Detecting abnormal and collusive bids in capped tendering

Recent developments in the area of Bid Tender Forecasting have enabled bidders to implement new types of easy-to-use tools for increasing their chances of winning contracts. Although these new tools (such as iso-Score Curve Graphs, Scoring Probability Graphs, and Position Probability Graphs) are designed for bidders in capped tendering (tenders with an upper price limit), some of their principles can also be applied by a Contracting Authority to detect which bidders do not follow a standard pattern, that is, their bids are extremely high or low. Since a collusive bid generally needs to be sufficiently high or low to make an impact on the bid distribution, any person in charge of supervising capped tenders can be alerted to any bidder that might be involved in a cartel after identifying the same abnormal behavior in a series of tenders through simple calculations and a new type of graph.

Rapid, automated detection of stem canker symptoms in woody perennials using artificial neural network analysis

Background Pseudomonas syringae can cause stem necrosis and canker in a wide range of woody species including cherry, plum, peach, horse chestnut and ash. The detection and quantification of lesion progression over time in woody tissues is a key trait for breeders to select upon for resistance. Results In this study a general, rapid and reliable approach to lesion quantification using image recognition and an artificial neural network model was developed. This was applied to screen both the virulence of a range of P. syringae pathovars and the resistance of a set of cherry and plum accessions to bacterial canker. The method developed was more objective than scoring by eye and allowed the detection of putatively resistant plant material for further study. Conclusions Automated image analysis will facilitate rapid screening of material for resistance to bacterial and other phytopathogens, allowing more efficient selection and quantification of resistance responses.

Decimal growth stages for precision wheat production in changing environments?

The utility of the decimal growth stage (DGS) scoring system for cereals is reviewed. The DGS is the most widely used scale in academic and commercial applications because of its comprehensive coverage of cereal developmental stages, the ease of use and definition provided and adoption by official agencies. The DGS has demonstrable and established value in helping to optimise the timing of agronomic inputs, particularly with regard to plant growth regulators, herbicides, fungicides and soluble nitrogen fertilisers. In addition, the DGS is used to help parameterise crop models, and also in understanding the response and adaptation of crops to the environment. The value of the DGS for increasing precision relies on it indicating, to some degree, the various stages in the development of the stem apex and spike. Coincidence of specific growth stage scores with the transition of the apical meristem from a vegetative to a reproductive state, and also with the period of meiosis, is unreliable. Nonetheless, in pot experiments it is shown that the broad period of booting (DGS 41–49) appears adequate for covering the duration when the vulnerability of meiosis to drought and heat stress is exposed. Similarly, the duration of anthesis (61–69) is particularly susceptible to abiotic stresses: initially from a fertility perspective, but increasingly from a mean grain weight perspective as flowering progresses to DGS 69 and then milk development. These associations with DGS can have value at the crop level of organisation: for interpreting environmental effects, and in crop modelling. However, genetic, biochemical and physiological analysis to develop greater understanding of stress acclimation during the vegetative state, and tolerance at meiosis, does require more precision than DGS can provide. Similarly, individual floret analysis is needed to further understand the genetic basis of stress tolerance during anthesis.

In the mood: the dynamics of collective sentiments on Twitter

We study the relationship between the sentiment levels of Twitter users and the evolving network structure that the users created by @-mentioning each other. We use a large dataset of tweets to which we apply three sentiment scoring algorithms, including the open source SentiStrength program. Specifically we make three contributions. Firstly we find that people who have potentially the largest communication reach (according to a dynamic centrality measure) use sentiment differently than the average user: for example they use positive sentiment more often and negative sentiment less often. Secondly we find that when we follow structurally stable Twitter communities over a period of months, their sentiment levels are also stable, and sudden changes in community sentiment from one day to the next can in most cases be traced to external events affecting the community. Thirdly, based on our findings, we create and calibrate a simple agent-based model that is capable of reproducing measures of emotive response comparable to those obtained from our empirical dataset.

Scanning Electron Microscopic Preliminary Study of the Efficacy of SmearClear and EDTA for Smear Layer Removal after Root Canal Instrumentation in Permanent Teeth

This study aimed to evaluate the efficacy of SmearClear (SybronEndo, Orange, CA) and EDTA for smear layer removal from root canals of permanent teeth after instrumentation. Thirty extracted human permanent teeth (n = 10) were randomly assigned to the following groups: group 1 = 14.3% EDTA, group 2 = SmearClear, and group 3 = no smear layer removal procedure was undertaken (control). The specimens were submitted to scanning electron microscopy analysis. Magnifications of 200x and 750x were used to evaluate cleaning at the apical, middle, and cervical thirds according to a three-point scoring system. Data were analyzed statistically by the Mann-Whitney U test (5% significance level). Groups 1 and 2 differed significantly from group 3 (p < 0.01). However, there was no statistically significant difference (p > 0.05) between groups 1 and 2. In conclusion, SmearClear was able to remove the smear layer from the root canals of permanent teeth similarly as 14.3% EDTA, suggesting that both solutions may be indicated for such purpose. (J Endod 2008,34:1541-1544)

Evaluation of three indices for biofilm accumulation on complete dentures

Objectives: The objective of this study was to evaluate the accuracy and reproducibility of three complete denture biofilm indices (Prosthesis Hygiene Index; Jeganathan et al. Index; Budtz-J circle divide rgensen Index) by means of a computerised comparison method. Background: Clinical studies into denture hygiene have employed a large number of biofilm indices among their outcome variables. However, the knowledge about the validity of these indices is still scarce. Materials and methods: Sixty-two complete denture wearers were selected. The internal surfaces of the upper complete dentures were stained (5% erythrosine) and photographed. The slides were projected on paper, and the biofilm indices were applied over the photos by means of a scoring method. For the computerised method, the areas (total and biofilm-covered) were measured by dedicated software (Image Tool). In addition, to compare the results of the computerised method and Prosthetic Hygiene Index, a new scoring scale (including four and five graded) was introduced. For the Jeganathan et al. and Budtz-J circle divide rgensen indices, the original scales were used. Values for each index were compared with the computerised method by the Friedman test. Their reproducibility was measured by means of weighed kappa. Significance for both tests was set at 0.05. Results: The indices tested provided similar mean measures but they tended to overestimate biofilm coverage when compared with the computerised method (p < 0.001). Agreement between the Prosthesis Hygiene Index and the computerised method was not significant, regardless of the scale used. Jeghanathan et al. Index showed weak agreement, and consistent results were found for Budtz-Jorgensen Index (kappa = 0.19 and 0.39 respectively). Conclusion: Assessment of accuracy for the biofilm indices showed instrument bias that was similar among the tested methods. Weak inter-instrument reproducibility was found for the indices, except for the Budtz-J circle divide rgensen Index. This should be the method of choice for clinical studies when more sophisticated approaches are not possible.

Bayesian Analysis for Nonlinear Regression Model Under Skewed Errors, with Application in Growth Curves

We have considered a Bayesian approach for the nonlinear regression model by replacing the normal distribution on the error term by some skewed distributions, which account for both skewness and heavy tails or skewness alone. The type of data considered in this paper concerns repeated measurements taken in time on a set of individuals. Such multiple observations on the same individual generally produce serially correlated outcomes. Thus, additionally, our model does allow for a correlation between observations made from the same individual. We have illustrated the procedure using a data set to study the growth curves of a clinic measurement of a group of pregnant women from an obstetrics clinic in Santiago, Chile. Parameter estimation and prediction were carried out using appropriate posterior simulation schemes based in Markov Chain Monte Carlo methods. Besides the deviance information criterion (DIC) and the conditional predictive ordinate (CPO), we suggest the use of proper scoring rules based on the posterior predictive distribution for comparing models. For our data set, all these criteria chose the skew-t model as the best model for the errors. These DIC and CPO criteria are also validated, for the model proposed here, through a simulation study. As a conclusion of this study, the DIC criterion is not trustful for this kind of complex model.

Integration of Ligand- and Target-Based Virtual Screening for the Discovery of Cruzain Inhibitors

A myriad of methods are available for virtual screening of small organic compound databases. In this study we have successfully applied a quantitative model of consensus measurements, using a combination of 3D similarity searches (ROCS and EON), Hologram Quantitative Structure Activity Relationships (HQSAR) and docking (FRED, FlexX, Glide and AutoDock Vina), to retrieve cruzain inhibitors from collected databases. All methods were assessed individually and then combined in a Ligand-Based Virtual Screening (LBVS) and Target-Based Virtual Screening (TBVS) consensus scoring, using Receiving Operating Characteristic (ROC) curves to evaluate their performance. Three consensus strategies were used: scaled-rank-by-number, rank-by-rank and rank-by-vote, with the most thriving the scaled-rank-by-number strategy, considering that the stiff ROC curve appeared to be satisfactory in every way to indicate a higher enrichment power at early retrieval of active compounds from the database. The ligand-based method provided access to a robust and predictive HQSAR model that was developed to show superior discrimination between active and inactive compounds, which was also better than ROCS and EON procedures. Overall, the integration of fast computational techniques based on ligand and target structures resulted in a more efficient retrieval of cruzain inhibitors with desired pharmacological profiles that may be useful to advance the discovery of new trypanocidal agents.

Docking and molecular dynamics simulation of quinone compounds with trypanocidal activity

In this work, two different docking programs were used, AutoDock and FlexX, which use different types of scoring functions and searching methods. The docking poses of all quinone compounds studied stayed in the same region in the trypanothione reductase. This region is a hydrophobic pocket near to Phe396, Pro398 and Leu399 amino acid residues. The compounds studied displays a higher affinity in trypanothione reductase (TR) than glutathione reductase (GR), since only two out of 28 quinone compounds presented more favorable docking energy in the site of human enzyme. The interaction of quinone compounds with the TR enzyme is in agreement with other studies, which showed different binding sites from the ones formed by cysteines 52 and 58. To verify the results obtained by docking, we carried out a molecular dynamics simulation with the compounds that presented the highest and lowest docking energies. The results showed that the root mean square deviation (RMSD) between the initial and final pose were very small. In addition, the hydrogen bond pattern was conserved along the simulation. In the parasite enzyme, the amino acid residues Leu399, Met400 and Lys402 are replaced in the human enzyme by Met406, Tyr407 and Ala409, respectively. In view of the fact that Leu399 is an amino acid of the Z site, this difference could be explored to design selective inhibitors of TR.

Automatic and objective assessment of alternating tapping performance in Parkinson’s disease

This paper presents the development and evaluation of a method for enabling quantitative and automatic scoring of alternating tapping performance of patients with Parkinson’s disease (PD). Ten healthy elderly subjects and 95 patients in different clinical stages of PD have utilized a touch-pad handheld computer to perform alternate tapping tests in their home environments. First, a neurologist used a web-based system to visually assess impairments in four tapping dimensions (‘speed’, ‘accuracy’, ‘fatigue’ and ‘arrhythmia’) and a global tapping severity (GTS). Second, tapping signals were processed with time series analysis and statistical methods to derive 24 quantitative parameters. Third, principal component analysis was used to reduce the dimensions of these parameters and to obtain scores for the four dimensions. Finally, a logistic regression classifier was trained using a 10-fold stratified cross-validation to map the reduced parameters to the corresponding visually assessed GTS scores. Results showed that the computed scores correlated well to visually assessed scores and were significantly different across Unified Parkinson’s Disease Rating Scale scores of upper limb motor performance. In addition, they had good internal consistency, had good ability to discriminate between healthy elderly and patients in different disease stages, had good sensitivity to treatment interventions and could reflect the natural disease progression over time. In conclusion, the automatic method can be useful to objectively assess the tapping performance of PD patients and can be included in telemedicine tools for remote monitoring of tapping.

A Computer Vision Framework For Finger-Tapping Evaluation In Parkinson's Disease

Objective: To define and evaluate a Computer-Vision (CV) method for scoring Paced Finger-Tapping (PFT) in Parkinson's disease (PD) using quantitative motion analysis of index-fingers and to compare the obtained scores to the UPDRS (Unified Parkinson's Disease Rating Scale) finger-taps (FT). Background: The naked-eye evaluation of PFT in clinical practice results in coarse resolution to determine PD status. Besides, sensor mechanisms for PFT evaluation may cause patients discomfort. In order to avoid cost and effort of applying wearable sensors, a CV system for non-invasive PFT evaluation is introduced. Methods: A database of 221 PFT videos from 6 PD patients was processed. The subjects were instructed to position their hands above their shoulders besides the face and tap the index-finger against the thumb consistently with speed. They were facing towards a pivoted camera during recording. The videos were rated by two clinicians between symptom levels 0-to-3 using UPDRS-FT. The CV method incorporates a motion analyzer and a face detector. The method detects the face of testee in each video-frame. The frame is split into two images from face-rectangle center. Two regions of interest are located in each image to detect index-finger motion of left and right hands respectively. The tracking of opening and closing phases of dominant hand index-finger produces a tapping time-series. This time-series is normalized by the face height. The normalization calibrates the amplitude in tapping signal which is affected by the varying distance between camera and subject (farther the camera, lesser the amplitude). A total of 15 features were classified using K-nearest neighbor (KNN) classifier to characterize the symptoms levels in UPDRS-FT. The target ratings provided by the raters were averaged. Results: A 10-fold cross validation in KNN classified 221 videos between 3 symptom levels with 75% accuracy. An area under the receiver operating characteristic curves of 82.6% supports feasibility of the obtained features to replicate clinical assessments. Conclusions: The system is able to track index-finger motion to estimate tapping symptoms in PD. It has certain advantages compared to other technologies (e.g. magnetic sensors, accelerometers etc.) for PFT evaluation to improve and automate the ratings

Salesperson’s Personality, Motivation and Selling Performance : The Study of New Product Selling

In the highly competitive environment businesses invest big amounts of money into the new product development. New product success potentially depends on different factors among which salespeople play an important role. The aim of this paper is to explore the potential link between salespeople’s personality, motivation to sell new products and performance in selling new products. Based on the theoretical background of the Big Five personality dimensions, motivation and selling performance hypotheses were formulated and tested using statistical methods of correlation and regression analysis. The data was collected within one technologically intensive organization – ABB AB in Sweden using online web questionnaire and self-assessment measurements. Total investigation was conducted among organization’s salesforce. The findings confirm the importance of salesperson’s personality empirically showing that the latter significantly predicts both motivation and performance in selling new products. From all the Big Five Extraversion was confirmed to be the most important predictor of both motivation and performance in selling new products. Extraversion was found positively related with both motivation and performance in selling new products. Salespeople scoring high in Extraversion and especially possessing such characteristics as confident, energetic and sociable tend to be more motivated to sell new products and show higher performance results. Other personality dimensions such as Agreeableness, Conscientiousness, Neuroticism, and Openness to experience complexly approached are not proved to be significantly related neither with motivation nor performance in selling new products. The results are explained by the extreme importance of Extraversion in new product selling situation which analyzing in combination with the other personality dimensions suppresses the others. Finding regarding controlling for certain demographical characteristics of salespeople reveal that performance in selling new products is determined by selling experience. Salespeople’s age is not proved to be significantly related neither with motivation nor performance in selling new products. Findings regarding salespeople’s gender though proposing that males are more motivated to sell new products cannot be generalized due to the study limitations.

Automatic spiral analysis for objective assessment of motor symptoms in Parkinson's disease

Objective: To develop a method for objective quantification of PD motor symptoms related to Off episodes and peak dose dyskinesias, using spiral data gathered by using a touch screen telemetry device. The aim was to objectively characterize predominant motor phenotypes (bradykinesia and dyskinesia), to help in automating the process of visual interpretation of movement anomalies in spirals as rated by movement disorder specialists. Background: A retrospective analysis was conducted on recordings from 65 patients with advanced idiopathic PD from nine different clinics in Sweden, recruited from January 2006 until August 2010. In addition to the patient group, 10 healthy elderly subjects were recruited. Upper limb movement data were collected using a touch screen telemetry device from home environments of the subjects. Measurements with the device were performed four times per day during week-long test periods. On each test occasion, the subjects were asked to trace pre-drawn Archimedean spirals, using the dominant hand. The pre-drawn spiral was shown on the screen of the device. The spiral test was repeated three times per test occasion and they were instructed to complete it within 10 seconds. The device had a sampling rate of 10Hz and measured both position and time-stamps (in milliseconds) of the pen tip. Methods: Four independent raters (FB, DH, AJ and DN) used a web interface that animated the spiral drawings and allowed them to observe different kinematic features during the drawing process and to rate task performance. Initially, a number of kinematic features were assessed including ‘impairment’, ‘speed’, ‘irregularity’ and ‘hesitation’ followed by marking the predominant motor phenotype on a 3-category scale: tremor, bradykinesia and/or choreatic dyskinesia. There were only 2 test occasions for which all the four raters either classified them as tremor or could not identify the motor phenotype. Therefore, the two main motor phenotype categories were bradykinesia and dyskinesia. ‘Impairment’ was rated on a scale from 0 (no impairment) to 10 (extremely severe) whereas ‘speed’, ‘irregularity’ and ‘hesitation’ were rated on a scale from 0 (normal) to 4 (extremely severe). The proposed data-driven method consisted of the following steps. Initially, 28 spatiotemporal features were extracted from the time series signals before being presented to a Multilayer Perceptron (MLP) classifier. The features were based on different kinematic quantities of spirals including radius, angle, speed and velocity with the aim of measuring the severity of involuntary symptoms and discriminate between PD-specific (bradykinesia) and/or treatment-induced symptoms (dyskinesia). A Principal Component Analysis was applied on the features to reduce their dimensions where 4 relevant principal components (PCs) were retained and used as inputs to the MLP classifier. Finally, the MLP classifier mapped these components to the corresponding visually assessed motor phenotype scores for automating the process of scoring the bradykinesia and dyskinesia in PD patients whilst they draw spirals using the touch screen device. For motor phenotype (bradykinesia vs. dyskinesia) classification, the stratified 10-fold cross validation technique was employed. Results: There were good agreements between the four raters when rating the individual kinematic features with intra-class correlation coefficient (ICC) of 0.88 for ‘impairment’, 0.74 for ‘speed’, 0.70 for ‘irregularity’, and moderate agreements when rating ‘hesitation’ with an ICC of 0.49. When assessing the two main motor phenotype categories (bradykinesia or dyskinesia) in animated spirals the agreements between the four raters ranged from fair to moderate. There were good correlations between mean ratings of the four raters on individual kinematic features and computed scores. The MLP classifier classified the motor phenotype that is bradykinesia or dyskinesia with an accuracy of 85% in relation to visual classifications of the four movement disorder specialists. The test-retest reliability of the four PCs across the three spiral test trials was good with Cronbach’s Alpha coefficients of 0.80, 0.82, 0.54 and 0.49, respectively. These results indicate that the computed scores are stable and consistent over time. Significant differences were found between the two groups (patients and healthy elderly subjects) in all the PCs, except for the PC3. Conclusions: The proposed method automatically assessed the severity of unwanted symptoms and could reasonably well discriminate between PD-specific and/or treatment-induced motor symptoms, in relation to visual assessments of movement disorder specialists. The objective assessments could provide a time-effect summary score that could be useful for improving decision-making during symptom evaluation of individualized treatment when the goal is to maximize functional On time for patients while minimizing their Off episodes and troublesome dyskinesias.