On a vector-valued generalisation of viscosity solutions for general PDE systems

We propose a theory of non-differentiable solutions which applies to fully nonlinear PDE systems and extends the theory of viscosity solutions of Crandall-Ishii-Lions to the vectorial case. Our key ingredient is the discovery of a notion of extremum for maps which extends min-max and allows "nonlinear passage of derivatives" to test maps. This new PDE approach supports certain stability and convergence results, preserving some basic features of the scalar viscosity counterpart. In this first part of our two-part work we introduce and study the rudiments of this theory, leaving applications for the second part.Comment: 34 pages, 6 figure

Genetic variants in eleven central and peripheral chemoreceptor genes in sudden infant death syndrome

Background: Sudden infant death syndrome (SIDS) is still one of the leading causes of postnatal infant death in developed countries. The occurrence of SIDS is described by a multifactorial etiology that involves the respiratory control system including chemoreception. It is still unclear whether genetic variants in genes involved in respiratory chemoreception might play a role in SIDS. Methods: The exome data of 155 SIDS cases were screened for variants within 11 genes described in chemoreception. Pathogenicity of variants was assigned based on the assessment of variant types and in silico protein predictions according to the current recommendations of the American College of Medical Genetics and Genomics. Results: Potential pathogenic variants in genes encoding proteins involved in respiratory chemoreception could be identified in 5 (3%) SIDS cases. Two of the variants (R137S/A188S) were found in the KNCJ16 gene, which encodes for the potassium channel Kir5.1, presumably involved in central chemoreception. Electrophysiologic analysis of these KCNJ16 variants revealed a loss-of-function for the R137S variant but no obvious impairment for the A188S variant. Conclusions: Genetic variants in genes involved in respiratory chemoreception may be a risk factor in a fraction of SIDS cases and may thereby contribute to the multifactorial etiology of SIDS. Impact: What is the key message of your article? Gene variants encoding proteins involved in respiratory chemoreception may play a role in a minority of SIDS cases. What does it add to the existing literature? Although impaired respiratory chemoreception has been suggested as an important risk factor for SIDS, genetic variants in single genes seem to play a minor role. What is the impact? This study supports previous findings, which indicate that genetic variants in single genes involved in respiratory control do not have a dominant role in SIDS

Self-supervised Multi-modality Image Feature Extraction for the Progression Free Survival Prediction in Head and Neck Cancer

Long-term survival of oropharyngeal squamous cell carcinoma patients (OPSCC) is quite poor. Accurate prediction of Progression Free Survival (PFS) before treatment could make identification of high-risk patients before treatment feasible which makes it possible to intensify or de-intensify treatments for high- or low-risk patients. In this work, we proposed a deep learning based pipeline for PFS prediction. The proposed pipeline consists of three parts. Firstly, we utilize the pyramid autoencoder for image feature extraction from both CT and PET scans. Secondly, the feed forward feature selection method is used to remove the redundant features from the extracted image features as well as clinical features. Finally, we feed all selected features to a DeepSurv model for survival analysis that outputs the risk score on PFS on individual patients. The whole pipeline was trained on 224 OPSCC patients. We have achieved a average C-index of 0.7806 and 0.7967 on the independent validation set for task 2 and task 3. The C-indices achieved on the test set are 0.6445 and 0.6373, respectively. It is demonstrated that our proposed approach has the potential for PFS prediction and possibly for other survival endpoints.</p

Comparison of computed tomography image features extracted by radiomics, self-supervised learning and end-to-end deep learning for outcome prediction of oropharyngeal cancer

Background and purpose: To compare the prediction performance of image features of computed tomography (CT) images extracted by radiomics, self-supervised learning and end-to-end deep learning for local control (LC), regional control (RC), locoregional control (LRC), distant metastasis-free survival (DMFS), tumor-specific survival (TSS), overall survival (OS) and disease-free survival (DFS) of oropharyngeal squamous cell carcinoma (OPSCC) patients after (chemo)radiotherapy.Methods and materials: The OPC-Radiomics dataset was used for model development and independent internal testing and the UMCG-OPC set for external testing. Image features were extracted from the Gross Tumor Volume contours of the primary tumor (GTVt) regions in CT scans when using radiomics or a self-supervised learning-based method (autoencoder). Clinical and combined (radiomics, autoencoder or end-to-end) models were built using multivariable Cox proportional-hazard analysis with clinical features only and both clinical and image features for LC, RC, LRC, DMFS, TSS, OS and DFS prediction, respectively.Results: In the internal test set, combined autoencoder models performed better than clinical models and combined radiomics models for LC, RC, LRC, DMFS, TSS and DFS prediction (largest improvements in C-index: 0.91 vs. 0.76 in RC and 0.74 vs. 0.60 in DMFS). In the external test set, combined radiomics models performed better than clinical and combined autoencoder models for all endpoints (largest improvements in LC, 0.82 vs. 0.71). Furthermore, combined models performed better in risk stratification than clinical models and showed good calibration for most endpoints.Conclusions: Image features extracted using self-supervised learning showed best internal prediction performance while radiomics features have better external generalizability.</p

DNA metabarcoding quantifies the relative biomass of arthropod taxa in songbird diets:Validation with camera-recorded diets

Ecological research is often hampered by the inability to quantify animal diets. Diet composition can be tracked through DNA metabarcoding of fecal samples, but whether (complex) diets can be quantitatively determined with metabarcoding is still debated and needs validation using free-living animals. This study validates that DNA metabarcoding of feces can retrieve actual ingested taxa, and most importantly, that read numbers retrieved from sequencing can also be used to quantify the relative biomass of dietary taxa. Validation was done with the hole-nesting insectivorous Pied Flycatcher whose diet was quantified using camera footage. Size-adjusted counts of food items delivered to nestlings were used as a proxy for provided biomass of prey orders and families, and subsequently, nestling feces were assessed through DNA metabarcoding. To explore potential effects of digestion, gizzard and lower intestine samples of freshly collected birds were subjected to DNA metabarcoding. For metabarcoding with Cytochrome Oxidase subunit I (COI), we modified published invertebrate COI primers LCO1490 and HCO1777, which reduced host reads to 0.03%, and amplified Arachnida DNA without significant changing the recovery of other arthropod taxa. DNA metabarcoding retrieved all commonly camera-recorded taxa. Overall, and in each replicate year (N = 3), the relative scaled biomass of prey taxa and COI read numbers correlated at R =.85 (95CI:0.68–0.94) at order level and at R =.75 (CI:0.67–0.82) at family level. Similarity in arthropod community composition between gizzard and intestines suggested limited digestive bias. This DNA metabarcoding validation demonstrates that quantitative analyses of arthropod diet is possible. We discuss the ecological applications for insectivorous birds

Topological Interference Management With Transmitter Cooperation

Interference networks with no channel state information at the transmitter except for the knowledge of the connectivity graph have been recently studied under the topological interference management framework. In this paper, we consider a similar problem with topological knowledge but in a distributed broadcast channel setting, i.e., a network where transmitter cooperation is enabled. We show that the topological information can also be exploited in this case to strictly improve the degrees of freedom (DoF) as long as the network is not fully connected, which is a reasonable assumption in practice. Achievability schemes from graph theoretic and interference alignment perspectives are proposed. Together with outer bounds built upon generator sequence, the concept of compound channel settings, and the relation to index coding, we characterize the symmetric DoF for the so-called regular networks with constant number of interfering links, and identify the sufficient and/or necessary conditions for the arbitrary network topologies to achieve a certain amount of symmetric DoF

Survival prediction for stage I-IIIA non-small cell lung cancer using deep learning

BACKGROUND AND PURPOSE: The aim of this study was to develop and evaluate a prediction model for 2-year overall survival (OS) in stage I-IIIA non-small cell lung cancer (NSCLC) patients who received definitive radiotherapy by considering clinical variables and image features from pre-treatment CT-scans. MATERIALS AND METHODS: NSCLC patients who received stereotactic radiotherapy were prospectively collected at the UMCG and split into a training and a hold out test set including 189 and 81 patients, respectively. External validation was performed on 228 NSCLC patients who were treated with radiation or concurrent chemoradiation at the Maastro clinic (Lung1 dataset). A hybrid model that integrated both image and clinical features was implemented using deep learning. Image features were learned from cubic patches containing lung tumours extracted from pre-treatment CT scans. Relevant clinical variables were selected by univariable and multivariable analyses. RESULTS: Multivariable analysis showed that age and clinical stage were significant prognostic clinical factors for 2-year OS. Using these two clinical variables in combination with image features from pre-treatment CT scans, the hybrid model achieved a median AUC of 0.76 [95% CI: 0.65-0.86] and 0.64 [95% CI: 0.58-0.70] on the complete UMCG and Maastro test sets, respectively. The Kaplan-Meier survival curves showed significant separation between low and high mortality risk groups on these two test sets (log-rank test: p-value < 0.001, p-value = 0.012, respectively) CONCLUSION: We demonstrated that a hybrid model could achieve reasonable performance by utilizing both clinical and image features for 2-year OS prediction. Such a model has the potential to identify patients with high mortality risk and guide clinical decision making. Short title: OS prediction for stage I-IIIA NSCLC using DL

Anthropogenic substrate-borne vibrations impact anuran calling

Anthropogenic disturbance is a major cause of the biodiversity crisis. Nevertheless, the role of anthropogenic substrate vibrations in disrupting animal behavior is poorly understood. Amphibians comprise the terrestrial vertebrates most sensitive to vibrations, and since communication is crucial to their survival and reproduction, they are a suitable model for investigating this timely subject. Playback tests were used to assess the effects of substrate vibrations produced by two sources of anthropogenic activity– road traffic and wind turbines– on the calling activity of a naïve population of terrestrial toads. In their natural habitat, a buried tactile sound transducer was used to emit simulated traffic and wind turbine vibrations, and changes in the toads’ acoustic responses were analyzed by measuring parameters important for reproductive success: call rate, call duration and dominant frequency. Our results showed a significant call rate reduction by males of Alytes obstetricans in response to both seismic sources, whereas other parameters remained stable. Since females of several species prefer males with higher call rates, our results suggest that anthropogenically derived substrate-borne vibrations could reduce individual reproductive success. Our study demonstrates a clear negative effect of anthropogenic vibrations on anuran communication, and the urgent need for further investigation in this area

Challenging claims in the study of migratory birds and climate change

Recent shifts in phenology in response to climate change are well established but often poorly understood. Many animals integrate climate change across a spatially and temporally dispersed annual life cycle, and effects are modulated by ecological interactions, evolutionary change and endogenous control mechanisms. Here we assess and discuss key statements emerging from the rapidly developing study of changing spring phenology in migratory birds. These well-studied organisms have been instrumental for understanding climate-change effects, but research is developing rapidly and there is a need to attack the big issues rather than risking affirmative science. Although we agree poorly on the support for most claims, agreement regarding the knowledge basis enables consensus regarding broad patterns and likely causes. Empirical data needed for disentangling mechanisms are still scarce, and consequences at a population level and on community composition remain unclear. With increasing knowledge, the overall support (‘consensus view’) for a claim increased and between-researcher variability in support (‘expert opinions') decreased, indicating the importance of assessing and communicating the knowledge basis. A proper integration across biological disciplines seems essential for the field's transition from affirming patterns to understanding mechanisms and making robust predictions regarding future consequences of shifting phenologies

Deep Learning and Radiomics Based PET/CT Image Feature Extraction from Auto Segmented Tumor Volumes for Recurrence-Free Survival Prediction in Oropharyngeal Cancer Patients

Aim: The development and evaluation of deep learning (DL) and radiomics based models for recurrence-free survival (RFS) prediction in oropharyngeal squamous cell carcinoma (OPSCC) patients based on clinical features, positron emission tomography (PET) and computed tomography (CT) scans and GTV (Gross Tumor Volume) contours of primary tumors and pathological lymph nodes. Methods: A DL auto-segmentation algorithm generated the GTV contours (task 1) that were used for imaging biomarkers (IBMs) extraction and as input for the DL model. Multivariable cox regression analysis was used to develop radiomics models based on clinical and IBMs features. Clinical features with a significant correlation with the endpoint in a univariable analysis were selected. The most promising IBMs were selected by forward selection in 1000 times bootstrap resampling in five-fold cross validation. To optimize the DL models, different combinations of clinical features, PET/CT imaging, GTV contours, the selected radiomics features and the radiomics model predictions were used as input. The combination with the best average performance in five-fold cross validation was taken as the final input for the DL model. The final prediction in the test set, was an ensemble average of the predictions from the five models for the different folds. Results: The average C-index in the five-fold cross validation of the radiomics model and the DL model were 0.7069 and 0.7575, respectively. The radiomics and final DL models showed C-indexes of 0.6683 and 0.6455, respectively in the test set. Conclusion: The radiomics model for recurrence free survival prediction based on clinical, GTV and CT image features showed the best predictive performance in the test set with a C-index of 0.6683.</p