INDEPTH Network Research Articles

Identification of WSN Compromised Nodes and Performance Degradation Prevention using Cooperative Cache

Wireless Sensor Networks (WSNs) are critical in a variety of applications such as environmental monitoring, surveillance, and healthcare. WSNs, on the other hand, are vulnerable to assaults due to their dispersed and resource-constrained nature, resulting in compromised nodes and performance deterioration. Using network characteristics analysis and cooperative cache management with data and process affinity, this study provides a unique way to identifying compromised WSN nodes and mitigating performance deterioration. The initial portion of our suggested technique focuses on finding compromised nodes using in-depth network characteristics research. This suggested system can precisely locate probable hacked nodes by monitoring metrics such as traffic patterns, node activity, and communication abnormalities. Machine learning algorithms improve the identification process by learning and evolving adaptively to identify new assault patterns. Once affected nodes have been discovered, the second component of this suggested solution leverages cooperative cache management to prevent performance deterioration. Data and process affinity is presented, in which nearby nodes share caches to effectively store and retrieve data relevant to their processing activities. This affinity-based cache collaboration increases data availability, decreases communication costs, and improves overall network performance.

The roles of key suppliers in network formation when reshoring

Purpose Reshoring implies the reconfiguration of supply networks. Focusing on the specific case of a born-offshored firm that engaged in reshoring of outsourced activities, this study aims to analyse how initial key suppliers in the home country can help the reshoring firm to organize a new supply network. Design/methodology/approach The research is qualitative and based on a single exploratory case study. The data was collected from multiple primary and secondary sources, and using different techniques, such as in-depth interviews, direct observation and network pictures. Findings The study provides insights on how an initial key supplier can enable its customers’ reshoring in the home country. Specifically, the authors identify 10 roles played by a key supplier. The results also identify supply network formation as an essential component of the reshoring process. Originality/value This paper provides unique contributions. Firstly, it presents the specific and under-explored case of a born-offshored firm engaged in outsourced reshoring; secondly, it sheds light on the roles that key suppliers may play for the reshoring process; thirdly, it explores the formation of business relationships in the context of reshoring. In essence, the paper contributes to the reshoring literature, by stressing the importance of supply network formation in the reshoring process and by identifying the multiplicity of roles that key suppliers can play when implementing reshoring, and to the industrial marketing and purchasing (IMP) literature, by showing how relationships and networks develop during reshoring, a phenomenon so far understudied by IMP scholars.

New molecular mechanisms of quercetin in improving recurrent spontaneous abortion based on in-depth network pharmacology and molecular docking.

The increasing prevalence of recurrent spontaneous abortion (RSA) poses significant physical and psychological challenges for affected individuals. Quercetin, a natural plant flavonoid, shows promise in reducing miscarriage rates, yet its precise mechanism remains elusive. This study uses network pharmacology, molecular docking, and experimental validation to explore the molecular pathways through which quercetin mitigates RSA. Quercetin-related target genes were sourced from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), and RSA target genes were retrieved from the Comparative Toxicogenomics Database (CTD), with overlapping targets identified using Venn diagrams. All genes were visualized using the STRING database, and core targets were selected with Cytoscape 3.7.3. Gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted using the DAVID and Reactome online resources. Subsequently, HTR-8/SVneo cells were stimulated with lipopolysaccharide (LPS) and treated with varying concentrations of quercetin (1, 5, and 10μM), then subjected to CCK-8, wound healing, transwell, and annexin V-FITC/PI apoptosis assays. Reverse-transcription quantitative PCR was used to determine the mRNA expression levels of IL-1β, TNF-α, and IL-6 in LPS-induced cells post-quercetin intervention, and western blotting was used to measure AKT1, MMP9, and caspase-3 protein levels. A total of 139 quercetin-associated target genes were identified from the TCMSP database, and 98 disease-associated target genes were obtained from the CTD, resulting in 25 shared target genes. Gene ontology enrichment highlighted the involvement of these targets in positive regulation of apoptosis, response to hypoxia, and intrinsic apoptotic signaling pathway in response to DNA damage. KEGG pathway analysis indicated enrichment in pathways related to interleukin-4 and interleukin-13 signaling, cytokine signaling in the immune system, and apoptosis. Molecular docking studies revealed robust binding of quercetin with MMP9, AKT1, IL-1β, TNF, and caspase-3. In vitro experiments demonstrated that quercetin enhanced LPS-induced cell activity, fostering proliferation, migration, and invasion, and reducing apoptosis. Moreover, quercetin reduced IL-1β, TNF-α, and IL-6 mRNA expression, increased AKT1 and MMP9 protein levels, and reduced caspase-3 expression. Quercetin could mitigate the incidence of RSA by modulating inflammatory responses and apoptotic processes, through upregulation of AKT1 and MMP9, and downregulation of caspase-3, IL-1β, TNF-α, and IL-6. Quercetin opens up a new way of thinking about treating RSA.

Unpacking the agricultural innovation and diffusion for modernizing the smallholders in rural China: From the perspective of agricultural innovation system and its governance

Innovation is perceived as a crucial tool for addressing the serious global challenges faced by the agriculture sector. This paper focuses on a new local public agricultural innovation project in the context of rural China, and intends to capture the embodied agricultural innovation and dissemination network for modernizing the smallholders. Through integrating the concepts of Agricultural Innovation System and Agricultural Innovation Governance, we develop a comprehensive framework for understanding the partnerships between agri-technicians and small farmers within the network structured by innovation governance practices. In-depth interview and network analysis methods were used to scrutinize the linkages among actors engaged with the agricultural innovation programme in case area and detect the achievements in enabling smallholders to upgrade technical trajectories in production and fostering modern farmers at the local levels. Our empirical analysis suggests that the single-center and multi-level network produced in the agricultural innovation practices, which is distinctive to the previous simplistic linear model for technology transfer, demonstrates obvious effectiveness in diffusing the new knowledge both vertically and horizontally, and that the grassroots sub-network generates the interaction space between agronomic experts and farmers for building mutual trust and co-producing technologies suitable for local conditions. Through allocating financial resources and supervising the public agricultural innovation projects, the provincial government indirectly dominates the directionality of local agricultural innovation for enabling smallholders to learn emerging technologies and cultivating the new generation of professional farmers. Nevertheless, the current agricultural innovation practice in case area has not completely avoided the social selection bias due to the clear marginalization of some vulnerable smallholders, and the duration of innovation projects imposed negative influence on the persistent collaborations between agri-technicians and smallholders in places. It is argued that for meeting the national agenda of agricultural modernization and common prosperity in rural area, how to reform the management measures of public innovation projects and enable the vulnerable farmers to equally engage with the innovation activities and share the interests brought by new technologies are the urgent challenges need to be addressed in the future.

Exploring the therapeutic mechanism of curcumin in prostate cancer using network pharmacology and molecular docking

ObjectiveCurcumin, a phenolic compound extracted from turmeric rhizomes, exhibits antitumour effects in preclinical models of tumours. However, its mechanism of action in prostate cancer remains unclear. Exploring the molecular mechanisms of curcumin in prostate cancer based on network pharmacology and molecular docking provides a new theoretical basis for prostate cancer treatment. MethodUsing tools such as PharmMapper, SuperPred, TargetNet, and SwissTargetPrediction, we obtained information on curcumin-related targets. We comprehensively collected prostate cancer-related targets from several databases, including GeneCards, CTD, DisGeNET, OMIM, and PharmGKB. Cross-cutting drug-disease targets were then derived by screening using the Venny 2.1.0 tool. Subsequently, we used the DAVID platform to perform in-depth GO and KEGG enrichment analyses of the drug-disease-shared targets. To construct a PPI network map of the cross-targets and screen the 10 core targets, we combined the STRING database and Cytoscape 3.7.2. Molecular docking experiments were performed using AutoDockTools 1.5.7 software. Finally, we used several databases such as GEPIA, HPA, cBioPortal, and TIMER to further analyse the screened core targets in detail. ResultWe identified 307 key targets of curcumin in cancer treatment. After GO functional enrichment analysis, we obtained 1119 relevant entries, including 782 biological progression (BP) entries, 112 cellular component (CC) entries, and 225 molecular function (MF) entries. In addition, KEGG pathway enrichment analysis revealed 126 signalling pathways, which were mainly involved in the cancer pathway, such as lipid and atherosclerosis pathway, PI3K-Akt signal pathway, MAPK signal pathway, Ras signal pathways, and chemical carcinogenesis-reactive oxygen species. By applying Cytoscape 3.7.2 software, we identified SRC, PIK3R1, STAT3, AKT1, HSP90AA1, ESR1, EGFR, HSP90AB1, MAPK8, and MAPK1 as core targets. Molecular docking experiments showed that the binding energies of curcumin to these core targets were all below −1.85 kJ mol−1, which fully demonstrated that curcumin could spontaneously bind to these core targets. Finally, these results were validated at multiple levels, including mRNA expression, protein expression, and immune infiltration. ConclusionThrough in-depth network pharmacology and molecular docking studies, we have found that curcumin may have anticancer potential by upregulating the expression of PIK3R1 and STAT3, and downregulating the binding ability of molecules such as SRC, AKT1, HSP90AA1, ESR1, EGFR, HSP90AB1, MAPK8, and MAPK1. In addition, curcumin may interfere with the cyclic process of prostate cancer cells by inhibiting key signalling pathways such as the PI3K-Akt signalling pathway, MAPK signalling pathway, and Ras, thereby inhibiting their growth. This study not only reveals the potential molecular mechanism of curcumin in the treatment of prostate cancer but also provides an important theoretical basis for subsequent research.

Effect of Cross-Well Natural Fractures and Fracture Network on Production History Match and Well Location Optimization in an Ultra-Deep Gas Reservoir

Understanding subsurface natural fracture systems is crucial to characterize well production dynamics and long-term productivity potential. In addition, the placement of future wells can benefit from in-depth fracture network connectivity investigations, vastly improving new wells’ profitability and life cycles if they are placed in dense, well-connected natural fracture zones. In this study, a novel natural fracture calibration workflow is proposed. This workflow starts with the extraction of sector geology and a natural fracture model from the pre-built full-field model. Then, a cross wellbore discrete fracture network (CW-DFN) is created using a novel CW-DFN generation tool, based on image log data. An innovative fracture network identification tool is developed to detect the interconnected regional fracture network (IcFN) with CW-DFN. The non-intrusive embedded discrete fracture model (EDFM) is utilized to numerically incorporate the complex IcFN and CW-DFN in a reservoir simulation, and it is history-matched by tuning their conductivities. This workflow is applied to a single vertical well within a natural fracture carbonate reservoir in Northwest China. The study results show that the number of CW-DFNs is 11, and the number of IcFNs is 72. The non-intersected natural fractures only account for 5.5% of the production, and thus can be removed to improve simulation efficiency. The history-matching absolute average relative deviation (AARD) is 15.16%. The calibrated effective fracture permeability is 280 millidarcy, with an aperture of 0.001 m, equating to a conductivity of 0.28 millidarcy-meter. The 30-year gas production forecast is estimated to be 1.66 billion cubic meters based on a history-matched model. Finally, if the well is drilled to the east of the sector, 30-year production declines to 1.33 billion cubic meters (a reduction of 20%). However, if the well is drilled to the west of the sector, 30-year production increases to 2 billion cubic meters (an improvement of 20.5%).

A VALUE-BASED NETWORK ANALYSIS FOR STAKEHOLDER ENGAGEMENT THROUGH PREFABRICATED CONSTRUCTION LIFE CYCLE: EVIDENCE FROM CHINA

Over the past decade, prefabricated construction has increasingly gained popularity in addressing environmental concerns while meeting the high construction demand, particularly in developing countries. Accordingly, the concept of stakeholder engagement increasingly garnered attention as it is highly relevant for understanding and explaining the relationships among various stakeholders, like contractors, suppliers, and producers, especially within the fragmented context of prefabrication. To further the field, this study aims to provide a comprehensive qualitative and quantitative analysis for stakeholder engagement in the realm of prefabricated construction via a value-based network. This value-based network analysis is utilized to model multiple relationships between stakeholders as value exchanges, thus enabling to engage them though management with a focus. Specifically, A total of 110 values were identified and analyzed among 12 stakeholders throughout the whole lifecycle via an extensive literature review, interviews, and questionnaires directed at prefabrication practitioners in China. This data collection process garnered 194 valid responses with a 64.7% response rate. The findings show that enhancing stakeholder engagement requires improvements in both the abilities and experiences of developers, and in the collaborative relationships among key stakeholders. These improvements can be achieved by prioritizing the engagement of prefabrication consultants and encouraging the government to issue policies that support developers, producers, and consultants in fostering collaboration. This in-depth network analysis offers practical insights for decision-makers in the management of core stakeholders and value-based relationships, thereby improving stakeholder engagement. Additionally, it expands the current body of knowledge on stakeholder engagement by incorporating the value-based network analysis into the exploration of its stakeholders and relationships.

High-throughput single-cell mass spectrometry enables metabolic network analysis by resolving phospholipid C[double bond, length as m-dash]C isomers.

Single-cell mass spectrometry (MS) is an essential technology for sensitive and multiplexed analysis of metabolites and lipids for cell phenotyping and pathway studies. However, the structural elucidation of lipids from single cells remains a challenge, especially in the high-throughput scenario. Technically, there is a contradiction between the inadequate sample amount (i.e. a single cell, 0.5-20 pL) for replicate or multiple analysis, on the one hand, and the high metabolite coverage and multidimensional structure analysis that needs to be performed for each single cell, on the other hand. Here, we have developed a high-throughput single-cell MS platform that can perform both lipid profiling and lipid carbon-carbon double bond (C[double bond, length as m-dash]C) location isomer resolution analysis, aided by C[double bond, length as m-dash]C activation in unsaturated lipids by the Paternò-Büchi (PB) reaction and tandem MS, termed single-cell structural lipidomics analysis. The method can achieve a single-cell analysis throughput of 51 cells per minute. A total of 145 lipids were structurally characterized at the subclass level, of which the relative abundance of 17 isomeric lipids differing in the location of C[double bond, length as m-dash]C from 5 lipid precursors was determined. While cell-to-cell variations in MS1-based lipid profiling can be large, an advantage of quantifying lipid C[double bond, length as m-dash]C location isomers is the significantly improved quantitation accuracy. For example, the relative standard deviations (RSDs) of the relative amounts of PC 34:1 C[double bond, length as m-dash]C position isomers in MDA-MB-468 cells are half smaller than those measured for PC 34:1 as a whole by MS1 abundance profiling. Taken together, the developed method can be effectively used for in-depth structural lipid metabolism network analysis by high-throughput analysis of 142 MDA-MB-468 human breast cancer cells.

Network Dynamic GCN Influence Maximization Algorithm With Leader Fake Labeling Mechanism

Influence maximization is an important technique for its significant value on various social network applications, such as viral marketing, advertisement, and recommendation. Traditional heuristic algorithms for influence maximization suffer from different issues, such as accuracy descent and information loss during network exploration. Besides, recently proposed deep learning-based approaches cannot extract the in-depth structural information of social networks well. In light of these problems, we propose a novel heuristic method called the network dynamic GCN influence maximization algorithm based on the leader fake labeling mechanism. To exploit the in-depth network topology information for the influence maximization task, we design a network dynamic GCN that owns adaptive layer numbers in terms of different network scales to obtain node representations. Then, considering that there are few labels in the network or the labels are irrelevant to the task of influence maximization, we establish a leader fake labeling mechanism to automatically generate node labels that are helpful to seed nodes selecting for model training. Finally, a heuristic method based on the Mahalanobis distance is developed to quickly select influential seed nodes with the learned node representations. Three real-world datasets are used in our experiments, and the experimental results demonstrate that our algorithm has a better performance for seed set identification under the premise of high efficiency compared with some latest heuristic influence maximization algorithms.

Exploring international collaboration and language dynamics in Digital Humanities: insights from co-authorship networks in canonical journals

PurposeThis paper presents a follow-on study that quantifies geolingual markers and their apparent connection with authorship collaboration patterns in canonical Digital Humanities (DH) journals. In particular, it seeks to detect patterns in authors' countries of work and languages in co-authorship networks.Design/methodology/approachThrough an in-depth co-authorship network analysis, this study analysed bibliometric data from three canonical DH journals over a range of 52 years (1966–2017). The results are presented as visualised networks with centrality calculations.FindingsThe results suggest that while DH scholars may not collaborate as frequently as those in other disciplines, when they do so their collaborations tend to be more international than in many Science and Engineering, and Social Sciences disciplines. DH authors in some countries (e.g. Spain, Finland, Australia, Canada, and the UK) have the highest international co-author rates, while others have high national co-author rates but low international rates (e.g. Japan, the USA, and France).Originality/valueThis study is the first DH co-authorship network study that explores the apparent connection between language and collaboration patterns in DH. It contributes to ongoing debates about diversity, representation, and multilingualism in DH and academic publishing more widely.

"I won't get to live my life the way I planned it":A qualitative analysis of the experiences of adolescents and young adults with advanced cancer.

Individuals with advanced cancer face complex challenges, including prognostic uncertainty and evolving goals of care. Despite the unique psychosocial support needs of adolescents and young adults (AYAs), few studies have specifically examined AYA perspectives of and experiences with advanced cancer. The objective of this study was to describe the experience, needs, and perspectives of pediatric AYAs with advanced cancer. We invited English-speaking AYAs (age 14-25 years) who were receiving treatment for advanced cancer at our single tertiary pediatric cancer center to participate in semi-structured interviews. We used directed content analysis for codebook development and then applied in-depth thematic network analysis to describe their perspectives and experiences with advanced cancer. A total of 32 AYAs (86% of approached) completed interviews. A slight majority were male (59%) and non-Hispanic White (56%). Most were diagnosed with leukemia/lymphoma, had recurrent disease (84%), and were a mean 53 months from initial diagnosis. Organizing themes of "not being able to beat this," "not wanting to miss out," and "living each day" generated the global theme "do I have a future?" "Making tough medical decisions," "adjusting life/plans/perspectives," and "decisions about dying" were organized into the global theme "those decisions … were really hard." "Feeling like there is no one to talk to," "being away from family and friends," and "feeling like a burden" generated the global theme "I felt very alone." Pediatric AYAs with advanced cancer describe unique challenges. Psychological support interventions are needed to empower AYAs to navigate difficult decisions and to cope with isolation.

An Efficient Federated Learning System for Network Intrusion Detection

Network intrusion detection is used to detect unauthorized activities on a digital network, with which the cybersecurity teams of organizations can then kick-start prevention protocols to protect the security of their networks and data. In real-life scenarios, due to the lack of high-quality attack instance data, building an in-depth network intrusion detection system (NIDS) is always challenging for a single enterprise, in terms of handling complex network security threats. To remedy the problem, this article proposes an efficient intrusion detection system called dynamic weighted aggregation federated learning (DAFL) based on federated learning. Specifically, DAFL has used the full advantages of federated learning for data privacy preservation. Moreover, compared to a conventional federated-learning based intrusion detection system, our scheme has implemented dynamic filtering and weighting strategies for local models. In this way, DAFL can perform better in detecting network intrusions with less communication overhead. We give the detailed designs of DAFL, and our experimental results demonstrate that DAFL can achieve excellent detection performance with a low network communication overhead, with data privacy preserved.

Quantitative proteomics and phosphoproteomics of urinary extracellular vesicles define putative diagnostic biosignatures for Parkinson’s disease

BackgroundMutations in the leucine-rich repeat kinase 2 (LRRK2) gene have been recognized as genetic risk factors for Parkinson’s disease (PD). However, compared to cancer, fewer genetic mutations contribute to the cause of PD, propelling the search for protein biomarkers for early detection of the disease.MethodsUtilizing 138 urine samples from four groups, healthy individuals (control), healthy individuals with G2019S mutation in the LRRK2 gene (non-manifesting carrier/NMC), PD individuals without G2019S mutation (idiopathic PD/iPD), and PD individuals with G2019S mutation (LRRK2 PD), we applied a proteomics strategy to determine potential diagnostic biomarkers for PD from urinary extracellular vesicles (EVs).ResultsAfter efficient isolation of urinary EVs through chemical affinity followed by mass spectrometric analyses of EV peptides and enriched phosphopeptides, we identify and quantify 4476 unique proteins and 2680 unique phosphoproteins. We detect multiple proteins and phosphoproteins elevated in PD EVs that are known to be involved in important PD pathways, in particular the autophagy pathway, as well as neuronal cell death, neuroinflammation, and formation of amyloid fibrils. We establish a panel of proteins and phosphoproteins as novel candidates for disease biomarkers and substantiate the biomarkers using machine learning, ROC, clinical correlation, and in-depth network analysis. Several putative disease biomarkers are further partially validated in patients with PD using parallel reaction monitoring (PRM) and immunoassay for targeted quantitation.ConclusionsThese findings demonstrate a general strategy of utilizing biofluid EV proteome/phosphoproteome as an outstanding and non-invasive source for a wide range of disease exploration.

RNA-dependent interactome allows network-based assignment of RNA-binding protein function.

RNA-binding proteins (RBPs) form highly diverse and dynamic ribonucleoprotein complexes, whose functions determine the molecular fate of the bound RNA. In the model organism Sacchromyces cerevisiae, the number of proteins identified as RBPs has greatly increased over the last decade. However, the cellular function of most of these novel RBPs remains largely unexplored. We used mass spectrometry-based quantitative proteomics to systematically identify protein-protein interactions (PPIs) and RNA-dependent interactions (RDIs) to create a novel dataset for 40 RBPs that are associated with the mRNA life cycle. Domain, functional and pathway enrichment analyses revealed an over-representation of RNA functionalities among the enriched interactors. Using our extensive PPI and RDI networks, we revealed putative new members of RNA-associated pathways, and highlighted potential new roles for several RBPs. Our RBP interactome resource is available through an online interactive platform as a community tool to guide further in-depth functional studies and RBP network analysis (https://www.butterlab.org/RINE).

AMDNet: A Modern UAV RGB Remote-Sensing Tree Species Image Segmentation Model Based on Dual-Attention Residual and Structure Re-Parameterization

Tree species classification is an important and challenging task in image recognition and the management of forest resources. Moreover, the task of tree species classification based on remote sensing images can significantly improve the efficiency of the tree species survey and save costs. In recent years, many large models have achieved high accuracy in the task of tree species classification in an airborne remote-sensing manner, but due to their fixed geometric structure, traditional convolutional neural networks are inherently limited to the local receptive field and can only provide segmental context information. The limitation of insufficient context information greatly affects the segmentation accuracy. In this paper, a dual-attention residual network (AMDNet) and a re-parameterized model approach are proposed to capture the global context information, fuse the weight, reduce the model volume, and maintain the computational efficiency. Firstly, we propose MobileNetV2 as the backbone network for feature extraction to further improve the feature identification by modeling semantic dependencies in the spatial dimension and channel dimension and adding the output of the two attention modules. Then, the attention perception features are generated by stacking the attention modules, and the in-depth residual attention network is trained using attention residual learning, through which more accurate segmentation results can be obtained. Secondly, we adopt the approach of structure re-parameterization, use a multi-branch topology for training, carry out weighted averaging on multiple trained models, and fuse multiple branch modules into a completely equivalent module in inference. The proposed approach results in a reduction in the number of parameters and an accelerated inference speed while also achieving improved classification accuracy. In addition, the model training strategy is optimized based on Transformer to enhance the accuracy of segmentation. The model was used to conduct classification experiments on aerial orthophotos of Hongya Forest Farm in Sichuan, China, and the MIOU of tree species recognition using the test equipment reached 93.8%. Compared with current models such as UNet, our model exhibits a better performance in terms of both speed and accuracy, in addition to its enhanced deployment capacity, and its speed advantage is more conducive to real-time segmentation, thereby representing a novel approach for the classification of tree species in remote sensing imagery with significant potential for practical applications.

Bilingualism caught in a net: A new approach to understanding the complexity of bilingual experience.

The growing importance of research on bilingualism in psychology and neuroscience motivates the need for a psychometric model that can be used to understand and quantify this phenomenon. This research is the first to meet this need. We reanalyzed two data sets (N = 171 and N = 112) from relatively young adult language-unbalanced bilinguals and asked whether bilingualism is best described by the factor structure or by the network structure. The factor and network models were established on one data set and then validated on the other data set in a fully confirmatory manner. The network model provided the best fit to the data. This implies that bilingualism should be conceptualized as an emergent phenomenon arising from direct and idiosyncratic dependencies among the history of language acquisition, diverse language skills, and language-use practices. These dependencies can be reduced to neither a single universal quotient nor to some more general factors. Additional in-depth network analyses showed that the subjective perception of proficiency along with language entropy and language mixing were the most central indices of bilingualism, thus indicating that these measures can be especially sensitive to variation in the overall bilingual experience. Overall, this work highlights the great potential of psychometric network modeling to gain a more accurate description and understanding of complex (psycho)linguistic and cognitive phenomena. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Network Mapping Interface Analysis between Carbon and Trees Corresponding to Climate Change

Background and objective: There have been increasing correlations between the keywords 'carbon' and 'tree' in response to issues related to climate change adaptation. Given this, a study that explores how these keywords relate to issues on climate change adaptation would be significant contribution to the literature. Therefore, this study aims to determine the correlation between these keywords and their network.Methods: To address the aim, the study employed a bibliometric analysis using the VOSviewer software to conduct network-mapping interface analysis using co-occurrence-based visualization and quantification. A two-step approach was applied: the first approach identifies recent driver changes of the keywords 'carbon' and 'tree' and further meaningful keywords. The second approach conducts an in-depth network mapping analysis based on the results drawn by the first approach.Results: The results suggested that, first, 'carbon' and 'tree' were shown to reflect the assessment system and contemporary issues based on the keywords 'carbon sequestration' and 'climate change.' In addition, 'biomass,' 'tree growth,' 'tree ring' and 'trade-off' were shown to be important keywords, which indicated the need for an in-depth correlation analysis. Second, 'biomass' was the key factor in the assessment system for 'carbon sequestration,' in which 'tree growth' and 'tree ring' were included. Third, the network analysis did not report varying results on 'trade-off' compared to 'carbon' and 'tree' with respect to the basic issues on climate change.Conclusion: The conventional assessment system based on the keywords 'carbon' and 'tree' should be improved by incorporating the issues drawn from the keywords 'biomass' and 'trade-off' so that it may reflect the latest contemporary needs. The network analysis on the keywords 'carbon' and 'tree' nevertheless indicated that the two keywords were at the centre of the latest contemporary issues and the assessment system.

Beyond integration versus homeland attachment: how migrant organizations affect processes of anchoring and embedding

ABSTRACT The links between integration and transnationalism have attracted considerable attention in the study of migration. In contributing to these debates, this article examines the increasingly diverse landscape of self-organizations of people with migratory biographies, the so-called migrant organizations (MOs), to show how these provide opportunities for establishing and maintaining attachments in changing social and spatial contexts. Using in-depth interviews and qualitative network analysis to explore the connectivity of migrants with different actors and places, we show that the key contribution to developing experiences of belonging essentially lies in their role as places associated with home. They have a significant impact on processes of anchoring and embedding within prevailing politics of belonging. In addition to illustrating the interconnectedness between local and transnational involvement in adaptation processes, we emphasize the transformative role of MOs as unique spaces where people can develop coherent forms of belonging beyond essentialized notions of ethnicity, nationality, and “we” identities.

RNAseq Analysis of Brown Adipose Tissue and Thyroid of Newborn Lambs Subjected to Short-Term Cold Exposure Reveals Signs of Early Whitening of Adipose Tissue.

During the early postnatal period, lambs have the ability to thermoregulate body temperature via non-shivering thermogenesis through brown adipose tissue (BAT), which soon after birth begins to transform into white adipose tissue. An RNA seq approach was used to characterize the transcriptome of BAT and thyroid tissue in newborn lambs exposed to cold conditions. Fifteen newborn Romney lambs were selected and divided into three groups: group 1 (n = 3) was a control, and groups 2 and 3 (n = 6 each) were kept indoors for two days at an ambient temperature (20–22 °C) or at a cold temperature (4 °C), respectively. Sequencing was performed using a paired-end strategy through the BGISEQ-500 platform, followed by the identification of differentially expressed genes using DESeq2 and an enrichment analysis by g:Profiler. This study provides an in-depth expression network of the main characters involved in the thermogenesis and fat-whitening mechanisms that take place in the newborn lamb. Data revealed no significant differential expression of key thermogenic factors such as uncoupling protein 1, suggesting that the heat production peak under cold exposure might occur so rapidly and in such an immediate way that it may seem undetectable in BAT by day three of life. Moreover, these changes in expression might indicate the start of the whitening process of the adipose tissue, concluding the non-shivering thermogenesis period.

Peter Waiswa: building better health systems in Africa and beyond

Growing up in Iganga, a rural town in Uganda, Peter Waiswa was influenced by his late father, who instilled a love for education in him, and by the newborn deaths he saw in his large family. Waiswa now leads the Makerere University Centre of Excellence for Maternal, Newborn and Child Health in Kampala, Uganda, and the INDEPTH Network's Maternal, Newborn and Child Health Working Group. His work involves paving the way locally, nationally, and globally to develop health systems and health policy in maternal, newborn, and child health, development, and health services.