Automatic Head and Neck Tumor segmentation and outcome prediction relying on FDG-PET/CT images: Findings from the second edition of the HECKTOR challenge.

By focusing on metabolic and morphological tissue properties respectively, FluoroDeoxyGlucose (FDG)-Positron Emission Tomography (PET) and Computed Tomography (CT) modalities include complementary and synergistic information for cancerous lesion delineation and characterization (e.g. for outcome prediction), in addition to usual clinical variables. This is especially true in Head and Neck Cancer (HNC). The goal of the HEad and neCK TumOR segmentation and outcome prediction (HECKTOR) challenge was to develop and compare modern image analysis methods to best extract and leverage this information automatically. We present here the post-analysis of HECKTOR 2nd edition, at the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2021. The scope of the challenge was substantially expanded compared to the first edition, by providing a larger population (adding patients from a new clinical center) and proposing an additional task to the challengers, namely the prediction of Progression-Free Survival (PFS). To this end, the participants were given access to a training set of 224 cases from 5 different centers, each with a pre-treatment FDG-PET/CT scan and clinical variables. Their methods were subsequently evaluated on a held-out test set of 101 cases from two centers. For the segmentation task (Task 1), the ranking was based on a Borda counting of their ranks according to two metrics: mean Dice Similarity Coefficient (DSC) and median Hausdorff Distance at 95th percentile (HD95). For the PFS prediction task, challengers could use the tumor contours provided by experts (Task 3) or rely on their own (Task 2). The ranking was obtained according to the Concordance index (C-index) calculated on the predicted risk scores. A total of 103 teams registered for the challenge, for a total of 448 submissions and 29 papers. The best method in the segmentation task obtained an average DSC of 0.759, and the best predictions of PFS obtained a C-index of 0.717 (without relying on the provided contours) and 0.698 (using the expert contours). An interesting finding was that best PFS predictions were reached by relying on DL approaches (with or without explicit tumor segmentation, 4 out of the 5 best ranked) compared to standard radiomics methods using handcrafted features extracted from delineated tumors, and by exploiting alternative tumor contours (automated and/or larger volumes encompassing surrounding tissues) rather than relying on the expert contours. This second edition of the challenge confirmed the promising performance of fully automated primary tumor delineation in PET/CT images of HNC patients, although there is still a margin for improvement in some difficult cases. For the first time, the prediction of outcome was also addressed and the best methods reached relatively good performance (C-index above 0.7). Both results constitute another step forward toward large-scale outcome prediction studies in HNC

Selecting social work students:lessons from research in Scotland

The issue of selection of students to social work programmes is one that remains highly contested. While it is clear that there is no single way of choosing the next generation of social work students, nevertheless, there are a number of strongly held beliefs about what ‘best practice’ means in this fraught field. These can be difficult to challenge, and even harder to shift, in spite of contrary evidence. This paper presents research conducted in Scotland in 2016 as part of the Scottish Government-sponsored Review of Social Work Education. The research set out to consider what selection processes were being used in Scotland and why; more fundamentally, it sought to explore the views of those involved in social work education alongside evidence about the outcomes of the selection processes (that is, data on student retention and success). The article concludes that while there is little evidence that one method of selection to social work programmes is intrinsically better than another, issues of fairness and transparency in selection, as well as diversity, remain pressing

Water-soluble, stable and azide-reactive strained dialkynes for biocompatible double strain-promoted click chemistry.

The Sondheimer dialkyne is extensively used in double strain-promoted azide-alkyne cycloadditions. This reagent suffers with poor water-solubility and rapidly decomposes in aqueous solutions. This intrinsically limits its application in biological systems, and no effective solutions are currently available. Herein, we report the development of novel highly water-soluble, stable, and azide-reactive strained dialkyne reagents. To demonstrate their extensive utility, we applied our novel dialkynes to a double strain-promoted macrocyclisation strategy to generate functionalised p53-based stapled peptides for inhibiting the oncogenic p53-MDM2 interaction. These functionalised stapled peptides bind MDM2 with low nanomolar affinity and show p53 activation in a cellular environment. Overall, our highly soluble, stable and azide-reactive dialkynes offer significant advantages over the currently used Sondheimer dialkyne, and could be utilised for numerous biological applications

Obstetric near miss and deaths in public and private hospitals in Indonesia

This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens

Temporary storage or permanent removal? The division of nitrogen between biotic assimilation and denitrification in stormwater biofiltration systems

The long-term efficacy of stormwater treatment systems requires continuous pollutant removal without substantial re-release. Hence, the division of incoming pollutants between temporary and permanent removal pathways is fundamental. This is pertinent to nitrogen, a critical water body pollutant, which on a broad level may be assimilated by plants or microbes and temporarily stored, or transformed by bacteria to gaseous forms and permanently lost via denitrification. Biofiltration systems have demonstrated effective removal of nitrogen from urban stormwater runoff, but to date studies have been limited to a 'black-box' approach. The lack of understanding on internal nitrogen processes constrains future design and threatens the reliability of long-term system performance. While nitrogen processes have been thoroughly studied in other environments, including wastewater treatment wetlands, biofiltration systems differ fundamentally in design and the composition and hydrology of stormwater inflows, with intermittent inundation and prolonged dry periods. Two mesocosm experiments were conducted to investigate biofilter nitrogen processes using the stable isotope tracer 15NO3(-) (nitrate) over the course of one inflow event. The immediate partitioning of 15NO3(-) between biotic assimilation and denitrification were investigated for a range of different inflow concentrations and plant species. Assimilation was the primary fate for NO3(-) under typical stormwater concentrations (∼1-2 mg N/L), contributing an average 89-99% of 15NO3(-) processing in biofilter columns containing the most effective plant species, while only 0-3% was denitrified and 0-8% remained in the pore water. Denitrification played a greater role for columns containing less effective species, processing up to 8% of 15NO3(-), and increased further with nitrate loading. This study uniquely applied isotope tracing to biofiltration systems and revealed the dominance of assimilation in stormwater biofilters. The findings raise important questions about nitrogen release upon plant senescence, seasonally and in the long term, which have implications on the management and design of biofiltration systems.Emily G. I. Payne, Tim D. Fletcher, Douglas G. Russell, Michael R. Grace, Timothy R. Cavagnaro, Victor Evrard, Ana Deletic, Belinda E. Hatt, Perran L. M. Coo

Temporal and structural genetic variation in reindeer (Rangifer tarandus) associated with the pastoral transition in Northwestern Siberia

Funding Information NordForsk. Grant Number: 76915 ERC Advanced Grant. Grant Numbers: 295458, ESRC ES/, M011054/1 JPI HUMANOR ERC Starting GrantPeer reviewedPublisher PD