Defining Canada’s Extended Continental Shelves

Article 76 of the United Nations Convention on the Law of the Sea provides a process to delineate Canada’s continental shelf where it extends beyond 200 nautical miles. After ratification of the Convention in 2003, Canada started a program to acquire and analyze the scientific data required for a submission to the Commission on the limits of the continental shelf. This submission will assist Canada in defining the outer limits of its continental shelf, thereby determining, with precision, the area where Canada may exercise its sovereign rights over natural resources. This paper outlines the scope of that program and summarizes the scientific activities to date. Data collection along the Atlantic margin was completed according to plan and the data are presently being analyzed. Data collection in the Arctic has been challenging because of ice and weather conditions, and several innovative solutions were implemented to collect seismic and bathymetry data using ice-breakers and large ice camps. The narrow Pacific margin provides no clear prospects for an extended continental shelf. Overall, the program is on schedule to meet the December 2013 deadline for submission to the Commission. SOMMAIRE L’article 76 de la Convention des Nations Unies sur le droit de la mer comporte une procédure permettant de définir l’étendue du plateau continental canadien au-delà des 200 miles nautiques. Après ratification de la Convention en 2003, le Canada a lancé un programme d’acquisition et d’analyse des données scientifiques exigées par la Commission pour définir les limites du plateau continental. Le dépôt de ces données aidera le Canada à définir les limites extérieures de son plateau continental, et donc, de déter-miner précisément le territoire où il pourra exercer des droits souverains sur les ressources naturelles. Le présent article décrit ce programme et résume les activités scientifiques réalisées à ce jour. Le long du plateau continental de l’Atlantique la collecte des données s’est terminée comme prévu et leur analyse est en cours. Dans l’Arctique, la collecte des données n’a pas été facile en raison de la glace et les conditions météorologiques, et plusieurs solutions innovantes ont dues être mises en œuvre afin de recueillir des données sismiques et bathymétriques, comme l’utilisation de brise-glaces et le recours à de grands camps de glace. Sur la côte du Pacifique, l'étroitesse du plateau continental exclu toutes possibilités d'extension évidemment. En gros, le programme se déroule comme prévu, et la date limite du décembre 2013 pour soumission du dossier à la Commission devrait être respectée

Preoperative Classification of Peripheral Nerve Sheath Tumors on MRI Using Radiomics

Malignant peripheral nerve sheath tumors (MPNSTs) are aggressive soft-tissue tumors prevalent in neurofibromatosis type 1 (NF1) patients, posing a significant risk of metastasis and recurrence. Current magnetic resonance imaging (MRI) imaging lacks decisiveness in distinguishing benign peripheral nerve sheath tumors (BPNSTs) and MPNSTs, necessitating invasive biopsies. This study aims to develop a radiomics model using quantitative imaging features and machine learning to distinguish MPNSTs from BPNSTs. Clinical data and MRIs from MPNST and BPNST patients (2000–2019) were collected at a tertiary sarcoma referral center. Lesions were manually and semi-automatically segmented on MRI scans, and radiomics features were extracted using the Workflow for Optimal Radiomics Classification (WORC) algorithm, employing automated machine learning. The evaluation was conducted using a 100× random-split cross-validation. A total of 35 MPNSTs and 74 BPNSTs were included. The T1-weighted (T1w) MRI radiomics model outperformed others with an area under the curve (AUC) of 0.71. The incorporation of additional MRI scans did not enhance performance. Combining T1w MRI with clinical features achieved an AUC of 0.74. Experienced radiologists achieved AUCs of 0.75 and 0.66, respectively. Radiomics based on T1w MRI scans and clinical features show some ability to distinguish MPNSTs from BPNSTs, potentially aiding in the management of these tumors.</p

Possible futures for groundwater in Burkina Faso under a changing climate

This narrative describes three possible future scenarios for water resources in Burkina Faso and the human and socio-economic impacts that might be experienced by people living in rural areas. The narratives aim to stimulate discussion towards realistic policy responses and the decision support tools needed to assist future planning needs. It is important to recognise that the scenarios do not represent every possible outcome projected by hydroclimate models, and resulting impacts will be contextualised by local circumstances. See here for information about BRAVE (www.walker.ac.uk) and the UPGro programme: upgro.org/consortium/brave2/

Synchronous beta rhythms of frontoparietal networks support only behaviorally relevant representations

Categorization has been associated with distributed networks of the primate brain, including the prefrontal cortex (PFC) and posterior parietal cortex (PPC). Although category-selective spiking in PFC and PPC has been established, the frequency-dependent dynamic interactions of frontoparietal networks are largely unexplored. We trained monkeys to perform a delayed-match-to-spatial-category task while recording spikes and local field potentials from the PFC and PPC with multiple electrodes. We found category-selective beta- and delta-band synchrony between and within the areas. However, in addition to the categories, delta synchrony and spiking activity also reflected irrelevant stimulus dimensions. By contrast, beta synchrony only conveyed information about the task-relevant categories. Further, category-selective PFC neurons were synchronized with PPC beta oscillations, while neurons that carried irrelevant information were not. These results suggest that long-range beta-band synchrony could act as a filter that only supports neural representations of the variables relevant to the task at hand.National Institute of Mental Health (U.S.) (4R01MH065252)Prop. 63 the Mental Health Services ActUniversity of California, Davis. Behavioral Health Center of Excellenc

Preoperative Classification of Peripheral Nerve Sheath Tumors on MRI Using Radiomics

Malignant peripheral nerve sheath tumors (MPNSTs) are aggressive soft-tissue tumors prevalent in neurofibromatosis type 1 (NF1) patients, posing a significant risk of metastasis and recurrence. Current magnetic resonance imaging (MRI) imaging lacks decisiveness in distinguishing benign peripheral nerve sheath tumors (BPNSTs) and MPNSTs, necessitating invasive biopsies. This study aims to develop a radiomics model using quantitative imaging features and machine learning to distinguish MPNSTs from BPNSTs. Clinical data and MRIs from MPNST and BPNST patients (2000–2019) were collected at a tertiary sarcoma referral center. Lesions were manually and semi-automatically segmented on MRI scans, and radiomics features were extracted using the Workflow for Optimal Radiomics Classification (WORC) algorithm, employing automated machine learning. The evaluation was conducted using a 100× random-split cross-validation. A total of 35 MPNSTs and 74 BPNSTs were included. The T1-weighted (T1w) MRI radiomics model outperformed others with an area under the curve (AUC) of 0.71. The incorporation of additional MRI scans did not enhance performance. Combining T1w MRI with clinical features achieved an AUC of 0.74. Experienced radiologists achieved AUCs of 0.75 and 0.66, respectively. Radiomics based on T1w MRI scans and clinical features show some ability to distinguish MPNSTs from BPNSTs, potentially aiding in the management of these tumors

Differential diagnosis and mutation stratification of desmoid-type fibromatosis on MRI using radiomics

Purpose: Diagnosing desmoid-type fibromatosis (DTF) requires an invasive tissue biopsy with β-catenin staining and CTNNB1 mutational analysis, and is challenging due to its rarity. The aim of this study was to evaluate radiomics for distinguishing DTF from soft tissue sarcomas (STS), and in DTF, for predicting the CTNNB1 mutation types. Methods: Patients with histologically confirmed extremity STS (non-DTF) or DTF and at least a pretreatment T1- weighted (T1w) MRI scan were retrospectively included. Tumors were semi-automatically annotated on the T1w scans, from which 411 features were extracted. Prediction models were created using a combination of various machine learning approaches. Evaluation was performed through a 100x random-split cross-validation. The model for DTF vs. non-DTF was compared to classification by two radiologists on a location matched subset. Results: The data included 203 patients (72 DTF, 131 STS). The T1w radiomics model showed a mean AUC of 0.79 on the full dataset. Addition of T2w or T1w post-contrast scans did not improve the performance. On the location matched cohort, the T1w model had a mean AUC of 0.88 while the radiologists had an AUC of 0.80 and 0.88, respectively. For the prediction of the CTNNB1 mutation types (S45 F, T41A and wild-type), the T1w model showed an AUC of 0.61, 0.56, and 0.74. Conclusions: Our radiomics model was able to distinguish DTF from STS with high accuracy similar to two radiologists, but was not able to predict the CTNNB1 mutation status

Radiomics approach to distinguish between well differentiated liposarcomas and lipomas on MRI

Background: Well differentiated liposarcoma (WDLPS) can be difficult to distinguish from lipoma. Currently, this distinction is made by testing for MDM2 amplification, which requires a biopsy. The aim of this study was to develop a noninvasive method to predict MDM2 amplification status using radiomics features derived from MRI. Methods: Patients with an MDM2-negative lipoma or MDM2-positive WDLPS and a pretreatment T1-weighted MRI scan who were referred to Erasmus MC between 2009 and 2018 were included. When available, other MRI sequences were included in the radiomics analysis. Features describing intensity, shape and texture were extracted from the tumour region. Classification was performed using various machine learning approaches. Evaluation was performed through a 100 times random-split cross-validation. The performance of the models wa

Reproducible radiomics through automated machine learning validated on twelve clinical applications

Radiomics uses quantitative medical imaging features to predict clinical outcomes. Currently, in a new clinical application, findingthe optimal radiomics method out of the wide range of available options has to be done manually through a heuristic trial-anderror process. In this study we propose a framework for automatically optimizing the construction of radiomics workflows perapplication. To this end, we formulate radiomics as a modular workflow and include a large collection of common algorithms foreach component. To optimize the workflow per application, we employ automated machine learning using a random search andensembling. We evaluate our method in twelve different clinical applications, resulting in the following area under the curves: 1)liposarcoma (0.83); 2) desmoid-type fibromatosis (0.82); 3) primary liver tumors (0.80); 4) gastrointestinal stromal tumors (0.77);5) colorectal liver metastases (0.61); 6) melanoma metastases (0.45); 7) hepatocellular carcinoma (0.75); 8) mesenteric fibrosis(0.80); 9) prostate cancer (0.72); 10) glioma (0.71); 11) Alzheimer’s disease (0.87); and 12) head and neck cancer (0.84). Weshow that our framework has a competitive performance compared human experts, outperforms a radiomics baseline, and performssimilar or superior to Bayesian optimization and more advanced ensemble approaches. Concluding, our method fully automaticallyoptimizes the construction of radiomics workflows, thereby streamlining the search for radiomics biomarkers in new applications.To facilitate reproducibility and future research, we publicly release six datasets, the software implementation of our framework,and the code to reproduce this study

Hypothesis-driven genome-wide association studies provide novel insights into genetics of reading disabilities

Peer reviewe