A new article "A decadal view of biodiversity informatics: challenges and priorities"  published by BMC Ecology focuses on the challenges and perspectives for biodiversity informatics after a decade of development. The authors Alex Hardisty and Dave Roberts alongside 77 contributions from the biodiversity informatics community share experience and set future directions of biodiversity informatics as a tool for addressing conservation and ecological issues.

Biodiversity informatics plays a central enabling role in the research community's efforts to address scientific conservation and sustainability issues. This community consultation paper positions the role of biodiversity informatics, for the next decade, presenting the actions needed to link the various biodiversity infrastructures invisibly and to facilitate understanding that can support both business and policy-makers. The community considers the goal in biodiversity informatics to be full integration of the biodiversity research community, including citizens’ science, through a commonly-shared, sustainable e-infrastructure across all sub-disciplines that reliably serves science and society alike.

The full text of the article can be accessed here.

Full article available at: http://dx.doi.org/10.1186/1471-2105-12-S15-S1 

Abstract: In an effort to increase conservation effectiveness through the use of Earth observation technologies, a group of remote sensing scientists affiliated with government and academic institutions and conservation organizations identified 10 questions in conservation for which the potential to be answered would be greatly increased by use of remotely sensed data and analyses of those data. Our goals were to increase conservation practitioners’ use of remote sensing to support their work, increase collaboration between the conservation science and remote sensing communities, identify and develop new and innovative uses of remote sensing for advancing conservation science, provide guidance to space agencies on how future satellite missions can support conservation science, and generate support from the public and private sector in the use of remote sensing data to address the 10 conservation questions. We identified a broad initial list of questions on the basis of an email chain-referral survey. We then used a workshop-based iterative and collaborative approach to whittle the list down to these final questions (which represent 10 major themes in conservation): How can global Earth observation data be used to model species distributions and abundances? How can remote sensing improve the understanding of animal movements? How can remotely sensed ecosystem variables be used to understand, monitor, and predict ecosystem response and resilience to multiple stressors? How can remote sensing be used to monitor the effects of climate on ecosystems? How can near real-time ecosystem monitoring catalyze threat reduction, governance and regulation compliance, and resource management decisions? How can remote sensing inform configuration of protected area networks at spatial extents relevant to populations of target species and ecosystem services? How can remote sensing-derived products be used to value and monitor changes in ecosystem services? How can remote sensing be used to monitor and evaluate the effectiveness of conservation efforts? How does the expansion and intensification of agriculture and aquaculture alter ecosystems and the services they provide? How can remote sensing be used to determine the degree to which ecosystems are being disturbed or degraded and the effects of these changes on species and ecosystem functions?

A new EU BON acknowledged paper "Advancing species diversity estimate by remotely sensed proxies: A conceptual review" has been recently published in the journal Ecological Informatics.

Abstract: 

Many geospatial tools have been advocated in spatial ecology to estimate biodiversity and its changes over space and time. Such information is essential in designing effective strategies for biodiversity conservation and management. Remote sensing is one of the most powerful approaches to identify biodiversity hotspots and predict changes in species composition in reduced time and costs. This is because, with respect to field-based methods, it allows to derive complete spatial coverages of the Earth surface under study in a short period of time. Furthermore, remote sensing provides repeated coverages of field sites, thus making studies of temporal changes in biodiversity possible. In this paper we discuss, from a conceptual point of view, the potential of remote sensing in estimating biodiversity using various diversity indices, including alpha- and beta-diversity measurements.

Original Source: 

Rocchini D, Hernández-Stefanoni J L, He KS (2014) Advancing species diversity estimate by remotely sensed proxies: A conceptual review. Ecological Informatics. DOI: 10.1016/j.ecoinf.2014.10.006

A recent paper by Duccio Rocchini et al. (2015) has been classified as the fourth hottest article in Ecological Informatics. The paper is part of the EU BON project, and discusses from a conceptual point of view, the potential of remote sensing in estimating biodiversity using various diversity indices, including alpha- and beta-diversity measurements. 

Abstract:

Many geospatial tools have been advocated in spatial ecology to estimate biodiversity and its changes over space and time. Such information is essential in designing effective strategies for biodiversity conservation and management. Remote sensing is one of the most powerful approaches to identify biodiversity hotspots and predict changes in species composition in reduced time and costs. This is because, with respect to field-based methods, it allows to derive complete spatial coverages of the Earth surface under study in a short period of time. Furthermore, remote sensing provides repeated coverages of field sites, thus making studies of temporal changes in biodiversity possible. In this paper we discuss, from a conceptual point of view, the potential of remote sensing in estimating biodiversity using various diversity indices, including alpha- and beta-diversity measurements.

Original source:

Rocchini D, Hernández Stefanoni JL, He, KS (2015) Advancing species diversity estimate by remotely sensed proxies: a conceptual review. Ecological Informatics, 25: 22-28. doi:10.1016/j.ecoinf.2014.10.006

A new aticle published in Ecology Letters  looks into the indirect interactions among tropical tree species through shared rodent seed predators. The reasearch is part of the work of EU BON postdoc Carol X. Garzon-Lopez.

Abstract: The coexistence of numerous tree species in tropical forests is commonly explained by negative dependence of recruitment on the conspecific seed and tree density due to specialist natural enemies that attack seeds and seedlings (‘Janzen–Connell’ effects). Less known is whether guilds of shared seed predators can induce a negative dependence of recruitment on the density of different species of the same plant functional group. We studied 54 plots in tropical forest on Barro Colorado Island, Panama, with contrasting mature tree densities of three coexisting large seeded tree species with shared seed predators. Levels of seed predation were far better explained by incorporating seed densities of all three focal species than by conspecific seed density alone. Both positive and negative density dependencies were observed for different species combinations. Thus, indirect interactions via shared seed predators can either promote or reduce the coexistence of different plant functional groups in tropical forest.

Carol X. Garzon-Lopez et. al. (2015) Indirect interactions among tropical tree species through shared rodent seed predators: a novel mechanism of tree species coexistence. Ecology Letters. doi: 10.1111/ele.12452

One of our recent associated partners, the EU project GLOBIS-B has published its first paper: "Towards global interoperability for supporting biodiversity research on essential biodiversity variables (EBVs)". You can find the article here.

Abstract: 

Essential biodiversity variables (EBVs) have been proposed by the Group on Earth Observations Biodiversity Observation Network (GEO BON) to identify a minimum set of essential measurements that are required for studying, monitoring and reporting biodiversity and ecosystem change. Despite the initial conceptualisation, however, the practical implementation of EBVs remains challenging. There is much discussion about the concept and implementation of EBVs: which variables are meaningful; which data are needed and available; at which spatial, temporal and topical scales can EBVs be calculated; and how sensitive are EBVs to variations in underlying data? To advance scientific progress in implementing EBVs we propose that both scientists and research infrastructure operators need to cooperate globally to serve and process the essential large datasets for calculating EBVs. We introduce GLOBIS-B (GLOBal Infrastructures for Supporting Biodiversity research), a global cooperation funded by the Horizon 2020 research and innovation framework programme of the European Commission. The main aim of GLOBIS-B is to bring together biodiversity scientists, global research infrastructure operators and legal interoperability experts to identify the research needs and infrastructure services underpinning the concept of EBVs. The project will facilitate the multi-lateral cooperation of biodiversity research infrastructures worldwide and identify the required primary data, analysis tools, methodologies and legal and technical bottlenecks to develop an agenda for research and infrastructure development to compute EBVs. This requires development of standards, protocols and workflows that are ‘self-documenting’ and openly shared to allow the discovery and analysis of data across large spatial extents and different temporal resolutions. The interoperability of existing biodiversity research infrastructures will be crucial for integrating the necessary biodiversity data to calculate EBVs, and to advance our ability to assess progress towards the Aichi targets for 2020 of the Convention on Biological Diversity (CBD).

Original Source:

W. Daniel Kissling et. al. (2015) Towards global interoperability for supporting biodiversity research on essential biodiversity variables (EBVs). Biodiversity. DOI: 10.1080/14888386.2015.1068709

A new article published in the Journal of Marine Science and Engineering looks at how Aphia, the core platform that underpins the World Register of Marine Species (WoRMS),  can Serve the taxonomic community and the field of biodiversity informatics.

Abstract

The Aphia platform is an infrastructure designed to capture taxonomic and related data and information, and includes an online editing environment. The latter allows easy access to experts so they can update the content of the database in a timely fashion. Aphia is the core platform that underpins the World Register of Marine Species (WoRMS) and its more than 80 related global, regional and thematic species databases, but it also allows the storage of non-marine data. The content of Aphia can be consulted online, either by individual users or via machine-to-machine interactions. Aphia uses unique and stable identifiers for each available name in the database through the use of Life Science Identifiers (LSIDs). The system not only allows the storage of accepted and unaccepted names, but it also documents the relationships between names. This makes it a very powerful tool for taxonomic quality control, and also allows the linking of different pieces of information through scientific names, both within the Aphia platform and in relation to externally hosted databases. Through these LSIDs, Aphia has become an important player in the field of (marine) biodiversity informatics, allowing interactions between its own taxonomic data and e.g., biogeographic databases. Some applications in the field of biodiversity informatics encompass the coupling of species traits and taxonomy, as well as the creation of diverse, expert validated data products that can be used by policy makers, for example. Aphia also supplies (part of) its content to other data integrators and the infrastructure can be used to host orphan databases in danger of being lost.

Original Source: http://www.mdpi.com/2077-1312/3/4/1448/htm

The Tropical Ecology Assessment and Monitoring (TEAM) network has the aim to measure and compare plants, terrestrial mammals, ground-dwelling birds and climate using a standard methodology in a range of tropical forests, from relatively pristine places to those most affected by people. TEAM currently operates in sixteen tropical forest sites across Africa, Asia and Latin America supporting a network of scientists committed to standardized methods of data collection to quantify how plants and animals respond to pressures such as climate change and human encroachment.

A recent TEAM network paper published in PLOS Biology deals with the standartization of methods in assessing biodiversity trends in tropical forest protected areas.

Abstract: 

A new EU BON acknowledging data article provides a virtual species set as a valuable tool in biodiversity monitoring. 

Abstract

Predicting species potential and future distribution has become a relevant tool in biodiversity monitoring and conservation. In this data article we present the suitability map of a virtual species generated based on two bioclimatic variables, and a dataset containing more than 700,000 random observations at the extent of Europe. The dataset includes spatial attributes such as: distance to roads, protected areas, country codes, and the habitat suitability of two spatially clustered species (grassland and forest species) and a wide-spread species.

Original Source:

Garzon-Lopez, C.X., Bastin, L., Foody, G.M., Rocchini, D. (2016). A virtual species set for robust and reproducible Species Distribution Modelling tests. Data in Brief, 7: 476-479. doi: http://dx.doi.org/10.1016/j.dib.2016.02.058

A new EU BON acknowledged article looks at the recently introduced in scientific literature methods for constructing Spatially Explicit Rarefaction (SER) and their implication for ecologists and conservation biologist. The research was published in the journal Ecological Indicators.

Abstract: 

Recently, methods for constructing Spatially Explicit Rarefaction (SER) curves have been introduced in the scientific literature to describe the relation between the recorded species richness and sampling effort and taking into account for the spatial autocorrelation in the data. Despite these methodological advances, the use of SERs has not become routine and ecologists continue to use rarefaction methods that are not spatially explicit. Using two study cases from Italian vegetation surveys, we demonstrate that classic rarefaction methods that do not account for spatial structure can produce inaccurate results. Furthermore, our goal in this paper is to demonstrate how SERs can overcome the problem of spatial autocorrelation in the analysis of plant or animal communities. Our analyses demonstrate that using a spatially-explicit method for constructing rarefaction curves can substantially alter estimates of relative species richness. For both analyzed data sets, we found that the rank ordering of standardized species richness estimates was reversed between the two methods. We strongly advise the use of Spatially Explicit Rarefaction methods when analyzing biodiversity: the inclusion of spatial autocorrelation into rarefaction analyses can substantially alter conclusions and change the way we might prioritize or manage nature reserves.

Original Source: 

Bacaro, G., Altobelli, A., Camelletti, M., Ciccarelli, D., Martellos, S., Palmer, M.W., Ricotta, C., Rocchini, D., Scheiner, S.M., Tordoni, E., Chiarucci, A. (2016). Incorporating spatial autocorrelation in rarefaction methods: implications for ecologists and conservation biologists. Ecological Indicators, 69: 233-238. [5years-IF: 3.494] doi: http://dx.doi.org/10.1016/j.ecolind.2016.04.026

A new opinion piece published in the journal Global Change Biology looks at the development of biodiversity scenarios and their inclusion of  future land-use changes.

Abstract: 

A new research paper Linking Earth Observation and taxonomic, structural and functional biodiversity: Local to ecosystem perspectives published in the journal Ecological Indicators looks at the ways in which earth observation (EO) techniques may provide a solution to overcome shortcomings in biodiversity monitoring by measuring entities of interest at different spatial and temporal scales. 

Abstract: 

Impacts of human civilization on ecosystems threaten global biodiversity. In a changing environment, traditional in situ approaches to biodiversity monitoring have made significant steps forward to quantify and evaluate BD at many scales but still, these methods are limited to comparatively small areas. Earth observation (EO) techniques may provide a solution to overcome this shortcoming by measuring entities of interest at different spatial and temporal scales.

This paper provides a comprehensive overview of the role of EO to detect, describe, explain, predict and assess biodiversity. Here, we focus on three main aspects related to biodiversity taxonomic diversity, functional diversity and structural diversity, which integrate different levels of organization molecular, genetic, individual, species, populations, communities, biomes, ecosystems and landscapes. In particular, we discuss the recording of taxonomic elements of biodiversity through the identification of animal and plant species. We highlight the importance of the spectral traits (ST) and spectral trait variations (STV) concept for EO-based biodiversity research.

Furthermore we provide examples of spectral traits/spectral trait variations used in EO applications for quantifying taxonomic diversity, functional diversity andstructural diversity. We discuss the use of EO to monitor biodiversity and habitat quality using differ-ent remote-sensing techniques. Finally, we suggest specifically important steps for a better integrationof EO in biodiversity research.EO methods represent an affordable, repeatable and comparable method for measuring, describing,explaining and modelling taxonomic, functional and structural diversity. Upcoming sensor developmentswill provide opportunities to quantify spectral traits, currently not detectable with EO, and will surelyhelp to describe biodiversity in more detail. Therefore, new concepts are needed to tightly integrate EOsensor networks with the identification of biodiversity. This will mean taking completely new directionsin the future to link complex, large data, different approaches and models.

Original reseach:

A. Lausch, L. Bannehr, M. Beckmann, C. Boehm, H. Feilhauer, J.M. Hacker, M. Heurich, A. Jung, R. Klenke, C. Neumann, M. Pause, D. Rocchini, M.E. Schaepman, S. Schmidtlein, K. Schulz, P. Selsam, J. Settele, A.K. Skidmore, A.F. Cord, Linking Earth Observation and taxonomic, structural and functional biodiversity: Local to ecosystem perspectives, Ecological Indicators, Volume 70, November 2016, Pages 317-339, ISSN 1470-160X, http://dx.doi.org/10.1016/j.ecolind.2016.06.022

The planetary boundaries framework attempts to set limits for biodiversity loss within which ecological function is relatively unaffected. In a recent article in Science Newbold et al. present a quantitative global analysis of the extent to which the proposed planetary boundary has been crossed. 

Abstract: 

Land use and related pressures have reduced local terrestrial biodiversity, but it is unclear how the magnitude of change relates to the recently proposed planetary boundary ("safe limit"). We estimate that land use and related pressures have already reduced local biodiversity intactness—the average proportion of natural biodiversity remaining in local ecosystems—beyond its recently proposed planetary boundary across 58.1% of the world’s land surface, where 71.4% of the human population live. Biodiversity intactness within most biomes (especially grassland biomes), most biodiversity hotspots, and even some wilderness areas is inferred to be beyond the boundary. Such widespread transgression of safe limits suggests that biodiversity loss, if unchecked, will undermine efforts toward long-term sustainable development.

The study is available at http://dx.doi/10.1126/science.aaf2201

Are protected areas working when it comes to promoting biodivesity? A new study, published in Nature Communications, shows that local biodiversity is actually higher within, rather than outside protected areas.

Abstract: 

Protected areas are widely considered essential for biodiversity conservation. However, few global studies have demonstrated that protection benefits a broad range of species. Here, using a new global biodiversity database with unprecedented geographic and taxonomic coverage, we compare four biodiversity measures at sites sampled in multiple land uses inside and outside protected areas. Globally, species richness is 10.6% higher and abundance 14.5% higher in samples taken inside protected areas compared with samples taken outside, but neither rarefaction-based richness nor endemicity differ significantly. Importantly, we show that the positive effects of protection are mostly attributable to differences in land use between protected and unprotected sites. Nonetheless, even within some human-dominated land uses, species richness and abundance are higher in protected sites. Our results reinforce the global importance of protected areas but suggest that protection does not consistently benefit species with small ranges or increase the variety of ecological niches.

Original Source:

The original article is openly accessible at:

http://dx.doi.org/10.1038/ncomms12306

Key in ensuring the effectiveness of conservation efforts and maintaining ecosystem health, measuring biodiversity can benefit greatly when remote sensing data comes into the equation. A new EU BON related paper, published in the journal Ecological Indicators, proposes open source solutions for measuring the important Rao's Q index, when it comes to remote sensing data.

Abstract: 

Measuring biodiversity is a key issue in ecology to guarantee effective indicators of ecosystem health at different spatial and time scales. However, estimating biodiversity from field observations might present difficulties related to costs and time needed. Moreover, a continuous data update for biodiversity monitoring purposes might be prohibitive. From this point of view, remote sensing represents a powerful tool since it allows to cover wide areas in a relatively low amount of time. One of the most common indicators of biodiversity is Shannon's entropy H′, which is strictly related to environmental heterogeneity, and thus to species diversity. However, Shannon's entropy might show drawbacks once applied to remote sensing data, since it considers relative abundances but it does not explicitly account for distances among pixels’ numerical values. In this paper we propose the use of Rao's Q applied to remotely sensed data, providing a straightforward R-package function to calculate it in 2D systems. We will introduce the theoretical rationale behind Rao's index and then provide applied examples based on the proposed R function.

Original Source: 

Rocchini, D., Marcantonio, M., Ricotta, C. (2017). Measuring Rao's Q diversity index rom remote sensing: an open source solution. Ecological Indicators, 72: 234-238. [5years-IF: 3.649] DOI:10.1016/j.ecolind.2016.07.039

A new EU BON derived paper, publsihed recently in the journal Remote Sensing, introduces eHabitat+, a habitat modelling service supporting the European Commission’s Digital Observatory for Protected Areas.

Abstract:

Protected areas (PAs) need to be assessed systematically according to biodiversity values and threats in order to support decision-making processes. For this, PAs can be characterized according to their species, ecosystems and threats, but such information is often difficult to access and usually not comparable across regions. There are currently over 200,000 PAs in the world, and assessing these systematically according to their ecological values remains a huge challenge. However, linking remote sensing with ecological modelling can help to overcome some limitations of conservation studies, such as the sampling bias of biodiversity inventories. The aim of this paper is to introduce eHabitat+, a habitat modelling service supporting the European Commission’s Digital Observatory for Protected Areas, and specifically to discuss a component that systematically stratifies PAs into different habitat functional types based on remote sensing data. eHabitat+ uses an optimized procedure of automatic image segmentation based on several environmental variables to identify the main biophysical gradients in each PA. This allows a systematic production of key indicators on PAs that can be compared globally. Results from a few case studies are illustrated to show the benefits and limitations of this open-source tool.

Original Source: 

Martínez-López, J.; Bertzky, B.; Bonet-García, F.J.; Bastin, L.; Dubois, G. Biophysical Characterization of Protected Areas Globally through Optimized Image Segmentation and Classification. Remote Sens. 2016, 8, 780. DOI: 0.3390/rs8090780

A new editorial, published in the Journal of Apllied Ecology looks at modelling and monitoring as methods for adaptive biodiversity management in the 21st century.

Abstract: 

With increasing threats on biodiversity, informed conservation decisions need to be based on currently observed and future predicted trends of biodiversity (Pereira, Navarro & Martins 2012; Guisan et al. 2013). In this regard, two essential components supporting informed biodiversity conservation decisions are good monitoring data to assess recent and ongoing trends (Collen et al. 2013; Pereira et al. 2013) and robust models to anticipate possible future trends (Pereira et al. 2010a; Akc
akaya et al. 2016). Models benefit from robust monitoring data sets, that is repeated observations of biodiversity, as they need data to be fitted or validated, but models can also help assess data representativeness (e.g. by highlighting any bias), support proper data collection (e.g. covering the relevant gradients) or be used to make more effective use of biodiversity observations (Guisan et al. 2006, 2013; Ferrier 2011).

Read more in the open access paper.

The DiSCmini is the smallest handheld instrument for the measurement of nanoparticles. It measures particle number, average particle diameter and lung-deposited surface area with time resolution and logging at one second (1 Hz).

The PC-5000 Series Wall-Mount Particle/Mass Monitor is a branded six-channel particulate meter that optionally incorporates sensors for additional environmental parameters such as total volatile organic compounds, carbon dioxide, temperature and relative humidity.

State College, PA — Using a 3D device on a microchip that mimics the behavior of human lungs, researchers from Penn State University will use a $400,000 grant from NIOSH to study the effects of nano-scale coal dust on the lungs of underground miners, the university has announced.

Washington – The American Industrial Hygiene Association has published a fact sheet that aims to improve safety for workers in the nanotechnology industry.

Washington — In an effort to dispel “incorrect claims” about the efficacy of N95 respirators to protect wearers against COVID-19 infection, OSHA has added a section on respirators and particle size to its series of answers to frequently asked questions on protecting workers from exposure to the coronavirus.

Children's Geographies; 06/01/2023
(AN 164286248); ISSN: 14733285
Academic Search Premier

So-called "XYZ states" defy the standard picture of particle behavior and have given rise to several attempts to understand their nature. But researchers with the Center for Theoretical and Computational Physics (Theory Center) at the U.S. Department of Energy's Thomas Jefferson National Accelerator Facility say there is a simpler way to explain the abundance of exotic charmonium particles using lattice quantum chromodynamics.

Small-angle X-ray scattering (SAXS) is widely used to analyze the shape and size of nanoparticles in solution. A multitude of models, describing the SAXS intensity resulting from nanoparticles of various shapes, have been developed by the scientific community and are used for data analysis. Choosing the optimal model is a crucial step in data analysis, which can be difficult and time-consuming, especially for non-expert users. An algorithm is proposed, based on machine learning, representation learning and SAXS-specific preprocessing methods, which instantly selects the nanoparticle model best suited to describe SAXS data. The different algorithms compared are trained and evaluated on a simulated database. This database includes 75 000 scattering spectra from nine nanoparticle models, and realistically simulates two distinct device configurations. It will be made freely available to serve as a basis of comparison for future work. Deploying a universal solution for automatic nanoparticle model selection is a challenge made more difficult by the diversity of SAXS instruments and their flexible settings. The poor transferability of classification rules learned on one device configuration to another is highlighted. It is shown that training on several device configurations enables the algorithm to be generalized, without degrading performance compared with configuration-specific training. Finally, the classification algorithm is evaluated on a real data set obtained by performing SAXS experiments on nanoparticles for each of the instrumental configurations, which have been characterized by transmission electron microscopy. This data set, although very limited, allows estimation of the transferability of the classification rules learned on simulated data to real data.

Small-angle X-ray scattering (SAXS) is a widely used method for nanoparticle characterization. A common approach to analysing nanoparticles in solution by SAXS involves fitting the curve using a parametric model that relates real-space parameters, such as nanoparticle size and electron density, to intensity values in reciprocal space. Selecting the optimal model is a crucial step in terms of analysis quality and can be time-consuming and complex. Several studies have proposed effective methods, based on machine learning, to automate the model selection step. Deploying these methods in software intended for both researchers and industry raises several issues. The diversity of SAXS instrumentation requires assessment of the robustness of these methods on data from various machine configurations, involving significant variations in the q-space ranges and highly variable signal-to-noise ratios (SNR) from one data set to another. In the case of laboratory instrumentation, data acquisition can be time-consuming and there is no universal criterion for defining an optimal acquisition time. This paper presents an approach that revisits the nanoparticle model selection method proposed by Monge et al. [Acta Cryst. (2024), A80, 202–212], evaluating and enhancing its robustness on data from device configurations not seen during training, by expanding the data set used for training. The influence of SNR on predictor robustness is then assessed, improved, and used to propose a stopping criterion for optimizing the trade-off between exposure time and data quality.

This work investigates the performance of the electrospray aerosol generator at the European X-ray Free Electron Laser (EuXFEL). This generator is, together with an aerodynamic lens stack that transports the particles into the X-ray interaction vacuum chamber, the method of choice to deliver particles for single-particle coherent diffractive imaging (SPI) experiments at the EuXFEL. For these experiments to be successful, it is necessary to achieve high transmission of particles from solution into the vacuum interaction region. Particle transmission is highly dependent on efficient neutralization of the charged aerosol generated by the electrospray mechanism as well as the geometry in the vicinity of the Taylor cone. We report absolute particle transmission values for different neutralizers and geometries while keeping the conditions suitable for SPI experiments. Our findings reveal that a vacuum ultraviolet ionizer demonstrates a transmission efficiency approximately seven times greater than the soft X-ray ionizer used previously. Combined with an optimized orifice size on the counter electrode, we achieve >40% particle transmission from solution into the X-ray interaction region. These findings offer valuable insights for optimizing electrospray aerosol generator configurations and data rates for SPI experiments.

In situ and operando investigation of photocatalysts plays a fundamental role in understanding the processes of active phase formation and the mechanisms of catalytic reactions, which is crucial for the rational design of more efficient materials. Using a custom-made operando photocatalytic cell, an in situ procedure to follow the formation steps of Pd/TiO2 photocatalyst by synchrotron-based X-ray absorption spectroscopy (XAS) is proposed. The procedure resulted in the formation of ∼1 nm Pd particles with a much narrower size distribution and homogeneous spreading over TiO2 support compared with the samples generated in a conventional batch reactor. The combination of in situ XAS spectroscopy with high-angle annular dark-field scanning transmission electron microscopy demonstrated the formation of single-atom Pd(0) sites on TiO2 as the initial step of the photodeposition process. Palladium hydride particles were observed for all investigated samples upon exposure to formic acid solutions.

Aerosol science is of utmost importance for both climate and public health research, and in recent years X-ray techniques have proven effective tools for aerosol-particle characterization. To date, such methods have often involved the study of particles collected onto a substrate, but a high photon flux may cause radiation damage to such deposited particles and volatile components can potentially react with the surrounding environment after sampling. These and many other factors make studies on collected aerosol particles challenging. Therefore, a new aerosol sample-delivery system dedicated to X-ray photoelectron spectroscopy studies of aerosol particles and gas molecules in-flight has been developed at the MAX IV Laboratory. The aerosol particles are brought from atmospheric pressure to vacuum in a continuous flow, ensuring that the sample is constantly renewed, thus avoiding radiation damage, and allowing measurements on the true unsupported aerosol. At the same time, available gas molecules can be used for energy calibration and to study gas-particle partitioning. The design features of the aerosol sample-delivery system and important information on the operation procedures are described in detail here. Furthermore, to demonstrate the experimental range of the aerosol sample-delivery system, results from aerosol particles of different shape, size and composition are presented, including inorganic atmospheric aerosols, secondary organic aerosols and engineered nanoparticles.

A section in the Acta Crystallographica Section C article by Raymond & Girolami [Acta Cryst. (2023), C79, 445–455] stated that the product of the reaction of [(Cp*Rh)2(μ-OH)3]+ (Cp* is 1,2,3,4,5-penta­methyl­cyclo­penta­diene) with 1-methyl­thymine (1-MT) at pH 10 and 60 °C, to synthesize the anionic com­ponent [RhI(η1-N3-1-MT)2]−, was not an RhI com­plex, but rather an AgI com­plex, due to the use of silver triflate (AgOTf) to remove Cl− from [Cp*RhCl2]2 to synthesize [Cp*Rh(H2O)3](OTf)2, a water-soluble crystalline com­plex. We will clearly show that this premise, as stated, is invalid, while the authors have simply avoided several important facts, including that Cp*OH, a reductive elimination product, at pH 10 and 60 °C, was unequivocally identified, thus leading to the RhI anionic com­ponent [RhI(η1-N3-1-MT)2]−. More importantly, AgOH, from the reaction of NaOH at pH 10 with any potentially remaining AgOTf, after the AgCl was filtered off, would be insoluble in water. Furthermore, a control experiment with the inorganic com­plex Rh(OH)3, reacting with 1-methyl­thymine at pH 10, provided no product, and this bodes well for a similar fate with AgOTf and 1-methyl­thymine, i.e. at pH 10, AgOTf would again be converted to the water-insoluble AgOH; therefore, no reaction would occur! Finally, a 1H NMR spectroscopy experiment was carried out with synthesized and crystallized [Cp*Rh(H2O)3](OTf)2 in D2O at various pD values; at pD 8.65 no reaction took place, while at pD 13.6, and at 60 °C for 2 h, a reductive elimination reaction caused the precipitation of Cp*OH. The subsequent 1H NMR spectrum clearly demonstrated, in the absence of any AgI com­plexes, that the solution structure and the X-ray crystals in D2O were similar. A postulated mechanism for this novel anionic com­ponent structure, as published previously [Smith et al. (2014). Organometallics, 33, 2389–2404], will be presented, along with the experimental data, to insure the credibility of our results. We will also answer the comments in the response of Drs Raymond and Girolami to this rebuttal.

We stand fully behind our earlier suggestion [Raymond & Girolami (2023). Acta Cryst. C79, 445–455] that the claim by Fish and co-workers [Chen et al. (1995). J. Am. Chem. Soc. 117, 9097–9098; Smith et al. (2014). Organometallics, 33, 2389–2404] of a linear two-coordinate rhodium(I) species is incorrect, and that the putative rhodium atom is in fact silver.

Single-particle imaging using X-ray free-electron lasers (XFELs) is a promising technique for observing nanoscale biological samples under near-physiological conditions. However, as the sample's orientation in each diffraction pattern is unknown, advanced algorithms are required to reconstruct the 3D diffraction intensity volume and subsequently the sample's density model. While most approaches perform 3D reconstruction via determining the orientation of each diffraction pattern, a correlation-based approach utilizes the averaged spatial correlations of diffraction intensities over all patterns, making it well suited for processing experimental data with a poor signal-to-noise ratio of individual patterns. Here, a method is proposed to determine the 3D structure of a sample by analyzing the double, triple and quadruple spatial correlations in diffraction patterns. This ab initio method can reconstruct the basic shape of an irregular unsymmetric 3D sample without requiring any prior knowledge of the sample. The impact of background and noise on correlations is investigated and corrected to ensure the success of reconstruction under simulated experimental conditions. Additionally, the feasibility of using the correlation-based approach to process incomplete partial diffraction patterns is demonstrated. The proposed method is a variable addition to existing algorithms for 3D reconstruction and will further promote the development and adoption of XFEL single-particle imaging techniques.

The orientation ordering and assembly behavior of silica–nickel Janus particles in a static external magnetic field were probed by ultra small-angle X-ray scattering (USAXS). Even in a weak applied field, the net magnetic moments of the individual particles aligned in the direction of the field, as indicated by the anisotropy in the recorded USAXS patterns. X-ray photon correlation spectroscopy (XPCS) measurements on these suspensions revealed that the corresponding particle dynamics are primarily Brownian diffusion [Zinn, Sharpnack & Narayanan (2023). Soft Matter, 19, 2311–2318]. At higher fields, the magnetic forces led to chain-like configurations of particles, as indicated by an additional feature in the USAXS pattern. A theoretical framework is provided for the quantitative interpretation of the observed anisotropic scattering diagrams and the corresponding degree of orientation. No anisotropy was detected when the magnetic field was applied along the beam direction, which is also replicated by the model. The method presented here could be useful for the interpretation of oriented scattering patterns from a wide variety of particulate systems. The combination of USAXS and XPCS is a powerful approach for investigating asymmetric colloidal particles in external fields.

Ultra-intense, ultra-fast X-ray free-electron lasers (XFELs) enable the imaging of single protein molecules under ambient temperature and pressure. A crucial aspect of structure reconstruction involves determining the relative orientations of each diffraction pattern and recovering the missing phase information. In this paper, we introduce a predicted model-aided algorithm for orientation determination and phase retrieval, which has been tested on various simulated datasets and has shown significant improvements in the success rate, accuracy and efficiency of XFEL data reconstruction.

Ultrafast, high-intensity X-ray free-electron lasers can perform diffraction imaging of single protein molecules. Various algorithms have been developed to determine the orientation of each single-particle diffraction pattern and reconstruct the 3D diffraction intensity. Most of these algorithms rely on the premise that all diffraction patterns originate from identical protein molecules. However, in actual experiments, diffraction patterns from multiple different molecules may be collected simultaneously. Here, we propose a predicted model-aided one-step classification–multireconstruction algorithm that can handle mixed diffraction patterns from various molecules. The algorithm uses predicted structures of different protein molecules as templates to classify diffraction patterns based on correlation coefficients and determines orientations using a correlation maximization method. Tests on simulated data demonstrated high accuracy and efficiency in classification and reconstruction.

Fluctuation X-ray scattering (FXS) offers a complementary approach for nano- and bioparticle imaging with an X-ray free-electron laser (XFEL), by extracting structural information from correlations in scattered XFEL pulses. Here a workflow is presented for single-particle structure determination using FXS. The workflow includes procedures for extracting the rotational invariants from FXS patterns, performing structure reconstructions via iterative phasing of the invariants, and aligning and averaging multiple reconstructions. The reconstruction pipeline is implemented in the open-source software xFrame and its functionality is demonstrated on several simulated structures.

Controlling the shape and size dispersivity and crystallinity of nanoparticles (NPs) has been a challenge in identifying these parameters' role in the physical and chemical properties of NPs. The need for reliable quantitative tools for analyzing the dispersivity and crystallinity of NPs is a considerable problem in optimizing scalable synthesis routes capable of controlling NP properties. The most common tools are electron microscopy (EM) and X-ray scattering techniques. However, each technique has different susceptibility to these parameters, implying that more than one technique is necessary to characterize NP systems with maximum reliability. Wide-angle X-ray scattering (WAXS) is mandatory to access information on crystallinity. In contrast, EM or small-angle X-ray scattering (SAXS) is required to access information on whole NP sizes. EM provides average values on relatively small ensembles in contrast to the bulk values accessed by X-ray techniques. Besides the fact that the SAXS and WAXS techniques have different susceptibilities to size distributions, SAXS is easily affected by NP–NP interaction distances. Because of all the variables involved, there have yet to be proposed methodologies for cross-analyzing data from two techniques that can provide reliable quantitative results of dispersivity and crystallinity. In this work, a SAXS/WAXS-based methodology is proposed for simultaneously quantifying size distribution and degree of crystallinity of NPs. The most reliable easy-to-access size result for each technique is demonstrated by computer simulation. Strategies on how to compare these results and how to identify NP–NP interaction effects underneath the SAXS intensity curve are presented. Experimental results are shown for cubic-like CeO2 NPs. WAXS size results from two analytical procedures are compared, line-profile fitting of individual diffraction peaks in opposition to whole pattern fitting. The impact of shape dispersivity is also evaluated. Extension of the proposed methodology for cross-analyzing EM and WAXS data is possible.

Coherent diffractive imaging with X-ray free-electron lasers could enable structural studies of macromolecules at room temperature. This type of experiment could provide a means to study structural dynamics on the femtosecond timescale. However, the diffraction from a single protein is weak compared with the incoherent scattering from background sources, which negatively affects the reconstruction analysis. This work evaluates the effects of the presence of background on the analysis pipeline. Background measurements from the European X-ray Free-Electron Laser were combined with simulated diffraction patterns and treated by a standard reconstruction procedure, including orientation recovery with the expand, maximize and compress algorithm and 3D phase retrieval. Background scattering did have an adverse effect on the estimated resolution of the reconstructed density maps. Still, the reconstructions generally worked when the signal-to-background ratio was 0.6 or better, in the momentum transfer shell of the highest reconstructed resolution. The results also suggest that the signal-to-background requirement increases at higher resolution. This study gives an indication of what is possible at current setups at X-ray free-electron lasers with regards to expected background strength and establishes a target for experimental optimization of the background.

Here, it is investigated how optical properties of single scatterers in interacting multi-particle systems influence measurable structure factors. Both particles with linear gradients of their scattering length density and core–shell structures evoke characteristic deviations between the weighted sum 〈S(Q)〉 of partial structure factors in a multi-component system and experimentally accessible measurable structure factors SM(Q). While 〈S(Q)〉 contains only the structural information of self-organizing systems, SM(Q) is additionally influenced by the optical properties of their constituents, resulting in features such as changing amplitudes, additional peaks in the low-wavevector region or splitting of higher-order maxima, which are not related to structural reasons. It is shown that these effects can be systematically categorized according to the qualitative behaviour of the form factor in the Guinier region, which enables assessing the suitability of experimentally obtained structure factors to genuinely represent the microstructure of complex systems free from any particular model assumption. Hence, a careful data analysis regarding size distribution and optical properties of single scatterers is mandatory to avoid a misinterpretation of measurable structure factors.

Article by Catawba County Public Health School Nurse Margaret Sides on vision screenings for students is published nationally.

The National Academies Committee on Genetically Engineered Crops - Past Experiences and Future Prospects authored an almost 600-page landmark report, released in May 2016. It was perhaps the most comprehensive analysis of genetically engineered crops to date.

The U.S. National Academies recognize the significance of the opioid crisis and have been at the forefront of efforts to advise our nation on how to combat it.