Complex Structure Research Articles

DEMO-EMol: modeling protein-nucleic acid complex structures from cryo-EM maps by coupling chain assembly with map segmentation.

Atomic structure modeling is a crucial step in determining the structures of protein complexes using cryo-electron microscopy (cryo-EM). This work introduces DEMO-EMol, an improved server that integrates deep learning-based map segmentation and chain fitting to accurately assemble protein-nucleic acid (NA) complex structures from cryo-EM density maps. Starting from a density map and independently modeled chain structures, DEMO-EMol first segments protein and NA regions from the density map using deep learning. The overall complex is then assembled by fitting protein and NA chain models into their respective segmented maps, followed by domain-level fitting and optimization for protein chains. The output of DEMO-EMol includes the final assembled complex model along with overall and residue-level quality assessments. DEMO-EMol was evaluated on a comprehensive benchmark set of cryo-EM maps with resolutions ranging from 1.96 to 12.77 Å, and the results demonstrated its superior performance over the state-of-the-art methods for both protein-NA and protein-protein complex modeling. The DEMO-EMol web server is freely accessible at https://zhanggroup.org/DEMO-EMol/.

An Engineered PfAgo with Wide Catalytic Temperature Range and Substrate Spectrum.

PfAgo, a thermophilic Argonaute nuclease from Pyrococcus furiosus, is widely used in various fields due to its high DNA-guided DNA cleavage activity. However, its high-temperature-dependent cleavage activity largely restricts its applications in moderate-temperature scenarios. In this study, PfAgo is engineered for cold adaptation based on its ternary complex structure and the attributes of cold-adapted enzymes, yielding a series of variants with better performance at moderate temperatures. Among those, mPfAgo (K617G, L618G) exhibits significantly promoted cleavage activity at 37°C and a wider catalytic temperature range of 30-95°C. Its high-temperature cleavage activity is also greatly improved, enabling its application in DNA detection with attomolar sensitivity in the presence of Mg2+. Additionally, mPfAgo shows versatile cleavage activities, including DNA cleavage guided by 5'OH-gDNA, 5'P-gDNA, or 5'COOH-gDNA, as well as RNA cleavage with 5'OH-gDNA, 5'P-gDNA, 5'P-gRNA, or 5'COOH-gDNA as guides. Further analysis through far-UV CD spectra and DSF indicates that mPfAgo has a more flexible structure than wild-type PfAgo. Furthermore, this established strategy is applied to engineer TtdAgo, likewise obtaining its variants with enhanced moderate-temperature activity and expanded substrate spectrum. In summary, this work provides a novel method for the rational design of thermophilic Agos, thereby greatly expanding their application scopes.

Few-shot small vessel segmentation using a detail-preserving network enhanced by discriminator.

Accurate segmentation of small vessels, such as coronary and pulmonary arteries, is crucial for early detection and treatment of vascular diseases. However, challenges persist due to the vessel's small size, complex structures, morphological variations, and limited annotated data. To address these challenges, we propose a detail-preserving network enhanced by a discriminator to improve the few-shot small vessel segmentation performance. The detail-preserving network constructs a complex module with multi-residual hybrid dilated convolution, which can enhance the network's receptive field while preserving the image's full detail features, enabling it to better capture the small vessel's structural features. Simultaneously, discriminator enhancement is incorporated into the training process through adversarial learning, effectively utilizing large amounts of unlabeled data to boost the generalization and robustness of the segmentation model. We validate the proposed method on in-house and public coronary artery datasets and public pulmonary artery datasets. Experimental results demonstrate that the proposed method significantly improves segmentation accuracy, particularly for small vessels. Compared with other state-of-the-art methods, the proposed method achieves higher accuracy, a lower false positive rate, and superior generalization capability, effectively assisting the clinical diagnosis of vessel diseases.

Exciton-phonon coupling and phonon-assisted exciton relaxation dynamics in In1-xGaxP quantum dots

Quantum dots leverage quantum confinement to modify the electronic structure of materials, separating electronic transitions from the composition of the corresponding bulk material. With ternary quantum dots, the composition may be varied continuously so that both composition and size may be used to tune the bandgap. As composition influences electron-phonon coupling which in turn governs relaxation dynamics, the composition of ternary quantum dots may be adjusted to change dynamics. Here, we show that exciton-phonon coupling and phonon-assisted exciton relaxation dynamics remain strongly correlated to material composition in ternary In0.62Ga0.38P/ZnS and In0.35Ga0.65P/ZnS quantum dots using both experimental two-dimensional electronic spectroscopy measurements and quantum dynamical simulations. Theoretical calculations show that alloyed In1-xGaxP quantum dots have more complex exciton level structure than parent InP quantum dots. We identify a slower hot exciton cooling rate in In0.62Ga0.38P/ZnS, attributed to the presence of ‘energy-retaining’ valley exciton states with strong exciton-phonon coupling. Experimental quantum beating maps reveal a more localized quantum beat pattern for In0.35Ga0.65P/ZnS quantum dots, which may relate to the increased number of ‘dim’ exciton levels with reduced spacings. These findings highlight that exciton relaxation dynamics and exciton-phonon coupling in an alloyed In1-xGaxP quantum dot system are composition-dependent.

Interplay between NETosis and the lncRNA-microRNA regulatory axis in the immunopathogenesis of cancer.

Neutrophil extracellular traps (NETs), web-like complex structures secreted by neutrophils, have emerged as key players in the modulation of immune responses and the immunopathogenesis of immune disorders. Initially described for their antimicrobial function, NETs now play a part in the fundamental processes of cancer biology, including cancer initiation, metastatic dissemination, and immune evasion strategies. NETs hijack anti-tumor immunity by entrapping circulating cancer cells, fostering the growth of tumors, and reorganizing the tumor microenvironment such that it is pro-malignancy. Emerging evidence emphasizes the role of NETosis coupled with non-coding RNAs-long non-coding RNAs (lncRNAs) and microRNAs (miRNAs)-as key regulators of gene expression and controllers of processes vital for cancer growth, such as immune response and programmed cell death processes like apoptosis, necroptosis, pyroptosis, and ferroptosis. Aberrantly expressed non-coding RNAs have been attributed to immune dysregulation and excessive NET production, promoting tumor growth. NETs are also associated with a myriad of pathological conditions, such as autoimmune disorders, cystic fibrosis, sepsis, and thrombotic disorders. New therapeutic approaches-such as DNase therapy and PAD4 inhibitors-target NET production and their degradation to modify immune function and the efficiency of immunotherapies. Further clarification of the intricate interactions of NETosis, lncRNAs, and miRNAs has the potential to establish new strategies for the suppression of the growth of tumors and preventing immune evasion. This review seeks to elucidate the interactions between NETosis and the regulatory networks involving non-coding RNAs that significantly contribute to the immunopathogenesis of cancer.

AI-Based Impact Location in Structural Health Monitoring for Aerospace Application Evaluation Using Explainable Artificial Intelligence Techniques

Due to the nature of composites, the ability to accurately locate low-energy impacts on structures is crucial for Structural Health Monitoring (SHM) in the aerospace sector. For this purpose, several techniques have been developed in the past, and, among them, Artificial Intelligence (AI) has demonstrated promising results with high performance. The non-linear behavior of AI-based solutions has made them able to withstand scenarios where complex structures and different impact configurations have been introduced, making accurate location predictions. However, the black-box nature of AI poses a challenge in the aerospace field, where reliability, trustworthiness, and validation capability are paramount. To overcome this problem, Explainable Artificial Intelligence (XAI) techniques emerge as a solution, enhancing model transparency, trust, and validation. This research presents a case study: a previously trained Impact-Locator-AI model is, initially, demonstrating a promising location accuracy; however, its behavior in real-life scenarios is unknown, and before embedding it in an aerospace structure as an SHM system its reliability must be tested. By applying XAI methodologies, the Impact-Locator-AI model can be critically evaluated to assess its reliability and potential suitability for aerospace applications, while also laying the groundwork for future research at the intersection of XAI and impact location in SHM.

Piezo Channels in Dentistry: Decoding the Functional Effects of Forces.

The oral system is a highly complex mechanosensory structure that continuously adapts to changes in mechanical stimuli, exerting mechanical forces on cells and tissues. Understanding how these forces are converted into biochemical signals and how they mediate gene expression and cellular activities has been a significant focus in dentistry. Piezo channels, including Piezo1 and Piezo2, are mechanically activated cation channels characterized by an extracellular "cap" domain and 3 peripheral mechanosensitive blades. Recent research has demonstrated that mechanical forces applied to tissues can induce deformation of cell membranes, leading to conformational changes in Piezo channels that facilitate cation influx, thereby regulating cellular activities. The influx of Ca2+, the most discussed outcome of Piezo channel activation, initiates diverse signaling pathways that regulate dentin hypersensitivity, alveolar bone remodeling, and temporomandibular joint (TMJ) osteoarthritis. The chemical inhibition of Piezo channels has been shown to alleviate dentinal hypersensitivity, reduce the rate of orthodontic tooth movement, and slow the progression of TMJ osteoarthritis in rat models. Mice deficient in piezo channels exhibit impaired reactive dentin formation, reduced alveolar bone volume, and developmental deformities of the jawbone. Considering their roles in decoding the functional effects of mechanical forces, this review summarizes the involvement of Piezo channels in dentistry, organized by anatomical sites, to provide comprehensive knowledge of Piezo channels and their mediated signal crosstalk, which offers promising therapeutic prospects for the treatment of various force-related oral diseases.

JUSTIFICATION OF TECHNOLOGY TO INCREASE RESERVOIR COVERAGE AND LIMIT UNWANTED FLUID INFLOW

This article substantiates a comprehensive technology for increasing the coverage of the impact on the terrigenous formations of the Bobrikov horizon of the lower part of the coal system of the Volga-Ural oil and gas province using tools to limit the influx of unwanted fluid. It has been shown that the deposits in question are characterized by a complex geological structure due to the heterogeneous lithological and facies composition, the presence of extensive water-oil zones, inconsistency of reservoirs in thickness and reservoir properties, as well as the content of oil of the high-viscosity and extra-viscous category. Three fields, which belong to the class of medium and small, were selected as objects of study. Each field has been mapped to specific facies conditions. The main problems of development and depletion of residual oil reserves were identified. It was noted that for further effective development, it is necessary to develop and adapt new technologies that increase the coverage of the impact (drainage) on the formation and provide for measures to combat forward flooding (limiting the influx of unwanted fluid). The efficiency and results of drilling horizontal and directional wells within the study targets were analyzed. It was found that the efficiency of drilling horizontal wells is three times higher than that of directional wells. Also, in zones with underlying water, when using horizontal wells, fairly high rates of flooding are observed. Therefore, other technologies with limited water inflow are required to recover residual reserves from such zones. To improve the efficiency of the current development system, as well as to preserve the drainage zone, but at the same time limiting the flow of water to the well, the technology of drilling a multilateral well with autonomous inflow control devices was proposed and considered. As a result of numerical studies, a solution was proposed for the design of the lower completion of multilateral wells with autonomous inflow control devices. Optimal segmentation parameters of the main horizontal wellbore of the multilateral well were selected for the considered geological and field conditions of the Bobrikov horizon.

Application of improved graph convolutional network for cortical surface parcellation

Accurate cortical surface parcellation is essential for elucidating brain organizational principles, functional mechanisms, and the neural substrates underlying higher cognitive and emotional processes. However, the cortical surface is a highly folded complex geometry, and large regional variations make the analysis of surface data challenging. Current methods rely on geometric simplification, such as spherical expansion, which takes hours for spherical mapping and registration, a popular but costly process that does not take full advantage of inherent structural information. In this study, we propose an Attention-guided Deep Graph Convolutional network (ADGCN) for end-to-end parcellation on primitive cortical surface manifolds. ADGCN consists of a deep graph convolutional layer with a symmetrical U-shaped structure, which enables it to effectively transmit detailed information of the original brain map and learn the complex graph structure, help the network enhance feature extraction capability. What’s more, we introduce the Squeeze and Excitation (SE) module, which enables the network to better capture key features, suppress unimportant features, and significantly improve parcellation performance with a small amount of computation. We evaluated the model on a public dataset of 100 artificially labeled brain surfaces. Compared with other methods, the proposed network achieves Dice coefficient of 88.53% and an accuracy of 90.27%. The network can segment the cortex directly in the original domain, and has the advantages of high efficiency, simple operation and strong interpretability. This approach facilitates the investigation of cortical changes during development, aging, and disease progression, with the potential to enhance the accuracy of neurological disease diagnosis and the objectivity of treatment efficacy evaluation.

SA-Seg: Annotation-Efficient Segmentation for Airway Tree Using Saliency-based Annotation.

Segmentation of the airway tree plays a vital role in clinical practice. However, the complex airway tree structure makes it quite challenging to annotate accurately. Although some annotation-efficient methods have shown promising results in medical image segmentation, most are developed for locally focused segmentation objects and are incompatible with the airway. In this work, we propose an annotation-efficient segmentation method to improve annotation efficiency and tree completeness. It includes a new efficient annotation way and an accompanying segmentation method. The saliency-based annotation method only needs to annotate high-saliency regions, thus greatly improving the annotation efficiency. Inspired by positive-unlabeled learning, we model the dependency relationship between key items in the annotation process to learn from biased weak annotation. The probabilistic model of the annotation process is divided into the score function and the bias function. The score function models the uniform foreground feature representation of the airway, while the bias function models the saliency bias between labeled and unlabeled airway regions. Then, the two models are implemented with convolutional neural networks and optimized by applying an EM algorithm during training. Experimental results reveal that our approach saves 89% annotation time and significantly narrows the performance gap between weak and full annotations. This highlights its potential for clinical applications.

Not all is male biased: sexual dimorphism in carapace size and shape of the kelp crab Taliepus dentatus (Decapoda: Majoidea: Epialtdae)

ABSTRACT Different selection pressures acting on males and females generate biases in the cephalothoracic size and shape of many decapod crustaceans. We assessed such patterns in the carapace components (dorsal cephalothorax, ventral cephalothorax, and pleotelson) of the kelp crab Taliepus dentatus, with morphological variables based on Euclidean and Procrustes coordinates of homologous landmarks, which describe variations in size and shape, respectively. The dorsal and ventral cephalothorax exhibited male-biased sexual size dimorphism, differing from the female-biased sexual size dimorphism expressed by the shape and size of the pleotelson. Size allometry was more restricted in the cephalothorax than in the pleotelson. Shape allometry showed greater restriction in larger females, with robust and parallel limits of variation in males. In contrast, the shape of the pleotelson (female-biased) varied within parallel limits in both sexes. The variations in shape and size of both groups of traits are mainly attributable to adaptive factors that favour population fitness in females through natural selection and individual fitness explained through sexual selection in males. However, the variation in the shape of the dorsal and ventral cephalothorax indicates inherent structural complexity, where developmental modules favour the muscular performance of the chelipeds and the remaining pereiopods. Our results suggest that stabilising selection acting differentially on the shape and size of the caparace components directs the expression of this body plan towards optimality, promoting morphological convergences and taxonomic conflicts for this species, which is consistent with highly integrated complex structures.

Comprehensive High-Spatial-Resolution Imaging Metabolomics Workflow for Heterogeneous Tissues.

Mass spectrometry imaging is a developing technique that maps the molecular composition of samples in a label-free manner. However, highly heterogeneous samples, including bones, usually cannot be easily analyzed due to challenging sample preparation, particularly in minimizing cracks and maintaining flatness. To comprehensively address these issues, we developed a sample preparation method for the fresh frozen heterogeneous samples such as knee joint and skull of murine, which includes complex structures and tissue types, such as neuronal tissue, peripheral nerve, muscle, tracks, connective tissue, cartilage, mineralized bone, and bone marrow. By controlling the sample thickness and employing an optimized drying method, we achieved minimal cracking. We found that a combination of lyophilization and 5 μm section thickness, when attached to a cryofilm, was readily achievable and significantly reduced cracking in bone tissue. Additionally, we implemented a contactless spin-flattening technique to ensure surface uniformity. Centrifuging the section at 7000g improved surface flatness, bringing the height variation within the range typically observed in soft tissues while also removing excess mounting medium and bubbles. This approach enhances the sample quality and reliability without requiring complex manual skills, making it more practical and reproducible for routine analysis. High molecular coverage was achieved, including small metabolites, metals, and lipids, by using the N-(1-naphthyl) ethylenediamine dihydrochloride (NEDC) matrix. To further explore the potential of our workflow, high-resolution MSI was performed on rat tibial growth plates at different growth stages. Numbers of N-acetyl disaccharide sulfate and PE (34:1) are found to be complementary expressed in growth plate cartilage and have different intensities at different growth stages. Our findings suggested the potential involvement of those metabolites in bone development. By addressing the challenges of sample preparation, including surface flatness, bubbles, and severe cracking, our approach significantly improves the quality of the MS imaging. Additionally, this method offers a broad detection range that encompasses both metal ions and metabolites. This advancement enables detailed and accurate molecular characterization of rigid biological samples, enhancing the potential for applications in biomedical research.

Assessment of climate change impacts and land cover dynamics in the Upper Sakarya Basin, Türkiye using the Collect Earth approach

Abstract Long-term monitoring and evaluation of climate change effects in a basin help identify trends, assess ecosystem resilience, and guide adaptive management for sustainable resource use and conservation. The Upper Sakarya Basin (USB), which has the ability to represent semiarid areas of Turkey due to its climate and environmental conditions and land use integrity, was selected as the study area. The study focused on Land Cover and Land Use Transformations, soil moisture, evapotranspiration, Normalized Difference Vegetation Cover (NDVI), Net Primary Production (NPP), water deficit criteria in the basin for a 20-year period (2000–2020) in order to support adaptive management and conservation strategies and to evaluate the effects of climate change on ecosystem dynamics, water balance and vegetation health. As a field monitoring and evaluation method, Collect Earth (CE), which is a sample-based visual interpretation method, was selected because it can better explain the complex structure of land cover types and provides the opportunity to easily access and interpret many graphical data through the GEE code editor. In the context of Land Use, Land-use Change and Forestry (LULUCF), it was observed that the increase in Forestland was due to afforestation activities in Grassland. Although this situation was observed in NDVI observations, it was not evaluated as a net gain area compared to the past situation of Forestland in terms of the ecosystem and the services they provide. It was determined that the conversion to Settlement in the basin was rapid and large in area. As an indicator of vegetation productivity, it was determined that the areas with decreasing trend in NPP were Forestland and Settlement. According to water deficit data, it was determined that water deficit was high in regions where intensive agriculture was carried out in the basin. Evapotranspiration value was continuously increasing for the basin in general, while soil moisture was on a decreasing trend. These findings emphasize the vulnerability of the basin to climate change and emphasize the need for sustainable land and water management strategies. It is recommended that decision makers at the basin scale develop policies based on these findings and evaluate interventions within the framework of a balancing approach against land degradation.

Molecular insights into the interaction mechanism of EDCs and DNA in laccase-induced polymerization transfer

Abstract Endocrine-disrupting chemicals (EDCs), such as 17β-estradiol (E2) and bisphenol A (BPA), can induce DNA damage, leading to genomic instability and cell death. Laccase, an enzyme secreted by diverse organisms, plays a critical role in mitigating the cytotoxicity of these contaminants. Despite its importance, the dynamic evolution and interaction mechanisms of EDCs and DNA in laccase catalysis remain poorly understood. This study investigates the interactions between EDCs and DNA during laccase-induced polymerization transfer at a molecular level. As the DNA concentration was increased from 0 to 7.575 nM, the pseudo-first-order kinetic constants for E2 and BPA decreased by 2.03 and 2.10 times, respectively. DNA-bound EDCs disrupted the catalytic activity and stability of laccase, thereby delaying the polymerization transfer rate of EDCs. E2 and BPA bound to DNA base pairs via groove and intercalative modes, respectively. Laccase-induced polymerization reduced damage to the DNA helix and base stacking caused by EDC binding. Moreover, the resulting DNA-EDC precipitated polymers, formed through continuous laccase polymerization, exhibited denser and more complex structures compared to spherical EDC precipitated polymers, confirming DNA encapsulation and/or binding. This work underscores the intramolecular mechanisms of EDC interaction with DNA in vitro during the laccase-induced polymerization, offering efficient ways to mitigate the genotoxicity of EDCs.

Development of medical education in the context of european integration

European countries are united in their views on the value of human life, education, and healthcare through intellectual, cultural, and social proximity. Human health is the most sensitive issue for different population segments and is often exploited by political forces to create social instability. The European Union is a complex structure that unites member countries with their long-established views on healthcare. An important innovation of today is the introduction of digital technologies into the healthcare system. The events of recent years (pandemic, economic instability, war) have exposed the heterogeneity of European countries' economic and technological levels and changed the proportional possibilities of individual states' contributions to a single system. To create a single, coordinated intellectual space, it is necessary to create a single level of quality of higher education in the countries participating in this space. Strengthening cooperation between Ukrainian scientific and industrial organizations with their partners in the EU, expanding the research space, will serve as an incentive for Ukrainian scientists not only to comply with the agreed rules, but also to actively, equally participate in the improvement and development of new recommendation and regulatory documents of European educational and research spaces, and to make the most of the opportunities of our scientists, teaching staff, and young specialists.

Design and Analysis of an Integrated Additively Manufactured Test Article for Plasma-Facing Components

Current plasma-facing components (PFCs) used in helium-cooled divertor modules are complex structures with tungsten tile, steel sleeve components, and cartridges, all assembled in a helium-cooled multiple jet (HEMJ) structure. The goal of this project is to simplify the complex PFC design using additive manufacturing techniques to create a single integrated tungsten test article. Apart from the flexibility this opens up in exploring a wide array of geometries for the article, having a single integrated article significantly reduces the number of joints and parts in the article, thus reducing chances of leaks. A process called electron beam melting has shown to produce very high-density samples and unique geometries, enabling HEMJ or similar designs. To validate and optimize this novel design, the model underwent a series of computational fluid dynamics and finite element analysis simulations to replicate steady-state heat flux in the divertors. The simulations presented in this study consider a steady-state base heat flux of 5 MW/m2, with water serving as the coolant. Future research will explore the use of helium as a coolant, simulate edge-localized-mode conditions, and include experimental validation. Since 3D-printed tungsten is anisotropic, the build direction versus build plane of the article are taken into consideration for the test article strength. Because of the high operating temperatures and low ductility of tungsten, thermal creep and brittle fracture are important failure mechanisms to consider. The cap is evaluated with various flow velocities and nozzle diameters, and an optimal design choice is made for which this cap will survive the divertor conditions with a conservative safety margin.

3d stability modelling of Tutankhamen,s Tomb (Kv62) using Plaxis 3d with jointed rock model

The tomb of the young pharaoh Tutankhamen (KV62) is the most famous tomb in the world. This tomb has been subjected to long-term impacts from both flash flooding and major faults. As a result, it has experienced varying degrees of instability and damage, which have progressively worsened over time. This study represents geotechnical modeling and calculations of stresses and deformation patterns using high-performance PLAXIS 3D software with a jointed rock model. Numerical simulations were employed to investigate the effects of flash flooding and the major fault intersecting the tomb on its stability. The study identifies the main factors influencing the stability of the ceiling of the antechamber and burial chamber, the deformation and failure modes and key areas, and the current stage of the tomb’s stability was determined. These findings provide valuable suggestions for future reinforcement measures and offer a reference for the stability analysis of other similarly complex underground structures.

Power, class, and gender in dementia care: Stories of carer precariousness from culturally and linguistically diverse female family carers in Australia.

Dementia care provision is a global challenge. However, dynamics to provide care to a family member living with dementia in their home are far more complex. Evidence suggests that dementia care among culturally and linguistically diverse (CALD) communities is largely offered by family members within communities. But at a family level, care happens at the intersection of unequal gender relations, complex cultural constructions of dementia, and longstanding family values and traditions. While these dynamics show the intersection of power, class, and gender, these aspects have not gained widespread currency in dementia policy plans. Yet they shape the identities and social positioning of carers and consequently carer precariousness. This article reveals that the role of caring for family members living with dementia is embedded in complex power structures that stigmatise the identities of carers and those cared for, reproduces gendered social hierarchies, exacerbate economic uncertainties, and diminish the merits of filial piety - a valued cultural practice. By centering the voices of female carers from a CALD background, the authors highlight the need for policymakers to devote particular attention to how the intersection of the diverse cultural environments with dementia care at a family level induce carer insecurities and vulnerabilities - precariousness. This calls for an emancipatory dementia policy agenda that values the lived experiences of female carers' cumulative disadvantage over the course of providing care.

AI-Signaturer: AI Automatic Signature Generation Based on Pix2pix

This paper explores AI automatic signature generation technology based on the Pix2pix conditional generative adversarial network (cGAN), aiming to solve the issues of low efficiency and poor consistency associated with traditional manual signing. Leveraging the learning capability of deep learning models, this study is dedicated to achieving automatic, efficient, and high-quality signature generation, providing technical support for areas such as electronic documents. For this purpose, the paper has designed and trained a Pix2pix model consisting of a generator and a discriminator that can convert input images into signature images with specific font styles. In experiments, 900 signature samples collected manually by students were used for training. To optimize model performance, strategies such as early stopping and model checkpoints were introduced, and hyperparameters like learning rate and iteration counts were adjusted during training. Ultimately, the model achieved an average accuracy of 76% across three different types of input fonts—Kaiti, YaHei, and FangSong—and clearly displayed Chinese character features. However, for complex and dense font styles, there is room for improvement in the model's performance. Future research will focus on more refined data preprocessing and exploring more complex model structures like CycleGAN to enhance performance in multi-font recognition tasks, improving the model's generalization and robustness.

Implementation of plastinated specimens in a Doctor of Physical Therapy human anatomy curriculum.

This descriptive report explores the implementation of the use of plastinated specimens in a Doctor of Physical Therapy human anatomy curriculum as an alternative to traditional body donor dissection. Plastination, a preservation technique developed in the 1970s, creates durable and flexible specimens that retain detailed anatomical features. The use of plastinated specimens offers significant advantages, including ease of handling, enhanced visibility, and reduced maintenance costs. Feedback from physical therapy students highlighted the benefits of combining lectures with hands-on learning, using plastinated specimens to improve understanding of anatomical structures and functions. The inclusion of camera technology during lab sessions further facilitated students in visualizing complex structures, enhancing learning in small group settings. However, limitations were identified by the faculty, such as a diminished sense of discovery and connection to the human donor compared to full-body donor dissections. Despite these challenges, plastinated specimens provide a time-efficient, cost-effective solution that allows for greater flexibility in human anatomy curriculum design and a valuable teaching method in physical therapy education. Further research is needed to explore the long-term educational impact and emotional engagement of physical therapy faculty and students when using plastinated specimens compared to traditional dissection methods.