Level Of Resolution Research Articles

Laser ablation inductively coupled plasma mass spectrometry measurements for high-resolution chemical ice core analyses with a first application to an ice core from Skytrain Ice Rise (Antarctica)

Abstract. Conventional methods of inorganic impurity analysis do not provide high enough depth resolution for many scientific questions in ice core science. In this study, we present a setup of laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) for high-resolution glacier ice impurity analysis to the sub-millimetre scale. This setup enables ice core chemical impurity analysis to a depth resolution of ∼182 µm while consuming only very small amounts of ice. The system performs simultaneous analysis of sodium, magnesium and aluminium incorporated in the ice matrix. In this case study within the framework of the WACSWAIN (WArm Climate Stability of the West Antarctic Ice Sheet in the last INterglacial) project, our method is applied to a selection of samples from the Skytrain ice core (West Antarctica) over a total length of 6.7 m consisting of about 130 single samples. The main goal of this study is to use the new LA-ICP-MS method to extract meaningful climate signals on a depth resolution level beyond the limits of continuous-flow analysis (CFA). A comparison between low-resolution CFA data and the high-resolution LA-ICP-MS data reveals generally good agreement on the decimetre scale. Stacking of parallel laser measurements together with frequency analysis is used to analyse the high-resolution LA-ICP-MS data at millimetre resolution. Spectral analysis reveals that despite effects of impurity accumulation along ice crystal grain boundaries, periodic concentration changes in the Skytrain ice core on the millimetre scale can be identified in ice from 26.8 ka (kiloyears before present, i.e. 1950 CE). These findings open new possibilities for climate data interpretation with respect to fast changes in the last glacial period and beyond, for example within the Beyond EPICA oldest-ice project.

Ranking Antibody Binding Epitopes and Proteins Across Samples from Whole Proteome Tiled Linear Peptides.

Ultradense peptide binding arrays that can probe millions of linear peptides comprising the entire proteomes of human or mouse, or hundreds of thousands of microbes, are powerful tools for studying the antibody repertoire in serum samples to understand adaptive immune responses. There are few tools for exploring high-dimensional, significant and reproducible antibody targets for ultradense peptide binding arrays at the linear peptide, epitope (grouping of adjacent peptides), and protein level across multiple samples/subjects (i.e. epitope spread or immunogenic regions of proteins) for understanding the heterogeneity of immune responses. We developed HERON (Hierarchical antibody binding Epitopes and pROteins from liNear peptides), an R package, which identifies immunogenic epitopes, using meta-analyses and spatial clustering techniques to explore antibody targets at various resolution and confidence levels, that can be found consistently across a specified number of samples through the entire proteome to study antibody responses for diagnostics or treatment. Our approach estimates significance values at the linear peptide (probe), epitope, and protein level to identify top candidates for validation. We test the performance of predictions on all three levels using correlation between technical replicates and comparison of epitope calls on two datasets, which shows HERON's competitiveness in estimating false discovery rates and finding general and sample-level regions of interest for antibody binding. The HERON R package is available at Bioconductor https://bioconductor.org/packages/release/bioc/html/HERON.html. Supplementary data are available at Bioinformatics online.

The impact of data resolution on dynamic causal inference in multiscale ecological networks.

While it is commonly accepted that ecosystem dynamics are nonlinear, what is often not acknowledged is that nonlinearity implies scale-dependence. With the increasing availability of high-resolution ecological time series, there is a growing need to understand how scale and resolution in the data affect the construction and interpretation of causal networks-specifically, networks mapping how changes in one variable drive changes in others as part of a shared dynamic system ("dynamic causation"). We use Convergent Cross Mapping (CCM), a method specifically designed to measure dynamic causation, to study the effects of varying temporal and taxonomic/functional resolution in data when constructing ecological causal networks. As the system is viewed at different scales relationships will appear and disappear. The relationship between data resolution and interaction presence is not random: the temporal scale at which a relationship is uncovered identifies a biologically relevant scale that drives changes in population abundance. Further, causal relationships between taxonomic aggregates (low-resolution) are shown to be influenced by the number of interactions between their component species (high-resolution). Because no single level of resolution captures all the causal links in a system, a more complete understanding requires multiple levels when constructing causal networks.

GroupMorph: Medical Image Registration via Grouping Network With Contextual Fusion.

Pyramid-based deformation decomposition is a promising registration framework, which gradually decomposes the deformation field into multi-resolution subfields for precise registration. However, most pyramid-based methods directly produce one subfield per resolution level, which does not fully depict the spatial deformation. In this paper, we propose a novel registration model, called GroupMorph. Different from typical pyramid-based methods, we adopt the grouping-combination strategy to predict deformation field at each resolution. Specifically, we perform group-wise correlation calculation to measure the similarities of grouped features. After that, n groups of deformation subfields with different receptive fields are predicted in parallel. By composing these subfields, a deformation field with multi-receptive field ranges is formed, which can effectively identify both large and small deformations. Meanwhile, a contextual fusion module is designed to fuse the contextual features and provide the inter-group information for the field estimator of the next level. By leveraging the inter-group correspondence, the synergy among deformation subfields is enhanced. Extensive experiments on four public datasets demonstrate the effectiveness of GroupMorph. Code is available at https://github.com/TVayne/GroupMorph.

Optical temperature sensing properties of novel RE3+ (RE=Er, Ho) doped BaLa2Ti3O10 phosphors

A novel luminescent material of BaLa2Ti3O10:RE3+ was synthesized by the high-temperature solid phase method, with a typical layered structure belonging to a special crystal system. Under the excitation of 980 nm, BaLa2Ti3O10:4%Er3+ exhibits characteristic emissions centered at 531 nm, 551 nm and 668 nm. It is noteworthy that the color of BaLa2Ti3O10:3%Ho3+ shows a strong green emission at 551 nm and a weak red emission near 664 nm, also represents adjustable yellow-green light with the change of 980nm laser pumped powers. The optical temperature sensing properties were checked by employing different strategies, relating to the thermally-coupled-levels (TCLs) and nonthermally-coupled-levels (NTCLs). The results show that the maximum relative sensitivity of TCLs based on BaLa2Ti3O10:Er3+ is 0.68% K-1(305 K). Similarly, the maximum sensitivity of TCLs is 0.89% K-1(313 K) in BaLa2Ti3O10:Ho3+, which performs well from 313 K to 513 K. It has been found the samples using FIRs of TCLs can produce higher absolute (Sa) and relative (Sr) sensitivities compared with those using Fluorescence Intensity Ratios (FIRs) of NTCLs. The multiple FIRs also achieved superior levels of temperature resolution and repeatability in all cases. Generally, this work provides favorable candidates for the temperature sensors.

OMIP-109: 45-color full spectrum flow cytometry panel for deep immunophenotyping of the major lineages present in human peripheral blood mononuclear cells with emphasis on the T cell memory compartment.

The need for more in-depth exploration of the human immune system has moved the flow cytometry field forward with advances in instrumentation, reagent development and availability, and user-friendly implementation of data analysis methods. We developed a high-quality human 45-color panel, for comprehensive characterization of major cell lineages present in circulation including T cells, γδ T cells, NKT-like cells, B cells, NK cells, monocytes, basophils, dendritic cells, and ILCs. Assay optimization steps are described in detail to ensure that each marker in the panel was optimally resolved. In addition, we highlight the outstanding discernment of cell activation, exhaustion, memory, and differentiation states of CD4+ and CD8+ T cells using this 45-color panel. The panel enabled an in-depth description of very distinct phenotypes associated with the complexity of the T cell memory response. Furthermore, we present how this panel can be effectively used for cell sorting on instruments with a similar optical layout to achieve the same level of resolution. Functional evaluation of sorted specific rare cell subsets demonstrated significantly different patterns of immunological responses to stimulation, supporting functional and phenotypic differences within the T cell memory subsets. In summary, the combination of full spectrum profiling technology and careful assay design and optimization results in a high resolution multiparametric 45-color assay. This panel offers the opportunity to fully characterize immunological profiles present in peripheral blood in the context of infectious diseases, autoimmunity, neurodegeneration, immunotherapy, and biomarker discovery.

Superposition of standing waves induced ultra high-resolution atom localization

Abstract In this study, we have theoretically demonstrated ultra-high resolution two-dimensional atom localization within a three-level $\lambda$-type atomic medium via superposition of asymmetric and symmetric standing wave fields. Our analysis provides understanding into the precise spatial localization of atomic positions at the atomic level, utilizing advanced theoretical approaches and principles of quantum mechanics. The dynamical behavior of a three-level atomic system is thoroughly analyzed using the density matrix formalism within the realm of quantum mechanics. A theoretical approach is constructed to describe the interaction between the system and external fields, specifically a control field and a probe field. The absorption spectrum of the probe field is thoroughly examined to clarify the spatial
localization of the atom within the proposed configuration. We have theoretically investigated that symmetric and asymmetric superposition phenomena significantly influence the localized peaks within a two-dimensional spatial domain. Specifically, the emergence of one and two sharp localized peaks has been observed within a region of one wavelength domain. We observed notable influences of the intensity of the control field, probe field detuning, and decay rates on atom localization. Ultimately, we have achieved an unprecedented level of ultra-high resolution and precision in localizing an atom within an area smaller than λ/35×λ/35. These findings hold promise for potential applications in domains such as Bose-Einstein condensation, nanolithography, laser cooling, trapping of neutral atoms, and the measurement of center-of-mass wave-functions.

Learning infrared degradations for coherent visible image fusion in the undecimated dual-tree complex wavelet domain

Traditional infrared (IR) and visible (VIS) image fusion methods demand identical resolution levels for source images, which can be problematic due to the inherent low-resolution nature of IR imagery. In this paper, we introduce an innovative image fusion approach that harmonizes resolution across IR-VIS source images, leading to the generation of fused images with higher resolution. We employ a convolutional neural network model to recover the real-time image degradations in IR data, with a particular focus on super-resolution through the multi degradation resolution enhancement network (MDREN). We adopt undecimated dual-tree complex wavelet transform (UDT-CWT) in our fusion process due to its near shift invariance and better directionality capabilities. This results in coherent information of the fused images with minimized noise and loss. Experiments employing five image quality assessment measures are used to compare the proposed method to nine state-of-the-art approaches and show its efficacy.

Integrative taxonomy of Tethya: description of four new species based on morphology, phylogeny and microbiome diversity

The genus Tethya, one of the most iconic groups in the phylum Porifera. includes nearly 100 valid species. Tethya shows a nearly cosmopolitan distribution, and thus this clade could help elucidate global mechanisms of speciation in sponges. Contrasting with many other marine organisms, Tethya peaks in diversity in the temperate region, with only ∼30% of diversity occurring in the tropics. This pattern may however be related to a lack of studies in the tropics, masked by dubious taxonomic identifications. To address this question, we studied new collections from the Western Atlantic (Caribbean and Brazil) and the Northeastern Atlantic, as well as museum material from the Indian Ocean. Combining morphological investigation with molecular phylogenetics and the study of the sponge’s microbial communities, we conclude that four constitute new species that we describe here: Tethya martini Riesgo, Giribet, & Santodomingo, sp. nov.; Tethya simoni Santodomingo, Zea, & Riesgo, sp. nov.; Tethya erici Díez-Vives, Santodomingo, Moles, & Riesgo, sp. nov.; Tethya orioni Kenny, Santodomingo & Riesgo, sp. nov. Some species thought to be widespread (e.g., T. aurantium and T. seychellensis), each represent multiple species with unique geographic distributions. A phylogenetic analysis of Tethya (based on COI and 28S rRNA sequence data) recovered five main clades, which were also characterized by distinct prokaryotic communities. This suggests that microbiomes could be used as a guide for taxonomic identification in Tethya. We finally explored the existence of a phylosymbiotic pattern in sponges at different levels of prokaryotic taxonomic resolution (i.e., at phylum, class, and genus-level analysis of prokaryotes). Remarkably, our analysis revealed high levels of coevolution of Tethya and their associated microbial communities, even when microbiomes were analysed at the genus level. Our findings support the use of an integrative approach to better understand the evolutionary history of sponges. http://zoobank.org/urn:lsid:zoobank.org:pub:54F80E5E-99A4-43CE-BA3A-BCD01E715595

The Power of Reagent Titration in Flow Cytometry.

Flow cytometry facilitates the detection of multiple cell parameters simultaneously with a high level of resolution and throughput, enabling in-depth immunological evaluations. High data resolution in flow cytometry depends on multiple factors, including the concentration of reagents used in the staining protocol, and reagent validation and titration should be the first step in any assay optimization. Titration is the process of finding the concentration of the reagent that best resolves a positive signal from the background, with the saturation of all binding sites, and minimal antibody excess. The titration process involves the evaluation of serial reagent dilutions in cells expressing the antigen target for the tested antibody. The concentration of antibody that provides the highest signal to noise ratio is calculated by plotting the percentage of positive cells and the intensity of the fluorescence of the stained cells with respect to the negative events, in a concentration-response curve. The determination of the optimal antibody concentration is necessary to ensure reliable and reproducible results and is required for each sample type, reagent clone and lot, as well as the methods used for cell collection, staining, and storage conditions. If the antibody dilution is too low, the signal will be too weak to be accurately determined, leading to suboptimal data resolution, high variability across measurements, and the underestimation of the frequency of cells expressing a specific marker. The use of excess antibodies could lead to non-specific binding, reagent misuse, and detector overloading with the signal off scale and higher spillover spreading. In this publication, we summarized the titration fundamentals and best practices, and evaluated the impact of using a different instrument, sample, staining, acquisition, and analysis conditions in the selection of the optimal titer and population resolution.

What animals live there? Exploring the factors influencing primary school children’s awareness of native and exotic fauna

ABSTRACT Education can help combat the current biodiversity crisis, particularly in Western societies where citizens often lack knowledge about biodiversity. This study investigates the factors affecting the low awareness of native species. Primary school students (ages 6–12; n = 960) from northern Spain were shown a photograph from a local temperate forest and another from an exotic African savannah, and were asked to mention what animals they thought might live there. Listings for the exotic biome were overall longer, included more animals correctly belonging to the biome in the picture, and displayed a finer taxonomic resolution level. Children mentioned TV, internet and books as the main sources of information regarding exotic fauna, whereas the most chosen source for local fauna was their families. Both interest in nature and in animals were positively related to greater knowledge about fauna, especially from the exotic biome, which was also linked to students’ educational level. It was also found that the smaller the student’s hometown, the greater their knowledge about local fauna. The implications of the results for the education community as well as for traditional and online media are discussed, and some educational strategies, adjusted to children’s current cultural context, are proposed.

Multiple measures for self-identification improve matching donors with patients in unrelated hematopoietic stem cell transplant

BackgroundQuestions persist around whether and how to use race or geographic ancestry in biomedical research and medicine, but these forms of self-identification serve as a critical tool to inform matching algorithms for human leukocyte antigen (HLA) of varying levels of resolution for unrelated hematopoietic stem cell transplant in large donor registries.MethodsHere, we examined multiple self-reported measures of race and ancestry from a survey of a cohort of over 100,000 U.S. volunteer bone marrow donors alongside their high-resolution HLA genotype data.ResultsWe find that these self-report measures are often non-overlapping, and that no single self-reported measure alone provides a better fit to HLA genetic ancestry than a combination including both race and geographic ancestry. We also found that patterns of reporting for race and ancestry appear to be influenced by participation in direct-to-consumer genetic ancestry testing.ConclusionsWhile these data are not used directly in matching for transplant, our results demonstrate that there is a place for the language of both race and geographic ancestry in the critical process of facilitating accurate prediction of HLA in the donor registry context.

Review of Innovative Cavity Designs in Metal–Insulator-Metal Waveguide-Based Plasmonic Sensors

AbstractPlasmonic sensors utilizing metal–insulator-metal (MIM) waveguides represent a significant advancement in sensing technology due to their high sensitivity and versatility. These sensors leverage surface plasmon polaritons to detect minute changes in the surrounding environment, making them highly effective for a range of applications. For instance, they can precisely measure variations in the Refractive Index, which is crucial for monitoring chemical concentrations and biological interactions. Additionally, MIM waveguides can be adapted to sense temperature fluctuations, pressure changes, and the presence of specific gases, providing valuable insights in fields such as environmental surveillance, medical diagnostics, and industrial processes. In recent years, a variety of sensor cavity shapes have been proposed to enhance sensor performance. This review examines how these innovative geometries optimize sensor cavities to achieve unprecedented levels of resolution and sensitivity, underscoring their transformative potential across a broad spectrum of scientific and practical applications.

Towards a high resolution simulation framework for building integrated photovoltaics under partial shading in urban environments

This paper presents a framework for modeling the performance of photovoltaic (PV) systems, which is intended to be used in the design, optimization, and deployment of PV systems in urban environments. Urban PV and building integrated PVs (BIPV) are a crucial component of the transition to a low-carbon society. However, existing frameworks for simulating PV systems accurately characterize PV under partial shading, leading to performance gaps with systems deployed in environments such as cities where shading is more common than in large open fields. In this paper, we extend a framework from literature for modeling parametric BIPV arrays using a power model that characterizes IV-curves at the PV cell. We describe the framework and perform a validation against a measured rooftop PV array dataset. We then evaluate the extended framework to other PV modeling and simulation frameworks found in the literature through the simulation of a simplified facade under various levels of shading. Results show that the extended framework is necessary to account for partial shading, but there may be use cases when lower resolution frameworks may be just as accurate. Amongst the frameworks a range of predicted power outputs under the shading conditions was observed; from 6% to 846% with a mean of 162% from the other frameworks relative to the proposed framework. The range is due to the extensive set of conditions tested, where it was observed that in conditions where shading is present but sparse, there is a larger range of predicted output from the various frameworks. Whereas in conditions where shading coverage is high this range is lower. Lastly, we demonstrate the use of the framework on three arrays. Again it was observed that homogeneous shading conditions may not require the level of resolution provided by the proposed framework, while heterogeneous could benefit the most from the approach. This indicates, and is discussed in the paper, that modelers should consider the shading conditions present when deciding on which framework to apply.

A novel deep domain adaptation method based on three-way decisions for identifying autistic patients

Combining machine learning with functional magnetic resonance imaging (fMRI) technology to build an effective autism recognition model has become a research hotspot. However, the performance of existing machine learning models is highly dependent on large-scale labelled data, and the cost of fMRI data acquisition and labelling limits the application of models in autism recognition. Although the availability of multisite data increases the sample size, the resolution, image contrast, and noise level of fMRI data acquired at different sites vary considerably due to the different scanning devices or scanning protocols used. The heterogeneity of fMRI data between sites leads to limited generalisation of the trained models. To tackle this problem, we propose a deep domain adaptation approach based on three-way decisions. First, we introduce a pseudolabel optimisation method based on three-way decisions, which fully considers the structural information of the target domain and makes secondary decisions on the uncertain objects in the target domain, thus improving the accuracy of the pseudolabel in the target domain. Then, we construct a new autism recognition network using a stacked autoencoder and combine the proposed pseudolabel optimisation model to adapt the domain shift. Finally, the self-training strategy is used to retrain the network after domain adaptation to improve the generalisability of the model to the target domain. Experimental results on several cross-domain autism recognition tasks show the effectiveness of the proposed method.

Integrating Multi‐Scale Feature Boundary Module and Feature Fusion With CNN for Accurate Skin Cancer Segmentation and Classification

ABSTRACTThe skin, a crucial organ, plays a protective role in the human body, emphasizing the significance of early detection of skin diseases to prevent potential progression to skin cancer. The challenge lies in diagnosing these diseases at their early stages, where visual resemblance complicates differentiation, highlighting the need for an innovative automated method for precisely identifying skin lesions in biomedical images. This paper introduces a holistic methodology that combines DenseNet, multi‐scale feature boundary module (MFBM), and feature fusion and decoding engine (FFDE) to tackle challenges in existing deep‐learning image segmentation methods. Furthermore, a convolutional neural network model is designed for the classification of segmented images. The DenseNet encoder efficiently extracts features at four resolution levels, leveraging dense connectivity to capture intricate hierarchical features. The proposed MFBM plays a crucial role in extracting boundary information, employing parallel dilated convolutions with various dilation rates for effective multi‐scale information capture. To overcome potential disadvantages related to the conversion of features during segmentation, our approach ensures the preservation of context features. The proposed FFDE method adaptively fuses features from different levels, restoring skin lesion location information while preserving local details. The evaluation of the model is conducted on the HAM10000 dataset, which consists of 10 015 dermoscopy images, yielding promising results.

Analysis of Patients Who Undergo Index Arthroscopy With Biopsy but Not Implantation for Staged Chondrocyte Cell Transplantation.

Autologous chondrocyte implantation (ACI) and matrix-induced autologous chondrocyte implantation (MACI) are 2-stage procedures requiring an index full-thickness cartilage biopsy. Only a portion of patients ultimately undergo second-stage ACI/MACI. To identify patients with articular cartilage defects who underwent arthroscopic debridement with biopsy for ACI/MACI and compare those who did with those who did not proceed with implantation within 2 years after biopsy. Additionally, the authors sought to identify why patients did not proceed with implantation. Case-control study; Level of evidence, 3. Patients who underwent arthroscopy and autologous chondrocyte biopsy from January 1, 2015, to December 31, 2019, and who had minimum 2-year follow-up data were grouped into those who proceeded with second-stage ACI/MACI (implant group; n = 97) and those who did not (biopsy group; n = 63). Demographic factors, cartilage defect characteristics, and preoperative International Knee Documentation Committee (IKDC) scores were analyzed. Patients in both groups were evaluated postoperatively using the IKDC, Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Single Assessment Numeric Evaluation (SANE), and visual analog scale (VAS) for pain, and patients who did not undergo implantation were asked for their reasoning. Body mass index (BMI) (P < .001) and Outerbridge grades at index arthroscopy (P = .047) were significantly higher for the implant group than the biopsy group. Both groups had significantly improved IKDC scores from their initial presentation to final follow-up (implant group: 46.4 ± 16.2 preoperative vs 69.6 ± 20.6 postoperative [P < .001]; biopsy group: 47.2 ± 15.9 preoperative vs 70.7 ± 19.1 postoperative [P < .001]); however, the level of improvement did not differ significantly between groups. Postoperative WOMAC, SANE, and VAS pain scores were also similar between groups. In the biopsy group, 23 patients (37%) cited symptom resolution or activity level improvement after initial arthroscopy as the reason for not proceeding with implantation. Patients who proceeded to the second stage of chondrocyte implantation via either ACI or MACI had higher-grade articular defects and higher BMI compared with those who underwent biopsy with concomitant debridement chondroplasty alone. Postoperative outcomes were similar between the groups.

Improved subarray coherence factor beamformer for ultrasound plane-wave imaging

Abstract Coherent plane-wave compounding (CPWC) has gained significant traction in medical ultrasound imaging. The coherence factor (CF) weighting algorithm, a classical adaptive beamforming strategy, aims to enhance the CPWC imaging lateral resolution, but it also noticeably compromises the speckle patterns, particularly.at a low signal-to-noise ratio (SNR). Delay multiply and sum (DMAS) has been proposed to reduce side lobes. However, the reconstructed image quality with DMAS is limited in terms of resolution and speckle level. In the present study, an improved subarray coherence factor (ISCF) algorithm is introduced by combining CF with DMAS strategies, aiming to improve the contrast and resolution while preserving the speckle level. The performance of the proposed algorithm is evaluated using simulation and phantom datasets. Results indicate that the resolution and the contrast of ISCF can be improved by 41.6% and 79.4%, respectively; compared with delay and sum (DAS) algorithm. Additionally, the resolution and the speckle level of ISCF can be improved by 10.6% and 35.7% compared with CF algorithm, respectively. The proposed ISCF algorithm outperforms CF in terms of resolution, contrast, and speckle preservation.

Federal fractals: review of Stephen Tierney, The Federal Contract

Stephen Tierney’s new book presents an exciting new theory of federal constitutionalism. His core argument is that each federal constitution is founded on a ‘federal contract’, which is an agreement among a plurality of territorial communities to form a federal political community while retaining their distinct constitutional identity as constituent states. This offers an alternative to the ‘social contact’ of liberal political theory in which the isolated individual is the sole basic unit of the constitutional order. The explanatory power of Tierney’s thesis is impressive. He shows how the idea of the federal contract can be manifested in many varied forms but in a manner that recognises the constitutional status and autonomy of the constituent states, formed into an association under which they agree to act cooperatively with each other and with the federation as a whole, under a mutual commitment of reciprocity that underwrites inter-governmental cooperation and solidarity, while also maintaining inter-governmental distinctiveness and autonomy. Under the auspices of this simple and elegant idea, Tierney offers an account of federal constitutionalism that resembles the fractal patterns of mathematical geometry insofar as each federal system reflects a particular federating logic that drives its initial creation and ongoing evolution. This opens up a promising area of further research involving an ever-closer examination of federal systems to see whether the fractal-like patterns he has identified continue to be apparent at progressively higher levels of resolution.

Unveiling hidden aspects of GPS deployment on wildlife: A multistep and transdisciplinary approach to urban wild boar monitoring

Studies of free-ranging wildlife often involve individual tracking by sequentially recording animals’ positions over a continuous and extended period. Automatic, programmable, operational continuously, and user-friendly thanks to the development of intuitive software, GPS (Global Positioning System) enable the acquisition of large quantities of data, day and night, regardless of field and weather conditions, while allowing for levels of spatial and temporal resolution in the location data never before achieved in wildlife tracking studies. However, GPS collars deployment on wild fauna does not directly translate into scientific outcomes. This article delves into the hidden aspects of telemetry programs, offering a reflective account of our transdisciplinary experience (between researchers and wildlife managers) in GPS tracking of urban wild boar. The described protocol and its discussion aim to outline the necessary conditions to benefit from GPS programs. The program first requires a common construction of the protocol, which meets the objectives of each partner. Second, raw data collection and transformation into information involve four steps. Finally, both technical and human-related dimensions are to be anticipated and considered for further analyses.•Transdisciplinary research requires a common construction of the protocol in line with the research question.•Technical constraints and negotiations between partners need to be considered.•Multiple steps are required to leverage the scientific advantages of the monitoring.