Fingerprinting Technique Research Articles

Soil erosion and sediment sourcing in the Hyrcanian forests, Northern Iran: an integration approach of the G2loss model and sediment fingerprinting technique

Soil erosion and sediment sourcing in the Hyrcanian forests, Northern Iran: an integration approach of the G2loss model and sediment fingerprinting technique

Molecular characterization of multidrug resistant (MDR) clinical isolates of Pseudomonas aeruginosa from Nsukka, South Eastern Nigeria.

P. aeruginosa is an important nosocomial pathogen with increasing resistance to antibiotics. Objective: This study was performed to evaluate the genetic relatedness of MDR clinical isolates of P. aeruginosa. A total of 1000 samples were analysed in the study. Antibiotic resistance profiles of the isolates were determined using Kirby Bauer disk diffusion method. Polymerase chain reaction (PCR) and sequencing were simultaneously used to detect the consensus region of 16S rRNA. Genetic relatedness of the isolates was determined using restriction patterns from ALU 1 digest and random amplified polymorphic DNA. Out of the 192 P. aeruginosa isolates recovered, 136 (78.83%) were multidrug resistant. Sequence analysis of the confirmed isolates (80.68%) revealed that all the isolates shared homology with each other and also showed sequence similarity to known strains of P. aeruginosa (ATCC 27853; KT 315654; KU 321274 and KT894767). The PCR-Restriction fragment length polymorphism (PCR-RFLP) analysis revealed that there was a lot of genetic relatedness among the isolates. The RFLP finger printing technique detected seven distinct RFLP types among the isolates. Thus, study shows that there is high prevalence of MDRPA and high degree of genetic relatedness among the MDRPA isolates circulating in Nsukka area.

Information Fusion for 5G IoT: An Improved 3D Localisation Approach Using K-DNN and Multi-Layered Hybrid Radiomap

Indoor positioning is a core enabler for various 5G identity and context-aware applications requiring precise and real-time simultaneous localisation and mapping (SLAM). In this work, we propose a K-nearest neighbours and deep neural network (K-DNN) algorithm to improve 3D indoor positioning. Our implementation uses a novel data-augmentation concept for the received signal strength (RSS)-based fingerprint technique to produce a 3D fused hybrid. In the offline phase, a machine learning (ML) approach is used to train a model on a radiomap dataset that is collected during the offline phase. The proposed algorithm is implemented on the constructed hybrid multi-layered radiomap to improve the 3D localisation accuracy. In our implementation, the proposed approach is based on the fusion of the prominent 5G IoT signals of Bluetooth Low Energy (BLE) and the ubiquitous WLAN. As a result, we achieved a 91% classification accuracy in 1D and a submeter accuracy in 2D.

DNA barcodes, ISSR, RAPD and SCAR markers as potential quality control tools for molecular authentication of black and white mulberry

Morus species, especially black mulberry (Morus nigra L.), are well known for their nutritive and economic values. Black mulberry is often a subject for deliberate and undeliberate substitution with the morphologically similar white mulberry (Morus alba L.). In the current study, the genetic diversity of Morus nigra L. and Morus alba L. was assessed using different genetic profiling and fingerprinting techniques namely namely DNA barcoding, Random Amplified Polymorphic DNA (RAPD) and Inter Simple Sequence Repeat (ISSR). DNA barcoding was implemented using 2 plastid loci (matK, and rbcL-1) and 2 nuclear spacers (ITS and ITS2) aiming at accurate authentication of the studied species. DNA barcoding, targeting the selected loci succeeded to identify and authenticate the studies Morus species but failed to differentiate them, owing to their close genetic resemblance. Out of 52 screened RAPD primers, 27 were able to produce reproducible and clear profiles with 115 amplified fragments of which 37 were polymorphic accounting for 32.17% polymorphism. The number of amplified bands varied from 2 to 7 ranging in size from 100 to 700 bp. Moreover, 55 ISSR primers were evaluated, 108 fragments were produced, using 20 primers, of which 28 were polymorphic accounting for 25.92% polymorphism with size range of 200–800 bp. Sequence Characterized Amplified Region (SCAR) marker was developed based on reproducible banding pattern obtained by RAPD primer (OPG-03), for the purpose of accelerating the initial screening of Morus batches and enabling the preventative rejection of adulterated samples. The polymorphic band obtained was sequenced and specific primers were designed to develop a SCAR marker of approximately 558 bp exclusively for M. nigra, allowing detection of down to 1 ng, of M. nigra. The SCAR marker showed 100% success in differentiating authentic M. nigra and M. alba samples. In addition, the application of the developed SCAR marker was extended to distinguish M. nigra from its four common adulterants namely Ficus carica L., Fragaria vesca L., Ficus racemosa L. and Vitis vinifera L.. The proposed protocol was successfully implemented for the evaluation of 47 commercial M. nigra samples collected from different Egyptian locations.

Exploring Suspended Sediment Dynamics Using a Novel Indexing Framework Based on X‐Ray Diffraction Spectral Fingerprinting

AbstractUnderstanding the origins and hotspots of suspended sediments is crucial for safeguarding the biotic communities and for management of water quality in river‐reservoir systems. This study is an attempt to develop a new analytical framework to deconvolve the suspended sediment sources contribution using a novel index (i.e., erosion susceptibility index, ESI). The ESI is computed using sediment fingerprinting information, sediment yield, and the areal coverage of the sediment sources (land use). We have used X‐ray diffraction (XRD) and Partial least square regression (PLSR) based spectral fingerprinting technique to apportion the land use classes as sources of sediments in a representative catchment of Chotanagpur Plateau (CP), India (Konar reservoir catchment). PLSR model on the XRD spectra has performed well with R2 > 0.88 and identified agricultural areas as the major contributor of suspended sediments by delivering 28%–43%, varying seasonally (in the period of 2018–2019). However, based on the ESI values barren lands and human settlements were found to be the most crucial land use classes (with highest ESI value of 1.31 and 1.08 respectively) in terms of sediment generation (22%–29% and 14%–23% respectively) with only 14% and 12% areal coverage. The results have demonstrated that the state of critical conservation urgency of different land uses can be quantified more effectively when combining spectral tracers based knowledge with ESI.

Gotta Catch ’em All: A Multistage Framework for Honeypot Fingerprinting

Honeypots are decoy systems that lure attackers by presenting them with a seemingly vulnerable system. They provide an early detection mechanism as well as a method for learning how adversaries work and think. However, over the past years, several researchers have shown methods for fingerprinting honeypots. This significantly decreases the value of a honeypot; if an attacker is able to recognize the existence of such a system, they can evade it. In this article, we revisit the honeypot identification field, by providing a holistic framework that includes state-of-the-art and novel fingerprinting components. We decrease the probability of false positives by proposing a rigid multi-step approach for labeling a system as a honeypot. We perform extensive scans covering 2.9 billion addresses of the IPv4 space and identify a total of 21,855 honeypot instances. Moreover, we present several interesting side findings such as the identification of around 355,000 non-honeypot systems that represent potentially misconfigured or unpatched vulnerable servers (e.g., SSH servers with default password configurations and vulnerable versions). We ethically disclose our findings to network administrators about the default configuration and the honeypot developers about the gaps in implementation that lead to possible honeypot fingerprinting. Last, we discuss countermeasures against honeypot fingerprinting techniques.

Quantifying the contributions of natural and anthropogenic dust sources in Shanxi Province, northern China

Dust storms have direct or indirect impacts on climate change and human health. Identifying and quantifying natural/anthropogenic dust sources can facilitate effective prevention and control of dust events. Based on surface real-time PM10 monitoring data, satellite remote sensing and the HYSPLIT model, this study determined the specific timing, coverage and sources of dust events in Shanxi Province, Northern China. Thus, a composite fingerprinting technique was established to quantify potential dust sources and dust contributions of single dust events. The dust oxidation model was validated, indicating that the composite fingerprinting technique was well suited to the study region. The results show that natural dust sources (67%) contributed more to the study region than anthropogenic dust sources. They were mainly from the northwest and north of the study region. Particularly, the contributions of Taiyuan (TY) and Linfen (LF) accounted for the largest (82%) and smallest (55%) proportions, respectively, both exceeding 50%. Anthropogenic dust sources contributed 33%, mainly from the east and south of the study region. The contribution of anthropogenic dust sources increased in the study region from north to south. In terms of potential dust sources, the Tengger Desert and Badain Jaran Desert (TDBD) contributed the most (26%), followed by the Otindag Sandy Land (OL) (22%). The Taklimakan Desert (TD) contributed the least (2%). The Middle Farmland region of the Hexi Corridor (HMF) in the west (15%) had the largest proportion of anthropogenic dust sources. Differences in the regional contribution of potential dust sources mainly resulted from winter winds, surface drought severity and particle size. At an insignificant distance from the study region, the contribution of potential dust sources was larger in the west than in the east and increased from south to north overall. These methods and findings can contribute to improving the ecological environment in Northern China.

A short review on recent developments in the computational techniques (2021) to mitigate SARS-CoV-19 disease

Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2) or SARS Corona Virus 2019 (SARS-CoV-19) disease, which was first reported in December 2019 in China, affected nearly all countries of the world with 65,19,18,402 confirmed cases and causing 66,56,601 deaths worldwide till 12th December 2022. No specific drug has been discovered till now because the discovery of effective and reliable drug requires prolonged research and clinical trials. The efforts to discover treatment against Corona Virus Disease 2019 (CoVID-19) have been expedited by the techniques like high-throughput screening, bioinformatics and cheminformatics revealing molecular pathogenesis of coronaviruses. The application of multidisciplinary studies like computational chemistry, drug repurposing, protein-binding, nano-QSAR, fingerprint techniques have offered invaluable information about atomic viral host receptor interaction, biochemical basis of infection, strains evolution, identification of important viral protein for host-ligand binding studies. In the present review, we discuss the role of various techniques like Quantitative Structure Activity Relationship (QSAR), Machine Learning (ML), Deep Learning (DL), Virtual Screening (VS), Drug repurposing (DR), Molecular Dynamics (MD), nano-QSAR, docking study, Artificial Intelligence (AI) and Language Models (ML) in the process of potent drug/vaccine development against coronavirus. This review is an effort to summarize the reported database and tools for computational studies by bringing together resources in the public domain with respect to structure and pathophysiology of the corona virus. The work will offer an insight in prediction of therapeutics of the coronavirus disease.

Dual research on the quality and efficacy of antiviral oral liquid based on fingerprint techniques and chemometric analysis combined with network pharmacology

Antiviral oral liquid (AOL), which had antipyretic, antiviral, and anti-inflammatory pharmacological effects, was currently used to treat Coronavirus Disease 2019 (COVID-19). However, the mechanism of action was still unclear. Additionally, there had long been a lack of a comprehensive quality assessment and in vitro antioxidant activity study. The aim was to construct fingerprints of AOL and conduct the fingerprint-efficacy relationship study, as well as to investigate the mechanism of action of AOL in treating COVID-19. Firstly, four fingerprints of AOL were established using HPLC, GC, UV, and electrochemical analysis methods. Then, the quality consistency of AOL was evaluated qualitatively and quantitatively using the average linear quantitative fingerprint method and chemometric method. In addition, the fingerprint efficacy relationship between the fingerprint peaks of AOL and the antioxidant activity measured by DPPH was constructed using the OPLS model, while Pearson's correlation coefficient was used to explore the relationship between the fingerprint peaks and electrochemical analysis. Finally, network pharmacology and molecular docking were used to study AOL's potential active substances, targets, and mechanisms to treat COVID-19. The 22 samples were divided into different grades. AOL showed good antioxidant predictive power, providing a favorable direction for finding and predicting bioactive compounds and potential UV wavelengths. 100 compounds and 263 potential targets were identified, providing a theoretical basis for subsequent clinical studies. This paper investigated AOL in two dimensions of quality consistency and efficacy, providing a new way of thinking for other herbal compound preparations to achieve dual control of quality and effectiveness.

COMBINED METHOD FOR PROTECTING USER PRIVACY WHEN USING A WEB BROWSER

The article examines the issue of digital tracking, which allows users to be identified using cookies and digital fingerprints. A comparative analysis of current web tracking methods shows that approaches based on detecting and blocking web trackers are less effective than methods using value substitution. These approaches do not protect against passive fingerprinting techniques and are easily detected, making them vulnerable to user identification. To solve this problem, a hybrid method of forging transmitted data is proposed, including random generation and data fusion, as well as detecting trackers for immediate protection. Attributes with a wide range of possible values and complex compatibility are proposed to be replaced by real values with minor distortions.

Quality control of camellia oil based on HPLC fingerprinting techniques combined with chemometric methods for triglycerides

AbstractTriglycerides (TAGs) play an important role in the physiological function of vegetable oils. Camellia oil has a variety of good physiological functions. Therefore, a qualitative and quantitative method for the determination of TAGs in camellia oil by high‐performance liquid chromatography‐diode array detector/electro spray ionization mass spectrometry was developed. The 16 TAGs that separated were identified and quantified by peak‐area normalization. In addition, the major TAGs in camellia oil were OOO (25.25%), OLO (20.72%), POL (9.23%), OLL (8.22%), and OOP (8.22%). Moreover, the content of oleic acid in camellia oil was 81.74%. Using fingerprint techniques, 15 common peaks in 12 samples were selected as characteristic peaks to evaluate the differences in the composition of camellia oil samples. In addition, the samples were further classified by chemometric methods. It is shown that the camellia oil samples with high similarity from two cities in the main producing areas of Zhejiang province could be further classified according to the planting areas by the combination of chromatographic fingerprints and chemometric methods.

Recent Advances and Perspective in Electrically Regulated Surface‐Enhanced Raman Spectroscopy

Surface‐enhanced Raman spectroscopy (SERS) has emerged as a distinctive fingerprinting technique, offering a broad spectrum of applications encompassing environmental monitoring, chemical detection, and biomolecular analysis. Electrically modulated SERS (E‐SERS) stands as a technique which leverages an electric field to finely control the Raman signal based on the foundational principles of SERS. Through the manipulation of the physicochemical attributes of a metal or 2D material substrate, E‐SERS brings about an enhancement of the SERS signal. This enhancement occurs through a charge transfer process and modulation of the plasma resonance. This article showcases recent strides in the domain of E‐SERS, encompassing advancements such as the integration of paired integrated electrodes, piezoelectric materials, and pyroelectric materials in the construction of substrates. An exhaustive analysis of the mechanism underpinning the enhancement of the Raman signal is undertaken, alongside an exploration of the prominent attributes characterizing diverse methods for conditioning Raman signals. Elucidations are provided on the significance of employed electric fields in the augmentation of SERS and their manifold applications in drug identification, chemical detection, and catalysis. In summation, a delineation of challenges and perspectives inherent to E‐SERS substrates is presented, delineating a path for prospective comprehensive exploration within this auspicious realm of research.

Indoor localization for an unknown radio emitter employing graph-based optimization and improved RSSD

Accurately locating an indoor unknown radio emitter (URE) is a challenging target to ensure telecommunication security. The URE positioning method based on received signal strength difference (RSSD) has attracted considerable attention due to the advantage of not being affected by transmit power and frequency. However, the RSSD-based fingerprint technique cannot accurately express the constraint equations between signal characteristics and geographic coordinates because of redundant databases and false matching. In this paper, a novel RSSD-based indoor positioning method using factor graph (FG) for an URE is proposed to improve positioning accuracy and reduce computational complexity. Firstly, the databases are reconstructed by singular value decomposition (SVD) to eliminate redundant factor nodes. Secondly, Pearson correlation coefficient (PCC) is utilized to determine the sub-positioning area. Combing SVD with PCC, the hyperplane equations are reconstructed to build an optimized FG model, called SP-FG. Considering simulation and experiment, the proposed SP-FG algorithm improves the cumulative distribution function (CDF) of average positioning error within 0.5 m by 10% and 14% compared with conventional FG algorithm and K-nearest neighbor (KNN) algorithm, respectively. In addition, this paper discusses the superiority of proposed SP-FG algorithm in positioning accuracy under different reference side lengths, access point (AP) coordinates and numbers.

Fast deep learning reconstruction techniques for preclinical magnetic resonance fingerprinting.

We propose a deep learning (DL) model and a hyperparameter optimization strategy to reconstruct T1 and T2 maps acquired with the magnetic resonance fingerprinting (MRF) methodology. We applied two different MRF sequence routines to acquire images of ex vivo rat brain phantoms using a 7-T preclinical scanner. Subsequently, the DL model was trained using experimental data, completely excluding the use of any theoretical MRI signal simulator. The best combination of the DL parameters was implemented by an automatic hyperparameter optimization strategy, whose key aspect is to include all the parameters to the fit, allowing the simultaneous optimization of the neural network architecture, the structure of the DL model, and the supervised learning algorithm. By comparing the reconstruction performances of the DL technique with those achieved from the traditional dictionary-based method on an independent dataset, the DL approach was shown to reduce the mean percentage relative error by a factor of 3 for T1 and by a factor of 2 for T2 , and to improve the computational time by at least a factor of 37. Furthermore, the proposed DL method enables maintaining comparable reconstruction performance, even with a lower number of MRF images and a reduced k-space sampling percentage, with respect to the dictionary-based method. Our results suggest that the proposed DL methodology may offer an improvement in reconstruction accuracy, as well as speeding up MRF for preclinical, and in prospective clinical, investigations.

Fingerprinting sediment sources in two typical watersheds in the dry-hot valleys of Southwest China: The role of gully and orchard land

Identifying the contributions of sediment sources is important for effectively understanding soil erosion processes, adopting appropriate management measures, and optimizing soil conservation strategies. Currently, the sediment fingerprinting technique has gained significant attention worldwide to assess the contributions of sediment sources; however, such studies in the dry-hot valleys of Southwest China, one of the most important sediment sources for several major rivers (e.g., the Yangtze, Mekong, Salween and Yuanjiang River), are largely lacking. In this study, two typical, small watersheds in the dry-hot valley in the Yuanjiang river, which were representative of the landscape, were selected to quantify the relative contributions of potential sediment sources based on the sediment fingerprinting technique. In the orchard-forest watershed, four potential sediment sources, including the slope and level of orchard land, forest, and gully, were identified, while two sources (gully and slope) were identified for the shrub-grass watershed, and the relative contributions of these sources were estimated based on the Bayesian model. The results indicate the following:1) The gully contributed to 55.43% of the total sediment in the shrub-grass watershed, indicating that gully erosion is still significant in the dry-hot valleys, which is in accordance with several previous studies; 2) the ecological restoration in the gully in the orchard-forest watershed has greatly improved the vegetation cover from 2014 to 2021 and substantially reduced the sediment contribution of the gully, indicating that vegetation restoration is an effective ways to reduce sediment yield at the watershed scale; and 3) the orchard land, with an area proportion of<40%, in the orchard-forest watershed contributed to more than 90% of the sediment; this highlights the dominant role of orchard land in sediment yield in the watershed of the dry-hot valley, implying that suitable conservation strategies are necessary to protect soil in the orchard-related watersheds. This study quantitatively evaluated the relative contributions of the main sediment sources in dry-hot valleys, a natural gully, a gully with vegetation restoration, and orchard land, through combined studies. The results of this study could be helpful for deploying appropriate measures to reduce soil erosion and sediment yield in dry-hot valleys.

NuMY-A qPCR Assay Simultaneously Targeting Human Autosomal, Y-Chromosomal, and Mitochondrial DNA.

The accurate quantification of DNA in forensic samples is of utmost importance. These samples are often present in limited amounts; therefore, it is indicated to use the appropriate analysis route with the optimum DNA amount (when possible). Also, DNA quantification can inform about the degradation stage and therefore support the decision on which downstream genotyping method to use. Consequently, DNA quantification aids in getting the best possible results from a forensic sample, considering both its DNA quantity and quality limitations. Here, we introduce NuMY, a new quantitative real-time PCR (qPCR) method for the parallel quantification of human nuclear (n) and mitochondrial (mt) DNA, assessing the male portion in mixtures of both sexes and testing for possible PCR inhibition. NuMY is based on previous work and follows the MIQE guidelines whenever applicable. Although quantification of nuclear (n)DNA by simultaneously analyzing autosomal and male-specific targets is available in commercial qPCR kits, tools that include the quantification of mtDNA are sparse. The quantification of mtDNA has proven relevant for samples with low nDNA content when conventional DNA fingerprinting techniques cannot be followed. Furthermore, the development and use of new massively parallel sequencing assays that combine multiple marker types, i.e., autosomal, Y-chromosomal, and mtDNA, can be optimized when precisely knowing the amount of each DNA component present in the input sample. For high-quality DNA extracts, NuMY provided nDNA results comparable to those of another quantification technique and has also proven to be a reliable tool for challenging, forensically relevant samples such as mixtures, inhibited, and naturally degraded samples.

Image Steganography-based Copyright and Privacy-Protected Image Trading Systems

This paper proposes steganography-based copyright- and privacy-protected image trading systems using image transformation, i.e., either discrete cosine transform (DCT) or Hadamard transform (HT). In the systems, there are a content provider (CP), a consumer, the first trusted third party (TTP), and the second TTP. To protect the copyright of the image, the consumer ID is embedded into the amplitude components of the commercial image by the first TTP using the digital fingerprinting technique, and to protect the consumer's privacy against the first TTP and a malicious third party (s), the image steganography is applied to the commercial image by using image transformation. A color dummy image is used instead of a gray dummy image for security purposes. After applying the image transformation to both images, the coefficient signs of the commercial image are replaced by the coefficient signs of the dummy image pixel-by-pixel so that the inversely transformed commercial image looks like the dummy image instead of the commercial image. Once the consumer receives the fingerprinted image from the first TTP and the coefficient signs of the commercial image from the second TTP, the consumer reconstructs the fingerprinted commercial image without losing the hidden fingerprint at all because of the compatibility of the proposed image steganography method and the amplitude-based fingerprinting method. The experimental results confirm that the stego-images generated by the proposed systems do not look suspicious with higher qualities compared with those generated by existing systems. Moreover, the fingerprinted image quality and the correct fingerprint extracting rate have been improved by the proposed systems.

Leveraging Deep Learning for IoT Transceiver Identification.

With the increasing demand for Internet of Things (IoT) network applications, the lack of adequate identification and authentication has become a significant security concern. Radio frequency fingerprinting techniques, which utilize regular radio traffic as the identification source, were then proposed to provide a more secured identification approach compared to traditional security methods. Such solutions take hardware-level characteristics as device fingerprints to mitigate the risk of pre-shared key leakage and lower computational complexity. However, the existing studies suffer from problems such as location dependence. In this study, we have proposed a novel scheme for further exploiting the spectrogram and the carrier frequency offset (CFO) as identification sources. A convolutional neural network (CNN) is chosen as the classifier. The scheme addressed the location-dependence problem in the existing identification schemes. Experimental evaluations with data collected in the real world have indicated that the proposed approach can achieve 80% accuracy even if the training and testing data are collected on different days and at different locations, which is 13% higher than state-of-the-art approaches.

Increasing the Robustness of SIFT-MS Volatilome Fingerprinting by Introducing Notional Analyte Concentrations.

Selected ion flow tube-mass spectrometry (SIFT-MS) is an analytical technique for volatile detection and quantification. SIFT-MS can be applied in a "white box" approach, measuring concentrations of target compounds, or as a "black box" fingerprinting technique, scanning all product ions during a full scan. Combining SIFT-MS full scan data acquired from multibatches or large-scale experiments remains problematic due to signal fluctuation over time. The standard approach of normalizing full scan data to the total signal intensity was insufficient. This study proposes a new approach to correct SIFT-MS fingerprinting data. In this concept, all of the product ions from a full scan are considered individual compounds for which notional concentrations can be calculated. Converting ion count rates into notional analyte concentrations accounts for any changes in the instrument parameters. The benefits of the proposed approach are demonstrated on three years of data from both multibatches and long-term experiments showing a significant reduction of system-induced fluctuations providing a better focus on the changes of interest.

The Ecological Quality Status Assessment of Marine and Transitional Ecosystems: New Methods and Perspectives for the Future

Worldwide legislation emphasizes the need to monitor the health of aquatic ecosystems based on the response of biological quality elements to environmental conditions. A plethora of methodologies have been suggested in this sense. Lately, substantial efforts have led to the exploration of new biological quality elements from the meiobenthic compartment and the implementation of new methodologies based on environmental DNA. Due to their short life-cycles, meiofaunal organisms respond quickly to environmental variability. Changes in population dynamics and species composition are indicative of changes in environmental conditions. Recent pioneer studies have shown that biotic indices based on benthic foraminifera and nematodes can efficiently assess the health of transitional and marine ecosystems. The use of environmental DNA, as well as other fingerprinting techniques, is increasing in biomonitoring studies, and further calibrations are still needed to implement this method. The published papers in this Special Issue represent well the wide applicability of meiobenthic groups, i.e., benthic foraminifera and nematodes, allowing us to address a key ecological knowledge gap in order to convince decision makers and stakeholders about the advantage of introducing new biological quality elements in environmental biomonitoring.