3D Technique Research Articles

Leveraging drone technology for enhanced thermal performance analysis in sustainable architecture: a case study of GBI-certified buildings in Malaysia

ABSTRACT In recent years, the realm of architecture has witnessed a significant transformation marked by a notable surge in focus on sustainable practices and energy efficiency. Architects and engineers increasingly recognize the innovative role of thermal efficiency in enhancing building performance. Such recognition emphasizes the critical importance of thermal analysis in understanding the thermal attributes of building materials and structures. Against this backdrop, this paper aims to bridge a current gap in architectural practices and sustainable design within the Malaysian context. It proposes a case study leveraging drone thermal mapping techniques on Green Building Index (GBI)-certified buildings. The study seeks to examine how architectural designs interact with environmental conditions and identify areas for improvement in sustainable building practices, particularly within Malaysia’s tropical climate context. The primary objective is to assess the effectiveness of drone technology in analysing building thermal performance and to explore how the findings can inform design interventions to enhance sustainability. To achieve this, a structured methodology comprising five procedural phases is employed in a case study focused on three iconic GBI-certified buildings in Putrajaya, namely Putrajaya Holdings Tower, Energy Commission Building, and Heriot-Watt University Malaysia. By integrating thermal imaging with visuospatial data and 3D modeling techniques, the study demonstrates the effectiveness of drones in analysing heat patterns, highlighting their potential as a tool for strategic design interventions. As an exploratory study, this paper serves as a catalyst for further investigations into the integration of drone technologies with architectural design, particularly within the context of tropical climates.

Performance Properties and Finite Element Modelling of Forest-Based Bionanomaterials/Activated Carbon Composite Film for Sustainable Future

Thanks to its highly crystalline structure and excellent thermal, optical, electrical and mechanical properties, carbon and its derivatives are considered the preferred reinforcement material in composites used in many industrial applications, especially in the forest and forest products sector, including oil, gas and aviation. Since hydroxyethyl cellulose (HEC) is a biopolymer, it has poor mechanical and thermal properties. These properties need to be strengthened with various additives. This study aims to improve the thermal and mechanical properties of hydroxyethyl cellulose by preparing hydroxyethyl cellulose/activated carbon (HEC/AC) composite materials. With this study, composites were obtained for the first time and their mechanical properties were examined using a 3D numerical modeling technique. The thermal stability of the prepared composite materials was investigated via thermal gravimetric analysis (TGA). The samples were heated from 30 °C to 750 °C with a heating rate of 10 °C/min under a nitrogen atmosphere and their masses were measured subsequently. The mechanical properties of the composites were investigated via the tensile test. The viscoelastic properties of the composite films were determined with dynamic mechanical thermal analyses (DMTA) and their morphologies were examined with scanning electron microscopy (SEM) images. According to the results, the best F3 sample (films containing 3 wt.% activated carbon) had an elastic modulus of 168.3 MPa, a thermal conductivity value of 0.068 W/mK, the maximum mass loss was at 328.20 °C and the initial storage modulus at 30 °C was 206.13 MPa. It was determined that the hydroxyethyl cellulose composite films containing 3 wt.% activated carbon revealed the optimum results in terms of both thermal conductivity and viscoelastic response and showed that the obtained composite films could be used in industrial applications where thermal conductivity was required.

Innovations in Bioink Materials and 3D Bioprinting for Precision Tissue Engineering

The progress in bioink materials and 3D bioprinting techniques has created new opportunities in tissue engineering. The goal is to develop cellular implants that closely resemble native tissues in structure, function, and microenvironment, addressing challenges such as nutrient transport and mechanical support for tissue regeneration. This literature review evaluates current trends in bioink development and 3D bioprinting methodologies, assessing their effectiveness for enhancing tissue-engineered constructs for clinical applications. Recent studies were comprehensively analyzed, focusing on novel bioink formulations, optimization of 3D bioprinting processes, and evaluate on of printed constructs' mechanical and biological properties. Various fabrication techniques and their implications for tissue integration were examined. The review shows significant progress in bioink compositions that enhance cell viability and nutrient diffusion within printed scaffolds. Constructs demonstrated improved mechanical properties and biological functionality, enabling better integration with host tissues. In vivo studies highlighted the potential of these bioprinted tissues to support cellular activity and regeneration, signifying significant advancements in clinical viability. The findings emphasize the crucial role of bioink materials and bioprinting technology in advancing tissue engineering. Continual innovation in bioink formulation and printing techniques is essential to overcome current limitations and achieve widespread clinical application. Future research should focus on personalized bioink development and expanding the range of tissues that can be effectively engineered, ultimately improving patient outcomes in regenerative medicine.

Comparison of Bond Strength of Soft Denture Liner on the Denture Base Materials Produced by Different Methods and Effect of Thermocycling

Aim: This study set out to determine the tensile bond strength between denture bases (produced by 3D printing technology, conventional technique, and computer-aided design and computer-aided manufacturing [CAD/CAM] milled) and silicone-based soft lining material. The consequence of thermocycling on the bonding strength was also investigated. Materials and Methods: The bonding between denture foundation materials produced through three distinct techniques (conventional, CAD/CAM milled, and 3D printed) and silicone-based soft lining material was examined. Before tensile testing, half of the samples underwent thermocycling (5–55°C, 5,000 cycles) in 37°C distilled water for 48 hours. A universal testing apparatus employed a crosshead speed of 5 mm/min. The failure type was identified visually, and the maximum tensile strength was noted. The Shapiro-Wilk test and analysis of variances ( P = .05) were used to assess the statistics. Results: CAD/CAM milled denture base material (1.56 ± 0.62/1.36 ± 0.16 MPa) showed higher bond strength values than the other denture bases in the tensile test conducted before and after thermocycling ( P < .001). The denture base material made conventionally had the lowest bond strength (1.02 ± 0.24/0.77 ± 0.1 MPa) ( P < .001). The tensile bond strength values of the conventional and 3D printing groups showed a statistically significant drop before and after thermocycling ( P = .001). Regardless of thermocycling, adhesive failure was primarily seen in all groups (76.6%). Conclusion: Compared to conventionally produced denture bases, the bond strength of soft relining materials to CAD/CAM milled and 3D printed denture base is different. In denture base materials that are conventionally, CAD/CAM and 3D printed, the thermocycling method reduced bonding strength values.

Evaluation of Myocarditis with a Free-breathing 3D Isotropic Whole-Heart Joint T1 and T2 Mapping Sequence

BackgroundThe diagnosis of myocarditis by CMR requires the use of T2 and T1 weighted imaging, ideally incorporating parametric mapping. Current 2D mapping sequences are acquired sequentially and involve multiple breath-holds resulting in prolonged scan times and anisotropic image resolution. We developed an isotropic free-breathing 3D whole-heart sequence which allows simultaneous T1 and T2 mapping and validated it in patients with suspected acute myocarditis. MethodsEighteen healthy volunteers and 28 patients with suspected myocarditis underwent conventional 2D T1 and T2 mapping with whole heart coverage and 3D joint T1/T2 mapping on a 1.5T scanner. Acquisition time, image quality, and diagnostic performance were compared. Qualitative analysis was performed using a 4-point Likert scale. Bland-Altman plots were used to assess the quantitative agreement between 2D and 3D sequences. ResultsThe 3D T1/T2 sequence was acquired in 8mins 26s under free breathing, whereas 2D T1 and T2 sequences were acquired with breath holds in 11mins 44s (p=0.0001). All 2D images were diagnostic. For 3D images, 89% of T1 and 96% of T2 images were diagnostic with no significant difference in the proportion of diagnostic images for the 3D and 2D T1 (p=0.2482) and T2 maps (p=1.0000). Systematic bias in T1 was noted with biases of 102ms, 115ms, and 152ms for basal-apical segments, with a larger bias for higher T1 values. Good agreement between T2 values for 3D and 2D techniques was found (bias of 1.8ms, 3.9ms, and 3.6ms for basal-apical segments). The sensitivity and specificity of the 3D sequence for diagnosing acute myocarditis was 74% (95% confidence interval [CI] 49-91%) and 83% (36-100%) respectively, with an estimated c-statistic (95% CI) of 0.85 (0.79-0.91) and no statistically significant difference between the 2D and 3D sequences for the detection of acute myocarditis for T1 (p=0.2207) or T2 (p=1.0000). ConclusionFree-breathing whole heart 3D joint T1/T2 mapping was comparable to 2D mapping sequences with respect to diagnostic performance, but with the added advantages of free-breathing, and shorter scan times. Further work is required to address the bias noted at high T1 values, but this did not significantly impact on diagnostic accuracy.

Multiphoton Lithography of Interpenetrating Polymer Networks for Tailored Microstructure Thermal and Micromechanical Properties.

Multiphoton lithography (MPL), an emerging truly 3D microfabrication technique, exhibits substantial potential in biomedical applications, including drug delivery and tissue engineering. Fabricated micro-objects are often expected to undergo shape morphing or bending of the entire structure or its parts. Furthermore, ensuring precise property tuning is detrimental to the realization of the functionality of MPL microstructures. Herein, novel MPL materials based on interpenetrating polymer networks (IPNs) are presented that effectively combine the advantages of acrylate and epoxy systems. IPNs with varying component ratios are investigated for their microfabrication performance and structural integrity with respect to thermal and micromechanical properties. A variety of high-resolution techniques is applied to comprehensively evaluate IPN properties at the bulk, micron, and segmental levels. This study shows that the MPL laser scanning velocity and power, photoinitiator content, and multi-step exposure can be used to tune the morphology and properties of the IPN. As a result, a library of 3D MPL IPN microstructures with high 3D structural stability and tailored thermal and micromechanical properties is achieved. New IPN microstructures with Young's moduli of 3-4MPa demonstrate high-to-fully elastic responses to deformations, making them promising for applications in morphable microsystems, soft micro-robotics, and cell engineering.

An Interview with Adam Lowe: To Re-produce Works of Art

The relationship Bruno Latour had with Adam Lowe illustrates the close link between art and technique, and the problem of representation. Thanks to 3D digitisation and rematerialisation techniques of cultural objects, Adam reproduces all kinds of artworks at all scales with an unprecedented degree of precision. He does not do that in the customary sense today of producing an image of them. Breaking with the division between physical objects and the digital world, Lowe really produces them again, creating their material doubles in 3D, with their volume, their thickness, their grain. Latour was fascinated by Lowe’s project and its achievements, particularly in that they call into question the controversial problem raised by Walter Benjamin of the aura of works of art. The reverse was true, as this interview makes abundantly clear: Lowe has been captivated by the radical way in which Latour redefined modes of representation.

3D Scene Creation and Rendering via Rough Meshes: A Lighting Transfer Avenue.

This paper studies how to flexibly integrate reconstructed 3D models into practical 3D modeling pipelines such as 3D scene creation and rendering. Due to the technical difficulty, one can only obtain rough 3D models (R3DMs) for most real objects using existing 3D reconstruction techniques. As a result, physically-based rendering (PBR) would render low-quality images or videos for scenes that are constructed by R3DMs. One promising solution would be representing real-world objects as Neural Fields such as NeRFs, which are able to generate photo-realistic renderings of an object under desired viewpoints. However, a drawback is that the synthesized views through Neural Fields Rendering (NFR) cannot reflect the simulated lighting details on R3DMs in PBR pipelines, especially when object interactions in the 3D scene creation cause local shadows. To solve this dilemma, we propose a lighting transfer network (LighTNet) to bridge NFR and PBR, such that they can benefit from each other. LighTNet reasons about a simplified image composition model, remedies the uneven surface issue caused by R3DMs, and is empowered by several perceptual-motivated constraints and a new Lab angle loss which enhances the contrast between lighting strength and colors. Comparisons demonstrate that LighTNet is superior in synthesizing impressive lighting, and is promising in pushing NFR further in practical 3D modeling workflows.

Identification of Novel Drug Compound for the Mutated Protein (PKBB-Protein Kinase B, Beta) Responsible for Endometrial Cancer Using Advanced Insilico Drug Designing Technique

Currently, one of the most important health problems affecting individuals worldwide is endometrial cancer. Numerous studies have been conducted by oncologists in an effort to create preventive medications that will lower the cancer's death rate. Using 3D Insilico drug docking techniques, we investigate the potential interactions between the mutant target protein, PKBB-Protein Kinase B, Beta, and the anti-cancer pharmaceutical (control drug), Mercaptopurine, and the proposed de novo drug derivative. To perform drug docking procedures, the translated amino acid sequence and three-dimensional chemical compound were acquired from the NCBI database. The most sophisticated 3D molecular imaging instruments were used to conduct post-docking studies. The de novo medicine directly blocks amino acid mutational sites, as the results of the docking investigation clearly showed (PKBB). The suggested de novo drug has a higher binding score than the control medication, mercaptopurine. Using molecular dynamics techniques, a three-dimensional image of the H-bond contact force between PKBB and the de novo drug is produced. Therefore, we deduce that the suggested anti-cancer de novo drug may aid in the treatment of endometrial cancer. Thus, we suggest the de novo medicine, which lessens discomfort while reducing the signs and symptoms of malignancy.

Application of 3D-Printed Bioinks in Chronic Wound Healing: A Scoping Review.

Chronic wounds, such as diabetic foot ulcers, pressure ulcers, and venous ulcers, pose significant clinical challenges and burden healthcare systems worldwide. The advent of 3D bioprinting technologies offers innovative solutions for enhancing chronic wound care. This scoping review evaluates the applications, methodologies, and effectiveness of 3D-printed bioinks in chronic wound healing, focusing on bioinks incorporating living cells to facilitate wound closure and tissue regeneration. Relevant studies were identified through comprehensive searches in databases, including PubMed, Scopus, and Web of Science databases, following strict inclusion criteria. These studies employ various 3D bioprinting techniques, predominantly extrusion-based, to create bioinks from natural or synthetic polymers. These bioinks are designed to support cell viability, promote angiogenesis, and provide structural integrity to the wound site. Despite these promising results, further research is necessary to optimize bioink formulations and printing parameters for clinical application. Overall, 3D-printed bioinks offer a transformative approach to chronic wound care, providing tailored and efficient solutions. Continued development and refinement of these technologies hold significant promise for improving chronic wound management and patient outcomes.

Quantitative ultrashort echo time MR imaging of knee osteochondral junction: An ex vivo feasibility study.

Compositional changes can occur in the osteochondral junction (OCJ) during the early stages and progressive disease evolution of knee osteoarthritis (OA). However, conventional magnetic resonance imaging (MRI) sequences are not able to image these regions efficiently because of the OCJ region's rapid signal decay. The development of new sequences able to image and quantify OCJ region is therefore highly desirable. We developed a comprehensive ultrashort echo time (UTE) MRI protocol for quantitative assessment of OCJ region in the knee joint, including UTE variable flip angle technique for T1 mapping, UTE magnetization transfer (UTE-MT) modeling for macromolecular proton fraction (MMF) mapping, UTE adiabatic T1ρ (UTE-AdiabT1ρ) sequence for T1ρ mapping, and multi-echo UTE sequence for T2* mapping. B1 mapping based on the UTE actual flip angle technique was utilized for B1 correction in T1, MMF, and T1ρ measurements. Ten normal and one abnormal cadaveric human knee joints were scanned on a 3T clinical MRI scanner to investigate the feasibility of OCJ imaging using the proposed protocol. Volumetric T1, MMF, T1ρ, and T2* maps of the OCJ, as well as the superficial and full-thickness cartilage regions, were successfully produced using the quantitative UTE imaging protocol. Significantly lower T1, T1ρ, and T2* relaxation times were observed in the OCJ region compared with those observed in both the superficial and full-thickness cartilage regions, whereas MMF showed significantly higher values in the OCJ region. In addition, all four UTE biomarkers showed substantial differences in the OCJ region between normal and abnormal knees. These results indicate that the newly developed 3D quantitative UTE imaging techniques are feasible for T1, MMF, T1ρ, and T2* mapping of knee OCJ, representative of a promising approach for the evaluation of compositional changes in early knee OA.

An Exploration of Spatial Visualization Skills: Investigating Students' Use of 3D Models in Science Problems during Think-Aloud Interviews.

Effective spatial visualization and reasoning skills are often credited for students' success in science and engineering courses. However, students enrolled in these science courses are not always exposed to or trained properly on the best ways to utilize models to aid in their learning. Improving spatial visualization techniques with 3D models, such as molecular and DNA modeling kits, is often suggested to facilitate students' ability to conceptualize compounds in two and three dimensions. Here, we investigate what techniques students use to conceptualize 2D representations of various biomolecules with the use of 3D models by interviewing undergraduate students from various natural science and engineering disciplines in task-based, think-aloud sessions. After scoring and analyzing the participant data we explored some of the techniques used among successful scoring participants, including the use of informal models to transition between 2D and 3D. Additional techniques used by students who were able to successfully conceptualize 3D images included starting with smaller, granular details to inductively make conclusions when thinking between two and three dimensions. We find that (1) students who anchor their thinking in 3D models show a deeper level of understanding in initially solving science problems successfully, and (2) proper 3D model use and spatial visualization techniques may improve students' abilities to accurately visualize 2D and 3D representations of molecules in science courses. Our results demonstrate that implementing spatial visualization training to teach students how to effectively use 3D models may improve students' problem-solving techniques in science curricula.

TopoRoot+: computing whorl and soil line traits of field-excavated maize roots from CT imaging

BackgroundThe use of 3D imaging techniques, such as X-ray CT, in root phenotyping has become more widespread in recent years. However, due to the complexity of the root structure, analyzing the resulting 3D volumes to obtain detailed architectural root traits remains a challenging computational problem. When it comes to image-based phenotyping of excavated maize root crowns, two types of root features that are notably missing from existing methods are the whorls and soil line. Whorls refer to the distinct areas located at the base of each stem node from which roots sprout in a circular pattern (Liu S, Barrow CS, Hanlon M, Lynch JP, Bucksch A. Dirt/3D: 3D root phenotyping for field-grown maize (zea mays). Plant Physiol. 2021;187(2):739–57. https://doi.org/10.1093/plphys/kiab311.). The soil line is where the root stem meets the ground. Knowledge of these features would give biologists deeper insights into the root system architecture (RSA) and the below- and above-ground root properties.ResultsWe developed TopoRoot+, a computational pipeline that produces architectural traits from 3D X-ray CT volumes of excavated maize root crowns. Building upon the TopoRoot software (Zeng D, Li M, Jiang N, Ju Y, Schreiber H, Chambers E, et al. Toporoot: A method for computing hierarchy and fine-grained traits of maize roots from 3D imaging. Plant Methods. 2021;17(1). https://doi.org/10.1186/s13007-021-00829-z.) for computing fine-grained root traits, TopoRoot + adds the capability to detect whorls, identify nodal roots at each whorl, and compute the soil line location. The new algorithms in TopoRoot + offer an additional set of fine-grained traits beyond those provided by TopoRoot. The addition includes internode distances, root traits at every hierarchy level associated with a whorl, and root traits specific to above or below the ground. TopoRoot + is validated on a diverse collection of field-grown maize root crowns consisting of nine genotypes and spanning across three years. TopoRoot + runs in minutes for a typical volume size of \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:40{0}^{3}$$\\end{document} on a desktop workstation. Our software and test dataset are freely distributed on Github.ConclusionsTopoRoot + advances the state-of-the-art in image-based phenotyping of excavated maize root crowns by offering more detailed architectural traits related to whorls and soil lines. The efficiency of TopoRoot + makes it well-suited for high-throughput image-based root phenotyping.

Developing a 3D Segmentation Technique by a Region-Growing Algorithm of CT Scan Lung Images

Developing a 3D Segmentation Technique by a Region-Growing Algorithm of CT Scan Lung Images

Characters of a new species of the Cretaceous genus †Loeblitoides affirm affinities to the extant ‘Syndicus group’ of Stenichnini (Coleoptera: Staphylinidae: Scydmaeninae)

A new species of Cenomanian Stenichnini is described, based on an inclusion in Myanmar amber: †Loeblitoides latus sp. nov. As a close relationship between †Loeblitoides and the extant genera Syndicus Motschulsky, Loeblites Franz, and Horaeomorphus Schaufuss was postulated, we used 3D visualization techniques based on micro-computed tomography to search for otherwise impossible to observe morphological details and find arguments supporting this hypothesis. A novel structure, a pair of basal elytral foveae connected in a peculiar way, found in †L. latus, indeed supports the previous views, as it is unique for the extant ‘Syndicus group’. The diagnosis of †Loeblitoides is emended to include new findings.

Mapping distribution of fractures and minerals in rock samples using Res-VGG-UNet and threshold segmentation methods

Understanding the internal structures of rock and their subsequent evolution is essential across multiple disciplines. Fractures and mineral grains are significant components of internal structures. These components influence natural processes and human activities, such as oil migration and extraction. With advancements in non-destructive evaluation (NDE) techniques, especially X-ray computed tomography (CT), in-depth analyses of internal structures of rock have become feasible. However, the data-intensive nature of CT scans necessitates more efficient post-processing algorithms. This research uses the Residual Network-Visual Geometry Group-UNet (Res-VGG-UNet) deep learning framework in conjunction with two threshold segmentation methods to capture components like fractures, the drilled hole, and minerals. The performance of Res-VGG-UNet is evaluated against DeepLabv3+ and Weka3D. Key findings include the capability of the deep learning technique to distinguish between various components and the performance of single-slice-based versus multi-slice-based threshold segmentation methods. VGG16 and VGG19 outperform DeepLabv3+ and Weka3D in the performance of fracture classification. Moreover, a 3D reconstruction technique is introduced, providing enhanced insights into internal features of the rock. We conclude this paper by showing the potential to use the outcomes of the deep learning and multi-slice-based threshold segmentation methods to develop discrete element models, accurately reflecting the physical characteristics of rock specimens.

Working in Tandem to Uncover 3D Artefact Distribution in Archaeological Excavations: Mathematical Interpretation through Positional and Relational Methods

In recent years, the most advanced pioneering techniques in the computing field have found application in assorted areas. Deep learning approaches, including artificial neural networks (ANNs), have become popular thanks to their ability to draw inferences from intricate and seemingly unconnected datasets. Additionally, 3D clustering techniques manage to associate groups of elements by identifying the specific inherent structures exhibited by such objects based on similarity measures. Generally, the characteristics of archaeological information gathered after extraction operations align with the previously mentioned challenges. Hence, an excavation could be an opportunity to use these prior innovative computing approaches. Our objective is to integrate software techniques to organise recovered artefacts and derive logical conclusions from their spatial location and the correlation between tangible attributes. These results can statistically improve our approach to investigations and provide a mathematical interpretation of archaeological excavations.

Efficient and Accurate 3D Thickness Measurement in Vessel Wall Imaging: Overcoming Limitations of 2D Approaches Using the Laplacian Method.

The clinical significance of measuring vessel wall thickness is widely acknowledged. Recent advancements have enabled high-resolution 3D scans of arteries and precise segmentation of their lumens and outer walls; however, most existing methods for assessing vessel wall thickness are 2D. Despite being valuable, reproducibility and accuracy of 2D techniques depend on the extracted 2D slices. Additionally, these methods fail to fully account for variations in wall thickness in all dimensions. Furthermore, most existing approaches are difficult to be extended into 3D and their measurements lack spatial localization and are primarily confined to lumen boundaries. We advocate for a shift in perspective towards recognizing vessel wall thickness measurement as inherently a 3D challenge and propose adapting the Laplacian method as an outstanding alternative. The Laplacian method is implemented using convolutions, ensuring its efficient and rapid execution on deep learning platforms. Experiments using digital phantoms and vessel wall imaging data are conducted to showcase the accuracy, reproducibility, and localization capabilities of the proposed approach. The proposed method produce consistent outcomes that remain independent of centerlines and 2D slices. Notably, this approach is applicable in both 2D and 3D scenarios. It allows for voxel-wise quantification of wall thickness, enabling precise identification of wall volumes exhibiting abnormal wall thickness. Our research highlights the urgency of transitioning to 3D methodologies for vessel wall thickness measurement. Such a transition not only acknowledges the intricate spatial variations of vessel walls, but also opens doors to more accurate, localized, and insightful diagnostic insights.

From BIM to thermal comfort: Leveraging BIM for rapid outdoor comfort assessments

This study introduces a novel system within a Building Information Modeling (BIM) environment to rapidly evaluate Outdoor Thermal Comfort (OTC). Traditionally, direct microclimate analysis in the design phase is hindered by cumbersome and resource-intensive processes. By utilizing information from BIM and integrating the COMFA thermal energy budget model, this approach facilitates immediate OTC evaluation. The system automates the Sky View Factor simulation using 3D modeling techniques and utilizes BIM model data to assess the thermal environment. Tested on a university campus, it effectively processes geospatial BIM data to compute microclimate parameters and estimate OTC using COMFA. This research represents a significant advancement in BIM-integrated OTC analysis, streamlining the design feedback process and aiding practitioners in improving outdoor thermal environments amidst climate change challenges.

3D genomic features across >50 diverse cell types reveal insights into the genomic architecture of childhood obesity.

The prevalence of childhood obesity is increasing worldwide, along with the associated common comorbidities of type 2 diabetes and cardiovascular disease in later life. Motivated by evidence for a strong genetic component, our prior genome-wide association study (GWAS) efforts for childhood obesity revealed 19 independent signals for the trait; however, the mechanism of action of these loci remains to be elucidated. To molecularly characterize these childhood obesity loci we sought to determine the underlying causal variants and the corresponding effector genes within diverse cellular contexts. Integrating childhood obesity GWAS summary statistics with our existing 3D genomic datasets for 57 human cell types, consisting of high-resolution promoter-focused Capture-C/Hi-C, ATAC-seq, and RNA-seq, we applied stratified LD score regression and calculated the proportion of genome-wide SNP heritability attributable to cell type-specific features, revealing pancreatic alpha cell enrichment as the most statistically significant. Subsequent chromatin contact-based fine-mapping was carried out for genome-wide significant childhood obesity loci and their linkage disequilibrium proxies to implicate effector genes, yielded the most abundant number of candidate variants and target genes at the BDNF, ADCY3, TMEM18 and FTO loci in skeletal muscle myotubes and the pancreatic beta-cell line, EndoC-BH1. One novel implicated effector gene, ALKAL2 - an inflammation-responsive gene in nerve nociceptors - was observed at the key TMEM18 locus across multiple immune cell types. Interestingly, this observation was also supported through colocalization analysis using expression quantitative trait loci (eQTL) derived from the Genotype-Tissue Expression (GTEx) dataset, supporting an inflammatory and neurologic component to the pathogenesis of childhood obesity. Our comprehensive appraisal of 3D genomic datasets generated in a myriad of different cell types provides genomic insights into pediatric obesity pathogenesis.