Connected Component Analysis Research Articles

A Systematic Method Combining Rotated Convolution and State Space Augmented Transformer for Digitizing and Classifying Paper ECGs

Billions of paper Electrocardiograms (ECGs) are recorded annually worldwide, particularly in the Global South. Manual review of this massive dataset is time-consuming and inefficient. Accurate digital reconstruction of these records is essential for efficient cardiac disease diagnosis. This paper proposes a systematic framework for digitizing paper ECGs with 12 symmetrically distributed leads and identifying abnormal samples. This method consists of three main components. First, we introduce an adaptive rotated convolution network to detect the positions of lead waveforms. By exploiting the symmetric distribution of 12 leads, a novel loss is proposed to improve the detection model’s performance. Second, image processing techniques, including denoising and connected component analysis, are employed to digitize ECG waveforms. Finally, we propose a transformer-based classification method combined with a state space model. Our process is evaluated on a large synthetic dataset, including ECG images characterized by rotations, noise, and creases. The results demonstrate that the proposed detection method can effectively reconstruct paper ECGs, achieving an 11% improvement in SNR compared to the baseline. Moreover, our classification model exhibits slightly higher performance than other counterparts. The proposed approach offers a promising solution for the automated analysis of paper ECGs, supporting clinical decision-making.

A STEDable BF2-Azadipyrromethene Fluorophore for Nuclear Membrane and Associated Endoplasmic Reticulum Imaging.

The endoplasmic reticulum and the internal nuclear compartments are intrinsically connected through the nuclear membrane, pores and lamina. High resolution imaging of each of these cellular features concurrently remains a significant challenge. To that end we have developed a new molecular nuclear membrane-endoplasmic reticulum (NM-ER) staining fluorophore with emission maxima at 650 nm. NM-ER is compatible with fixed and live cell imaging and stimulated emission depletion microscopy (STED) showing significant improvement in resolution when compared to comparable confocal laser scanning microscopy. The imaging versatility of NM-ER was illustrated through its compatible use with other fluorophores for co-imaging with DNA, nuclear pores and lamina allowing cellular abnormalities to be identified. NM-ER alone, or in use with other nuclear region labels could be an important tool for the investigation of nuclear transport and associated cellular processes.

TurboCas: A method for locus-specific labeling of genomic regions and isolating their associated protein interactome.

Regulation of gene expression during development and stress response requires the concerted action of transcription factors and chromatin-binding proteins. Because this process is cell-type specific and varies with cellular conditions, mapping of chromatin factors at individual regulatory loci is crucial for understanding cis-regulatory control. Previous methods only characterize static protein binding. We present "TurboCas," a method combining a proximity-labeling (PL) enzyme, miniTurbo, with CRISPR-dCas9 that allows for efficient and site-specific labeling of chromatin factors in mammalian cells. Validating TurboCas at the FOS promoter, we identify proteins recruited upon heat shock, cross-validated via RNA polymerase II and P-TEFb immunoprecipitation. These methodologies reveal canonical and uncharacterized factors that function to activate expression of heat-shock-responsive genes. Applying TurboCas to the MYC promoter, we identify two P-TEFb coactivators, the super elongation complex (SEC) and BRD4, as MYC co-regulators. TurboCas provides a genome-specific targeting PL, with the potential to deepen our molecular understanding of transcriptional regulatory pathways in development and stress response.

Automated layered personification methodology of structural drawings with embedded design information

As artificial intelligence continues to reshape various industries, the demand for digitalization and intelligent design in the fields of architecture and structural engineering is increasingly pronounced. In the pursuit of digital and intelligent design, it is necessary to identify and extract information on various structural components from the construction drawings. In the current stages of intelligent structural design, the preprocessing of construction component drawings predominantly relies on manual efforts, highlighting a notable gap in automated solutions. To address this challenge, this paper introduces an innovative automated layered method to mimic human efforts while reading the architectural and structural drawings. The proposed method could both identify and embed the relevant information of the structural components within bitmap drawings. The proposed method employs the Connected Component Labeling algorithm, a novel Intersection Recognition and Connection approach, which is a straightforward method for information embedding. This approach automates the process of preprocessing and integrating basic information into the structural drawings, facilitating the transition from manual to intelligent systems. Then, the proposed method was validated by case studies and algorithm performance evaluations for its effectiveness and efficiency.

Quantitative analysis of patient motion in walk-through PET scanner and standard axial field of view pet scanner using infrared-based tracking

BackgroundLong-axial field-of-view (LAFOV) Positron Emission Tomography (PET) scanners provide high sensitivity, but throughput is limited because of time-consuming patient positioning. To enhance throughput, a novel Walk-Through PET (WT-PET) scanner has been developed, allowing patients to stand upright, supported by an adjustable headrest and hand supports. This study evaluates the degree of motion in the WT-PET system and compares it with the standard PET-CT.MethodsThree studies were conducted with healthy volunteers to estimate motion. The first two studies assessed motion in the WT-PET’s Design I (Study 1) and Design II (Study 2), while the third study compared motion on a standard PET-CT scanner bed (Study 3). Infrared markers placed on the head, shoulders, chest, and abdomen were tracked and processed using image-processing techniques involving thresholding and connected component analysis. Videos were recorded for normal breathing and breath-holding conditions, and 2D centroids were transformed into 3D coordinates using depth information.ResultsThe results shows a significant reduction in motion during breath-holding, especially for the abdomen. Mean motion distances decreased from 2.63 mm to 2.18 mm in Study 1 and from 2.42 mm to 1.67 mm in Study 2. Statistical analysis revealed notable differences in motion between the WT-PET and mCT scanners. The Shapiro-Wilk test indicated non-normal motion distributions in the head, right shoulder, and abdomen for both systems, leading to the use of the Wilcoxon signed-rank test for all markers. Significant differences were found in the right shoulder (p = 0.0266), left shoulder (p = 0.0004) and chest (p < 0.0001) but no significant differences were observed in the head (p = 0.1327) and abdomen (p = 0.8404).ConclusionThis study provides a comprehensive analysis of patient motion in a WT-PET scanner with respect to the standard PET. The findings highlight a significant increase in shoulder and chest motion, while the head and abdomen regions showed more stability.

Comprehensive study of rapid capacity fade in prismatic Li-ion cells with flexible packaging

Prismatic lithium-ion batteries (LIBs) are considered promising electric energy sources in electromobility applications due to their efficient space utilization. However, their sensitivity to external and internal influences and reduced durability lead to inflation risk and potential explosions throughout their lifecycle. These critical processes are strongly influenced by the inner construction of the cell, especially concerning the coating and mechanical fixation. This study subjects a commercially available prismatic LIB cell to comprehensive, correlative analysis employing various imaging techniques. The inner structure of the entire cell is visualized non-destructively by X-ray computed tomography (CT), enabling the identification of critical design flaws prior to electrochemical cycling. Electrochemical cycling simulates the battery lifecycle, and the cell is subsequently disassembled in the fully charged state. The usage of the inert-gas transfer system allowed the preparation of Broad Ion Beam (BIB) electrodes cross-sections in a fully native state and for the first time to observe the tearing of graphite particles due to over-lithiation. Established region labeling system allowed to use CT and scanning electron microscopy (SEM) correlatively to identify critical regions. After 100 cycles, a 40% capacity loss was observed and event diagram describing deagradation mechanisms, related both to the cell design and to the processes occurring at high load, was created.

On-the-fly clustering for exascale molecular dynamics simulations

Computational resources have experienced exponential growth in the last decades enabling the simulation of complex physical problems at the cost of a massive increase in data storage. This is especially true for N-body simulations now reaching billions or trillions particles in certain cases. To overcome the drawbacks of data storage on disk for post-processing purposes, on-the-fly analysis has gained momentum but still represents a challenge in both its implementation and efficiency without impacting the simulation engine performances. This work provides a new in-situ procedure for features detection in massive N-body simulations, leveraging state-of-the-art techniques from various fields. Based on a discrete-to-continuum paradigm shift, particles and their respective physical quantities are projected onto a 3D regular grid before applying image analysis algorithms to group voxels based on specific user-defined criteria. A significant extension to the hybrid parallelism of connected component analysis within the image processing community is also introduced in the present study. Traditionally operating in shared memory parallelism, this extension now incorporates both distributed and shared memory approaches. The implementation is carried out within the exaStamp classical Molecular Dynamics code, a variant of the open-source exaNBody platform [39]. This adaptation allows for the on-the-fly analysis of multi-billion atoms samples with at most a 1.3% overhead. In addition, the entire framework is benchmarked up to 32768 cores. The applicability of the present approach is demonstrated on the case of a spall fracture in a tantalum sample as well as high velocity impact of a tin droplets on a rigid surface.

Alpha-2 nicotinic acetylcholine receptors regulate spectral integration in auditory cortex.

In primary auditory cortex (A1), nicotinic acetylcholine receptors (nAChRs) containing α2 subunits are expressed in layer 5 Martinotti cells (MCs)-inhibitory interneurons that send a main axon to superficial layers to inhibit distal apical dendrites of pyramidal cells (PCs). MCs also contact interneurons in supragranular layers that, in turn, inhibit PCs. Thus, MCs may regulate PCs via inhibition and disinhibition, respectively, of distal and proximal apical dendrites. Auditory inputs to PCs include thalamocortical inputs to middle layers relaying information about characteristic frequency (CF) and near-CF stimuli, and intracortical long-distance ("horizontal") projections to multiple layers carrying information about spectrally distant ("nonCF") stimuli. CF and nonCF inputs integrate to create broad frequency receptive fields (RFs). Systemic administration of nicotine activates nAChRs to "sharpen" RFs-to increase gain within a narrowed RF-resulting in enhanced responses to CF stimuli and reduced responses to nonCF stimuli. While nicotinic mechanisms to increase gain have been identified, the mechanism underlying RF narrowing is unknown. Here, we examine the role of α2 nAChRs in mice with α2 nAChR-expressing neurons labeled fluorescently, and in mice with α2 nAChRs genetically deleted. The distribution of fluorescent neurons in auditory cortex was consistent with previous studies demonstrating α2 nAChRs in layer 5 MCs, including nonpyramidal somata in layer 5 and dense processes in layer 1. We also observed label in subcortical auditory regions, including processes, but no somata, in the medial geniculate body, and both fibers and somata in the inferior colliculus. Using electrophysiological (current-source density) recordings in α2 nAChR knock-out mice, we found that systemic nicotine failed to enhance CF-evoked inputs to layer 4, suggesting a role for subcortical α2 nAChRs, and failed to reduce nonCF-evoked responses, suggesting that α2 nAChRs regulate horizontal projections to produce RF narrowing. The results support the hypothesis that α2 nAChRs function to simultaneously enhance RF gain and narrow RF breadth in A1. Notably, a similar neural circuit may recur throughout cortex and hippocampus, suggesting widespread conserved functions regulated by α2 nAChRs.

Automated Surface Defect Detection Based on CycleGAN Model

Abstract Defect detection occupies an increasingly important position in the manufacturing industry, and most of approaches for the traditional defect detection are based on manual extraction of defective region traits and labeling work. This paper presents a novel defect detection approach based on Generative Adversarial Network (GAN) to automatically detect and extract defects from the target dataset. The method extends the samples using GAN model to solve the problem of insufficient samples in reality, and also provides paired samples for the second stage of defective pixel accumulation, after which the defective pixel images are output as binarized defect maps using difference accumulation and threshold segmentation. The experimental results verify that the proposed method can very accurately highlight the defects at the defect locations, and can be generated without manual labeling of defect traits.

Interval type-2 fuzzy approach for retinopathy detection in fundus images

In the manuscript, an automatic approach for analysis and detection of various stages of retinopathy defects in human eyes has been proposed. The approach consists of a robust preprocessing technique of the retina fundus image to mitigate the effects of noise and poor lightening in the image. To realize a compressive analysis of the defects, methods for extracting blood vessels and optic disc in the fundus image has also been developed. Adaptive Histogram Equalization (AHE), median filtering and Connected Component Analysis techniques were used in separating blood vessels and optic disc from each fundus image. The pre-processing utilizes canny edge detection and Morphological Closing on the fundus image. An interval type-2 fuzzy (IT2F) clustering is applied to segments an input image into four clusters. These four clusters from the fuzzy segmentation are further analyzed to extract various stages of retinopathy abnormalities (e.g., Hemorrhage, hard exudates etc.). The extracted blood vessels and optic disc are removed from the analysis to enhance the defects detection process. Experiments were conducted on DIARETDB1 database. The experimental results obtained are validated using the ground-truth images contained in DIARETDB1 database. Impressive results are recorded throughout the experiment. Hard-Exudates and Hemorrhage were detected from the fundus images and results from similarity indexes such as, accuracy (94.11%) sensitivity (93.03%) and specificity (98.45%) were recorded.

Measuring global urban complexity from the perspective of living structure

As urban critic Jane Jacobs conceived, a city is essentially the problem of organized complexity. What underlies the complexity refers to a structural factor, called living structure, which is defined as a mathematical structure composed of hierarchically organized substructures. Through these substructures, the complexity of cities, or equivalent to the livingness of urban space (L), can be measured by the multiplication the number of cities or substructures (S) and their scaling hierarchy (H), indicating that complexity is about both quantity of cities and how well the city is organized hierarchically. In other words, complexity emerges from a hierarchical structure where there are far more small cities or substructures than large ones across all scales, and cities are more or less similar within each individual hierarchy. In this paper, we conduct comprehensive case studies to investigate urban complexity on a global scale using multisource geospatial data. We develop an efficient approach to recursively identifying all natural cities with their inner hotspots worldwide through connected component analysis. To characterize urban complexity, urban space is initially represented as a hierarchy of recursively defined natural cities, and all the cities are then represented as a network for measuring the degree of complexity or livingness of the urban space. The results show the Earth’s surface is growing more complex from an economic perspective, and the dynamics of urban complexity are more explicit from nighttime light imagery than from population data. We further discuss the implications in city science, aiming to help create and recreate urban environments that are more resilient and livable by fostering organized complexity from the perspective of living structure.

FESTA: A Polygon-Based Approach for Extracting Relevant Structures from Free Energy Surfaces Obtained in Molecular Simulations.

An easy-to-use script named FESTA (Free Energy Surface Trajectory Analysis) is provided to streamline the extraction of equilibrium structures from free energy maps obtained with enhanced sampling simulation techniques and their associated trajectories. This approach efficiently identifies relevant structures by automatically selecting minima on the free energy surface using a connected-component labeling algorithm and extracts them utilizing a Shapely-polygon-based trajectory analysis process. The script is general and portable; it incorporates an automatic periodicity detection system; and multiprocessing is utilized to leverage all available computational resources for enhanced efficiency. The effectiveness of the proposed polygon-based approach is demonstrated through comparison with a naïve and largely inefficient loop-based script and its application across three distinct systems for benchmarking purposes.

Validation of an Improved Vision-Based Web Page Parsing Pipeline

In this article, we present a novel approach to quantitative evaluation of a model for parsing web pages as visual images, intended to provide improvements for users with assistive needs (cognitive or visual deficits, enabling decluttering or zooming and supporting more effective screen reader output). This segmentation-classification pipeline is tested in stages: We first discuss the validation of the segmentation algorithm, showing that our approach produces automated segmentations that are very similar to those produced by real users when making use of a drawing interface to designate edges and regions. We also examine the properties of these ground truth segmentations produced under different conditions. We then describe our Hidden Markov tree approach for classification and present results which serve provide important validation for this model. The analysis is set against effective choices for dataset and pruning options, measured with respect to manual ground truth labelling of regions. In all, we offer a detailed quantitative validation (focused on complex news pages) of a fully pipelined approach for interpreting web pages as visual images, an approach which enables important advances for users with assistive needs.

Total lightning signatures in a tornadic thunderstorm over the Pearl River Delta of Southern China

Tornado rises rotating air columns extending from the bottom of cumulonimbus clouds to the ground, often accompanied by strong convection with thunderstorms, hail, and short-term heavy precipitation, which seriously threaten people's lives and property safety. Studying the characteristics of thunderstorm activity during a tornado is crucial for comprehending the atmospheric electrical mechanisms involved in its generation process and developing cooperative methods for tornado warning and forecasting. Based on the VLF/LF total lightning location system and radar echo data, this paper analyzes the spatial-temporal evolution, frequency, polarity, and height of total lightning during a strong tornado (EF3) in the Pearl River Delta region on June 3, 2014. The lightning activity lasted for about 3.5 h, with a total of 41,117 total lightning flashes, of which intra-cloud (IC) flashes accounted for 74.7%, cloud-to-ground (CG) flashes accounted for 21.6%, and narrow bipolar events (NBEs) accounted for 3.7%. Connected-Component Labeling (CCL) algorithm was used to divide the thunderstorm into four stages: initiation, development, maturation, and dissipation, and it was observed that the occurrence of tornadoes was closely related to total lightning activities. The observed characteristics of lightning activity in the tornado are summarized through comparison with other studies.

A graph-learning based model for automatic diagnosis of Sjögren’s syndrome on digital pathological images: a multicentre cohort study

BackgroundSjögren’s Syndrome (SS) is a rare chronic autoimmune disorder primarily affecting adult females, characterized by chronic inflammation and salivary and lacrimal gland dysfunction. It is often associated with systemic lupus erythematosus, rheumatoid arthritis and kidney disease, which can lead to increased mortality. Early diagnosis is critical, but traditional methods for diagnosing SS, mainly through histopathological evaluation of salivary gland tissue, have limitations.MethodsThe study used 100 labial gland biopsy, creating whole-slide images (WSIs) for analysis. The proposed model, named Cell-tissue-graph-based pathological image analysis model (CTG-PAM) and based on graph theory, characterizes single-cell feature, cell-cell feature, and cell-tissue feature. Building upon these features, CTG-PAM achieves cellular-level classification, enabling lymphocyte recognition. Furthermore, it leverages connected component analysis techniques in the cell graph structure to perform SS diagnosis based on lymphocyte counts.FindingsCTG-PAM outperforms traditional deep learning methods in diagnosing SS. Its area under the receiver operating characteristic curve (AUC) is 1.0 for the internal validation dataset and 0.8035 for the external test dataset. This indicates high accuracy. The sensitivity of CTG-PAM for the external dataset is 98.21%, while the accuracy is 93.75%. In comparison, the sensitivity and accuracy for traditional deep learning methods (ResNet-50) are lower. The study also shows that CTG-PAM’s diagnostic accuracy is closer to skilled pathologists compared to beginners.InterpretationOur findings indicate that CTG-PAM is a reliable method for diagnosing SS. Additionally, CTG-PAM shows promise in enhancing the prognosis of SS patients and holds significant potential for the differential diagnosis of both non-neoplastic and neoplastic diseases. The AI model potentially extends its application to diagnosing immune cells in tumor microenvironments.

Thermal Image Based Occupant Count Measurement Model using Human Body Temperature for Smart Building

Objectives: The proposed Occupant Count Measurement (OCM) model aims to enhance sustainability, energy efficiency, comfort, and safety in smart buildings by accurately determining occupant count using thermal camera images and body temperature data. Methods: The model leverages real-time thermal camera images without the need for a pre-existing dataset. Key parameters include temperature threshold, occupant motion, size, and shape to ensure accurate occupancy estimation. The K-means algorithm identifies and clusters regions of interest (ROI) in thermal images corresponding to human body temperatures. The model also employs sensors like PIR, RGB cameras, and thermal image sensors. Manual counting serves as a benchmark for comparison. Findings: The K-means algorithm extracts regions with elevated temperatures related to human bodies from thermal images, partitioning them into K-clusters based on temperature ranges and assigning each pixel to one of the clusters. A temperature threshold differentiates human clusters in the thermal image, while connected component labeling refines human object segmentation by identifying blobs, which are then used for occupant counting. The model's precision is assessed using diverse image sensors and compared to the actual number of occupants. The proposed OCM model achieves an accuracy of about 90.2% compared to traditional methods. Novelty: This study introduces an OCM model that uses thermal images to estimate the number of occupants in a room based on their body temperature. The method focuses on detecting and counting occupants by their overall thermal body signature, providing a novel approach to occupant measurement in smart buildings. Keywords: Thermal Images, Segmentation, Occupant Estimation, Occupant Comfort, Smart Building

An image processing approach for fatigue crack identification in cellulose acetate replicas

The cellulose acetate replication technique is an important method for studying material fatigue. However, extracting accurate information from pictures of cellulose replicas poses challenges because of distortions and numerous artifacts. This paper presents an image processing procedure for effective fatigue crack identification in plastic replicas. The approach employs thresholding, adaptive Gaussian thresholding, and Otsu binarization to convert gray-scale images into binary ones, enhancing crack visibility. Morphological operations refine object shapes, and Connected Components Analysis facilitates crack identification. Despite limited data, the handcrafted feature extraction algorithm proves robust, addressing challenges. The algorithm shows efficacy in detecting cracks as small as 30 μm, even in the presence of cellulose replication artifacts. The results highlight ability to capture significant cracks’ orientation, length, and growth stages, essential for understanding fatigue mechanisms. Analysis of results, especially evaluation metrics encompassing false positives and false negatives, provides a comprehensive understanding of the algorithm’s strengths and limitations. The proposed tool is available on GitHub.

Quantitative cellular pathology of the amygdala in temporal lobe epilepsy and correlation with magnetic resonance imaging volumetry, tissue microstructure, and sudden unexpected death in epilepsy risk factors.

Amygdala enlargement can occur in temporal lobe epilepsy, and increased amygdala volume is also reported in sudden unexpected death in epilepsy (SUDEP). Apnea can be induced by amygdala stimulation, and postconvulsive central apnea (PCCA) and generalized seizures are both known SUDEP risk factors. Neurite orientation dispersion and density imaging (NODDI) has recently provided additional information on altered amygdala microstructure in SUDEP. In a series of 24 surgical temporal lobe epilepsy cases, our aim was to quantify amygdala cellular pathology parameters that could predict enlargement, NODDI changes, and ictal respiratory dysfunction. Using whole slide scanning automated quantitative image analysis methods, parallel evaluation of myelin, axons, dendrites, oligodendroglia, microglia, astroglia, neurons, serotonergic networks, mTOR-pathway activation (pS6) and phosphorylated tau (pTau; AT8, AT100, PHF) in amygdala, periamygdala cortex, and white matter regions of interest were compared with preoperative magnetic resonance imaging data on amygdala size, and in 13 cases with NODDI and evidence of ictal-associated apnea. We observed significantly higher glial labeling (Iba1, glial fibrillary acidic protein, Olig2) in amygdala regions compared to cortex and a strong positive correlation between Olig2 and Iba1 in the amygdala. Larger amygdala volumes correlated with lower microtubule-associated protein (MAP2), whereas higher NODDI orientation dispersion index correlated with lower Olig2 cell densities. In the three cases with recorded PCCA, higher MAP2 and pS6-235 expression was noted than in those without. pTau did not correlate with SUDEP risk factors, including seizure frequency. Histological quantitation of amygdala microstructure can shed light on enlargement and diffusion imaging alterations in epilepsy to explore possible mechanisms of amygdala dysfunction, including mTOR pathway activation, that in turn may increase the risk for SUDEP.

Classifying 8 Years of MMS Dayside Plasma Regions via Unsupervised Machine Learning

AbstractThe Magnetospheric Multiscale (MMS) mission has probed Earth's magnetosphere, magnetosheath, and near‐Earth solar wind for over 8 years. We utilize an unsupervised learning algorithm, Gaussian mixture model clustering, along with feature generation and simple post‐cleaning methods to automatically classify 8 years of MMS dayside observations into four plasma regions (magnetosphere, magnetosheath, solar wind, and ion foreshock) at 1‐min resolution. With these plasma regions distinguished, we have also identified boundary surfaces (e.g., magnetopause, bow shock). We validate our results on manually generated and rule based region labels described in the literature. We report overlap rates in our cluster determined magnetopauses and bow shocks against Scientist‐in‐the Loop (SITL) identified transitions and published databases. Our features are general and our model is extensible, potentially making it applicable to observational data from multiple other missions.

Can markets for nature conservation be successful? An integrated assessment of a product label for biodiversity practices in Germany

Biodiversity is declining in many ecosystems, particularly in agricultural landscapes. So far, instruments such as the Common Agricultural Policy (CAP) have not been able to halt this trend. Stricter legal regulations tend to be poorly received by farmers and this leads to conflicts. However, their acceptance is crucial as agriculture is a major stakeholder in biodiversity conservation. In this context, market-based approaches are alternative or complementary instruments for biodiversity conservation. However, there are few examples in Germany and the potential economic feasibility on a larger scale has hardly been investigated. We, therefore, analyzed the economic and agronomic impacts of a potential new market label for biodiversity-friendly products on conventional arable farms in two German case study regions – Brandenburg and North Rhine-Westphalia (NRW) – with different agricultural frameworks. The label requirements were defined in advance by a group of experts in the field of nature conservation. In terms of methodology, we combined a survey of nine companies in the food sector, for instance food retailers, that looked at potential price premium scenarios for products with the label and economic land use modelling. The participation of farms led to an average reduction of gross margins per hectare of arable land of approximately 64 € in Brandenburg and about 96 € in NRW. This revealed spatial disparities. Regarding biodiversity impacts, pesticide use was reduced by about 50 % in both regions. This indicated a high level of effectiveness coupled with comparably moderate farm adaptation costs. Price premiums under the market label for potatoes, wheat, and rye averaging 10 % could almost compensate for the loss of farm income in both regions. Premiums between 5 % and 10 % were also the order of magnitude estimated by the companies that took part in the survey. At the same time, the survey did show that potential transaction costs could be quite high. We, therefore, concluded that the market alone was unlikely to finance the introduction of the label in the case study regions. There was a need for accompanying instruments such as the CAP or private business investment. However, given the case study character, the results should be interpreted with caution and cannot be considered representative of all German or European agricultural conditions. They require further research. Nevertheless, our results are highly relevant for policy makers, as they show that biodiversity conservation, at least in the study regions considered, cannot be tackled with one single instrument, but requires a mix of different ones. For instance, a combination of policies and private initiatives, such as the CAP and a private market label like the one considered in the study.