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Buddhist statues hold profound spiritual and cultural significance, yet their comprehensive analysis and identification remain largely reliant on manual annotation and expert scrutiny. Despite advancements in 3D modeling, the nuanced representation of these statues, essential for automated analysis, remains elusive. Integrating semantic information such as symbolic and cultural elements poses a further challenge. There currently exists no standardized method for modeling Buddhist heads in 3D with semantic information. This paper proposes a framework for the component-based analysis of Buddhist statues, particularly focusing on their heads, to bridge this gap. Drawing from diverse disciplines including archaeology, art history, and conservation, the framework leverages advancements in 3D reconstruction to provide a detailed depiction of intricate features. Building upon existing research outcomes, the framework facilitates the seamless integration of semantic information, capturing not only the physical attributes but also the cultural, historical, and spiritual dimensions of these sculptures. This approach aids in overcoming challenges in automated analysis and virtual restoration, providing a dynamic tool for exploration, preservation, and interpretation of cultural heritage. By enabling interdisciplinary synergy, the framework emerges as a dynamic tool for exploring, preserving, and interpreting the profound heritage of Buddhist art with unprecedented depth and clarity.

Painted artefacts, such as murals and paintings, are the treasures of human civilization. Pigment is an important component of their surfaces. It is crucial to study the composition and proportion of pigments on the surface of painted artefacts for the field of heritage conservation. In this study, hyperspectral remote sensing was used to invert the abundance of mixed pigments by spectral unmixing. Spectral variability effects are often present in hyperspectral images. Hyperspectral images of cultural artefacts also suffer from spectral variability due to factors such as particle size and impurities within the pigment, and acquisition conditions. Therefore, spectral variability was incorporated into the Linear Mixed Model of the pigment spectral unmixing. The unmixing methods are classified into two categories based on whether or not spectral libraries are used. In the experiment, computer synthesized data and laboratory-made sample blocks of mixed pigments, which mixed with different ratios of Azurite and Malachite, were selected as validation data. Five commonly used algorithms for solving the spectral variability problem, namely MESMA, Fractional Sparse SU, ELMM, RUSAL, and BCM, are used for pigment unmixing and compared with FCLS, which does not consider spectral variability. The results show that ELMM has the highest unmixing accuracy of aRMSE and xRMSE compared to other methods. At the same time, it can be seen from the metric of SAM that ELMM is able to extract reliable endmember variability spectral, which is more suitable for solving the problem of spectral variability in pigment unmixing. Finally, we apply ELMM to real artefacts Yungang Grottoes mural paintings, and obtain a lower xRMSE than FCLS, which improves the unmixing accuracy.

Buddhist niches in grottoes can be represented in three-dimensional (3D) for their detailed geometries on surfaces by using triangular meshes generated from point clouds. However, not all applications require 3D models with high geometric detail. The mesh models of niches have drawbacks such as large data volumes, lack of semantic information, and absence of spatial relationships between structural components and members within niches. Those limitations make mesh models suitable only for visualization and challenging to use directly in tasks like spatial analysis, simulation experiments, mechanical analysis, and disease investigation. To address this problem, this study defines four Levels of Detail (LoDs) for Buddhist niches, ranging from LoD0 to LoD3, drawing on the concept of LoDs for urban buildings in CityGML 3.0. As the LoD level increases, 3D models contain more detailed geometries, including spatial points, bounding boxes, niche structural components, and component members. Those 3D models at different LoDs can represent niches with varying degrees of abstraction, making them suitable for different applications and guiding the production of standardized 3D semantic models. To validate the feasibility of the LoDs definition for Buddha niches, this paper reconstructs 3D models of niches at different LoDs based on high-precision mesh models. Finally, a comparison is made between the original mesh model and models at different LoDs in terms of data size and potential application scenarios.

Our study utilizes a global reanalysis of near-surface daily air temperature data spanning the years from 1949 to 2019 to construct climate networks. By employing community detection for each year, we reveal the evolving community structure of the climate network within the context of global warming. Our findings indicate significant changes in measures such as network modularity and the number of communities over the past 30 years. Notably, the community structure of the climate network has undergone a discernible transition since the early 1980s. We attribute this transition to the substantial increase in isolated nodes since the 1980s, primarily concentrated in equatorial ocean regions. Additionally, we demonstrate that nodes experiencing amplified isolation tend to diminish connectivity with other nodes globally, particularly those within the same oceanic basin, while showing a significant strengthening of connections with the Eurasian and North African continents. We deduce that the mechanism driving amplified isolation in the climate network may be comprehended through the weakening of tropical circulations, such as the Hadley cell and Walker circulation, in response to increasing greenhouse gases.

Abstract The receptor-binding domain, region II, of the Plasmodium vivax Duffy binding protein (PvDBPII) binds the Duffy antigen on the reticulocyte surface to mediate invasion. A heterologous vaccine challenge trial recently showed that a delayed dosing regimen with recombinant PvDBPII SalI variant formulated with adjuvant Matrix-MTM reduced the in vivo parasite multiplication rate (PMR) in immunized volunteers challenged with the Thai P. vivax isolate PvW1. Here, we describe extensive analysis of the polyfunctional antibody responses elicited by PvDBPII immunization and identify immune correlates for PMR reduction. A classification algorithm identified antibody features that significantly contribute to PMR reduction. These included antibody titre, receptor-binding inhibitory titre, dissociation constant of the PvDBPII-antibody interaction, complement C1q and Fc gamma receptor binding and specific IgG subclasses. These data suggest that multiple immune mechanisms elicited by PvDBPII immunization are likely to be associated with protection and the immune correlates identified could guide the development of an effective vaccine for P. vivax malaria. Importantly, all the polyfunctional antibody features that correlated with protection cross-reacted with both PvDBPII SalI and PvW1 variants, suggesting that immunization with PvDBPII should protect against diverse P. vivax isolates.

A method of virtual restoration of inscribed texts is proposed for blurred inscriptions. Firstly, the image enhancement method is used to enhance the image of the current topos, and the optimal method is selected to highlight the text information. Secondly, the extracted text outline is extracted from the enhanced image, and the extracted text outline is partially processed to obtain a smooth outline, and finally the fill function is used to fill the text outline to complete the virtual restoration of the inscribed text. The experimental results prove that the method can restore for blurred text and retain the original stele text writing style. The results of this experiment can provide a reference basis for the virtual restoration of other inscriptions on the central axis.

Grottoes in China are crucial components of immovable cultural heritage. According to the 2020 national survey conducted by the State Administration of Cultural Heritage, the Sichuan-Chongqing region boasts a total of 2,850 grottoes and rock carvings, accounting for approximately half of the national total. Among these, Anyue County, situated in the central part of Sichuan Province, stands out as one of the most concentrated and distinctive areas for grottoes in the Sichuan-Chongqing region. Therefore, conducting a spatial pattern analysis of grottoes holds significant theoretical and practical value for the protection and cultural dissemination of cultural heritage in Anyue County. Building upon the spatial distribution of grottoes in Anyue County, this study integrates multiple geospatial data sources, such as water systems and elevation. By employing methods such as buffer analysis, kernel density analysis, and standard deviation ellipse analysis, it delineates the spatial distribution pattern in Anyue County and preliminarily identifies their Spatial distribution rules. The findings of this study are as follows: (1) Within the administrative boundaries of Anyue County, grottoes exhibit a distribution pattern characterized by "less in the west and more in the east" and "one belt with three cores." (2) Grottoes and rock carvings in Anyue County primarily concentrate within a 1500-meter range on both sides of rivers, with the number decreasing as the distance from the rivers increases. Moreover, there are significantly fewer sites near inward-flowing rivers compared to outward-flowing rivers. (3) Natural factors influence the distribution of grottoes, with the number of grottoes being much lower in higher-altitude areas than in lower-altitude areas.

As one of the most characteristic components of ancient Chinese architecture, Dougong has an important significance in the history of ancient Chinese architecture, but with the passage of time, wooden building components are prone to decay and missing, so it is particularly important to digitally retain the Dougong. In order to realize the Dougong component segmentation, this paper proposes a super voxel-based Dougong component primitive segmentation method, which is divided into two parts: firstly, super voxelizing the Dougong point cloud data, and secondly, realizing the Dougong component primitive segmentation by using multi-geometric constrained region growth algorithm. In order to verify the effectiveness of the method, this paper selects three main Dougong types of the Qing Dynasty, including pingshenke, jiaoke, zhutouke, for experimental validation, and the results show that the combination of super voxel and regional growth algorithm can effectively extract the geometric primitive surface of the Dougong components with high extraction efficiency and good adaptability, which can be prepared for the subsequent knowledge of Dougong construction method to achieve component segmentation.

As a carrier of history, culture, religion and art, grotto temples are an important part of China's splendid cultural heritage. Among them, there are many grotto statues with similar shapes and scattered on the grotto temples, which have high artistic value. It is of great significance to classify them.The existing classification methods of grotto temple statues are mainly based on traditional manual classification and machine learning classification, which often consumes a lot of labor costs and time costs. To solve these problems, this paper improves the PointNet network and applies it to the classification of statues in grottoes, which greatly improves the automation of the classification of statues. And the point cloud data set of the grotto temple is made for experiment. The results show that the overall accuracy of the method in this paper reaches 89.73%, the average intersection and combination ratio reaches 68.9%, and the accuracy is increased by 5.47% and 4.3% respectively compared with the random forest classification method. It is of great significance to the value research, status assessment and virtual restoration of the subsequent grotto temples.

To solve the problem that Dougong has various shapes and complex structures, the corresponding solutions are proposed in this paper. Our proposed method mainly consist of two parts. At first, the surface primitives were segmented using the machine learning (Random Forest). In this stage, the features including the curvature, normas and other features based on covariance matrix. Then, the knowledge from the construction rules were applied to label the segmented surface primitives into correct categories. The corresponding height constraint, concave-convex constraint, and symmetry constraint are proposed as the judgment conditions to mark the geometric elements belonging to the same dougong component and complete the point cloud segmentation of the dougong component. To verify the performance of our proposed method, the point cloud of a Qing-style single-arch flat-bodied Dougong was tested. The experimental results show that the classification accuracy of point cloud is 96.0%.

This paper focuses on VR/AR applications in immovable tangible cultural heritage (ITCH) tourism by considering both academic research and cases in the XR industry. The goal is to understand how VR/AR can be used to present heritage information to the public. A framework is developed, including the purpose of using the technology, hardware, software, and user experience to have an overall understanding of VR/AR applications. Cases collected based on the framework are divided into four heritage types to answer the following questions: under a certain heritage context, what is the commonly used technology, is there a correlation between the purpose of using VR/AR, the enabling technology, and the user experience, and is there a difference in the concentration point between academia and the XR industry in China.

Identification and manipulation of cellular energy regulation mechanisms may be a strategy to increase productivity in photosynthetic organisms. This work tests the hypothesis that polyphosphate synthesis and degradation play a role in energy management by storing or dissipating energy in the form of ATP. A polyphosphate kinase (ppk) knock-out strain unable to synthesize polyphosphate was generated in the cyanobacterium Synechocystis sp. PCC 6803. This mutant strain demonstrated higher ATP levels and faster growth than the wildtype strain in high-carbon conditions and had a growth defect under multiple stress conditions. In a strain that combined ppk deletion with heterologous expression of ethylene-forming enzyme, higher ethylene productivity was observed than in the wildtype background. These results support the role of polyphosphate synthesis and degradation as an energy regulation mechanism and suggest that such mechanisms may be effective targets in biocontainment design.

We have previously reported primary endpoints of a clinical trial testing two vaccine platforms for the delivery of Plasmodium vivax malaria DBPRII: viral vectors (ChAd63, MVA), and protein/adjuvant (PvDBPII with 50µg Matrix-M™ adjuvant). Delayed boosting was necessitated due to trial halts during the pandemic and provides an opportunity to investigate the impact of dosing regimens. Here, using flow cytometry – including agnostic definition of B cell populations with the clustering tool CITRUS – we report enhanced induction of DBPRII-specific plasma cell and memory B cell responses in protein/adjuvant versus viral vector vaccinees. Within protein/adjuvant groups, delayed boosting further improved B cell immunogenicity compared to a monthly boosting regimen. Consistent with this, delayed boosting also drove more durable anti-DBPRII serum IgG. In an independent vaccine clinical trial with the P. falciparum malaria RH5.1 protein/adjuvant (50µg Matrix-M™) vaccine candidate, we similarly observed enhanced circulating B cell responses in vaccinees receiving a delayed final booster. Notably, a higher frequency of vaccine-specific (putatively long-lived) plasma cells was detected in the bone marrow of these delayed boosting vaccinees by ELISPOT and correlated strongly with serum IgG. Finally, following controlled human malaria infection with P. vivax parasites in the DBPRII trial, in vivo growth inhibition was observed to correlate with DBPRII-specific B cell and serum IgG responses. In contrast, the CD4+ and CD8+ T cell responses were impacted by vaccine platform but not dosing regimen and did not correlate with in vivo growth inhibition in a challenge model. Taken together, our DBPRII and RH5 data suggest an opportunity for protein/adjuvant dosing regimen optimisation in the context of rational vaccine development against pathogens where protection is antibody-mediated.

Tibetan Buddhist architecture embodies ancient Chinese architectural culture and religious culture. In the past, the information retention mechanisms for ancient buildings were implemented as photos, tracings, and rubbings, which cannot fundamentally document the authenticity of architectural heritage. To explore the digital retention method for the unique style of Han Tibetan architecture，this research that based on the idea of reverse documentation first collects point cloud data with the technical support of unmanned aerial vehicle (UAV) photogrammetry and terrestrial laser scanner (TLS) and then uses registration method to obtain the integral of the point cloud model of Baoguang Hall. This paper explores the possibility of extracting 2D and 3D information, such as architectural plans, facades, decorative components, and models of the temple architecture, by processing point cloud data. Finally, this study proves the feasibility of using digital technology for the preservation and protection of architectural heritage.

The ancient city wall is affected by factors such as environment and man-made factors, and the formed bulging disease is particularly prominent, and even caused irreversible damage to the ancient city wall. Therefore, the bulging monitoring after the repair of the city wall should be the top priority of the deformation monitoring of the city wall. This paper proposes a new plan for monitoring the bulge after the repair of the city wall. According to the point cloud data before the repair, the characteristic plane is fitted to determine the bulge range of the city wall, and then observation signs are placed on the surface of the repaired city wall at the corresponding location where the bulging deformation is severe. By using a total station to monitor the space coordinates of the observation signs, the deformations perpendicular to the wall can be obtained through coordinate conversion, and then the bulging of the repaired city wall can be determined. The actual application results show that this method can effectively monitor the swelling of the ancient city wall after the repair.

Aiming at solving the problem of how to obtain the different evidence and deduce the disappeared sections of the Great Wall site, this paper quoted the concept of evidence scale and proposed a virtual restoration framework. In this paper, taking the Nine-eyes watchtower as an example, we modified the evidence-scale from 10 to 3: the existed relic, the direct evidence and the indirect evidence. The framework consisted of three parts, namely, the classification of evidence scale, the evidence checks and the generation of three-dimensional (3D) virtual models. Base on the support of the restoration scheme and 3DMAX technology, the disappeared parts of Nine-eyes watchtower was finally restored to its original appearance. The result of virtual restoration could not only help the tourists to visit the original appearance of cultural heritage, but also experience the culture value and historical vicissitude. Therefore, it was of great scientific significance and cultural value to study how to carry out virtual restoration for the ancient relics.

Chinese ancient architecture is a valuable heritage wealth, especially for roof that reflects the construction age, structural features and cultural connotation. Point cloud data, as a flexible representation with characteristics of fast, precise, non-contact, plays a crucial role in a variety of applications for ancient architectural heritage, such as 3D fine reconstruction, HBIM, disaster monitoring etc. However, there are still many limitations in data editing tasks that need to be worked out manually, which is time-consuming, labor-intensive and error-prone. In recent years, the theoretical advance on deep learning has stimulated the development of various domains, and digital heritage is not in exception. Whenever, deep learning algorithm need to consume a huge amount of labeled date to achieve the purpose for segmentation, resulting a actuality that high labor costs also be acquired. In this paper, inspired by the architectural style similarity between mimetic model and real building, we proposed a method supported by deep learning, which aims to give a solution for the point cloud automatic extraction of roof structure. Firstly, to generate real point cloud, Baoguang Temple, unmanned Aerial Vehicle (UAV) is presented to obtain image collections that are subsequently processed by reconstruction technology. Secondly, a modified Dynamic Graph Convolutional Neural Network (DGCNN) which can learn local features with taking advantage of an edge attention convolution is trained using simulated data and additional attributes of geometric attributes. The mimetic data is sampled from 3DMAX model surface. Finally, we try to extract roof structure of ancient building from real point clouds scenes utilizing the trained model. The experimental results show that the proposed method can extract the rooftop structure from real scene of Baoguang, which illustrates not only effectiveness of approach but also a fact that the simulated source perform potential value when real point cloud datasets are scarce.

Due to the influence of natural and human factors, the linear features in the murals are partially blurred, which brings great challenges to the digital preservation and virtual restoration of cultural heritage. Taking the advantages of non-invasive measurement as well as the rich image and spectral information of hyperspectral technology, we proposed a linear feature enhancement method by combining semi-supervised superpixel segmentation with block dimension reduction. The main research work includes: (1) The true color composite image was segmented to obtain the label data by using the local spatial information of the superpixel image and the global feature information extracted by fuzzy c-means (FCM) clustering.(2) According to the label data, the preprocessed hyperspectral data were divided into homogeneous regions, whose dimensionality was reduced by principal component analysis (PCA) and kernel principal component analysis (KPCA). (3) The principal component images with the largest gradient after dimensionality reduction were respectively selected and normalized. The optimal principal component images normalized by the block PCA and block KPCA dimensionality reduction algorithms are superimposed to produce the linear feature enhancement images of murals. The hyperspectral images of some murals in Qutan Temple, Qinghai Province, China were used to verify the method. The results show that the spatial information and the spectral information of different pattern areas in the hyperspectral image can be fully used by combining the superpixel FCM image segmentation algorithm with the dimensionality reduction algorithm. of. It can highlight the linear information in the hyperspectral images of fades murals.

The niche for Buddha is an important part of the cultural relics in the grottoes, but it has caused serious spalling disease due to human activities and natural disasters. This paper presents a new quantitative evaluation method for the spalling disease of niche for Buddha. First, the random forest-based method was used to identify the niche for Buddha . Then, the spatial clustering of the head of the niche for Buddha was performed by DBSCAN, and the reference surface was determined by the RANSAC algorithm. Based on this, the spallation depth of the grotto surface was extracted and the relevant index elements were established. The experiment of point cloud data in Cave 18 of Yungang Grottoes proves the rationality of this method, which is of great significance to the protection and virtual restoration of cultural relics in grottoes.

In recent years, air pollution related to PM2.5 has caused a significant impact on human health. The Grand Canal (GC) is not only a great Cultural heritage created in ancient China but also the longest and largest canal in the world. Based on remotely sensed PM2.5 gridded data in the GC region covering 2000 to 2018, we used the holistic methods of standard deviation ellipse, local moran index, slope trend analysis to reveal the spatiotemporal evolutions of PM2.5 concentrations in the GC regions and investigated the driving factors of PM2.5 concentrations by using the geographically weighted regression (GWR) model. Results show that (1) PM2.5 concentrations in the GC region exhibited an increasing trend and followed by a decreasing trend from 2000 to 2018 (the turning point emerged in 2010). (2) The standard deviation ellipse analyses show that the spatial distributions of PM2.5 concentrations featured more and more concentrated over time, whereas, after the year 2010, the distributions gradually featured scattered. (3) The concentrations of PM2.5 exhibited the strong effects of local spatial autocorrelation and areas with "high-high" agglomeration were mainly located in the central and west regions of the GC region and gradually expanded to the north over time. (4) The areas of regions with rapidly increasing in PM2.5 concentrations gradually decreased over time, however, those with rapidly decreasing in PM2.5 concentrations increased. (5) The influences of the natural factors and socio-economic factors on the distributions of PM2.5 concentrations varied spatially. In detail, the elevation was negatively correlated with PM2.5 concentrations, whereas an opposite relationship between industrial structure and PM2.5 concentrations was observed. The coefficients of rainfall, population density, GDP per capita and foreign investment show different results in positive and negative correlations depending on the position.

Mural painting is one of the important cultural heritage reflecting the historical migration of the nation. In order to inherit these precious historical and cultural heritage, how to non - destructively and digitally protect and restore the existing murals has become an urgent task. The use of computer - assisted restoration of murals can not only save manpower and material resources, but also avoid secondary damage to the murals.However, most of the existing computer-assisted mural restoration algorithms use similar blocks with priority calculations and matching blocks in adjacent areas to guide mural restoration. There are some problems such as incoherent overall semantic structure, unnatural detail texture and inability to effectively repair large area missing remain to be solved. Aiming at the problems existing in the restoration of large area diseases such as paint loss and color fading in murals, we constructed a fine image restoration network model which based on generative adversarial network. A multi-scale dense matching repair network based on a generative adversarial network is constructed. First, the dense combination of dilated convolutions is used to improve the repair effect of detailed textures, Then, mean absolute Error, (Visual Geometry Group, VGG) feature matching, auto-guided regression, and geometric alignment are used as the loss function to guide the training of the generative network. Second, the discriminator with local and global branches is used to train the discriminant network, so that the repaired image is in the local and global content. Experiments were performed on the three mural data sets one by one. The results show that the network model can effectively restore the lines and faces in the murals. The images restored are not only coherent in semantic details, but also natural in color, which is conducive to the appreciation and display of murals. Thus, as one of the important directions of cultural heritage digital protection，the use of generative adversarial network in the digital restoration of ancient murals have been proved to be effective. It not only provides a reference for the true restoration of the murals but also means a lot to the preservation of murals.

Cultural relics are often threatened by nature and human, especially stone carving relics which have immovable characteristics. Compared with other cultural relics, the diseases of stone carving relics are more complex, and they can affect carvings’ cultural and artistic value to a great extent. This article selects Dazu thousand-hand bodhisattva as a case, not only because it has a strong representation in Chinese stone carving art, but also considering that there are many complex diseases in the whole range after a long history, therefore the Dazu thousand-hand bodhisattva has high research value, and scientific investigation methods are essential for the protection and research of cultural relics. The main purpose of this paper is to investigate the arm diseases of Dazu thousand-hand bodhisattva by using the two methods of GIS spatial analysis: kernel density analysis and trend analysis. The past investigation methods are difficult to achieve the expected results because of their strong subjectivity and narrow range, and the use of spatial analysis to investigate the diseases of stone carvings can study the spatial characteristics and coupling relationship of stone carvings from the macro level.

Urban architectural heritage as a part of cultural heritage, its surrounding buildings will have a certain visual impact on the cultural heritage itself. This paper presents a method for calculating visual impact and applies it to the field of urban architectural heritage. In this method, the visual radius and visual range are calculated based on the theoretical knowledge related to the range of human eye line of sight and the curvature of the earth, and then the visual projection plane is established. Then, through skyline analysis and spatial relationship calculation, the urban architectural heritage and its surrounding buildings are projected. Calculate the visual impairment index through the projection area and the projection surface area to quantify the visual impact. By calculating the visual impact in The Lama Temple area, it is proved that the method presented in this paper is effective and advanced in practical application.

With thousands of years of glorious history and culture, China, which is the origin of Chinese civilization has also survived with a magnificent amount of stone cultural heritage. Stone monuments are one of the most important components of Chinese monuments and contain a wealth of historical information by themselves. Due to the fact that some stone monuments and stone architectural elements are often exposed to the natural environment, they are subject to physical, biological, chemical and human damage over a long period of time, which leads to the formation of various kinds of damages. Therefore, it is an important issue to extract and analysis the diseases information effectively. In this paper, we were going to examine these issues in the context of existing surveying and mapping techniques.

In endemic settings it is known that natural malaria immunity is gradually acquired following repeated exposures. Here we sought to assess whether similar acquisition of blood-stage malaria immunity would occur following repeated parasite exposure by controlled human malaria infection (CHMI). We report the findings of repeat homologous blood-stage Plasmodium falciparum (3D7 clone) CHMI studies VAC063C (ClinicalTrials.gov NCT03906474) and VAC063 (ClinicalTrials.gov NCT02927145). In total, 24 healthy, unvaccinated, malaria-naïve UK adult participants underwent primary CHMI followed by drug treatment. Ten of these then underwent secondary CHMI in the same manner, and then six of these underwent a final tertiary CHMI. As with primary CHMI, malaria symptoms were common following secondary and tertiary infection, however, most resolved within a few days of treatment and there were no long term sequelae or serious adverse events related to CHMI. Despite detectable induction and boosting of anti-merozoite serum IgG antibody responses following each round of CHMI, there was no clear evidence of anti-parasite immunity (manifest as reduced parasite growth in vivo) conferred by repeated challenge with the homologous parasite in the majority of volunteers. However, three volunteers showed some variation in parasite growth dynamics in vivo following repeat CHMI that were either modest or short-lived. We also observed no major differences in clinical symptoms or laboratory markers of infection across the primary, secondary and tertiary challenges. However, there was a trend to more severe pyrexia after primary CHMI and the absence of a detectable transaminitis post-treatment following secondary and tertiary infection. We hypothesize that this could represent the initial induction of clinical immunity. Repeat homologous blood-stage CHMI is thus safe and provides a model with the potential to further the understanding of naturally acquired immunity to blood-stage infection in a highly controlled setting.Clinical Trial RegistrationClinicalTrials.gov, identifier NCT03906474, NCT02927145.

Since the digital technologies emerge, digital heritage has become an integral part of the world's cultural heritage under the leadership of UNESCO. With the development of digitization and accumulation of data, the digital information processing system for cultural heritage keeps updating. However, there still exist the problems of low information integration degree and weak information sharing ability, severely restricting the promotion and integration of cultural heritage and society. This paper proposes a digital cultural heritage cloud platform, where basic data service, knowledge service, engineering application, visual exhibition were realized through HTTP request. This platform completely encapsulated a large number of cultural heritage data processing algorithms. Accordingly, a cultural heritage data management system, a cultural heritage knowledge construction and application system, a cultural heritage display, analysis and evaluation system, and a cultural heritage microenvironment index monitoring system were embedded in this platform. In addition, the platform provided API for professional customized development to provide effective support. This platform can be flexibly adapted and extended, laying solid foundation for digital information sharing of cultural heritage.

Plane segmentation from the point cloud is an important step in various types of geo-information related to human activities. In this paper, we present a new approach to accurate segment planar primitives simultaneously by transforming it into the best matching issue between the over-segmented super-voxels and the 3D plane models. The super-voxels and its adjacent topological graph are firstly derived from the input point cloud as over-segmented small patches. Such initial 3D plane models are then enriched by fitting centroids of randomly sampled super-voxels, and translating these grouped planar super-voxels by structured scene prior (e.g. orthogonality, parallelism), while the generated adjacent graph will be updated along with planar clustering. To achieve the final super-voxels to planes assignment problem, an energy minimization framework is constructed using the productions of candidate planes, initial super-voxels, and the improved adjacent graph, and optimized to segment multiple consistent planar surfaces in the scenes simultaneously. The proposed algorithms are implemented, and three types of point clouds differing in feature characteristics (e.g. point density, complexity) are mainly tested to validate the efficiency and effectiveness of our segmentation method.

As there usually exist widespread crack, decay, deformation and other damages in the wooden architectural heritage (WAH). It is of great significance to detect the damages automatically and rapidly in order to grasp the status for daily repairs. Traditional methods use artificial feature-driven point clouds and image processing technology for object detection. With the development of big data and GPU computing performance, data-driven deep learning technology has been widely used for monitoring WAH. Deep learning technology is more accurate, faster, and more robust than traditional methods.In this paper, we conducted a case study to detect timber-crack damages in WAH, and selected the YOLOv3 algorithm with DarkNet-53 as the backbone network in the deep learning technology according to the characteristics of the crack. A large timber-crack dataset was first constructed, based on which the timber-crack detection model was trained and tested. The results were analyzed both qualitatively and quantitatively, showing that our proposed method was able to reach an accuracy of more than 90% through processing each image for less than 0.1s. The promising results illustrate the validity of our self-constructed dataset as well as the reliability of YOLOv3 algorithm for the crack detection of wooden heritage.

Different from the field of remote sensing, artificial lights are often utilized as the energy source for spectral imaging in the ground hyperspectral applications. The kind of double-spot light source is widely adopted in some large scale ground hyperspectral applications. However, it is hard to reach a satisfied lighting without difference in light intensity in many cases although the lamps are tuned carefully. Therefore, a reflectance calibration of hyperspectral imaging based on the data of diffuse reflectance standard and Gaussian surface fitting is proposed in this paper. The purpose is to improve the reconstruction accuracy of hyperspectral reflectance image by minimized the error caused by the uneven illumination of artificial light source. The method has a higher accuracy than traditional one according to the experiment results.

Laser scanning or photogrammetry are useful individual techniques for digital documentation of cultural heritage sites. However, these techniques are of limited usage if cultural heritage such as the Great Wall is in harsh geographical conditions. The Great Wall is usually built on the ridge with cliffs on both sides, so it is very difficult to construct scaffolding. Therefore, the three-dimensional (3D) data obtained from the traditional 3D laser scanning is not complete. As UAV cannot enter the enemy tower, the 3D structure data inside the enemy tower with unmanned aerial vehicle (UAV) photogrammetry is missing. In order to explore effective methods to completely collect the 3D data of cultural heritage under harsh geographical environment, this study focuses on establishing a 3D model and the associated digital documentation for the No.15 enemy tower of the New Guangwu Great Wall using a combination of terrestrial laser scanning and UAV photogrammetry. This paper proposes an integrated data collection method and reduces the layout of image control points using RTK-UAV technology, which improved work efficiency and reduced work risks as well. In this paper, the internal structure data of the Great Wall enemy tower was collected by laser scanning, the external structure data was collected by UAV photogrammetry, and data fusion was based on ICP algorithm. Finally, we obtained the complete and high quality 3D digital documentation of the Great Wall enemy tower, the data can be displayed digitally and help heritage experts complete the Great Wall's restoration. This study demonstrates the potential of integrating terrestrial laser scanning and UAV photogrammetry in 3D digital documentation of cultural heritage sites.

A constitutive model based on damage mechanics and statistical strength theory for sandstone under water-rock coupling is proposed and verified by laboratory tests and numerical tests. The damage mechanism of sandstone under the coupling action of water and force is clarified based on the microscopic test of sandstone under the coupling action of water and rock in this paper. The stress state of sandstone is determined by analyzing the results of laboratory tests. Based on the energy theory method, the strength criterion of sandstone under the interaction of water and rock is obtained, which is introduced into the statistical damage constitutive model of sandstone considering the interaction of water and rock; thus, a relatively perfect damage constitutive model of sandstone considering the interaction of water and rock is established. Finally, the influence of the number of cycles on the model parameters was further analyzed by analyzing the model parameters and the water-rock coupling test. Compared with the existing test results, the model established in this paper can perfectly reflect the deterioration characteristics of sandstone under the coupling action of water and rock.

For positioning an object with m references, there are m-1 linearly independent range differences and measuring them is essential. However, none of m(m-1) possible range differences should be considered redundant unless their measurements are free of noise and locations of the references are exactly known. From all available range-difference measurements, m range measurements are obtained for positioning based on the least squares principle. The problem formulation treats missing and weighted range-difference measurements simultaneously. The exact relationships among several formulations of least squares positioning are established. A numerical example illustrates the results.

Controlled human malaria infection (CHMI) provides a highly informative means to investigate host-pathogen interactions and enable in vivo proof-of-concept efficacy testing of new drugs and vaccines. However, unlike Plasmodium falciparum, well-characterized P. vivax parasites that are safe and suitable for use in modern CHMI models are limited. Here, 2 healthy malaria-naive United Kingdom adults with universal donor blood group were safely infected with a clone of P. vivax from Thailand by mosquito-bite CHMI. Parasitemia developed in both volunteers, and prior to treatment, each volunteer donated blood to produce a cryopreserved stabilate of infected RBCs. Following stringent safety screening, the parasite stabilate from one of these donors (PvW1) was thawed and used to inoculate 6 healthy malaria-naive United Kingdom adults by blood-stage CHMI, at 3 different dilutions. Parasitemia developed in all volunteers, who were then successfully drug treated. PvW1 parasite DNA was isolated and sequenced to produce a high-quality genome assembly by using a hybrid assembly method. We analyzed leading vaccine candidate antigens and multigene families, including the vivax interspersed repeat (VIR) genes, of which we identified 1145 in the PvW1 genome. Our genomic analysis will guide future assessment of candidate vaccines and drugs, as well as experimental medicine studies.

Among the immovable cultural relics such as grottoes, stone carvings of the same period usually show similar artistic styles and repetitive statues. Through the study of the similarity between cultural relics in the same period, we can have reference value for the historical significance, artistic characteristics and cultural relics restoration of cultural relics. The traditional discrimination of cultural relics similarity often depends on the subjective experience judgment of experts, which is time-consuming and inefficient. Often, different experts may issue different conclusions, the lack of convincing objective evaluation basis, can not provide effective scientific support for the protection and restoration of cultural relics. In order to provide objective criteria for evaluating the similarity of cultural relics and avoid the interference of human factors, this paper proposes the concept of similarity index of cultural relics based on perceptual Hash (pHsah) algorithm. For orthophoto images of cultural relics with similarity index within the threshold, the SIFT operator is further used to find the corresponding feature points. This paper takes the Buddha statues in Yungang Grottoes as the research object, objectively calculates the similarity index of cultural relics, combines the pHash algorithm and Sift operator, realizes the objective quantitative discrimination of cultural relics similarity, effectively avoids the subjective influence of experts on the judgment of cultural relics similarity.

The rapid development of 3D laser scanning and 3D printing technology provides new technologies and ideas for cultural relic protection and reproduction. Aiming at the requirement of equal proportional reproduction of large-scale grottoes, this paper takes the point cloud data of the 18th Cave of Yungang Grottoes obtained by 3D laser scanning as an example, and proposes a data processing and reproduction block partitioning method for equal proportion reproduction. The Cyclone, Geomagic and AutoCAD software were used to construct the 3D model of the grotto, and the 3D printing technology was used to realize the secondary design and model print. Among them, the research focuses on the modeling of massive point clouds and the method of model partitioning based on voxels. It can meet the requirements of movable and assembly while realizing the equal proportional reproduction of the whole grotto. The research results and application can be a good reference for the future grotto reproduction work.

A demand-oriented Building Information Model (BIM) model built using high-fidelity point cloud data can better protect architectural heritage. The multi-level detail (mutli-LoD) parametric model emphasizes the different protection requirements of typical components and the automatic extraction of corresponding parameters of high-fidelity point clouds, which are two related key issues. Taking the typical Chinese wooden architectural heritage as an example, according to different requirements, the multi-LoD principle of typical components is proposed. On this basis, the automatic extraction method of the above parameters is developed, and the key parameters of the method are recommended. In order to solve the above problems, taking the three typical Dou-Gong used in Liao Dynasty and Song Dynasty, including Zhutou Puzuo, Bujian Puzuo and Zhuanjiao Puzuo, as an example, briefly introduced the standardization characteristics of the typical components of the "Yingzao Fashi". Subsequently, the corresponding multiple LoD principles are recommended according to different requirements. Based on this and high-fidelity point cloud data, an automatic extraction method for multi-LoD BIM model parameters for typical components of wooden architectural heritage is proposed.

Suffered from the environmental changes and human’s influence, Chinese paintings are facing a worrying situation in documentation, which leads urgent into establishing digital archives to realize the permanent preservation of its surface information. Non-destructive identification of pigment types using hyperspectral techniques is the key to ensure the colour restoration scientifically. At present, one commonly used method for spectral identification is to calculate the similarities between unknown spectra and standard spectra in spectral library. The purpose of this study is to establish a spectral library of typical pigments specifically for the information preservation and pigment types identification on surface of Chinese paintings. The main works of the research are: (1) collecting the standard spectra in laboratory and the pigment spectra on surface of real cultural relics as the data source of the spectral library; (2) adding the expert knowledge of traditional pigments and normative descriptions of spectra measurement as part of the spectral library; and (3) constructing the spectral library website with the Browser/Server structure, and by using MySQL database for storage, SSM framework for background building and JSP page for displaying. The significance of the design and construction of the Typical Pigments of Chinese Paintings (TPCP) spectral library is not only to establish a standard spectral library for pigment types identification, but also a digital archive of pigment information on the surface of real cultural relics, to achieve permanent preservation, and provide spectral data sources and pigment information references for conservation workers and researchers in related fields for further research.

With the development of social economy, the definition, scope and connotation of architectural heritage have also evolved. However, the architectural heritage is facing the threat of destruction and destruction. The sustainable protection of architectural heritage has become a major strategic need of the country to enhance cultural self-confidence. In the past century, the concept of architectural heritage protection has evolved many times. At present, preventive protection and research protection have become the consensus in the field of architectural heritage protection. The idea of preventive protection is to delay the destruction or extinction of architectural heritage by controlling the main factors that cause the changes in the ontology. Preventive protection is different from emergency protection after the destruction of architectural heritage. It mainly discovers and eliminates hidden dangers through scientific records, periodic inspection, systematic monitoring and routine maintenance. It emphasizes the analysis of damage evolution law through risk assessment and scientific monitoring and takes effective protection measures. Based on the concept of preventive protection and research protection, combining the accumulation of prior art development and protection practices, this paper proposes a four-in-one sustainable preservation method system for architectural heritage. It mainly includes four aspects: architectural heritage value and protection demand cognition, all-round information access technology, architectural heritage monitoring and evaluation, architectural heritage performance improvement and value continuation.

The National Biosafety Laboratory level 4 in Wuhan was accredited according to the Guo Biao (National standard) 19489:2008 standard in 2017 by the China National Accreditation Service for Conformity Assessment and was then licensed to store and handle microorganisms of risk group 4. The current aim of laboratory management is to accredit this high-containment laboratory according to the ISO/IEC 17025:2017 standard to set up a quality management system. To complement this standard, the Comité Européen de Normalisation (CEN, i.e., European Committee for Standardization) Working Agreement 15793:2011 is also used for safety and security against biorisks. The Chinese Guo Biao 19489:2008 standard is also taken into account to achieve two aims: to comply with Chinese legislation and to improve the level of detail in the laboratory’s documentation. Furthermore, the management proposes to apply Good Research Laboratory Practices (published by the China National Accreditation Service for Conformity Assessment), in which ethical rules are laid down. Only a well-integrated and well-managed quality management system can master the implementation of all these different guidelines. Nonetheless, it would be better if all the requirements were combined into one standard. Therefore, there is a real need for drafting a new standard or guideline specifically for containment laboratories. Keywords: Biocontainment, Biosecurity, Bbiosafety, BSL4, Accreditation

As a common feature, bilateral symmetry of biological forms is ubiquitous, but in fact rarely exact. In a setting of analytic geometry, bilateral symmetry is defined with respect to a point, line or plane, and the well-known notions of fluctuating asymmetry, directional asymmetry and antisymmetry are recast. A meticulous scheme for asymmetry assessments is proposed and explicit solutions to them are derived. An investigation into observational errors of points representing the geometric structure of an object offers a baseline reference for asymmetry assessment of the object. The proposed assessments are applicable to individual, part or all point pairs at both individual and collective levels. The exact relationship between the developed treatments and the widely used Procrustes method in asymmetry assessment is examined. An application of the proposed assessments to a large collection of human skull data in the form of 3D landmark coordinates finds: (a) asymmetry of most skulls is not fluctuating, but directional if measured about a plane fitted to shared landmarks or side landmarks for balancing; (b) asymmetry becomes completely fluctuating if one side of a skull could be slightly rotated and translated with respect to the other side; (c) female skulls are more asymmetric than male skulls. The methodology developed in this study is rigorous and transparent, and lays an analytical base for investigation of structural symmetries and asymmetries in a wide range of biological and medical applications.

There are a large number of materials with important historical information in ancient tombs. However, in many cases, these substances could become obscure and indistinguishable by human naked eye or true colour camera. In order to classify and identify materials in ancient tomb effectively, this paper applied hyperspectral imaging technology to archaeological research of ancient tomb in Shanxi province. Firstly, the feature bands including the main information at the bottom of the ancient tomb are selected by the Principal Component Analysis (PCA) transformation to realize the data dimension. Then, the image classification was performed using Support Vector Machine (SVM) based on feature bands. Finally, the material at the bottom of ancient tomb is identified by spectral analysis and spectral matching. The results show that SVM based on feature bands can not only ensure the classification accuracy, but also shorten the data processing time and improve the classification efficiency. In the material identification, it is found that the same matter identified in the visible light is actually two different substances. This research result provides a new reference and research idea for archaeological work.

3D data fusion is a research hotspot in the field of computer vision and fine mapping, and plays an important role in fine measurement, risk monitoring, data display and other processes. At present, the research of 3D data fusion in the field of Surveying and mapping focuses on the 3D model fusion of terrain and ground objects. This paper summarizes the basic methods of 3D data fusion of terrain and ground objects in recent years, and classified the data structure and the establishment method of 3D model, and some of the most widely used fusion methods are analysed and commented.

Since the colour in painting cultural relics observed by our naked eyes or hyperspectral cameras is usually a mixture of several kinds of pigments, the mixed pigments analysis will be an important subject in the field of ancient painting conservation and restoration. This paper aims to find a more effective method to confirm the types of every pure pigment from mixture on the surface of paintings. Firstly, we adopted two kinds of blind source separation algorithms, which are independent component analysis and non-negative matrix factorization, to extract the pure pigment component from mixed spectrum respectively. Moreover, we matched the separated pure spectrum with the pigments spectra library built by our team to determine the pigment type. Furthermore, three kinds of data including simulation data, mixed pigments spectral data measured in laboratory, and the spectral data of an ancient painting were chosen to evaluate the performance of the different algorithms. And the accuracy was compared between the two algorithms. Finally, the experimental results show that non-negative matrix factorization method is more suitable for endmember extraction in the field of ancient painting conservation and restoration.