Heterogeneous Complex Research Articles

The Emerging Field of Viroimmunotherapy for Pediatric Brain Tumors.

Pediatric brain tumors are the most common solid tumors in children. Even to date, with the advances in multimodality therapeutic management, survival outcomes remain dismal in some types of tumors, such as pediatric-type diffuse high-grade gliomas or central nervous system (CNS) embryonal tumors. Failure to understand the complex molecular heterogeneity and the elusive tumor and microenvironment interplay continues to undermine therapeutic efficacy. Developing a strategy that would improve survival for these fatal tumors remains unmet in pediatric neuro-oncology. Oncolytic viruses (OVs) are emerging as a feasible, safe, and promising therapy for brain tumors. The new paradigm in virotherapy implies that the direct cytopathic effect is followed, under certain circumstances, by an antitumor immune response responsible for the partial or complete debulking of the tumor mass. OVs alone or combined with other therapeutic modalities have been primarily used in adult neuro-oncology. A surge in encouraging preclinical studies in pediatric brain tumor models recently led to the clinical translation of OVs with encouraging results in these tumors. In this review, we summarize the different virotherapy tested in preclinical and clinical studies in pediatric brain tumors, and we discuss the limitations and future avenues necessary to improve the response of these tumors to this type of therapy.

A multi-agent motion simulation method for emergency scenario deduction

Simulating crowd motion in emergency scenarios remains a challenge in computer graphics due to crowd heterogeneity and environmental complexity. However, existing crowd simulation methods homogenize the agent model and simplify target selection and motion navigation of emergency crowds. To address these problems, we propose a multi-agent motion simulation method for emergency scenario deduction. First, we propose a multi-agent model to simulate crowd heterogeneity. This model includes a personality-based heterogeneous agent model and an agent perception model that considers vision, hearing, and familiarity with the environment. Second, we propose a target selection strategy based on the motion patterns of actual pedestrians. This strategy employs mathematical models and our agent perception model to guide agents in selecting appropriate targets. Finally, we propose a global navigation algorithm that combines random sampling with heuristic search methods. Concurrently, we use our multi-agent model to adjust the agent’s local motion planning to deduce the motion states of emergency crowds naturally. Experimental results validate that our method can realistically and reasonably simulate crowd motion in emergency scenarios.

»Relationships are Key« A Semantic Relationship Awareness Framework for Operational Technology Monitoring

Critical infrastructures in areas like road traffic management naturally rely on the broad use of "Operational Technology (OT)" to ensure efficient and safe road traffic monitoring (RTM) through "OT objects", like sensors and actuators, whereby monitoring OT itself ("OTM") is evenly crucial. OTM is highly challenging, not least due to massive heterogeneity of OT, immense complexity and size, and omnipresence of evolution. As a consequence, knowledge about interdependencies between OT objects in form of semantic relationships is often outdated or simply not available. Thus, in case of incidents, detection of cause and effect in the sense of a situational picture of OT is missing. In order to counteract this fundamental deficiency, we aim to automatically discover semantic relationships between OT objects, to build up an ontological knowledge base as prerequisite for achieving OT situation awareness. Thereby, the contribution of this paper is threefold. First, a systematic exploration of the induced challenges is provided, derived from an in-depth analysis of real-world OT message logs in the area of RTM. Based on that, we sketch out a research roadmap, thereby guiding the identification of existing concepts and technologies appearing to be useful for realizing a framework for semantic relationship awareness, being the crucial pre-step for achieving OT situation awareness. Finally, a first proof-of-concept prototype is put forward, complemented by an evaluation of its applicability and a detailed comparison to related approaches.

Evaluating effects of silvicultural treatments on forest canopy structure outcomes

Traditional forest management can homogenize forests, and management strategies that can restore complexity and promote adaptive capacity to global change are needed. However, effects of different silvicultural practices on complexity of forest structural components, such as the canopy, are not well understood mechanistically. We conducted a coupled field and simulation-modeling study to evaluate: 1) how near-term effects of silviculture on canopy structure and complexity differ across treatment types, and 2) how outcomes of common silvicultural treatments compare to a spectrum of randomly implemented removals. Thirteen different silvicultural treatments replicated across 12 study plots were simulated within 3D models derived from terrestrial lidar data. Treatment types often differed in their multi-dimensional structural outcomes, including vertical heterogeneity and canopy structural complexity. Moderate intensity thinning treatments that preferentially removed smaller trees increased near-term canopy structural complexity, while diameter-limit cutting often reduced complexity. Silvicultural treatments collectively produced a wide range of residual canopy conditions; however, variability among structural outcomes within individual treatment types or categories was limited relative to the range of possible outcomes from random removals. Most treatments induced shifts in canopy structure outside the spectrum of random removal, suggesting ample space for adapting management to promote forest heterogeneity.

Trioxane-based MS-cleavable Cross-linking Mass Spectrometry for Profiling Multimeric Interactions of Cellular Networks.

Cross-linking mass spectrometry (XL-MS) is a powerful technology for mapping protein-protein interactions (PPIs) at the systems-level. By covalently connecting pairs of proximal residues, cross-linking reagents provide distance restraints to infer protein conformations and interaction interfaces. While binary cross-links have been remarkably informative, multimeric cross-links can offer enhanced spatial resolution to facilitate the characterization of dynamic and heterogeneous protein complexes. However, the identification of multimeric cross-links remains extremely challenging due to fragmentation complexity and the vast expansion of database search space. Here, we present a novel trioxane-based MS-cleavable homotrifunctional cross-linker TSTO, which can target three proximal lysine residues simultaneously. Owing to its unique structure and MS-cleavability, TSTO enables fast and unambiguous identification of cross-linked peptides using LC-MS n analysis. Importantly, we have demonstrated that the TSTO-based XL-MS platform is effective for mapping PPIs of protein complexes and cellular networks. The trimeric interactions captured by TSTO have uncovered new structural details that cannot be easily revealed by existing reagents, allowing in-depth description of PPIs to facilitate structural modeling. This development not only advances XL-MS technologies for global PPI profiling from living cells, but also offers a new direction for creating multifunctional MS-cleavable cross-linkers to further push structural systems biology forward in the future.

High-throughput gene expression analysis with TempO-LINC sensitively resolves complex brain, lung and kidney heterogeneity at single-cell resolution.

We report the development and performance of a novel genomics platform, TempO-LINC, for conducting high-throughput transcriptomic analysis on single cells and nuclei. TempO-LINC works by adding cell-identifying molecular barcodes onto highly selective and high-sensitivity gene expression probes within fixed cells, without having to first generate cDNA. Using an instrument-free combinatorial-indexing approach, all probes within the same fixed cell receive an identical barcode, enabling the reconstruction of single-cell gene expression profiles across as few as several hundred cells and up to 100,000+ cells per run. The TempO-LINC approach is easily scalable based on the number of barcodes and rounds of barcoding performed; however, for the experiments reported in this study, the assay utilized over 5.3 million unique barcodes. TempO-LINC has a robust protocol for fixing and banking cells and displays high-sensitivity gene detection from multiple diverse sample types. We show that TempO-LINC has an observed multiplet rate of less than 1.1% and a cell capture rate of ~50%. Although the assay can accurately profile the whole transcriptome (19,683 human or 21,400 mouse genes), it can be targeted to measure only actionable/informative genes and molecular pathways of interest - thereby reducing sequencing requirements. In this study, we applied TempO-LINC to profile the transcriptomes of 89,722 cells across multiple sample types, including nuclei from mouse lung, kidney and brain tissues. The data demonstrated the ability to identify and annotate at least 50 unique cell populations and positively correlate expression of cell type-specific molecular markers within them. TempO-LINC is a robust new single-cell technology that is ideal for large-scale applications/studies across thousands of samples with high data quality.

Head circumference growth in children with Autism Spectrum Disorder: trend and clinical correlates in the first five years of life.

Macrocephaly is described in almost 15% of children with Autism Spectrum Disorder (ASD). Relationships between head growth trajectories and clinical findings in ASD children show a high degree of variability, highlighting the complex heterogeneity of the disorder. The aim of this study was to measure differences of the early growth trajectory of head circumference (HC) in children with ASD and macrocephaly compared to ASD normocephalic children, examining clinical correlates in the two groups of patients. HC data were collected from birth to 5 years of age in a sample of children with a confirmed diagnosis of ASD. Participants were classified into two groups: ASD macrocephaly (ASD-M, Z-scores ≥1.88 in at least two consecutive HC measurements), and ASD non-macrocephaly (ASD-N). Based on the distribution of HC measurements (Z-scores), five age groups were identified for the longitudinal study. Developmental and behavioral characteristics of the ASD-M children compared to the ASD-N group were compared by using standardized scores. 20,8% of the children sample met criteria for macrocephaly. HC values became indicative of macrocephaly in the ASD-M group at the age range from 1 to 6 months, and persisted thereafter throughout the first five years of age. ASD-M children showed significantly higher developmental quotients of Griffiths III B and D subscales compared to ASD-N group. No significant differences in the severity of ASD symptoms assessed by ADOS-2 were observed between ASD-M and ASD-N groups. In this study HC size from birth to 5 years links to accelerated HC growth rate as early as the first 6 months of age in children with ASD and macrocephaly, preceding the onset and diagnosis of ASD. We found that in early childhood, children with ASD-M may exhibit some advantages in language and social communication and emotional skills without differences in autism severity, when compared with age-matched normocephalic ASD children. Longitudinal analyses are required to catch-up prospectively possible relationships between head size as proxy measure of brain development and neuro-developmental and behavioral features in children with ASD.

DCAI-CLUD: a data-centric framework for the construction of land-use datasets

A high-quality land-use dataset is crucial for constructing a high-performance land-use classification model. Due to the complexity and spatial heterogeneity of land-use, the dataset construction process is inefficient and costly. This challenge affects the quality of datasets, consequently impacting the model’s performance. The emerging field of Data-Centric Artificial Intelligence (DCAI) is expected to deliver techniques for dataset optimization, offering a promising solution to the problem. Therefore, this study proposes a data-centric framework named DCAI-CLUD for the construction of land-use datasets. Based on this framework, the accuracy and rate of data labeling are improved by 5.93 and 28.97%. The Gini index of the dataset and the proportion of samples with non-mixed land-use categories are enhanced by 3.27 and 8.52%. The overall accuracy (OA) and Kappa of the land-use classification model improved significantly by 27.87 and 58.08%. This study is the first to introduce DCAI into the field of geographic information and remote sensing and verify its effectiveness. The proposed framework can effectively improve the construction efficiency and quality of the dataset and synchronously optimize the model performance. Based on the proposed framework, we constructed a multi-source land-use dataset of major cities in China named CN-MSLU-100K.

Spatiotemporal characteristics and driving mechanisms of land-use transitions and landscape patterns in response to ecological restoration projects: A case study of mountainous areas in Guizhou, Southwest China

With the acceleration of urbanization and industrialization, the land-use mode in mountainous areas has changed significantly, causing serious ecological destruction. Therefore, improving the efficiency of land-resource use in mountainous areas and enhancing ecological protection are important issues. This study focuses on typical mountainous areas in Guizhou Province to explore their significance not only for ecological restoration but also for sustainable socioeconomic development. In this study, we integrated the framework of the interaction between land-use transitions and landscape patterns and employed Spearman's rank correlation coefficient and a spatial bivariate model to reveal the spatiotemporal heterogeneity of landscape patterns and land-use transitions. The results showed that for 1990–2020, the cultivated, forest, and grassland areas decreased by −1353.66, −749.98 and − 541.72 km2 respectively, whereas the water area, urban, and other construction land increased by 799.69, 542.62, and 1230.90 km2, respectively. Moreover, the cultivated land areas converted into urban and other construction land were 358.03 and 661.77 km2 respectively, and the grassland areas converted into forest and other construction land were 3550.08 and 277.32 km2, respectively. Different land-use transitions presented rapid expansion and contraction characteristics, resulting in an increase in overall patch fragmentation, a decrease in uniformity, and an increase in irregular patches, which enhanced the spatial heterogeneity of diversity and complexity. Finally, the land-use transition effects on landscape patterns were primarily elevation, slope, and primary road facility factors. In-depth research on land-use transitional evolution and its impact on landscape patterns has important theoretical and practical value for formulating land-management policies and promoting sustainable development in mountainous areas.

A new flow path: eDNA connecting hydrology and biology

AbstractEnvironmental DNA (eDNA) has revolutionized ecological research, particularly for biodiversity assessment in various environments, most notably aquatic media. Environmental DNA analysis allows for non‐invasive and rapid species detection across multiple taxonomic groups within a single sample, making it especially useful for identifying rare or invasive species. Due to dynamic hydrological processes, eDNA samples from running waters may represent biodiversity from broad contributing areas, which is convenient from a biomonitoring perspective but also challenging, as hydrological knowledge is required for meaningful biological interpretation. Hydrologists could also benefit from eDNA to address unsolved questions, particularly concerning water movement through catchments. While naturally occurring abiotic tracers have advanced our understanding of water age distribution in catchments, for example, current geochemical tracers cannot fully elucidate the timing and flow paths of water through landscapes. Conversely, biological tracers, owing to their immense diversity and interactions with the environment, could offer more detailed information on the sources and flow paths of water to the stream. The informational capacity of eDNA as a tracer, however, is determined by the ability to interpret the complex biological heterogeneity at a study site, which arguably requires both biological and hydrological expertise. As eDNA data has become increasingly available as part of biomonitoring campaigns, we argue that accompanying eDNA surveys with hydrological observations could enhance our understanding of both biological and hydrological processes; we identify opportunities, challenges, and needs for further interdisciplinary collaboration; and we highlight eDNA's potential as a bridge between hydrology and biology, which could foster both domains.This article is categorized under: Science of Water > Hydrological Processes Science of Water > Methods Water and Life > Nature of Freshwater Ecosystems

Study on the fluidity of the pore-fracture binary system in a tight sandstone reservoir-NMR

Fluid movability in tight sands may not be accurately characterized by pore size-based classification methods solely because of the complex pore structure and heterogeneity in pore size. In this study, on the basis of casting thin slices and scanning electron microscope observation, pore structure was analyzed using mercury injection, NMR, and micron CT to classify and evaluate the tight oil reservoir. The experiment suggest that the quality of tight reservoir is determined by its pore structure, particularly the throat radius, with the microthroat being an essential factor in permeability. Uniquely, we divide the reservoir by Q-cluster with throat radius, displacement pressure, permeability and other parameters. Based on reservoir classification, this study proposed a method for studying the pore size classification of samples on the T2 spectrum by combining CT scanning with mercury intrusion and a NMR experiment. Pore fluids are generally classified into movable fluid and irreducible fluid by one or two NMR T2 cut-offs. The pore size distributions and capillarity boundaries are converted from T2 and mercury injection capillary pressure (MICP). We categorized pores into micropores (T2 < 1), macropores (T2 > 10, with T2 > 300 as fractures), and medium pores (the rest). The saturation of movable fluid and the percentage of micro-fractures can characterize the seepage characteristics of tight reservoirs, which is of great significance for the later periods of oilfield development.

Evolution of bioactive 3, 4-dihydropyrimidin-2(1H)-ones by an efficient heterogeneous Ni-phen MCM-41 mixed ligand complex under green path

Evolution of bioactive 3, 4-dihydropyrimidin-2(1H)-ones by an efficient heterogeneous Ni-phen MCM-41 mixed ligand complex under green path

Empirical relationship between vadose zone properties and diesel attenuation capacity: A complement for intrinsic vulnerability models

Evaluating and predicting the natural attenuation capacity (AC) of a vadose zone is essential for determining groundwater vulnerability to contamination from upper sources. However, it remains unclear how the physicochemical properties of vadose zone soils affect AC owing to their complexity and spatial heterogeneity. In this study, we developed a regression model for estimating the AC of a vadose zone against diesel using datasets from different soils with a wide range of physicochemical properties. Among the 17 properties, six (i.e., organic matter (OM), total phosphorous (TP), coefficient of uniformity, particle size (D30), van Genuchten’s n, saturation degree (SD)) were selected as primary regressors. The results indicate that biogeochemical factors, including OM and TP, have decisive effects on the AC. Finally, the regression model was expanded to a GIS-based spatial model and applied to Namyangju, Korea using the index-overlay method. The produced AC map showed a nonmonotonic decrease along the depth, and the areas closer to the water bodies generally represented low AC values, most likely due to the lower OM, TP, and higher SD. This study provides an empirical basis for future research initiatives for spatial and temporal AC dynamics, which complements conventional intrinsic groundwater vulnerability models such as DRASTIC.

Predicting multiphase flow behavior of methane in shallow unconfined aquifers using conditional deep convolutional generative adversarial network

Numerical modeling is an essential tool for geoscience applications involving multiphase flow behavior. Performing numerical simulation is, however, computationally intensive due to multi-scale, non-linear, and multi-physics nature of mechanisms controlling multiphase flow dynamics. Additionally, the computational challenges associated with traditional numerical simulations limit their applicability for repetitive simulations required in inverse modeling, uncertainty analysis, and optimization tasks. To overcome these limitations, surrogate modeling has recently emerged as a viable solution. A surrogate model, which functions as a regression model, is trained using numerical simulation data. It approximates the input–output relationships within a dynamic fluid environment. We design surrogate models, based on conditional deep convolutional generative adversarial network (cDC-GAN), to map the cross-domain between input and output pairs in a multiphase multicomponent system, focusing on methane (CH4) migration in shallow unconfined aquifers. The cDC-GAN technique is selected due to its ability to generate high-fidelity surrogate models that effectively capture complex spatial distributions and heterogeneities. This technique excels in handling high-dimensional data, learning the intricate relationships between inputs and outputs, and producing realistic representations of subsurface phenomena. The trained cDC-GANs consider capillary entry pressure heterogeneity as input because this geologic attribute places first-order control on CH4 migration in subsurface. The outputs include CH4 saturation, water saturation, residual saturation of CH4, CH4 mole fraction, and CH4 molality. For each output, a separate cDC-GAN model is trained. Once trained, cDC-GANs can at any given time determine CH4 distribution in a shallow unconfined aquifer, both qualitatively and quantitatively. The cDC-GAN approach can be considered as a valuable complement to numerical simulations for predicting multiphase flow behavior in other geoscience-, energy-, and environmental applications such as contaminant transport, hydrocarbon production, and geological carbon dioxide and hydrogen storage.

SmartImpute: A Targeted Imputation Framework for Single-cell Transcriptome Data.

Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular heterogeneity and tissue transcriptomic complexity. However, the high frequency of dropout events in scRNA-seq data complicates downstream analyses such as cell type identification and trajectory inference. Existing imputation methods address the dropout problem but face limitations such as high computational cost and risk of over-imputation. We present SmartImpute, a novel computational framework designed for targeted imputation of scRNA-seq data. SmartImpute focuses on a predefined set of marker genes, enhancing the biological relevance and computational efficiency of the imputation process while minimizing the risk of model misspecification. Utilizing a modified Generative Adversarial Imputation Network architecture, SmartImpute accurately imputes the missing gene expression and distinguishes between true biological zeros and missing values, preventing overfitting and preserving biologically relevant zeros. To ensure reproducibility, we also provide a function based on the GPT4 model to create target gene panels depending on the tissue types and research context. Our results, based on scRNA-seq data from head and neck squamous cell carcinoma and human bone marrow, demonstrate that SmartImpute significantly enhances cell type annotation and clustering accuracy while reducing computational burden. Benchmarking against other imputation methods highlights SmartImpute's superior performance in terms of both accuracy and efficiency. Overall, SmartImpute provides a lightweight, efficient, and biologically relevant solution for addressing dropout events in scRNA-seq data, facilitating deeper insights into cellular heterogeneity and disease progression. Furthermore, SmartImpute's targeted approach can be extended to spatial omics data, which also contain many missing values.

Method for Testing Etiologic Heterogeneity Among Noncompeting Diagnoses, Applied to Impact of Perinatal Exposures on Autism and Attention Deficit Hyperactivity Disorder.

Testing etiologic heterogeneity, whether a disorder subtype is more or less impacted by a risk factor, is important for understanding causal pathways and optimizing statistical power. The study of mental health disorders especially benefits from strategic subcategorization because these disorders are heterogeneous and frequently co-occur. Existing methods to quantify etiologic heterogeneity are not appropriate for noncompeting events in an open cohort of variable-length follow-up. Thus, we developed a new method. We estimated risks from urban residence, maternal smoking during pregnancy, and parental psychiatric history, with subtypes defined by the presence or absence of a codiagnosis: autism alone, attention deficit hyperactivity disorder (ADHD) alone, and joint diagnoses of autism + ADHD. To calculate the risk of a single diagnosis (e.g., autism alone), we subtracted the risk for autism + ADHD from the risk for autism overall. We tested the equivalency of average risk ratios over time, using a Wald-type test and bootstrapped standard errors. Urban residence was most strongly linked with autism + ADHD and least with ADHD only; maternal smoking was associated with ADHD only but not autism only; and parental psychiatric history exhibited similar associations with all subgroups. Our method allowed the calculation of appropriate P values to test the strength of association, informing etiologic heterogeneity wherein two of these three risk factors exhibited different impacts across diagnostic subtypes. The method used all available data, avoided neurodevelopmental outcome misclassification, exhibited robust statistical precision, and is applicable to similar heterogeneous complex conditions using common diagnostic data with variable follow-up.

Protein complex heterogeneity and topology revealed by electron capture charge reduction and surface induced dissociation.

We illustrate the utility of native mass spectrometry (nMS) combined with a fast, tunable gas-phase charge reduction, electron capture charge reduction (ECCR), for the characterization of protein complex topology and glycoprotein heterogeneity. ECCR efficiently reduces the charge states of tetradecameric GroEL, illustrating Orbitrap m/z measurements to greater than 100,000 m/z. For pentameric C-reactive protein and tetradecameric GroEL, our novel device combining ECCR with surface induced dissociation (SID) reduces the charge states and yields more topologically informative fragmentation. This is the first demonstration that ECCR yields more native-like SID fragmentation. ECCR also significantly improved mass and glycan heterogeneity measurements of heavily glycosylated SARS-CoV-2 spike protein trimer and thyroglobulin dimer. Protein glycosylation is important for structural and functional properties and plays essential roles in many biological processes. The immense heterogeneity in glycosylation sites and glycan structure poses significant analytical challenges that hinder a mechanistic understanding of the biological role of glycosylation. Without ECCR, average mass determination of glycoprotein complexes is available only through charge detection mass spectrometry or mass photometry. With narrow m/z selection windows followed by ECCR, multiple glycoform m/z values are apparent, providing quick global glycoform profiling and providing a future path for glycan localization on individual intact glycoforms.

Enhanced Declustering Enables Native Top-Down Analysis of Membrane Protein Complexes using Ion-Mobility Time-Aligned Fragmentation.

Native mass spectrometry (MS) is proving to be a disruptive technique for studying the interactions of proteins, necessary for understanding the functional roles of these biomolecules. Recent research is expanding the application of native MS towards membrane proteins directly from isolated membrane preparations or from purified detergent micelles. The former results in complex spectra comprising several heterogeneous protein complexes; the latter enables therapeutic protein targets to be screened against multiplexed preparations of compound libraries. In both cases, the resulting spectra are increasingly complex to assign/interpret, and the key to these new directions of native MS research is the ability to perform native top-down analysis, which allows unambiguous peak assignment. To achieve this, detergent removal is necessary prior to MS analyzers, which allow selection of specific m/z values, representing the parent ion for downstream activation. Here, we describe a novel, enhanced declustering (ED) device installed into the first pumping region of a cyclic IMS-enabled mass spectrometry platform. The device enables declustering of ions prior to the quadrupole by imparting collisional activation through an oscillating electric field applied between two parallel plates. The positioning of the device enables liberation of membrane protein ions from detergent micelles. Quadrupole selection can now be utilized to isolate protein-ligand complexes, and downstream collision cells enable the dissociation and identification of binding partners. We demonstrate that ion mobility (IM) significantly aids in the assignment of top-down spectra, aligning fragments to their corresponding parent ions by means of IM drift time. Using this approach, we were able to confidently assign and identify a novel hit compound against PfMATE, obtained from multiplexed ligand libraries.

Could Gas6/TAM Axis Provide Valuable Insights into the Pathogenesis of Systemic Sclerosis?

Systemic sclerosis (SSc) is a connective tissue disorder characterized by microvascular injury, extracellular matrix deposition, autoimmunity, inflammation, and fibrosis. The clinical complexity and high heterogeneity of the disease make the discovery of potential therapeutic targets difficult. However, the recent progress in the comprehension of its pathogenesis is encouraging. Growth Arrest-Specific 6 (Gas6) and Tyro3, Axl, and MerTK (TAM) receptors are involved in multiple biological processes, including modulation of the immune response, phagocytosis, apoptosis, fibrosis, inflammation, cancer development, and autoimmune disorders. In the present manuscript, we review the current evidence regarding SSc pathogenesis and the role of the Gas6/TAM system in several human diseases, suggesting its likely contribution in SSc and highlighting areas where further research is necessary to fully comprehend the role of TAM receptors in this condition. Indeed, understanding the involvement of TAM receptors in SSc, which is currently unknown, could provide valuable insights for novel potential therapeutic targets.

DoS attacks resilience of heterogeneous complex networks via dynamic event-triggered impulsive scheme for secure quasi-synchronization

This paper addresses the secure quasi-synchronization issue of heterogeneous complex networks (HCNs) under aperiodic denial-of-service (DoS) attacks with dynamic event-triggered impulsive scheme (ETIS). The heterogeneity of networks and the aperiodic DoS attacks, which hinder communication channels and synchronization goals, present challenges to the analysis of secure quasi-synchronization. The ETIS leverages impulsive control and dynamic event-triggered scheme (ETS) to handle the network heterogeneity and the DoS attacks. We give specific bounds on the attack duration and frequency that the network can endure, and obtain synchronization criteria that relate to event parameters, attack duration, attack frequency, and impulsive gain by the variation of parameter formula and recursive methods. Moreover, we prove that the dynamic ETS significantly reduces the controller updates, saves energy without sacrificing the system decay rate, and prevents the Zeno phenomenon. Finally, we validate our control scheme with a numerical example.