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Instead of “2Tianjin Medical University Cancer Institute and Hospital, Tianjin China, 3National Clinical Research Center for Cancer, Tianjin, China, 4Key Laboratory of Cancer Prevention and Therapy, Tianjin China, 5Tianjin’s Clinical Research Center for Cancer, Tianjin China, 6Key Laboratory of Cancer Immunology and Biotherapy, Tianjin, China, 7Department of Immunology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China, 8Wenzhou Central Hospital, Wenzhou, Zhejiang, China, 9Department of Head and Neck Surgery, Shanxi Province Cancer Hospital/ Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, Shanxi, China, 10Department of Pathology, Shanxi Bethune Hospital, Taiyuan, Shanxi, China, 11General Surgery Department, Shanxi Bethune Hospital, Taiyuan, Shanxi, China, 12Haihe Laboratory of Cell Ecosystem, Tianjin, China, 13Department of Biotherapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.”, it should be:” 2National Clinical Research Center for Cancer, Tianjin, China, 3Key Laboratory of Cancer Prevention and Therapy, Tianjin, China, 4Tianjin’s Clinical Research Center for Cancer, Tianjin, China, 5Key Laboratory of Cancer Immunology and Biotherapy, Tianjin, China, 6Department of Immunology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China, 7Wenzhou Central Hospital, Wenzhou, Zhejiang, China, 8Department of head and neck surgery, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, Shanxi, China, 9Department of Pathology, Shanxi Bethune Hospital, Taiyuan, Shanxi, China, 10General Surgery Department, Shanxi Bethune Hospital, Taiyuan, Shanxi, China, 11Haihe Laboratory of Cell Ecosystem, Tianjin, China, 12Department of Biotherapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.” The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. In the published article, there was an error regarding the affiliation(s) for Siyuan Zhang, Qiuru Zhou, Fei Han, Gang Du, Lin Wang, Xuena Yang, Xiying Zhang, Wenwen Yu, Feng Wei, Xishan Hao, Xiubao Ren and Hua Zhao.

ObjectiveThis study aimed to explore the value of clinical-radiomics features for predicting response to immunotherapy in extensive-stage small cell lung cancer (ES-SCLC).MethodsThis retrospective study enrolled patients with ES-SCLC who received immunotherapy as first-line treatment from two centers. Patients were divided into a training and an external test cohort. Chest Computed Tomography (CT) images were obtained at baseline and after 2–3 cycles of immunotherapy. Each lesion was segmented based on intratumoral regions (ITR) in the plain scan (PS) and venous phase (VP) CT images. Radiomic features, including absolute and relative delta features were extracted. Four signatures were established by the least absolute shrinkage and selection operator (LASSO) after selecting relevant features. Multivariable logistic regression incorporating signature scores and clinical predictors was used to generate a nomogram. The performance of the nomogram was evaluated through area under the curves (AUC) analysis, calibration curves, and decision curve analysis (DCA). Tertiary lymphoid structures (TLS) and the tumor immune microenvironment (TIME) of tumors were investigated via multiplex immunohistochemistry (mIHC). Kaplan-Meier curves were constructed to illustrate Overall Survival (OS) in different patients groups.ResultsThe nomogram was built based on two radiomics signatures (ITR before treatment; relative delta radiomics) and two clinical factors (age; node). This model showed powerful predictive ability for both training and external test sets with AUCs of 0.919 and 0.839, respectively. Calibration curves and DCA showed a favorable predictive performance of the nomogram.ConclusionThe nomogram that included ITR, delta radiomic features, and clinical risk factors had the best performance in predicting prognosis for patients with ES-SCLC who received immunotherapy compared to models relying solely on radiomic features or clinical risk factors, and has the potential to assist clinicians in making personalized treatment decisions.

There is increasing evidence that tertiary lymphoid structures (TLS) control not only local adaptive B cell responses at melanoma tumor sites but also the cellular composition and function of other immune cells. In human melanoma, however, a comprehensive analysis of TLS phenotypes, density and spatial distribution at different disease stages is lacking. Here we used 7-color multiplex immunostaining of whole tissue sections from 103 human melanoma samples to characterize TLS phenotypes along the expression of established TLS-defining molecular and cellular components. TLS density and spatial distribution were determined by referring TLS counts to the tissue area within defined intra- and extratumoral perimeters around the invasive tumor front. We show that only a subgroup of primary human melanomas contains TLS. These TLS rarely formed germinal centers and mostly located intratumorally within 1 mm distance to the invasive tumor front. In contrast, melanoma metastases had a significantly increased density of secondary follicular TLS. They appeared preferentially in stromal areas within an extratumoral 1 mm distance to the invasive tumor front and their density varied over time and site of metastasis. Interestingly, secondary follicular TLS in melanoma often lacked BCL6 + lymphatic cells and canonical germinal center polarity with the formation of dark and light zone areas. Our work provides an integrated qualitative, quantitative and spatial analysis of TLS in human melanoma and shows disease progression- and site-associated changes in TLS phenotypes, density and spatial distribution. The frequent absence of canonical germinal center polarity in melanoma TLS highlights the induction of TLS maturation as a potential additive to future immunotherapy studies. Given the variable evaluation strategies used in previous TLS studies of human tumors, an important asset of this study is the standardized quantitative evaluation approach that provides a high degree of reproducibility.

As a revolutionary technology, terrestrial laser scanning (TLS) is attracting increasing interest in the fields of architecture, engineering and construction (AEC), with outstanding advantages, such as highly automated, non-contact operation and efficient large-scale sampling capability. TLS has extended a new approach to capturing extremely comprehensive data of the construction environment, providing detailed information for further analysis. This paper presents a systematic review based on scientometric and qualitative analysis to summarize the progress and the current status of the topic and to point out promising research efforts. To begin with, a brief understanding of TLS is provided. Following the selection of relevant papers through a literature search, a scientometric analysis of papers is carried out. Then, major applications are categorized and presented, including (1) 3D model reconstruction, (2) object recognition, (3) deformation measurement, (4) quality assessment, and (5) progress tracking. For widespread adoption and effective use of TLS, essential problems impacting working effects in application are summarized as follows: workflow, data quality, scan planning, and data processing. Finally, future research directions are suggested, including: (1) cost control of hardware and software, (2) improvement of data processing capability, (3) automatic scan planning, (4) integration of digital technologies, (5) adoption of artificial intelligence.

Terrestrial laser scanning (TLS) is providing new, very detailed three-dimensional (3D) measurements of forest canopy structure. The information that TLS measurements can provide in describing detailed, accurate 3D canopy architecture offers fascinating new insights into the variety of tree form, environmental drivers and constraints, and the relationship between form and function, particularly for tall, hard-to-measure trees. TLS measurements are helping to test fundamental ecological theories and enabling new and better exploitation of other measurements and models that depend on 3D structural information. This Tansley insight introduces the background and capabilities of TLS in forest ecology, discusses some of the barriers to progress, and identifies some of the directions for new work.

In recent years, LIght Detection And Ranging (LiDAR) and especially Terrestrial Laser Scanning (TLS) systems have shown the potential to revolutionise forest structural characterisation by providing unprecedented 3D data. However, manned Airborne Laser Scanning (ALS) requires costly campaigns and produces relatively low point density, while TLS is labour intense and time demanding. Unmanned Aerial Vehicle (UAV)-borne laser scanning can be the way in between. In this study, we present first results and experiences with the RIEGL RiCOPTER with VUX ® -1UAV ALS system and compare it with the well tested RIEGL VZ-400 TLS system. We scanned the same forest plots with both systems over the course of two days. We derived Digital Terrain Model (DTMs), Digital Surface Model (DSMs) and finally Canopy Height Model (CHMs) from the resulting point clouds. ALS CHMs were on average 11.5 c m higher in five plots with different canopy conditions. This showed that TLS could not always detect the top of canopy. Moreover, we extracted trunk segments of 58 trees for ALS and TLS simultaneously, of which 39 could be used to model Diameter at Breast Height (DBH). ALS DBH showed a high agreement with TLS DBH with a correlation coefficient of 0.98 and root mean square error of 4.24 c m . We conclude that RiCOPTER has the potential to perform comparable to TLS for estimating forest canopy height and DBH under the studied forest conditions. Further research should be directed to testing UAV-borne LiDAR for explicit 3D modelling of whole trees to estimate tree volume and subsequently Above-Ground Biomass (AGB).

The Light Detection And Ranging (LiDAR) system has become a prominent tool in structural health monitoring. Among such systems, Terrestrial Laser Scanning (TLS) is a potential technology for the acquisition of three-dimensional (3D) information to assess structural health conditions. This paper enhances the application of TLS to damage detection and shape change analysis for structural element specimens. Specifically, estimating the deflection of a structural element with the aid of a Lidar system is introduced in this study. The proposed approach was validated by an indoor experiment by inducing artificial deflection on a simply supported beam. A robust genetic algorithm method is utilized to enhance the accuracy level of measuring deflection using lidar data. The proposed research primarily covers robust optimization of a genetic algorithm control parameter using the Taguchi experiment design. Once the acquired data is defined in terms of plane, which has minimum error, using a genetic algorithm and the deflection of the specimen can be extracted from the shape change analysis.

[This corrects the article DOI: 10.3389/fimmu.2024.1439413.].

ForCrops - Evaluarea directa a productivității de biomasa din culturile forestiere cu ciclul scurt de producţie (SRF) cu scanerul laser terestru (TLS), este un proiect finanțat de  Unitatea Executiva pentru Finanțarea Învățământului Superior, a Cercetării, Dezvoltării și Inovării (UEFISCDI) prin programul 1 – Dezvoltarea sistemului național de cercetare-dezvoltare, subprogramul 1.1. – Resurse umane (Proiect de cercetare postdoctorală). Scopul principal al acestui proiect este de a estima biomasei lemnoase pe părți componente de arbore cu ajutorul tehnologiei TLS în vederea calibrării unor ecuații alometrice în condițiile colinare din NE României. Se urmărește în acest sens dezvoltarea unui protocol de scanare complet pentru aceste culturi, crescând precizia de estimare a biomasei, scăzând efortul și timpul de lucru în teren, cât și oferirea către industriei a unui instrument pragmatic.

Over the last decade, particular interest in using state-of-the-art emerging technologies for inspection, assessment, and management of civil infrastructures has remarkably increased. Advanced technologies, such as laser scanners, have become a suitable alternative for labor intensive, expensive, and unsafe traditional inspection and maintenance methods, which encourage the increasing use of this technology in construction industry, especially in bridges. This paper aims to provide a thorough mixed scientometric and state-of-the-art review on the application of terrestrial laser scanners (TLS) in bridge engineering and explore investigations and recommendations of researchers in this area. Following the review, more than 1500 research publications were collected, investigated and analyzed through a two-fold literature search published within the last decade from 2010 to 2020. Research trends, consisting of dominated sub-fields, co-occurrence of keywords, network of researchers and their institutions, along with the interaction of research networks, were quantitatively analyzed. Moreover, based on the collected papers, application of TLS in bridge engineering and asset management was reviewed according to four categories including (1) generation of 3D model, (2) quality inspection, (3) structural assessment, and (4) bridge information modeling (BrIM). Finally, the paper identifies the current research gaps, future directions obtained from the quantitative analysis, and in-depth discussions of the collected papers in this area.