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Abstract Controlling many-body interactions in two-dimensional systems remains a formidable task from the perspective of both fundamental physics and application. Here, we explore remarkable electronic structure alterations of MoS2 monolayer islands on graphene on Ir(111) induced by non-invasive self-intercalation. This introduces significant differences in morphology and strain of MoS2 as a result of the modified interaction with the substrate. Consequently, considerable changes of the band gap and non-rigid electronic shifts of valleys are detected, which are a combined effect of the screening of the many-body interactions and strain in MoS2. Furthermore, theory shows that each substrate leaves a unique stamp on the electronic structure of two-dimensional material in terms of those two parameters, restricted by their correlation.

Freshwater ecosystems are characterized by their unique longitudinal and lateral habitat connectivity. As a result, spatial units in freshwater-specific analyses can often not be considered independent of each other. Accounting for this connectivity in modelling analyses requires advanced skills in Geographic Information Systems (GIS) for adequately processing and managing the data. To address this challenge, we developed the GeoFRESH online platform, which is available at https://geofresh.org. The platform provides a graphical, easy-to-use interface to create freshwater-specific analysis-ready data for any given location in the world, based on a high-resolution stream network (https://hydrography.org/hydrography90m/hydrography90m_layers). Users can (i) upload and visualize point coordinates, (ii) automatically assign points to the closest stream network segment, (iii) annotate the point data with a suite of 104 local and/or upstream-aggregated topographic, climatic, land-cover and soil variables, (iv) visualize summary plots, and (v) download the data in csv-format for further analyses. The platform can be expanded given its modular structure and it can serve as a key element to support freshwater science and management relying on high-resolution geospatial analyses. GeoFRESH provides a low-entry interface while being complementary to the hydrographr R-package, and contributes importantly to the re-usability of data as an important aspect of the FAIR principles.

ObjectiveTo evaluate incidence and predictors of early silent bypass occlusion following coronary bypass surgery using cardiac computed tomography angiography.MethodsA total of 439 consecutive patients with mean age of 66 ± 10 years comprising 17% (n = 75) females underwent isolated coronary bypass surgery followed by CT scan before discharge. Graft patency was evaluated in 1,319 anastomoses where 44% (n = 580) arterial and 56% (n = 739) vein graft anastomosis were performed. Cardiovascular risk factors, demographics, and intraoperative variables were analyzed. We conducted univariable and multivariable logistic regression analyses to analyze variables potentially associated with graft occlusion following CABG. Variables included gender, surgery duration, graft flow, pulsatility index, vein vs. artery graft, and recent MI.ResultsOverall incidence of graft occlusion was 2.4% (31/1,319), and it was diagnosed in 6.6% (29/439) of patients. The difference in occlusion between arterial (2.1%) and vein (2.6%) grafts was not significant, p = 0.68. The duration of intervention p = 0.034, cross clamp time p = 0.024 as well the number of distal anastomosis p = 0.034 were significantly higher in occlusion group. The univariate and multivariate logistic regression indicated duration of surgery being predictive for bypass graft occlusion with OR = 1.18; 95% CI: 1.01–1.38; p = 0.035.ConclusionsEarly graft occlusion was associated with surgical factors. The number of distant anastamoses, along duration of surgical intervention were, significantly influenced the risk of EGO. Prolonged procedural time reflecting complex coronary pathology and time-consuming revascularization procedure was as well associated to the elevated risk of occlusion.

Abstract Freshwater ecosystems are considered biodiversity hotspots, but assessing the spatial distribution of species remains challenging. One major obstacle lies in the complex geospatial processing of large amounts of data, such as stream network, sub‐catchment and basin data, that are necessary for addressing the longitudinal connectivity among water bodies. Workflows thus need to be scalable, especially when working across large spatial extents and at high spatial resolution. This in turn requires advanced command‐line GIS skills and programming language integration, which often poses a challenge for freshwater researchers. To address this challenge, we developed the package hydrographr that provides scalable hydrographic data processing in R. The package contains functions for downloading data of the high‐resolution Hydrography90m dataset, processing, reading and extracting information, as well as assessing network distances and connectivity. While the functions are, by default, tailored toward the Hydrography90m data, they can also be generalised toward other data and purposes, such as efficient cropping and merging of raster and vector data, point‐raster extraction, raster reclassification and data aggregation. The package depends on the open‐source software GDAL/OGR, GRASS‐GIS and the AWK programming language in the Linux environment, allowing a seamless language integration. Since the data is processed outside R, hydrographr allows creating scalable geo‐processing workflows. We illustrate the hydrographr functions using two workflows that focus on (i) a freshwater species distribution modelling approach, and (ii) assessing stream connectivity given the fragmentation by dams. We also provide a detailed guide for the initial installation of the required software. Windows users need to first enable the Windows Subsystem for Linux (WSL) feature, and can then follow the same software installation as Linux users. hydrographr is maintained on GitHub at https://github.com/glowabio/hydrographr. hydrographr provides a set of key functions for processing freshwater geospatial data. We expect that the package will support the freshwater‐related research communities given the easy‐to‐use wrapper functions that allow capitalizing on powerful open‐source command‐line software, which may otherwise require a steep learning curve. Users can thus perform large‐scale freshwater‐specific longitudinal connectivity and network analyses across large geographic extents while staying within the R environment.

Aging affects the cardiovascular system, and this process may be accelerated in individuals with cardiovascular risk factors. The main vascular changes include arterial wall thickening, calcification, and stiffening, together with aortic dilatation and elongation. With aging, we can observe left ventricular hypertrophy with myocardial fibrosis and left atrial dilatation. These changes may lead to heart failure and atrial fibrillation. Using multimodality imaging, including ultrasound, computed tomography (CT), and magnetic resonance imaging, it is possible to detect these changes. Additionally, multimodality imaging, mainly via CT measurements of coronary artery calcium or ultrasound carotid intima-media thickness, enables advanced cardiovascular risk stratification and helps in decision-making about preventive strategies. The focus of this manuscript is to briefly review cardiovascular changes that occur with aging, as well as to describe how multimodality imaging may be used for the assessment of these changes and risk stratification of asymptomatic individuals.

Abstract Background Chest X-ray offers high sensitivity and acceptable specificity as a tuberculosis screening tool, but in areas with a high burden of tuberculosis, there is often a lack of radiological expertise to interpret chest X-ray. Computer-aided detection systems based on artificial intelligence are therefore increasingly used to screen for tuberculosis-related abnormalities on digital chest radiographies. The CAD4TB software has previously been shown to demonstrate high sensitivity for chest X-ray tuberculosis-related abnormalities, but it is not yet calibrated for the detection of non-tuberculosis abnormalities. When screening for tuberculosis, users of computer-aided detection need to be aware that other chest pathologies are likely to be as prevalent as, or more prevalent than, active tuberculosis. However, non­-tuberculosis chest X-ray abnormalities detected during chest X-ray screening for tuberculosis remain poorly characterized in the sub-Saharan African setting, with only minimal literature. Case presentation In this case series, we report on four cases with non-tuberculosis abnormalities detected on CXR in TB TRIAGE + ACCURACY (ClinicalTrials.gov Identifier: NCT04666311), a study in adult presumptive tuberculosis cases at health facilities in Lesotho and South Africa to determine the diagnostic accuracy of two potential tuberculosis triage tests: computer-aided detection (CAD4TB v7, Delft, the Netherlands) and C-reactive protein (Alere Afinion, USA). The four Black African participants presented with the following chest X-ray abnormalities: a 59-year-old woman with pulmonary arteriovenous malformation, a 28-year-old man with pneumothorax, a 20-year-old man with massive bronchiectasis, and a 47-year-old woman with aspergilloma. Conclusions Solely using chest X-ray computer-aided detection systems based on artificial intelligence as a tuberculosis screening strategy in sub-Saharan Africa comes with benefits, but also risks. Due to the limitation of CAD4TB for non-tuberculosis-abnormality identification, the computer-aided detection software may miss significant chest X-ray abnormalities that require treatment, as exemplified in our four cases. Increased data collection, characterization of non-tuberculosis anomalies and research on the implications of these diseases for individuals and health systems in sub-Saharan Africa is needed to help improve existing artificial intelligence software programs and their use in countries with high tuberculosis burden.

Conclusion: CMR-derived PTT is a readily obtainable hemodynamic parameter. It is elevated in patients with DD and moderate to severe MVR. Low PTT values make the presence of DD and MVR—as assessed by echocardiography—unlikely.

A rare case of a previously treated thoraco-abdominal aortic aneurysm eroding into the thoracic spine is described. Initially, several follow-up CT angiography scans showed an increasing aneurysm sack, but no endoleak could be depicted. Then, a new rapidly developing erosion into the thoracic spine was noted. MRI imaging excluded any other underlying infectious or malignant process. Additional contrast-enhanced ultrasound excluded an endoleak. A 3D-printed model of the aneurysm and spine and cinematic renderings were created to improve visualization. She underwent relining of the thoracic stent graft. Follow-up imaging showed a stable aneurysm size and no progression of the vertebral erosions.

Abstract Aims New‐onset atrial fibrillation (NOAF) is the most common complication after cardiac surgery, occurring in 25–50% of patients. It is associated with post‐operative stroke, increased mortality, prolonged hospital length of stay, and higher treatment costs. Previous small observational studies have identified the left atrium as a source of the electrical rotors and foci maintaining NOAF, but confirmation by a large prospective clinical study is still missing. The aim of the proposed study is to investigate whether the source of NOAF lies in the left atrium. The correct identification of NOAF‐maintaining structures in cardiac surgical patients might offer potential therapeutic targets for prophylactic perioperative ablation strategies. Methods and results This is a prospective single‐centre observational study of patients developing NOAF after cardiac surgery. The primary outcome is the description of NOAF‐maintaining structures within the atria. Key secondary outcomes include overall mortality, intensive care unit length of stay, hospital–ventilator‐free days, and proportion of persistent NOAF. In NOAF patients, the non‐invasive electrophysiological mapping will be conducted using a 252‐electrode electrocardiogram vest. After mapping, a low‐dose computed tomography scan of the chest will be performed to integrate the electrophysiological mapping results into a 3D picture of the heart. The study will include approximately 570 patients, of whom 30% (n = 170) are expected to develop NOAF. Sample size calculation revealed that 157 NOAF patients are necessary to assess the primary outcome. Patients will be tracked for a total of 5 years. Conclusions This is the largest prospective study to date describing the electrophysiological mechanisms of NOAF using non‐invasive mapping.

This article proposes a new research approach to teachers' lesson planning. While numerous guidelines have been dominating lesson planning as an object of teacher education, we utilize teacher cognition and expertise research to view lesson planning from a different perspective: We argue that lesson planning typically demands specific cognitive skills that teachers must master to create high quality instructional practice. The so-called CODE-PLAN model ("co"gnitive "de"mands of lesson "plan"ning) forms the general theoretical framework for our conceptualization of six demands (content transformation, task creation, adaptation to student learning dispositions, clarity of learning objectives, unit contextualization, and phasing) to empirically describe and analyze teachers' planning competence. We investigate how these demands are met through content analysis of 337 plans for demonstration lessons written by teacher candidates during induction in Germany. Statistical findings on the construct, curricular, and predictive validity of planning competence measures show that the CODE-PLAN model may open new empirical research perspectives on teacher planning competence and stimulate curriculum design in teacher education.

Background To improve tuberculosis case-finding, rapid, non-sputum triage tests need to be developed according to the World Health Organization target product profile (TPP) (>90% sensitivity, >70% specificity). We prospectively evaluated and compared artificial intelligence–based, computer-aided detection software, CAD4TBv7, and C-reactive protein assay (CRP) as triage tests at health facilities in Lesotho and South Africa. Methods Adults (≥18 years) presenting with ≥1 of the 4 cardinal tuberculosis symptoms were consecutively recruited between February 2021 and April 2022. After informed consent, each participant underwent a digital chest X-ray for CAD4TBv7 and a CRP test. Participants provided 1 sputum sample for Xpert MTB/RIF Ultra and Xpert MTB/RIF and 1 for liquid culture. Additionally, an expert radiologist read the chest X-rays via teleradiology. For primary analysis, a composite microbiological reference standard (ie, positive culture or Xpert Ultra) was used. Results We enrolled 1392 participants, 48% were people with HIV and 24% had previously tuberculosis. The receiver operating characteristic curve for CAD4TBv7 and CRP showed an area under the curve of.87 (95% CI:.84–.91) and.80 (95% CI:.76–.84), respectively. At thresholds corresponding to 90% sensitivity, specificity was 68.2% (95% CI: 65.4–71.0%) and 38.2% (95% CI: 35.3–41.1%) for CAD4TBv7 and CRP, respectively. CAD4TBv7 detected tuberculosis as well as an expert radiologist. CAD4TBv7 almost met the TPP criteria for tuberculosis triage. Conclusions CAD4TBv7 is accurate as a triage test for patients with tuberculosis symptoms from areas with a high tuberculosis and HIV burden. The role of CRP in tuberculosis triage requires further research. Clinical Trials Registration Clinicaltrials.gov identifier: NCT04666311. [ABSTRACT FROM AUTHOR]

Introduction: Pulmonary transit time (PTT) is the time it takes blood to pass from the right ventricle to the left ventricle via the pulmonary circulation, making it a potentially useful marker for heart failure. We assessed the association of PTT with diastolic dysfunction (DD) and mitral valve regurgitation (MVR). Methods: We evaluated routine stress perfusion cardiovascular magnetic resonance (CMR) scans in 83 patients including assessment of PTT with simultaneously available echocardiographic assessment. Relevant DD and MVR were defined as exceeding Grade I (impaired relaxation and mild regurgitation). PTT was determined from CMR rest perfusion scans. Normalized PTT (nPTT), adjusted for heart rate, was calculated using Bazett's formula. Results: Higher PTT and nPTT values were associated with higher grade DD and MVR. The diagnostic accuracy for the prediction of DD as quantified by the area under the ROC curve (AUC) was 0.73 (CI 0.61–0.85; p = 0.001) for PTT and 0.81 (CI 0.71–0.89; p < 0.001) for nPTT. For MVR, the diagnostic performance amounted to an AUC of 0.80 (CI 0.68–0.92; p < 0.001) for PTT and 0.78 (CI 0.65–0.90; p < 0.001) for nPTT. PTT values < 8 s rule out the presence of DD and MVR with a probability of 70% (negative predictive value 78%). Conclusion: CMR‐derived PTT is a readily obtainable hemodynamic parameter. It is elevated in patients with DD and moderate to severe MVR. Low PTT values make the presence of DD and MVR—as assessed by echocardiography—unlikely. [ABSTRACT FROM AUTHOR]

In this article, we analyse pre-service language teachers' written plans for demonstration lessons in Germany. Our objective is to reconstruct and quantify generic and subject-specific planning decisions related to adaptive teaching, specified as the ways in which a lesson's main task fits the learning group's cognitive level. The focus is on aspects of generic planning featured in subject specific planning rather than very specific subject planning that reflects the unique aspect of that particular subject discipline. The sample comprises pre-service teachers during induction, surveyed at two time points. The findings show that planning skills can be measured in a reliable way and that subject-specific decisions are more difficult to implement into planning than generic decisions. Skills increase substantially during induction and can be explained by institutional and individual factors using regression analysis. Implications for the design of learning opportunities in teacher education and perspectives for future research are discussed.

Lesson planning of teachers as a research field has received little attention in terms of modelling and measuring relevant competences. As an innovative measurement approach, we developed a standardised method for analysing written plans of demonstration lessons. Our focus is on the demand of pedagogical adaptivity, i.e., the ways in which lesson assignments fit with the cognitive level of learners so that they are guided into their zone of proximal development. This conceptualisation is operationalised by using several indicators (content analysis criteria) to reconstruct and quantify situation-specific planning perception, interpretation, and decision-making. We use the data from 172 preservice teachers in Germany with their first demonstration lesson during induction. Findings show their declarative general pedagogical knowledge of adaptivity (assessed via a standardised test) is a significant predictor for the situation-specific skill of pedagogical adaptivity in written lesson plans, and the latter effects preservice teachers' self-reported instructional practice of teaching that lesson. Findings are discussed towards their implication for the design of teacher education.

In nature, basically 2 types of myocardial vascular patterns exist: the sinusoidal and the coronary type. In the sinusoidal type, the sinusoid is completely fed by blood coming directly from the ventricle through a spongy sinusoidal network. This pattern is found in cold-blooded animals and in the early embryologic development of human (warm-blooded) hearts. A 61-year-old man with atrial fibrillation developed severe tachymyopathy with a severely reduced left-ventricular ejection fraction (LVEF) of 20%. The patient had no history of prior heart surgery or other cardiac interventions. He was referred for a computed tomography (CT) scan for assessment of pulmonary vein anatomy prior to their isolation. Incidentally, a focal myocardial defect of the midventricular infero-septal wall with tail-like extension into the right ventricular cavity was detected. In a cardiac magnetic resonance (CMR) scan there was no evidence of a myocardial infarction or fibrosis. In the absence of a ventricular septal defect by CT, CMR and echocardiography the diagnosis of a persistent myocardial sinusoid was evident. In this case, we used state-of-the art methods for pathology visualization, illustrating the effectiveness of CT and CMR in the precise detection and differential diagnosis of myocardial anomalies including a multi-coloured 3D-printed model that may further enhance visuospatial appreciation of those anomalies.

Abstract Background Artificial intelligence can assist in cardiac image interpretation. Here, we achieved a substantial reduction in time required to read a cardiovascular magnetic resonance (CMR) study to estimate left atrial volume without compromising accuracy or reliability. Rather than deploying a fully automatic black-box, we propose to incorporate the automated LA volumetry into a human-centric interactive image-analysis process. Methods and results Atri-U, an automated data analysis pipeline for long-axis cardiac cine images, computes the atrial volume by: (i) detecting the end-systolic frame, (ii) outlining the endocardial borders of the LA, (iii) localizing the mitral annular hinge points and constructing the longitudinal atrial diameters, equivalent to the usual workup done by clinicians. In every step human interaction is possible, such that the results provided by the algorithm can be accepted, corrected, or re-done from scratch. Atri-U was trained and evaluated retrospectively on a sample of 300 patients and then applied to a consecutive clinical sample of 150 patients with various heart conditions. The agreement of the indexed LA volume between Atri-U and two experts was similar to the inter-rater agreement between clinicians (average overestimation of 0.8 mL/m2 with upper and lower limits of agreement of − 7.5 and 5.8 mL/m2, respectively). An expert cardiologist blinded to the origin of the annotations rated the outputs produced by Atri-U as acceptable in 97% of cases for step (i), 94% for step (ii) and 95% for step (iii), which was slightly lower than the acceptance rate of the outputs produced by a human expert radiologist in the same cases (92%, 100% and 100%, respectively). The assistance of Atri-U lead to an expected reduction in reading time of 66%—from 105 to 34 s, in our in-house clinical setting. Conclusions Our proposal enables automated calculation of the maximum LA volume approaching human accuracy and precision. The optional user interaction is possible at each processing step. As such, the assisted process sped up the routine CMR workflow by providing accurate, precise, and validated measurement results.

BackgroundA concise visualization framework of related reports would increase readability and improve patient management. To this end, temporal referrals to prior comparative exams are an essential connection to previous exams in written reports. Due to unstructured narrative texts' variable structure and content, their extraction is hampered by poor computer readability. Natural language processing (NLP) permits the extraction of structured information from unstructured texts automatically and can serve as an essential input for such a novel visualization framework. ObjectiveThis study proposes and evaluates an NLP-based algorithm capable of extracting the temporal referrals in written radiology reports, applies it to all the radiology reports generated for 10 years, introduces a graphical representation of imaging reports, and investigates its benefits for clinical and research purposes. MethodsIn this single-center, university hospital, retrospective study, we developed a convolutional neural network capable of extracting the date of referrals from imaging reports. The model's performance was assessed by calculating precision, recall, and F1-score using an independent test set of 149 reports. Next, the algorithm was applied to our department's radiology reports generated from 2011 to 2021. Finally, the reports and their metadata were represented in a modulable graph. ResultsFor extracting the date of referrals, the named-entity recognition (NER) model had a high precision of 0.93, a recall of 0.95, and an F1-score of 0.94. A total of 1,684,635 reports were included in the analysis. Temporal reference was mentioned in 53.3% (656,852/1,684,635), explicitly stated as not available in 21.0% (258,386/1,684,635), and omitted in 25.7% (317,059/1,684,635) of the reports. Imaging records can be visualized in a directed and modulable graph, in which the referring links represent the connecting arrows. ConclusionsAutomatically extracting the date of referrals from unstructured radiology reports using deep learning NLP algorithms is feasible. Graphs refined the selection of distinct pathology pathways, facilitated the revelation of missing comparisons, and enabled the query of specific referring exam sequences. Further work is needed to evaluate its benefits in clinics, research, and resource planning.

Abstract Background Transbronchial cryobiopsy in the evaluation of patients with interstitial lung diseases (ILD) is expected to reduce the need for surgical lung biopsy (SLB). Objective To evaluate the diagnostic value of cryobiopsy in combination with bronchoalveolar lavage (BAL), radiologic and clinical data in patients with ILD. Methods Between 08/15 and 01/20 patients with ILD underwent cryobiopsy if they: did not have (i) an usual interstitial pneumonia (UIP)-pattern on CT, (ii) predominant ground-glass opacities suggesting alveolitis, (iii) findings suggestive of sarcoidosis on CT, or if they had (i) a CT showing UIP-pattern, but had findings suggesting alternative diagnosis than idiopathic pulmonary fibrosis (IPF), or (ii) had previous non-diagnostic conventional transbronchial forceps biopsy. Histological findings were integrated into the multidisciplinary team discussion (MDTD) and a diagnostic consensus was sought. Results One hundred patients underwent cryobiopsy. In 88/100 patients, cryobiopsy was representative with diagnostic findings in 45/88 and non-specific histological findings in 43/88 patients. In 25/43 with non-specific findings, a consensus diagnosis was reached after MDTD integrating BAL, radiologic and clinical data; eight of the remaining 18 patients with non-specific findings were referred to SLB. In 12/100 patients cryobiopsy was not representative and three of these patients were also referred to SLB. In 7/11 patients (64%) SLB was diagnostic. Complications of cryobiopsy included pneumothorax (14%) and locally controlled bleeding (24%). Conclusions The diagnostic yield of cryobiopsy was 70%:45% of cryobiopsies were diagnostic based on histology alone and an additional 25% provided non-specific, but valuable findings allowing a consensus diagnosis after MDTD. Our data demonstrate that the diagnostic value of cryobiopsy is high if combined with BAL, radiologic and clinical data.

PurposeThoracic aortic (TA) dilatation (TAD) is a risk factor for acute aortic syndrome and must therefore be reported in every CT report. However, the complex anatomy of the thoracic aorta impedes TAD detection. We investigated the performance of a deep learning (DL) prototype as a secondary reading tool built to measure TA diameters in a large-scale cohort.Material and methodsConsecutive contrast-enhanced (CE) and non-CE chest CT exams with “normal” TA diameters according to their radiology reports were included. The DL-prototype (AIRad, Siemens Healthineers, Germany) measured the TA at nine locations according to AHA guidelines. Dilatation was defined as >45 mm at aortic sinus, sinotubular junction (STJ), ascending aorta (AA) and proximal arch and >40 mm from mid arch to abdominal aorta. A cardiovascular radiologist reviewed all cases with TAD according to AIRad. Multivariable logistic regression (MLR) was used to identify factors (demographics and scan parameters) associated with TAD classification by AIRad.Results18,243 CT scans (45.7% female) were successfully analyzed by AIRad. Mean age was 62.3 ± 15.9 years and 12,092 (66.3%) were CE scans. AIRad confirmed normal diameters in 17,239 exams (94.5%) and reported TAD in 1,004/18,243 exams (5.5%). Review confirmed TAD classification in 452/1,004 exams (45.0%, 2.5% total), 552 cases were false-positive but identification was easily possible using visual outputs by AIRad. MLR revealed that the following factors were significantly associated with correct TAD classification by AIRad: TAD reported at AA [odds ratio (OR): 1.12, p < 0.001] and STJ (OR: 1.09, p = 0.002), TAD found at >1 location (OR: 1.42, p = 0.008), in CE exams (OR: 2.1–3.1, p < 0.05), men (OR: 2.4, p = 0.003) and patients presenting with higher BMI (OR: 1.05, p = 0.01). Overall, 17,691/18,243 (97.0%) exams were correctly classified.ConclusionsAIRad correctly assessed the presence or absence of TAD in 17,691 exams (97%), including 452 cases with previously missed TAD independent from contrast protocol. These findings suggest its usefulness as a secondary reading tool by improving report quality and efficiency.