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The Global Navigation Satellite System (GNSS) is a key asset for tropospheric monitoring. Currently, GNSS meteorology relies primarily on geodetic-grade stations. However, such stations are too costly to be densely deployed, which limits the contribution of GNSS to tropospheric monitoring. In 2016, Google released the raw GNSS measurement application programming interface for smartphones running on Android version 7.0 and higher. Given that nowadays there are billions of Android smartphones worldwide, utilizing those devices for atmospheric monitoring represents a remarkable scientific opportunity. In this study, smartphone GNSS data collected in Germany as part of the Application of Machine Learning Technology for GNSS IoT Data Fusion (CAMALIOT) crowdsourcing campaign in 2022 were utilized to investigate this idea. Approximately 20 000 raw GNSS observation files were collected there during the campaign. First, a dedicated data processing pipeline was established that consists of two major parts: machine learning (ML)-based data selection and ionosphere-free precise point positioning (PPP)-based zenith total delay (ZTD) estimation. The proposed method was validated with a dedicated smartphone data collection experiment conducted on the rooftop of the ETH campus. The results confirmed that ZTD estimates of millimeter-level precision could be achieved with smartphone data collected in an open-sky environment. The impacts of observation time span and utilization of multi-GNSS observations on ZTD estimation were also investigated. Subsequently, the crowdsourced data from Germany were processed by PPP with the ionospheric delays interpolated using observations from surrounding satellite positioning service of the German National Survey (SAPOS) GNSS stations. The ZTDs derived from ERA5 and an ML-based ZTD product served as benchmarks. The results revealed that an accuracy of better than 10 mm can be achieved by utilizing selected high-quality crowdsourced smartphone data. This study demonstrates high-precision ZTD determination with crowdsourced smartphone GNSS data and reveals success factors and current limitations.

BackgroundWe sought to describe trends in extracorporeal membrane oxygenation (ECMO) use, and define the impact on PGD incidence and early mortality in lung transplantation.MethodsPatients were enrolled from August 2011 to June 2018 at 10 transplant centers in the multi-center Lung Transplant Outcomes Group prospective cohort study. PGD was defined as Grade 3 at 48 or 72 hours, based on the 2016 PGD ISHLT guidelines. Logistic regression and survival models were used to contrast between group effects for event (i.e., PGD and Death) and time-to-event (i.e., death, extubation, discharge) outcomes respectively. Both modeling frameworks accommodate the inclusion of potential confounders.ResultsA total of 1,528 subjects were enrolled with a 25.7% incidence of PGD. Annual PGD incidence (14.3%-38.2%, p = .0002), median LAS (38.0-47.7 p = .009) and the use of ECMO salvage for PGD (5.7%-20.9%, p = .007) increased over the course of the study. PGD was associated with increased 1 year mortality (OR 1.7 [95% C.I. 1.2, 2.3], p = .0001). Bridging strategies were not associated with increased mortality compared to non-bridged patients (p = .66); however, salvage ECMO for PGD was significantly associated with increased mortality (OR 1.9 [1.3, 2.7], p = .0007). Restricted mean survival time comparison at 1-year demonstrated 84.1 days lost in venoarterial salvaged recipients with PGD when compared to those without PGD (ratio 1.3 [1.1, 1.5]) and 27.2 days for venovenous with PGD (ratio 1.1 [1.0, 1.4]).ConclusionsPGD incidence continues to rise in modern transplant practice paralleled by significant increases in recipient severity of illness. Bridging strategies have increased but did not affect PGD incidence or mortality. PGD remains highly associated with mortality and is increasingly treated with salvage ECMO.

Raw observations (carrier-phase and code observations) from the Global Navigation Satellite System (GNSS) can now be accessed from Android mobile phones (Version 7.0 onwards). This paves the way for GNSS data to be utilized for low-cost precise positioning or in ionospheric or tropospheric applications. This paper presents results from data collection campaigns using the CAMALIOT mobile app. In the first campaign, 116.3 billion measurements from 11,828 mobile devices were collected from all continents. Although participation decreased during the second campaign, data are still being collected globally. In this contribution, we demonstrate the potential of volunteered geographic information (VGI) from mobile phones to fill data gaps in geodetic station networks that collect GNSS data, e.g. in Brazil, but also how the data can provide a denser set of observations than current networks in countries across Europe. We also show that mobile phones capable of dual-frequency reception, which is an emerging technology that can provide a richer source of GNSS data, are contributing in a substantial way. Finally, we present the results from a survey of participants to indicate that participation is diverse in terms of backgrounds and geography, where the dominant motivation for participation is to contribute to scientific research.

Arnica montana L. is a medicinal plant with significant conservation importance. It is crucial to monitor this species, ensuring its sustainable harvesting and management. The aim of this study is to develop a practical system that can effectively detect A. montana inflorescences utilizing unmanned aerial vehicles (UAVs) with RGB sensors (red–green–blue, visible light) to improve the monitoring of A. montana habitats during the harvest season. From a methodological point of view, a model was developed based on a convolutional neural network (CNN) ResNet101 architecture. The trained model offers quantitative and qualitative assessments of A. montana inflorescences detected in semi-natural grasslands using low-resolution imagery, with a correctable error rate. The developed prototype is applicable in monitoring a larger area in a short time by flying at a higher altitude, implicitly capturing lower-resolution images. Despite the challenges posed by shadow effects, fluctuating ground sampling distance (GSD), and overlapping vegetation, this approach revealed encouraging outcomes, particularly when the GSD value was less than 0.45 cm. This research highlights the importance of low-resolution image clarity, on the training data by the phenophase, and of the need for training across different photoperiods to enhance model flexibility. This innovative approach provides guidelines for mission planning in support of reaching sustainable management goals. The robustness of the model can be attributed to the fact that it has been trained with real-world imagery of semi-natural grassland, making it practical for fieldwork with accessible portable devices. This study confirms the potential of ResNet CNN models to transfer learning to new plant communities, contributing to the broader effort of using high-resolution RGB sensors, UAVs, and machine-learning technologies for sustainable management and biodiversity conservation.

Plastic scintillators are inexpensive to manufacture and therefore a popular alternative to inorganic crystalline scintillators. For many applications, their advantages outweigh their lower light yield. Additionally, it is easier to structure plastic scintillators with well‐developed processing techniques which is of growing relevance in modern applications. One technique to structure plastic material is 3D printing, with noteworthy recent advances in one‐photon‐based approaches. However, some applications require high spatial resolution and optically smooth surfaces, which can be achieved by multi‐photon 3D laser micro‐printing. One application example is the improvement of sensitivity of the Karlsruhe Tritium Neutrino (KATRIN) experiment. This improvement can be realized by printing a 3D scintillator structure as an active transverse energy filter directly onto the detector. Herein, the first two‐photon printable plastic scintillator providing a printing resolution in the micrometer regime is presented. Using the benefits of two‐photon grayscale lithography, optical‐grade surfaces are achieved. The light output is estimated to be 930 photons MeV−1. A prototype structure printed directly on a single‐photon avalanche diode array is demonstrated. [ABSTRACT FROM AUTHOR]

RationalePrimary graft dysfunction (PGD) is a form of acute lung injury that occurs after lung transplantation. The definition of PGD was standardized in 2005. Since that time, clinical practice has evolved, and this definition is increasingly used as a primary endpoint for clinical trials; therefore, validation is warranted.ObjectivesWe sought to determine whether refinements to the 2005 consensus definition could further improve construct validity.MethodsData from the Lung Transplant Outcomes Group multicenter cohort were used to compare variations on the PGD definition, including alternate oxygenation thresholds, inclusion of additional severity groups, and effects of procedure type and mechanical ventilation. Convergent and divergent validity were compared for mortality prediction and concurrent lung injury biomarker discrimination.Measurements and main resultsA total of 1,179 subjects from 10 centers were enrolled from 2007 to 2012. Median length of follow-up was 4 years (interquartile range = 2.4-5.9). No mortality differences were noted between no PGD (grade 0) and mild PGD (grade 1). Significantly better mortality discrimination was evident for all definitions using later time points (48, 72, or 48-72 hours; P

Unoccupied aerial vehicles (UAV) with RGB-cameras are affordable and versatile devices for the generation of a series of remote sensing products that can be used for forest inventory tasks, such as creating high-resolution orthomosaics and canopy height models. The latter may serve purposes including tree species identification, forest damage assessments, canopy height or timber stock assessments. Besides flight and image acquisition parameters such as image overlap, flight height, and weather conditions, the focal length, which determines the opening angle of the camera lens, is a parameter that influences the reconstruction quality. Despite its importance, the effect of focal length on the quality of 3D reconstructions of forests has received little attention in the literature. Shorter focal lengths result in more accurate distance estimates in the nadir direction since small angular errors lead to large positional errors in narrow opening angles. In this study, 3D reconstructions of four UAV-acquisitions with different focal lengths (21, 35, 50, and 85 mm) on a 1 ha mature mixed forest plot were compared to reference point clouds derived from high quality Terrestrial Laser Scans. Shorter focal lengths (21 and 35 mm) led to a higher agreement with the TLS scans and thus better reconstruction quality, while at 50 mm, quality losses were observed, and at 85 mm, the quality was considerably worse. F1-scores calculated from a voxel representation of the point clouds amounted to 0.254 with 35 mm and 0.201 with 85 mm. The precision with 21 mm focal length was 0.466 and 0.302 with 85 mm. We thus recommend a focal length no longer than 35 mm during UAV Structure from Motion (SfM) data acquisition for forest management practices.

RationalePulmonary hypertension from pulmonary arterial hypertension or parenchymal lung disease is associated with an increased risk for primary graft dysfunction after lung transplantation.ObjectiveWe evaluated the clinical determinants of severe primary graft dysfunction in pulmonary hypertension and developed and validated a prognostic model.MethodsWe conducted a retrospective cohort study of patients in the multicenter Lung Transplant Outcomes Group with pulmonary hypertension at transplant listing. Severe primary graft dysfunction was defined as PaO2/FiO2 ≤200 with allograft infiltrates at 48 or 72 hours after transplantation. Donor, recipient, and operative characteristics were evaluated in a multivariable explanatory model. A prognostic model derived using donor and recipient characteristics was then validated in a separate cohort.ResultsIn the explanatory model of 826 patients with pulmonary hypertension, donor tobacco smoke exposure, higher recipient body mass index, female sex, listing mean pulmonary artery pressure, right atrial pressure and creatinine at transplant, cardiopulmonary bypass use, transfusion volume, and reperfusion fraction of inspired oxygen were associated with primary graft dysfunction. Donor obesity was associated with a lower risk for primary graft dysfunction. Using a 20% threshold for elevated risk, the prognostic model had good negative predictive value in both derivation and validation cohorts (89.1% [95% confidence interval, 85.3-92.8] and 83.3% [95% confidence interval, 78.5-88.2], respectively), but low positive predictive value.ConclusionsSeveral recipient, donor, and operative characteristics were associated with severe primary graft dysfunction in patients with pulmonary hypertension, including several risk factors not identified in the overall transplant population. A prognostic model with donor and recipient clinical risk factors alone had low positive predictive value, but high negative predictive value, to rule out high risk for primary graft dysfunction.

The Global Navigation Satellite System (GNSS) is a key asset for tropospheric monitoring. Currently, GNSS meteorology relies primarily on geodetic-grade stations. However, such stations are too costly to be densely deployed, which limits the contribution of GNSS to tropospheric monitoring. In 2016, Google released the raw GNSS measurement application programming interface for smartphones running on Android version 7.0 and higher. Given that nowadays there are billions of Android smartphones worldwide, utilizing those devices for atmospheric monitoring represents a remarkable scientific opportunity. In this study, smartphone GNSS data collected in Germany as part of the Application of Machine Learning Technology for GNSS IoT Data Fusion (CAMALIOT) crowdsourcing campaign in 2022 were utilized to investigate this idea. Approximately 20 000 raw GNSS observation files were collected there during the campaign. First, a dedicated data processing pipeline was established that consists of two major parts: machine learning (ML)-based data selection and ionosphere-free precise point positioning (PPP)-based zenith total delay (ZTD) estimation. The proposed method was validated with a dedicated smartphone data collection experiment conducted on the rooftop of the ETH campus. The results confirmed that ZTD estimates of millimeter-level precision could be achieved with smartphone data collected in an open-sky environment. The impacts of observation time span and utilization of multi-GNSS observations on ZTD estimation were also investigated. Subsequently, the crowdsourced data from Germany were processed by PPP with the ionospheric delays interpolated using observations from surrounding satellite positioning service of the German National Survey (SAPOS) GNSS stations. The ZTDs derived from ERA5 and an ML-based ZTD product served as benchmarks. The results revealed that an accuracy of better than 10 mm can be achieved by utilizing selected high-quality crowdsourced smartphone data. This study demonstrates high-precision ZTD determination with crowdsourced smartphone GNSS data and reveals success factors and current limitations. [ABSTRACT FROM AUTHOR]

Arnica montana L. is a medicinal plant with significant conservation importance. It is crucial to monitor this species, ensuring its sustainable harvesting and management. The aim of this study is to develop a practical system that can effectively detect A. montana inflorescences utilizing unmanned aerial vehicles (UAVs) with RGB sensors (red–green–blue, visible light) to improve the monitoring of A. montana habitats during the harvest season. From a methodological point of view, a model was developed based on a convolutional neural network (CNN) ResNet101 architecture. The trained model offers quantitative and qualitative assessments of A. montana inflorescences detected in semi-natural grasslands using low-resolution imagery, with a correctable error rate. The developed prototype is applicable in monitoring a larger area in a short time by flying at a higher altitude, implicitly capturing lower-resolution images. Despite the challenges posed by shadow effects, fluctuating ground sampling distance (GSD), and overlapping vegetation, this approach revealed encouraging outcomes, particularly when the GSD value was less than 0.45 cm. This research highlights the importance of low-resolution image clarity, on the training data by the phenophase, and of the need for training across different photoperiods to enhance model flexibility. This innovative approach provides guidelines for mission planning in support of reaching sustainable management goals. The robustness of the model can be attributed to the fact that it has been trained with real-world imagery of semi-natural grassland, making it practical for fieldwork with accessible portable devices. This study confirms the potential of ResNet CNN models to transfer learning to new plant communities, contributing to the broader effort of using high-resolution RGB sensors, UAVs, and machine-learning technologies for sustainable management and biodiversity conservation. [ABSTRACT FROM AUTHOR]

BackgroundDonor smoking history and higher fraction of inspired oxygen (FIO2) at reperfusion are associated with primary graft dysfunction (PGD) after lung transplantation. We hypothesized that oxidative injury biomarkers would be elevated in PGD, with higher levels associated with donor exposure to cigarette smoke and recipient hyperoxia at reperfusion.MethodsWe performed a nested case-control study of 72 lung transplant recipients from the Lung Transplant Outcomes Group cohort. Using mass spectroscopy, F2-isoprostanes and isofurans were measured in plasma collected after transplantation. Cases were defined in 2 ways: grade 3 PGD present at day 2 or day 3 after reperfusion (severe PGD) or any grade 3 PGD (any PGD).ResultsThere were 31 severe PGD cases with 41 controls and 35 any PGD cases with 37 controls. Plasma F2-isoprostane levels were higher in severe PGD cases compared with controls (28.6 pg/ml vs 19.8 pg/ml, p = 0.03). Plasma F2-isoprostane levels were higher in severe PGD cases compared with controls (29.6 pg/ml vs 19.0 pg/ml, p = 0.03) among patients reperfused with FIO2 >40%. Among recipients of lungs from donors with smoke exposure, plasma F2-isoprostane (38.2 pg/ml vs 22.5 pg/ml, p = 0.046) and isofuran (66.9 pg/ml vs 34.6 pg/ml, p = 0.046) levels were higher in severe PGD compared with control subjects.ConclusionsPlasma levels of lipid peroxidation products are higher in patients with severe PGD, in recipients of lungs from donors with smoke exposure, and in recipients exposed to higher Fio2 at reperfusion. Oxidative injury is an important mechanism of PGD and may be magnified by donor exposure to cigarette smoke and hyperoxia at reperfusion.

The authors previously identified plasma plasminogen activator inhibitor-1 (PAI-1) level as a quantitative lung injury biomarker in primary graft dysfunction (PGD). They hypothesized that plasma levels of PAI-1 used as a quantitative trait could facilitate discovery of genetic loci important in PGD pathogenesis. A two-stage cohort study was performed. In stage 1, they tested associations of loci with PAI-1 plasma level using linear modeling. Genotyping was performed using the Illumina CVD Bead Chip v2. Loci meeting a p < 5 × 10(-4) cutoff were carried forward and tested in stage 2 for association with PGD. Two hundred ninety-seven enrollees were evaluated in stage 1. Six loci, associated with PAI-1, were carried forward to stage 2 and evaluated in 728 patients. rs3168046 (Toll interacting protein [TOLLIP]) was significantly associated with PGD (p = 0.006). The increased risk of PGD for carrying at least one copy of this variant was 11.7% (95% confidence interval 4.9-18.5%). The false-positive rate for individuals with this genotype who did not have PGD was 6.1%. Variants in the TOLLIP gene are associated with higher circulating PAI-1 plasma levels and validate for association with clinical PGD. A protein quantitative trait analysis for PGD risk prioritizes genetic variations in TOLLIP and supports a role for Toll-like receptors in PGD pathogenesis.

Primary graft dysfunction (PGD) is a major cause of early mortality after lung transplant. We aimed to define objective estimates of PGD risk based on readily available clinical variables, using a prospective study of 11 centers in the Lung Transplant Outcomes Group (LTOG). Derivation included 1255 subjects from 2002 to 2010; with separate validation in 382 subjects accrued from 2011 to 2012. We used logistic regression to identify predictors of grade 3 PGD at 48/72 h, and decision curve methods to assess impact on clinical decisions. 211/1255 subjects in the derivation and 56/382 subjects in the validation developed PGD. We developed three prediction models, where low-risk recipients had a normal BMI (18.5-25 kg/m(2) ), chronic obstructive pulmonary disease/cystic fibrosis, and absent or mild pulmonary hypertension (mPAP

RationaleBiologic pathways with significant genetic conservation across human populations have been implicated in the pathogenesis of primary graft dysfunction (PGD). The evaluation of the role of recipient genetic variation in PGD has thus far been limited to single, candidate gene analyses.ObjectivesWe sought to identify genetic variants in lung transplant recipients that are responsible for increased risk of PGD using a two-phase large-scale genotyping approach.MethodsPhase 1 was a large-scale candidate gene association study of the multicenter, prospective Lung Transplant Outcomes Group cohort. Phase 2 included functional evaluation of selected variants and a bioinformatics screening of variants identified in phase 1.Measurements and main resultsAfter genetic data quality control, 680 lung transplant recipients were included in the analysis. In phase 1, a total of 17 variants were significantly associated with PGD, four of which were in the prostaglandin E2 family of genes. Among these were a coding variant in the gene encoding prostaglandin E2 synthase (PTGES2; P = 9.3 × 10(-5)) resulting in an arginine to histidine substitution at amino acid position 298, and three variants in a block containing the 5' promoter and first intron of the PTGER4 gene (encoding prostaglandin E2 receptor subtype 4; all P < 5 × 10(-5)). Functional evaluation in regulatory T cells identified that rs4434423A in the PTGER4 gene was associated with differential suppressive function of regulatory T cells.ConclusionsFurther research aimed at replication and additional functional insight into the role played by genetic variation in prostaglandin E2 synthetic and signaling pathways in PGD is warranted.

Inherent recipient factors, including pretransplant diagnosis, obesity and elevated pulmonary pressures, are established primary graft dysfunction (PGD) risks. We evaluated the relationship between preoperative lung injury biomarkers and PGD to gain further mechanistic insight in recipients. We performed a prospective cohort study of recipients in the Lung Transplant Outcomes Group enrolled between 2002 and 2010. Our primary outcome was Grade 3 PGD on Day 2 or 3. We measured preoperative plasma levels of five biomarkers (CC-16, sRAGE, ICAM-1, IL-8 and Protein C) that were previously associated with PGD when measured at the postoperative time point. We used multivariable logistic regression to adjust for potential confounders. Of 714 subjects, 130 (18%) developed PGD. Median CC-16 levels were elevated in subjects with PGD (10.1 vs. 6.0, p

State‐of‐the‐art 3D two‐photon laser printing systems already use pre‐compensation algorithms to reduce systematic deviations between the printed and the targeted structures. Nevertheless, the remaining deviations are often still larger than the uncontrollable or "statistical" deviations. In principle, it is straightforward to correct for systematic deviations by measuring the difference between printed structure and target and by subtracting the difference from the first target to obtain the next‐iteration target. However, in reality, one faces several issues such as noise and systematic errors of the characterization measurement itself, as well as unwanted translations and rotations between the coordinate systems of the characterization setup and the printer, respectively. Two examples of printed structures requiring sub‐micrometer accuracy are considered, a large 1D micro‐lens array and a specific diffractive optical element. For both, the device performance before the pre‐compensation workflow described herein is insufficient for the targeted application and has become sufficient after this workflow. The workflow involving optimizations using cross‐correlations with confocal‐optical‐microscopy data is documented by an open‐access program (available via GitLab). This program includes an easy‐to‐use graphical user interface so that other researchers can immediately profit from it. [ABSTRACT FROM AUTHOR]

BackgroundThere is significant heterogeneity within the primary graft dysfunction (PGD) syndrome. We aimed to identify distinct grade 3 PGD phenotypes based on severity of lung dysfunction and patterns of resolution.MethodsSubjects from the Lung Transplant Outcomes Group (LTOG) cohort study with grade 3 PGD within 72 h after transplantation were included. Latent class analysis (LCA) was used to statistically identify classes based on changes in PGD International Society for Heart & Lung Transplantation grade over time. Construct validity of the classes was assessed by testing for divergence of recipient, donor, and operative characteristics between classes. Predictive validity was assessed using time to death.ResultsOf 1,255 subjects, 361 had grade 3 PGD within the first 72 h after transplantation. LCA identified three distinct phenotypes: (1) severe persistent dysfunction (class 1), (2) complete resolution of dysfunction within 72 h (class 2), and (3) attenuation, without complete resolution within 72 h (class 3). Increased use of cardiopulmonary bypass, greater RBC transfusion, and higher mean pulmonary artery pressure were associated with persistent PGD (class 1). Subjects in class 1 also had the greatest risk of death (hazard ratio, 2.39; 95% CI, 1.57-3.63; P < .001).ConclusionsThere are distinct phenotypes of resolution of dysfunction within the severe PGD syndrome. Subjects with early resolution may represent a different mechanism of lung pathology, such as resolving pulmonary edema, whereas those with persistent PGD may represent a more severe phenotype. Future studies aimed at PGD mechanism or treatment may focus on phenotypes based on resolution of graft dysfunction.

RationalePrimary graft dysfunction (PGD) is the main cause of early morbidity and mortality after lung transplantation. Previous studies have yielded conflicting results for PGD risk factors.ObjectivesWe sought to identify donor, recipient, and perioperative risk factors for PGD.MethodsWe performed a 10-center prospective cohort study enrolled between March 2002 and December 2010 (the Lung Transplant Outcomes Group). The primary outcome was International Society for Heart and Lung Transplantation grade 3 PGD at 48 or 72 hours post-transplant. The association of potential risk factors with PGD was analyzed using multivariable conditional logistic regression.Measurements and main resultsA total of 1,255 patients from 10 centers were enrolled; 211 subjects (16.8%) developed grade 3 PGD. In multivariable models, independent risk factors for PGD were any history of donor smoking (odds ratio [OR], 1.8; 95% confidence interval [CI], 1.2-2.6; P = 0.002); FiO2 during allograft reperfusion (OR, 1.1 per 10% increase in FiO2; 95% CI, 1.0-1.2; P = 0.01); single lung transplant (OR, 2; 95% CI, 1.2-3.3; P = 0.008); use of cardiopulmonary bypass (OR, 3.4; 95% CI, 2.2-5.3; P < 0.001); overweight (OR, 1.8; 95% CI, 1.2-2.7; P = 0.01) and obese (OR, 2.3; 95% CI, 1.3-3.9; P = 0.004) recipient body mass index; preoperative sarcoidosis (OR, 2.5; 95% CI, 1.1-5.6; P = 0.03) or pulmonary arterial hypertension (OR, 3.5; 95% CI, 1.6-7.7; P = 0.002); and mean pulmonary artery pressure (OR, 1.3 per 10 mm Hg increase; 95% CI, 1.1-1.5; P < 0.001). PGD was significantly associated with 90-day (relative risk, 4.8; absolute risk increase, 18%; P < 0.001) and 1-year (relative risk, 3; absolute risk increase, 23%; P < 0.001) mortality.ConclusionsWe identified grade 3 PGD risk factors, several of which are potentially modifiable and should be prioritized for future research aimed at preventative strategies. Clinical trial registered with www.clinicaltrials.gov (NCT 00552357).

RationaleElevated long pentraxin-3 (PTX3) levels are associated with the development of primary graft dysfunction (PGD) after lung transplantation. Abnormalities in innate immunity, mediated by PTX3 release, may play a role in PGD pathogenesis.ObjectivesOur goal was to test whether variants in the gene encoding PTX3 are risk factors for PGD.MethodsWe performed a candidate gene association study in recipients from the multicenter, prospective Lung Transplant Outcomes Group cohort enrolled between July 2002 and July 2009. The primary outcome was International Society for Heart and Lung Transplantation grade 3 PGD within 72 hours of transplantation. Targeted genotyping of 10 haplotype-tagging PTX3 single-nucleotide polymorphisms (SNPs) was performed in lung transplant recipients. The association between PGD and each SNP was evaluated by logistic regression, adjusting for pretransplantation lung disease, cardiopulmonary bypass use, and population stratification. The association between SNPs and plasma PTX3 levels was tested across genotypes in a subset of recipients with idiopathic pulmonary fibrosis.Measurements and main resultsSix hundred fifty-four lung transplant recipients were included. The incidence of PGD was 29%. Two linked 5' region variants, rs2120243 and rs2305619, were associated with PGD (odds ratio, 1.5; 95% confidence interval, 1.1 to 1.9; P = 0.006 and odds ratio, 1.4; 95% confidence interval, 1.1 to 1.9; P = 0.007, respectively). The minor allele of rs2305619 was significantly associated with higher plasma PTX3 levels measured pretransplantation (P = 0.014) and at 24 hours (P = 0.047) after transplantation in patients with idiopathic pulmonary fibrosis.ConclusionsGenetic variants of PTX3 are associated with PGD after lung transplantation, and are associated with increased PTX3 plasma levels.

BackgroundWe aimed to identify combinations of biomarkers to enhance the definition of primary graft dysfunction (PGD) for translational research.MethodsBiomarkers reflecting lung epithelial injury (soluble receptor for advance glycation end products [sRAGE] and surfactant protein-D [SP-D]), coagulation cascade (plasminogen activator inhibitor-1 [PAI-1] and protein C), and cell adhesion (intracellular adhesion molecule-1 [ICAM-1]) were measured in the plasma of 315 individuals derived from the Lung Transplant Outcomes Group cohort at 6 and 24 hours after transplantation. We assessed biomarker utility in 2 ways: first, we tested the discrimination of grade 3 PGD within 72 hours; second, we tested the predictive utility of plasma biomarkers for 90-day mortality.ResultsPGD developed in 86 of 315 individuals (27%). Twenty-patients (8%) died within 90 days of transplantation, of which 16 (70%) had PGD. Biomarkers measured at 24 hours had greater discrimination than at 6 hours. Individually, sRAGE (area under the curve [AUC], 0.71) and PAI-1 (AUC, 0.73) had the best discrimination of PGD. The combinations of sRAGE with PAI-1 (AUC, 0.75), PAI-1 with ICAM-1 (AUC, 0.75), and PAI-1 with SP-D (AUC, 0.76) had the best discrimination. Combinations of greater than 2 biomarkers did not significantly enhance discrimination of PGD. ICAM-1 with PAI-1 (AUC, 0.72) and ICAM-1 with sRAGE (AUC, 0.72) had the best prediction for 90-day mortality. The addition of ICAM-1, PAI-1, or sRAGE to the concurrent clinical PGD grade significantly improved the prediction of 90-day mortality (p < 0.001 each).ConclusionsMeasurement of the combination of a marker of impaired fibrinolysis with an epithelial injury or cell adhesion marker had the best discrimination for PGD and prediction for early death and may provide an alternative outcome useful in future research.

The Global Navigation Satellite System (GNSS) is a key asset for tropospheric monitoring. Currently, GNSS meteorology relies primarily on geodetic-grade stations. However, such stations are too costly to be densely deployed, which limits the contribution of GNSS to tropospheric monitoring. In 2016, Google released the raw GNSS measurement application programming interface for smartphones running on Android version 7.0 and higher. Since nowadays there are billions of Android smartphones worldwide, utilizing those devices for atmospheric monitoring represents a remarkable scientific opportunity. In this study, smartphone GNSS data collected in Germany as part of the Application of Machine Learning Technology for GNSS IoT Data Fusion (CAMALIOT) crowdsourcing campaign in 2022 were utilized to investigate this idea. Approximately twenty thousand raw GNSS observation files were collected there during the campaign. First, a dedicated data processing pipeline was established that consists of two major parts: machine learning (ML)-based data selection and ionosphere-free Precise Point Positioning (PPP)-based Zenith Total Delay (ZTD) estimation. The proposed method was validated with a dedicated smartphone data collection experiment conducted on the rooftop of the ETH campus. The results confirmed that ZTD estimates of mm-level precision could be achieved with smartphone data collected in an open-sky environment. The impacts of observation time span and utilization of multi-GNSS observations on ZTD estimation were also investigated. Subsequently, the crowdsourced data from Germany were processed by PPP with the ionospheric delays interpolated using observations from surrounding SAPOS (Satellite Positioning Service of the German State Survey) GNSS stations. The ZTDs derived from ERA5 and an ML-based ZTD product served as benchmarks. The results revealed that an accuracy of better than 10 mm can be achieved by utilizing selected high-quality crowdsourced smartphone data. This study marks the first successful demonstration of high-precision ZTD determination with crowdsourced smartphone GNSS data and reveals success factors and current limitations. [ABSTRACT FROM AUTHOR]

An accurate understanding of urban objects is critical for urban modeling, intelligent infrastructure planning and city management. The semantic segmentation of light detection and ranging (LiDAR) point clouds is a fundamental approach for urban scene analysis. Over the last years, several methods have been developed to segment urban furniture with point clouds. However, the traditional processing of large amounts of spatial data has become increasingly costly, both time-wise and financially. Recently, deep learning (DL) techniques have been increasingly used for 3D segmentation tasks. Yet, most of these deep neural networks (DNNs) were conducted on benchmarks. It is, therefore, arguable whether DL approaches can achieve the state-of-the-art performance of 3D point clouds segmentation in real-life scenarios. In this research, we apply an adapted DNN (ARandLA-Net) to directly process large-scale point clouds. In particular, we develop a new paradigm for training and validation, which presents a typical urban scene in central Europe (Munzingen, Freiburg, Baden-Württemberg, Germany). Our dataset consists of nearly 390 million dense points acquired by Mobile Laser Scanning (MLS), which has a rather larger quantity of sample points in comparison to existing datasets and includes meaningful object categories that are particular to applications for smart cities and urban planning. We further assess the DNN on our dataset and investigate a number of key challenges from varying aspects, such as data preparation strategies, the advantage of color information and the unbalanced class distribution in the real world. The final segmentation model achieved a mean Intersection-over-Union (mIoU) score of 54.4% and an overall accuracy score of 83.9%. Our experiments indicated that different data preparation strategies influenced the model performance. Additional RGB information yielded an approximately 4% higher mIoU score. Our results also demonstrate that the use of weighted cross-entropy with inverse square root frequency loss led to better segmentation performance than when other losses were considered.

El artículo presenta una experiencia de análisis y gestión de información científica y tecnológica para la focalización de la Investigación, Desarrollo e Innovación (I+D+i) en la Universidad de La Frontera, Chile. El objetivo fue identificar y visualizar áreas temáticas de la producción científica de la universidad visibles en el sistema de publicación científica y en el sistema de financiamiento público. La muestra consideró publicaciones y proyectos disponibles durante el periodo 2007-2011. La identificación de las áreas temáticas se aborda desde la utilización complementaria del método de las palabras asociadas y la validación de expertos, mediante el análisis temático de contextos elementales y la utilización de herramientas de visualización de redes sociales. Se extraen aprendizajes de gestión de información y gestión de capacidades científicas y tecnológicas.

Accurate and updated knowledge of forest tree heights is fundamental in the context of forest management. However, measuring canopy height over large forest areas using traditional inventory techniques is laborious, time-consuming and excessively expensive. In this study, image-based point clouds produced from stereo aerial photographs (AP) were used to estimate forest height, and compared to Airborne Laser Scanning (ALS) data. We generated image-based Canopy Height Models (CHM) using different image-matching algorithms (SGM: Semi-Global Matching; eATE: enhanced Automatic Terrain Extraction), which were compared with a pure ALS-derived CHM. Additionally, plot-level height and density metrics were extracted from CHMs and used as explanatory variables for predicting the Lorey’s mean height (LMH), which was measured at 296 reference points on the ground. CHMSGM and CHMALS showed similar results in predicting LMH at sample plot locations (RMSE% = 8.54 vs. 7.92, respectively), while CHMeATE had lower accuracy (RMSE% = 13.23). Similarly, CHMSGM showed a lower normalized median absolute deviation (NMAD) from CHMALS (0.68 m) compared to CHMeATE (1.1 m). Our study revealed that image-based point clouds using SGM in the presence of high-resolution ALS-derived digital terrain model (DTM) provide comparable results with ALS data, while the performance of image-based point clouds using eATE is poorer than ALS for forest height estimation. The findings of this study provide a viable and cost-effective option for assessing height-related forest structural parameters. The proposed methodology can be usefully applied in all those countries where AP are updated on a regular basis and pre-existing historical ALS-derived DTMs are available.