3D Structural Model Research Articles

Integration of seismic and well log data for evaluating the upper cretaceous reservoirs of Horus oil field, Alamein Basin, Northern Western Desert, Egypt

Horus field stands out as one of the main oil-rich areas in Alamein Basin. Formation and evolution of Horus field were influenced by tectonic processes occurring from Jurassic to Cretaceous periods. The main objectives of this study are to characterize structural elements, identify hydrocarbon potential, and pinpoint promising zones within Early Cretaceous rocks for hydrocarbon accumulations in Horus reservoirs. By utilizing high-quality 2D seismic reflection sections enhanced with structural smoothing attribute, which help eliminate random noise while preserving structural details and horizon geometry, building a 3D structural model becomes facile. Analysis of well logging data provides a comprehensive understanding of Cretaceous reservoirs, simplified by 3D static reservoir modeling to depict their varying ranges and anisotropies. 3D structural model shows that the trap of Horus field is linked to a structural uplift in an ENE-WSW trend, bordered to north and south by sediment-filled lows. This anticlinal ridge within the model is segmented into blocks by a series of NW-SE normal faults. 3D static reservoir models reveal high-quality reservoirs in Upper Bahariya and Abu Roash (G) Dolomite, characterized by substantial thicknesses of net pay zones. 3D static modeling of Cretaceous reservoirs guides the oilfield development and optimizes production strategies in Horus area.

Kinetics of Secoisolariciresinol Glucosyltransferase LuUGT74S1 and Its Mutants.

The lignan secoisolariciresinol (SECO) diglucoside (SDG) is a phytoestrogen with diverse effects. LuUGT74S1 glucosylates SECO to SDG, whereby only small amounts of the monoglucoside SMG are formed intermediately, which exhibit increased activity. To identify critical amino acids that are important for enzymatic activity and the SMG/SDG ratio, 3D structural modeling and docking, as well as site-directed mutation studies, were performed. Enzyme assays with ten mutants revealed that four of them had identical kinetic data to LuUGT74S1, while three showed reduced and one increased catalytic efficiency kcat/Km. S82F and E189L substitutions resulted in the complete absence of activity. A17 and Q136 are crucial for the conversion of SMG to SDG as A17S and Q136F mutants exhibited the highest SMG/SDG ratios of 0.7 and 0.4. Kinetic analyses show that diglucosylation is an essentially irreversible reaction, while monoglycosylation is kinetically favored. The results lay the foundation for the biotechnological production of SMG.

Shaping the crustal structure of the SW-Alpine Foreland: Insights from 3D Geological modeling

Reactivation processes play a significative role in the localization of deformation but still remain hard to establish at the lithospheric scale. In this work, we built a 3D structural model, which enables to bridge the gap between the main tectonic structures observed at the surface and the geometry of the major interfaces (the Mohorovičić-discontinuity (hereafter Moho) and top of the basement) inferred from geophysical data acquired in the external Western Alps and their foreland. The geometry of these tectonic structures is interpreted in relation to their geodynamic evolution. The main results of this study highlight: (1) a strong contribution of thick-skinned Pyrenean-Provence and Alpine tectonics, (2) a lithospheric rooting of Variscan shear zones and related faults, and (3) the regional-scale influence of these inherited structures on the post-Paleozoic strain localization in the study area. Our 3D model shows that the pattern of Variscan shear zones that were developed at the end of the Paleozoic involved the whole crust, as emphasized by the Moho offsets. These shear zones were reactivated and localized Meso-Cenozoic deformation. The Variscan deformation pattern controlled the geometry of extensional basins, the propagation of Pyrenean-Provence deformation, and finally the Alpine deformation at crustal scale. Our 3D model shows minor crustal thickening (ca. 40 km) located below the Pelvoux External Crystalline Massif, which probably resulted from both Pyrenean and Alpine tectonic phases. In contrast, the southern part of the Alpine front shows a thinned crust (ca. 18 km) resulting from extensional Meso-Cenozoic phases between the Cévennes margin and the Durance basin.

Integrated BIM-GA approach for slab formwork design optimization

Purpose Formwork design and construction for reinforced concrete buildings take significant time, effort and money. Construction procedures are time-consuming for designers and costly for the contractor. Poor engineering decisions have led to several workplace accidents in the construction industry. This paper aims to present an integrated building information modeling – genetic algorithm (BIM-GA) model to automate formwork design, 3D visualization and optimization. Design/methodology/approach Data are precisely extracted from a 3D structural model and used to optimize formwork design based on available formwork components and prices. Optimization models are made using GA approach. A library of 3D formwork components was modeled and stored using Revit. The optimized design solution thereafter would be visualized automatically in Revit to readily acquire formwork quantities schedules and shop drawings. Findings A case study illustrating the proposed approach demonstrated that using BIM will reduce formwork design, quantification and drawing time by more than 50% of the traditional approach with safer design and accurate results due to process automation and optimize cost for the given data. Originality/value This research introduces an innovative integrated BIM and GA model for the optimization and automation of slab formwork design, which has significantly benefited the construction industry. The utilization of GA in the optimization process allows for the attainment of an optimal formwork design, ultimately leading to a reduction in construction cost and time.

In silico and experimental characterization of a new polyextremophilic subtilisin-like protease from Microbacterium metallidurans and its application as a laundry detergent additive.

Considering the current growing interest in new and improved enzymes for use in a variety of applications, the present study aimed to characterize a novel detergent-stable serine alkaline protease from the extremophilic actinobacterium Microbacterium metallidurans TL13 (MmSP) using a combined in silico and experimental approach. The MmSP showed a close phylogenetic relationship with high molecular weight S8 peptidases of Microbacterium species. Moreover, its physical and chemical parameters computed using Expasy's ProtParam tool revealed that MmSP is hydrophilic, halophilic and thermo-alkali stable. 3D structure modelling and functional prediction of TL13 serine protease resulted in the detection of five characteristic domains: [catalytic subtilase domain, fibronectin (Fn) type-III domain, peptidase inhibitor I9, protease-associated (PA) domain and bacterial Ig-like domain (group 3)], as well as the three amino acid residues [aspartate (D182), histidine (H272) and serine (S604)] in the catalytic subtilase domain. The extremophilic strain TL13 was tested for protease production using agricultural wastes/by-products as carbon substrates. Maximum enzyme activity (390U/gds) was obtained at 8th day fermentation on potato peel medium. Extracellular extract was concentrated and partially purified using ammonium sulfate precipitation methodology (1.58 folds purification fold). The optimal pH, temperature and salinity of MmSP were 9, 60°C and 1M NaCl, respectively. The MmSP protease showed broad pH stability, thermal stability, salt tolerance and detergent compatibility. In order to achieve the maximum stain removal efficacy by the TL 13 serine protease, the operation conditions were optimized using a Box-Behnken Design (BBD) with four variables, namely, time (15-75min), temperature (30-60°C), MmSP enzyme concentration (5-10U/mL) and pH (7-11). The maximum stain removal yield (95 ± 4%) obtained under the optimal enzymatic operation conditions (treatment with 7.5U/mL of MmSP during 30min at 32°C and pH9) was in good agreement with the value predicted by the regression model (98 ± %), which prove the validity of the fitted model. In conclusion, MmSP appears to be a good candidate for industrial applications, particularly in laundry detergent formulations, due to its high hydrophilicity, alkali-halo-stability, detergent compatibility and stain removal efficiency.

Enhancing Open BIM Interoperability: Automated Generation of a Structural Model from an Architectural Model

Building information modelling (BIM) is an appreciated technology in the field of architecture and construction management. Collaboration of information in BIM has not been fully utilized in the structural engineering stream as many engineers keep on working with previous prevailing design approaches. Failure to adequately facilitate automation in design could lead to structural defects, construction rework, or even structural clashes, with major financial implications. Given the inherent complexity of large-scale construction projects, the ‘manual design and detailing’ of structure is a challenging task and prone to human errors. Against this backdrop, this study developed a 4D building information management approach to facilitate automated structural models for professionals designing all the elements required in reinforced concrete (RC) structures like slabs, beams, and columns. The main contribution of this study is to obtain structural models directly from architecture models automatically, which reduces effort and possible errors in the previous prevailing approaches. The framework enables execution of all the model design works automatically through coding. This is achieved by executing a script which is beneficial for integrated project delivery (IPD). The 3D structural model in BIM software presented in this study extracts and transfers the geometrical data and links these data in Industry Foundation Classes (IFC) files using integration facilitated by Python 3.6 and IFCopenshell. The developed automated programme framework offers a cost-effective and accurate methodology to address the limitations and inefficiencies of traditional methods of structural modelling, which had been carried out manually. The authors have developed a novel tool to extract structural models from architectural models without proprietary software, greatly benefiting BIM managers by enhancing 3D BIM models. This advancement toward Open BIM, crucial for the architecture, engineering, and construction (AEC) industry’s future, is accessible to educators and beginners and highlights BIM’s effectiveness in improving structural analysis and productivity. The core finding of this study is to generate a structural model from an architecture model by automating the script with Python integration of IFC and IFCopenshell. The merits of the developed framework are reduced clashes, more economical structural modelling, and fully automated smart work as functions of the IPD.

VUStruct: a compute pipeline for high throughput and personalized structural biology.

Effective diagnosis and treatment of rare genetic disorders requires the interpretation of a patient's genetic variants of unknown significance (VUSs). Today, clinical decision-making is primarily guided by gene-phenotype association databases and DNA-based scoring methods. Our web-accessible variant analysis pipeline, VUStruct, supplements these established approaches by deeply analyzing the downstream molecular impact of variation in context of 3D protein structure. VUStruct's growing impact is fueled by the co-proliferation of protein 3D structural models, gene sequencing, compute power, and artificial intelligence. Contextualizing VUSs in protein 3D structural models also illuminates longitudinal genomics studies and biochemical bench research focused on VUS, and we created VUStruct for clinicians and researchers alike. We now introduce VUStruct to the broad scientific community as a mature, web-facing, extensible, High Performance Computing (HPC) software pipeline. VUStruct maps missense variants onto automatically selected protein structures and launches a broad range of analyses. These include energy-based assessments of protein folding and stability, pathogenicity prediction through spatial clustering analysis, and machine learning (ML) predictors of binding surface disruptions and nearby post-translational modification sites. The pipeline also considers the entire input set of VUS and identifies genes potentially involved in digenic disease. VUStruct's utility in clinical rare disease genome interpretation has been demonstrated through its analysis of over 175 Undiagnosed Disease Network (UDN) Patient cases. VUStruct-leveraged hypotheses have often informed clinicians in their consideration of additional patient testing, and we report here details from two cases where VUStruct was key to their solution. We also note successes with academic research collaborators, for whom VUStruct has informed research directions in both computational genomics and wet lab studies.

Exome sequencing reveals neurodevelopmental genes in simplex consanguineous Iranian families with syndromic autism

Background and objectiveAutosomal recessive genetic disorders pose significant health challenges in regions where consanguineous marriages are prevalent. The utilization of exome sequencing as a frequently employed methodology has enabled a clear delineation of diagnostic efficacy and mode of inheritance within multiplex consanguineous families. However, these aspects remain less elucidated within simplex families.MethodsIn this study involving 12 unrelated simplex Iranian families presenting syndromic autism, we conducted singleton exome sequencing. The identified genetic variants were validated using Sanger sequencing, and for the missense variants in FOXG1 and DMD, 3D protein structure modeling was carried out to substantiate their pathogenicity. To examine the expression patterns of the candidate genes in the fetal brain, adult brain, and muscle, RT-qPCR was employed.ResultsIn four families, we detected an autosomal dominant gene (FOXG1), an autosomal recessive gene (CHKB), and two X-linked autism genes (IQSEC2 and DMD), indicating diverse inheritance patterns. In the remaining eight families, we were unable to identify any disease-associated genes. As a result, our variant detection rate stood at 33.3% (4/12), surpassing rates reported in similar studies of smaller cohorts. Among the four newly identified coding variants, three are de novo (heterozygous variant p.Trp546Ter in IQSEC2, heterozygous variant p.Ala188Glu in FOXG1, and hemizygous variant p.Leu211Met in DMD), while the homozygous variant p.Glu128Ter in CHKB was inherited from both healthy heterozygous parents. 3D protein structure modeling was carried out for the missense variants in FOXG1 and DMD, which predicted steric hindrance and spatial inhibition, respectively, supporting the pathogenicity of these human mutants. Additionally, the nonsense variant in CHKB is anticipated to influence its dimerization – crucial for choline kinase function – and the nonsense variant in IQSEC2 is predicted to eliminate three functional domains. Consequently, these distinct variants found in four unrelated individuals with autism are likely indicative of loss-of-function mutations.ConclusionsIn our two syndromic autism families, we discovered variants in two muscular dystrophy genes, DMD and CHKB. Given that DMD and CHKB are recognized for their participation in the non-cognitive manifestations of muscular dystrophy, it indicates that some genes transcend the boundary of apparently unrelated clinical categories, thereby establishing a novel connection between ASD and muscular dystrophy. Our findings also shed light on the complex inheritance patterns observed in Iranian consanguineous simplex families and emphasize the connection between autism spectrum disorder and muscular dystrophy. This underscores a likely genetic convergence between neurodevelopmental and neuromuscular disorders.

3DGEN: a framework for generating custom-made synthetic 3D datasets for civil structure health monitoring

The availability of high-quality datasets is increasingly critical in the field of computer vision-based civil structural health monitoring, where deep learning approaches have gained prominence. However, the lack of specialized datasets for such tasks poses a significant challenge for training a reliable model. To address this challenge, a framework, 3DGEN, is proposed to swiftly generate realistic synthetic 3D datasets which can be targeted for specific tasks. The framework is based on diverse 3D civil structural models, rendering them from various angles and providing depth information and camera parameters for training neural networks. By employing mathematical methods, such as analytical solutions and/or numerical simulations, deformation of civil engineering structures can be generated, ensuring a reliable representation of their real-world shapes and characteristics in the 3D datasets. For texture generation, a generative 3D texturing method enables users to specify desired textures using plain English sentences. Two successful experiments are conducted to (1) assess the efficiency of generating the 3D datasets using two distinct structures, (2) train a monocular depth estimation network to perform 3D surface reconstruction with the generated dataset. Notably, 3DGEN is not limited to 3D surface reconstruction; it can also be used for training neural networks for various other tasks. The code and dataset are available at: https://github.com/YANDA-SHAO/Beam-Dataset-SE

3D structural modeling approach by discretization of a basic structural map associated with magnetic data

ABSTRACT This study proposes a 3D structural modeling approach based on the discretization of a 2D structural model associated with magnetic data. The approach proposed in this study requires first to identify the parameters involved in the modeling (geographical position, direction, dip direction and dip angle) and then to transform the whole 2D model into fault segments. The first discretization will allow determining the position, the direction and the dip direction of each fault segment. This discretization phase was done by coupling the Excel program to existing GIS software. The last discretization will allow evaluating the variation of inclinations along the fault segment. It required the elaboration of a depth model from the magnetic data grid. It is this model that provided all the necessary data for this last discretization, which was automated thanks to the DipAngle code developed for this study. All these discretizations have provided to the used modeler and all the databases necessary for the realization of the various models. The application of this methodology to the Central Domain of the Mbere Division has made it possible to develop different models that can be used as basic documents in exploration projects.

Three-Dimensional Structure of Human Epididymis Protein 4 (HE4): A Protein Modelling of an Ovarian Cancer Biomarker Through In Silico Approach

The Human Epididymis Protein 4 (HE4) biomarker has been extensively investigated for its potential in diagnosing ovarian cancer (OC). For the application of diagnostic techniques and drug delivery, it is crucial to understand the protein tertiary structure. However, the Protein Data Bank (PDB) does not currently contain the three-dimensional (3D) structure of HE4. Therefore, an in silico analysis was conducted to model the HE4 protein using AlphaFold, I-TASSER, and Robetta servers, with the sequence retrieved from UniProt (ID: Q14508). These three servers employed deep learning algorithms, threading templates, and de novo methods, respectively. Subsequently, Molecular Dynamics (MD) simulation using the GROMACS software package improved each 3D structure model, resulting in optimized and refined structures: RF1, RF2, and RF3. PROCHECK and ERRAT programs were employed to assess the structure quality. The Ramachandran plots from PROCHECK indicated that 100% of residues were within the allowed regions for all servers except for I-TASSER. For the refined structures, RF1 and RF3, all residues were concentrated within the allowed regions. According to the ERRAT program, the RF1 model exhibited the highest overall quality factor of 97.701, followed by RF3 and AlphaFold models with scores of 94.643 and 93.750, respectively. After these validations, RF1 emerged as the most accurately predicted 3D structure of HE4 and has one tunnel identified by CAVER 3.0 tool that facilitates the transportation of small particles to the active site, supported by FTsite and PrankWeb binding site predictions. This model holds potential for various computational studies, including the development of OC diagnostic kits. It will enhance our comprehension of the interactions between the protein and other biomolecules.

Synthesis, characterization, and anti-cancer potential of novel p53-mediated Mdm2 and Pirh2 modulators: an integrated In silico and In vitro approach.

Introduction: Leukemia is a global health concern that requires alternative treatments due to the limitations of the FDA-approved drugs. Our focus is on p53, a crucial tumor suppressor that regulates cell division. It appears possible to stabilize p53 without causing damage to DNA by investigating dual-acting inhibitors that target both ligases. The paper aims to identify small molecule modulators of Mdm2 and Pirh2 by using 3D structural models of p53 residues and to further carry out the synthesis and evaluation of hit candidates for anti-cancer potency by in vitro and in silico studies. Methods: We synthesized structural analogues of MMs02943764 and MMs03738126 using a 4,5-(substituted) 1,2,4-triazole-3-thiols with 2-chloro N-phenylacetamide in acetone with derivatives of PAA and PCA were followed. Cytotoxicity assays, including MTT, Trypan Blue Exclusion, and MTS assays, were performed on cancer cell lines. Anti-proliferation activity was evaluated using K562 cells. Cell cycle analysis and protein expression studies of p53, Mdm2, and Pirh2 were conducted using flow cytometry. Results: As for results obtained from our previous studies MMs02943764, and MMs03738126 were selected among the best-fit hit molecules whose structural analogues were further subjected to molecular docking and dynamic simulation. Synthesized compounds exhibited potent anti-proliferative effects, with PAC showing significant cytotoxicity against leukemia cells. PAC induced cell cycle arrest and modulated p53, Mdm2, and Pirh2 protein expressions in K562 cells. Molecular docking revealed strong binding affinity of PAC to p53 protein, further confirmed by molecular dynamics simulation. Discussion: The study presents novel anticancer compounds targeting the p53 ubiquitination pathway, exemplified by PAC. Future perspectives involve further optimization and preclinical studies to validate PAC's potential as an effective anticancer therapy.

LociPARSE: a locality-aware invariant point attention model for scoring RNA 3D structures.

A scoring function that can reliably assess the accuracy of a 3D RNA structural model in the absence of experimental structure is not only important for model evaluation and selection but also useful for scoring-guided conformational sampling. However, high-fidelity RNA scoring has proven to be difficult using conventional knowledge-based statistical potentials and currently-available machine learning-based approaches. Here we present lociPARSE, a locality-aware invariant point attention architecture for scoring RNA 3D structures. Unlike existing machine learning methods that estimate superposition-based root mean square deviation (RMSD), lociPARSE estimates Local Distance Difference Test (lDDT) scores capturing the accuracy of each nucleotide and its surrounding local atomic environment in a superposition-free manner, before aggregating information to predict global structural accuracy. Tested on multiple datasets including CASP15, lociPARSE significantly outperforms existing statistical potentials (rsRNASP, cgRNASP, DFIRE-RNA, and RASP) and machine learning methods (ARES and RNA3DCNN) across complementary assessment metrics. lociPARSE is freely available at https://github.com/Bhattacharya-Lab/lociPARSE.

Spatial Chromosome Organization and Adaptation of Escherichia coli under Heat Stress.

The spatial organization of bacterial chromosomes is crucial for cellular functions. It remains unclear how bacterial chromosomes adapt to high-temperature stress. This study delves into the 3D genome architecture and transcriptomic responses of Escherichia coli under heat-stress conditions to unravel the intricate interplay between the chromosome structure and environmental cues. By examining the role of macrodomains, chromosome interaction domains (CIDs), and nucleoid-associated proteins (NAPs), this work unveils the dynamic changes in chromosome conformation and gene expression patterns induced by high-temperature stress. It was observed that, under heat stress, the short-range interaction frequency of the chromosomes decreased, while the long-range interaction frequency of the Ter macrodomain increased. Furthermore, two metrics, namely, Global Compactness (GC) and Local Compactness (LC), were devised to measure and compare the compactness of the chromosomes based on their 3D structure models. The findings in this work shed light on the molecular mechanisms underlying thermal adaptation and chromosomal organization in bacterial cells, offering valuable insights into the complex inter-relationships between environmental stimuli and genomic responses.

TRENDS OF ARTIFICIAL INTELLIGENCE AND IMMERSIVE DESIGN USE IN THE FORMATION OF ARCHITECTURAL ENVIRONMENT

There is increasing attention to a new design methodology incorporating the latest achievements of a non-hierarchical way of forming based on the learning process of computers or machines, namely the concepts of artificial intelligence (AI) and immersive design, using virtual and other types of reality. The study aims to reveal the necessity to identify the main trends of artificial intelligence and immersive design use in architectural environment formation. The results can contribute to rethinking architectural design algorithms at the present stage and identifying potential opportunities for improvement. The paper describes the possibilities of AI and immersive design in architectural environment formation. Analysis of current research in this field has made it possible to identify trends that likely will continue to develop further and impact the architectural profession. One of these trends is the improvement of the architect’s toolkit, currently provided by additional plug-ins to existing programmes or separate services based on AI algorithms, but in the future, it may be a change in the programme code itself with a completely updated structure. In this case, the role of software from the architect-managed toolkit may shift to another plane, such as a digital assistant that partially or fully manages its responsibilities in working on an architectural project. The symbiosis of multidirectional but powerful immersive design technologies (VR, AR, MR) and AI algorithms is another trend in architectural environment formation. It can also be characterised as the most experimental and, at the same time, dynamically progressing, given the active development of its components. Another modern trend is to bring architectural design to a new level of a balanced combination of virtual and real. The immersion of the user in virtual reality for design work and demonstration of the relevant results is gradually changing as the 3D model of an architectural structure is increasingly manifested in the physical world, including at the implementation site, and interactive equipment with flexible and constantly evolving functionality provides architects with more opportunities for creativity. Keywords: artificial intelligence, immersive design, architectural environment.

Electronic Health Literacy Scale-Web3.0 for Older Adults with Noncommunicable Diseases: Validation Study.

In the current digital era, eHealth literacy plays an indispensable role in health care and self-management among older adults with noncommunicable diseases (NCDs). Measuring eHealth literacy appropriately and accurately ensures the successful implementation and evaluation of pertinent research and interventions. However, existing eHealth literacy measures focus mainly on individuals' abilities of accessing and comprehending eHealth information (Web1.0), whereas the capabilities for web-based interaction (Web2.0) and using eHealth information (Web3.0) have not been adequately evaluated. This study aimed to examine the reliability, validity, and measurement invariance of the eHealth Literacy Scale-Web3.0 (eHLS-Web3.0) among older adults with NCDs. A total of 642 Chinese older adults with NCDs (mean age 65.78, SD 3.91 years; 55.8% female) were recruited in the baseline assessment, of whom 134 (mean age 65.63, SD 3.99 years; 58.2% female) completed the 1-month follow-up assessment. Baseline measures included the Chinese version of the 24-item 3D eHLS-Web3.0, the Chinese version of the 8-item unidimensional eHealth Literacy Scale (eHEALS), and demographic information. Follow-up measures included the 24-item eHLS-Web3.0 and accelerometer-measured physical activity and sedentary behavior. A series of statistical analyses, for example, Cronbach α, composite reliability coefficient (CR), confirmatory factor analysis (CFA), and multigroup CFA, were performed to examine the internal consistency and test-retest reliabilities, as well as the construct, concurrent, convergent, discriminant, and predictive validities, and the measurement invariance of the eHLS-Web3.0 across gender, education level, and residence. Cronbach α and CR were within acceptable ranges of 0.89-0.94 and 0.90-0.97, respectively, indicating adequate internal consistency of the eHLS-Web3.0 and its subscales. The eHLS-Web3.0 also demonstrated cross-time stability, with baseline and follow-up measures showing a significant intraclass correlation of 0.81-0.91. The construct validity of the 3D structure model of the eHLS-Web3.0 was supported by confirmatory factor analyses. The eHLS-Web3.0 exhibited convergent validity with an average variance extracted value of 0.58 and a CR value of 0.97. Discriminant validity was supported by CFA results for a proposed 4-factor model integrating the 3 eHLS-Web3.0 subscales and eHEALS. The predictive validity of the eHLS-Web3.0 for health behaviors was supported by significant associations of the eHLS-Web3.0 with light physical activity (β=.36, P=.004), moderate to vigorous physical activity (β=.49, P<.001), and sedentary behavior (β=-.26, P=.002). Finally, the measurement invariance of the eHLS-Web3.0 across gender, education level, and residence was supported by the establishment of configural, metric, strong, and strict invariances. The present study provides timely empirical evidence on the reliability, validity, and measurement invariance of the eHLS-Web3.0, suggesting that the 24-item 3D eHLS-Web3.0 is an appropriate and valid tool for measuring eHealth literacy among older adults with NCDs within the Web3.0 sphere.

The essential role of aggregation for the emulsifying ability of a fungal CYS-rich protein

Biosurfactants are in demand by the global market as natural commodities suitable for incorporation into commercial products or utilization in environmental applications. Fungi are promising producers of these molecules and have garnered interest also for their metabolic capabilities in efficiently utilizing recalcitrant and complex substrates, like hydrocarbons, plastic, etc. Within this framework, biosurfactants produced by two Fusarium solani fungal strains, isolated from plastic waste-contaminated landfill soils, were analyzed. Mycelia of these fungi were grown in the presence of 5% olive oil to drive biosurfactant production. The characterization of the emulsifying and surfactant capacity of these extracts highlighted that two different components are involved. A protein was purified and identified as a CFEM (common in fungal extracellular membrane) containing domain, revealing a good propensity to stabilize emulsions only in its aggregate form. On the other hand, an unidentified cationic smaller molecule exhibits the ability to reduce surface tension. Based on the 3D structural model of the protein, a plausible mechanism for the formation of very stable aggregates, endowed with the emulsifying ability, is proposed.Key points• Two Fusarium solani strains are analyzed for their surfactant production.• A cationic surfactant is produced, exhibiting the ability to remarkably reduce surface tension.• An identified protein reveals a good propensity to stabilize emulsions only in its aggregate form.

STVANet: A spatio-temporal visual attention framework with large kernel attention mechanism for citywide traffic dynamics prediction

Enhancing the efficiency and safety of the Intelligent Transportation System requires effective modeling and prediction of citywide traffic dynamics. Most studies employ convolutional neural networks (CNNs) with a 3D convolutional structure or spatio-temporal models with self-attention mechanisms to capture the spatio-temporal information of traffic distribution. Although 3D CNNs excel at capturing local contextual information, they are computationally complex due to the large number of parameters and cannot capture long-range dependence. By contrast, although self-attention mechanisms originally designed to address challenges in natural language processing can capture long-range dependence, their application to 2D image structures requires breaking down the inherent 2D context into a 1D sequence, increasing the computational complexity and neglecting the adaptability between local contextual information and channels. Accordingly, we propose a spatio-temporal visual attention neural network (STVANet), a novel spatio-temporal visual attention 2D CNN, which integrates a unique visual attention module with a large kernel attention (LKA) mechanism, a squeeze-and-excitation (SE) mechanism and a feedforward component to capture long-range dependence and channel information in urban traffic data while preserving the 2D image structure. LKA-based spatio-temporal attention networks extract spatial and temporal features from weekly, daily, and recent hourly periods, and aggregate them with weighted consideration of external features to make predictions. Evaluation of real-world datasets demonstrates STVANet’s superiority over baseline models, showcasing its potential in citywide traffic prediction.

Head model dataset for mixed reality navigation in neurosurgical interventions for intracranial lesions

Mixed reality navigation (MRN) technology is emerging as an increasingly significant and interesting topic in neurosurgery. MRN enables neurosurgeons to “see through” the head with an interactive, hybrid visualization environment that merges virtual- and physical-world elements. Offering immersive, intuitive, and reliable guidance for preoperative and intraoperative intervention of intracranial lesions, MRN showcases its potential as an economically efficient and user-friendly alternative to standard neuronavigation systems. However, the clinical research and development of MRN systems present challenges: recruiting a sufficient number of patients within a limited timeframe is difficult, and acquiring low-cost, commercially available, medically significant head phantoms is equally challenging. To accelerate the development of novel MRN systems and surmount these obstacles, the study presents a dataset designed for MRN system development and testing in neurosurgery. It includes CT and MRI data from 19 patients with intracranial lesions and derived 3D models of anatomical structures and validation references. The models are available in Wavefront object (OBJ) and Stereolithography (STL) formats, supporting the creation and assessment of neurosurgical MRN applications.

A 3D-Based Modeling and Analysis of an Artificial Palm Tree for Video Surveillance Activities

A study on the 3-D modeling and analysis of an artificial palm tree structure capable of housing a camera was made. The proposed system's design, simulation, and virtual testing were done using SolidWorks software. This work's primary objective was an aesthetically pleasing and functional solution that integrates with the urban landscape while providing an enhanced concealed security system. The natural form of a palm tree was incorporated into the artificial palm tree structure. The hidden camera ensures optimal coverage without compromising the overall appearance and presence of the device. This is so because, in the Nigerian situation, Miscreants will pull down the structure if they know of its presence. A detailed analysis of the system's structural integrity and stability was achieved using a 3D model of the artificial palm tree structure. Factors such as wind load, weight distribution, and material strength are considered to ensure the design can cope with environmental conditions. Virtual simulations allow for the maximization of the structure, ensuring it complies with safety standards, and the results of VonMise's stress show that the materialsselected for the design are suitable for the work.