Spatial Sampling Resolution Research Articles

Unsupervised deep denoising for four-dimensional scanning transmission electron microscopy

By simultaneously achieving high spatial and angular sampling resolution, four dimensional scanning transmission electron microscopy (4D STEM) is enabling analysis techniques that provide great insight into the atomic structure of materials. Applying these techniques to scientifically and technologically significant beam-sensitive materials remains challenging because the low doses needed to minimise beam damage lead to noisy data. We demonstrate an unsupervised deep learning model that leverages the continuity and coupling between the probe position and the electron scattering distribution to denoise 4D STEM data. By restricting the network complexity it can learn the geometric flow present but not the noise. Through experimental and simulated case studies, we demonstrate that denoising as a preprocessing step enables 4D STEM analysis techniques to succeed at lower doses, broadening the range of materials that can be studied using these powerful structure characterization techniques.

Autonomous data sampling for high-resolution spatiotemporal fish biomass estimates

Many key ecological dynamics such as biomass distributions are only detectable on a fine spatiotemporal scale. Autonomous data collection with Unmanned Surface Vehicles (USV) creates new possibilities for cost efficient and high-resolution aquatic data sampling. However, the spatial coverage and sampling resolution remain uncertain due to the novelty of the technology. Further, there is no established method for analysing such fine-scale autocorrelated data without aggregation, potentially compromising data resolution. We here used a USV with an echosounder, a conductivity-temperature sensor and a flourometer to collect data from April–July 2019–2023 in a 60x80km area in the central Baltic Sea. The USV covered a total distance of 8000 nmi, over 42–81 days per year, with an average speed of 0.5 m/s. We combined the hydroacoustic data with publicly available oceanographic data from Copernicus Marine Service Information (CMSI) to describe seasonal distribution dynamics of a small pelagic fish community. Key oceanographic variables collected by the USV were correlated with CMSI estimates at daily/monthly resolution, respectively, to test for suitability to scale (Temperature 0.99/0.97; Salinity −0.77/−0.26; Chlorophyll-a 0.12/0.28). We investigated two approaches of Species Distribution Models (SDMs): generalized additive models (GAM) versus spatiotemporal generalized linear mixed effect models (GLMM). The GLMMs explained the observed data better than the GAMs (R2 0.31 and 0.20, respectively). The addition of environmental variables increased the explanatory capability of GAM and GLMM by 25 % and ~ 3 %, respectively. Due to the high data resolution, we found significant amounts of positive autocorrelation (R: 0.05–0.30) across more than 50 lags of observation. However, we found that diel patterns in fish detection strongly affected the abundance estimates due to diel vertically migrating species hiding in the ‘acoustic dead zone’ near the seabed. Such dynamics could only be estimated and corrected for in predictions on the high-resolution data, complicating the trade-off between autocorrelation and high-resolution for SDMs even further. We compared estimates and effect sizes/directions in identical SDMs on 2x2km/month aggregated (i.e non-autocorrelated) observations and non-aggregated (i.e. autocorrelated) observations, and found relatively little difference in spatiotemporal estimates (r = 0.80). For the first time, we predicted the distribution of a small pelagic fish community at a high spatial resolution, in an area essential to breeding top predators, opening up for new applications in ecological studies locally and globally.

Solving East Nile Delta's imaging challenges through new ocean-bottom-node acquisition and advanced imaging

The East Nile Delta, located offshore Egypt, is a challenging area for seismic imaging due to the presence of substantial velocity heterogeneity created by the complex Messinian unconformity and overlaying fault structures. Existing towed-streamer (TS) seismic data have been pushed to their technical limit but remain insufficient for adequate representation of the subsurface. Previous studies have shown that a step change in imaging is possible if advanced acquisition, processing, and velocity model building techniques are employed. Atoll Field is a candidate to benefit from such improvements in seismic data. Results show that ocean-bottom-node acquisition provides improved spatial sampling and higher resolution. Acquiring seismic data with offsets of more than 30 km facilitated the generation of a robust high-frequency velocity model that better captures subsurface complexity. This paper outlines the acquisition and processing, with a focus on the velocity model building process. The generated subsurface model is effectively used for imaging with conventional migration algorithms and full-waveform-inversion-derived reflectivity. Results show a significant step change from previous TS images at the Oligocene target, with channel features clearly imaged. This provides an improved understanding of the depositional systems and reduced depth uncertainty.

Critical dynamics and interictal epileptiform discharge: a comparative analysis with respect to tracking seizure risk cycles.

Epilepsy is characterized by recurrent, unprovoked seizures. Accurate prediction of seizure occurrence has long been a clinical goal since this would allow to optimize patient treatment, prevent injuries due to seizures, and alleviate the patient burden of unpredictability. Advances in implantable electroencephalographic (EEG) devices, allowing for long-term interictal EEG recordings, have facilitated major progress in this field. Recently, it has been discovered that interictal brain activity demonstrates circadian and multi-dien cycles that are strongly aligned, or phase locked, with seizure risk. Thus, cyclical brain activity patterns have been used to forecast seizures. However, in the effort to develop a clinically useful EEG based seizure forecasting system, challenges remain. Firstly, multiple EEG features demonstrate cyclical patterns, but it remains unclear which feature is best suited for predicting seizures. Secondly, the technology for long-term EEG recording is currently limited in both spatial and temporal sampling resolution. In this study, we compare five established EEG metrics:synchrony, spatial correlation, temporal correlation, signal variance which have been motivated from critical dynamics theory, and interictal epileptiform discharge (IED) which are a traditional marker of seizure propensity. We assess their effectiveness in detecting 24-h and seizure cycles as well as their robustness under spatial and temporal subsampling. Analyzing intracranial EEG data from 23 patients, we report that all examined features exhibit 24-h cycles. Spatial correlation, signal variance, and synchrony showed the highest phase locking with seizures, while IED rates were the lowest. Notably, spatial and temporal correlation were also found to be highly correlated to each other, as were signal variance and IED-suggesting some features may reflect similar aspects of cortical dynamics, whereas others provide complementary information. All features proved robust under subsampling, indicating that the dynamic properties of interictal activity evolve slowly and are not confined to specific brain regions. Our results may aid future translational research by assisting in design and testing of EEG based seizure forecasting systems.

Wind machines for frost damage mitigation: A quantitative 3D investigation based on observations

Wind machines have been increasingly used for frost damage mitigation in the agricultural community. During radiative frost nights, wind machines are used to erode near-surface thermal inversion by air mixing. The underlying mixing processes remain poorly understood. A full picture of warming effects caused by air mixing requires measurements with wide coverage and high resolution. Our study aimed to quantify the magnitude and area of warming by air mixing and identify the characteristic mixing processes downwind and upwind. We installed 9 km of fiber optic cables in a 6.75 ha orchard block, creating two horizontal planes and three vertical profiles. Quasi-3D temperature responses with spatial sampling and temporal resolution of 25 cm and 10 s, respectively, were obtained before and during machine operation. We found a 50% reduction of the local inversion strength (8 K) over 0.42 ha at 1 m and 0.46 ha at 2 m height. The warming area for a 30% reduction extends to 2.81 and 2.52 ha, respectively. As the propeller rotates 360°, the weak background wind substantially impacts the air mixing processes downwind and upwind. When jets blow along with background wind, the warming plumes arrive earlier than the jet due to horizontal advection from earlier warmed sections. The warming plumes consequently accumulate downwind and penetrate deep into the canopy. In contrast, in upwind direction, wind drag resistance causes warming plumes arrive later than the jet. Quadrant analysis reveals that flux transport during the machine operation is dominated by sweeping and ejection motions. Intermittent downdrafts of warm air and updrafts of cool air result in efficient vertical heat exchange. This feature makes wind machines highly effective in raising canopy airspace temperature to mitigate frost damage.

Dissolved Solute Sampling Across an Oxic-Anoxic Soil-Water Interface Using Microdialysis Profilers.

Biogeochemical processes shift rapidly in both spatial (millimeter scale) and temporal (hour scale to day scale) dimensions at the oxic-anoxic interface in response to disturbances. Deciphering the rapid biogeochemical changes requires in situ, minimally invasive tools with high spatial and temporal sampling resolution. However, the available passive sampling devices are not very useful in many cases either due to their disposable nature or the complexity and extensive workload for sample preparation. To address this problem, a microdialysis profiler with 33 individual polyethersulfone nanomembrane tubes (semipermeable, <20 nm pore size) integrated into the one-dimensional skeleton (60 mm) was established to iteratively sample the dissolved compounds in porewater across the soil-water interface at a high resolution of 1.8 mm (outer diameter plus one spacing, i.e., 0.1 mm between probes). The sampling mechanism is based on the principle of concentration gradient diffusion. The automatic loading of degassed water allows minimal disturbance to the chemical species across the oxic-anoxic interface. This paper describes the procedures of device setup and continuous porewater sampling across the soil-water interface on a daily basis. Concentration-depth profiles were selectively measured before (on Day 6) and after (on Day 7) disturbances induced by irrigation. The results showed that concentration-depth profiles were undergoing rapid changes, especially for redox-sensitive elements (i.e., iron and arsenic). These protocols can help investigate the biogeochemical responses across the soil-water interface under various disturbances caused by physical, chemical, and biological factors. The paper thoroughly discusses the advantages and disadvantages of this method for potential use in the environmental sciences.

Towards Real-Time Hyperspectral Multi-Image Super-Resolution Reconstruction Applied to Histological Samples.

Hyperspectral Imaging (HSI) is increasingly adopted in medical applications for the usefulness of understanding the spectral signature of specific organic and non-organic elements. The acquisition of such images is a complex task, and the commercial sensors that can measure such images is scarce down to the point that some of them have limited spatial resolution in the bands of interest. This work proposes an approach to enhance the spatial resolution of hyperspectral histology samples using super-resolution. As the data volume associated to HSI has always been an inconvenience for the image processing in practical terms, this work proposes a relatively low computationally intensive algorithm. Using multiple images of the same scene taken in a controlled environment (hyperspectral microscopic system) with sub-pixel shifts between them, the proposed algorithm can effectively enhance the spatial resolution of the sensor while maintaining the spectral signature of the pixels, competing in performance with other state-of-the-art super-resolution techniques, and paving the way towards its use in real-time applications.

An assessment of high temporal frequency satellite data for historic environment applications. A case study from Scotland

AbstractThis paper assesses the value of high temporal frequency satellite data with various spatial sampling resolutions for multi‐scalar historic environment survey and management use cases in Scotland, specifically for broad‐brush landscape characterisation, for monitoring the condition of monuments and for the discovery of otherwise unknown sites. Dealing with a part of the world where applications of satellite imagery are almost entirely unexplored, this study takes a real‐world approach, which foregrounds the purpose at hand rather than presenting a case study from an optimal setting. The study highlights the importance of detailed imagery to support interpretation in some instances, and the challenges of obtaining time‐critical optical imagery in a part of the world that experiences significant periods of cloud cover. The real‐world availability of data in such settings is assessed, highlighting that even with daily revisits, useable imagery cannot be guaranteed. The implications of current and past tasking patterns for availability of high‐resolution data now and in the future are discussed. The study identifies the complementary roles that satellite imagery can fulfil, while identifying the limitations that remain to fuller applications of such data, in a study that will be relevant to many parts of Europe and beyond.

Study on Laser Parameter Measurement System Based on Cone-Arranged Fibers and CCD Camera.

This paper proposes a new laser parameter measuring method based on cone-arranged fibers to further improve the measurable spot size, allowable incident angle range, and spatial sampling resolution. This method takes a conical array composed of flexible fibers to sample and shrink the cross-section spot of the laser beam, facilitating low-distortion shooting with a charge-coupled diode (CCD) camera, and adopts homogenized processing and algorithm analysis to correct the spot. This method is experimentally proven to achieve high-accuracy measurements with a decimeter-level spot-receiving surface, millimeter-level resolution, and high tolerance in order to incite skew angle. Comparing the measured spot under normal incidence with the real one, the root mean square error (RMSE) of their power in the bucket (PIB) curves is less than 1%. When the incident angle change is between −8° and 8°, the RMSE is less than 2% and the measurement error of total power is less than 5% based on the premise that the fiber’s numerical aperture (NA) is 0.22. The possibility of further optimizing the measurement method by changing the fiber parameters and array design is also reported.

Measuring Estuarine Total Exchange Flow From Discrete Observations

The exchange between estuaries and the coastal ocean is a key dynamical driver impacting nutrient and phytoplankton concentrations and regulating estuarine residence time, hypoxia, and acidification. Estuarine exchange flows can be particularly challenging to monitor because many systems have strong vertical and lateral velocity shear and sharp gradients in water properties that vary over space and time, requiring high-resolution measurements in order to accurately constrain the flux. The total exchange flow (TEF) method provides detailed information about the salinity structure of the exchange, but requires observations (or model resolution) that resolve the time and spatial co-variability of salinity and currents. The goal of this analysis is to provide recommendations for measuring TEF with the most efficient spatial sampling resolution. Results from three realistic hydrodynamic models were investigated. These model domains included three estuary types: a bay (San Diego Bay), a salt-wedge (Columbia River), and a fjord (Salish Sea). Model fields were sampled using three different mooring strategies, varying the number of mooring locations (lateral resolution) and sample depths (vertical resolution) with each method. The exchange volume transport was more sensitive than salinity to the sampling resolution. Most (>90%) of the exchange flow magnitude was captured by three to four moorings evenly distributed across the estuarine channel with a minimum threshold of 1-5 sample depths, which varied depending on the vertical stratification. These results can improve our ability to observe and monitor the exchange and transport of water masses efficiently with limited resources.

Multiplex Chemical Imaging Based on Broadband Stimulated Raman Scattering Microscopy

Stimulated Raman scattering (SRS) microscopy is a nonlinear optical technique for label-free chemical imaging. This analytical tool delivers chemical maps at high speed, and high spatial resolution of thin samples by directly interrogating their molecular vibrations. In its standard implementation, SRS microscopy is narrowband and forms images with only a single vibrational frequency at a time. However, this approach not only hinders the chemical specificity of SRS but also neglects the wealth of information encoded within vibrational spectra. These limitations can be overcome by broadband SRS, an implementation capable of extracting a vibrational spectrum per pixel of the image in parallel. This delivers hyperspectral data that, when coupled with chemometric analysis, maximizes the amount of information retrieved from the specimen. Thus, broadband SRS improves the chemical specificity of the system, allowing the quantitative determination of the concentration of the different constituents of a sample. Here, we report a protocol for chemical imaging with broadband SRS microscopy, based on a home-built SRS microscope operating with a custom differential multichannel-lock-in amplifier detection. It discusses the sample preparation, alignment of the SRS apparatus, and chemometric analysis. By acquiring vibrational Raman spectra, the protocol illustrates how to identify different chemical species within a mixture, determining their relative concentrations.

Multiplex Chemical Imaging Based on Broadband Stimulated Raman Scattering Microscopy.

Stimulated Raman scattering (SRS) microscopy is a nonlinear optical technique for label-free chemical imaging. This analytical tool delivers chemical maps at high speed, and high spatial resolution of thin samples by directly interrogating their molecular vibrations. In its standard implementation, SRS microscopy is narrowband and forms images with only a single vibrational frequency at a time. However, this approach not only hinders the chemical specificity of SRS but also neglects the wealth of information encoded within vibrational spectra. These limitations can be overcome by broadband SRS, an implementation capable of extracting a vibrational spectrum per pixel of the image in parallel. This delivers hyperspectral data that, when coupled with chemometric analysis, maximizes the amount of information retrieved from the specimen. Thus, broadband SRS improves the chemical specificity of the system, allowing the quantitative determination of the concentration of the different constituents of a sample. Here, we report a protocol for chemical imaging with broadband SRS microscopy, based on a home-built SRS microscope operating with a custom differential multichannel-lock-in amplifier detection. It discusses the sample preparation, alignment of the SRS apparatus, and chemometric analysis. By acquiring vibrational Raman spectra, the protocol illustrates how to identify different chemical species within a mixture, determining their relative concentrations.

Vertical SOC distribution and aromatic carbon in centuries old charcoal‐rich Technosols

AbstractCharcoal‐rich Technosols on century‐old relict charcoal hearths (RCHs) are the subject of ongoing research regarding potential legacy effects that result from historic charcoal production and subsequent charcoal amendments on forest soil properties and forest ecosystems today. RCHs consist mostly of Auh horizons that are substantially enriched in soil organic carbon (SOC), of which the largest part seems to be of pyrogenic origin (PyC). However, the reported range of SOC and PyC contents in RCH soil also suggests that they are enriched in nonpyrogenic SOC. RCH soils are discussed as potential benchmarks for the long‐term influence of biochar amendment and the post‐wildfire influences on soil properties. In this study, we utilised a large soil sample dataset (n = 1245) from 52 RCH sites in north‐western Connecticut, USA, to quantify SOC contents by total element analysis. The contents of condensed highly aromatic carbon as a proxy for black carbon (BC) were predicted by using a modified benzene polycarboxylated acid (BPCA) marker method in combination with diffuse reflectance infrared Fourier transform (DRIFT) spectroscopy‐based partial least square regression (r2 = 0.89). A high vertical spatial sampling resolution allowed the identification of soil organic matter (SOM) enrichment and translocation processes. The results show an average 75% and 1862% increase in TOC and BPCA‐derived carbon, respectively, for technogenic Auh horizons compared to reference soils. In addition to an increase in aromatic properties, increased carboxylic properties of the RCH SOC suggest self‐humification effects of degrading charcoal and thereby the continuing formation of leachable aromatic carbon compounds, which could have effects on pedogenic processes in buried soils. Indeed, we show BPCA‐derived carbon concentrations in intermediate technogenic Cu horizons and buried top/subsoils that suggest vertical translocation of highly aromatic carbon originating in RCH Auh horizons. Topmost Auh horizons showed a gradual decrease in total organic carbon (TOC) contents with increasing depth, suggesting accumulation of recent, non‐pyrogenic SOM. Lower aliphatic absorptions in RCH soil spectra suggest different SOM turnover dynamics compared to reference soils. Furthermore, studied RCH soils featured additional TOC enrichment, which cannot be fully explained now.Highlights BC to TOC ratio and high resolution vertical SOC distribution in 52 RCH sites were studied. RCH soils non‐BC pool was potentially different to reference soils. RCH soils feature TOC accumulation in the topmost horizon. There is BC translocation into buried soils on RCH sites.

Importance of Weighting High-Resolution Proxy Data From Bivalve Shells to Avoid Bias Caused by Sample Spot Geometry and Variability in Seasonal Growth Rate

Shells of bivalve mollusks serve as archives for past climates and ecosystems, and human-environmental interactions as well as life history traits and physiology of the animals. Amongst other proxies, data can be recorded in the shells in the form of element chemical properties. As demonstrated here with measured chemical data (10 elements) from 12 Arctica islandica specimens complemented by numerical simulations, mistakes during sclerochronological data processing can introduce significant bias, adding a further source of error to paleoenvironmental or biological reconstructions. Specifically, signal extraction from noisy LA-ICP-MS (Laser Ablation—Inductively Coupled Plasma—Mass Spectrometry) data generated in line scan mode with circular LA spots requires a weighted rather than an arithmetic moving average. Otherwise, results can be in error by more than 41%. Furthermore, if variations of seasonal shell growth rate remain unconsidered, arithmetic annual averages of intra-annual data will be biased toward the fast-growing season of the year. Actual chemical data differed by between 3.7 and 33.7% from weighted averages. Numerical simulations not only corroborated these findings, but indicated that arithmetic annual means can overestimate or underestimate the actual environmental variable by nearly 40% relative to its seasonal range. The magnitude and direction of the error depends on the timing and rate of both seasonal shell growth and environmental change. With appropriate spatial sampling resolution, weighting can reduce this bias to almost zero. On average, the error reduction attains 80% at a sample depth of 10, 92% when 20 samples were analyzed and nearly 100% when 100 samples were taken from an annual increment. Under some exceptional, though unrealistic circumstances, arithmetic means can be superior to weighted means. To identify the presence of such cases, a numerical simulation is advised based on the shape, amplitude and phase relationships of both curves, i.e., seasonal shell growth and the environmental quantity. To assess the error of the offset induced by arithmetic averaging, Monte Carlo simulations should be employed and seasonal shell growth curves randomly generated based on observed variations.

3-D Auxiliary Classifier GAN for Hyperspectral Anomaly Detection via Weakly Supervised Learning

Hyperspectral anomaly detection (AD) is important in Earth observation and remote sensing. However, the low spatial resolution of hyperspectral images, insufficient samples and lack of prior information limit the detection accuracy. To solve these problems, in this paper, we propose an auxiliary classifier generative adversarial network model based on a three-dimensional (3D) convolutional neural network named 3D AC-GAN. Firstly, the model is based on a 3D convolutional neural network design, with 3D tensors as samples. The network maintains valuable image spatial spectrum joint features to achieve good detection results. It can also generate sufficient samples to achieve dataset augmentation, solving the overfitting problem in GAN training. Secondly, we train the model with a weakly supervised method. The label of the samples is obtained through the coarse scanning method. Then, the AC-GAN is trained with the bootstrapping method to mitigate the impact of noise labels. The experimental results show that our proposed algorithm outperforms state-of-the-art AD algorithms.

Regional Variation in Communities of Demersal Fishes and Scavengers Across the CCZ and Pacific Ocean

We synthesize and analyze data from visual transecting approaches and baited camera studies to evaluate fish and invertebrate scavenger communities across the Clarion-Clipperton Zone (CCZ), an area of intense deep-sea mining interest, and neighboring areas of the abyssal Pacific. In abyssal regions including the CCZ most of the top predators are large mobile fishes and crustaceans, and the majority of these are also opportunistic scavengers. Top predators can exert important ecosystem influences and they can be susceptible to sustained anthropogenic disturbances, necessitating their study in the CCZ mining region. In total 157 baited camera deployments from 3 mining exploration license areas, 4 APEIs (Areas of Particular Environmental Interest – one type of no mining zone) and 4 other areas in the Pacific (Hawaii, California, New Zealand and Guam) and 122 visual transects from 7 exploration license areas, 4 no mining zones, and the Peru Basin (DISCOL area) were examined. Many taxa were observed in both sampling techniques but visual transects viewed few fishes overall. Fish and scavenger communities and diversity varied across the CCZ, significantly for baited camera data with a parallel but insignificant pattern for visual transects suggesting that even for these highly mobile species, not all regions of the CCZ are equivalent and the CCZ cannot be managed as one homogenous region. Further CCZ communities were different than communities elsewhere in the abyssal Pacific. The regional variations in community composition are largely the result of varying abundances of species rather than species presence/absence given that most, but not all, of the fishes and scavengers observed have very large ranges. On a more local scale, seamounts had a significantly different scavenger community than neighboring abyssal plains and thus contribute to regional diversity. Visual transect data revealed a similar but insignificant pattern due to low sample sizes. Given the coarse spatial resolution of sampling of fish and scavenger communities in the CCZ, it is not possible to evaluate if no mining zones (APEIs) adequately represent these communities nor where, or if, any biogeographic boundaries exist in the CCZ region. It is possible to conclude that a network of APEIs that covers the spectrum of available habitats at regional and more local scales will be key to conserving fish and scavenger biodiversity.

Capturing the spatial variability of algal bloom development in a shallow temperate lake

Abstract Algal blooms can have profound effects on the structure and function of aquatic ecosystems and have the potential to interrupt valuable ecosystem services. Despite the potential ecological and economic consequences of algal blooms, the spatial dynamics of bloom development in spatially complex ecosystems such as shallow lakes remain poorly characterised. Our goal was to evaluate the magnitude and drivers of spatial variability of algal biomass, dissolved oxygen, and pH over the course of a season, in a shallow lake in order to better understand the spatial dynamics of algal blooms in these ecosystems. We sampled 98 locations in a small eutrophic lake on a 65‐m grid for several parameters (chlorophyll a, phycocyanin, dissolved oxygen, pH, and temperature), weekly over 122 days. This was done to estimate the dynamics of variability and spatial autocorrelation during the course of multiple bloom events. We also compared the spatial measurements to a high frequency sensor deployed at a fixed station and estimated the optimal spatial sampling resolution by performing a rarefaction analysis. Spatial heterogeneity of algal pigments was high, particularly during bloom events, and this pattern and the overall severity of the bloom were not well captured with the fixed station monitoring. The pattern of algal pigments and other limnologically important variables (dissolved oxygen and pH) was related to the direction of prevailing winds 24 hr prior to sampling, the shallow northern basin where the main surface inlet is located, and heavy precipitation. Additionally, a dense bed of floating‐leaf macrophytes contributed to local patchiness in all variables. Finally, from the rarefaction analysis we found that minimal information about the mean state of the ecosystem was gained after c. 30 locations had been sampled. This study revealed how spatially heterogeneous shallow lakes are over the course of a single season, and that the magnitude of variability was highest during biologically intensive periods such as algal blooms. As such, continued research is needed across a range of trophic conditions to better understand the structure of horizontal variability in lakes. Overall, these data demonstrate the need for spatially explicit monitoring to better understand the dynamics and drivers of algal blooms in shallow lakes and to better manage ecosystem services.

Improving pest monitoring networks using a simulation-based approach to contribute to pesticide reduction

Conventional pest management mainly relies on the use of pesticides. However, the negative externalities of pesticides are now well known. More sustainable practices, such as Integrated Pest Management, are necessary to limit crop damage from pathogens, pests and weeds in agroecosystems. Reducing pesticide use requires information to determine whether chemical treatments are really needed. Pest monitoring networks (PMNs) are key contributors to this information. However, the effectiveness of a PMN in delivering relevant information about pests depends on its spatial sampling resolution and its memory length. The trade-off between the monitoring efforts and the usefulness of the information provided is highly dependent on pest ecological traits, the damage they can cause (in terms of crop losses), and economic drivers (production costs, agriculture product prices and incentives). Due to the high complexity of optimising PMNs, we have developed a theoretical model that belongs to the family of Dynamic Bayesian Networks in order to compare several PMNs performances. This model links the characteristics of a PMN to treatment decisions and the resulting pest dynamics. Using simulation and inference tools for graphical models, we derived the proportion of impacted fields, the number of pesticide treatments and the overall gross margins for three types of pest with contrasting levels of endocyclism. The term “endocyclic” refers to an organism whose development is mostly restricted to a field and highly depends on the inoculum present in the considered field. The presence of purely endocyclic pests at a given time increases the probability of reoccurrence. Conversely, slightly endocyclic pests have a low persistence. The simulation analysis considered ten scenarios: an expected margin-based strategy with a spatial resolution of four PMNs and two memory lengths (one year or eight years), as well as two extreme crop protection strategies (systematic treatments on all fields and systematic no treatment). For purely and mainly endocyclic pests (e.g. soil-borne pathogens and most weeds, respectively), we found that increasing the spatial resolution of PMNs made it possible to significantly decrease the number of treatments required for pest control. Taking past observations into account was also effective, but to a lesser extent. PMN information had virtually no influence on the control of non-endocyclic pests (such as flying insects or airborne plant pathogens) which may be due to the spatial coverage addressed in our study. The next step is to extend the analysis of PMNs and to integrate the information generated by PMNs into sustainable pest management strategies, both at the field and the landscape level.

How chromatic cues can guide human eye growth to achieve good focus.

The postnatal growing eye uses visual cues to actively control its own axial elongation to achieve and maintain sharp focus, a process termed emmetropization. The primary visual cue may be the difference in image sharpness as sensed by the arrays of short- and long-wavelength sensitive cone photoreceptors caused by longitudinal chromatic aberration: Shorter wavelengths focus in front of longer wavelengths. However, the sparse distribution of short-wavelength sensitive cones across the retina suggests that they do not have sufficient spatial sampling resolution for this task. Here, we show that the spacing of the short-wavelength sensitive cones in humans is sufficient for them, in conjunction with the longer wavelength cones, to use chromatic signals to detect defocus and guide emmetropization. We hypothesize that the retinal spacing of the short-wavelength sensitive cones in many mammalian species is an evolutionarily ancient adaption that allows the efficient use of chromatic cues in emmetropization.

Mini-distributed acoustic sensing module for submarine application

High-resolution submarine seismic survey techniques have become necessary in many actual geological and geophysical investigations. A new type of mini-distributed acoustic sensing (DAS) module is developed for working at the bottom of the sea with several kilometers long single-mode fiber cable for tens of thousands of channels at the same time. Integrated designs of optics and electrics help to significantly reduce volume and power consumption. Compared with a common land-based DAS system, the size and power consumption of the mini-DAS module are significantly optimized. The size is 150 × 300 × 110 mm3 (width × depth × height), and the power consumption is down to 25 W. The spatial sampling resolution of ∼0.8 m is retained for high-resolution seismic profile in the deep sea survey. The upper limit of response frequency is set by 500 Hz for the channel sample rate of 1 kS / s to realize the long-term data storage. It presents a powerful signal acquisition ability with the average system noise of 4.79 × 10 − 4 rad / √Hz and the minimum detectable strain is 10.4 pε / √Hz. The innovative mini-DAS module has high enough capabilities for real-time seismic wave signal detection in deep sea.