Global Network Research Articles

Psychiatric Comorbidities in Pediatric Trichotillomania: AMulticenter Cohort Study.

Trichotillomania (TTM) significantly increases the risk of psychiatric comorbidities. Sparse research in pediatric populations necessitates larger studies to assess these risks. This study investigates the risk of developing psychiatric comorbidities in pediatric TTM patients. This case-control study assessed pediatric patients (< 18 years old) with TTM diagnosed between May 18, 2013, and January 1, 2024, using US-based data from the TriNetX global research network. TTM patients (ICD-10 diagnostic category F63.3) aged 18 years or younger at diagnosis and control patients (ICD-10 code Z00.129) matched for age, sex, race, and ethnicity were assessed. Propensity score matching yielded 16,590 patients in each cohort. The analysis assessed subsequent diagnoses of ADHD, conduct disorders, tic disorders, obsessive-compulsive disorder, anxiety disorders, dissociative, stress-related, and somatoform disorders, mood disorders, and suicide attempts compared to controls. TTM patients under 18 years exhibited significantly greater risks of subsequent diagnoses for ADHD (OR: 2.002; CI 1.841-2.178; p < 0.001), conduct disorders (OR: 3.668; 3.2-3.668; p < 0.0001), tic disorders (OR: 2.247; 1.826-2.765; p < 0.0001), obsessive-compulsive disorder (OR: 11.047; 8.822-13.832; p < 0.0001), anxiety disorders (OR: 3.583; 3.387-3.7; p < 0.0001), dissociative, stress-related, and somatoform disorders (OR: 6.179; 3.935-9.701; p < 0.0001), mood disorders (OR: 2.476; 2.288-2.68; p < 0.0001), and suicide attempts (OR: 1.81; 1.121-2.924; p = 0.0139) compared to controls. TTM patients had the greatest risk of psychiatric diagnosis 1 year postindex event. Pediatric TTM patients have higher psychiatric comorbidity risks, necessitating timely intervention and comprehensive management. Dermatologists can facilitate access to behavioral and pharmacological care, enhancing patient outcomes.

The resilience and determinants of global mineral resource supply chains: a network percolation perspective

Mineral resources are the basic materials for global economic development. Assessing mineral resource supply chain resilience is an important pillar of mineral resource supply chain stability management. The globality, heterogeneity and complexity of supply chain bring challenges to the resilience assessment of global mineral resource supply chain. To solve this problem, a method based on multi-region input-output model, network percolation model and econometric model is proposed, which is able to measure the resilience of global mineral resource supply chain and its influencing factors from the perspective of the whole system. The percolation phase transition is introduced to measure the critical state of global mineral resource supply chain system collapse facing external disruption. Using the proposed method, this paper conducts an empirical study on the evolution of global mineral resource supply chain resilience from 2005 to 2014. The results show that the resilience of global mineral resource supply chain declined by 39.6% in 2005–2014. Most of the critical links that caused the collapse of the global mineral resource supply chain network are the manufacturing sector and its upstream and downstream sectors. The structure of supply chain network plays a key role in network resilience. Increasing the number of linkages in upstream and downstream could improve network resilience, but the increase of linkage strength would deteriorate network resilience.

The Impact of Industrial Robot Applications on Global Value Chains Division Position

This paper surveys the GVC division position across forty-five countries and fourteen manufacturing sub-sectors during the period from 1999 to 2018 to illustrate the impact of applications of industrial robots on the GVC position. The study proves that an increasing level of utilization of industrial robots significantly improves the GVC division position in the manufacturing sector. By automating repetitive and precision tasks, robots enhance efficiency and productivity, therefore allowing deeper specialization in specific stages of the manufacturing process. This technological progress allows for a more connected global production network and hence better integration into GVC. The study also evaluates the spillover effects across value chains and the moderating impact of industrial agglomeration on smart manufacturing.The empirical results show that the application of industrial robots enhances productivity and encourages innovation in related sectors, thereby driving downstream industry growth and promoting industry specialization.Despite the improvement in GVC division positions, robots also contribute to making global value chains more diversified and resilient. Flexible and adaptive manufacturing systems can adapt to disruptions, such as pandemics and geopolitical tensions, ensuring continuity in production and increasing stability and competitiveness in smart manufacturing systems.

Network efficiency of functional brain connectomes altered in type 2 diabetes patients with and without mild cognitive impairment

AimTo explore the topological organization alterations of functional connectomes in type 2 diabetes (T2DM) patients with and without mild cognitive impairment (MCI), and compare these with structural connectomes changes.MethodsTwenty-six T2DM patients with MCI (DM-MCI), 26 without cognitive impairment (DM-NC), and 28 healthy controls were included. Diffusion tensor imaging (DTI) and resting-state functional MRI images were acquired. Networks were constructed and graph-theory based network measurements were calculated. The global network parameters and nodal efficiencies were compared across the three groups using one-way ANOVA and a false-discovery rate correction was applied for multiple comparisons. Partial correlation analyses were performed to investigate relationships between network parameters, cognitive performance and clinical variables.ResultsIn the structural connectome, the DM-MCI group exhibited significantly decreased global efficiency (Eglob) and local efficiency (Eloc) compared to the DM-NC and control groups. In the functional connectome, the DM-MCI group exhibited increased Eloc and clustering coefficient (Cp) compared to the controls. No significant differences were found in Eglob, Eloc, or Cp between the DM-NC and the control group, both in structural and functional connectomes. Nodal efficiencies decreased in some brain regions of structural and functional networks in the DM-MCI and DM-NC groups, but increased in five regions in functional network, some of which were involved in the default-mode network.ConclusionUnlike the consistently decreased global properties and nodal efficiencies in the structural connectome of T2DM patients, increases in Eloc, Cp, and nodal efficiencies in the functional connectome may be viewed as a compensatory mechanism due to functional plasticity and reorganization. Altered nodal efficiency can hint at cognitive decrements at an early stage in T2DM patients.

The Infrastructural Power of the Cayman Islands and the US State Power: A Financial Networks Centrality Approach

ABSTRACT This paper explores the symbiotic relationship between the Cayman Islands and the centrality of the United States (US) in global financial networks, using their connection as a test case for a broader theory of how infrastructural power of states is achieved through transnational and networked strategies. The legal and financial infrastructure of the Cayman Islands is extensively used by US financial institutions. This infrastructure supports the development of a significantly US-centric fund industry, facilitating substantial investments into US capital markets. Additionally, it serves as a global conduit, channelling funds from regions such as Asia and Latin America into US markets, streamlining the process by which foreign investors acquire US securities, and supporting the development of complex USD-denominated financial products. This dynamic enhances the depth, liquidity, and complexity of US capital markets, thereby reinforcing US centrality in global financial networks and bolstering its geopolitical power through financial diplomacy, economic sanctions, regulatory influence, and control over critical financial infrastructure. The relationship underscores the infrastructural power of the Cayman Islands, whose financial and legal framework is essential for sustaining and amplifying US centrality. Consequently, this paper aims to integrate the transnational perspective on infrastructural power within the International Political Economy (IPE) and Geopolitics literature, demonstrating how the Cayman Islands function as a multifaceted networked site that strengthens, projects, and sustains US state power on a global scale.

The Comprehensive Profiling of the Chemical Components in the Raw and Processed Roots of Scrophularia ningpoensis by Combining UPLC-Q-TOF-MS Coupled with MS/MS-Based Molecular Networking

Scrophulariae Radix (SR), the dried root of Scrophularia ningpoensis Hemsl (S. ningpoensis), has been extensively used as traditional Chinese medicine for thousands of years. However, since the mid-20th century, the traditional processing technology of S. ningpoensis has been interrupted. Therefore, ultra-high performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry technology, together with a Global Natural Product Social Molecular Networking (GNPS) method, was applied to comprehensively analyze the characteristic changes and mutual transformation of chemical constituents in the differently processed roots of S. ningpoensis, as well as to scientifically elucidate the processing mechanism of differently processed SR. As a result, a total of 149 components were identified. Notably, with the help of the GNPS data platform and MS2 fragment ions, the possible structures of four new compounds (47, 48, 50, and 73) were deduced in differently processed SR samples, in which 47, 48, and 50 are iridoid glycosides, and 73 is a phenylpropanoid glycoside. Five cyclopeptides (78, 86, 97, 99, and 104) derived from leucine (isoleucine) were identified in SR for the first time. The heatmaps analysis results indicated that leucine or isoleucine may be converted to cyclopeptides under the prolonged high-temperature conditions. Moreover, it is found that short-time steaming can effectively prevent the degradation of glycosides by inactivating enzymes. This study provides a new and efficient technical strategy for systematically identifying the chemical components, rapidly discovering the components, and preliminarily clarifying the processing mechanism of S. ningpoensis, as well as also providing a scientific basis for the improvement of the quality standards and field processing of S. ningpoensis.

The role of two major nucleoid-associated proteins in Streptomyces, HupA and HupS, in stress survival and gene expression regulation

BackgroundStreptomyces are sporulating soil bacteria with enormous potential for secondary metabolites biosynthesis. Regulatory networks governing Streptomyces coelicolor differentiation and secondary metabolites production are complex and composed of numerous regulatory proteins ranging from specific transcriptional regulators to sigma factors. Nucleoid-associated proteins (NAPs) are also believed to contribute to regulation of gene expression. Upon DNA binding, these proteins impact DNA accessibility. Among NAPs, HU proteins are the most widespread and abundant. Unlike other bacteria, the Streptomyces genomes encode two HU homologs: HupA and HupS, which differ in structure and expression profile. However, it remained unclear whether the functions of both homologs overlap. Additionally, although both proteins have been shown to bind the chromosome, their rolesin transcriptional regulation have not been studied so far.ResultsIn this study, we explore whether HupA and HupS affect S. coelicolor growth under optimal and stressful conditions and how they control global gene expression. By testing both single and double mutants, we address the question of the complementarity of both HU homologs. We show that the lack of both hup genes led to growth and sporulation inhibition, as well as increased spore fragility. We also demonstrate that both HU homologs can be considered global transcriptional regulators, influencing expression of between 2% and 6% genes encoding among others proteins linked to global regulatory networks and secondary metabolite production.ConclusionsWe identify the independent HupA and HupS regulons, as well as genes under the control of both HupA and HupS proteins. Our data indicate a partial overlap between the functions of HupA and HupS during S. coelicolor growth. HupA and HupS play important roles in Streptomyces regulatory network and impact secondary metabolite clusters.

Intra-individual structural covariance network in schizophrenia patients with persistent auditory hallucinations

Neuroimaging studies have revealed that the mechanisms of auditory hallucinations are related to morphological changes in multiple cortical regions, but studies on brain network properties are lacking. This study aims to construct intra-individual structural covariance networks and reveal network changes related to auditory hallucinations. T1-weighted MRI images were acquired from 90 schizophrenia patients with persistent auditory hallucinations (pAH group), 55 schizophrenia patients without auditory hallucinations (non-pAH group), and 83 healthy controls (HC group). Networks were constructed using the voxel-based gray matter volume and the intra-individual structural covariance was based on the similarity between the morphological variations of any two regions. One-way ANCOVA was employed to compare global and local network metrics among the three groups, and edge analysis was conducted via network-based statistics. In the pAH group, Pearson correlation analysis between network metrics and clinical symptoms was conducted. Compared with the HC group, both the pAH group (p = 0.01) and the non-pAH group (p = 3.56 × 10−4) had lower nodal efficiency of the left medial superior frontal gyrus. Compared to the non-pAH group and HC group, the pAH group presented lower nodal efficiency of the temporal pole of the left superior temporal gyrus (p = 1.09 × 10−3; p = 7.67 × 10−4) and right insula (p = 0.02; p = 8.99 × 10−6), and lower degree centrality of the right insula (p = 0.04; p = 1.65 × 10−5). The pAH group had a subnetwork with reduced structural covariance centered by the left temporal pole of the superior temporal gyrus. In the pAH group, the normalized clustering coefficient (r = −0.36, p = 8.45 × 10−3) and small-worldness (r = −0.35, p = 9.89 × 10−3) were negatively correlated with the PANSS positive scale score. This study revealed network changes in schizophrenia patients with persistent auditory hallucinations, and provided new insights into the structural architecture related to auditory hallucinations at the network level.

From here to eternity: The rise and fall of global art worlds and the contemporary private museum

The process of converting a private collection into a public museum is often presented as a means of ensuring a collection&amp;rsquo;s future by transforming it into an enduring asset for future generations. This case study assesses contemporary collection models as well as entrepreneurial investor-collectors and their respective museum-building strategies by addressing two fundamental questions: (1) What is the role of the contemporary art collector and private museum and (2) How do their interactions with art markets challenge the established protocols and hierarchies of the global art network? It shall be argued that private collection museums often have a limited lifespan. Furthermore, the future of private collections and museums is often determined within the lifetimes of their creators. This can play out in one of four ways: first, collectors may offer artworks at high-profile auction houses; second, they may sell the art collection and close the museum or collector&amp;rsquo;s exhibition space; third, they may sell the museum or exhibition space. Alternatively, private collectors, along with their museums and art collections, can play an important role on the global stage. However, more rigorous planning is necessary for these collectors to distinguish themselves, expand the cultural landscape, and ensure their collections endure within or beyond their creators&amp;rsquo; lifetime.

Citizen Science for Invertebrate Biodiversity: Mobilizing Spider Occurrence Data through Facebook in India

India accommodates a huge diversity of plant and animal life across a variety of biomes. However, the degree of research, funding, and attention is asymmetric, largely focused on its charismatic vertebrates. Invertebrates, despite their megadiversity, are generally overlooked with some exceptions (for example, lepidoterans). One species-rich group, spiders, exemplifies this knowledge gap. More than 1,800 species from 63 families have been reported in the country (Mondal et al. 2020), even though the true number may be much higher, in part, owing to the need for taxonomic revisions. Several spider systematics and biogeographic studies have pointed out that spatial distribution, seasonality, and natural history data are lacking from India. The lack of foundational biodiversity information has led to poor opportunities to share knowledge between researchers and community scientists. Mining occurrences through scientific publications or databases (in some cases behind paywalls) may require specialized training. This restricted access inhibits more people from using the abundance of information. The prevalence of social media posts showcasing photographs of species in biodiversity contexts has experienced a significant increase, with a multitude of users sharing wildlife photographs on social media sites such as Instagram®, Facebook®, and Flickr®. These observations on social media are a potential source of primary biodiversity data when curated and validated (Barman and Barve 2022, Barman et al. 2022a). Such data can enhance biodiversity monitoring by expanding spatial and temporal coverage and involving a global network of citizen scientists in conservation research (Barve et al. 2023, Roy 2022, Kulkarni 2023). The India Biodiversity Portal (IBP, Vattakaven et al. 2016) has been cataloguing species diversity and spider data through citizen science. Meanwhile, major social media sites report many more species sightings than citizen science platforms. SpiderIndia, a popular Facebook group, has collected over 20,000 observations from 8,500 spider enthusiasts. However, these data remain unorganised and inaccessible to academic researchers and the public. In order to tackle these problems, we implemented a methodical process to enhance the occurrence data on spiders from well-known social media platforms, such as the SpiderIndia project. The procedure involved retrieving the relevant data, and ingesting it through a custom pipeline to parse and extract, scientific names, spatial and temporal data to generate occurrence records on the IBP. The data was then presented on a curation interface, enabling taxonomic experts to verify the records, and finally publishing the verified records on the Global Biodiversity Information Facility (GBIF) (Global Biodiversity Information Facility 2022, Barman et al. 2022b) to improve the records of spider species occurrence in India. This project showcases the capacity of citizen science via social media to involve citizen scientists in generating extensive datasets that make a significant contribution to scientific knowledge and improve our comprehension of invertebrate biodiversity. The final dataset encompasses over 15,000 observations, providing valuable insights into spider diversity and distribution across India (Fig. 1). This data is publicly available on GBIF, facilitating further research on Indian spider populations.

Identification and interaction analysis of molecular markers in myocardial infarction by bioinformatics and next-generation sequencing data analysis

BackgroundCardiovascular diseases are prevalent worldwide with any age, and it is characterized by sudden blockage of blood flow to heart and permanent damage to the heart muscle, whose cause and underlying molecular mechanisms are not fully understood. This investigation aimed to explore and identify essential genes and signaling pathways that contribute to the progression of MI.MethodsThe aim of this investigation was to use bioinformatics and next-generation sequencing (NGS) data analysis to identify differentially expressed genes (DEGs) with diagnostic and therapeutic potential in MI. NGS dataset (GSE132143) was downloaded from the Gene Expression Omnibus (GEO) database. DEGs between MI and normal control samples were identified using the DESeq2 R bioconductor tool. The gene ontology (GO) and REACTOME pathway enrichment analyses of the DEGs were performed using g:Profiler. Next, four kinds of algorithms in the protein–protein interaction (PPI) were performed to identify potential novel biomarkers. Next, miRNA-hub gene regulatory network analysis and TF-hub gene regulatory network were constructed by miRNet and NetworkAnalyst database, and Cytoscape software. Finally, the diagnostic effectiveness of hub genes was predicted by receiver operator characteristic curve (ROC) analysis and AUC more than 0.800 was considered as having the capability to diagnose MI with excellent specificity and sensitivity.ResultsA total of 958 DEGs were identified, consisting of 480 up-regulated genes and 478 down-regulated genes. The enriched GO terms and pathways of the DEGs include immune system, neuronal system, response to stimulus and multicellular organismal process. Ten hub genes (namely cftr, cdk1, rps13, rps15a, rps27, notch1, mrpl12, nos2, ccdc85b and atn1) were obtained via protein–protein interaction analysis results. MiRNA-hub gene regulatory network and TF-hub gene regulatory network showed that hsa-mir-409-3p, hsa-mir-3200-3p, creb1 and tp63 might play an important role in the MI.ConclusionsAnalysis of next-generation sequencing dataset combined with global network information and validation presents a successful approach to uncover the risk hub genes and prognostic markers of MI. Our investigation identified four risk- and prognostic-related gene signatures, including cftr, cdk1, rps13, rps15a, rps27, notch1, mrpl12, nos2, ccdc85b and atn1. This gene sets contribute a new perspective to improve the diagnostic, prognostic, and therapeutic outcomes of MI.

Low-degree gravity field coefficients based on inverse GNSS method: insights into hydrological and ice mass change studies

The relative displacements of stations from a global network of Global Navigation Satellite System (GNSS) sites provide information on global mass transport. In this study, we use 19 years of global GNSS station displacements from the 3rd International GNSS Service reprocessing campaign to estimate the coefficients of the spherical harmonics of the Earth’s gravity field up to degree and order 8 using the inverse GNSS method based on elastic loading theory. The results indicate that the C30 coefficient can be derived based on GNSS station displacements as an alternative to solutions provided by Satellite Laser Ranging (SLR) and Gravity Recovery and Climate Experiment (GRACE). GNSS may support GRACE solutions that face the problems of deriving C30, which has fundamental meaning in estimating ice mass changes in polar regions. The recovery of Antarctic ice sheet mass change from January 2007 to December 2020 based on coefficients replaced by GNSS estimates results in a linear trend of − 152 ± 4 Gt/year, in comparison to − 149 ± 2 Gt/year for the replacement based on SLR from GRACE Technical Note #14. The results indicate that the spatial and seasonal patterns of terrestrial water storage changes derived from GNSS are consistent with those estimated using GRACE/GRACE Follow-On and SLR at a few-millimeter level in the Amazon and Brahmaputra River basin regions.

Global hake production and trade: Insights for food security and supply chain resilience

Hake, a key species in global seafood trade, substantially influences market dynamics and consumption patterns. This study delves into the global hake trade network (HGTN), spotlighting the roles and connections of key countries such as Spain, Namibia, Argentina, South Africa, the USA, and Italy, with Spain being the primary importer and countries like Namibia and South Africa notable exporters. Our findings unveil a multifaceted trade network, with Spain and Namibia as central nodes, and reveal growth in trade volume and value, signifying an evolving marketplace. The network’s susceptibility to economic, political, and ecological shifts underscores the necessity for resilient, adaptable trading practices to sustainably manage hake trade. Our findings emphasise the importance of managing resources and maintaining market balance to support global food security and reflect the dynamic seafood industry.

Reconstruction of the Temperature Index Series of China in 1368–1911 based on REACHES database

This study reports the methodology for reconstructing anomalous temperature index series of China in 1368–1911 based on the REACHES database which digitizes the Chinese records quoted in the Compendium of Meteorological Records of China in the Last 3000 Years. The reconstruction adopts an ordinal scale index approach ranging from −2 (very cold) to 1 (warm). Based on the grading criteria, a total of 12,871 records were retrieved through a standard coding system established at REACHES. Sensitivity experiments were performed to test robustness of the index system and a reasonability test was conducted to develop an appropriate method for deriving areal mean temperature index. The reconstructed series were validated through comparison with early instrumental data from Global Historical Climatology Network which shows good correlations and reliability of the REACHES reconstructed index data. Annual and semi-annual (winter and summer) temperature index data series were released for the whole domain as well as the 3- and 15-subregion geographical domains in China.

COVID-19 Vaccination Rates and Vaccine Hesitancy in Pregnant Women in Seven Low- and Middle-Income Countries Through May 2023: An Observational Study From the Global Network.

COVID-19 Vaccination Rates and Vaccine Hesitancy in Pregnant Women in Seven Low- and Middle-Income Countries Through May 2023: An Observational Study From the Global Network.

Knowledge source switching under state interventions of latecomer regions: A case study of Shenzhen

This paper aims to unpack the process and analyze the mechanisms of knowledge source switching in latecomer regions that have evolved from peripheral to core positions. Drawing on four decades of matched patent-company data from Shenzhen, we classify patents into various knowledge classifications and calculate their percentage changes to observe the dynamics of knowledge source switching. Furthermore, we employ Difference-in-Differences (DiD) estimation to examine the causal effects of state interventions on this dynamic. The findings indicate that Shenzhen's knowledge sources have switched from multinational corporations (MNCs) and foreign subsidiaries to global inventors. Notably, state interventions exhibit a significant positive causal effect on this dynamic, particularly following the implementation of independent innovation policies. These findings contribute to the cross-fertilization of regional innovation studies and the Global Production Networks (GPN) approach, highlight the critical role of the state in ensuring the right and dynamic mode of global knowledge sourcing and offer implications for local innovation policies in latecomer regions.

Enhancing network robustness with structural prior and evolutionary techniques

Robustness optimization in complex networks is a critical research area due to its implications for the reliability and stability of various systems. However, existing algorithms encounter two key challenges: the lack of integration of prior network knowledge, leading to suboptimal solutions, and high computational costs, which hinder their practical application. To address these challenges, this paper introduces Eff-R-Net, an efficient evolutionary algorithm framework aimed at enhancing the robustness of complex networks through accelerated evolution. Eff-R-Net leverages global and local network information, featuring a novel three-part composite crossover operator. Prior network knowledge is incorporated in mutation and local search operators to expedite the construction of networks with superior robustness. Additionally, a simplified method for calculating robustness enhances efficiency, while adaptive hyper-parameters dynamically adjust operators execution probabilities for optimal evolution. Extensive evaluations on both synthetic (Scale-Free, Erdös-Rényi, and Small-World) and three infrastructure real-world networks demonstrate the superiority of Eff-R-Net. The algorithm improves robustness by 12.8% and reduces computational time by 25.4% compared to state-of-the-art algorithm in real-world network experiments. These findings underscore Eff-R-Net's versatility and potential in enhancing network robustness across different domains.

Estimating transpiration globally by integrating the Priestley-Taylor model with neural networks

Abstract Transpiration (T), the component of evapotranspiration (ET) controlled by the vegetation, dominates terrestrial ET in many ecosystems; however, estimating it accurately, especially at the global scale, remains a considerable challenge. Existing approaches mostly rely on the relationship between T and photosynthesis, but untangling this relationship is difficult and leads to diverging T estimates. Limited in-situ measurements and the inability to directly measure transpiration from space further complicate the reliable assessment of this crucial process in the terrestrial water cycle. Here, we developed a new hybrid Priestley-Taylor (PT) model combined with an Artificial Neural Network (ANN) using globally available remote sensing and reanalysis data of soil moisture, vapor pressure deficit and windspeed. We also take advantage of the newly released global sap flow measurement network SAPFLUXNET. In the proposed approach, we avoid the parameterization of stomatal conductance by training the ANN on the PT-Coefficient α, obtained by inverting the PT equation. The results showed that our model framework can estimate T in different forest ecosystems based on few predictors. By utilizing forcings from independent datasets, we eliminate the reliance on in-situ measurements for predicting T. Through upscaling actual observations to a larger scale, this model framework helps alleviate the scarcity of T products. Intercomparison of T with ET partitioning methods based on eddy covariance data, shows high performances (KGE of 0.69 in Europe and 0.60 in North America), slightly improving estimates compared to other models. Analysis of contribution of T to ET across 100 FLUXNET sites result in a global mean of 55.2%. We believe that modelling T independent from the carbon cycle can support our understanding of land-atmosphere feedbacks and climate extremes in future research.

Strategic coupling beyond borders: Germany’s extraterritorial agency in Namibia’s green hydrogen industry

Abstract Amidst increasing geopolitical threats, geo-economic competition, and climate change concerns, this article reflects on why and how states alter strategic coupling processes abroad. Scrutinizing Germany’s involvement in Namibia’s green hydrogen industry, we conceptualize the decarbonization rationale as a state project that drives extraterritorial agency. Extraterritorial institutions accompany private enterprises with public institutional support, thus leading to three mechanisms through which extraterritorial agency shapes strategic coupling dynamics abroad: extraterritorial de-risking, extraterritorial asset creation, and market creation. We show that, even in liberal and coordinated market economies, states develop extraterritorial strategies to align global production networks with their strategic goals.

Large language models recover scientific collaboration networks from text

Science is a collaborative endeavor. Yet, unlike co-authorship, interactions within and across teams are seldom reported in a structured way, making them hard to study at scale. We show that Large Language Models (LLMs) can solve this problem, vastly improving the efficiency and quality of network data collection. Our approach iteratively applies filtering with few-shot learning, allowing us to identify and categorize different types of relationships from text. We compare this approach to manual annotation and fuzzy matching using a corpus of digital laboratory notebooks, examining inference quality at the level of edges (recovering a single link), labels (recovering the relationship context) and at the whole-network level (recovering local and global network properties). Large Language Models perform impressively well at each of these tasks, with edge recall rate ranging from 0.8 for the highly contextual case of recovering the task allocation structure of teams from their unstructured attribution page to 0.9 for the more explicit case of retrieving the collaboration with other teams from direct mentions, showing a 32% improvement over a fuzzy matching approach. Beyond science, the flexibility of LLMs means that our approach can be extended broadly through minor prompt revision.