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Fig. 6 in Biometric analysis of the teeth of fossil and Recent hexanchid sharks and its taxonomic implications
Fig. 6. Acrocone shape of lower teeth in Late Eocene (Early Lutetian) Hexanchus agassizi from south−western France. Bivariate plot constructed as in Fig. 4. Data for Recent Hexanchus species are indicated by symbols for H. nakamurai teeth but only by regression lines for H. griseus teeth. Holotypes, paratypes and figured teeth of the fossil species from the work of Ward (1979) are distinguished on the graph. Some H. microdon specimens (Paleocene − early Eocene) have been added for comparison.
Fig. 5 in Biometric analysis of the teeth of fossil and Recent hexanchid sharks and its taxonomic implications
Fig. 5. Acrocone shape of lower teeth in Recent hexanchid species. Small pictures illustrate the changes in acrocone size according to the L2/L3 values. Interrupted lines are regression curves from data for the two living Hexanchus species. For H. griseus, different symbols have been used to indicate the sex of the sharks (m, male; f, female; ind, indeterminate).
Fig. 3 in Biometric analysis of the teeth of fossil and Recent hexanchid sharks and its taxonomic implications
Fig. 3. Fossil Hexanchus sp. lower teeth from the Donzacq Formation of Saint−Géours−d'Auribat (late Ypresian/early Lutetian, south−western France). A. UMC−SG13, antero−lateral file, labial view. B. UMC−SG130, antero−lateral file, lingual view. C. UMC−SG15, lateral file, labial view. D. UMCSG14, antero−lateral file, labial view. E. UMC−SG131, lateral file, lingual view. F. UMC−SG132, antero−lateral file, lingual view. G. UMC−SG17, lateral file, labial view. H. UMC−SG133, lateral file, labial view. I. UMCSG134, broken lateral lower tooth with peculiar deep root, labial (I1) and lingual (I2) views. J. UMC−SG135, lateral file, labial view. K. UMC−SG12, symphysial file, lingual view.
Fig. 2 in Biometric analysis of the teeth of fossil and Recent hexanchid sharks and its taxonomic implications
Fig. 2. Overview of dental variations in lower teeth of living species of Hexanchus. Labial view of some lower teeth of Hexanchus griseus (A–L) and Hexanchus nakamurai (M–O). A. ♀uncertain (UMC−REC204bM),>300 cm TL; 1st left file (A), 3rd (uncertain) left file (A), and lateral left 1 2 file (A). B. ♂ (UMC−REC204M), 300 cm TL; 3rd right file (B), 1st right 3 1 file (B), symphysial file (B), and 1st left file (B). C. ♂ (UMC− 2 3 4 REC162M), 223 cm TL; 1st left file (C), 2nd right file (C), and 3rd left 1 2 file (C). D. ♂ (UMC−REC175M), 195 cm TL; 1st left file (D), 3rd left file 3 1 with enlargement on the mesial serrated cutting edge of the first cusp (D2), 4th left file (D), and 5th left file (D). E. ♀ (UMC−REC161M), 191 cm 3 4 TL; 1st left file (E) and 3rd left file (E). F. ♂ (UMC−REC611M), 1.08 cm 1 2 TL; 1st left file (F), 4th left file (F). G. ♀ (UMC−REC163M), 114 cm TL, 1 2 1st right file. H. ♀ (UMC−REC164M), 117 cm TL, 3nd left file. I. Unsexued (UMC−REC201M), 58 cm TL 2nd right file. J. Unsexued (UMCREC172M), 72 cm TL, 2nd left file. K. Unsexued (UMC−REC173M), 75 cm TL, 1st left file. L. ♂ (UMC−REC174M), 80 cm TL, 1st right file. M. ♀ uncertain (UMC−REC197M), 148 cm TL; 4th rigth file (M), 1st 1 right file with enlargement on the mesial serrated cutting edge of the first cusp (M2), symphysial file (M3). N. ♂ uncertain (UMC−REC192M), 145 cm TL; 3rd right file (N) and 1st left file (N). O. Sex undetermined 1 2 (UMC−REC196M), 1 m TL, 1st right file (O), 1st left file (O), 3rd lateral 1 2 file (O), and 4th lateral file (O). A–D and F–O are respectively at the 3 4 same magnification (vertical white bars). Abbreviation: TL, total length.
Fig. 1. A in Biometric analysis of the teeth of fossil and Recent hexanchid sharks and its taxonomic implications
Fig. 1. A. Simplified terminology of a Hexanchus lower tooth (Hexanchus griseus, 4th right file,>300 cm total length, UMC−REC204bM). B. Schematic drawing of measurements (homologuous points and intermediate distances) discussed in the text.
Data from: Enhancing the security of pattern unlock with surface EMG-based biometrics
Pattern unlock is a popular screen unlock scheme that protects the sensitive data and information stored in mobile devices from unauthorized access. However, it is also susceptible to various attacks, including guessing attacks, shoulder surfing attacks, smudge attacks, and side-channel attacks, which can achieve a high success rate in breaking the patterns. In this paper, we propose a new two-factor screen unlock scheme that incorporates surface electromyography (sEMG)-based biometrics with patterns for user authentication. sEMG signals are unique biometric traits suitable for person identification, which can greatly improve the security of pattern unlock. During a screen unlock session, sEMG signals are recorded when the user draws the pattern on the device screen. Time-domain features extracted from the recorded sEMG signals are then used as the input of a one-class classifier to identify the user is legitimate or not. We conducted an experiment involving 10 subjects to test the effectiveness of the proposed scheme. It is shown that the adopted time-domain sEMG features and one-class classifiers achieve good authentication performance in terms of the F 1 score and Half of Total Error Rate (HTER). The results demonstrate that the proposed scheme is a promising solution to enhance the security of pattern unlock.
Dataset for CardioPRINT-based Biometric Identification
<p>This repository contains ECG and ICG signals with timestamp tables (timestamps_with_neutral.csv, timestamps_without_neutral.csv) indicating the beginning and the end of each emotional state used in the paper titled " CardioPRINT: Biometric identification based on the individual characteristics derived from the cardiogram". The dataset is shared openly on the Zenodo repository with a Creative Commons Attribution 4.0 International license Additionally, the repository comprises extracted timestamps for segments that describe emotional states and feature sets for both ECG and ICG recordings. We applied a selective editing process to the signal, where segments deemed irrelevant were omitted. The remaining segments, identified as significant due to their association with changes in emotions as per the timestamp table, were concatenated. This resulted in a non-continuous signal, characterized by discontinuities at the specific timestamps where emotional shifts were noted.</p> <p>Moreover, the repository contains a Supplementary to the paper titled "<a href="https://doi.org/10.1016/j.eswa.2024.126018">CardioPRINT: Biometric identification based on the individual characteristics derived from the cardiogram</a>".</p> <p>If you find provided signals and code useful for your own research and teaching class, please cite the following references:</p> <ol> <li>Tanasković, I., Lazarević, L. B., Knežević, G., Milosavljević, N., Dubljević, O., Bjegojević, B., & Miljković, N. (2023). CardioPRINT-based Biometric Identification with Machine Learning [Computer software]. <a href="https://github.com/Luck032/CardioPRINT-based-biometric-identification-with-machine-learning">https://github.com/Luck032/CardioPRINT-based-biometric-identification-with-machine-learning</a>, <a href="https://doi.org/10.5281/zenodo.10204894">https://doi.org/10.5281/zenodo.10204894</a></li> <li>Tanasković, I., Lazarević, L. B., Knežević, G., Milosavljević, N., Dubljević, O., Bjegojević, B., & Miljković, N. (2024). CardioPRINT: Biometric identification based on the individual characteristics derived from the cardiogram. Expert Systems with Applications, 126018. <a href="https://doi.org/10.1016/j.eswa.2024.126018">https://doi.org/10.1016/j.eswa.2024.126018</a></li> <li>Bjegojević B, Milosavljević N, Dubljević O, Purić D, Knežević G. In pursuit of objectivity: Physiological Measures as a Means of Emotion Induction Procedure Validation. Empirical Studies in Psychology 2020:17.</li> <li>Tanasković, I., Lazarević, L. B., Knežević, G., Milosavljević, N., Dubljević, O., Bjegojević, B., & Miljković, N. (2023). Dataset for CardioPRINT-based Biometric Identification [Dataset]. <a href="https://doi.org/10.5281/zenodo.10204955">https://doi.org/10.5281/zenodo.1020495</a></li> </ol>
Gender and age-related differences of ccular biometric parameters in patients undergoing cataract surgery in Bosnia and Herzegovina
<p><strong>Purpose</strong>: To determine the distribution and mutual relationship of ocular biometric parameters, as well as to evaluate gender- and age-related differences in patients undergoing cataract surgery in Bosnia and Herzegovina.</p> <p><strong>Materials and methods</strong>: It was a retrospective cross-sectional study of consecutive patients who underwent cataract surgery between January 2017 and December 2021 in a tertiary care clinic. All biometric measurements were performed using the optical biometer OA-2000 (Tomey, Nagoya, Japan).</p> <p><strong>Results</strong>: The study evaluated 1278 eyes from 1278 consecutive cataract patients. The average age of all included patients was 69.4 ± 9.98 (range 40–96). A total of 672 eyes (52.58%) were from females. The mean axial length (AL), anterior chamber depth (ACD), lens thickness (LT), and mean keratometry were 23.46±1.18mm, 3.17±0.40mm, 4.54±0.48mm, 43.42±1.55D respectively. Corneal astigmatism of ≥1D, >2D and >3D was found in 33.4%, 7.8% and 2.5% patients, respectively. Females were found to have shorter AL (p<0.0001), shallower ACD (p<0.0001) and steeper corneas (p<0.0001). In both genders, AL, ACD and with-the-rule astigmatism showed a decreasing trend (p=0.0001), while keratometry, the average cylinder, and against-the-rule astigmatism showed an increasing trend (p=0.0001) with increasing age. Furthermore, in both genders, there was an increasing trend in ACD (p=0.0001), and a decreasing trend in keratometry (p=0.0001) and LT (p=0.0001) with increasing AL.</p> <p><strong>Conclusions</strong>: This study provides useful reference data on ocular biometry for cataract surgeons in Bosnia and Herzegovina. Female patients tend to have steeper corneas, shorter AL and shallower AC than males, and these differences are independent of age or AL.</p>
Gauging Ambient Environmental Carbon Dioxide Concentration Solely Using Biometric Observations: A Machine Learning Approach
<p>Data and code in form of Jupyter Notebook to accompany an unpublished paper with the title " Gauging Ambient Environmental Carbon Dioxide Concentration Solely Using Biometric Observations: A Machine Learning Approach". This work makes use of biometric variables of a participant to estimate the inhaled carbon dioxide in microenvironments and understand various physiological and cognitive responses. </p><p>Github link: <a href="https://github.com/mi3nts/Estimate-CO2">mi3nts/Estimate-CO2: Data and code to estimate inhaled CO2 (github.com)</a></p>
Figure 3 in FlorAl biometrics And phenologicAl chArActeriZAtion of flowering And fruiting of the passion fruit PAssiflorA TrinTAE in southwestern BAhiA, BrAZil
Figure 3. Flowers of Passiflora trintae in the sampling site.
Figure 2 in Morphological and biometrical comparisons of the baculum in the genus Nannospalax Palmer, 1903 (Rodentia: Spalacidae) from Turkey with consideration of its taxonomic importance
Figure 2. Morphometric variation among the species of the 6 measurements taken from the baculum.
Figure 3 in Preliminary report of a biometric analysis of greater pipefish Syngnathus acus Linnaeus, 1758 for the western Black Sea
Figure 3. Length–weight relationship of S. acus from catches in the western Black Sea.
Figure 2 in Preliminary report of a biometric analysis of greater pipefish Syngnathus acus Linnaeus, 1758 for the western Black Sea
Figure 2. Diagram of morphometric measurements of pipefishes.
Figure 1 in Preliminary report of a biometric analysis of greater pipefish Syngnathus acus Linnaeus, 1758 for the western Black Sea
Figure 1. Sampling stations.
Revolutionizing Biometric Security
<p><span><span>The illustrated study delves further into the topic of deep learning in fingerprint recognition, focusing on writing dated between 2019 and 2024. Key examples and disclosures were discovered through rigorous steps of inspection, approval, examination, extraction, and blending, revealing insights into the feasibility and advancements in this sector. With 22 studies demonstrating its proficiency in tackling the complexities of fingerprint authentication tasks, Convolutional Neural Networks (CNN) emerged as a significant focal point. Particularly, CNNs' capacity to process and analyse image data with exceptional accuracy, ensuring robust performance even in challenging conditions, demonstrated their adaptability and effectiveness. LSTM-RNN and Support Vector Machines (SVM) also showed a lot of utility, highlighting different ways to deal with authentication issues. Notwithstanding periodic assessment challenges, the chose articles exhibited importance by laying out clear goals, utilizing sound exploration philosophies, and yielding exhaustive discoveries. Information blend uncovered obvious proof of the viability of profound learning approaches in unique mark confirmation, especially CNN-based models, with great execution measurements including critical AUC values going from 83% to 86.6%. Extraordinarily, CNN outflanked elective strategies as far as exactness and mistake rates while managing complex models like electromyogram (EMG) signals. Since they vow to improve precision, proficiency, and security across a great many applications and spaces, these discoveries by and large require the far-reaching execution of cutting-edge learning methods in biometric validation frameworks not long from now. By encouraging innovation in biometric authentication technologies, this research contributes to the achievement of Sustainable Development Goal 9 objectives for resilient infrastructure and inclusive societies. </span></span><span><span> </span></span></p>
Migration statistics and animal biometrics for mule deer that migrated long-distances (2011–2020), Wyoming, USA
<p>Billions of animals migrate to track seasonal pulses in resources. Optimally timing migration is a key strategy, yet the ability of animals to compensate for phenological mismatches en route is largely unknown. We studied a population of mule deer (<em>Odocoileus hemionus</em>) in Wyoming that lack reliable cues on their desert winter range, causing them to start migration 70 days ahead to 52 days behind the wave of spring green-up. By adjusting movement speed and stopover use, however, individual deer arrive at the summer range within an average 6-day window. Late migrants move 2.5 times faster and spend 72% less time on stopovers than early migrants, which allows them to catch the green wave. Ungulates, and potentially other migratory species, possess cognitive abilities to recognize where they are in space and time relative to key resources. Such behavioral capacity may allow migratory taxa to maintain foraging benefits amid rapidly changing phenology.</p>
Clinical Trial Intelligent Biometrics for PTSD - Clinical Trial
ClinicalTrials.gov study NCT04471207. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Migration statistics and animal biometrics for mule deer that migrated long-distances (2011–2020), Wyoming, USA
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Data from: Enhancing the security of pattern unlock with surface EMG-based biometrics
Open the record for dataset details and reuse information.
Gender and age-related differences of ccular biometric parameters in patients undergoing cataract surgery in Bosnia and Herzegovina
Open the record for dataset details and reuse information.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.