Candidate Recognition Research Articles

A support vector machine and image processing based approach for counting open cotton bolls and estimating lint yield from UAV imagery

Cotton boll count is an important phenotypic trait that aids in a better understanding of the genetic and physiological mechanisms of cotton growth. Several computer vision technologies are available for cotton boll segmentation. However, estimating the number of cotton bolls in a segmented cluster of cotton bolls is a challenging task due to the complex shapes of cotton bolls. This study proposed a combination of spectral-spatial and supervised machine learning based methods for cotton boll candidate recognition and counting from high resolution RGB images obtained from unmanned aerial vehicles (UAVs). An algorithm consisting of machine vision, band-mean filter, Otsu thresholding, red/blue band ratio filter, and geometrical characteristics-based error removal techniques, was employed to detect open cotton boll pixels under several environmental settings. In addition, a support vector machine (SVM) based encoding method was developed using geometric features of cotton boll candidates to predict the number of cotton bolls from the segmented cotton boll candidates. This algorithm was implemented over three experiment sites with three cotton varieties, two tillage practices, seven cover crop treatments, two irrigation regimes (irrigated and rainfed), 26 irrigation levels, and two sensors (DJI FC6310 RGB and MicaSense Rededge) capturing images at two spatial resolutions (0.75 cm and 1.07 cm) over two growing seasons (2019 and 2021). These different experimental settings allowed the proposed approaches to be validated against a variety of complex backgrounds. A visual inspection of 1000 randomly selected pixels revealed that the proposed cotton boll candidate recognition approach was highly effective in segmenting cotton bolls and background pixels, with high classification accuracy (> 95%) and a low number of falsely classified pixels (precision > 0.96; recall > 0.93). A high correlation between ground truth observations and predicted cotton boll count indicated that the use of geometric features of segmented candidates as predictors in association with the SVM model demonstrated a good performance in estimating boll count from recognized cotton boll candidates. Furthermore, linear regression analyses revealed that both boll count and candidate area are potential predictors of lint yield, with boll count being a better predictor than candidate area. Overall, the study demonstrated that machine vision/learning techniques can be potentially used on UAV images to count the number of cotton bolls and predict lint yield over large acreages with reasonable accuracy.

Applications of Smartphone-Based Aptasensor for Diverse Targets Detection

Aptamers are a particular class of functional recognition ligands with high specificity and affinity to their targets. As the candidate recognition layer of biosensors, aptamers can be used to sense biomolecules. Aptasensors, aptamer-based biosensors, have been demonstrated to be specific, sensitive, and cost-effective. Furthermore, smartphone-based devices have shown their advantages in binding to aptasensors for point-of-care testing (POCT), which offers an immediate or spontaneous responding time for biological testing. This review describes smartphone-based aptasensors to detect various targets such as metal ions, nucleic acids, proteins, and cells. Additionally, the focus is also on aptasensors-related technologies and configurations.

Awareness of Spitzenkandidaten in the 2019 European elections: The effects of news exposure in domestic campaign contexts

With the introduction of the so-called Spitzenkandidaten procedure, by which European party families nominate lead candidates for the post of President of the European Commission for European elections, the European Parliament (EP) sought to raise voter awareness and engagement by personalizing the campaigns. This article studies candidate recognition with respect to Spitzenkandidaten, which is an important prerequisite in the study of personalization effects on voter behaviour. We use novel survey data collected in 10 European countries in the 2019 EP election campaign ( n = 17,027). The article focuses on the role of voters’ news exposure in various media and argues that news exposure is crucial for candidate recognition, but its effect is contingent upon the domestic campaign context. The article is the first to show that the campaign context matters to the extent that news exposure is particularly relevant in those domestic contexts in which Spitzenkandidaten were not previously present. In other campaign contexts additional news exposure adds little to the effect of news exposure on candidate recognition. The results have important implications for understanding EP election campaigns.

A Review of Research on Pulsar Candidate Recognition Based on Machine Learning

Abstract The pulsar is a highly magnetized rotating neutron star that provides the first indirect evidence for the existence of gravitational waves and also provides the possibility to reveal extreme phenomena in neutron star astrophysics. Therefore, the identification of pulsars in the universe is a prerequisite for the study of pulsars and gravitational waves. At present, a large number of pulsar searches have produced millions of pulsar candidates. In the face of these large-scale data, if only relying on manual visual classification by experts in related fields, it will be a huge project. Since the emergence of machine learning, its theory and technology have become increasingly mature, and has been successfully applied to astronomical research fields such as pulsar candidate screening. This paper introduces the related machine learning theory of pulsar candidate recognition firstly, and then reviews the research status of pulsar candidate recognition based on machine learning in recent years. Finally, we discuss and prospect the identification of pulsars in the future.

YTHDF2 Binds to 5-Methylcytosine in RNA and Modulates the Maturation of Ribosomal RNA.

5-Methylcytosine is found in both DNA and RNA; although its functions in DNA are well established, the exact role of 5-methylcytidine (m5C) in RNA remains poorly defined. Here we identified, by employing a quantitative proteomics method, multiple candidate recognition proteins of m5C in RNA, including several YTH domain-containing family (YTHDF) proteins. We showed that YTHDF2 could bind directly to m5C in RNA, albeit at a lower affinity than that toward N6-methyladenosine (m6A) in RNA, and this binding involves Trp432, a conserved residue located in the hydrophobic pocket of YTHDF2 that is also required for m6A recognition. RNA bisulfite sequencing results revealed that, after CRISPR-Cas9-mediated knockout of the YTHDF2 gene, the majority of m5C sites in rRNA (rRNA) exhibited substantially augmented levels of methylation. Moreover, we found that YTHDF2 is involved in pre-rRNA processing in cells. Together, our data expanded the functions of the YTHDF2 protein in post-transcriptional regulations of RNA and provided novel insights into the functions of m5C in RNA biology.

Informing the Public: How Party Communication Builds Opportunity Structures

We argue that the attention parties devote to a topic contributes to expanding the opportunity structure to acquire information that party supporters have. We evaluate this proposition in a comparative manner by focusing on an elite-driven new topic, namely the Spitzenkandidaten system in European Parliament elections. We link candidate recognition survey data from 28 countries with over 175 party electoral programs, press releases, and Twitter communication before the 2014 European Parliament elections. Our results show that especially what parties emphasize or decide to talk about on Twitter contributes to what their supporters will know. As proposed, this is an indirect effect through a general contribution to the information environment in election campaigns. However, party communication portfolios should not discount traditional tools given that these can also contribute to the opportunity structures available to party supporters.

Automatic classification of cells in microscopic fecal images using convolutional neural networks.

The analysis of fecal-type components for clinical diagnosis is important. The main examination involves the counting of red blood cells (RBCs), white blood cells (WBCs), and molds under the microscopic. With the development of machine vision, some vision-based detection schemes have been proposed. However, these methods have a single target for detection, with low detection efficiency and low accuracy. We proposed an algorithm to identify the visible image of fecal composition based on intelligent deep learning. The algorithm mainly includes region proposal and candidate recognition. In the process of segmentation, we proposed a morphology extraction algorithm in a complex background. As for the candidate recognition, we proposed a new convolutional neural network (CNN) architecture based on Inception-v3 and principal component analysis (PCA). This method achieves high-average Precision of 90.7%, which is better than the other mainstream CNN models. Finally, the images within the rectangle marks were obtained. The total time for detection of an image was roughly 1200 ms. The algorithm proposed in the present paper can be integrated into an automatic fecal detection system.

Pulsar candidate recognition with deep learning

In this paper, we present a deep learning-based recognition algorithm to identify pulsars by observing data containing millions of candidates including radio frequency interference and noise sources. The dataset is obtained from the High Time Resolution Universe survey created and updated by the Parkes telescope. We investigate several effective single and combined features via simple logistic regression. To deal with the imbalanced dataset, we oversimplify the original dataset at different sampling rates, which is also one of the learning parameters. After training the pre-processed dataset via a convolutional neural network, we provide a cross-validated evaluation of all candidates. Results show that the deep-learning based recognition algorithm can identify the pulsar and radio frequency interference signals with high accuracy. The precision and recall of radio frequency interference are both 100%, and those of pulsars are 91% and 94%, respectively.

An Aptamer-Based Capacitive Sensing Platform for Specific Detection of Lung Carcinoma Cells in the Microfluidic Chip.

Improvement of methods for reliable and early diagnosis of the cellular diseases is necessary. A biological selectivity probe, such as an aptamer, is one of the candidate recognition layers that can be used to detect important biomolecules. Lung cancer is currently a typical cause of cancer-related deaths. In this work, an electrical sensing platform is built based on amine-terminated aptamer modified-gold electrodes for the specific, label-free detection of a human lung carcinoma cell line (A549). The microdevice, that includes a coplanar electrodes configuration and a simple microfluidic channel on a glass substrate, is fabricated using standard photolithography and cast molding techniques. A procedure of self-assembly onto the gold surface is proposed. Optical microscope observations and electrical impedance spectroscopy measurements confirm that the fabricated microchip can specifically and effectively identify A549 cells. In the experiments, the capacitance element that is dominant in the change of the impedance is calculated at the appropriate frequency for evaluation of the sensitivity of the biosensor. Therefore, a simple, inexpensive, biocompatible, and selective biosensor that has the potential to detect early-stage lung cancer would be developed.

Real-time pedestrian crossing lights detection algorithm for the visually impaired

In defect of intelligent assistant approaches, the visually impaired feel hard to cross the roads in urban environments. Aiming to tackle the problem, a real-time Pedestrian Crossing Lights (PCL) detection algorithm for the visually impaired is proposed in this paper. Different from previous works which utilize analytic image processing to detect the PCL in ideal scenarios, the proposed algorithm detects PCL using machine learning scheme in the challenging scenarios, where PCL have arbitrary sizes and locations in acquired image and suffer from the shake and movement of camera. In order to achieve the robustness and efficiency in those scenarios, the detection algorithm is designed to include three procedures: candidate extraction, candidate recognition and temporal-spatial analysis. A public dataset of PCL, which includes manually labeled ground truth data, is established for tuning parameters, training samples and evaluating the performance. The algorithm is implemented on a portable PC with color camera. The experiments carried out in various practical scenarios prove that the precision and recall of detection are both close to 100%, meanwhile the frame rate is up to 21 frames per second (FPS).

Remembering One's Representative: How District Magnitude and List Type Affect Candidate Recognition.

District magnitude and list type shape the incentives for politicians to develop a personal vote. If voters also react to these strategies, their knowledge about candidates should be influenced by these features of the electoral system. This article directly tests the responsiveness of voters by employing individual-level survey data from the Comparative Study of Electoral Systems. As district magnitude increases, more people remember at least one candidate in closed list systems, but there is no such effect in open list systems. These influences are also larger for non-voters than for voters. A measure of political contact is not affected in this way. The differential effect of district magnitude can be explained by a different campaign focus.

The role of candidate evaluations in the 2014 European Parliament elections: Towards the personalization of voting behaviour?

We study the personalization of voting behaviour in European Parliament elections. We argue that information from the media is crucial for providing linkages between candidates and voters. Moreover, we contend that candidates can serve as information short-cuts given the complexity of European Union politics. We use a four-wave Dutch panel survey and a media study that enable us to link evaluations of lead candidates, party preferences, and vote choice to exposure to news about these candidates. We show, firstly, that exposure to candidate news is a strong explanatory factor for candidate recognition. Secondly, we find that candidate evaluations positively affect party choice, albeit mainly for those voters who tend to be politically aware. Our research has implications for debates about the European Union’s accountability deficit.

Ultrasensitive electrochemical aptasensor based on sandwich architecture for selective label-free detection of colorectal cancer (CT26) cells

Colorectal cancer is one of the most common cancers in the world and has no effective treatment. Therefore, development of new methods for early diagnosis is instantly required. Biological recognition probes such as synthetic receptor and aptamer is one of the candidate recognition layers to detect important biomolecules. In this work, an electrochemical aptasensor was developed by fabricating an aptamer–cell–aptamer sandwich architecture on an SBA-15-3-aminopropyltriethoxysilane (SBA-15-pr-NH2) and Au nanoparticles (AuNPs) modified graphite screen printed electrode (GSPE) surface for the selective, label-free detection of CT26 cancer cells. Based on the incubation of the thiolated aptamer with CT26 cells, the electron-transfer resistance of Fe (CN)63−/4− redox couple increased considerably on the aptasensor surface. The results obtained from cyclic voltammetry and electrochemical impedance spectroscopy studies showed that the fabricated aptasensor can specifically identify CT26 cells in the concentration ranges of 10–1.0×105cells/mL and 1.0×105–6.0×106 cells/mL, respectively, with a detection limit of 2cells/mL. Applying the thiol terminated aptamer (5TR1) as a recognition layer led to a sensor with high affinity for CT26 cancer cells, compared to control cancer cells of AGS cells, VERO Cells, PC3 cells and SKOV-3 cells. Therefore a simple, rapid, label free, inexpensive, excellent, sensitive and selective electrochemical aptasensor based on sandwich architecture was developed for detection of CT26 Cells.

If You Don't Know Me by Now: Explaining Local Candidate Recognition

For the personal vote to be cast in a meaningful way it is a minimal condition that voters recognise candidates. However, from earlier studies we know that there is huge variation in the number of candidates recognised. Little to nothing is known about candidate recognition and its determinants. This study explores the sources of candidate recognition from three different angles: candidates; citizens; and context. Furthermore, it enables the distinction of campaign-related from other factors. A unique multi-level within-subject design was set up for the analyses of the 2013 German Federal Election to ensure a meaningful validation of our theoretical framework. Our results suggest that, indeed, many factors lead to recognition but as well that earlier studies overestimated the effects of political interest or incumbency status. Moreover, we show that a good campaign makes a difference for recognition – as does the context in which it takes place.

Does personalization increase turnout? Spitzenkandidaten in the 2014 European Parliament elections

The 2014 European Parliament elections were the first elections where the major political groups each nominated a lead candidate ( Spitzenkandidat) for the Commission presidency in the hope that this would increase the visibility of the elections and mobilize more citizens to turn out. Using data from the 2014 European Elections Study, an EU-wide post-election survey, we analyse whether and how the presence of the lead candidates influenced the individual probability to participate in these elections. Our findings show that the recognition of the candidates increased the propensity to turn out, even when controlling for a host of other individual-level factors explaining turnout and the context factors known to facilitate participation. Furthermore, the campaign efforts of the lead candidates are associated with higher turnout levels and are reinforced by candidate recognition.

PD42-03 ROBUST AUTOMATIC RENAL STONE DETECTION IN ULTRASONIC LIVE STREAMS FOR IMPROVING EXTRACORPOREAL SHOCK WAVE THERAPY

You have accessJournal of UrologyStone Disease: New Technology1 Apr 2015PD42-03 ROBUST AUTOMATIC RENAL STONE DETECTION IN ULTRASONIC LIVE STREAMS FOR IMPROVING EXTRACORPOREAL SHOCK WAVE THERAPY Werner Pomwenger, Peter Ott, Stefan Wegenkittl, Reinhold Zimmermann, Olaf Gleibe, and Axel Koch Werner PomwengerWerner Pomwenger More articles by this author , Peter OttPeter Ott More articles by this author , Stefan WegenkittlStefan Wegenkittl More articles by this author , Reinhold ZimmermannReinhold Zimmermann More articles by this author , Olaf GleibeOlaf Gleibe More articles by this author , and Axel KochAxel Koch More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2015.02.2588AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES As the gold standard, Extracorporeal Shock Wave Lithotripsy (ESWL) predominantly uses X-ray as a localization system. Ultrasound (US) for stone detection is an alternative method allowing for continuous assessment during the treatment while avoiding radiation exposure. Despite its evident advantages, comparably lower image quality and staff training costs have been identified as major barriers against a wider acceptance. We aim to develop a reliable automatic US-based renal stone detection system to support urologists in ESWL treatment by accurately locating targets in real-time. METHODS Our prototype combines i) a single, frame-based multistage candidate recognition system with ii) a multiple frame-based probabilistic tracking. For i), image enhancement is followed by feature extraction to identify stone candidates. Subsequently, these are examined for the presence of shadowing effects and classified using a finite state machine. The high detection rate (52% to 78%) is achieved by ii) posterior probabilities based on accumulated stone evidence in successive US frames in order to substantially increase sensitivity. To demonstrate robustness, the prototype is evaluated against a set of 5 recorded US sequences (Aloka 3500SX, 3.5MHz transducer, average 60 sec, 17 fps ), annotated by a medical expert, well-trained in US-based stone diagnosis. RESULTS The recorded video sequences showed a strongly varying image quality and typical artefacts (e.g. speckle, ring down, rib shadow or electronic spiking), which was intended as it reflects the real case of clinical application. The correct detection rates vary from 61.7 – 95.0% with sensitivity being from 96.4 – 98.1% with respect to the established ground truth. In this context the positive predictive value was 86.8% in average. CONCLUSIONS As future medical guidelines are expected to call for higher avoidance of radiation exposure of patients, the use of US in ESWL will become desirable. To bring US on a par with X-ray based location, it will strongly benefit from an automatic stone detection system. By combining single- and multi-frame features of US and analyzing these in a decision logic, a substantial increase in stone detection rates was achieved. The moderate to low US video quality used in the tests raises our expectations with respect to the currently ongoing clinical evaluation. With this real-time system US-only based ESWL will perform better in regards of radiation hygiene, stone identification, focus accuracy and treatment efficiency. © 2015 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 193Issue 4SApril 2015Page: e886 Advertisement Copyright & Permissions© 2015 by American Urological Association Education and Research, Inc.MetricsAuthor Information Werner Pomwenger More articles by this author Peter Ott More articles by this author Stefan Wegenkittl More articles by this author Reinhold Zimmermann More articles by this author Olaf Gleibe More articles by this author Axel Koch More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...

Examining Teacher Candidate Use of Data-Based Formative Assessment for Instructional Decision-Making

Using accounts of critical events, this study examines teacher candidate decisions to modify instruction in response to ongoing classroom interaction and assessment during student teaching. Instructional changes made by candidates who reported incidents in which students had difficulty with learning goals were explored across two years. Analysis of decision making suggested that candidates during the first year lacked variation in instructional interventions and continued presenting content to students through previously used teaching strategies and materials. In the second year, candidates improved their instructional interventions, but had difficulty using objective data to describe outcomes resulting from changes made. The discussion examines changes made to teacher education curriculum after study year one to prompt teacher candidate recognition of and accommodation for diverse student needs when making instruction and assessment decisions. The use of data in reflective practice is argued as a continued need for teacher candidate development.

The Consequences of Concurrent Campaigns for Citizen Knowledge of Congressional Candidates

Congressional campaigns do not occur in isolation, but concurrently with many other races. What are the effects of overlapping campaigns for how people learn about congressional candidates? On one hand, intensely fought races on the same ballot may encourage people to extend interest to contemporaneous races. Alternatively, competing campaigns may distract voters and limit learning about down-ticket contests. Using American National Election Studies data from 1996 to 2002, I test the consequences of state level presidential, senatorial, and gubernatorial campaign intensity on citizen learning about congressional candidates. I find that while the local intensity of the presidential race has little effect on knowledge of House candidates, the intensity of the Senate race is positively associated with candidate recognition. These effects vary depending on the nature of the election, where House candidates benefit from intense Senate races on the ballot particularly in midterm election years.

Using data mining techniques and rough set theory for language modeling

In this article, we propose a new postprocessing strategy, word suggestion, based on a multiple word trigger-pair language model for Chinese character recognizers. With the word suggestion strategy, Chinese character recognizers may even achieve a recognition rate greater than the top-n candidate recognition rate. To construct the multiple word trigger-pair model, data mining techniques are used to alleviate the intensive computation problem. Furthermore, rough set theory is first used in the study to discover negatively correlated relationships between words in order to prevent introducing wrong words in the process of word suggestion.

Genome-wide transcription map of an archaeal cell cycle

Relative RNA abundance was measured at different cell-cycle stages in synchronized cultures of the hyperthermophilic archaeon Sulfolobus acidocaldarius. Cyclic induction was observed for >160 genes, demonstrating central roles for transcriptional regulation and cell-cycle-specific gene expression in archaeal cell-cycle progression. Many replication genes were induced in a cell-cycle-specific manner, and novel replisome components are likely to be among the genes of unknown function with similar induction patterns. Candidate genes for the unknown genome segregation and cell division machineries were also identified, as well as seven transcription factors likely to be involved in cell-cycle control. Two serine-threonine protein kinases showed distinct cell-cycle-specific induction, suggesting regulation of the archaeal cell cycle also through protein modification. Two candidate recognition elements, CCR boxes, for transcription factors in control of cell-cycle regulons were identified among gene sets with similar induction kinetics. The results allow detailed characterization of the genome segregation, division, and replication processes and may, because of the extensive homologies between the archaeal and eukaryotic information machineries, also be applicable to core features of the eukaryotic cell cycle.