Application Of Sampling Research Articles

Optical Time-Domain Quantum State Tomography on a Subcycle Scale

Following recent progress in the experimental application of electro-optic sampling to the detection of the quantum fluctuations of the electromagnetic-field ground state and ultrabroadband squeezed states on a subcycle scale, we propose an approach to elevate broadband electro-optic sampling from a spectroscopic method to a full quantum tomography scheme, able to reconstruct a free-space quantum state directly in the time domain. By combining two recently developed methods to theoretically describe quantum electro-optic sampling, we analytically relate the photon-count probability distribution of the electro-optic signal to a transformed phase-space quasiprobability distribution of the sampled quantum state as a function of the time delay between the sampled midinfrared pulsed state and an ultrabroadband near-infrared probe pulse. We catalog and analyze sources of noise and show that in quantum electro-optic sampling with an ultrabroadband probe pulse one can expect to observe thermalization due to entanglement breaking. Mitigation of the thermalization noise enables a tomographic reconstruction of broadband quantum states while granting access to its dynamics on a subcycle scale. Published by the American Physical Society 2024

Regularisation and central limit theorems for an inverse problem in network sampling applications

ABSTRACT An inverse problem motivated by packet sampling in communication networks and edge sampling in directed complex networks is studied through the operator perspective. The problem is shown to be ill-posed, with the resulting naive estimator potentially having very heavy tails, satisfying non-Gaussian central limit theorem and showing poor statistical performance. Regularisation of the problem leads to the Gaussian central limit theorem and superior performance of the regularised estimator, as a result of desirable properties of underlying operators. The limiting variance and convergence rates of the regularised estimator are also investigated. The results are illustrated on synthetic and real data from communication and complex networks.

DeepPBI-KG: a deep learning method for the prediction of phage-bacteria interactions based on key genes.

Phages, the natural predators of bacteria, were discovered more than 100years ago. However, increasing antimicrobial resistance rates have revitalized phage research. Methods that are more time-consuming and efficient than wet-laboratory experiments are needed to help screen phages quickly for therapeutic use. Traditional computational methods usually ignore the fact that phage-bacteria interactions are achieved by key genes and proteins. Methods for intraspecific prediction are rare since almost all existing methods consider only interactions at the species and genus levels. Moreover, most strains in existing databases contain only partial genome information because whole-genome information for species is difficult to obtain. Here, we propose a new approach for interaction prediction by constructing new features from key genes and proteins via the application of K-means sampling to select high-quality negative samples for prediction. Finally, we develop DeepPBI-KG, a corresponding prediction tool based on feature selection and a deep neural network. The results show that the average area under the curve for prediction reached 0.93 for each strain, and the overall AUC and area under the precision-recall curve reached 0.89 and 0.92, respectively, on the independent test set; these values are greater than those of other existing prediction tools. The forward and reverse validation results indicate that key genes and key proteins regulate and influence the interaction, which supports the reliability of the model. In addition, intraspecific prediction experiments based on Klebsiella pneumoniae data demonstrate the potential applicability of DeepPBI-KG for intraspecific prediction. In summary, the feature engineering and interaction prediction approaches proposed in this study can effectively improve the robustness and stability of interaction prediction, can achieve high generalizability, and may provide new directions and insights for rapid phage screening for therapy.

Application of low-density polyethylene (LDPE) passive samplers for monitoring PAHs in groundwater

Equilibrium passive sampling continues to find increasing use for performing in situ assessments and monitoring of hydrophobic organic compounds (HOCs). Although this method has been successfully used in several field studies including open surface waters and sediments, comparatively, their use in groundwater has been very limited. In this study, low-density polyethylene (LDPE) passive samplers were deployed for 80 days in three groundwater wells contaminated with polycyclic aromatic hydrocarbons (PAHs). Prior to deployment, LDPE was loaded with performance reference compounds (PRCs) consisting of deuterated PAHs and their release used to ascertain system equilibrium. Within the 80-day deployment period, LDPE-groundwater equilibrium was confirmed for PAHs with molecular weights (MWs) in the range of 178 to 228 (i.e. anthracene, chrysene). Measured freely dissolved concentrations (Cw) were between one to three orders of magnitude lower than the total filtered concentrations (Ctotal) in the studied wells. The sum of PAHs (ΣPAHs) measured based on Cw and Ctotal were 2.05, 0.07 and 29.2 μg L−1 and 197, 59.7 and 1010 μg L−1, at wells 1, 2 and 3, respectively. A separate dataset, comprising long-term (2010 to 2022) concentrations of PAHs in total (i.e., unfiltered) groundwater, is also presented to provide insight into PAH contamination levels at the assessed groundwater wells based on conventional measurement. Estimated in situ LDPE daily clearance volumes (2.34 to 27.56 Ld−1) for the target analytes were far less than the daily turnover of ground water (144 to 348 Ld−1) encountered in the wells eliminating the possibility of depletive sampling of the groundwater by the passive samplers. These results represent the first published study on the practical application of equilibrium passive sampling using LDPE for monitoring and quantitatively assessing PAHs in groundwater. Also, this work demonstrates that LDPEs are a useful tool for measuring the Cw of PAHs in groundwater, a critical contaminant in many ecological and human health risk assessments.

Adoption of new banking models from a consumer perspective: the case of Iran

PurposeTraditionally, the banking and financial sectors have pioneered adoption of new technologies and business models. One important digital banking model that has proven its efficacy in recent times, is Digital-Only Banking (DOB) where consumers interact with their banks through digital channels only. Having detailed knowledge of what actually happens at the consumer level during the adoption of new digital models and technologies is paramount to the success of these technological initiatives. The present study aims to investigate DOB adoption behavior and possible barriers using a quantitative approach at the consumer level. A conceptual model is developed by extending the Unified Theory of Acceptance and Use of Technology (UTAUT) model, incorporating Trust (TR), Perceived Risk (PR) constructs and cultural moderators of Individualism (IDV) and Uncertainty Avoidance (UA).Design/methodology/approachFor this study, an online survey instrument was created and administered in Iran. The research sample was selected through the application of purposive sampling. Data from 788 respondents were analyzed. The proposed model was tested using Partial Least Square.?.s Structural Equation Modeling (PLS-SEM).FindingsThe results show that DOB adoption is positively influenced by Effort Expectancy (EE), Social Influence (SI), and Facilitating Conditions (FC), while PR negatively influences DOB adoption intention. Unexpectedly, the results indicate that TR has no significant impact on DOB usage intention. Additionally, this study demonstrates that with individuals having a low level of IDV, the relationship between PE and BI is stronger, and with individuals having a low level of UA, the impact of SI on BI is stronger. It also reveals that the impact of TR on BI is stronger in low individualistic cultures.Practical implicationsDOB providers should enhance support features of their services or provide facilities that make it simpler for users to accomplish online transactions. Here, in order to improve the UI/UX design of their apps, DOB product managers should carefully observe the technical guidelines of the operating systems of digital devices, such as the Human Interface Guidelines (HIG) for iOS and Material You for Android. Additionally, DOB providers should build partnerships with mega online retailers to provide hassle-free and easy to use payment solutions for consumers.Originality/valueDOB, as a novel and business model, has been investigated in very few studies, especially regarding any which focus on its adoption. To fill this gap, this research investigates DOB adoption through a modified version of the UTAUT model. The findings of this study suggest that future research regarding DOB should consider sources of TR, types of non-adopters, and context. This study, as the first of its kind in DOB literature, also highlights the significant role played by cultural values in customer behavior regarding DOB adoption.

Healthcare Awareness Survey

This prospective cross-sectional study employs a survey quantitative research method to delve into the realm of healthcare awareness among students currently enrolled at GD Goenka University, located in Gurgaon. Through meticulous application of simple random sampling, a sample size ranging between 50 to 70 students is carefully selected, ensuring each individual within the population has an equal opportunity of inclusion, thereby providing a representative snapshot of the student community’s health care preferences.

Microneedle patch casting using a micromachined carbon master for enhanced drug delivery

For successful treatment of diseases, sufficient therapeutics must be provided to the body. Microneedle applications in therapeutic delivery and analytics sampling are restricted because of various issues, including smaller area for drug loading and analytics sampling. To achieve sufficient drug loading and analytics sampling and improve drug penetration while maintaining painless administration, patch-type microneedle arrays were designed and fabricated using polymer casting from a conical cavity mold. Microcavities were formed on a carbon plate via micromechanical machining. A porous polymer layer was coated on a microneedle patch (MNP). The pores of the porous polymer layer provided space and channels for drug delivery. A pH-sensitive polymer layer was employed to cap the porous polymer layer, which prevented drug leakage during storage and provided a stimulus drug release in response to body pH conditions. The drug can be delivered through holes connected to both sides of the patch. The drug release of the MNP was investigated in vitro and in vivo and showed conceptual proof that these MNs have the potential to enhance treatment protocols for various diseases with the flexibility of coating and therapeutic materials and offer significant scope for further variations and advancement.

Detection of Epstein‒Barr virus DNA methylation as tumor markers of nasopharyngeal carcinoma patients in saliva, oropharyngeal swab, oral swab, and mouthwash.

Saliva biopsy of nasopharyngeal carcinoma (NPC) has been developed in our latest study, indicating the application of oral sampling in NPC detection. Further exploration of the potential for self-sampling from the oral cavity is necessary. A total of 907 various samples from oral cavity, including saliva (n=262), oropharyngeal swabs (n=250), oral swabs (n=210), and mouthwash (n=185), were collected. Epstein‒Barr virus (EBV) DNA methylation at the 12,420bp CpG site in EBV genome from the repeat-copy W promoter (Wp) region and at the 11,029bp CpG site in the single-copy C promoter (Cp) region were simultaneously detected in these samples. A significant increase in EBV methylation, no matter at Wp or Cp region, was found in all types of samples from NPC patients. However, EBV DNA methylation in saliva and oropharyngeal swab showed a better diagnostic performance in detecting NPC. The combination of these two sample types and two markers could help to improve the detection of NPC. Our study further explored the optimal self-sampling methods and detection target in the detection of NPC and may facilitate the application of EBV DNA methylation detection in a home-based large-scale screening of NPC.

Does Negative Sampling Matter? a Review With Insights Into its Theory and Applications.

Negative sampling has swiftly risen to prominence as a focal point of research, with wide-ranging applications spanning machine learning, computer vision, natural language processing, data mining, and recommender systems. This surge in interest prompts us to question the fundamental impact of negative sampling: Does negative sampling really matter? Is there a general framework that can incorporate all negative sampling methods? In what fields is it applied? Addressing these questions, we propose a general framework that using negative sampling. Delving into the history of negative sampling, we chart its evolution across five distinct trajectories. We dissect and categorize the strategies used to select negative sample candidates, detailing global, local, mini-batch, hop, and memory-based approaches. Our comprehensive review extends to an analysis of current negative sampling methodologies, systematically grouping them into five classifications: static, hard, GAN-based, Auxiliary-based, and In-batch. Beyond detailed categorization, we explore the practical application of negative sampling across various fields. Finally, we briefly discuss open problems and future directions for negative sampling.

Sigmoni: classification of nanopore signal with a compressed pangenome index.

Improvements in nanopore sequencing necessitate efficient classification methods, including pre-filtering and adaptive sampling algorithms that enrich for reads of interest. Signal-based approaches circumvent the computational bottleneck of basecalling. But past methods for signal-based classification do not scale efficiently to large, repetitive references like pangenomes, limiting their utility to partial references or individual genomes. We introduce Sigmoni: a rapid, multiclass classification method based on the r-index that scales to references of hundreds of Gbps. Sigmoni quantizes nanopore signal into a discrete alphabet of picoamp ranges. It performs rapid, approximate matching using matching statistics, classifying reads based on distributions of picoamp matching statistics and co-linearity statistics, all in linear query time without the need for seed-chain-extend. Sigmoni is 10-100× faster than previous methods for adaptive sampling in host depletion experiments with improved accuracy, and can query reads against large microbial or human pangenomes. Sigmoni is the first signal-based tool to scale to a complete human genome and pangenome while remaining fast enough for adaptive sampling applications. Sigmoni is implemented in Python, and is available open-source at https://github.com/vshiv18/sigmoni.

Refraction color based contact detection for single cell micromanipulation: a cornerstone for precise automation

Abstract Single cell studies represent a cutting-edge field in life science. For cells growing on a soft substrate, a precise micromanipulation is indispensable in order for high-quality single-cell sampling. The foundation of micromanipulation is the accurate triaxial movement control, of which the major challenge lies in the z-axis positioning. Conventional methods rely on manual operation but are hampered by low efficiency. Resorting to additional sensors and cameras might help, whereas the system configuration becomes inevitably complicated. In this work, a vision-based approach utilizing interface refraction color is reported. By monitoring the color change induced by refraction during the contact process, we realized an accurate movement control of the end-effector. The reported method is capable of detecting the z-axis contact between the capillary tip and the substrate in a fully automated manner. Tested by 70 batches of a total of 3840 images, the proposed technique achieves an overall accuracy of 94.28% with precision of 2.73 μm, sufficing in the applications of single cell sampling. Detecting z-axis contact by interface refraction color marks an innovative computer vision method and it is proposed and validated in this work. Our method holds a promise of being a potent part of a close-loop control mechanism for automated single cell micromanipulation.

Dual-comb operation in an all-polarization-maintaining ultrafast holmium-doped fiber laser

We present a dual-comb fiber laser in the wavelength range of 2.1 μm from an all-polarization-maintaining holmium-doped fiber laser based on a reflective nonlinear amplifying loop mirror (Figure- 9) scheme. The dynamics of the laser, where net cavity dispersion is controlled by varying the length of the intra-cavity dispersion-compensating fiber (DCF), has been fully investigated. The laser is found capable of mode-locking with a net cavity dispersion value ranging from net anomalous (−0.19 ps2) to net normal (0.11 ps2). More interestingly, without any intentional design, dual-comb operation with a relatively small offset frequency, down to a few Hertz, is observed in the system. The dual-comb operation is believed to arise from direction multiplexing in the loop cavity and polarization multiplexing in the linear arm, and is likely to be a generic solution for Figure- 9 lasers. Such a dual-comb system with a characteristic small frequency difference can provide sample interval with higher precision in asynchronous optical sampling (ASOP) applications.

Application of video adaptive sampling based on soft computing in higher education evaluation and E-learning interactive environment

The level of teaching quality largely reflects the overall strength of universities and has a direct impact on the quality of cultivating high-quality talents. In this context, higher education institutions are constantly striving to explore innovative methods, but this diverse educational environment also brings challenges to the evaluation methods of teacher teaching quality. This article explores the application of video adaptive sampling in AI data analysis of university education evaluation based on soft computing technology. In order to solve the data sampling problem in university education evaluation, this paper proposes a video adaptive sampling method based on soft computing. In this paper, an E-learning virtual interactive system is designed, multimedia teaching materials matching the requirements of E-learning environment are created, tools suitable for E-learning virtual interactive environment are selected and integrated, soft computing technology is adopted, intelligent algorithms and data analysis methods are combined. By extracting and analyzing the key information in the video, the video sampling strategy is dynamically adjusted to realize the adaptive video sampling. Modeling the content characteristics of videos through fuzzy logic, and using genetic algorithms to optimize the sampling frequency and accuracy of videos. The experimental results indicate that the video adaptive sampling method based on soft computing can effectively improve the accuracy and efficiency of AI data analysis in university education evaluation. Compared with traditional fixed sampling methods, this method can dynamically adjust according to the characteristics of video content, reduce redundant data sampling, and improve the accuracy of data analysis.

Distance and T-square sampling for spatial measures of tree diversity

Distance sampling and its statistically improved variant, T-square sampling, are important sampling methods in plant ecology. They have often been applied in the context of plant density estimations and are comparatively easy to implement, since they intuitively follow the nearest-neighbour principle and thus do not require the layout of sample plots. Previous research studying distance sampling suggested that T-square sampling may also lead to an improved estimation of spatial tree diversity indices. We simulated distance and T-square sampling in six large fully mapped forest areas for seven tree diversity indices of which some competed for the same diversity aspect, i.e. tree location (dispersion), tree species and tree size diversity. Our results demonstrated that both distance and T-square sampling are indeed robust methods for sampling spatial measures of tree diversity. The sample size required for a sampling error of 10% does not exceed 20% of the total number of trees in a sampling area. T-square sampling has the ability to adapt to different spatial patterns of tree locations and this ability is key to the way the method controls estimation bias. The sample size required for species mingling and size differentiation clearly depends on the underlying spatial tree pattern in the sampling area. With most diversity indices, sample size reductions between 0.06% and 40% could be achieved by the application of T-square sampling compared to traditional distance sampling. All other conditions being equal, we could identify the uniform angle index, the species mingling index and the size differentiation index as those diversity indices achieving lower sampling error values than their competitors. For tree density estimations the Diggle and Byth estimators performed best. Based on our results, T-square sampling can be considered a robust sampling method for spatial tree diversity indices that is easy to apply in the field.

Importance Sampling BRDF Derivatives

We propose a set of techniques to efficiently importance sample the derivatives of a wide range of Bidirectional Reflectance Distribution Function (BRDF) models. In differentiable rendering, BRDFs are replaced by their differential BRDF counterparts, which are real-valued and can have negative values. This leads to a new source of variance arising from their change in sign. Real-valued functions cannot be perfectly importance sampled by a positive-valued PDF, and the direct application of BRDF sampling leads to high variance. Previous attempts at antithetic sampling only addressed the derivative with the roughness parameter of isotropic microfacet BRDFs. Our work generalizes BRDF derivative sampling to anisotropic microfacet models, mixture BRDFs, Oren-Nayar, Hanrahan-Krueger, among other analytic BRDFs. Our method first decomposes the real-valued differential BRDF into a sum of single-signed functions, eliminating variance from a change in sign. Next, we importance sample each of the resulting single-signed functions separately. The first decomposition, positivization, partitions the real-valued function based on its sign, and is effective at variance reduction when applicable. However, it requires analytic knowledge of the roots of the differential BRDF, and for it to be analytically integrable too. Our key insight is that the single-signed functions can have overlapping support, which significantly broadens the ways we can decompose a real-valued function. Our product and mixture decompositions exploit this property, and they allow us to support several BRDF derivatives that positivization could not handle. For a wide variety of BRDF derivatives, our method significantly reduces the variance (up to 58× in some cases) at equal computation cost and enables better recovery of spatially varying textures through gradient-descent-based inverse rendering.

Well-spread samples with dynamic sample sizes.

A spatial sampling design determines where sample locations are placed in a study area so that population parameters can be estimated with relatively high precision. If the response variable has spatial trends, spatially balanced or well-spread designs give precise results for commonly used estimators. This article proposes a new method that draws well-spread samples over arbitrary auxiliary spaces and can be used for master sampling applications. All we require is a measure of the distance between population units. Numerical results show that the method generates well-spread samples and compares favorably with existing designs. We provide an example application using several auxiliary variables to estimate total aboveground biomass over a large study area in Eastern Amazonia, Brazil. Multipurpose surveys are also considered, where the totals of aboveground biomass, primary production, and clay content (3 responses) are estimated from a single well-spread sample over the auxiliary space.

Infrared: a declarative tree decomposition-powered framework for bioinformatics.

Many bioinformatics problems can be approached as optimization or controlled sampling tasks, and solved exactly and efficiently using Dynamic Programming (DP). However, such exact methods are typically tailored towards specific settings, complex to develop, and hard to implement and adapt to problem variations. We introduce the Infrared framework to overcome such hindrances for a large class of problems. Its underlying paradigm is tailored toward problems that can be declaratively formalized as sparse feature networks, a generalization of constraint networks. Classic Boolean constraints specify a search space, consisting of putative solutions whose evaluation is performed through a combination of features. Problems are then solved using generic cluster tree elimination algorithms over a tree decomposition of the feature network. Their overall complexities are linear on the number of variables, and only exponential in the treewidth of the feature network. For sparse feature networks, associated with low to moderate treewidths, these algorithms allow to find optimal solutions, or generate controlled samples, with practical empirical efficiency. Implementing these methods, the Infrared software allows Python programmers to rapidly develop exact optimization and sampling applications based on a tree decomposition-based efficient processing. Instead of directly coding specialized algorithms, problems are declaratively modeled as sets of variables over finite domains, whose dependencies are captured by constraints and functions. Such models are then automatically solved by generic DP algorithms. To illustrate the applicability of Infrared in bioinformatics and guide new users, we model and discuss variants of bioinformatics applications. We provide reimplementations and extensions of methods for RNA design, RNA sequence-structure alignment, parsimony-driven inference of ancestral traits in phylogenetic trees/networks, and design of coding sequences. Moreover, we demonstrate multidimensional Boltzmann sampling. These applications of the framework-together with our novel results-underline the practical relevance of Infrared. Remarkably, the achieved complexities are typically equivalent to the ones of specialized algorithms and implementations. Infrared is available at https://amibio.gitlabpages.inria.fr/Infrared with extensive documentation, including various usage examples and API reference; it can be installed using Conda or from source.

The Production Splitting Method of Offshore Multilayer Combined Water Flooding Gas Wells with Gas Dissolving.

Offshore gas reservoirs are characterized by thin interlayers, high production, few wells, etc., and are often exploited by multilayer combined mining, whereas the production dynamics of multilayer gas reservoirs are very different from those of single-layer gas reservoirs. Therefore, clarifying the gas production contribution of each layer in multilayer combined gas reservoirs is an important prerequisite for analyzing the potential of gas reservoirs and realizing efficient development. In this paper, unlike the past method of evaluating the gas production contribution of each layer by using the KH attribute of the reservoir, we combined the modified B-L equation considering CO2 dissolution and the multilayer multizone seepage equation to establish a dynamic split model of the production dynamics of multilayer water-driven gas reservoirs, verified the reliability of the model through the numerical model and the results of the production well logging, quantitatively analyzed the degree of influence of each parameter on the contribution of the layered gas production, and designed the orthogonal experiments. The main controlling factors of the gas production contribution of each layer were determined. The results of the study show that (1) the main controlling factors for the gas production contribution of each layer in the early stage of WDG are, in order, permeability, thickness, outer boundary distance, porosity, CO2 content, and total gas production rate; however, the main controlling factors for the gas production contribution of each layer in the late stage of WDG are, in order, thickness, permeability, outer boundary distance, porosity, CO2 content, and total gas production rate; and the combined view shows that the permeability and thickness have the greatest influence. (2) In multilayer production, the conditions of high permeability, close gas-water boundary, poor gas content, and low CO2 content will reduce the gas production contribution of the layer with the increase of production time. (3) Compared with the results of production logging and numerical simulation, the split model can better predict the gas production of each layer, and the prediction error is no more than 10%. (4) By comparing with the numerical simulation results, the model can realize the prediction of the time of seeing water in the layer with stronger water body capability. (5) The model takes into account the effect of the CO2 content, better reflects the actual gas composition of each layer, and can improve the production prediction accuracy by up to 4%. Considering the high cost of production logging in offshore oil and gas fields, the inability of the KH method to reflect the dynamic changes of gas production in each layer, the poor application of stratified sampling to dry gas reservoirs, and other limitations, the model in this paper can be utilized to simulate the multilayer water-driven gas drive process when the energy of the water body is strong by using the geological parameters of the reservoir and the fluid parameters, and the simulation results of this model provide directions for offshore multilayer water-driven gas reservoirs to improve the recovery rate, and for plugging and regulating the water and exploiting the potential of gas wells that have seen water.

Assessing the analytical reliability of traditional and novel sampling methods for the study of flower rewards quality

AbstractPollinator insects are declining worldwide also due to the alteration of their diet which plays a pivotal role in influencing their health status. Despite interspecific and intraspecific diversity in the diet, pollinators completely or partly rely on pollen and nectar as food sources. A precise characterization of the chemical composition of these flower resources represents a key step in the definition of pollinators' nutritional ecology. However, pollen and nectar represent challenging sources to collect and analyze, especially due to their small amounts per flower, and the application of suitable sampling and analysis tools is a pivotal step to perform dedicated studies and comparisons. Here, we compared a recently proposed tool based on a portable vacuum cleaner for floral pollen collection (E‐PoSa, i.e., Electronic Pollen Sampler) with traditional pollen sampling methods (i.e., anther collection and anther sieving) together with the evaluation of different nectar sampling techniques (i.e., centrifugation, microcapillaries, washing, and microrinsing) by looking at the differences in their quantitative recovery as well as their chemical profiling. Pollen and nectar were collected from three model flower species each. Our results demonstrated that different collection methods introduce biases in the nutritional profiling of floral rewards and specifically: (1) underestimation of the pollen protein and lipid content in the anther collection method; (2) reduction in the volume of recovered nectar by centrifugation; (3) overestimation of the glucose content in the nectar collected by flower washing and underestimation of the glucose content by microrinsing; and (4) relevant biases in the phytochemical profiles of pollen and nectar by analyzing the whole anthers and the nectar collected by washing the entire flower. Differences in methods were not directly related to the different productivity of pollen and nectar across species. The final goal of the study is to propose standardized, comparable, and easily accessible strategies for the study of flower resources that ultimately impact on pollinators' nutritional ecology. Acknowledging the potential influences of the sampling techniques and moving toward shared field protocols will advance the comprehension of species interactions, foraging patterns, and pollinators' nutritional needs.

A case study on the application of spore sampling for the monitoring of macrofungi.

Fungi play a vital role in ecosystem functioning, yet significant knowledge gaps persist in understanding their diversity and distribution leading to uncertainties about their threat status and extinction risk. This is partly owed to the difficulty of monitoring fungi using traditional fruiting body surveys. The present study evaluates airborne environmental DNA (eDNA) sampling as a monitoring tool with a focus on grassland macrofungi. We applied active and passive air sampling methods, complemented by extensive field surveys of waxcap and clavarioid fungi-species groups of high relevance for conservation. Twenty-nine species were recorded during the field surveys, 19 of which were also detectable by ITS2 metabarcoding of the collected samples. An additional 12 species from the studied genera were identified exclusively in air eDNA. We found that the patterns of species detection and read abundance in air samples reflected the abundance and occurrence of fruiting bodies on the field. Dispersal kernels fitted for the three dominant species predicted rapidly decreasing spore concentrations with increasing distance from fruitbodies. Airborne assemblages were dominated by a high diversity of common species, while rare and threatened red-listed species were under-represented, which underscores the difficulty in detecting rare species, not only in conventional surveys. Considering the benefits and drawbacks of air sampling and fruitbody surveys, we conclude that air sampling serves as a cost- and time-efficient tool to characterize local macrofungal communities, providing the potential to facilitate and improve future fungal monitoring efforts.