Detailed Mapping Research Articles

Scan-to-BIM Process and Architectural Conservation: Towards an Effective Tool for the Thematic Mapping of Decay and Alteration Phenomena

Ancient monumental complexes need continuous analysis and monitoring operations to preserve a good conservation status. For this reason, the analysis of decay and alteration phenomena represents one of the main activities for their preservation. At the same time, the diffusion of Heritage Building Information Modelling (HBIM) methodology opens new scenarios for the management of Architectural Heritage. The paper describes the workflow based on a Scan-to-BIM approach for the generation of a decay map in an HBIM model. The workflow was applied to a significant case study, the church of “Santa Maria della Grotta” in Marsala (Italy). This church, partially excavated in a sandstone bank, is part of a larger heritage site consisting of a series of hypogea and a Punic necropolis dating back more than a thousand years. The Scan-to-BIM process, relying on an integrated survey combining mobile laser scanning and photogrammetric technologies, enabled the achievement of a complete 3D parametric model of the monument and, altogether, a detailed decay map in a BIM environment. The mapping process focused on the production of thematic maps of perimetral walls according to an abacus of decays implemented in a BIM system, useful for the analysis and conservation of the church. The work demonstrates how the Scan-to-BIM process is an efficient approach for 3D data collection and how it could facilitate the identification and mapping of pathogenic phenomena. Furthermore, the inclusion of this kind of information in the BIM model represents an effective tool for the maintenance and restoration of built heritage.

MAPPING WOOD DENSITY VARIATION USING QGIS: AN INNOVATIVE APPROACH FOR CHARACTERIZATION OF OCHROMA PYRAMIDALE, ACACIA MANGIUM, EUCALYPTUS GRANDIS, AND PINUS SP.

This study explores the innovative application of QGIS for mapping radial wood density variation across the entire cross-section of selected native and non-native forest species, aiming to enhance wood characterization. Using samples from Ochroma pyramidale, Acacia mangium, Eucalyptus grandis, and Pinus sp., we applied X-ray densitometry to obtain high-resolution images, which were then analyzed with QGIS to create detailed density maps. These maps provided a clear visualization of radial density variation, offering insights into the internal structure of the wood. The integration of QGIS with X-ray densitometry proved to be an effective tool for assessing wood density variation, supporting more precise and sustainable forest management practices.

Worker well-being in the digital economy: Lens through bibliometrics analysis

Orientation: The study employed bibliometrics analysis to investigate key research themes and trends on digital economy and worker well-being.Research purpose: The study aims to provide a comprehensive understanding of worker well-being and digital economy to identify prominent and emerging research themes in this domain.Motivation for the study: Given the rapid digital transformation of workplaces, there is a need to assess literature trends on workers’ well-being and the digital economy.Research approach/design and method: The study employed a quantitative bibliometrics analysis to map literature on worker well-being and the digital economy. Data from Scopus were collected and screened through Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA). A total of 1473 documents were considered meeting the study’s criteria. The scientific literature production, country scientific production, conceptual structure (co-occurrence network, thematic map and thematic evolution) and keyword analysis were used to analyse the data.Main findings: The analysis revealed the following key themes, including task performance, emotional labour, employee voice, career adaptability, job satisfaction, employee well-being, work engagement, proactive personality and psychology of work.Practical/managerial implications: The findings underscore the need for organisations to implement employee well-being initiatives and support mechanisms to address the challenges posed by digital work environments.Contribution/value-add: This study contributes to the existing literature by providing a detailed map of the research landscape. The study shows the need to address well-being challenges because of poverty, vulnerability and changing psychology of work. The study also shows importance of adaptability, work engagement and support systems for employees.

Deep learning based highly accurate transplanted bioengineered corneal equivalent thickness measurement using optical coherence tomography

Corneal transplantation is the primary treatment for irreversible corneal diseases, but due to limited donor availability, bioengineered corneal equivalents are being developed as a solution, with biocompatibility, structural integrity, and physical function considered key factors. Since conventional evaluation methods may not fully capture the complex properties of the cornea, there is a need for advanced imaging and assessment techniques. In this study, we proposed a deep learning-based automatic segmentation method for transplanted bioengineered corneal equivalents using optical coherence tomography to achieve a highly accurate evaluation of graft integrity and biocompatibility. Our method provides quantitative individual thickness values, detailed maps, and volume measurements of the bioengineered corneal equivalents, and has been validated through 14 days of monitoring. Based on the results, it is expected to have high clinical utility as a quantitative assessment method for human keratoplasties, including automatic opacity area segmentation and implanted graft part extraction, beyond animal studies.

STATISTICAL INTEGRATION OF QGIS AND MULTI-CRITERIA DECISION ANALYSIS FOR SUSTAINABLE RURAL DEVELOPMENT IN BELSAR DEVELOPMENT BLOCK

The definition of “rural areas” varies significantly across regions, making the determination of rurality levels crucial for sustainable development and effective policy design. This study introduces a comprehensive rurality index to rank and categorize villages within the Belsar Development Block of Gonda District, India. Utilizing Multiple-criteria Decision-making Methods (MCDMs) such as Fuzzy Analytic Hierarchy Process (F-AHP), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Weighted Product Model (WPM), and Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE), we developed a robust methodology for this assessment. A thorough sensitivity analysis was also conducted to ensure the reliability of the results. The study identified fourteen critical factors, grouped into five domains: (1) socioeconomic status, (2) education, (3) land use, (4) employment, and (5) healthcare. Based on these factors, villages were ranked and classified into four categories according to their rurality levels, and a detailed rurality map was created with QGIS. The study revealed that the modified F-AHP emerged as the most effective MCDM method for this rurality assessment. Given the systematic execution of this assessment, it can serve as a model for comparative studies in other regions of India and globally, promoting sustainable rural development through informed decision-making.

Embankment Breach Simulation and Inundation Mapping Leveraging High-Performance Computing for Enhanced Flood Risk Prediction and Assessment

Abstract. Embankment breaches represent significant hazards to communities and infrastructure, precipitating catastrophic flood occurrences. The precise prediction of floods and understanding the scope of inundation stemming from embankment failures are imperative for effective disaster preparedness and response. This research delves into a case study on simulating embankment breaches to evaluate the extent of flooding. Leveraging advanced hydrodynamic models validated through high-performance computing (HPC) systems, and integrating real-time data assimilation, we aim to improve accuracy in flood forecasting. The study endeavours to bolster flood risk management by furnishing detailed inundation maps and insights into embankment breach dynamics, thereby facilitating enhanced preparedness and response strategies. Our findings reveal that simulations conducted on multicore processors offer superior performance compared to single-core setups, yielding enhanced result accuracy and providing administrators with increased lead time. It unlocks high-resolution simulations for intricate basin details, explores a wider range of flood scenarios quickly, and allows for efficient ensemble modelling to assess model uncertainty. Through HPC utilization, we can harness high-resolution digital elevation models (DEMs) for 2D-hydrodynamic modelling, enabling rapid assessment of water spread resulting from embankment breaches within a mere 20-minute timeframe, a significant improvement from the previous 3–4 hours duration.

The NEDD4-binding protein N4BP1 degrades mRNA substrates through the coding sequence independent of nonsense-mediated decay

3'-Untranslated regions (3'UTRs) are recognized for their role in regulating mRNA turnover while the turnover of a specific group of mRNAs mediated by coding sequences (CDS) remains poorly understood. N4BP1 is a critical inflammatory regulator in vivo with a molecular mechanism that is not yet clearly defined. Our study reveals that N4BP1 efficiently degrades its mRNA targets via CDS rather than the 3'-UTR. This CDS-dependent mRNA turnover mechanism appears to be a general feature of N4BP1, as evidenced by testing multiple mRNA substrates, such as Fos-C, Fos-B, Jun-B and CXCL1. Detailed mapping of the motif identified a crucial 33nt (289-322) sequence near the 5'-end of Fos-C-CDS, where the presence of polyC is necessary for N4BP1-mediated degradation. Functional studies involving domain deletion and point mutations showed that both the KH and NYN domains are essential for N4BP1 to restrict mRNA substrates. The function of N4BP1 in mRNA turnover is not dependent on nonsense-mediated decay as it efficiently restricts mRNA substrates even in cells deficient in UPF1, UPF3A, and UPF3B. Additionally, the function of N4BP1 is not reliant on LUC7L3 despite its known association with this protein. Our findings suggest that N4BP1 acts as an endoribonuclease to degrade mRNA substrates primarily through coding sequences containing a C-rich motif.

Role of supply chain digitalization and global supply chain in decarbonization of natural resources sector supply chain

Environmental management and decarbonization are becoming increasingly critical as industries seek sustainable development paths that align with global climate targets. As businesses aim to reduce their carbon footprint, integrating effective environmental strategies with technological advancements is essential. This necessitates a clear understanding of digital technologies and environmental policies to drive decarbonization efforts. In this context, this study explores the role of digitalization in reducing carbon emissions in the natural resources sector supply chain. Furthermore, it examines the role of supply chain mapping as a mediator in the relationship between supply chain digitalization and supply chain decarbonization efforts, providing a novel perspective on how technology can intersect to foster environmental sustainability. This research targets the metal sector supply chain, a significant contributor to global carbon emissions. The data for the study was gathered through a questionnaire administered to 246 employees from various levels of the metal industry in China. We employed Covariance-based Structural Equation Modelling (CB-SEM) using AMOs for analysis. Our results show that level digitalization of supply chain processes in the metal sector has a profound and direct impact on its supply chain decarbonization. Digital technologies facilitate more efficient resource use, improved energy efficiency, and the adoption of cleaner production methods. Importantly, the study identifies supply chain mapping as a crucial mechanism through which digitalization exerts its positive influence on decarbonization. Through detailed mapping, organizations can gain visibility over their entire supply chain, identifying carbon hotspots and opportunities for improvement that digital solutions can address. The novelty of this study lies in its systematic exploration of how supply chain mapping serves not just as a tool for transparency and efficiency but as a strategic facilitator of the beneficial impacts of digitalization on decarbonization.

Computational multiphysics modeling of radioactive aerosol deposition in diverse human respiratory tract geometries

The evaluation of aerosol exposure relies on generic mathematical models that assume uniform particle deposition profiles over the human respiratory tract and do not account for subject-specific characteristics. Here we introduce a hybrid-automated computational workflow that generates personalized particle deposition profiles in 3D reconstructed human airways from computed tomography scans using Computational Fluid and Particle Dynamics simulations. This is the first large-scale study to consider realistic airways variability, where 380 lower and 40 upper human respiratory tract 3D geometries are reconstructed and parameterized. The data is clustered into nine groups using random forest regression. Computational fluid and particle dynamics simulations are conducted on these representative geometries using a realistic heavy-breathing respiratory cycle and radioactive iodine-131 as a source term. Monte Carlo radiation transport simulations are performed to obtain detailed energy deposition maps. Our findings emphasize the importance of personalized studies, as minor respiratory tract variations notably influence deposition patterns rather than global parameters of the lower airways, observing more than 30% variance in the mass deposition fraction.

Development of a 3D Geologic Model Used in the Seismic Hazard and Liquefaction Hazard Analysis of Madison County, Tennessee

ABSTRACT Madison County of western Tennessee is approximately 100 km southeast of the New Madrid seismic zone and, thus, is subject to potentially dangerous seismic shaking and liquefaction events. Holocene floodplain alluvium and associated Pliocene/Pleistocene terraces of the Forked Deer and Hatchie Rivers cover a large part of Madison County. Underlying these unconsolidated sediments are approximately ∼650 m of poorly consolidated Paleogene and Cretaceous terrestrial and marine sediments, which are underlain by Paleozoic limestone bedrock. Uniform regional geology used in the 2014 United States Geological Survey seismic hazard maps assumes a homogenous seismic velocity structure above bedrock. Three-dimensional geologic mapping of the ∼700 m of Quaternary, Neogene, Paleogene, and Cretaceous sediments conducted in this study provides a more detailed velocity structure, resulting in a more accurate and detailed map of the expected ground motions in Madison County during future earthquakes. In addition, the surface geologic map reveals regions that are susceptible to liquefaction. This paper discusses the methods used to construct the surface geology map, subsurface structure contour maps, isopach maps, and 3D geologic model used in the seismic hazard and liquefaction hazard analyses of Madison County, Tennessee, and summaries of the results of these analyses are presented.

SmokeNav: Millimeter‐Wave‐Radar/Inertial Measurement Unit Integrated Positioning and Semantic Mapping in Visually Degraded Environments for First Responders

First responders often face hazardous and life‐threatening situations in environments filled with smoke, posing significant risks to their safety. The existing perception solutions, such as camera or light detection and ranging (LiDAR)‐based methods, are inadequate when faced with visually degraded conditions caused by smoke. In this work, SmokeNav, a novel system that combines data from an inertial sensor and millimeter‐wave (mmWave) radar, is proposed to enhance situational awareness for first responders in smoky environments. SmokeNav utilizes an inertial positioning module that exploits the human motion constraints with a foot‐mounted inertial measurement unit to provide accurate user localization. By integrating this location information with mmWave radar data, it employs a probabilistic occupancy map construction to reconstruct an accurate metric map. To enable semantic understanding of the environment, a DNN‐based semantic segmentation model that incorporates radar reflectivity and employs focal loss to improve performance is introduced. Herein, extensive real‐world experiments in smoky environments is conducted to demonstrate that SmokeNav precisely localizes the user and generates detailed maps with semantic segmentation. In this work, potentials are held for enhancing the safety and effectiveness of first responders in hazardous conditions.

Topographic and Geodetic Survey for Samarkand's Administrative Boundaries

General Background: Administrative and territorial boundary delineation is vital in the effective governance and economic planning of urban regions globally, with digital technologies increasingly integrated into these processes for accuracy and efficiency. Specific Background: In Uzbekistan, the city of Samarkand’s administrative boundaries are undergoing significant realignment as part of a broader initiative to incorporate strategic urban centers and enable digital land management across administrative zones. Knowledge Gap: Existing methods lack precise integration with digital geospatial frameworks, leaving a gap in high-resolution topographic data essential for clear administrative delineation. Aims: This study seeks to develop and apply an accurate, digitally supported methodology for determining the boundaries of Samarkand's administrative units using advanced geodetic and topographic techniques. Results: By utilizing the CREDO DAT program, this study achieved precise boundary coordinates through the automated processing of survey points, and further analysis was conducted using the Panorama program for detailed map orientation. Novelty: This research presents a unique approach by integrating CREDO DAT with geospatial tools in administrative boundary demarcation, enabling accurate and reproducible results that surpass traditional methods in efficiency and scalability. Implications: The findings support the strategic planning of Samarkand’s infrastructure and resource allocation while also laying the groundwork for applying similar methodologies in other urban regions, advancing Uzbekistan’s digital transformation efforts in land management.Highlights: Digital tools enhance accuracy in administrative boundary mapping. CREDO DAT and Panorama software streamline data processing for precise mapping. This method supports effective urban planning and resource management in Samarkand. Keywords: Administrative Zones, Border, Territory, District, City

Shifting Sands: Examining and Mapping the Population Structure of Greece Through the Last Three Censuses

This paper aims to facilitate a more nuanced understanding of regional disparities in the population age structure at a local scale by applying a recent method for visualizing these disparities. Utilizing data from the three most recent population censuses in Greece, this method applies advanced data visualization techniques to map age distributions, highlighting significant variations in aging patterns across municipalities, towns, and districts. Traditional demographic analysis often overlooks local heterogeneities, leading to broad policies that often fail to address the unique needs of specific regions. Detailed maps are created by integrating geographic data with census data (using R and GIS), enabling policymakers to pinpoint areas with specific demographic challenges and opportunities. This localized approach reveals critical insights, such as regions experiencing rapid population aging, areas with younger population profiles, and zones undergoing demographic transitions. The visualization tool significantly improves the formulation of targeted strategies, enhancing the effectiveness of policies related to healthcare, workforce planning, and social services distribution. Through case studies and comparative analysis, we demonstrate the practical applications and advantages of this method in shaping public policy and strategic planning. This paper contributes to the field of geodemography by introducing and demonstrating a visualization method that enhances the accuracy of demographic analysis, providing policy makers with useful information to better address local demographic challenges and tailor strategies to specific regional needs.

Late pleistocene glacial history of Mount Karadağ, SW Türkiye

Glacial landforms in the southwestern part of the Anatolian Peninsula are found at lower elevations than the rest, suggesting that glaciers were present during the Late Pleistocene. Karadağ, located west of the Western Taurus Mountains, provides evidence of the climatic conditions that facilitated this extensive glaciation. It is characterized by numerous peaks exceeding 2300 m above sea level (asl), with the highest peak reaching 2418 m, making it the region's largest glaciation area. This high mountain mass is composed mainly of limestone and dolomite. This study focuses on the glacial landforms in Karadağ to reconstruct the Late Pleistocene glacial chronology and palaeoclimate of the Western Taurus Mountains. We employed detailed UAV photogrammetry, extensive fieldwork and mapping, surface exposure dating with cosmogenic 36Cl, meteorological measurements, palaeoglacier reconstruction, and palaeo-equilibrium line altitude (pELA) calculations. Karadağ, a topographic barrier to humid air masses from the Mediterranean Sea, ranks as the second wettest area in the Western Taurus Mountains. At a meteorological station we installed in Karadağ, an annual precipitation of 1700 mm or more was recorded, highlighting its significant precipitation. In Karadağ, we have identified two glacial valleys with a maximum length of 4.5 km and six cirques. The glaciers reached their maximum extent around 22.4 ± 2.8 ka during the Last Glacial Maximum (LGM), facilitated by the lowering of the pELA to 2090 m. During the LGM, the glaciers covered an area of approximately 3.5 km2 and reached a maximum thickness of about 140 m. Sample TRKR 10 at 2015 m probably belongs to the LGM period. Although a single sample does not conclude definitive conclusions, the presence of the moraine at its highest position indicates the maximum thickness of the glacier. Following the LGM, two more significant glacier advances occurred during the Lateglacial (15.5 ± 2.7 ka) and the Younger Dryas (12.4 ± 1.1 ka). During the late Pleistocene glaciations, and especially during the LGM, Karadağ probably received more precipitation than today, leading to the formation of glaciers at relatively lower elevations than on the Anatolian Peninsula.

Spectral aging analysis of the 3C 219 double-double radio galaxy

Context. Double-double radio galaxies are characterized by intermittent jet-formation activity. The exact reasons for this behavior are not yet fully understood. Studying these objects and their environment allows us to find common characteristics and compare them with the general population of radio galaxies. 3C 219 is a well-known and thoroughly studied radio galaxy. Among the many explanations for its unusual “partial jet” structure, intermittent activity seems to be the leading one. However, this hypothesis has never been tested using aging analysis. Aims. The aim of this paper is to put constraints on the active galactic nucleus duty cycle and dynamics of radio lobe expansion in 3C 219, in both the inner and outer double. This will provide us with information on the behavior of the central engine and the interaction of the radio lobes with the ambient medium, allowing us to verify whether the structure of 3C 219 is the result of intermittent activity and to search for its possible causes. Methods. We performed a spectral aging analysis of the 3C 219 double-double radio galaxy using archival Karl G. Jansky Very Large Array and Low Frequency Array data. We present detailed spectral age maps and constructed a spectral age profile. We compared ratios of linear sizes and luminosities in individual phases of activity with lobe expansion velocities in the plane of the sky derived from the age profile to infer hot-spot advance velocities through the surrounding medium and the orientation of the jet axes. Results. The galaxy shows a general asymmetry in the distribution of plasma with respect to the inferred jet axis in the original phase of activity, suggesting an influence of the intracluster medium on the formation of the radio structure. The advance speed of hot spots in the outer double is typical of classical FRII sources expanding into an ambient thermal medium, while for the inner double, the lower limit is estimated to be ∼0.28 c. The radio galaxy has experienced a very short quiescent period of no more than 2 Myr, which is ≲7% of its total lifetime. Conclusions. The most plausible explanation for the double-double radio structure in 3C 219 is a rapid jet reorientation along the line of sight, which may be the result of a minor merger. Further study of the 3C 219 inner double is needed. High-resolution maps are required to probe its structure in sufficient detail, while high-frequency observations are necessary to better constrain the durations of the quiescent and restarted phases.

Bamboo Forests: Unleashing the Potential for Carbon Abatement and Local Income Improvements

Bamboo forests exhibit a unique efficient growth pattern that makes them invaluable in reducing atmospheric CO2 levels. Additionally, bamboo forests offer a diverse range of products, thus holding the potential to bolster local income. Despite these benefits, the comprehensive assessment of bamboo forests’ potential in both carbon abatement and improving local income enhancement has been hindered by the absence of a detailed bamboo biomass map. In this study, we address this gap by amalgamating a bamboo aboveground biomass (AGB) map covering three prominent producing provinces in southern China, utilizing multi-source remote sensing datasets. The results not only demonstrate a satisfactory consistency with China’s Ninth National Forest Inventory but also provide a more detailed spatial distribution. Based on this AGB estimation, we project an approximately threefold potential increase in annual bamboo culm harvest from existing bamboo forests. This represents a significant opportunity for expanding carbon abatement efforts, elevating local income levels, and facilitating the production of bamboo-derived biofuels. Furthermore, the adoption of an optimized management strategy has the potential to further enhance bamboo production. This study generates the first high-resolution bamboo AGB map and underscores the substantial potential of China’s bamboo forests in contributing to carbon sequestration and improving local income. The favorable income generated for local residents can serve as a compelling incentive for the implementation of sustainable forest management practices, offering a promising pathway toward achieving carbon-related objectives within the forestry sector and providing necessary support for forestry designation projects.

Flood Vulnerability Assessment in Paradip Coastal Area Using Weighted Overlay Index

Paradip, a prominent coastal city on India's eastern seaboard, presents a unique combination of environmental challenges and urban pressures. It has the largest tidal range among major regional tidal gauge stations, including Vishakhapatnam and Chennai. The city experiences an annual sea level rise of 2.33 mm, with projections indicating an increase of 0.183 feet by 2047 and 0.76 feet over the next century. The city's unique environmental and urban issues are exacerbated by its placement within the Mahanadi River's deltaic deposits, floodplains, and coastal marine deposits along the Bay of Bengal. The shoreline of Paradip is topographically uniform, with ground elevations ranging from 3.6 to 5.7 meters. According to India's Wind and Cyclone Hazard Map, Paradip is in the "Very High Damage Risk Zone," indicating the presence of significant wind speeds along the coast. The paper digs into these vulnerabilities and their possible influence on the coastal community, emphasizing the city's need to strike a balance between urban growth and ecological preservation. One of the main problems is flooding. Rainwater enters government buildings and impoverished areas because of encroachments on natural drainage systems. Dryland capable of residential, agricultural, and other economic activities is submerged by overflowing water which causes loss of life, property, and infrastructure. This study evaluates flood vulnerabilities in Paradip, a coastal city, using GIS and a weighted overlay index. By integrating satellite imagery, digital elevation models (DEM), and historical data on rainfall and soil types, we developed a detailed flood vulnerability map that categorises areas into high, moderate, and low risk. ArcGIS Pro 2.7.3 software and a weighted overlay index were utilized for the collection, processing, and display of spatial and attribute data. spatial analysis was the process used to produce the flood vulnerability map. The results provide essential insights for urban planning and flood mitigation strategies, with direct implications for improving disaster preparedness and climate resilience. The flood vulnerability map was then compared to the masterplan of Paradip which was proposed for 2030, to know the actual vulnerable area and to defy some major which were proposed by Paradip development authority. The study illustrates how remote sensing and GIS techniques can be effectively utilized to generate flood vulnerability data essential for informed decision-making.

Deep learning-assisted morphological segmentation for effective particle area estimation and prediction of interfacial properties in polymer composites.

The link between the macroscopic properties of polymer nanocomposites and the underlying microstructural features necessitates an understanding of nanoparticle dispersion. The dispersion of nanoparticles introduces variability, potentially leading to clustering and localized accumulation of nanoparticles. This non-uniform dispersion impacts the accuracy of predictive models. In response to this challenge, this study developed an automated and precise technique for particle recognition and detailed mapping of particle positions in scanning electron microscopy (SEM) micrographs. This was achieved by integrating deep convolutional neural networks with advanced image processing techniques. Following particle detection, two dispersion factors were introduced, namely size uniformity and supercritical clustering, to quantify the impact of particle dispersion on properties. These factors, estimated using the computer vision technique, were subsequently used to calculate the effective load-bearing area influenced by the particles. An adapted micromechanical model was then employed to quantify the interfacial strength and thickness of the nanocomposites. This approach enabled the establishment of a correlation between dispersion characteristics and interfacial properties by integrating experimental data, relevant micromechanical models, and quantified dispersion factors. The proposed systematic procedure demonstrates considerable promise in utilizing deep learning to capture and quantify particle dispersion characteristics for structure-property analyses in polymer nanocomposites.

Chromatic covalent organic frameworks enabling in-vivo chemical tomography

Covalent organic frameworks designed as chromatic sensors offer opportunities to probe biological interfaces, particularly when combined with biocompatible matrices. Particularly compelling is the prospect of chemical tomography – or the 3D spatial mapping of chemical detail within the complex environment of living systems. Herein, we demonstrate a chromic Covalent Organic Framework (COF) integrated within silk fibroin (SF) microneedles that probe plant vasculature, sense the alkalization of vascular fluid as a biomarker for drought stress, and provide a 3D in-vivo mapping of chemical gradients using smartphone technology. A series of Schiff base COFs with tunable pKa ranging from 5.6 to 7.6 enable conical, optically transparent SF microneedles with COF coatings of 120 to 950 nm to probe vascular fluid and the surrounding tissues of tobacco and tomato plants. The conical design allows for 3D mapping of the chemical environment (such as pH) at standoff distances from the plant, enabling in-vivo chemical tomography. Chromatic COF sensors of this type will enable multidimensional chemical mapping of previously inaccessible and complex environments.

Integrative 3D genomics with multi-omics analysis and functional validation of genetic regulatory mechanisms of abdominal fat deposition in chickens

Chickens are the most abundant agricultural animals globally, with controlling abdominal fat deposition being a key objective in poultry breeding. While GWAS can identify genetic variants associated with abdominal fat deposition, the precise roles and mechanisms of these variants remain largely unclear. Here, we use male chickens from two lines divergently selected for abdominal fat deposition as experimental models. Through the integration of genomic, epigenomic, 3D genomic, and transcriptomic data, we build a comprehensive chromatin 3D regulatory network map to identify the genetic regulatory mechanisms that influence abdominal fat deposition in chickens. Notably, we find that the rs734209466 variant functions as an allele-specific enhancer, remotely enhancing the transcription of IGFBP2 and IGFBP5 by the binding transcription factor IRF4. This interaction influences the differentiation and proliferation of preadipocytes, which ultimately affects phenotype. This work presents a detailed genetic regulatory map for chicken abdominal fat deposition, offering molecular targets for selective breeding.