scholarly journals Markerless tracking of an entire honey bee colony

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Katarzyna Bozek ◽  
Laetitia Hebert ◽  
Yoann Portugal ◽  
Greg J. Stephens
Keyword(s):  
Honey Bee ◽  
True Positive Rate ◽  
Computational Method ◽  
Visual Features ◽  
Brood Cell ◽  
Collective Behaviors ◽  
Markerless Tracking ◽  
Bee Colony ◽  
Positive Rate ◽  
Group Members

AbstractFrom cells in tissue, to bird flocks, to human crowds, living systems display a stunning variety of collective behaviors. Yet quantifying such phenomena first requires tracking a significant fraction of the group members in natural conditions, a substantial and ongoing challenge. We present a comprehensive, computational method for tracking an entire colony of the honey bee Apis mellifera using high-resolution video on a natural honeycomb background. We adapt a convolutional neural network (CNN) segmentation architecture to automatically identify bee and brood cell positions, body orientations and within-cell states. We achieve high accuracy (~10% body width error in position, ~10° error in orientation, and true positive rate > 90%) and demonstrate months-long monitoring of sociometric colony fluctuations. These fluctuations include ~24 h cycles in the counted detections, negative correlation between bee and brood, and nightly enhancement of bees inside comb cells. We combine detected positions with visual features of organism-centered images to track individuals over time and through challenging occluding events, recovering ~79% of bee trajectories from five observation hives over 5 min timespans. The trajectories reveal important individual behaviors, including waggle dances and crawling inside comb cells. Our results provide opportunities for the quantitative study of collective bee behavior and for advancing tracking techniques of crowded systems.

scholarly journals Markerless tracking of an entire insect colony

2020 ◽  
Author(s):  
Katarzyna Bozek ◽  
Laetitia Hebert ◽  
Yoann Portugal ◽  
Greg J. Stephens
Keyword(s):  
Cell Activity ◽  
True Positive Rate ◽  
Computational Method ◽  
Visual Features ◽  
Brood Cell ◽  
True Positive ◽  
Body Width ◽  
Markerless Tracking ◽  
Positive Rate ◽  
Fast Motion

AbstractWe present a comprehensive, computational method for tracking an entire colony of the honey bee Apis mellifera using high-resolution video on a natural honeycomb background. We adapt a convolutional neural network (CNN) segmentation architecture to automatically identify bee and brood cell positions, body orientations and within-cell states. We achieve high accuracy (~10% body width error in position, ~10° error in orientation, and true positive rate > 90%) and demonstrate months-long monitoring of sociometric colony fluctuations. We combine extracted positions with rich visual features of organism-centered images to track individuals over time and through challenging occluding events, recovering ~79% of bee trajectories from five observation hives over a span of 5 minutes. The resulting trajectories reveal important behaviors, including fast motion, comb-cell activity, and waggle dances. Our results provide new opportunities for the quantitative study of collective bee behavior and for advancing tracking techniques of crowded systems.

scholarly journals Author Correction: Markerless tracking of an entire honey bee colony

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Katarzyna Bozek ◽  
Laetitia Hebert ◽  
Yoann Portugal ◽  
Alexander S. Mikheyev ◽  
Greg J. Stephens
Keyword(s):  
Honey Bee ◽  
Markerless Tracking ◽  
Bee Colony ◽  
Honey Bee Colony

scholarly journals JUM is a computational method for comprehensive annotation-free analysis of alternative pre-mRNA splicing patterns

2018 ◽  
Vol 115 (35) ◽  
pp. E8181-E8190 ◽  
Author(s):  
Qingqing Wang ◽  
Donald C. Rio
Keyword(s):  
Intron Retention ◽  
False Positive Rate ◽  
True Positive Rate ◽  
Mrna Splicing ◽  
Computational Method ◽  
Sequencing Data ◽  
Detection Analysis ◽  
Positive Rate ◽  
Ir Detection ◽  
Splicing Patterns

Alternative pre-mRNA splicing (AS) greatly diversifies metazoan transcriptomes and proteomes and is crucial for gene regulation. Current computational analysis methods of AS from Illumina RNA-sequencing data rely on preannotated libraries of known spliced transcripts, which hinders AS analysis with poorly annotated genomes and can further mask unknown AS patterns. To address this critical bioinformatics problem, we developed a method called the junction usage model (JUM) that uses a bottom-up approach to identify, analyze, and quantitate global AS profiles without any prior transcriptome annotations. JUM accurately reports global AS changes in terms of the five conventional AS patterns and an additional “composite” category composed of inseparable combinations of conventional patterns. JUM stringently classifies the difficult and disease-relevant pattern of intron retention (IR), reducing the false positive rate of IR detection commonly seen in other annotation-based methods to near-negligible rates. When analyzing AS in RNA samples derived from Drosophila heads, human tumors, and human cell lines bearing cancer-associated splicing factor mutations, JUM consistently identified approximately twice the number of novel AS events missed by other methods. Computational simulations showed JUM exhibits a 1.2 to 4.8 times higher true positive rate at a fixed cutoff of 5% false discovery rate. In summary, JUM provides a framework and improved method that removes the necessity for transcriptome annotations and enables the detection, analysis, and quantification of AS patterns in complex metazoan transcriptomes with superior accuracy.

Do more bees imply higher fees? Honey bee colony strength as a determinant of almond pollination fees

Food Policy ◽  
2019 ◽  
Vol 83 ◽  
pp. 150-160 ◽  
Author(s):  
Brittney K. Goodrich
Keyword(s):  
Honey Bee ◽  
Bee Colony ◽  
Honey Bee Colony

scholarly journals PRATD: A Phased Remote Access Trojan Detection Method with Double-Sided Features

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1894
Author(s):  
Chun Guo ◽  
Zihua Song ◽  
Yuan Ping ◽  
Guowei Shen ◽  
Yuhei Cui ◽  
...  
Keyword(s):  
False Positive ◽  
Detection Method ◽  
False Positive Rate ◽  
True Positive Rate ◽  
Remote Access ◽  
Detection Methods ◽  
Security Threats ◽  
True Positive ◽  
Trojan Detection ◽  
Positive Rate

Remote Access Trojan (RAT) is one of the most terrible security threats that organizations face today. At present, two major RAT detection methods are host-based and network-based detection methods. To complement one another’s strengths, this article proposes a phased RATs detection method by combining double-side features (PRATD). In PRATD, both host-side and network-side features are combined to build detection models, which is conducive to distinguishing the RATs from benign programs because that the RATs not only generate traffic on the network but also leave traces on the host at run time. Besides, PRATD trains two different detection models for the two runtime states of RATs for improving the True Positive Rate (TPR). The experiments on the network and host records collected from five kinds of benign programs and 20 famous RATs show that PRATD can effectively detect RATs, it can achieve a TPR as high as 93.609% with a False Positive Rate (FPR) as low as 0.407% for the known RATs, a TPR 81.928% and FPR 0.185% for the unknown RATs, which suggests it is a competitive candidate for RAT detection.

scholarly journals Honey bee colony mortality and productivity with single and dual infestations of parasitic mite species

Apidologie ◽  
2001 ◽  
Vol 32 (6) ◽  
pp. 567-575 ◽  
Author(s):  
Danielle L. Downey ◽  
Mark L. Winston
Keyword(s):  
Honey Bee ◽  
Mite Species ◽  
Bee Colony ◽  
Parasitic Mite ◽  
Honey Bee Colony

scholarly journals Parameter identification modeling honey bee colony population dynamics

2021 ◽  
Author(s):  
Atanas Z. Atanasov ◽  
Slavi G. Georgiev
Keyword(s):  
Population Dynamics ◽  
Parameter Identification ◽  
Honey Bee ◽  
Bee Colony ◽  
Honey Bee Colony

Ascertaining an efficient eligibility cut-off for extended Medicare items for eating disorders

2021 ◽  
pp. 103985622110286
Author(s):  
Tracey Wade ◽  
Jamie-Lee Pennesi ◽  
Yuan Zhou
Keyword(s):  
Eating Disorders ◽  
Eating Disorder ◽  
False Positive Rate ◽  
Area Under The Curve ◽  
Rate Sensitivity ◽  
True Positive Rate ◽  
Eating Disorder Examination Questionnaire ◽  
Eating Disorder Examination ◽  
Positive Rate ◽  
The Relationship

Objective: Currently eligibility for expanded Medicare items for eating disorders (excluding anorexia nervosa) require a score ⩾ 3 on the 22-item Eating Disorder Examination-Questionnaire (EDE-Q). We compared these EDE-Q “cases” with continuous scores on a validated 7-item version of the EDE-Q (EDE-Q7) to identify an EDE-Q7 cut-off commensurate to 3 on the EDE-Q. Methods: We utilised EDE-Q scores of female university students ( N = 337) at risk of developing an eating disorder. We used a receiver operating characteristic (ROC) curve to assess the relationship between the true-positive rate (sensitivity) and the false-positive rate (1-specificity) of cases ⩾ 3. Results: The area under the curve showed outstanding discrimination of 0.94 (95% CI: .92–.97). We examined two specific cut-off points on the EDE-Q7, which included 100% and 87% of true cases, respectively. Conclusion: Given the EDE-Q cut-off for Medicare is used in conjunction with other criteria, we suggest using the more permissive EDE-Q7 cut-off (⩾2.5) to replace use of the EDE-Q cut-off (⩾3) in eligibility assessments.

scholarly journals Pretherapeutic Imaging for Axillary Staging in Breast Cancer: A Systematic Review and Meta-Analysis of Ultrasound, MRI and FDG PET

2021 ◽  
Vol 10 (7) ◽  
pp. 1543
Author(s):  
Morwenn Le Boulc’h ◽  
Julia Gilhodes ◽  
Zara Steinmeyer ◽  
Sébastien Molière ◽  
Carole Mathelin
Keyword(s):  
Systematic Review ◽  
Sensitivity And Specificity ◽  
Meta Analysis ◽  
False Negative ◽  
Significant Proportion ◽  
False Negative Rate ◽  
True Positive Rate ◽  
Axillary Staging ◽  
Positron Emission ◽  
Positive Rate

Background: This systematic review aimed at comparing performances of ultrasonography (US), magnetic resonance imaging (MRI), and fluorodeoxyglucose positron emission tomography (PET) for axillary staging, with a focus on micro- or micrometastases. Methods: A search for relevant studies published between January 2002 and March 2018 was conducted in MEDLINE database. Study quality was assessed using the QUality Assessment of Diagnostic Accuracy Studies checklist. Sensitivity and specificity were meta-analyzed using a bivariate random effects approach; Results: Across 62 studies (n = 10,374 patients), sensitivity and specificity to detect metastatic ALN were, respectively, 51% (95% CI: 43–59%) and 100% (95% CI: 99–100%) for US, 83% (95% CI: 72–91%) and 85% (95% CI: 72–92%) for MRI, and 49% (95% CI: 39–59%) and 94% (95% CI: 91–96%) for PET. Interestingly, US detects a significant proportion of macrometastases (false negative rate was 0.28 (0.22, 0.34) for more than 2 metastatic ALN and 0.96 (0.86, 0.99) for micrometastases). In contrast, PET tends to detect a significant proportion of micrometastases (true positive rate = 0.41 (0.29, 0.54)). Data are not available for MRI. Conclusions: In comparison with MRI and PET Fluorodeoxyglucose (FDG), US is an effective technique for axillary triage, especially to detect high metastatic burden without upstaging majority of micrometastases.

scholarly journals Classifying the Biological Status of Honeybee Workers Using Gas Sensors

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 166
Author(s):  
Jakub T. Wilk ◽  
Beata Bąk ◽  
Piotr Artiemjew ◽  
Jerzy Wilde ◽  
Maciej Siuda
Keyword(s):  
Gas Sensors ◽  
Laboratory Tests ◽  
Test Chamber ◽  
Ambient Air ◽  
True Positive Rate ◽  
True Positive ◽  
Positive Rate ◽  
Parameters Of Accuracy ◽  
Selection Of

Honeybee workers have a specific smell depending on the age of workers and the biological status of the colony. Laboratory tests were carried out at the Department of Apiculture at UWM Olsztyn, using gas sensors installed in two twin prototype multi-sensor detectors. The study aimed to compare the responses of sensors to the odor of old worker bees (3–6 weeks old), young ones (0–1 days old), and those from long-term queenless colonies. From the experimental colonies, 10 samples of 100 workers were taken for each group and placed successively in the research chambers for the duration of the study. Old workers came from outer nest combs, young workers from hatching out brood in an incubator, and laying worker bees from long-term queenless colonies from brood combs (with laying worker bee’s eggs, humped brood, and drones). Each probe was measured for 10 min, and then immediately for another 10 min ambient air was given to regenerate sensors. The results were analyzed using 10 different classifiers. Research has shown that the devices can distinguish between the biological status of bees. The effectiveness of distinguishing between classes, determined by the parameters of accuracy balanced and true positive rate, of 0.763 and 0.742 in the case of the best euclidean.1nn classifier, may be satisfactory in the context of practical beekeeping. Depending on the environment accompanying the tested objects (a type of insert in the test chamber), the introduction of other classifiers as well as baseline correction methods may be considered, while the selection of the appropriate classifier for the task may be of great importance for the effectiveness of the classification.

Sign in / Sign up

Export Citation Format

Share Document