Complex Architecture Research Articles

The role of mobile edge computing in advancing federated learning algorithms and techniques: A systematic review of applications, challenges, and future directions

Mobile Edge Computing (MEC) and Federated Learning (FL) have recently attracted considerable interest for their potential applications across diverse domains. MEC is an architecture for distributed computing that utilizes computational capabilities near the network edge, enabling quicker data processing and minimizing latency. In contrast, FL is a method in the field of Machine learning (ML) that allows for the simultaneous involvement of multiple participants to collectively train models without revealing their raw data, effectively tackling concerns related to security and privacy. This systematic review explores the core principles, architectures, and applications of FL within MEC and vice versa, providing a comprehensive analysis of these technologies. The study emphasizes FL and MEC's unique characteristics, advantages, and drawbacks, highlighting their attributes and limitations. The study explores the complex architectures of both technologies, showcasing the cutting-edge methods and tools employed for their implementation. Aside from examining the foundational principles, the review explores the depths of the internal mechanisms of FL and MEC, offering a valuable in-depth of their architecture understanding and the fundamental principles and processes that facilitate their operation. At last, the concluding remarks and future research directions are provided.

Pioneering anti-inflammatory therapy: The path to precision therapy with C3aR peptide libraries

Abstract. Inflammation, a pervasive process with profound implications for tissue, joint, and vascular health, underscores the critical importance of immune system regulation in maintaining bodily homeostasis. Central to this regulatory network is the complement system, a complex array of proteins that orchestrate immune responses to infections and cellular damage. Among these proteins, the complement component 3a receptor (C3aR) serves as a pivotal mediator, triggering inflammatory cascades upon binding to the anaphylatoxin C3a. Targeting C3aR has emerged as a promising strategy for anti-inflammatory therapy, driven by recent advances in structural biology elucidating the complex architecture of the C3a-C3aR interaction. This study focuses on the design of high-affinity peptides targeting the C3aR receptor, leveraging ligand design and docking techniques to optimize binding interactions. Utilizing computational tools with sophisticated algorithms, we modeled the binding affinity of mutated ligands with the C3aR receptor, providing insights into the potential efficacy of peptide-based therapeutics. Our findings reveal that mutations at residues G74 and A76 enhance the binding affinity of the ligand with the C3aR receptor, offering a promising avenue for the development of novel anti-inflammatory agents. By elucidating the molecular determinants underlying peptide-receptor interactions within the C3a system, this study advances our understanding of inflammation biology and lays the groundwork for the rational design of targeted therapeutics. These insights hold potential for the development of potent and selective C3aR agonists/antagonists, offering new avenues for the treatment of inflammatory diseases and enhancing patient outcomes.

A Low-Cost Modulated Laser-Based Imaging System Using Square Ring Laser Illumination for Depressing Underwater Backscatter

Underwater vision data facilitate a variety of underwater operations, including underwater ecosystem monitoring, topographical mapping, mariculture, and marine resource exploration. Conventional laser-based underwater imaging systems with complex system architecture rely on high-cost laser systems with high power, and software-based methods can not enrich the physical information captured by cameras. In this manuscript, a low-cost modulated laser-based imaging system is proposed with a spot in the shape of a square ring to eliminate the overlap between the illumination light path and the imaging path, which could reduce the negative effect of backscatter on the imaging process and enhance imaging quality. The imaging system is able to achieve underwater imaging at long distance (e.g., 10 m) with turbidity in the range of 2.49 to 7.82 NTUs, and the adjustable divergence angle of the laser tubes enables the flexibility of the proposed system to image on the basis of application requirements, such as the overall view or partial detail information of targets. Compared with a conventional underwater imaging camera (DS-2XC6244F, Hikvision, Hangzhou, China), the developed system could provide better imaging performance regarding visual effects and quantitative evaluation (e.g., UCIQUE and IE). Through integration with the CycleGAN-based method, the imaging results can be further improved, with the UCIQUE increased by 0.4. The proposed low-cost imaging system with a compact system structure and low consumption of energy could be equipped with platforms, such as underwater robots and AUVs, to facilitate real-world underwater applications.

Versatile synthesis of uniform mesoporous superparticles from stable monomicelle units.

Superstructures with architectural complexity and unique functionalities are promising for a variety of practical applications in many fields, including mechanics, sensing, photonics, catalysis, drug delivery and energy storage/conversion. In the past five years, a number of attempts have been made to build superparticles based on amphiphilic polymeric micelle units, but most have failed owing to their inherent poor stability. Determining how to stabilize micelles and control their superassembly is critical to obtaining the desired mesoporous superparticles. Here we provide a detailed procedure for the preparation of ultrastable polymeric monomicelle building units, the creation of a library of ultrasmall organic-inorganic nanohybrids, the modular superassembly of monomicelles into hierarchical superstructures and creation of novel multilevel mesoporous superstructures. The protocol enables precise control of the number of monomicelle units and the derived mesopores for superparticles. We show that ultrafine nanohybrids display enhanced mechanical antipressure performance compared with pristine polymeric micelles, and describe the functional characterization of mesoporous superstructures that exhibit excellent oxygen reduction reactivity. Except for the time (4.5 d) needed for the preparation of the triblock polystyrene-block-poly(4-vinylpyridine)-block-poly(ethylene oxide) PS-PVP-PEO or the polystyrene-block-poly(acrylic acid)-block-poly(ethylene oxide) (PS-PAA-PEO) copolymer, the synthesis of the ultrastable monomicelle, ultrafine organic-inorganic nanohybrids, hierarchical superstructures and mesoporous superparticles require ~6, 30, 8 and 24 h, respectively. The time needed for all characterizations and applications are 18 and 10 h, respectively.

Impact of Polyploidy on Crop Improvement and Plant Breeding Strategies: A Review

Polyploidy, the condition of possessing multiple sets of chromosomes, is a widespread phenomenon in plants that has had a profound impact on crop evolution and improvement. This review explores the role of polyploidy in plant breeding strategies, emphasizing its contributions to enhancing agronomic traits, overcoming genetic barriers, and expanding genetic diversity. Polyploid crops, such as wheat, cotton, and Brassica species, exhibit superior yield, increased biomass, and enhanced stress tolerance compared to their diploid counterparts. Polyploidy can arise naturally or be induced artificially, providing breeders with opportunities to create novel crop varieties through synthetic polyploidization. The complexity of polyploid genomes poses significant challenges, including fertility issues, meiotic instability, and difficulties in genome management. Advances in genomic and biotechnological tools, such as genomic selection and CRISPR-based genome editing, are beginning to address these obstacles, allowing for precise manipulation of polyploid genomes and improved breeding outcomes. Additionally, polyploidy plays a crucial role in overcoming reproductive barriers in interspecific hybrids, facilitating the transfer of desirable traits between species that would otherwise be genetically incompatible. The potential of polyploidy for developing climate-resilient crops is particularly noteworthy, as polyploid plants often exhibit greater tolerance to drought, salinity, and extreme temperatures. This makes polyploid breeding a promising approach for addressing the challenges of climate change and ensuring food security. Future research should focus on understanding the genetic and epigenetic mechanisms underlying polyploid genome stability and trait expression, as well as integrating advanced computational tools to predict and manipulate polyploid gene function. Emerging areas such as synthetic biology and multi-omics integration will further enhance our ability to engineer polyploid crops with complex trait architectures. Polyploidy remains a powerful and versatile tool for plant breeders, offering immense potential for crop improvement, genetic innovation, and the development of resilient agricultural systems. As the understanding and technological capabilities in polyploid breeding continue to advance, polyploid crops will play a central role in sustainable agricultural development and global food production.

Ultrastructure Expansion Microscopy (U-ExM) of the Fission Yeast Schizosaccharomyces pombe.

Among widely used super-resolution microscopy techniques, including stimulated emission depletion (STED), photoactivated localization microscopy (PALM), and stochastic optical reconstruction microscopy (STORM), expansion microscopy (ExM) is unique in achieving increased resolution through a physical manipulation of the actual sample rather than optics or postprocessing. Originally developed for applications in neuroscience, ExM now has a solid foothold across many fields and model systems, and has been adapted to work for organisms with cell walls, including budding and fission yeasts, through the inclusion of a pre-expansion enzymatic digestion step. A variant of the ExM technique optimized for preserving the architecture of protein complexes, ultrastructure expansion microscopy (U-ExM), enables super-resolution imaging of full 3D volumes at increased throughput using conventional microscopes and can be readily combined with commonly used antibodies, dyes, and stains. Here, we present its application to the fission yeast Schizosaccharomyces pombe.

Variants in LPA are associated with Familial Hypercholesterolaemia: whole genome sequencing analysis in the 100,000 Genomes Project.

Familial Hypercholesterolaemia (FH) is an inherited disease of high LDL-cholesterol (LDL-C) caused by defects in LDLR, APOB, APOE and PCSK9 genes. A pathogenic variant cannot be found in ∼60% of clinical FH patients. Using whole genome sequencing (WGS) we examined genetic determinants of FH. WGS data generated by the 100,000 Genomes Project (100KGP) included 536 FH patients diagnosed using the FH Simon Broome criteria. Rare variants in known FH genes were analysed. Genome-wide association study (GWAS) between 443 FH variant-negative unrelated FH cases and 77,275 control participants of the 100KGP was run using high coverage WGS data. Polygenic risk scores for LDL-C (LDL PRS) and lipoprotein(a) (Lp(a) PRS) were computed. An FH-causing variant was found in 17.4% of FH cases. GWAS identified the LPA gene locus being significantly associated (p<1x10-8). FH variant-negative participants had higher LDL and Lp(a) PRSs in comparison to the controls (p<1.0×10-16 and p<4.09×10-6, respectively). Similar associations were found in the monogenic FH with both LDL and Lp(a) PRSs being higher than in controls (p<4.03×10-4 and p<3.01x10-3, respectively). High LDL PRS was observed in 36.4% of FH variant-negative cases, whereas high Lp(a) PRS in 18.5%, with 7.0% having both high LDL and Lp(a) PRSs. This genome-wide analysis of monogenic and polygenic FH causes confirms a complex and heterogenous architecture of hypercholesterolaemia, with the LPA gene playing a significant role. Both Lp(a) and LDL-C should be measured for precision FH diagnosis. Specific therapies to lower Lp(a) should be targeted to those who will benefit most.

Homebuilt Imaging-Based Spatial Transcriptomics: Tertiary Lymphoid Structures as a Case Example.

Spatial transcriptomics methods provide insight into the cellular heterogeneity and spatial architecture of complex, multicellular systems. Combining molecular and spatial information provides important clues to study tissue architecture in development and disease. Here, we present a comprehensive do-it-yourself (DIY) guide to perform such experiments at reduced costs leveraging open-source approaches. This guide spans the entire life cycle of a project, from its initial definition to experimental choices, wet lab approaches, instrumentation, and analysis. As a concrete example, we focus on tertiary lymphoid structures (TLS), which we use to develop typical questions that can be addressed by these approaches.

Structure of the yeast ceramide synthase.

Ceramides are essential lipids involved in forming complex sphingolipids and acting as signaling molecules. They result from the N-acylation of a sphingoid base and a CoA-activated fatty acid, a reaction catalyzed by the ceramide synthase (CerS) family of enzymes. Yet, the precise structural details and catalytic mechanisms of CerSs have remained elusive. Here we used cryo-electron microscopy single-particle analysis to unravel the structure of the yeast CerS complex in both an active and a fumonisin B1-inhibited state. Our results reveal the complex's architecture as a dimer of Lip1 subunits bound to the catalytic subunits Lag1 and Lac1. Each catalytic subunit forms a hydrophobic crevice connecting the cytosolic site with the intermembrane space. The active site, located centrally in the tunnel, was resolved in a substrate preloaded state, representing one intermediate in ceramide synthesis. Our data provide evidence for competitive binding of fumonisin B1 to the acyl-CoA-binding tunnel.

Successful Invasion Into New Environments Without Evidence of Rapid Adaptation by a Predatory Marine Gastropod.

Invasive species with native ranges spanning strong environmental gradients are well suited for examining the roles of selection and population history in rapid adaptation to new habitats, providing insight into potential evolutionary responses to climate change. The Atlantic oyster drill (Urosalpinx cinerea) is a marine snail whose native range spans the strongest coastal latitudinal temperature gradient in the world, with invasive populations established on the US Pacific coast. Here, we leverage this system using genome-wide SNPs and environmental data to examine invasion history and identify genotype-environment associations indicative of local adaptation across the native range, and then assess evidence for allelic frequency shifts that would signal rapid adaptation within invasive populations. We demonstrate strong genetic structuring among native regions which aligns with life history expectations, identifying southern New England as the source of invasive populations. Then, we identify putatively thermally adaptive loci across the native range but find no evidence of allele frequency shifts in invasive populations that suggest rapid adaptation to new environments. Our results indicate that while these loci may underpin local thermal adaptation in their native range, selection is relaxed in invasive populations, perhaps due to complex polygenic architecture underlying thermal traits and/or standing capacity for phenotypic plasticity. Given the prolific invasion of Urosalpinx, our study suggests population success in new environments is influenced by factors other than selection on standing genetic variation that underlies local adaptation in the native range and highlights the importance of considering population history and environmental selection pressures when evaluating adaptive capacity.

BIOS-07. GRADUAL METABOLIC ALTERATIONS RESTRUCTURE THE SPATIAL ARCHITECTURE AND IMMUNITY IN GLIOBLASTOMA

Abstract The organized layered structure of the human neocortex contrasts sharply with the chaotic architecture of malignant CNS tumors. The heterogenous nature of glioblastoma (GBM) poses significant challenges to research and treatment, given that effective strategies require consistent biological patterns. Here, we leverage recent advances in spatial biology, integrating spatially resolved transcriptomics and metabolomics to identify drivers of GBM-specific architectural evolution. We performed spatial metabolomics (MALDI) and transcriptomics (Visium n=43 and MERFISH n=25) on glioblastoma patients, with computational analysis conducted using the SPATA2 R package. Our findings reveal that most spatial recurrent patterns in glioblastoma emerge in a gradient-like organization, particularly influenced by proximity to areas of necrosis and hypoxia. Samples without hypoxic regions demonstrated less spatial organization. Metabolomic and transcriptomic profiling indicated enhanced responses to hypoxia, reduced cell proliferation, and elevated expression indicative of the S phase in cell division near necrotic regions. The vicinity to hypoxic areas was associated with increased extracellular matrix remodeling and epithelial-to-mesenchymal transition, suggesting that the invasive capabilities of GBM cells originate from these hypoxic niches. By further integrating spatially resolved TCR-sequencing, we identified that hypoxic gradients drive defined T cell responses with a higher likelihood of T cell dysfunction within a distance of 235 µm (sd: 132 µm) to hypoxia. This suggests that the presence of necrosis, hypoxia, and hypoxia-associated metabolites could serve as potential predictive markers for T-cell behavior and exhaustion in glioblastoma. Our study offers an in-depth analysis of the influence of two prevalent niches in glioblastoma: necrosis and hypoxia. We reveal that the complex architecture of glioblastoma is not solely dictated by genetic randomness and stochastic occurrences but is significantly influenced by spatial proximity to key factors like necrosis and hypoxia. This insight contributes to an evolving model that seeks to unravel the intricate biology underpinning this formidable disease.

Novel SNPs Linked to Blast Resistance Genes Identified in Pearl Millet Through Genome-Wide Association Models.

Foliar blast, caused by Pyricularia grisea, poses a major challenge to pearl millet (Pennisetum glaucum (L.) R. Br) production, leading to severe yield losses, particularly in rainfed ecologies. This study aimed to elucidate the genetic basis of blast resistance through a genome-wide association study (GWAS) involving 281 diverse pearl millet inbreds. GWAS panel was phenotyped for blast resistance against three distinct isolates of P. grisea collected from Delhi, Gujarat, and Rajasthan locations, revealing a significant variability with 16.7% of the inbreds showing high resistance. Bayesian information and linkage disequilibrium iteratively nested keyway (BLINK) and Multi-Locus Mixed Model (MLMM) models using transformed means identified 68 significant SNPs linked to resistance, with hotspots for resistance-related genes on chromosomes 1, 2, and 6. These regions harbor genes involved in defense mechanisms, including immune response, stress tolerance, signal transduction, transcription regulation, and pathogen defense. Genes, namely 14-3-3-like proteins RGA2, RGA4, hypersensitive-induced response proteins, NHL3, NBS-LRR, LRR-RLK, LRRNT_2, and various transcription factors such as AP2/ERF and WRKY, played a crucial role in the stress-responsive pathways. Analyses of transporter proteins, redox processes, and structural proteins revealed additional mechanisms contributing to blast resistance. This study offers valuable insights into the complex genetic architecture of blast resistance in pearl millet, offering a solid foundation for marker-assisted breeding programs and gene-editing experiments.

Proposed Explainable Interference Control Technique in 6G Networks Using Large Language Models (LLMs)

After the advent of 5th generation (5G) and 6th generation (6G) cellular networks, the complexity of managing real-time signal interference has increased in dense and dynamic environments. Traditional interference techniques, such as frequency reuse and allocation, while effective, lack robust adaptability and transparency needed to reduce interference in advanced communication networks. This paper introduces a novel approach that fuses large language models (LLMs) and Explainable Artificial Intelligence (XAI) to mitigate interference and enhance interference management in the mathematical foundations of 6G networks. The proposed approach provides accurate interference predictions, which the LLM balances with its complex architecture, necessary to meet the demands of beyond 5G and 6G networks, along with interpretable explanations to ensure transparency in decision-making. The proposed framework has been evaluated across various performance metrics. Interference latency consistently achieves lower rates of 0.95 s, compared to traditional techniques, which average around 1 s. Furthermore, the confidence score of the LLM shows a stable value of 0.87 throughout the system, compared to 0.85 in techniques without LLMs. Overall, the XAI-driven LLM demonstrates the potential of incorporating LLMs and XAI into wireless networks to improve resilience in next-generation networks. This proof of concept introduces a novel framework that offers new dimensions in wireless communication, particularly for interference management, prediction, and mitigation.

Catalytic Aldehyde-Alkyne Couplings Triggered by Ketyl Radicals.

A general and flexible platform for catalytic aldehyde-alkyne couplings triggered by ketyl radicals is described. This open-shell strategy necessitates only a catalytic quantity of a photoredox catalyst, along with Hünig's base (DIPEA) as a halogen atom transfer reagent. The reaction proceeds through sequential steps involving activation, halogen atom transfer, and radical addition. This carbonyl-alkyne coupling exhibits a wide substrate scope and functional group compatibility and has been successfully applied to the late-stage modification of complex architectures.

Exploration and Practice of Digital Technology and Virtual Reality Technology in the Creation of Public Art with Chinese Characters: Take Qingshan Hongfang as an Example

Abstract: In the modern context of rapid development of artificial intelligence, it provides a new direction for the development of public art in the new situation. The digital reconstruction of modern and contemporary public art has provided new experiences in terms of interaction and emotion. The popularization of interactive devices and virtual reality technologies has made the forms of public art more diverse and the audience's experience more personalized. As the carrier of Chinese civilization, Chinese characters have their special cultural significance and resonance. Translating Chinese characters through public art is a good form of expression. Currently, there is a lack of public art works with cultural identity in urban public spaces in China. This article will use a combination of quantitative and qualitative methods to analyze the public art and Chinese character design cases of the Red Steel City Red House architectural complex in Qingshan District by sorting out relevant concepts and combining case study methods. Ultimately exploring the combination of digital technology and virtual reality, incorporating Chinese characters as a creative element in contemporary art and design. Intended to provide reference and inspiration for art creators and related practitioners.

Novel Insights Into the Ultrastructural and Immunofluorescence Characteristics of Limb Skin in the Red-Eared Slider Turtle (Trachemys scripta elegans).

The red-eared slider turtle, a species facing environmental challenges and habitat loss, exhibits a complex skin architecture that is crucial for its adaptation and survival. Our study aims to provide a comprehensive characterization of the turtle's skin structure and to elucidate the distribution and localization of its various cellular components, with a focus on understanding the skin's role in adaptation and ecological interactions. To achieve these goals, we employed light microscopy, transmission electron microscopy (TEM), and comprehensive immunofluorescence using 10 specific antibodies. The forelimb skin displays large- and moderate-sized scales with variations in color, including dark, yellow, and gray hues, likely contributing to camouflage and protection. The skin consists of corneous material, the epidermis, the dermis, and the hypodermis. The stratum basalis, stratum spinosum, and peri-corneous layer constitute the three distinct layers of the epidermis. There are four distinct types of chromatophores, including melanocytes located in the epidermis, while melanophores, xanthophores, and iridophores are found within the dermal layer. The skin also exhibits well-developed peripheral nerves, blood vessels, and subcutaneous muscles. Immunofluorescence staining further elucidates the distribution and localization of various skin cells. E-cadherin and CK14 are strongly expressed in the epidermal layers, excluding the corneous material. E-cadherin surrounds keratinocyte cells in the epidermis, facilitating cell-cell adhesion, while CK14 is present inside the keratinocyte cells, contributing to their internal structural integrity. Sox10 and CD117 identify the four chromatophore types, with Melan-A specifically detecting only melanocytes and melanophores and not labeling xanthophores and iridophores. Tom20 is used to detect mitochondrial distribution and intensity in the skin, revealing a high density of mitochondria in all epidermal layers, especially in melanocytes and melanophores, compared to xanthophores and iridophores. Numerous telocytes, spindle-shaped with extensions called telopods, are detected in the dermis using CD34, PDGFRα, and vimentin. The skin of the red-eared slider also shows abundant myofibroblasts and well-developed vascularization, with numerous blood vessels detected using α-SMA. This novel study offers an in-depth examination of the limb skin of the red-eared slider through the use of 10 distinct antibodies, uncovering the intricate interactions among its cellular components and providing valuable insights into its anatomical structure and physiological adaptations. Our findings contribute to a better understanding of the turtle's skin, which may aid in its conservation and management.

Design of multi-hidden-layer Bayesian neural networks for model updating

Structural health monitoring (SHM) based on artificial neural networks (ANN) has received a lot of attention in recent decades, especially the Bayesian neural network (BNN) approach, which is more robust to noise for training and generalization. Since building a high-quality BNN architecture suitable for a specific task depends highly on user experience, the existing literature generally studies BNNs with simple single-hidden-layer architectures designed by empirical formulas or very few tailor-made algorithms. Multiple hidden layer BNN (MBNN) has a more complex architecture than single hidden layer BNN (SBNN) and is expected to achieve better generalization performance for complex problems. But the corresponding architectural design problem is much more complex, and there is no previous experience in the literature. To achieve this for MBNN, this paper proposes two feasible algorithms to optimize the number of neurons in each hidden layer simultaneously, where a logarithmic evidence metric is introduced to quantitatively characterize the performance of MBNN for a given multi-hidden-layer architecture. The feasibility and effectiveness of the proposed method are verified by the finite element (FE) model updating of both a planar truss model and a real-life pedestrian bridge. The verification results show that the second algorithm is much more computationally efficient for the optimal design of the MBNN architecture and has the potential for practical application.

Population size rescaling significantly biases outcomes of forward-in-time population genetic simulations.

Simulations are an essential tool in all areas of population genetic research, used in tasks such as the validation of theoretical analysis and the study of complex evolutionary models. Forward-in-time simulations are especially flexible, allowing for various types of natural selection, complex genetic architectures, and non-Wright-Fisher dynamics. However, their intense computational requirements can be prohibitive to simulating large populations and genomes. A popular method to alleviate this burden is to scale down the population size by some scaling factor while scaling up the mutation rate, selection coefficients, and recombination rate by the same factor. However, this rescaling approach may in some cases bias simulation results. To investigate the manner and degree to which rescaling impacts simulation outcomes, we carried out simulations with different demographic histories and distributions of fitness effects using several values of the rescaling factor, Ǫ, and compared the deviation of key outcomes (fixation times, allele frequencies, linkage disequilibrium, and the fraction of mutations that fix during the simulation) between the scaled and unscaled simulations. Our results indicate that scaling introduces substantial biases to each of these measured outcomes, even at small values of Ʈ. Moreover, the nature of these effects depends on the evolutionary model and scaling factor being examined. While increasing the scaling factor tends to increase the observed biases, this relationship is not always straightforward, thus it may be difficult to know the impact of scaling on simulation outcomes a priori. However, it appears that for most models, only a small number of replicates was needed to accurately quantify the bias produced by rescaling for a given Ʈ. In summary, while rescaling forward-in-time simulations may be necessary in many cases, researchers should be aware of the rescaling procedure's impact on simulation outcomes and consider investigating its magnitude in smaller scale simulations of the desired model(s) before selecting an appropriate value of Ʈ.

3D in-system calibration method for PET detectors.

Light-sharing detector designs for positron emission tomography (PET) systems have sparked interest in the scientific community. Particularly, (semi-)monoliths show generally good performance characteristics regarding 2D positioning, energy-, and timing resolution, as well as readout area. This is combined with intrinsic depth-of-interaction (DOI) capability to ensure a homogeneous spatial resolution across the entire field of view (FoV). However, complex positioning calibration processes limit their use in PET systems, especially in large-scale clinical systems. This work proposes a new 3D positioning in-system calibration method for fast and convenient (re-)calibration and quality control of assembled PET scanners. The method targets all kinds of PET detectors that achieve the best performance with individual calibration, including complex segmented detector designs. The in-system calibration method is evaluated and empirically compared to a state-of-the-art fan-beam calibration for a small-diameter proof of concept (PoC) scanner. A simulation study evaluates the method's applicability to different scanner geometries. A PoC scanner geometry of 120mm inner diameter and 150mm axial extent was set up consisting of five identical finely segmented slab detectors (one detector under test and four collimation detectors). A 2 2Na point source was moved in a circular path inside the FoV. Utilizing virtual collimation and by selecting gamma rays incident approximately perpendicular to the detector normal of the detector under test, training data was created for the training of a 2D positioning model with the machine-learning technique gradient tree boosting (GTB). Data with oblique ray angles was acquired in the same measurement for subsequent angular DOI calibration. For this, a 2D position estimate in the detector under test was calculated first. On this basis, the DOI label was calculated geometrically from the ray path within the detector to finally establish up to 3D training data. With a mean absolute error (MAE) of 0.8 and 1.19mm full-width at half maximum (FWHM) along the planar-monolithic slab dimension, the in-system methods performed similarly within 1% to the fan-beam collimator results. The DOI performance was at ∼90% with 1.13mm MAE and 2.47mm FWHM to the fan-beam collimator. Analytical calculations suggest an improved performance for larger scanner geometries. The functionality of the 3D in-system positioning calibration method was successfully demonstrated with the measurements within a PoC scanner configuration with similar positioning performance as the bench-top fan-beam setup. The in-system calibration method can be used to calibrate and test fully assembled PET systems to enable more complex light-sharing detector architectures in, for example, large PET systems with many detectors. The acquired data can further be used for more complex energy and time calibrations.

Advanced Biomaterials for Lacrimal Tissue Engineering: A Review.

The lacrimal gland (LG) is vital for ocular health, producing tears that lubricate and protect the eye. Dysfunction of the LG leads to aqueous-deficient dry eye disease (DED), significantly impacting quality of life. Current treatments mainly address symptoms rather than the underlying LG dysfunction, highlighting the need for regenerative therapies. Tissue engineering offers a promising solution, with biomaterials playing crucial roles in scaffolding and supporting cell growth for LG regeneration. This review focuses on recent advances in biomaterials used for tissue engineering of the lacrimal gland. We discuss both natural and synthetic biomaterials that mimic the extracellular matrix and provide structural support for cell proliferation and differentiation. Natural biomaterials, such as Matrigel, decellularized extracellular matrices, chitosan, silk fibroin hydrogels, and human amniotic membrane are evaluated for their biocompatibility and ability to support lacrimal gland cells. Synthetic biomaterials, like polyethersulfone, polyesters, and biodegradable polymers (PLLA and PLGA), are assessed for their mechanical properties and potential to create scaffolds that replicate the complex architecture of the LG. We also explore the integration of growth factors and stem cells with these biomaterials to enhance tissue regeneration. Challenges such as achieving proper vascularization, innervation, and long-term functionality of engineered tissues are discussed. Advances in 3D bioprinting and scaffold fabrication techniques are highlighted as promising avenues to overcome current limitations.