Network Structure Research Articles

Cross-sector partnerships in the South City Choice Neighborhoods Initiative (CNI): An exploratory case study of collaboration network structure and process in Memphis, Tennessee

ABSTRACT Mixed-income initiatives represent promising anti-poverty strategies that assist families living in public housing in the United States. However, little is known about the extensive cross-sector partnerships required for implementation. We address these knowledge gaps by exploring partnerships within the South City Choice Neighborhoods Initiative (CNI) in Memphis, Tennessee. The following research questions guide the study: (1) what is the structure of inter-organizational relationships within the South City CNI partner network? and (2) what collaboration processes emerge within the network structure regarding communication, collaboration, and trust? Utilizing an exploratory case study design that leveraged social network analysis (SNA) and in-depth interviews, we found that the South City CNI was relatively dense and moderately centralized. A core group of partners was central for coordinating information and resources, but roles and positionality changed across each network. The presence of a high-capacity lead organization and multiple bridging organizations aided implementation. We conclude by discussing implications for practice, policy, and research.

Microservice-Based Vehicular Network for Seamless and Ultra-Reliable Communications of Connected Vehicles

The fifth-generation (5G) cellular infrastructure is expected to bring about the widespread use of connected vehicles. This technological progress marks the beginning of a new era in vehicular networks, which includes a range of different types and services of self-driving cars and the smooth sharing of information between vehicles. Connected vehicles have also been announced as a main use case of the sixth-generation (6G) cellular, with ultimate requirements beyond the 5G (B5G) and 6G eras. These networks require full coverage, extremely high reliability and availability, very low latency, and significant system adaptability. The significant specifications set for vehicular networks pose considerable design and development challenges. The goals of establishing a latency of 1 millisecond, effectively handling large amounts of data traffic, and facilitating high-speed mobility are of utmost importance. To address these difficulties and meet the demands of upcoming networks, e.g., 6G, it is necessary to improve the performance of vehicle networks by incorporating innovative technology into existing network structures. This work presents significant enhancements to vehicular networks to fulfill the demanding specifications by utilizing state-of-the-art technologies, including distributed edge computing, e.g., mobile edge computing (MEC) and fog computing, software-defined networking (SDN), and microservice. The work provides a novel vehicular network structure based on micro-services architecture that meets the requirements of 6G networks. The required offloading scheme is introduced, and a handover algorithm is presented to provide seamless communication over the network. Moreover, a migration scheme for migrating data between edge servers was developed. The work was evaluated in terms of latency, availability, and reliability. The results outperformed existing traditional approaches, demonstrating the potential of our approach to meet the demanding requirements of next-generation vehicular networks.

On the friendship paradox and inversity: A network property with applications to privacy-sensitive network interventions

We provide the mathematical and empirical foundations of the friendship paradox in networks, often stated as "Your friends have more friends than you." We prove a set of network properties on friends of friends and characterize the concepts of ego-based and alter-based means. We propose a network property called inversity that quantifies the imbalance in degrees across edges and prove that the sign of inversity determines the ordering between ego-based or alter-based means for any network, with implications for interventions. Network intervention problems like immunization benefit from using highly connected nodes. We characterize two intervention strategies based on the friendship paradox to obtain such nodes, with the alter-based and ego-based strategy. Both strategies provide provably guaranteed improvements for any network structure with variation in node degrees. We demonstrate that the proposed strategies obtain several-fold improvement (100-fold in some networks) in node degree relative to a random benchmark, for both generated and real networks. We evaluate how inversity informs which strategy works better based on network topology and show how network aggregation can alter inversity. We illustrate how the strategies can be used to control contagion of an epidemic spreading across a set of village networks, finding that these strategies require far fewer nodes to be immunized (less than 50%, relative to random). The interventions do not require knowledge of network structure, are privacy-sensitive, are flexible for time-sensitive action, and only require selected nodes to nominate network neighbors.

Burden of multiple high-risk factors in pregnancy before and after the universal two-child policy in Chinese women: An observational study.

The prevalence of high-risk pregnancy increased after the implementation of two-child policy in China, but the impact of this policy change on the burden and profile of multiple high-risk factors in pregnancy (MHFP) has been insufficiently explored. We hypothesised that the profile of MHFP might have changed after the two-child policy was implemented and aimed to estimate the prevalence, intercorrelation, and outcomes of MHFP before and after its introduction. We obtained data on the population of pregnant women before (2015) and after (2020/2021) the implementation of universal two-child policy in Huai'an. We then included 33 risk factors in our analysis based on the Five-Colour Management framework and defined MHFP as an individual having two or more of these factors. We also estimated the changes of the prevalence of each single factor and their coexistence. Lastly, we performed a network analysis to assess the intercorrelations across these factors and used logistic regression models to evaluate MHFP-related pregnancy outcomes. We observed an increase in the prevalence of MHFP after the implementation of the universal two-child policy (25.8% in 2015 vs 38.4% in 2020/2021, P < 0.01). Chronic conditions (e.g. gestational diabetes mellitus, abnormal body mass index) had the largest increase among the included factors, while cardiovascular disease and hypertensive disorders were central factors of the network structures. The correlations of advanced maternal age with abnormal pregnancy histories and scarred uteri increased significantly from 2015 to 2020/2021. MHFP was associated with multiple pregnancy outcomes, including preterm birth (adjusted odds ratio (aOR) = 2.57; 95% confidence interval (CI) = 2.39-2.75), low birthweight (aOR = 2.77; 95% CI = 2.54-3.02), low Apgar score (aOR = 1.41; 95% CI = 1.19-1.67), perinatal death (aOR = 1.75; 95% CI = 1.44-2.12), and neonatal death (aOR = 1.76; 95% CI = 1.42-2.18). Moreover, an increasing number and certain combinations of MHFP were associated with higher odds of pregnancy outcomes. For example, the aOR of preterm birth increased from 1.67 (95% CI = 1.52-1.87) for one risk factor to 8.03 (95% CI = 6.99-9.22) for ≥4 risk factors. Chinese women experienced a higher burden of multiple high-risk factors after the introduction of the two-child policy, particularly those with advanced maternal age, obesity, and chronic conditions. Strategies targeting chronic conditions for women with MHFP should be prioritised and a shift to a multiple-factor-oriented framework is needed in the expanding Chinese maternal health care system.

Advanced Hydrogels in Breast Cancer Therapy.

Breast cancer is the most common malignancy among women and is the second leading cause of cancer-related death for women. Depending on the tumor grade and stage, breast cancer is primarily treated with surgery and antineoplastic therapy. Direct or indirect side effects, emotional trauma, and unpredictable outcomes accompany these traditional therapies, calling for therapies that could improve the overall treatment and recovery experiences of patients. Hydrogels, biomimetic materials with 3D network structures, have shown great promise for augmenting breast cancer therapy. Hydrogel implants can be made with adipogenic and angiogenic properties for tissue integration. 3D organoids of malignant breast tumors grown in hydrogels retain the physical and genetic characteristics of the native tumors, allowing for post-surgery recapitulation of the diseased tissues for precision medicine assessment of the responsiveness of patient-specific cancers to antineoplastic treatment. Hydrogels can also be used as carrier matrices for delivering chemotherapeutics and immunotherapeutics or as post-surgery prosthetic scaffolds. The hydrogel delivery systems could achieve localized and controlled medication release targeting the tumor site, enhancing efficacy and minimizing the adverse effects of therapeutic agents delivered by traditional procedures. This review aims to summarize the most recent advancements in hydrogel utilization for breast cancer post-surgery tissue reconstruction, tumor modeling, and therapy and discuss their limitations in clinical translation.

Improved Dual Correlation Reduction Network With Affinity Recovery.

Deep graph clustering, which aims to reveal the underlying graph structure and divide the nodes into different clusters without human annotations, is a fundamental yet challenging task. However, we observe that the existing methods suffer from the representation collapse problem and tend to encode samples with different classes into the same latent embedding. Consequently, the discriminative capability of nodes is limited, resulting in suboptimal clustering performance. To address this problem, we propose a novel deep graph clustering algorithm termed improved dual correlation reduction network (IDCRN) through improving the discriminative capability of samples. Specifically, by approximating the cross-view feature correlation matrix to an identity matrix, we reduce the redundancy between different dimensions of features, thus improving the discriminative capability of the latent space explicitly. Meanwhile, the cross-view sample correlation matrix is forced to approximate the designed clustering-refined adjacency matrix to guide the learned latent representation to recover the affinity matrix even across views, thus enhancing the discriminative capability of features implicitly. Moreover, we avoid the collapsed representation caused by the oversmoothing issue in graph convolutional networks (GCNs) through an introduced propagation regularization term, enabling IDCRN to capture the long-range information with the shallow network structure. Extensive experimental results on six benchmarks have demonstrated the effectiveness and efficiency of IDCRN compared with the existing state-of-the-art deep graph clustering algorithms. The code of IDCRN is released at https://github.com/yueliu1999/IDCRN. Besides, we share a collection of deep graph clustering, including papers, codes, and datasets at https://github.com/yueliu1999/Awesome-Deep-Graph-Clustering.

Fabrication of resveratrol-loaded soy peptide nanogels with transglutaminase and in vitro gastrointestinal delivery and cholesterol-lowering effect

To improve the intestinal delivery of resveratrol, soy protein peptides (SPP) and transglutaminase (TGase) were used to prepare nanogels (NGs). The results demonstrated that, compared with native soy protein isolate (SPI) NG, SPP-NG showed a lower encapsulation efficiency (EE, 70.26%), a higher release efficiency (RE, 95.83%), and a smaller particle size (194.43 nm). However, the addition of TGase increased the EE and particle size to 74.37% and 217.73 nm, respectively. The RE of SPP-TGNG was also maintained at as high as 93.28%. Interestingly, SPP-TGNG exhibited optimal bile acid–binding capacity and cholesterol-lowering digests. The differences in delivery effects and morphology were attributed to structural changes in NGs. The unfolding and exposure of hydrophobic residues and weakened hydrogen bonds by hydrolysis resulted in a disordered and loose network structure, but it was beneficial to the production of cholesterol-lowering peptides and amino acids after digestion. The presence of TGase strengthened the crosslinking network by increasing β-sheet contents, hydrogen bonds, hydrophobic interactions, and isopeptide bonds. The alteration in multilevel structure would further affect the digestive products. The exposure of more hydrophobic residues and isopeptide crosslinking may be the intrinsic reason for the production of more hypocholesterolemic peptides and amino acids. This study indicated that the combination of hydrolysis and TGase treatment enhanced the delivery efficiency and bioactive properties of peptide-based NGs to resveratrol and revealed the internal mechanism between the structural changes and delivery efficiency. This work offers a theoretical foundation and technical suggestions for the production of peptide-based cargo systems and hydrophobic active substances.

Improving visual grounding with multi-modal interaction and auto-regressive vertex generation

We propose a concise and consistent network focusing on multi-task learning of Referring Expression Comprehension (REC) and Segmentation (RES) within Visual grounding (VG). To simplify the model architecture and achieve parameter sharing, we reconstruct the Visual grounding task as a floating-point coordinate generation problem based on both image and text inputs. Consequently, rather than separately predicting bounding boxes and pixel-level segmentation masks, we represent them uniformly as a sequence of coordinate tokens and output two corner points of bounding boxes and polygon vertices autoregressively. To improve the accuracy of point generation, we introduce a regression-based decoder. Inspired by bilinear interpolation, this decoder can directly predict precise floating-point coordinates, thus avoiding quantization errors. Additionally, we devise a Multi-Modal Interaction Fusion (M2IF) to address the imbalance between visual and language features in the model. This module focuses visual information on regions relevant to textual descriptions while suppressing the influence of irrelevant areas. Based on our model, Visual grounding is realized through a unified network structure. Experiments conducted on five benchmark datasets (RefCOCO, RefCOCO+, RefCOCOg, ReferItGame and Flickr30K Entities) demonstrate that the proposed unified network outperforms or is on par with many existing task-customized models. Codes are available at https://github.com/LFUSST/MMI-VG.

Greater Neighborhood Disadvantage Is Associated with Alterations in Fetal Functional Brain Network Structure

To determine the association between neighborhood disadvantage and functional brain development of in utero fetuses. We conducted an observational study utilizing Social Vulnerability Index (SVI) scores to assess the impact of neighborhood disadvantage on a prospectively recruited sample of healthy pregnant women from Washington, DC. Utilizing 79 functional MRI scans from 68 healthy pregnancies at a mean gestational age of 33.12 weeks, we characterized overall functional brain network structure using a graph metric approach. We utilized linear mixed effects models to assess the relationship between SVI and gestational age on five graph metrics, adjusting for multiple scans. Exposure to greater neighborhood disadvantage was associated with less well integrated functional brain networks, as observed by longer characteristic path lengths (L) and diminished global efficiency (GE) as well as diminished small world propensity (SWP). Across gestational ages, however, the association between SVI and network integration diminished to a negligible relationship in the third trimester. Conversely, SWP was significant across pregnancy, but the relationship changed such that there was a negative association with SWP earlier in the second trimester that inverted around the transition to the third trimester to a positive association. These data directly connect neighborhood disadvantage and altered functional brain maturation in fetuses. Our results suggest that even prior to birth, proximity to environmental stressors in the wider neighborhood environment are associated with altered brain development.

Integrative in vivo analysis of the ethanolamine utilization bacterial microcompartment in Escherichia coli.

Bacterial microcompartments (BMCs) are self-assembling protein megacomplexes that encapsulate metabolic pathways. Although approximately 20% of sequenced bacterial genomes contain operons encoding putative BMCs, few have been thoroughly characterized, nor any in the most studied Escherichia coli strains. We used an interdisciplinary approach to gain deep molecular and functional insights into the ethanolamine utilization (Eut) BMC system encoded by the eut operon in E. coli K-12. The eut genotype was linked with the ethanolamine utilization phenotype using deletion and overexpression mutants. The subcellular dynamics and morphology of the E. coli Eut BMCs were characterized in cellula by fluorescence microscopy and electron (cryo)microscopy. The minimal proteome reorganization required for ethanolamine utilization and the in vivo stoichiometric composition of the Eut BMC were determined by quantitative proteomics. Finally, the first flux map connecting the Eut BMC with central metabolism in cellula was obtained by genome-scale modeling and 13C-fluxomics. Our results reveal that contrary to previous suggestions, ethanolamine serves both as a nitrogen and a carbon source in E. coli K-12, while also contributing to significant metabolic overflow. Overall, this study provides a quantitative molecular and functional understanding of the BMCs involved in ethanolamine assimilation by E. coli.IMPORTANCEThe properties of bacterial microcompartments make them an ideal tool for building orthogonal network structures with minimal interactions with native metabolic and regulatory networks. However, this requires an understanding of how BMCs work natively. In this study, we combined genetic manipulation, multi-omics, modeling, and microscopy to address this issue for Eut BMCs. We show that the Eut BMC in Escherichia coli turns ethanolamine into usable carbon and nitrogen substrates to sustain growth. These results improve our understanding of compartmentalization in a widely used bacterial chassis.

Aproximación al Buen Vivir: Una experiencia de emprendimiento por mujeres de la zona rural

The concept of Good Living plays a crucial role in relation to the community and becomes an opportunity to improve the quality of life. This article systematizes the entrepreneurship experience with peasant women from the rural area of Villamaría, Caldas, Colombia, as a contribution to human flourishing. This research followed the guidelines of the Alforja Network, which proposes a systematization framed within dialectical, systemic, and holistic approaches to popular education. The results are presented in three phases: characterization of the families, the environment, and the entrepreneurship production; interdisciplinary accompaniment and sustainability through the structuring of networks and alliances. The experience reflects the ability of women for the Good Living of themselves and their families, collective organization, and innovation. Additionally, it highlights the importance of the role of the State in strengthening local development through the production chain, preserving its rural identity in the context of equitable processes.

Dual path features interaction network for efficient image super-resolution

Image super-resolution (SR) is a crucial task in computer vision that involves reconstructing a low-resolution (LR) image into its high-resolution (HR) counterpart. Transformer-based methods excel at establishing long-range dependency but face challenges with high-complexity computations and capturing fine-grained features. Conversely, CNN-based methods are advantageous in capturing fine-grained features but are limited by fixed receptive fields. Additionally, most SR methods suffer from channel redundancy, leading to higher computational overhead. In this paper, we propose a Dual Path Features Interaction Network (DPFINet) to achieve efficient image SR, which consists of two components: a) To alleviate the issue of feature channel redundancy, a Local–Global Features Modeling (LGFM) method is newly proposed, which concurrently models global and local features by splitting features along different channels. In LGFM, a Shift Window Linear Attention (SWLA) layer is adopted to effectively capture global information through a large shift window based on the split features. Meanwhile, a Multi-Scale Detail Enhancement (MSDE) layer is designed, where the split other features are encoded to facilitate detail reconstruction through an interactive fusion of semantic and local information, thereby addressing the limitations of SWLA in capturing fine-grained features. b) A Cross-Level Features Interaction (CLFI) method is proposed to fuse global and local features modeled by different network structures (SWLA and MSDE), where a novel residual fusion mechanism is designed to preserve both global and local information while complementing each other. Extensive experiments demonstrate that our method outperforms most state-of-the-art SR methods on five benchmark datasets. Notably, during inference, our approach improves performance by 0.41 dB and reduces memory consumption by approximately 79% compared to DiVANet (Behjati et al., 2023) on the Manga109 (×4) dataset.

Investigation of solution structures in a nonlinear electric transmission network incorporating dissipative elements through physical phenomena analysis

This paper presents an innovative approach to solving the voltage equation in a multi-coupled nonlinear transmission network with dissipative elements. By employing the G′G2 -expansion method and fractional derivatives, we explore the behavior of current and voltage, considering memory effects. Our investigation delves into the impact of fractional calculus on signal transmission, revealing insights into period, amplitude, and propagation direction. Additionally, we analyze the influence of obliqueness angles and various fractional-order derivatives on voltage waveforms Stability analysis demonstrates the significant role of dissipative elements and modulation parameters on signal stability and network resistance to interference. Furthermore, we examine the effects of free parameters on signal transmission characteristics. Our findings highlight the intricate interplay between fractional order, wave obliqueness, and physical parameters on voltage propagation, addressing gaps in previous research.

An anesthetic protocol for preserving functional network structure in the marmoset monkey brain

Abstract Initiatives towards acquiring large-scale neuroimaging data in non-human primates promise improving translational neuroscience and cross-species comparisons. Crucial among these efforts is the need to expand sample sizes while reducing the impact of anesthesia on the functional properties of brain networks. Yet, the effects of anesthesia on non-human primate brain networks remain unclear. Here, we demonstrate using functional magnetic resonance imaging (fMRI) at 9.4 tesla that isoflurane anesthesia induces a variety of brain states in the marmoset brain with dramatically altered functional connectivity profiles. As an alternative, we recommend using a continuous infusion of the sedative medetomidine, supplemented with a low concentration of isoflurane. Using this protocol in eight marmosets, we observed robust visual activation during flickering light stimulation and identified resting-state networks similar to the awake state. In contrast, isoflurane alone led to a suppressed visual activation and the absence of awake-like network patterns. Comparing states using a graph-theoretical approach, we confirmed that the structure of functional networks is preserved under our proposed anesthesia protocol but is lost using isoflurane alone at concentration levels greater than 1%. We believe that the widespread adoption of this protocol will be a step towards advancing translational neuroscience initiatives in non-human primate neuroimaging. To promote the collaborative use of neuroimaging resources, we openly share our datasets (https://zenodo.org/records/11118775).

Persistence, changes and robustness of nest webs along a latitudinal gradient in the Canadian boreal forest

IntroductionIn eastern Canada, the boreal forest is associated with an important latitudinal shift in forest composition and structure, which occurs in the transition between the mixed southern boreal forest and the coniferous northern boreal forest. Along this transition, upland mixedwood stands with large deciduous trees (important for cavity-dependent vertebrate species) are gradually replaced by forests with smaller conifer trees, primarily black spruce (Picea mariana). Concomitantly, the availability of lowland forests flooded by the American beaver (Castor canadensis), which can provide adequate conditions for tree-cavity users, is also decreasing.MethodsWe hypothesized that this latitudinal gradient would bring important changes in the functional diversity and network structure of vertebrate cavity-using communities. Along this latitudinal gradient we used a nest web approach to analyze the structure and robustness of networks of cavity users in upland forests and in lowland forests flooded by beavers.ResultsDespite their low availability in the northern forest region, we found that mixedwood stands persisted throughout the boreal forest in being the main drivers of nest webs network structure of upland forests whereas old black spruce stands contribution was low. In lowland forests, beaver ponds harbored nest webs with a rich and complex structure in both forest regions. Species removal simulations revealed that across our latitudinal gradient upland and lowland forest nest webs responded differently. In upland forests, the removal of trembling aspen and the Pileated Woodpecker (Dryocopus pileatus) caused the highest proportions of secondary extinctions, showing low robustness of nest webs given that these two species were highly connected to the other species. Contrastingly, nest webs in beaver ponds were more robust mainly because excavator species used a higher diversity of tree species despite the removal of the Northern Flicker (Colaptes auratus) which induced numerous secondary extinctions. The Pileated Woodpecker remained the pivotal species across the two forest regions in upland forests whereas the Northern Flicker became the main large cavity provider in beaver ponds across the latitudinal gradient.DiscussionWe discuss how mixedwood forests and beaver ponds, which are key habitat types for the cavity-using vertebrate community across our latitudinal gradient, should be maintained and protected in landscapes under industrial timber harvesting.

Gearbox Fault Diagnosis Method in Noisy Environments Based on Deep Residual Shrinkage Networks.

Gearbox fault diagnosis is essential in the maintenance and preventive repair of industrial systems. However, in actual working environments, noise frequently interferes with fault signals, consequently reducing the accuracy of fault diagnosis. To effectively address this issue, this paper incorporates the noise attenuation of the DRSN-CW model. A compound fault detection method for gearboxes, integrated with a cross-attention module, is proposed to enhance fault diagnosis performance in noisy environments. First, frequency domain features are extracted from the public dataset by using the fast Fourier transform (FFT). Furthermore, the cross-attention mechanism model is inserted in the optimal position to improve the extraction and recognition rate of global and local fault features. Finally, noise-related features are filtered through soft thresholds within the network structure to efficiently mitigate noise interference. The experimental results show that, compared to existing network models, the proposed model exhibits superior noise immunity and high-precision fault diagnosis performance.

Micelle-Containing Hydrogels and Their Applications in Biomedical Research.

Hydrogels are one of the most commonly used materials in our daily lives, which possess crosslinked three-dimensional network structures and are capable of absorbing large amounts of fluid. Due to their outstanding properties, such as flexibility, tunability, and biocompatibility, hydrogels have been widely employed in biomedical research and clinics, especially in on-demand drug release. However, traditional hydrogels face various limitations, e.g., the delivery of hydrophobic drugs due to their highly hydrophilic interior environment. Therefore, micelle-containing hydrogels have been designed and developed, which possess both hydrophilic and hydrophobic microenvironments and enable the storage of diverse cargos. Based on the functionalities of micelles, these hydrogels can be classified into micelle-doped and chemically/physically crosslinked types, which were reported to be responsive to varied stimuli, including temperature, pH, irradiation, electrical signal, magnetic field, etc. Here, we summarize the research advances of micelle-containing hydrogels and provide perspectives on their applications in the biomedical field based on the recent studies from our own lab and others.

BioPipeline Creator—a user-friendly Java-based GUI for managing and customizing biological data pipelines

Bioinformatics tools are essential for performing analyses in the omics sciences. Given the numerous experimental opportunities arising from advances in the field of omics and easier access to high-throughput sequencing platforms, these tools play a fundamental role in research projects. Despite the considerable progress made possible by the development of bioinformatics tools, some tools are tailored to specific analytical goals, leading to challenges for non-bioinformaticians who need to integrate the results of these specific tools into a customized pipeline. To solve this problem, we have developed the BioPipeline Creator, a user-friendly Java-based GUI that allows different software tools to be integrated into the repertoire while ensuring easy user interaction via an accessible graphical interface. Consisting of client and server software components, BioPipeline Creator provides an intuitive graphical interface that simplifies the use of various bioinformatics tools for users without advanced computer skills. It can run on less sophisticated devices or workstations, allowing users to keep their operating system without having to switch to another compatible system. The server is responsible for the processing tasks and can perform the analysis in the user's local or remote network structure. Compatible with the most important operating systems, available at https://github.com/allanverasce/bpc.git.

Gamma-Irradiated Gum Arabic Grafted with 2-Hydroxyethyl Methacrylate: A Novel Superabsorbent Polymer for Controlled Folic Acid Release.

This study explores the synthesis and characterization of superabsorbent hydrogels derived from chemically modified gum Arabic, designed for controlled folic acid release. The synthesis involves a two-step process: carboxymethylation followed by grafting with 2-hydroxyethyl methacrylate via gamma irradiation. The resulting hydrogels exhibit enhanced mechanical strength and controlled diffusivity, essential for nutrient delivery systems. Key factors such as copolymer composition and irradiation dose are investigated, affecting the synthesis process. Systematic studies of swelling behaviors reveal that the hydrogel achieves a maximum swelling of 888.1% at 40°C. The hydrogels are loaded with folic acid, and in vitro, sustained release profiles are examined under various pH conditions. The maximum release of 83.3% is observed after 24h at pH 7.0, following a Korsmeyer-Peppas release mechanism. Different characterization techniques, confirm the successful synthesis and unique properties of the superabsorbent hydrogels. Rheological behavior analysis, scanning electron microscopy, and biocompatibility assessments provide a comprehensive understanding of the hydrogel structures. Gamma irradiation ensures a homogeneous network structure, crucial for optimal swelling behavior and mechanical properties. This research highlights the potential of eco-friendly biopolymer hydrogels in precise drug delivery applications, leveraging the safety and process control benefits of gamma irradiation.

Endophytic and epiphytic metabarcoding reveals fungal communities on cashew phyllosphere in Kenya.

Plants intimately coexist with diverse taxonomically structured microbial communities that influence host health and productivity. The coexistence of plant microbes in the phyllosphere benefits biodiversity maintenance, ecosystem function, and community stability. However, differences in community composition and network structures of phyllosphere epiphytic and endophytic fungi are widely unknown. Using Illumina Miseq sequencing of internal transcribed spacer (ITS) and 28S rRNA gene amplicons, we characterised the epiphytic and endophytic fungal communities associated with cashew phyllosphere (leaf, flower and fruit) from Kwale, Kilifi and Lamu counties in Kenya. The ITS and 28S rRNA gene sequences were clustered into 267 and 108 operational taxonomic units (OTUs) at 97% sequence similarity for both the epiphytes and endophytes. Phylum Ascomycota was abundant followed by Basidiomycota, while class Saccharomycetes was most dominant followed by Dothideomycetes. The major non-ascomycete fungi were associated only with class Tremellales. The fungal communities detected had notable ecological functions as saprotrophs and pathotrophs in class Saccharomyectes and Dothideomycetes. The community composition of epiphytic and endophytic fungi significantly differed between the phyllosphere organs which was statistically confirmed by the Analysis of Similarity test (ANOSIM Statistic R: 0.3273, for 28S rRNA gene and ANOSIM Statistic R: 0.3034 for ITS). The network analysis revealed that epiphytic and endophytic structures were more specialized, modular and had less connectance. Our results comprehensively describe the phyllosphere cashew-associated fungal community and serve as a foundation for understanding the host-specific microbial community structures among cashew trees.