Graph Refinement based Airway Extraction using Mean-Field Networks and Graph Neural Networks

Graph refinement, or the task of obtaining subgraphs of interest from over-complete graphs, can have many varied applications. In this work, we extract trees or collection of sub-trees from image data by, first deriving a graph-based representation of the volumetric data and then, posing the tree extraction as a graph refinement task. We present two methods to perform graph refinement. First, we use mean-field approximation (MFA) to approximate the posterior density over the subgraphs from which the optimal subgraph of interest can be estimated. Mean field networks (MFNs) are used for inference based on the interpretation that iterations of MFA can be seen as feed-forward operations in a neural network. This allows us to learn the model parameters using gradient descent. Second, we present a supervised learning approach using graph neural networks (GNNs) which can be seen as generalisations of MFNs. Subgraphs are obtained by training a GNN-based graph refinement model to directly predict edge probabilities. We discuss connections between the two classes of methods and compare them for the task of extracting airways from 3D, low-dose, chest CT data. We show that both the MFN and GNN models show significant improvement when compared to one baseline method, that is similar to a top performing method in the EXACT'09 Challenge, and a 3D U-Net based airway segmentation model, in detecting more branches with fewer false positives.Comment: Accepted for publication at Medical Image Analysis. 14 page

A catalogue of bird bones: an exercise in semantic web practice

The vast databases of natural history collections are increasingly being made accessible through the internet. The challenge is to place this data in a wider context that may reach beyond the interests of scholars only. The North Atlantic Biocultural Organization and Icelandic Institute of Natural History are jointly developing a web based catalogue of bird bones, comprising digital images, and related information from the museum database. Linking the bird bone catalogue with the semantic web developed by STERNA will integrate the bird bone catalogue with diverse information on birds that is directed towards the general public

Diastereoselective synthesis of novel heterocyclic scaffolds through tandem Petasis 3-component/intramolecular Diels-Alder and ROM-RCM reactions

Complexity-generating tandem Petasis 3-component/intramolecular Diels–Alder and ROM–RCM reactions for the diastereoselective synthesis of sp3-rich heterocyclic compound libraries are presented.</p

Studies of hepatic synthesis in vivo of plasma proteins, including orosomucoid, transferrin, α-antitrypsin, C8, and factor B

Serum protein types were determined in eight recipients and donors in cases of hepatic homotransplantation. A change from recipient type to donor type was observed for factor B, C8, orosomucoid, haptoglobin, transferrin, α1-antitrypsin, C3 and C6, but not for Gm and Inv immunoglobulin markers. The results indicate that all the proteins studied (except immunoglobulins) are produced primarily by the liver in vivo. © 1980

Machine-learning vs. logistic regression for preoperative prediction of medical morbidity after fast-track hip and knee arthroplasty-a comparative study

BACKGROUND: Machine-learning models may improve prediction of length of stay (LOS) and morbidity after surgery. However, few studies include fast-track programs, and most rely on administrative coding with limited follow-up and information on perioperative care. This study investigates potential benefits of a machine-learning model for prediction of postoperative morbidity in fast-track total hip (THA) and knee arthroplasty (TKA).METHODS: Cohort study in consecutive unselected primary THA/TKA between 2014-2017 from seven Danish centers with established fast-track protocols. Preoperative comorbidity and prescribed medication were recorded prospectively and information on length of stay and readmissions was obtained through the Danish National Patient Registry and medical records. We used a machine-learning model (Boosted Decision Trees) based on boosted decision trees with 33 preoperative variables for predicting "medical" morbidity leading to LOS &gt; 4 days or 90-days readmissions and compared to a logistical regression model based on the same variables. We also evaluated two parsimonious models, using the ten most important variables in the full machine-learning and logistic regression models. Data collected between 2014-2016 (n:18,013) was used for model training and data from 2017 (n:3913) was used for testing. Model performances were analyzed using precision, area under receiver operating (AUROC) and precision recall curves (AUPRC), as well as the Mathews Correlation Coefficient. Variable importance was analyzed using Shapley Additive Explanations values.RESULTS: Using a threshold of 20% "risk-patients" (n:782), precision, AUROC and AUPRC were 13.6%, 76.3% and 15.5% vs. 12.4%, 74.7% and 15.6% for the machine-learning and logistic regression model, respectively. The parsimonious machine-learning model performed better than the full logistic regression model. Of the top ten variables, eight were shared between the machine-learning and logistic regression models, but with a considerable age-related variation in importance of specific types of medication.CONCLUSION: A machine-learning model using preoperative characteristics and prescriptions slightly improved identification of patients in high-risk of "medical" complications after fast-track THA and TKA compared to a logistic regression model. Such algorithms could help find a manageable population of patients who may benefit most from intensified perioperative care.</p

Extraction of Airways using Graph Neural Networks

We present extraction of tree structures, such as airways, from image data as a graph refinement task. To this end, we propose a graph auto-encoder model that uses an encoder based on graph neural networks (GNNs) to learn embeddings from input node features and a decoder to predict connections between nodes. Performance of the GNN model is compared with mean-field networks in their ability to extract airways from 3D chest CT scans.Comment: Extended Abstract submitted to MIDL, 2018. 3 page

A metal-catalyzed enyne-cyclization step for the synthesis of bi- and tricyclic scaffolds amenable to molecular library production

A facile metal-catalyzed diversification step for the synthesis of novel bi- and tricyclic scaffolds from enyne substrates is reported in this study for molecular library production.</p

Temporal Aspects of Endogenous Pain Modulation During a Noxious Stimulus Prolonged for 1 Day

Background This study investigated (a) if a prolonged noxious stimulus (24‐hr topical capsaicin) in healthy adults would impair central pain inhibitory and facilitatory systems measured as a reduction in conditioned pain modulation (CPM) and enhancement of temporal summation of pain (TSP) and (b) if acute pain relief or exacerbation (cooling and heating the capsaicin patch) during the prolonged noxious stimulus would affect central pain modulation. Methods Twenty‐eight participants (26.2 ± 1.0 years; 12 women) wore a transdermal 8% capsaicin patch on the forearm for 24 hr. Data were collected at baseline (Day 0), 1 hr, 3 hr, Day 1 (post‐capsaicin application) and Day 3/4 (post‐capsaicin removal) that included capsaicin‐evoked pain intensity, heat pain thresholds (HPTs), TSP (10 painful cuff pressure stimuli on leg) and CPM (cuff pressure pain threshold on the leg prior vs. during painful cuff pressure conditioning on contralateral leg). After 3 hr, cold (12°C) and heat (42°C) stimuli were applied to the capsaicin patch to transiently increase and decrease pain intensity. Results Participants reported moderate pain scores at 1 hr (2.5 ± 2.0), 3 hr (3.7 ± 2.4), and Day 1 (2.4 ± 1.8). CPM decreased 3‐hr post‐capsaicin (p = .001) compared to Day 0 and remained diminished while the capsaicin pain score was reduced (0.4 ± 0.7, p \u3c .001) and increased (6.6 ± 2.2, p \u3c .001) by patch cooling and heating. No significant differences occurred for CPM during patch cooling or heating compared to initial 3HR; however, CPM during patch heating was reduced compared with patch cooling (p = .01). TSP and HPT did not change. Conclusions This prolonged experimental pain model is useful to provide insight into subacute pain conditions and may provide insight into the transition from acute to chronic pain. Significance During the early hours of a prolonged noxious stimulus in healthy adults, CPM efficacy was reduced and did not recover by temporarily removing the ongoing pain indicating a less dynamic neuroplastic process