Semi-automatic Approach Research Articles

Revealing the technology development of natural language processing: A Scientific entity-centric perspective

Most studies on technology development have been conducted from a thematic perspective, but the topics are coarse-grained and insufficient to accurately represent technology. The development of automatic entity recognition techniques makes it possible to extract technology-related entities on a large scale. Thus, we perform a more accurate analysis of technology development from an entity-centric perspective. To begin with, we extract technology-related entities such as methods, datasets, metrics, and tools in articles on Natural Language Processing (NLP), and we apply a semi-automatic approach to normalize the entities. Subsequently, we calculate the z-scores of entities based on their co-occurrence networks to measure their impact. We then analyze the development trends of new technologies in the NLP domain since the beginning of the 21st century. The findings of this paper include three aspects: Firstly, the continued increase in the average number of entities per paper implies a growing burden on researchers to acquire relevant technical background knowledge. However, the emergence of pre-trained language models has injected new vitality into the technological innovation of the NLP domain. Secondly, Methods dominate among the 179 high-impact entities. An analysis of the z-score trend about the top 10 entities reveals that pre-trained language models, exemplified by BERT and Transformer, have become mainstream in recent years. Unlike the trend of the other eight method entities, the impact of Wikipedia dataset and BLEU metric has continued to rise in the long term. Thirdly, in recent years, there has been a remarkable surge in popularity for new high-impact technologies than ever before, and their acceptance by researchers has accelerated at an unprecedented speed. Our study provides a new perspective on analyzing technology development in a specific domain. This work can help researchers grasp the current hot technologies in the field of NLP and contribute to the future development of NLP technologies. The source code and dataset for this paper can be accessed at https://github.com/ZH-heng/technology_development.

Understanding basic multicellular unit activity in cortical bone through 3D morphological analysis: New methods to define zones of the remodeling space

The activity of basic multicellular units (BMU) in cortical bone is classically described as a sequential order of events— resorption, reversal and formation. This simplified portrayal of the remodeling process is pervasive despite the reported variability in remodeling space morphology. These variations may reflect meaningful nuances in BMU activity but methods to quantify 3D remodeling space morphology within the context of the cellular activity are currently lacking. This study developed new techniques to define zones of BMU activity based on the 3D morphology of remodeling spaces in rabbit cortical bone and integrated morphological data with the BMU longitudinal erosion rate (LER) to elucidate the spatial-temporal coordination of BMUs and estimate mineral apposition rate (MAR). The tibiae of New Zealand white rabbits (n = 5) were imaged in vivo using synchrotron radiation and two weeks later ex vivo with desktop microCT. The in vivo and ex vivo datasets were co-registered, and 27 remodeling spaces were identified at both timepoints. A radial profile representing the 3D morphology was the platform for partitioning the remodeling spaces into resorption, reversal and formation zones. Manual, automated and semi-automated partitioning approaches were compared, and the zone-segmentations were used to calculate the length, change in radius and slope of each zone. The manual approach most accurately defined the zones of idealized remodeling spaces with known dimensions (relative error = 0.9–9.2 %) while the semi-automated method reliably defined the zones in rabbit remodeling spaces (ICC = 0.85–1.00). Combining LER and the manually derived zone dimensions indicated that a BMU passes through a cross-section in approximately 18.8 days with resorption, reversal and formation taking 4.1, 2.2, and 12.5 days, respectively. MAR estimated by the 3D analysis was not significantly different than that determined with classic histomorphometry (p = 0.48). These techniques have the potential to assess dynamic parameters of bone resorption and formation, eliminate the need for fluorochrome labeling and provide a more comprehensive perspective of the remodeling process.

LTBK-06. IMPACT OF VORASIDENIB TREATMENT ON MUTANT IDH1 OR IDH2 DIFFUSE GLIOMA TUMOR GROWTH RATE: RESULTS FROM THE RANDOMIZED, DOUBLE-BLIND, PHASE 3 INDIGO STUDY

Abstract INTRODUCTION The INDIGO study (NCT04164901) showed that vorasidenib, an oral, brain-penetrant, dual inhibitor of mutant isocitrate dehydrogenase (mIDH) 1/2, significantly improved imaging-based progression-free survival and time-to-next-intervention compared with placebo in patients with grade 2 mIDH1/2 glioma previously treated with surgery only. Given the limitations of traditional bi-dimensional measurements, evaluating volumetry and tumor growth rate (TGR) is an additional method of measuring treatment effect in these diffuse growing tumors. METHODS Magnetic resonance imaging (MRI) scans were performed at baseline and every 12 weeks on-treatment; up to three pre-treatment MRI scans were requested when available. Tumor volumes were derived per blinded independent review committee using a semi-automated approach. TGR was defined as percentage change in tumor volume every 6 months. Patients with evaluable baseline and ≥ 1 MRI during the corresponding period were included in the analysis. The difference in TGR in each arm was assessed by slope of tumor growth over time using a linear mixed model. RESULTS 331 patients were randomized to vorasidenib (n=168) or placebo (n=163). Median follow-up was 14.2 months. On-treatment TGR was −2.5% (95% CI, −4.7, −0.2) with vorasidenib (n=167) and 13.9% (95% CI, 11.1, 16.8) with placebo (n=161). In patients with available imaging data, TGR pre- and post-treatment with vorasidenib (n=56) was 13.2% (95% CI, 10.3, 16.3) and −3.3% (95% CI, −5.2, −1.2), respectively, while placebo (n=67) was 18.3% (95% CI, 15.0, 21.7) and 12.2% (95% CI, 9.5, 14.9), respectively. In patients who crossed over from placebo with available imaging data (n=38), TGR pre- and post-crossover was 22.4% (95% CI, 15.7, 29.4) and 5.2% (95% CI, −3.8, 15.0), respectively. CONCLUSIONS Tumor growth was observed in patients with mIDH1/2 gliomas before receiving vorasidenib or placebo. Treatment with vorasidenib reduced the TGR and shrunk tumor volume, whereas continued growth in tumor volume was observed in patients receiving placebo.

Abstract 18634: Quantification of Coronary Volume to Left Ventricular Mass Ratio Using CT Angiography

Introduction: Coronary volume to left ventricular mass ratio (V/M) is a promising parameter that can detect physiological imbalances between coronary vasculature and the supplied myocardium. However, the existing method that assesses the V/M using epicardial vascular volume based on coronary CT angiography (CCTA) is labor intensive and prone to variability due to its dependence on coronary vasculature visualization. Hypothesis: A new technique based on CCTA and its associated bolus tracking data enables an efficient semi-automated approach that can reproducibly measure the V/M with less variability compared to the existing manual approach. Methods: The V/M was assessed by both existing manual and the new semi-automated techniques in 6 Yorkshire swine (49.1 ± 9.0 kg). Using a 320-slice CT scanner, 9 pairs of repeated CT measurements were made (100 kVp; 200 mA) with a 5-minute delay between each acquisition. Contrast (0.5 mL/kg; 370 mgI/mL) and a diluted contrast/saline chaser (0.5 mL/kg; 30:70contrast/saline) were injected peripherally at 5mL/s. Bolus tracking and triggering were followed by a single scan acquired at the peak of the aortic enhancement. Using a first-pass analysis model, the bolus tracking and single volume scan data were used to derive the total vascular blood volume (mL). Standard myocardial segmentation was used for myocardial mass (g) measurement. The V/M was also calculated from CCTA using the manual technique, by segmenting coronary luminal volume and myocardial mass. The reproducibility of the V/M was assessed using regression analysis. The variability of the two techniques for determining normal V/M was quantified using the coefficient of variation. Results: The first ((V/M) 1 ) and second ((V/M) 2 ) measurements were related by (V/M) 2 = 1.039 (V/M) 1 + 0.002 (Pearson’s r = 0.90; RMSE = 0.01 mL/g). The coefficient of variation was 0.13 and 0.24 for the V/M calculated from the new semi-automated and existing manual techniques, respectively. Conclusion: A novel semi-automated technique can be used for quantification of V/M with low variability. This technique can potentially be used for early detection of physiological imbalances between coronary vasculature and the supplied myocardium following coronary artery disease.

Automated pipeline for breast cancer diagnosis using US assisted diffuse optical tomography.

Ultrasound (US)-guided diffuse optical tomography (DOT) is a portable and non-invasive imaging modality for breast cancer diagnosis and treatment response monitoring. However, DOT data pre-processing and imaging reconstruction often require labor intensive manual processing which hampers real-time diagnosis. In this study, we aim at providing an automated US-assisted DOT pre-processing, imaging and diagnosis pipeline to achieve near real-time diagnosis. We have developed an automated DOT pre-processing method including motion detection, mismatch classification using deep-learning approach, and outlier removal. US-lesion information needed for DOT reconstruction was extracted by a semi-automated lesion segmentation approach combined with a US reading algorithm. A deep learning model was used to evaluate the quality of the reconstructed DOT images and a two-step deep-learning model developed earlier is implemented to provide final diagnosis based on US imaging features and DOT measurements and imaging results. The presented US-assisted DOT pipeline accurately processed the DOT measurements and reconstruction and reduced the procedure time to 2 to 3 minutes while maintained a comparable classification result with manually processed dataset.

Semi-automated visualization method for visual inspection of buildings on BIM using 3D point cloud

Regular diagnosis of a building can identify structural safety and latent hazards and establish efficient maintenance strategies. Traditional diagnoses are based on personal perspectives or knowledge of experts; therefore, they are laborious, time-consuming, and subjective. Moreover, the results may be unreliable. In addition, an undefined data format makes its practical use difficult. This study proposes a visualized semi-automated approach to extract building condition data and integrate them with BIM using Dynamo. The proposed framework uses Python code to improve efficiency when extracting coordinates. First, 3D point cloud data on the external walls of buildings obtained using a laser scanner are processed, and the relevant information, such as coordinates, is extracted. Second, the BIM model is implemented based on a point cloud using supplementary modeling tools. Finally, the extracted defects are modeled as new objects, and each BIM object is colored according to the modified criteria. To validate the feasibility of the proposed method, it was applied to actual buildings, improving the efficiency and accuracy of building inspections. This is expected to facilitate a reduction in building maintenance work and the identification of deterioration patterns.

Asynchronous contributions of decapod life history stages to the zooplankton of tropical estuarine, coastal and shelf ecosystems - new insights from semi-automatic image analysis

Decapod crustaceans include many species of socioeconomic importance and are key components of pelagic ecosystems, both as adults and as larval forms. Knowledge on seasonal and spatial synchronicity of planktonic larvae in coastal tropical ecosystems is fundamental to understand the dynamics of these complex ecosystems. The objective of the present study was to quantify the contributions of decapods to the zooplankton in adjacent tropical estuarine, coastal and shelf ecosystems, while considering their seasonal life cycles and probable dispersal strategies. We evaluated the contributions of decapod biomass and biovolume in three distinct ecosystems: Rio Formoso estuary, Tamandaré bay, and Pernambuco continental shelf off Tamandaré, northeastern Brazil. Zooplankton samples were taken bimonthly from June 2013 to May 2015, with a 300 μm mesh net, and analyzed using a ZooScan equipment. Decapods were the second most important organisms (after copepods), in abundance, biomass and biovolume, in all sampling areas. Total decapods contributed on average with 33.6%, 4.4% and 7.1% relative abundance and 30.9%, 30.9%, and 15.2% relative biovolume in estuary, bay, and shelf areas, respectively. Overall, the most relevant decapod taxa and stages found in the three sampling areas were brachyuran crab zoeae and megalopae, penaeid shrimp postlarvae (mostly Penaeus spp.), holoplanktonic luciferids (adults, protozoeae and mysis), anomuran zoeae (mostly Paguridae and Diogenidae hermit crabs), pistol shrimp zoeae (Alpheidae), and porcelain crab zoeae (Porcellanidae). Brachyuran zoeae contributed with up to 81.3% abundance and up to 69% biovolume, in the estuary. Penaeid postlarvae comprised up to 28.1% of total abundance and up to 94.7% of total biovolume, on the shelf. These postlarvae were transported shorewards from the offshore shelf. Decapod contributions were especially relevant during massive larval release events of crab zoeae and during shoreward migration of pre-settlement stages. Seasonal peaks were clearly asynchronous between taxa and areas. Possible functional relationships between copepods and key decapod groups are discussed, as well as the processes triggering and regulating the input of larvae. Our results show the importance of quantitative, semi-automatic approaches and the relevance of decapod larvae for pelagic food webs in tropical coastal areas.

HAZard and OPerability Study Analysis as a Semi-Automatic Approach

Risk analysis is crucial in industrial conception. HAZOP is the top risk analysis method for the oil and gas sector. This paper presents a semi-automatic method to address HAZOP's limitations and produce automatic results. The method uses a knowledge base, initially filled with gas liquefaction data, and is enhanced with subsequent case studies. An inference engine processes this data to conduct a HAZOP study. Propagation rules identify potential deviation paths, enabling risk analysis and consequence prediction based on the knowledge base. This method uniquely illustrates deviation paths and introduces nodes along these paths for further study. The findings derive from dynamic knowledge of each system in the knowledge base and can be reviewed and amended by experts.

Polygenic risk for depression and anterior and posterior hippocampal volume in children and adolescents

BackgroundDepression has frequently been associated with smaller hippocampal volume. The hippocampus varies in function along its anterior-posterior axis, with the anterior hippocampus more strongly associated with stress and emotion processing. The goals of this study were to examine the associations among parental history of anxiety/depression, polygenic risk scores for depression (PGS-DEP), and anterior and posterior hippocampal volumes in children and adolescents. To examine specificity to PGS-DEP, we examined associations of educational attainment polygenic scores (PGS-EA) with anterior and posterior hippocampal volume. MethodsParticipants were 350 3- to 21-year-olds (46 % female). PGS-DEP and PGS-EA were computed based on recent, large-scale genome-wide association studies. High-resolution, T1-weighted magnetic resonance imaging (MRI) data were acquired, and a semi-automated approach was used to segment the hippocampus into anterior and posterior subregions. ResultsChildren and adolescents with higher polygenic risk for depression were more likely to have a parent with a history of anxiety/depression. Higher polygenic risk for depression was significantly associated with smaller anterior but not posterior hippocampal volume. PGS-EA was not associated with anterior or posterior hippocampal volumes. LimitationsParticipants in these analyses were all of European ancestry. ConclusionsPolygenic risk for depression may lead to smaller anterior but not posterior hippocampal volume in children and adolescents, and there may be specificity of these effects to PGS-DEP rather than PGS-EA. These findings may inform the earlier identification of those in need of support and the design of more effective, personalized treatment strategies.Declarations of interest: none. Declarations of interestNone.

Scraping Relevant Images from Web Pages without Download

Automatically scraping relevant images from web pages is an error-prone and time-consuming task, leading experts to prefer manually preparing extraction patterns for a website. Existing web scraping tools are built on these patterns. However, this manual approach is laborious and requires specialized knowledge. Automatic extraction approaches, while a potential solution, require large training datasets and numerous features, including width, height, pixels, and file size, that can be difficult and time-consuming to obtain. To address these challenges, we propose a semi-automatic approach that does not require an expert, utilizes small training datasets, and has a low error rate while saving time and storage. Our approach involves clustering web pages from a website and suggesting several pages for a non-expert to annotate relevant images. The approach then uses these annotations to construct a learning model based on textual data from the HTML elements. In the experiments, we used a dataset of 635,015 images from 200 news websites, each containing 100 pages, with 22,632 relevant images. When comparing several machine learning methods for both automatic approaches and our proposed approach, the AdaBoost method yields the best performance results. When using automatic extraction approaches, the best f-Measure that can be achieved is 0.805 with a learning model constructed from a large training dataset consisting of 120 websites (12,000 web pages). In contrast, our approach achieved an average f-Measure of 0.958 for 200 websites with only six web pages annotated per website. This means that a non-expert only needs to examine 1,200 web pages to determine the relevant images for 200 websites. Our approach also saves time and storage space by not requiring the download of images and can be easily integrated into currently available web scraping tools, because it is based on textual data.

Semi-automation of keratopathy visual acuity grading of corneal events in belantamab mafodotin clinical trials: clinical decision support software.

Belantamab mafodotin (belamaf) has demonstrated clinically meaningful antimyeloma activity in patients with heavily pretreated multiple myeloma. However, it is highly active against dividing cells, contributing to off-target adverse events, particularly ocular toxicity. Changes in best corrected visual acuity (BCVA) and corneal examination findings are routinely monitored to determine Keratopathy Visual Acuity (KVA) grade to inform belamaf dose modification. We aimed to develop a semiautomated mobile app to facilitate the grading of ocular events in clinical trials involving belamaf. The paper process was semiautomated by creating a library of finite-state automaton (FSA) models to represent all permutations of KVA grade changes from baseline BCVA readings. The transition states in the FSA models operated independently of eye measurement units (e.g., Snellen, logMAR, decimal) and provided a uniform approach to determining KVA grade changes. Together with the FSA, the complex decision tree for determining the grade change based on corneal examination findings was converted into logical statements for accurate and efficient overall KVA grade computation. First, a web-based user interface, conforming to clinical practice settings, was developed to simplify the input of key KVA grading criteria. Subsequently, a mobile app was developed that included additional guided steps to assist in clinical decision-making. The app underwent a robust Good Clinical Practice validation process. Outcomes were reviewed by key stakeholders, our belamaf medical lead, and the systems integration team. The time to compute a patient's overall KVA grade using the Belamaf Eye Exam (BEE) app was reduced from a 20- to 30-min process to <1-2 min. The BEE app was well received, with most investigators surveyed selecting "satisfied" or "highly satisfied" for its accuracy and time efficiency. Our semiautomated approach provides for an accurate, simplified method of assessment of patients' corneal status that reduces errors and quickly delivers information critical for potential belamaf dose modifications. The app is currently available on the Apple iOS and Android platforms for use by investigators of the DREAMM clinical trials, and its use could easily be extended to the clinic to support healthcare providers who need to make informed belamaf treatment decisions.

A New Lunar Dome Detection Method Based on Improved YOLOv7.

Volcanism is an important geological evolutionary process on the Moon. The study of lunar volcanic features is of great significance and value to understanding the geological evolution of the Moon better. Lunar domes are one of the essential volcanic features of the Moon. However, the existing lunar dome detection methods are still traditional manual or semiautomatic identification approaches that require extensive prior knowledge and have a complex identification process. Therefore, this paper proposes an automatic detection method based on improved YOLOv7 for lunar dome detection. First, a new lunar dome dataset was created by digital elevation model (DEM) data, and the effective squeeze and excitation (ESE) attention mechanism module was added to the backbone and neck sections to reduce information loss in the feature map and enhance network expressiveness. Then, a new SPPCSPC-RFE module was proposed by adding the receptive field enhancement (RFE) module into the neck section, which can adapt to dome feature maps of different shapes and sizes. Finally, the bounding box regression loss function complete IOU (CIOU) was replaced by wise IOU (WIOU). The WIOU loss function improved the model's performance for the dome detection effect. Furthermore, this study combined several data enhancement strategies to improve the robustness of the network. To evaluate the performance of the proposed model, we conducted several experiments using the dome dataset developed in this study. The experimental results indicate that the improved method outperforms related methods with a mean average precision (mAP@0.5) value of 88.7%, precision (P) value of 85.6%, and recall (R) value of 86.4%. This study provides an effective solution for lunar dome detection.

Fat quantification in the sacroiliac joint syndesmosis: a new semi-automatic volumetric approach

Fat is physiologically embedded within the interosseous ligaments in the posterior part of the sacroiliac joint (PSIJ). This composite of fat and ligaments is hypothesized to serve a shock-absorbing, stabilizing function for the sacroiliac joint and the lumbopelvic transition region. Using a novel Python-based software (VolSEQ), total PSIJ volume and fat volume were computed semi-automatically. Differences within the cohort and the viability of the program for the quantification of fat in routine computed tomography (CT) scans were assessed. In 37 CT scans of heathy individuals, the PSIJ were first manually segmented as a region of interest in OSIRIX. Within VolSEQ, ‘fat’ Hounsfield units (− 150 to − 50 HU) are selected and the DICOM file of the patient scan and associated region of interest file from OSIRIX were imported and the pixel sub volumes were then automatically computed. Volume comparisons were made between sexes, sides and ages (≤ 30, 31–64 and > 65 years). PSIJ volumes in both software (VolSeq vs. OSIRIX) were non-different (both 9.7 ± 2.8cm3; p = 0.9). Total PSIJ volume (p = 0.3) and fat volume (p = 0.7) between sexes were non-different. A significant difference in total PSIJ volume between sexes (p < 0.01) but not in fat volume (p = 0.3) was found only in the ≥ 65 years cohort. Fat volume within the PSIJ remains unchanged throughout life. PSIJ volume is sex-dependent after 65 years. VolSEQ is a viable and user-friendly method for sub-volume quantification of tissues in CT.

Facial wrinkle segmentation using weighted deep supervision and semi-automatic labeling

Facial wrinkles are important indicators of human aging. Recently, a method using deep learning and a semi-automatic labeling was proposed to segment facial wrinkles, which showed much better performance than conventional image-processing-based methods. However, the difficulty of wrinkle segmentation remains challenging due to the thinness of wrinkles and their small proportion in the entire image. Therefore, performance improvement in wrinkle segmentation is still necessary. To address this issue, we propose a novel loss function that takes into account the thickness of wrinkles based on the semi-automatic labeling approach. First, considering the different spatial dimensions of the decoder in the U-Net architecture, we generated weighted wrinkle maps from ground truth. These weighted wrinkle maps were used to calculate the training losses more accurately than the existing deep supervision approach. This new loss computation approach is defined as weighted deep supervision in our study. The proposed method was evaluated using an image dataset obtained from a professional skin analysis device and labeled using semi-automatic labeling. In our experiment, the proposed weighted deep supervision showed higher Jaccard Similarity Index (JSI) performance for wrinkle segmentation compared to conventional deep supervision and traditional image processing methods. Additionally, we conducted experiments on the labeling using a semi-automatic labeling approach, which had not been explored in previous research, and compared it with human labeling. The semi-automatic labeling technology showed more consistent wrinkle labels than human-made labels. Furthermore, to assess the scalability of the proposed method to other domains, we applied it to retinal vessel segmentation. The results demonstrated superior performance of the proposed method compared to existing retinal vessel segmentation approaches. In conclusion, the proposed method offers high performance and can be easily applied to various biomedical domains and U-Net-based architectures. Therefore, the proposed approach will be beneficial for various biomedical imaging approaches. To facilitate this, we have made the source code of the proposed method publicly available at: https://github.com/resemin/WeightedDeepSupervision.

Atrophy of specific amygdala subfields in subjects converting to mild cognitive impairment.

Accumulating evidence indicates that the amygdala exhibits early signs of Alzheimer's disease (AD) pathology. However, it is still unknown whether the atrophy of distinct subfields of the amygdala also participates in the transition from healthy cognition to mild cognitive impairment (MCI). Our sample was derived from the AD Neuroimaging Initiative 3 and consisted of 97 cognitively healthy (HC) individuals, sorted into two groups based on their clinical follow-up: 75 who remained stable (s-HC) and 22 who converted to MCI within 48 months (c-HC). Anatomical magnetic resonance (MR) images were analyzed using a semi-automatic approach that combines probabilistic methods and a priori information from ex vivo MR images and histology to segment and obtain quantitative structural metrics for different amygdala subfields in each participant. Spearman's correlations were performed between MR measures and baseline and longitudinal neuropsychological measures. We also included anatomical measurements of the whole amygdala, the hippocampus, a key target of AD-related pathology, and the whole cortical thickness as a test of spatial specificity. Compared with s-HC individuals, c-HC subjects showed a reduced right amygdala volume, whereas no significant difference was observed for hippocampal volumes or changes in cortical thickness. In the amygdala subfields, we observed selected atrophy patterns in the basolateral nuclear complex, anterior amygdala area, and transitional area. Macro-structural alterations in these subfields correlated with variations of global indices of cognitive performance (measured at baseline and the 48-month follow-up), suggesting that amygdala changes shape the cognitive progression to MCI. Our results provide anatomical evidence for the early involvement of the amygdala in the preclinical stages of AD. Amygdala's atrophy marks elderly progression to mild cognitive impairment (MCI).Amygdala's was observed within the basolateral and amygdaloid complexes.Macro-structural alterations were associated with cognitive decline.No atrophy was found in the hippocampus and cortex.

Enhanced semi-automated Bayesian modal identification approach for Ultra-high voltage transmission towers

The accuracy and efficiency of the modal identification for Ultra-high voltage (UHV) transmission towers under harsh operational environments is seriously affected by environmental noises. In general, the initial natural frequency drowned in the noises is evaluated by the experience of engineers before performing a Bayesian modal identification approach. It will lead to potential errors and limitations to the application of the Bayesian modal identification approach. In this study, a coherent clustering enhanced semi-automated Bayesian modal identification method is proposed and then implemented in the operational modal analysis of an instrumented UHV transmission tower under ambient excitation. The coherent clustering is adopted to obtain the quantitative value of the initial natural frequency, in place of the empirical evaluation. Then, the Fast Bayesian Fast Fourier Transform (FBFFT) method could accurately identify the most probable values (MPV) and the corresponding posterior uncertainty of modal parameters. Moreover, the first ten modes of the UHV transmission tower are identified, and the results of modal parameters are compared to the stochastic subspace identification (SSI) method. The results show that the coherent clustering enhanced semi-automated Bayesian method could accurately identify modal parameters with posterior uncertainty, even for modes weakly excited. The proposed method could provide a reliable basis for the modal updating, damage detection, and dynamic reliability assessment of UHV transmission towers.

The role of unidentified bright objects in the neurocognitive profile of neurofibromatosis type 1 children: a volumetric MRI analysis.

Cognitive impairment is described in 80% of Neurofibromatosis type 1 (NF1) patients. Brain focal areas of T2w increased signal intensity on MRI, the so-called Unidentified Bright Objects (UBOs) have been hypothesized to be related to cognitive dysfunction, although conflicting results are available in literature. Here, we investigated the possible relation between UBOs' volume, cognitive impairment, and language disability in NF1 patients. In this retrospective study, clinical and MRI data of 21 NF1 patients (M/F = 12/9; mean age 10.1 ± 4.5) were evaluated. Brain intellectual functioning and language abilities were assessed with specific scales, while the analyzed MRI sequences included axial 2D-T2-weighted and FLAIR sequences. These images were used independently for UBOs segmentation with a semiautomatic approach and obtained volumes were normalized for biparietal diameters to take into account for brain volume. Possible differences in terms of normalized UBOs volumes were probed between cognitively affected and preserved patients, as well as between subjects with or without language impairment. Patients cognitively affected were not different in terms of UBOs volume compared to those preserved (p = 0.35 and p = 0.30, for T2-weighted and FLAIR images, respectively). Similarly, no differences were found between patients with and without language impairment (p = 0.47 and p = 0.40, for the two sequences). The relation between UBOs and cognition in children with NF1 has been already investigated in literature, although leading to conflicting results. Our study expands the current knowledge, showing a lack of correlation between UBOs volume and both cognitive impairment and language disability in NF1 patients.

Staphylococcus aureus surgical site infection rates in 5 European countries

ObjectiveTo determine the overall and procedure-specific incidence of surgical site infections (SSI) caused by Staphylococcus aureus (S. aureus) as well as risk factors for such across all surgical disciplines in Europe.MethodsThis is a retrospective cohort of patients with surgical procedures performed at 14 European centres in 2016, with a nested case–control analysis. S. aureus SSI were identified by a semi-automated crossmatching bacteriological and electronic health record data. Within each surgical procedure, cases and controls were matched using optimal propensity score matching.ResultsA total of 764 of 178 902 patients had S. aureus SSI (0.4%), with 86.0% of these caused by methicillin susceptible and 14% by resistant pathogens. Mean S. aureus SSI incidence was similar for all surgical specialties, while varying by procedure.ConclusionsThis large procedure-independent study of S. aureus SSI proves a low overall infection rate of 0.4% in this cohort. It provides proof of principle for a semi-automated approach to utilize big data in epidemiological studies of healthcare-associated infections.Trials registration The study was registered at clinicaltrials.gov under NCT03353532 (11/2017).

Constructing causal loop diagrams from large interview data sets

Abstract“Tackling the Root Causes Upstream of Unhealth Urban Development” is a trans‐disciplinary research project seeking to map and understand urban development decision‐making, visualise stakeholder mental models and codevelop improvement interventions. The project's primary data was gathered through 123 semistructured interviews. This article applies, compares, and discusses four variations on a method for constructing causal loop diagrams to illuminate mental models and collective decision‐making, based on manual and semiautomated processes applied to individual interview transcripts and datasets collected by thematic analysis. It concludes that while semiautomated approaches offer some time saving over manual approaches when applied to large data sets, care is required in interpreting and including peripheral contextual variables at the boundaries of the thematic analysis. Decisions regarding automation depend on the purpose of the modelling. Finally, the article recommends future applications record quantitative descriptors characterising the process of constructing CLDs from large qualitative data sets. © 2023 The Authors. System Dynamics Review published by John Wiley &amp; Sons Ltd on behalf of System Dynamics Society.

Two-photon imaging reveals histopathological changes in the gastric tumor microenvironment induced by neoadjuvant treatment.

There is a close association between tumor response and survival in gastric cancer patients after receiving neoadjuvant treatment. An accurate and rapid assessment of therapeutic efficacy would be helpful for subsequent treatments and individual prognosis. At present, pathological examination is the gold standard for evaluating treatment response, however, it requires additional staining and the process is tedious, labor-intensive, as well as time-consuming. Here, we introduce a label-free imaging technique, two-photon imaging, to evaluate histopathological changes induced by pre-operative therapy, with a focus on assessing tumor regression as well as stromal response. Imaging data show that two-photon imaging allows label-free, rapid visualization of various aspects of pathological alterations in tumor microenvironment such as fibrotic reaction, inflammatory cell infiltration, mucinous response, isolated residual tumor cells. Moreover, a semi-automatic image processing approach is developed to extract the collagen morphological features, and statistical results show that there are significant differences in collagen area, length, width, cross-link space between the gastric cancer tissues with and without treatment. With the advent of a portable, miniaturized two-photon imaging device, we have enough reason to believe that this technique will become as an important auxiliary diagnostic tool in assessing neoadjuvant treatment response and thereby tailoring the most appropriate therapy strategies for the patients.