Degree Of Connectivity Research Articles

Evolution of avian foot morphology through anatomical network analysis.

Avian evolution led to morphological adaptive variations in feet. Diverse foot types are accompanied by a diverse muscle system, allowing birds to adopt different primary lifestyles, and to display various locomotor and manipulative skills. We provide insights of evolutionary and functional significance on the avian foot architecture through Anatomical Network Analysis, a methodology focused on connectivity patterns of anatomical parts. Here, we show that: (1) anatomical parts largely conserved in living birds and already present in ancestral dinosaurs exhibit the highest connectivity degree, (2) there is no link between the more complex foot networks and the ability to perform more specialized skills or a higher number of different tasks, (3) there is a trend towards the simplification of foot networks on a macroevolutionary scale within birds, and (4) foot networks are phylogenetically constrained and conserved in all birds despite their foot type diversity, probably due to stabilizing selection.

Abstract 4140151: Network Analysis of Differentially Expressed Genes after Silencing Dynamin 2 in Pulmonary Arterial Hypertension

Rationale: Pulmonary arterial hypertension (PAH) is a rare cardiopulmonary disease that can cause right heart failure and even death. In PAH, excessive mitochondrial fission is responsible for the hyperproliferation of pulmonary artery smooth muscle cells (PASMC), which is incompletely mediated by the large GTPase dynamin-related protein 1 (Drp1). We hypothesize that another GTPase, Dynamin 2 (DNM2), elevated in PASMC of PAH patients, is responsible for the final stage of mitochondrial fission. Objectives: 1. Identify the differentially expressed gene (DEG) signature in PAH PASMC after silencing DNM2 using small interfering RNA (siRNA) by means of RNA sequencing (RNA-seq); 2. Identify the biological processes or pathways associated with these DEGs. Methods: Human PAH PASMCs (n=5 cell line) were transfected with control siRNA or siRNA targeting DNM2. 3’RNA-seq was used to create libraries which we sequenced on the Illumina Nextseq550. Bioinformatic analysis revealed DEGs which we placed into context using the STRING: protein query database and visualized using Cytoscape 3.10.2. Network functional enrichment analysis was performed using STRING. Results: Inhibiting DNM2 significantly reduced mitochondrial fission and slowed PAH PASMC proliferation. RNA-seq identified 156 DEGs (80 down-regulated, 56 up-regulated). The top 5 down-regulated DEGs were DNM2 , RGCC , TICAM1 , RASSF3 , MRPL39 and the top 5 up-regulated DEGs were ITIF1 , ARL6IP1 , POLR3G , CBX1 , CMPK2 . We tested the biologic relevance of RGCC which encodes the Regulator of Cell Cycle using siRNA and showed inhibition of cell proliferation in human PAH PASMC. Network analysis identified 152 edges amongst all DEGs. The largest subnetwork contains 77 nodes and 151 edges. PXDN , which encodes Peroxidasin, a heme-containing peroxidase that is secreted into the extracellular matrix, has the highest degree of connectivity (15 connections), the highest betweenness centrality (0.266), and the highest closeness centrality (0.441). Enrichment analysis found that the p53 signaling pathway and the cytokine signaling in the immune system are the top KEGG and Reactome pathways affected by siDNM2, respectively. Conclusions: DNM2 appears to be an important regulator of the increased mitochondrial fission in PAH. Silencing DNM2 not only inhibits fission but also slows cell proliferation, apparently by reducing the expression of RGCC. Silencing DNM2 in human PAH PASMC results in the disruption of 136 genes.

Analysis of Interconnectedness in Indian Subcontinent Remittances from the Gulf Cooperation Countries

Indian subcontinent is a major remittance receiver. With similar labor market characteristics, their workers go to same host countries. Therefore, their labor is substitutable for each other with high degree of connectedness with direct consequences on remittance inflows. Yet, due to social and political considerations in host countries, substitution may not take place. The issue is an empirical question. We study interactions of remittance inflows into India, Pakistan, Bangladesh and Sri Lanka via frequency connectedness. We find a low degree of spillovers across countries, i.e., dynamics of remittance inflows in each country are largely explained by internal factors, not due to spillovers from other countries. That is, remittance inflows in these countries are essentially independent. We also observe that information processing is faster in the short-run than the long-run indicating that market participants are aware of dynamics in other markets. The findings have policy implications for unemployment and foreign exchange.

Unveiling Biomarkers in Head and Neck Squamous Cell Carcinoma through Bioinformatics: The Role of SPP1 and KRT78

Head and neck squamous cell carcinoma (HNSCC) is the most common form of head and neck cancer, ranking sixth in global cancer incidence. Identifying molecular drivers of tumorigenesis and metastasis is essential for early detection and treatment. This study analyzed gene expression profiles from three datasets (GSE6791, GSE29330, and GSE58911) to identify differentially expressed genes (DEGs) in HNSCC. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses were employed to functionally annotate these DEGs. A protein–protein interaction (PPI) network was constructed for selecting hub genes using the STRING database. Finally, hub gene and protein expression levels were evaluated in patients with HNSCC, along with their association with overall survival. Our analysis identified twenty-eight co-DEGs comprising eight up-regulated and twenty down-regulated genes, primarily involved in extracellular matrix (ECM) organization, proteolysis, ECM disassembly, and keratinization processes. Furthermore, the PPI network revealed eight hub genes based on their high degree of connectivity. Notably, SPP1 demonstrated up-regulation, while KRT78 was down-regulated in HNSCC. Remarkably, the expression levels of these hub genes correlated with tumor grade, clinical cancer stage, and poor prognosis in HNSCC. Our findings hold significant clinical potential for early diagnosis and the development of novel therapeutic targets for patients with HNSCC.

The Agroecological Transition of Farms in the Ecuadorian Andes Through the Lens of the Main Agroecological Structure

ABSTRACTMany sectors of society have urged a transition from modern conventional agriculture to more sustainable alternatives. One of the most frequently mentioned is agroecology, due to its potential to improve soil health, promote agrobiodiversity, food sovereignty, and regenerate ecosystem functions. However, complex systems such as farms demand the transformation of a broad set of characteristics and practices for its transition. These factors are, in turn, conditioned by social, educational, economic, and political factors. Therefore, a clear understanding of the main drivers behind the management of farms is vital for supporting an effective transition. This research applied the main agroecological structure (MAS) methodology, which employs 10 indicators (5 biological, 5 cultural) to evaluate the degree of connectivity of a farm with the landscape, the agrobiodiversity present in living fences and crops, and the practices employed by farmers to support and preserve it. Finally, the MAS methodology gives farms a score from 0 to 100, understood as the degree of development of its agroecological structure, or as the “resilience” of a system in agroecological transition. Measurements were performed in 20 farms with different degrees of agroecological transition, in five locations of the northern‐central Andean region of Ecuador. The farms had a moderately developed structure on average (71.05 over 100), and MAS variation was not correlated to the size of the farm, nor with its degree of connectivity with the surrounding landscape. It was rather significantly related to the geographic location of the study site. One of the main differences between study sites was a more extended prevalence of cooperation and development programs, suggesting a strong connection between the development of agroecological practices and capacity building processes. For this reason, the author proposes that MAS measurements should be complemented with deeper analyses of the capacity‐building background of each site. This study aims to establish a baseline of the MAS in Ecuador, and to provide new tools for monitoring and supporting agroecological transition.

Impact of low temperature on bacterial community dynamics and process efficiency in anaerobic ammonia removal: A network analysis of metagenomic data

The implementation of anaerobic ammonia removal has potential for reducing the operating costs in wastewater treatment plants. However, the application of this process in mainstream treatment presents various challenges, particularly due to the sensitivity of anammox bacteria to low temperatures. To address these challenges, changes in the bacterial community were investigated under decreasing temperatures (from 30 to 12 °C) within a deammonification process conducted in a 10-L sequencing batch reactor (SBR) over a period of 114 days. The aim of the study was to determine which groups of microorganisms are most strongly linked by ecological relationships with anammox bacteria and how these relationships change in response to decreasing temperature. Community dynamics were analyzed by combining metagenomics, network analysis, and specific process rates. The nitrification process was most efficient at 25 °C, as indicated by high values of the ammonia and nitrite utilization rate, and nitrate production rate, all of which declined as the temperature decreased. In contrast, the activity and abundance of anammox bacteria remained stable throughout all phases of the experiment. The decreasing temperature negatively impacted Nitrospira bacteria, the main competitors of anammox in the deammonification system. Despite the reduced process efficiency, the SBR maintained stable functionality even at temperatures as low as 12 °C. Network analysis identified which bacterial groups most frequently co-occurred and interacted, as well as those most sensitive to temperature fluctuations. Among the bacterial nodes, Nitrospira, Phycisphaerae, and Prosthecobacter exhibited the highest degree of connectivity, suggesting that they participate in multiple interactions, such as metabolite exchange, cooperation, or competition. Nevertheless, the temperature node had a greater number of connections than any bacterial node, indicating that this environmental factor had a more important effect on microbial community formation than direct ecological relationships. These results contribute to a comprehensive understanding of the role of anammox bacteria in deammonification systems, their ecological interactions within microbial networks, and their activity under suboptimal temperature conditions, which is crucial for future practical applications.

How the synergy of government intervention and digital transformation affects carbon emission: evidence from a quasi-natural experiment in China

ABSTRACT Digital transformation (DT), government intervention (GI) and carbon emissions (CE) have always been the focus of academic attention, and arguments on the relationship amongst them exist. This study seeks to introduce the degree of political connection and government subsidies (GS) as GI into the relationship between DT and CE at the micro-level and aims to provide a new perspective for resolving previous arguments. By using a difference-in-differences model, the impact of DT on reducing CE is measured by taking a sample of 3925 observations from listed enterprises in China and dividing them according to the following degrees of political connection: none, moderate and high. Results show that DT is effective in reducing CE. Additionally, DT of enterprises with moderate political connections has had the greatest effect on reducing CE, followed by the effect of enterprises with high political connections. The effect of enterprises with no political connections is insignificant. If enterprises with a moderate or no political connections gains more GS, then the negative impact of DT on CE will be stronger. This study provides new evidence that DT reduces CE at the micro-level and presents a new perspective on reaching a consensus by introducing GS and the degrees of political connection.

Energy‐Aware Routing in WSN Utilizing Self‐Attention–Based Cycle‐Consistent Generative Adversarial Network With Honey Badger Algorithm

ABSTRACTEnergy efficiency and secure data transmission are considered as the main design targets of wireless sensor network (WSN). Previous energy‐aware cluster head (CH) selection approaches could not provide secured data transmission. To address this problem, a self‐attention–based cycle‐consistent generative adversarial network (SaCyCsGAN) with honey badger algorithm (HyBA)–adopted energy‐aware routing (EgAR) in WSN is proposed. Initially, the proposed system uses a CH to carry out the routing practice. Accordingly, SaCyCsGAN classifier is exploited for CH selection under firm fitness function: delay, distance, energy, cluster density, and traffic rate. Afterward CH selection, a spiteful node may enter the cluster and acts as a CH. Hence, best path selection is needed. For that, HyBA is utilized, it selects the best path depending upon triplet parameter: trust, connectivity, and service degree. Finally, data are conveying into base station (BS) and vice versa under best path. Finally, the proposed EgAR‐WSN‐SaCyCsGAN‐HyBA method attains 24.13%, 28.57%, 5.88%, and 20.37% higher throughput and 18.50%, 21.67%, 13.33%, and 22.22% lesser energy consumption evaluated to the existing methods such as multiple‐objective CH utilizing self‐attention basis progressive generative adversarial network for safe data aggregation (EgAR‐WSN‐SAPrGAN‐ArVOA), reinforcement learning–based energy‐efficient optimized routing protocol in WSN (EgAR‐WSN‐RLGT‐BCSA), data aggregation including clustering protocol in WSN under machine learning (EgAR‐WSN‐AfNN), and optimum cluster along trusted path for routing formation with categorization of intrusion under machine learning categorization (EgAR‐WSN‐RsBPDT‐HoCSOA).

Identification and validation of miR-21 key genes in cervical cancer through an integrated bioinformatics approach

Cervical cancer is one of the most prevalent female reproductive cancers. miR-21 is a multi-target oncomiR that has shown its potential in regulating several cancers including colon, pancreatic, breast, prostate, ovarian, and cervical cancer. However, the signaling network of miR-21 remains underexplored, and only a limited number of miR-21 gene targets in cervical cancer have been reported. In this context, the present study was undertaken to evaluate the role of miR-21 in cervical cancer by combining in silico analysis with in vitro validation in cervical cancer cells. The miR-21 target genes were predicted using four different prediction tools: miRwalk, DIANA, miRDBs, and TargetScan. A total of 113 overlapping target genes, common in at least three of the prediction tools, were shortlisted and subjected to functional enrichment analysis. The analysis predicted that JAK-STAT, MAPK, neurotrophin, and Ras signaling pathways are significantly (p≤0.05) targeted by miR-21. The MCODE plugin identified the potential cluster in the protein-protein interaction network based on the highest degree of connectivity. After GEPIA2 validation of all 20 hub genes, NTF3, LIFR, and IL-6R were shortlisted for validation in cervical cancer cell lines. The results showed that NTF3, LIFR, and IL-6R were significantly upregulated in the miR-21 knockdown CaSki cell lines in 6.27, 1.92 and 1.71 folds (p≤0.01), respectively. Similarly, in HeLa cell lines expression of NTF3, LIFR, and IL-6R were overexpressed in 4,06, 5.65, 2.42 folds (p≤0.001), respectively. confirming the role of miR-21 in regulating the expression of these genes. Additionally, the knockdown of miR-21 significantly inhibited the secretion of matrix metalloproteinases by CaSki cells. These results highlight that miR-21 could be a potential therapeutic target for cervical cancer, although further preclinical and clinical studies are required to validate its role and efficacy.

On the impact of zealots in a population of susceptible agents in a best-of-n problem within a heterogeneous network

Both humans and social animals live in groups and are frequently forced to choose between options with different qualities. When there are no leader agents controlling the group decision, consensus can be achieved through repeated interactions among group members. Various studies on collective decision-making illustrate how the dynamics of the opinions are determined by the structure of the social network and the methods that individuals use to share and update their opinion upon a social interaction. In this paper, we are interested in further exploring how cognitive, social, and environmental factors interactively contribute to determining the outcome of a collective best-of-n decision process involving asymmetric options, i.e., different costs and/or benefits for each option. We propose and study a novel model capturing those different factors, i) the cognitive load in processing social information, ii) the number of zealots (i.e., asocial agents who never change their opinion), iii) the option qualities, iv) the social connectivity structure, and v) the degree centrality of the asocial agents (i.e., the number of neighbours). By using the heterogeneous mean-field approach, we study the impact of the above-mentioned factors in the decision dynamics. Our findings indicate that when susceptible agents, i.e., individuals who change their opinion to conform with others, use the voter model as a mechanism to update their opinion, both the number and the degree of connectivity of the zealots can lead the population to converge towards the lowest quality option. Instead, when susceptible agents use methods demanding a larger cognitive cost (e.g., the majority rule), the group is marginally impacted by the presence of zealots. The results of the analytical model are complemented and extended by agent-based simulations. Our analysis also shows that the network topology can modulate the influence of zealots on group dynamics. In fact, in homogeneous networks where all nodes have the same degree, any location of the zealots has similar impact on the group dynamics. Instead, when the network is heterogeneous, our simulations confirm the model predictions showing that placing the zealots in the network hubs (nodes with several neighbours) has a much larger impact than placing them in lower-degree nodes.

Whole genome sequencing of Castanea mollissima and molecular mechanisms of sugar and starch synthesis.

The chestnut tree exhibits self-incompatibility, where the selection of the male parent (pollen xenia) significantly affects seed starch metabolism, as well as fruit yield and quality. Despite its importance, the molecular mechanisms underlying pollen xenia remains largely unknown. In this study, we utilized the 'Lan You' variety of C. mollissima to construct a high-quality reference genome. As a result, a first Telomere-to-telomere (T2T) gap-free genome for this species was successfully assembled. A total of 560 transcription factors and 22 structural genes were identified as consistent across the TO-GCNs, indicating a consistent regulation pattern in the co-expression of genes involved in starch accumulation. These networks were further divided into three sub-networks: T1, T2, and T3. Among these, the T1 and T2 sub-networks exhibited a higher number of structural genes with consistent regulation patterns and were closely associated with sugar biosynthesis. The gene SBE (Camol08G0254600) was identified as the hub gene with the highest degree of connectivity, encoding a key rate-limiting enzyme in the amylopectin biosynthesis pathway. This study provides a foundation for further research on C. mollissima population genetics, genetic improvement, and strategies aimed at enhancing yield and quality.

Understanding Family Resilience in Young-Onset Dementia: A Multiple Case Study.

Through the lens of family resilience, exploring the experiences of families in adapting to young-onset dementia. Qualitative multiple case study. This study examined the family as a single unit of analysis, focusing on a triad including a person with young-onset dementia, their spouse and their adult child. Three family triads were included. Data collection took place between April 20 and July 4, 2023, in South Korea. Data analysis involved a two-stage process, consisting of a directed content analysis based on the family resilience framework within cases, followed by a cross-case analysis to identify themes and unique patterns across cases. The study identified six key themes: (1) Stressors due to a young-onset dementia diagnosis, (2) perception of the young-onset dementia diagnosis, (3) degree of emotional connection, (4) coping approach to young-onset dementia, (5) availability and utilisation of external resources and (6) patterns of adaptation. Furthermore, our research uncovered the interplay between themes, and how adaptations patterns of families formed either resilient or non-resilient. Our triadic approach uncovered hidden family dynamics, emphasising family-centred care. This study provides insights into unique challenges and diverse adaptation processes, suggesting tailored interventions. This study emphasises the importance of family-centred care in young-onset dementia, recognising all family members as crucial clients. Also, we suggest that healthcare professionals should develop tailored treatment plans that reflect the unique needs of each family. This study presents the unique experiences of young-onset dementia families, laying the foundation for developing effective support strategies that reflect these insights. Additionally, we emphasise the utilisation of external resources and their existence, providing a comprehensive research direction. Our research follows the EQUATOR guidelines, specifically adhering to the COREQ guidelines. No Patient or Public Contribution.

An individual‐based model trained on multiple data sources estimates population connectivity and facilitates aggregation of harvest management units

Abstract Management boundaries are often delimited by political and social factors, whereas animal movements are affected by ecological and geophysical constraints. Thus, understanding connectivity among distinct management units is of considerable importance, particularly for harvested species, where quotas set in ignorance of connectivity may fail to meet management goals. We constructed an individual‐based model (IBM) to better understand wild turkey movements at large scales, benefiting from multiple data sources that are often available for harvested species. We built an IBM from spring seasonal movements of wild turkey, using data from ringed, radio‐, and GPS‐marked turkeys captured in Maine, USA. Our IBM accommodated variation in individual turkey response to landscape connectivity metrics and identified emergent migratory connectivity dynamics among harvest management regions. We calculated a low degree of connectivity among wildlife management districts (WMD) which, in combination with the substantial number of boundary crossings observed, indicated a more diffuse distribution of turkeys among WMDs. Synthesis and applications: Estimates of turkeys moving between districts provided a clear delineation of where immigration was strongest, identifying which WMDs should be managed as singular population units. This approach has widespread utility for any species or system where harvest management decisions are made at finer spatial scales than the movement dynamics affecting population processes.

Reduced-Order Modeling for Dynamic System Identification with Lumped and Distributed Parameters via Receptance Coupling Using Frequency-Based Substructuring (FBS)

Paper presents an effective technique for developing reduced-order models to predict the dynamic responses of systems using the receptance coupling and frequency-based substructuring (RCFBS) method. The proposed approach is particularly suited for reconfigurable dynamic systems across various applications, like cars, robots, mechanical machineries, and aerospace structures. The methodology focuses on determining the overall system receptance matrix by coupling the receptance matrices (FRFs) of individual subsystems in a disassembled configuration. Two case studies, one with distributed parameters and the other with lumped parameters, are used to illustrate the application of this approach. The first case involves coupling three substructures with flexible components under fixed–fixed boundary conditions, while the second case examines the coupling of subsystems characterized by multiple masses, springs, and dampers, with various internal and connection degrees of freedom. The accuracy of the proposed method is validated against a numerical finite element analysis (FEA), direct methods, and a modal analysis. The results demonstrate the reliability of RCFBS in predicting dynamic responses for reconfigurable systems, offering an efficient framework for reduced-order modeling by focusing on critical points of interest without the need to account for detailed modeling with numerous degrees of freedom.

Geography of research networks in the Big South of Russia

The geography of research and innovation activity is a traditional topic of research in human geography. Classical studies of the clustering and diffusion of innovation in the 1960s and 70s received a new impetus in the 21st century with the understanding of the openness of innovation and the importance of interregional collaborative networks. Digitalization has played an important role, providing access to new sources of large geocoded data on the movement of key elements of the knowledge economy, including publications as a formalized outcome of scientific activity. The purpose of the article is to identify the center-periphery research ties of one of the largest macroregions of Russia – the Big South of Russia. The aim was to assess the territorial patterns of interregional research networks with the identification of gravity poles and interconnections at the local and national levels. The study uses the method of spatial scientometrics. Geocoded data on publications from the Scopus bibliographic database were used. In the course of the study the hypotheses of “Moscow-centricity” and “St. Petersburg-centricity” of the knowledge domain of the macroregion are tested, the influence of the factor of geographical proximity and the role of the diversity of interregional ties are evaluated, and the “heights-lowlands” of the landscape are revealed. The results of the research have shown a high degree of connectivity between the regions of the Big South of Russia in the research area. The hypothesis about the presence of several centers of gravity of scientific activity in the macroregion was confirmed. First of all, the peaks of the “scientific ridge” of the Big South form Rostov oblast with the largest urban agglomeration in the south of the country, the Rostov urban agglomeration, as well as the Krasnodar krai and Volgograd oblast. The hypothesis of the existence of several centers of scientific activity in the macroregion was confirmed. The influence of the factor of territorial proximity in the formation of research ties is not high and is manifested mainly in the southern Russian regions with relatively low indicators of research output (primarily, it is typical for national republics). Interregional research cooperation between advanced regions is not limited to immediate geographical proximity but is due to a combination of non-territorial factors. The “Moscow-centricity” of the scientific agenda of the regions of the Big South of Russia is true for most of the southern regions of the Russian Federation, which is particularly pronounced in regions with low internal scientific potential and in new regions undergoing a period of transformation of their scientific systems.

Connections and Spatial Network Structure of the Tourism Economy in Beijing–Tianjin–Hebei: A Social Network Perspective

In the context of the coordinated development of Beijing–Tianjin–Hebei, examining the spatial connections and network structure characteristics of the tourism economy holds substantial theoretical and practical significance. This study constructs a tourism economic quality evaluation index system from four dimensions and optimizes the tourism economic gravity model by incorporating time distance and weight factors to dynamically simulate the intensity of tourism economic spatial connection (ITESC). Based on the new perspective of relational data, social network analysis is employed to investigate the spatial correlation network characteristics and group structure changes in the tourism economy within the Beijing–Tianjin–Hebei urban agglomeration (BTHUA). The results demonstrate a significant improvement in overall tourism economic quality from 2013 to 2022, with the average quality increasing from 0.128 in 2013 to 0.157 in 2022. Tourism economic spatial connections are prevalent among cities, exhibiting heterogeneous characteristics, characterized by density in the north and relatively sparse in the south. The network cohesion of ITESC strengthens, and the network structure evolves from a highly centralized to a multi-centralized configuration, with increasingly frequent cooperation and interaction in tourism between cities. Beijing, Tianjin, and Shijiazhuang occupy central positions in the network, exhibiting strong ITESC and a high degree of connectivity, generating a significant “trickle-down effect” on peripheral cities. Consequently, strengthening ITESC between peripheral cities and core areas, as well as enhancing the bidirectional spillover of tourism development between cities, is crucial for the future sustainable and coordinated development of tourism in the BTHUA.

Exploring the Antidiabetic Potential of Salvia officinalis Using Network Pharmacology, Molecular Docking and ADME/Drug-Likeness Predictions.

A combination of network pharmacology, molecular docking and ADME/drug-likeness predictions was employed to explore the potential of Salvia officinalis compounds to interact with key targets involved in the pathogenesis of T2DM. These were predicted using the SwissTargetPrediction, Similarity Ensemble Approach and BindingDB databases. Networks were constructed using the STRING online tool and Cytoscape (v.3.9.1) software. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis and molecular docking were performed using DAVID, SHINEGO 0.77 and MOE suite, respectively. ADME/drug-likeness parameters were computed using SwissADME and Molsoft L.L.C. The top-ranking targets were CTNNB1, JUN, ESR1, RELA, NR3C1, CREB1, PPARG, PTGS2, CYP3A4, MMP9, UGT2B7, CYP2C19, SLCO1B1, AR, CYP19A1, PARP1, CYP1A2, CYP1B1, HSD17B1, and GSK3B. Apigenin, caffeic acid, oleanolic acid, rosmarinic acid, hispidulin, and salvianolic acid B showed the highest degree of connections in the compound-target network. Gene enrichment analysis identified pathways involved in insulin resistance, adherens junctions, metabolic processes, IL-17, TNF-α, cAMP, relaxin, and AGE-RAGE in diabetic complications. Rosmarinic acid, caffeic acid, and salvianolic acid B showed the most promising interactions with PTGS2, DPP4, AMY1A, PTB1B, PPARG, GSK3B and RELA. Overall, this study enhances understanding of the antidiabetic activity of S. officinalis and provides further insights for future drug discovery purposes.

Efficient Distributed Transfer Learning for Large‐Scale Gaussian Graphic Models

ABSTRACTA transfer learning for the large‐scale Gaussian graphical models (GGMs) is considered in a distributed architecture for fitting these target and auxiliary studies scattered across multiple sites. A distributed transfer learning algorithm, DisPower‐Trans‐CLIME, is proposed to learn the target GGMs by incorporating the data from similar and related auxiliary studies. The algorithm is communication efficient via the distributed power method for matrix decomposition. We show that DisPower‐Trans‐CLIME has a fast convergence rate comparable to the centred Trans‐CLIME algorithm. A debiased DisPower‐Trans‐CLIME is constructed and is proved to be element‐wise asymptotically normal for statistical inference. Thus, a multiple testing procedure is developed to detect edge of GGMs with false discovery rate (FDR) control. Extensive simulation experiments have been conducted to demonstrate superior numerical performance of our proposed learning algorithm on estimation and edge detection. It is also applied to infer the regional connection networks in brain regions of interest (ROIs) based on a target hospital site by leveraging the graph from multiple other hospital sites for autism spectrum disorder. We observe that the degrees of regional connectivity in the right brain are balanced, while the ones of the left brain region are extremely uneven because of the presence of multiple strong connections. However, the specific association, between the degrees of connectivity of these regions and ASD disease, is unknown.

Comparative analysis of performance of different types of Tesla valves

Abstract The Tesla valve is a valve with no moving parts. Its special configuration also allows the pressure drop of fluid passing through the Tesla valve from the reverse inlet to be much greater than the pressure drop of fluid passing through the Tesla valve from the forward inlet. Therefore, the Tesla valve has a certain degree of single pilot connectivity and pressure reduction capability. This article selected 8 common types of Tesla valves, By using numerical calculation method through Fluent simulation, the differences of resistance coefficient, pressure drop and Di value of 8 types of single-stage Tesla valves under three different working conditions were compared. In order to explore the performance of 8 kinds of Tesla valves under the three working conditions. At the same time, the relationship between the flow state and the structure of each part of the fluid before and after passing through the distributary pipe is analyzed. The simulation results show that due to the different structure of various Tesla valves, the flow rate, pressure and turbulent kinetic energy of the fluid inside the valve also have different changes. At the same time, there are many similarities in the flow state of some Tesla valves with similar structural types. Finally, according to the calculation results, the advantages and disadvantages of various types of Tesla valves and their applications are analyzed.

Evil and allies: Opportunistic gulls as both spreaders and sentinels of antibiotic‐resistant bacteria in human‐transformed landscapes

Abstract Human‐transformed residuals, especially those derived from human waste (dumps), farmland, and livestock are involved in the emergence of antibiotic‐resistant bacteria (ARB) in the environment. Wildlife can act as vectors of ARB dispersal through different environments, but also as sentinels to detect the early spread and determine ARB sources. The development of integrated monitoring programmes focused on wildlife would help to anticipate the risks of ARB to humans and livestock. We used the yellow‐legged gull (Larus michahellis) as a model species to investigate and monitor the spatial patterns of ARB dispersal across an extensive farmland region located in northeastern Spain (Lleida). By integrating GPS tracking data and ARB clinical testing for 26 individuals within a network analysis framework, we modelled the risk of spatial pathogen spread through faeces during the bacteria‐transmission latency period (16 days after sample collection). Additionally, we created a connectivity network to determine the main sources of ARB in the area, focusing on three main habitats of special risk for infection: dumps, livestock facilities, and irrigation ponds. Seven individuals were infected by Escherichia coli, with one also co‐infected with Listeria monocytogenes and Salmonella spp. Potential pathogen dispersal distances ranged from 1.13 km to 23.13 km from the breeding colony. Our network analyses revealed 54 main nodes (i.e. high‐risk habitats recurrently visited by tracked gulls) and 1182 links among them. Our findings revealed a high degree of connectivity between the breeding area, located in a shallow lake, and nearby dumps, highlighting them as significant contributors to ARB dispersal. Synthesis and applications: The integration of GPS data, pathogen testing and network analyses can shed further light on pathogen dynamics by creating spatial risk maps and identifying ARB sources. In combination with complementary molecular epidemiology techniques within a One Health framework, our approach can emerge as an important tool for monitoring ARB dynamics within highly human‐transformed ecosystems. This may empower managers for the development of targeted ARB monitoring programmes and effective mitigation strategies, ultimately improving both animal and public health.