Scientific Community Research Articles

Economic growth in the Sub-Saharan African world region: convergence theory

Purpose The processes of convergence are particularly challenging in the Sub-Saharan African frameworks, considering the diversity of contexts and endogenous particularities. Creating conditions to support these nations to improve their socioeconomic dynamics and performance requires additional contributions from international organisations, governments and the scientific community. In this scenario, this paper aims to analyse the convergence process in Sub-Saharan African countries over the past three decades. Design/methodology/approach To achieve these objectives, data from the World Bank were considered for the gross domestic product (GDP) per capita over the period 1990–2021. This statistical information was assessed through panel data approaches based on the models from the convergence theory. Specifically, the concepts of sigma and beta convergence were addressed, as well as the concept of catch-up rates. Findings The findings obtained highlight evidence of the existence of clubs of convergence among the Sub-Saharan African countries and the processes of catching up. These results may be relevant support for the policymakers and international funds and programmes. Originality/value This research provides a new perspective on the convergence of GDP per capita in Sub-Saharan African countries, based on an analysis focused on groups of countries identified on the basis of catch-up rates. This approach presents a way of dealing with the different specificities of these nations.

Web-based diagnostic platform for microorganism-induced deterioration on paper-based cultural relics with iterative training from human feedback

PurposePaper-based artifacts hold significant cultural and social values. However, paper is intrinsically fragile to microorganisms, such as mold, due to its cellulose composition, which can serve as a microorganisms’ nutrient source. Mold not only can damage papers’ structural integrity and pose significant challenges to conservation works but also may subject individuals attending the contaminated artifacts to health risks. Current approaches for strain identification usually require extensive training, prolonged time for analysis, expensive operation costs, and higher risks of secondary damage due to sampling. Thus, in current conservation practices with mold-contaminated artifacts, little pre-screening or strain identification was performed before mold removal, and the cleaning techniques are usually broad-spectrum rather than strain-specific. With deep learning showing promising applications across various domains, this study investigated the feasibility of using a convolutional neural network (CNN) for fast in-situ recognition and classification of mold on paper.MethodsMolds were first non-invasively sampled from ancient Xuan Paper-based Chinese books from the Qing and Ming dynasties. Strains were identified using molecular biology methods and the four most prevalent strains were inoculated on Xuan paper to create mockups for image collection. Microscopic images of the molds as well as their stains situated on paper were collected using a compound microscope and commercial microscope lens for cell phone cameras, which were then used for training CNN models with a transfer learning scheme to perform the classification of mold. To enable involvement and contribution from the research community, a web interface that actuates the process while providing interactive features for users to learn about the information of the classified strain was constructed. Moreover, a feedback functionality in the web interface was embedded for catching potential classification errors, adding additional training images, or introducing new strains, all to refine the generalizability and robustness of the model.Results & ConclusionIn the study, we have constructed a suite of high-confidence classification CNN models for the diagnostic process for mold contamination in conservation. At the same time, a web interface was constructed that allows recurrently refining the model with human feedback through engaging the research community. Overall, the proposed framework opens new avenues for effective and timely identification of mold, thus enabling proactive and targeted mold remediation strategies in conservation.

Indigenous language and social justice as recognition: a participatory study with a Mapuce school

ABSTRACT The structure of the national educational system negatively affects the recognition of indigenous Mapuce people, who have been affected with regards to love, equal treatment and social esteem, as understood from the social justice approach of recognition described by Axel Honneth. This is evident in the indigenous knowledge and practices that have been removed from the Chilean school curriculum. This research was carried out through a participatory study using the dialogic-kishu kimkelay ta che methodology, forming a research community with professors, kimces and academics. The results relay, from the voices of the community, the work developed to reverse injustices such as disparagement toward the indigenous language at school, this was achieved through actions for reciprocal recognition based on the incorporation of educational practices associated with the language regarding sonority, nature and territory.

Sociotechnical Imaginaries of Sharing and Emerging Postdigital Meaning-Making Practices in the Astronomy Community

AbstractFor decades, science communities have had digital technology embedded in their everyday work. However, new research infrastructures are amplifying the presence and use of digital technologies for scientists. In that respect, radio astronomy is undergoing a major transformation causing the community to enter a phase of postdigital work, due to the construction of the most sensitive telescope, the Square Kilometer Array Observatory (SKAO), which scales the presence of digital technology and the amount of data generated. As new digital research infrastructure is set up, sociotechnical imaginaries—symbols and visions of a shared future—emerge, while others become obsolete thus impacting structures and practices of meaning-making. In this paper, we explore the disruptive potential of sociotechnical imaginaries and how astronomers using SKA pathfinder and precursor telescope data respond to these imaginaries and incorporate them into their meaning-making. The analysis shows that postdigital imaginaries related to data circulation, storage, archiving, and reuse have been amplified as SKA facilities and services are set up. Two changes are highlighted regarding a new postdigital condition within the astronomy community. Firstly, as astronomers engage in new postdigital forms of collaboration, they need to reach a consensus on what types of analyses to use by agreeing which methods are appropriate. This affects how scientific questions and research proposals are negotiated collectively, impacting the agency of astronomers. Secondly, as digital tools are increasingly part of astronomers’ daily work, they have to rely on new data analysis methods, which determine what evidence is uninteresting. Overall, these changes pose new questions regarding how meaning-making processes are altered and the way science is undertaken because of these new entanglements of human and non-human actors.

Shifting to machine supervision: annotation-efficient semi and self-supervised learning for automatic medical image segmentation and classification

Advancements in clinical treatment are increasingly constrained by the limitations of supervised learning techniques, which depend heavily on large volumes of annotated data. The annotation process is not only costly but also demands substantial time from clinical specialists. Addressing this issue, we introduce the S4MI (Self-Supervision and Semi-Supervision for Medical Imaging) pipeline, a novel approach that leverages advancements in self-supervised and semi-supervised learning. These techniques engage in auxiliary tasks that do not require labeling, thus simplifying the scaling of machine supervision compared to fully-supervised methods. Our study benchmarks these techniques on three distinct medical imaging datasets to evaluate their effectiveness in classification and segmentation tasks. Notably, we observed that self-supervised learning significantly surpassed the performance of supervised methods in the classification of all evaluated datasets. Remarkably, the semi-supervised approach demonstrated superior outcomes in segmentation, outperforming fully-supervised methods while using 50% fewer labels across all datasets. In line with our commitment to contributing to the scientific community, we have made the S4MI code openly accessible, allowing for broader application and further development of these methods. The code can be accessed at https://github.com/pranavsinghps1/S4MI.

TyBox: An Automatic Design and Code Generation Toolbox for TinyML Incremental On-Device Learning

Incremental on-device learning is one of the most relevant and interesting challenges in the field of Tiny Machine Learning (TinyML). Indeed, differently from traditional TinyML solutions where the training is typically carried out on the Cloud and inference only occurs on the tiny devices (e.g., embedded systems or Internet-of-Things units), incremental on-device TinyML allows both the inference and the training of TinyML models directly on tiny devices. This ability paves the way for TinyML-enabled intelligent devices that can learn directly on the field and adapt to evolving environments, different working conditions, or specific users. The literature in this field is quite limited with very few solutions focusing only on the incremental fine-tuning of machine learning models, whereas a general solution encompassing algorithms and code generation for incremental on-device TinyML is still perceived as missing. The aim of this article is to introduce, to the best of our knowledge for the first time in the literature, a toolbox called TyBox for the automatic design and code generation of incremental on-device TinyML classification models. In more detail, starting from a “static” TinyML model, TyBox is able to (i) automatically design the “incremental” on-device version of the TinyML model that has been suitably designed to take into account the technological constraint on the RAM memory of the target tiny device, and (ii) autonomously provide the C++ codes and libraries to support the inference and learning of the incremental on-device TinyML model directly on the tiny devices. TyBox has been extensively compared with a state-of-the-art incremental learning solution for TinyML and tested on an off-the-shelf tiny device (i.e., the Arduino Nano 33 BLE) in three relevant TinyML application tasks and scenarios: binary image classification, multi-class image classification, and ultra-wide-band human activity recognition. In addition, TyBox is released to the scientific community as a public repository.

Life cycle assessment of electric kick scooters: consolidating environmental impact quantification and concluding climate-friendly use options

BackgroundThe environmental impact of electric scooters has been the subject of critical debate in the scientific community for the past 5 years. The data published so far are very inhomogeneous and partly methodologically incomplete. Most of the data available in the literature suffer from an average bias of 34%, because end-of-life (EOL) impacts have not been modelled, reported or specified. In addition, the average lifetime mileage of shared fleets of e-scooters, as they are operated in cities around the world, has recently turned out to be much lower than expected. This casts the scooters in an unfavourable light for the necessary mobility transition. Data on impact categories other than the global warming potential (GWP) are scarce. This paper aims to quantify the strengths and weaknesses of e-scooters in terms of their contribution to sustainable transport by more specifically defining and extending the life cycle assessment (LCA) modelling conditions: the modelling is based on two genuine material inventories obtained by dismantling two different e-scooters, one based on a traditional aluminium frame and another, for the first time, based on plastic material.ResultsThis study provides complete inventory data to facilitate further LCA modelling of electric kick scooters. The plastic scooter had a 26% lower lifetime GWP than the aluminium vehicle. A favourable choice of electric motor promises a further reduction in GWP. In addition to GWP, the scooter's life cycles were assessed across seven other impact categories and showed no critical environmental or health impacts compared to a passenger car. On the other hand, only the resource extraction impact revealed clear advantages for electric scooters compared to passenger cars.ConclusionsUnder certain conditions, scooters can still be an important element of the desired mobility transition. To assure a lifetime long enough is the crucial factor to make the electric scooter a favourable or even competitive vehicle in a future sustainable mobility system. A scooter mileage of more than 5400 km is required to achieve lower CO2eq/pkm emissions compared to passenger cars, which seems unlikely in today's standard use case of shared scooter fleets. In contrast, a widespread use of e-scooters as a commuting tool is modelled to be able to save 4% of greenhouse gas (GHG) emissions across the German mobility sector.

Plasma biomarkers increase diagnostic confidence in patients with Alzheimer’s disease or frontotemporal lobar degeneration

BackgroundThe recent development of techniques to assess plasma biomarkers has changed the way the research community envisions the future of diagnosis and management of Alzheimer’s disease (AD) and other neurodegenerative disorders. This work aims to provide real world evidence on the clinical impact of plasma biomarkers in an academic tertiary care center.MethodsAnonymized clinical reports of patients diagnosed with AD or Frontotemporal Lobar Degeneration with available plasma biomarkers (Aβ42, Aβ42/Aβ40, p-tau181, p-tau231, NfL, GFAP) were independently assessed by two neurologists who expressed diagnosis and diagnostic confidence three times: (T0) at baseline based on the information collected during the first visit, (T1) after plasma biomarkers, and (T2) after traditional biomarkers (when available). Finally, we assessed whether clinicians’ interpretation of plasma biomarkers and the consequent clinical impact are consistent with the final diagnosis, determined after the conclusion of the diagnostic clinical and instrumental work-up by the actual managing physicians who had complete access to all available information.ResultsClinicians assessed 122 reports, and their concordance ranged from 81 to 91% at the three time points. At T1, the presentation of plasma biomarkers resulted in a change of diagnosis in 2% (2/122, p = 1.00) of cases, and in increased diagnostic confidence in 76% (91/120, p < 0.001) of cases with confirmed diagnosis. The change in diagnosis and the increase in diagnostic confidence after plasma biomarkers were consistent with the final diagnosis in 100% (2/2) and 81% (74/91) of cases, respectively. At T2, the presentation of traditional biomarkers resulted in a further change of diagnosis in 13% (12/94, p = 0.149) of cases, and in increased diagnostic confidence in 88% (72/82, p < 0.001) of cases with confirmed diagnosis.ConclusionsIn an academic tertiary care center, plasma biomarkers supported clinicians by increasing their diagnostic confidence in most cases, despite a negligible impact on diagnosis. Future prospective studies are needed to assess the full potential of plasma biomarkers on clinical grounds.

Leveraging co-production within ecosystem restoration to maximize benefits to coastal birds

Coastal Louisiana's ecosystems are threatened by anthropogenic factors exacerbated by climate change induced sea-level rise. The 2010 Deepwater Horizon oil spill resulted in injuries and deaths to coastal birds in Louisiana, and the ongoing loss of habitat has limited the potential for successful nesting of resident birds throughout the coast. Habitat loss is being addressed through increased large-scale ecosystem restoration as a result of settlement funds from the Deepwater Horizon oil spill. To further inform bird restoration in Louisiana, an avian restoration guidance document (Guidance for Coastal Ecosystem Restoration and Monitoring to Create or Improve Bird-NestingHabitat, 2023) was developed to maximize restoration benefits for coastal breeding birds while also achieving broader habitat restoration objectives. The developed restoration guidance was co-produced by subject-matter experts and professionals, including avian experts, engineers, and coastal restoration project managers. The result of this cross-disciplinary effort was specific and targeted guidance that presents designable habitat features that are in the control of project engineers and are also important high-value bird nesting habitats (e.g., shoreline access, elevation heterogeneity and edge habitat). For the first time in Louisiana, defined nest-site characteristics and monitoring approaches are readily available to inform ecosystem restoration project implementation. The restoration document specifically emphasizes bird species that breed and nest in coastal habitats in Louisiana, and restoration managers can use these guidelines to explicitly incorporate bird nesting habitat features into coastal restoration planning, design, and implementation. In developing this guidance, many knowledge gaps and data needs were identified specific to engineering and project design, enabling the research community to frame research questions around specific coastal restoration questions. The co-production of science model applied here for avian resources is applicable to a wide range of other living marine resources that may benefit from large-scale ecosystem restoration and is an example of the benefits of working relationships, communications, and common goal setting.

Negotiating Between Belief and Custom: The Encounters of Han Muslim Converts in China

ABSTRACT The conversion of Han Chinese in the Peoples Republic of China (PRC) to Islam is a complex and dynamic process that goes beyond the mere adoption of a new faith. Potential converts undergo a multifaceted journey involving learning about Islam, reflecting on Hui and Han ethnic cultures and customs, and understanding the historical relationship between Islam as a religion and ethnic Hui Chinese people. Through this process, Han converts gradually develop a pluralistic and multilayered identity that encompasses their Han ethnicity and Muslim faith. The article emphasizes the importance of daily inter-group interactions and the use of various strategies to resolve conflicts while adhering to Islamic beliefs. It introduces a theoretical framework consisting of discursive, local, and configurable values to analyze the nuances of everyday experiences among Han converts. This framework provides a structured approach to understanding the complexities of identity formation and enrich ongoing discussions within the Islamic anthropological research community.

Mapping the evolution of gender dysphoria research: a comprehensive bibliometric study

AbstractThe definition of gender dysphoria has been the subject of extensive scientific debate in various fields. The Diagnostic and Statistical Manual of Mental Disorders, 5th edition (DSM-V) characterizes gender dysphoria as a psychological condition due to a discrepancy between perceived and assigned gender. The scientific community has engaged in an extensive debate over the years regarding the classification of gender dysphoria, initially characterizing it as a gender identity disorder and subsequently removing it from the category of mental disorder. This paper aims to use bibliometric techniques to analyze scientific productivity and study the evolution of content on gender dysphoria from 1991 to 2022. It provides to map the scientific research production in this field through the science mapping approach highlighting the changes that have taken place over the past three decades.

ATENA: A Web-Based Tool for Modelling Metal Oxide Nanoparticles Based on NanoFingerprint Quantitative Structure–Activity Relationships

Modelling size-realistic nanomaterials to analyse some of their properties, such as toxicity, solubility, or electronic structure, is a current challenge in computational and theoretical chemistry. The representation of the all-atom three-dimensional structure of a nanocompound would be ideal, as it could account explicitly for structural effects. However, the use of the whole structure is tedious due to the high data management and the structural complexity that accompanies the surface of the nanoparticle. Developing appropriate tools that enable a quantitative analysis of the structure, as well as the selection of regions of interest such as the core-shell, is a crucial step toward enabling the efficient analysis and processing of model nanostructures. The aim of this study was twofold. First, we defined the NanoFingerprint, which is a representation of a nanocompound in the form of a vector based on its 3D structure. The local relationship between atoms, i.e., their coordination within successive layers of neighbours, allows the characterisation of the local structure through the atom connectivity, maintaining the information of the three-dimensional structure but increasing the management ability. Second, we present a web server, called ATENA, to generate NanoFingerprints and other tools based on the 3D structure of the nanocompounds. A case study is reported to show the validity of our new fingerprint tool and the usefulness of our server. The scientific community and also private companies have a new tool based on a public web server for exploring the toxicity of nanocompounds.

The Top Ranked 101 Articles in Pediatric Surgical Journals from an Altmetric Perspective.

In the era of scientific digitalization, online media platforms gain increasing popularity to accomplish research output awareness. The Altmetric Attention Score AAS weights these online mentions based on a privy algorithm. We aimed to characterize the top 100 articles with the highest (AAS) published in pediatric surgery journals. Publications from six core pediatric surgery journals were retrieved from www.altmetric.com in January 2023 and ranked by their AAS. The top 101 publications were analyzed for their bibliometric measures, study design, and quality as well as online media mentions. The top 101 AAS articles were published between 1974 and 2022, preferentially from the United States (64%) and mainly in Journal of Pediatric Surgery (73%), followed by Journal of Pediatric Surgery Case Reports, Pediatric Surgery International, Seminars in Pediatric Surgery, and European Journal of Pediatric Surgery. Their AAS ranged between 21 and 389 (median: 33), with Twitter/X being mostly responsible for online mentions (n = 2,189; 75%). The number of citations in peer-reviewed journals ranged between 0 and 358 (median: 16) and did not correlate to AAS. Retrospective study design (33%) with low evidence level IV (43%) dominated. The Journal of Pediatric Surgery is the main source of high-profile AAS publications in pediatric surgery. The altmetric popularity of articles is predominantly achieved by their propagation via X, irrespective of the study quality and recognition in the scientific community. Thus, active "twitterism" may play the key role to reach high AAS scores.

Recent Developments to the SimSphere Land Surface Modelling Tool for the Study of Land–Atmosphere Interactions

Soil–Vegetation–Atmosphere Transfer (SVAT) models are a promising avenue towards gaining a better insight into land surface interactions and Earth’s system dynamics. One such model developed for the academic and research community is the SimSphere SVAT model, a popular software toolkit employed for simulating interactions among the layers of vegetation, soil, and atmosphere on the land surface. The aim of the present review is two-fold: (1) to deliver a critical assessment of the model’s usage by the scientific and wider community over the last 15 years, and (2) to provide information on current software developments implemented in the model. From the review conducted herein, it is clearly evident that from the models’ inception to current day, SimSphere has received notable interest worldwide, and the dissemination of the model has continuously grown over the years. SimSphere has been used so far in several applications to study land surface interactions. The validation of the model performed worldwide has shown that it is able to produce realistic estimates of land surface parameters that have been validated, whereas detailed sensitivity analysis experiments conducted with the model have further confirmed its structure and architectural coherence. Furthermore, the recent inclusion of novel functionalities in the model, as outlined in the present review, has clearly resulted in improving its capabilities and in opening up new opportunities for its use by the wider community. SimSphere developments are also ongoing in different aspects, and its use as a toolkit towards advancing our understanding of land surface interactions from both educational and research points of view is anticipated to grow in the coming years.

“No-one’s contribution is more valid than another’s”: Committing to inclusive democratic methodologies

In this article, we explore the power and potential of democratic research methodologies in and beyond Critical Disability Studies research contexts. We centre two funded co-produced, participatory and arts-informed projects that have been co-designed and co-led with disabled young people and people living with chronic (respiratory) illness. We critically explore some key processes, which we suggest can mitigate forms of disablism and ableism inherent to research processes which traditionally make them undemocratic spaces of inequity. Our paper offers original analyses into the very notion of democratic research which have significant applications; driven as they are by the presence of disability. These include (i) Crip time - the recognition of (disabled) people’s need for flexible forms of time; (ii) virtual methods and intimacies as routes to equity in research leadership; and (iii) flexible and slow/er research approaches. We also draw upon the ways in which the Covid-19 global pandemic has reshaped methodologies and approaches to inquiry. We advocate that, as research communities, we must come together to keep hold of these new inclusive and hybridised ways of relating and engaging in what are problematically framed as “post-Covid” times. We conclude by emphasising the importance of always committing to disrupting power dynamics through centring flexibility, accessibility and inclusivity across our inquiry with marginalised others.

Ornithofauna seized by enforcement agencies from 2013 to 2022

In Brazil, birds are the most common targets of wildlife trade. Their diversity, beautiful plumage and elaborate songs are differentials among other animals. Therefore, they are coveted either by breeders, to compose zoo collections, or even to become pets to the general population. The Federal Highway Police (PRF), integrated with the Brazilian Institute of Environment and Renewable Natural Resources (IBAMA), aims to inspect and preserve the Brazilian natural environment. Most animals seized by enforcement agencies are sent to the Wild Animal Rehabilitation Centers (CETAS). This study aimed to evaluate illegal trade of wild birds in Brazil by analyzing, identifying and quantifying birds seized by IBAMA and PRF in Brazilian territory, to expand the knowledge of the scientific and popular community about the growth of poaching. This study used data from notices of seizure from 2013 to 2019 (IBAMA) and from 2018 to 2022 (PRF), both obtained via open databases of the corresponding agencies. In data consultation, information was collected on the number of species as well as the total number of animals seized from wildlife. During the evaluated period, the highest incidences of seizure correspond to the Columbiformes and Passeriformes orders. Thus, we identified the preference of certain species, families, and orders in illegal trade of birds in Brazil.

A Sole Case of the FGF23 Gene Mutation c.202A&gt;G (p.Thr68Ala) Associated with Multiple Severe Vascular Aneurysms and a Hyperphosphatemic Variant of Tumoral Calcinosis—A Case Report

Tumoral calcinosis is an extremely rare genetic disease caused by mutations in three genes, GALNT3, FGF23, and KL, which disrupt phosphorus metabolism. The hallmark of this condition is the formation of tumors in the soft tissues around the joints. Other phenotypic features of tumoral calcinosis are dental involvement and brain and vascular calcifications. The clinical case reported herein presents for the first time to the scientific community the c.202A&gt;G (p.Thr68Ala) mutation of the FGF23 gene, associated with a hyperphosphatemic variant of tumoral calcinosis and multiple severe vascular aneurysms. A female patient underwent multiple surgeries for tumor formations in her soft tissues that first appeared at the age of 12 months. On this occurrence, the patient was found to have hyperphosphatemia, low phosphate clearance, increased tubular reabsorption with normal levels of total and ionized calcium, vitamin D3, and parathyroid hormone, and no effect of treatment with sevelamer hydrochloride and a low-phosphate diet. At the age of 39, the patient underwent imaging studies due to edema and a pulsating formation in the neck area, which revealed multiple vascular aneurysms with thrombosis, for which she received operative and interventional treatment. In this connection, and because of the established phosphorus metabolism disturbance, a genetic disease was suspected. The sequence analysis and deletion/duplication testing of the 358 genes performed on this occasion revealed that the woman was homozygous for a variant of the c.202A&gt;G (p.Thr68Ala) mutation of the FGF23 gene. The established mutation is not present in population databases. The presented clinical case is the first and only one in the world to demonstrate the role of this type of FGF23 gene mutation in the development of a hyperphosphatemic variant of tumoral calcinosis characterized by aggressive formation of multiple vascular aneurysms.

Biodegradation of DEP, DIBP, and BBP by a psychrotolerant Sphingobium yanoikuyae strain P4: Degradation potentiality and mechanism study.

Phthalic acid esters (PAEs) are human made chemicals widely used as plasticizers to enhance the flexibility of plastic products. Due to the lack of chemical bonding between phthalates and plastics, these materials can easily enter the environment. Deleterious effects caused by this chemo-pollutant have drawn the attention of the scientific community to remediate them from different ecosystem. In this context, many bacterial strains have been reported across different habitats and Sphingobium yanoikuyae strain P4 is among the few psychrotolerant bacterial species reported to biodegrade simple and complex phthalates. In the present study, biodegradation of three structurally different PAEs viz., diethyl phthalate (DEP), di-isobutyl phthalate (DIBP), and butyl benzyl phthalate (BBP) have been investigated by the strain P4. Quantitative analyses through High-performance liquid chromatography (HPLC) revealed that the bacterium completely degraded 1g/L of DEP, DIBP, and BBP supplemented individually in minimal media pH 7.0 within 72, 54, and 120h of incubation, respectively, at 28°C and under shake culture condition (180rpm). In addition, the strain could grow in minimal media supplemented individually with up to 3g/L of DEP and 10.0g/L of DIBP and BBP at 28°C and pH 7.0. The strain also could grow in metabolites resulting from biodegradation of DEP, DIBP, and BBP, viz. n-butanol, isobutanol, butyric acid, ethanol, benzyl alcohol, benzoic acid, phthalic acid, and protocatechuic acid. Furthermore, phthalic acid and protocatechuic acid were also detected as degradation pathway metabolites of DEP and DIBP by HPLC, which gave an initial idea about the biodegradation pathway(s) of these phthalates.

Earthquake-triggered ground-failure inventory associated with the M7.1 2018 Southcentral Alaska earthquake

The 30 November 2018, magnitude (Mw) 7.1 earthquake in Southcentral Alaska triggered substantial landslides, liquefaction, and ground cracking throughout the region, resulting in widespread geotechnical damage to buildings and infrastructure. Despite a challenging reconnaissance and remote-sensing environment, we constructed a detailed digital inventory of ground failure associated with the event from several sources. Sources included information derived from remotely sensed data, and data compiled from literature, social media postings, and earthquake damage information compiled by local, state, and federal agencies. Each instance of ground failure within the inventory contains information on the location and type of observed ground failure, and the methods and data used to document the occurrence. Where high-quality data, such as LIDAR or satellite imagery, were available and showed the ground-failure instance clearly, the extent is mapped as a polygon or polyline. All other locations are mapped as points. There are a total of 886 ground-failure instances documented within the inventory (400 landslides, 286 liquefaction features, and 200 features unattributed to specific processes). A semi-quantitative confidence scheme is used to describe mapping certainty associated with each ground-failure feature. This inventory represents a relatively moderate ground-failure-triggering event that occurred in a subarctic environment. This data paper describes the content within the inventory, the inventory data collection procedures, and limitations of the data. Events of this type are not often documented in detail; thus, adding the inventory data to the US Geological Survey Open Repository of Earthquake-Triggered Ground-Failure Inventories further diversifies the datasets available to the scientific community to be used to better understand and model earthquake-triggered ground failure.

Forests high on CO2: A glimpse into how scientists study the biochemical machinery of forest ecosystems

Are forests across the planet storing more carbon as a result of rising atmospheric carbon dioxide (CO2) concentration? In this text, I propose to reflect on the work of researchers who are asking that very question. The focus is on a particular approach called free-air CO2 enrichment (FACE) experiments, in which forests in their local environment are subjected to a CO2 concentration higher than in the ambient air, to test the CO2 fertilization hypothesis. This hypothesis predicts that forests store more carbon in those conditions. FACE studies held the promise of establishing cause–effect relations and transcending the here and now. They emerged in the United States in the late 1980s, at a time when ecology scaled up to make itself relevant to the then nascent Earth System science. FACE experiments in forests are now conducted in Australia, the United Kingdom, and Brazil, and their results are closely scrutinized by a small research community. The text, first, presents the engineering feat that underpins FACE experiments in forests and discuss two problems highlighted by scientists because they impede hasty generalization from the data: the timescale needed to witness forests change and the spatial heterogeneity of forest soils. It then shows how scientists contend with their incomplete understanding of forest ecosystems as they bring together computer models and empirical data to build consensus around the sources of epistemic uncertainty. To conclude, I propose to rework the idea of “the macroscope” to conceptualize what scientists do when they seek to study forests as complex, layered, multi-scale systems. I wish to capture how researchers, who willingly endorse a planetary gaze, problematize their own predicament. This, as I suggest, has to do with the immanence of forests in the world.