring on construction sites. Without human intervention, however, both real-time reasoning about captured scenes and interpretation of recorded images are challenging tasks. This article presents an exploratory method for automated object identification using standard video cameras on construction sites. The proposed method supports real-time detection and classification of mobile heavy equipment and workers. The background subtraction algorithm extracts motion pixels from an image sequence, the pixels are then grouped into regions to represent moving objects, and finally the regions are identified as a certain object using classifiers. For evaluating the method, the formulated computer-aided process was implemented on actual construction sites, and promising results were obtained. This article is expected to contribute to future applications of automated monitoring systems of work zone safety or productivity.

Probabilistic subgroup identification using Bayesian finite mixture modelling : a case study in Parkinson’s disease phenotype identification

This article explores the use of probabilistic classification, namely finite mixture modelling, for identification of complex disease phenotypes, given cross-sectional data. In particular, if focuses on posterior probabilities of subgroup membership, a standard output of finite mixture modelling, and how the quantification of uncertainty in these probabilities can lead to more detailed analyses. Using a Bayesian approach, we describe two practical uses of this uncertainty: (i) as a means of describing a person’s membership to a single or multiple latent subgroups and (ii) as a means of describing identified subgroups by patient-centred covariates not included in model estimation. These proposed uses are demonstrated on a case study in Parkinson’s disease (PD), where latent subgroups are identified using multiple symptoms from the Unified Parkinson’s Disease Rating Scale (UPDRS).

Image-based safety assessment : automated spatial safety risk identification of earthmoving and surface mining activities

This paper presents an automated image‐based safety assessment method for earthmoving and surface mining activities. The literature review revealed the possible causes of accidents on earthmoving operations, investigated the spatial risk factors of these types of accident, and identified spatial data needs for automated safety assessment based on current safety regulations. Image‐based data collection devices and algorithms for safety assessment were then evaluated. Analysis methods and rules for monitoring safety violations were also discussed. The experimental results showed that the safety assessment method collected spatial data using stereo vision cameras, applied object identification and tracking algorithms, and finally utilized identified and tracked object information for safety decision making.