Native black poplar in the UK is a rare and endangered tree species that has been heavily reproduced vegetatively, leading to issues of widespread clonal replication. This data presents the DNA fingerprints from a panel of 7 microsats that are used to characterise unique clones for entry in the FRM National Register of Basic Material. Further information is available at http://www.forestry.gov.uk/frm Attribution statement:

PRINTS is a compendium of protein fingerprints. A fingerprint is a group of conserved motifs used to characterise a protein family or domain. PRINTS is based at the University of Manchester, UK.

Report contains data that can be used under Open Government Licence.

This project aims to develop techniques and a reference data library that will enable verification of the declared origin of imported timbers that are protected under the Convention on International Trade in Endangered Species of wild flora and fauna (CITES) The project will combine existing genetic and new isotopic information (e.g. carbon, sulphur and oxygen) into an enhanced tool to determine the provenance of timber. It will use some specially sourced reference samples and build on research done by Kew to determine ‘ fingerprints’ of tree species and the country of growth using DNA finger printing and population modelling, as well a statistical analysis and it will extend this capability by including the isotopic analysis- which relate to specific conditions where the tree was growing. Outputs include the final report on the success of the techniques as well as the data base and referenced samples that may be used to influence the international community for enhanced controls on CITES timber exports and imports.

EPSRC grant on Tactile Superresolution Sensing (EP/M02993X/1) The following data was obtained from 3 variants of the TacTip (with fingerprint, with fingerprint and cores, without fingerprint), an optical tactile sensor integrated on a 6-dof robotic arm The sensor performed localization on 9 stimuli with varying spatial frequency over a 30 mm range. The data was used to demonstrate the effect of fingerprints on tactile spatial perception.

The Europe Enterprise Biometrics Market is experiencing robust growth, projected to reach $1.26 billion in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 15.79% from 2025 to 2033. This expansion is fueled by several key factors. Increasing security concerns across various industries, coupled with the rising adoption of smart technologies and the demand for streamlined access control systems, are major drivers. The shift towards contactless authentication methods, driven by the post-pandemic hygiene focus, significantly boosts the market for non-contact biometric solutions like facial and iris recognition. Furthermore, the increasing integration of biometrics into time and attendance systems across businesses of all sizes contributes to market growth. Government regulations promoting data security and privacy also indirectly support market expansion by encouraging the adoption of secure biometric authentication. Specific growth within the market will be seen in multi-factor authentication solutions which offer increased security compared to single-factor methods. The market is segmented by authentication type (single-factor and multi-factor), contact type (contact and non-contact), product type (voice, facial, fingerprint, vein, and iris recognition), and application (door security, building access, and time and attendance). The leading players in the European market are leveraging technological advancements and strategic partnerships to gain a competitive edge. The significant growth trajectory is expected to continue throughout the forecast period (2025-2033), with multi-factor authentication and non-contact solutions anticipated to lead the growth segments. The strong presence of key players, coupled with ongoing technological innovations such as improved accuracy and speed of biometric systems, will continue to propel the market forward. However, concerns surrounding data privacy and security remain significant challenges. To mitigate these concerns, stringent data protection regulations and the adoption of robust security protocols are crucial for sustained market growth. The market’s success hinges on maintaining consumer trust by prioritizing ethical data handling and ensuring user privacy. Regions within Europe, such as the United Kingdom and Germany, are expected to lead market growth due to their strong technological infrastructure and relatively high adoption rates of advanced security systems. Recent developments include: October 2023: Global technology and security provider Thales unveiled its latest innovation: the SafeNet IDPrimeFIDO Bio Smart Card. This advanced security key is designed to bolster enterprise security through multi-factor authentication (MFA). Unlike traditional methods, this contactless smart card leverages fingerprints for swift and secure access to enterprise devices, applications, and cloud services., August 2023: HID Global Corporation announced a strategic partnership with GhelamcoPoland to provide mobile and physical access control solutions for high-profile projects in Warsaw, Poland. Ghelamco's physical access control strategy includes HID mobile access solutions and door readers to enhance secure access and provide maximum flexibility, ease of management, and upgrades across its four flagship locations in the capital.. Key drivers for this market are: Technological Advancements in The Field of Time and Attendance Systems. Potential restraints include: Technological Advancements in The Field of Time and Attendance Systems. Notable trends are: The Rising Demand for Safety and Security Measures in Organizations is Anticipated to Support Market Growth.

Docetaxel resistance presents a significant obstacle in the treatment of prostate cancer (PCa), resulting in unfavorable patient prognoses. Intratumoral heterogeneity, often associated with epithelial-to-mesenchymal transition (EMT), has previously emerged as a phenomenon facilitating adaptations to various stimuli, thus promoting cancer cell diversity and eventually resistance to chemotherapy, including docetaxel. Hence, comprehending intratumoral heterogeneity is essential for better patient prognosis and the development of personalized treatment strategies. To address this, we employed a high-throughput single-cell flow cytometry approach to identify a specific surface fingerprint associated with docetaxel-resistance in PCa cells and complement it with protein composition analysis of extracellular vesicles We further performed validation of selected antigens using docetaxel-resistant patient-derived xenografts in vivo and determine a 6-molecule surface fingerprint associated with docetaxel resistance in primary PCa specimens. Remarkably, we observed consistent overexpression of CD95 and SSEA-4 surface antigens in both in vitro and in vivo docetaxel-resistant models and in a cell subpopulation of primary PCa tumors exhibiting EMT features. Furthermore, CD95, along with the essential enzymes involved in SSEA-4 synthesis, ST3GAL1, and ST3GAL2, displayed a significant increase in PCa patients undergoing docetaxel-based therapy, correlating with poor survival outcomes. In conclusion, we demonstrate that the identified 6-molecule surface fingerprint associated with docetaxel resistance pre-exists in a subpopulation of primary PCa tumors before docetaxel treatment. This fingerprint, therefore, deserves further validation as a promising tool for predicting docetaxel response in PCa patients before therapy initiation.

The majority of Antarctic lichens produce sexual organs, and in many species sexual ascospores appear to be the only reproductive propagule. However, it is unknown whether sexual reproduction involves selfing (homothallism) or outcrossing (heterothallism). To investigate this issue we have established axenic cultures of sexual progeny in order to generate DNA fingerprints and thereby determine the breeding system.

Description: Acoustic Index and AudioSet Fingerprint data quantified derived from audio recorded between the 26th of February and the 2nd March 2019.The original raw audio was compressed, shortened and temporally subset to replicate common inconsistencies in ecoacoustic studies. This data frame show how this experimental variation affects how soundscapes are quantified by Analytical Indices and the AudioSet Fingerprint Project: This dataset was collected as part of the following SAFE research project: 3D Acoustics for Audio Monitoring of Rainforest Biodiversity Funding: These data were collected as part of research funded by:

NERC (NERC QMEE CDT Studentship, NE/P012345/1, http://gotw.nerc.ac.uk/list_full.asp?pcode=NE%2FP012345%2F1&cookieConsent=A) This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.

XML metadata: GEMINI compliant metadata for this dataset is available here Files: This consists of 1 file: Ecoacoustic_Method_Comparison.xlsx Ecoacoustic_Method_Comparison.xlsx This file contains dataset metadata and 2 data tables:

Analytical index values under differing experimental conditions (described in worksheet Analytical_Index_Data) Description: This dataset contains all the analytical indices derived from audio under different experimenatal conditions Number of fields: 16 Number of data rows: 87211 Fields:

id.no: Sample ID (Field type: id) file.size: File size as a % of uncompressed (Field type: numeric) compression: Compression level (Mp3) (Field type: ordered categorical) frame.size: Frame size (recording length) (Field type: ordered categorical) site: Field Site Location (Field type: location) req.freq: Recording Frequency (Field type: numeric) date: Date (Field type: date) time: Time ID (including subsamples) (Field type: id) max.freq: Nyquist (Maximum) frequency (Field type: numeric) ACI: Acoustic Complexity Index (Field type: numeric) ADI: Acoustic Diversity Index (Field type: numeric) Aeev: Acoustic Eveness (Field type: numeric) Bio: Biodiversity Index (Field type: numeric) H: Acoustic Entropy (Field type: numeric) M: Median of Acoustic Envelope (Field type: numeric) NDSI: Normalised Difference Soundscape Index (Field type: numeric)

AudioSet Fingerprint values under differing experimental conditions (described in worksheet AudioSet_Fingerprint_Data) Description: This dataset contains all the audioset fingerprint values derived from audio under different experimenatal conditions Number of fields: 137 Number of data rows: 87329 Fields:

id.no: Sample ID (Field type: id) file.size: File size as a % of uncompressed (Field type: numeric) frame.size: Frame size (recording length) (Field type: ordered categorical) compression: Compression level (Mp3) (Field type: ordered categorical) site: Field Site Location (Field type: location) req.freq: Recording Frequency (Field type: numeric) date: Date (Field type: date) time: Time ID (including subsamples) (Field type: id) max.freq: Nyquist (Maximum) frequency (Field type: numeric) feat1: Feature 1 (Field type: numeric) feat2: Feature 2 (Field type: numeric) feat3: Feature 3 (Field type: numeric) feat4: Feature 4 (Field type: numeric) feat5: Feature 5 (Field type: numeric) feat6: Feature 6 (Field type: numeric) feat7: Feature 7 (Field type: numeric) feat8: Feature 8 (Field type: numeric) feat9: Feature 9 (Field type: numeric) feat10: Feature 10 (Field type: numeric) feat11: Feature 11 (Field type: numeric) feat12: Feature 12 (Field type: numeric) feat13: Feature 13 (Field type: numeric) feat14: Feature 14 (Field type: numeric) feat15: Feature 15 (Field type: numeric) feat16: Feature 16 (Field type: numeric) feat17: Feature 17 (Field type: numeric) feat18: Feature 18 (Field type: numeric) feat19: Feature 19 (Field type: numeric) feat20: Feature 20 (Field type: numeric) feat21: Feature 21 (Field type: numeric) feat22: Feature 22 (Field type: numeric) feat23: Feature 23 (Field type: numeric) feat24: Feature 24 (Field type: numeric) feat25: Feature 25 (Field type: numeric) feat26: Feature 26 (Field type: numeric) feat27: Feature 27 (Field type: numeric) feat28: Feature 28 (Field type: numeric) feat29: Feature 29 (Field type: numeric) feat30: Feature 30 (Field type: numeric) feat31: Feature 31 (Field type: numeric) feat32: Feature 32 (Field type: numeric) feat33: Feature 33 (Field type: numeric) feat34: Feature 34 (Field type: numeric) feat35: Feature 35 (Field type: numeric) feat36: Feature 36 (Field type: numeric) feat37: Feature 37 (Field type: numeric) feat38: Feature 38 (Field type: numeric) feat39: Feature 39 (Field type: numeric) feat40: Feature 40 (Field type: numeric) feat41: Feature 41 (Field type: numeric) feat42: Feature 42 (Field type: numeric) feat43: Feature 43 (Field type: numeric) feat44: Feature 44 (Field type: numeric) feat45: Feature 45 (Field type: numeric) feat46: Feature 46 (Field type: numeric) feat47: Feature 47 (Field type: numeric) feat48: Feature 48 (Field type: numeric) feat49: Feature 49 (Field type: numeric) feat50: Feature 50 (Field type: numeric) feat51: Feature 51 (Field type: numeric) feat52: Feature 52 (Field type: numeric) feat53: Feature 53 (Field type: numeric) feat54: Feature 54 (Field type: numeric) feat55: Feature 55 (Field type: numeric) feat56: Feature 56 (Field type: numeric) feat57: Feature 57 (Field type: numeric) feat58: Feature 58 (Field type: numeric) feat59: Feature 59 (Field type: numeric) feat60: Feature 60 (Field type: numeric) feat61: Feature 61 (Field type: numeric) feat62: Feature 62 (Field type: numeric) feat63: Feature 63 (Field type: numeric) feat64: Feature 64 (Field type: numeric) feat65: Feature 65 (Field type: numeric) feat66: Feature 66 (Field type: numeric) feat67: Feature 67 (Field type: numeric) feat68: Feature 68 (Field type: numeric) feat69: Feature 69 (Field type: numeric) feat70: Feature 70 (Field type: numeric) feat71: Feature 71 (Field type: numeric) feat72: Feature 72 (Field type: numeric) feat73: Feature 73 (Field type: numeric) feat74: Feature 74 (Field type: numeric) feat75: Feature 75 (Field type: numeric) feat76: Feature 76 (Field type: numeric) feat77: Feature 77 (Field type: numeric) feat78: Feature 78 (Field type: numeric) feat79: Feature 79 (Field type: numeric) feat80: Feature 80 (Field type: numeric) feat81: Feature 81 (Field type: numeric) feat82: Feature 82 (Field type: numeric) feat83: Feature 83 (Field type: numeric) feat84: Feature 84 (Field type: numeric) feat85: Feature 85 (Field type: numeric) feat86: Feature 86 (Field type: numeric) feat87: Feature 87 (Field type: numeric) feat88: Feature 88 (Field type: numeric) feat89: Feature 89 (Field type: numeric) feat90: Feature 90 (Field type: numeric) feat91: Feature 91 (Field type: numeric) feat92: Feature 92 (Field type: numeric) feat93: Feature 93 (Field type: numeric) feat94: Feature 94 (Field type: numeric) feat95: Feature 95 (Field type: numeric) feat96: Feature 96 (Field type: numeric) feat97: Feature 97 (Field type: numeric) feat98: Feature 98 (Field type: numeric) feat99: Feature 99 (Field type: numeric) feat100: Feature 100 (Field type: numeric) feat101: Feature 101 (Field type: numeric) feat102: Feature 102 (Field type: numeric) feat103: Feature 103 (Field type: numeric) feat104: Feature 104 (Field type: numeric) feat105: Feature 105 (Field type: numeric) feat106: Feature 106 (Field type: numeric) feat107: Feature 107 (Field type: numeric) feat108: Feature 108 (Field type: numeric) feat109: Feature 109 (Field type: numeric) feat110: Feature 110 (Field type: numeric) feat111: Feature 111 (Field type: numeric) feat112: Feature 112 (Field type: numeric) feat113: Feature 113 (Field type: numeric) feat114: Feature 114 (Field type: numeric) feat115: Feature 115 (Field type: numeric) feat116: Feature 116 (Field type: numeric) feat117: Feature 117 (Field type: numeric) feat118: Feature 118 (Field type: numeric) feat119: Feature 119 (Field type: numeric) feat120: Feature 120 (Field type: numeric) feat121: Feature 121 (Field type: numeric) feat122: Feature 122 (Field type: numeric) feat123: Feature 123 (Field type: numeric) feat124: Feature 124 (Field type: numeric) feat125: Feature 125 (Field type: numeric) feat126: Feature 126 (Field type: numeric) feat127: Feature 127 (Field type: numeric) feat128: Feature 128 (Field type: numeric) Date range: 2019-02-26 to 2019-06-02 Latitudinal extent: 4.6644 to 4.7027 Longitudinal extent: 117.5351 to 117.5914

The main entity of this document is a structure with accession number 3rku

Stable and commonly bioactive cysteine-rich peptides (CRPs) have are typically discovered via mass shift analysis. However, the accurate assessment of unique CRP species in a given botanical species is often challenged by same mass species, post-translational modifications or modifications derived from sample handling, and incomplete MS2 fragmentation. Mass spectral fingerprint ions can be leveraged to gain additional information about a mass species prior to full sequence characterization and with only poor quality MS2 spectra. Herein we identify sets of mass spectral fingerprint ions characteristic of the CRP cyclotide family, which may indicate a mass belongs to a specific cyclotide subfamily, and “tell-tale” ions that are of importance when discriminating putative cyclotide species, including common oxidation and over-alkylation ions observed experimentally. Cyclotide-containing V. communis material is used as proof-of-principle, where experimental cyclotide fingerprint ions are explored. Fingerprint ions derived from a third type of CRP, the trypsin inhibitors, are assessed in the gourd L. siceraria. Combining mass shift analysis with the identification of prominent MS2 fingerprint ions is then used to identify three novel CRPs. We demonstrate that abundant mass spectral fingerprint ions can be used to quickly discern masses of interest in complex matrices and masses that are already characterized, aiding prioritization of the most promising novel mass species in a natural product sample for characterization.

MATLAB source files for the published figures. They contain all the data in extractable and readable form. See https://uk.mathworks.com/matlabcentral/answers/100687-how-do-i-extract-data-from-matlab-figuresNote the amended version (indicated by _corr at the end of the name) of Figure 1E and 1F (change in the unit of the z-axis, from K/year to K/day).Additional data are included for the PLASIM model runs. We include data from 1 of the 40 simulations with instantaneous CO2 increase. These are used for computing the Green's functions for the surface temperature field. We also include data from 1 of the 40 simulations with 1% annual increase of CO2 up to doubling used for performing the fingerprinting exercise. The theory is detailed in the text.Further details and data (storage constraints on figshare makes it impossible to upload the data from the 80 simulations) are available from the corresponding author Valerio Lucarini (v.lucarini@leicester.ac.uk).

Snapshot img

Biometric Access Control Systems Market Size 2025-2029

The biometric access control systems market size is forecast to increase by USD 6.41 billion at a CAGR of 8.4% between 2024 and 2029.

The market is driven by the increasing need for advanced surveillance and security measures in various sectors, including corporate offices, healthcare facilities, and educational institutions. This demand is fueled by the desire to enhance security and streamline access control processes, leading to significant market growth. Machine learning, artificial intelligence (AI), and deep learning enhance security and user experience (UX). However, high installation and maintenance costs associated with biometric access control systems pose a significant challenge for market expansion.
Companies must navigate these costs to effectively compete and capitalize on the market's potential. To remain competitive, organizations must explore cost-effective solutions, such as cloud-based systems, and focus on providing value-added services to offset the initial investment. The market's continuous dynamism is reflected in the evolving patterns of integration platforms and the adoption of cloud security solutions. By addressing these challenges and leveraging the latest technologies, market players can seize opportunities and thrive in the evolving biometric access control systems landscape.

What will be the Size of the Biometric Access Control Systems Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free Sample

The market is experiencing significant growth, driven by the increasing demand for advanced security solutions. Access control policies are becoming more complex, leading to the adoption of multi-factor authentication systems, including proximity readers and biometric readers. Privacy policies are a top concern, with vulnerability management and penetration testing playing crucial roles in mitigating risks. Encryption algorithms, directory services, and disaster recovery plans are essential components of robust security frameworks. Two-factor authentication and authorization servers enhance security by adding an extra layer of protection. Data masking, incident response, and user provisioning are essential for maintaining data privacy and ensuring regulatory compliance. Access control panels, business continuity, and password management are critical for maintaining smooth operations.

Blockchain technology, smart cards, and identity providers offer new possibilities for secure access control. Biometric keypads and authentication servers provide added convenience and security. Compliance frameworks, such as HIPAA and PCI-DSS, continue to shape the market. Cost is a critical consideration when selecting a biometric recognition system, such as multi-factor authentication or multimodal biometric systems. Security policies and audits are essential for maintaining the integrity of access control systems. Service providers play a vital role in implementing and managing these complex systems, offering expertise in areas such as encryption, incident response, and user de-provisioning. Overall, the market is dynamic and evolving, with a focus on advanced security solutions and regulatory compliance.

How is this Biometric Access Control Systems Industry segmented?

The biometric access control systems industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Application

  Commercial
  Residential


End-user

  Government and defence
  Manufacturing
  BFSI
  Transportation
  Others


Technology

  Fingerprint recognition
  Facial recognition
  Iris recognition
  Voice recognition
  Palm vein recognition


Mobility Type

  Fixed
  Portable


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Application Insights

The Commercial segment is estimated to witness significant growth during the forecast period. Biometric access control systems have become an integral part of modern security infrastructure, offering enhanced security and convenience. These systems use various biometric sensors, such as fingerprint, facial recognition, iris scanning, and voice recognition, for user authentication. The user interface is designed to be intuitive and user-friendly, ensuring a seamless experience for authorized personnel. Liveness detection technology is employed to prevent unauthorized access through fake biometric data. Database management and data encryption ensure the security of user information, while multi-fac