Deep learning for the rapid automatic quantification and characterization of rotator cuff muscle degeneration from shoulder CT datasets.

This study aimed at developing a convolutional neural network (CNN) able to automatically quantify and characterize the level of degeneration of rotator cuff (RC) muscles from shoulder CT images including muscle atrophy and fatty infiltration. One hundred three shoulder CT scans from 95 patients with primary glenohumeral osteoarthritis undergoing anatomical total shoulder arthroplasty were retrospectively retrieved. Three independent radiologists manually segmented the premorbid boundaries of all four RC muscles on standardized sagittal-oblique CT sections. This premorbid muscle segmentation was further automatically predicted using a CNN. Automatically predicted premorbid segmentations were then used to quantify the ratio of muscle atrophy, fatty infiltration, secondary bone formation, and overall muscle degeneration. These muscle parameters were compared with measures obtained manually by human raters. Average Dice similarity coefficients for muscle segmentations obtained automatically with the CNN (88% ± 9%) and manually by human raters (89% ± 6%) were comparable. No significant differences were observed for the subscapularis, supraspinatus, and teres minor muscles (p > 0.120), whereas Dice coefficients of the automatic segmentation were significantly higher for the infraspinatus (p < 0.012). The automatic approach was able to provide good-very good estimates of muscle atrophy (R <sup>2</sup> = 0.87), fatty infiltration (R <sup>2</sup> = 0.91), and overall muscle degeneration (R <sup>2</sup> = 0.91). However, CNN-derived segmentations showed a higher variability in quantifying secondary bone formation (R <sup>2</sup> = 0.61) than human raters (R <sup>2</sup> = 0.87). Deep learning provides a rapid and reliable automatic quantification of RC muscle atrophy, fatty infiltration, and overall muscle degeneration directly from preoperative shoulder CT scans of osteoarthritic patients, with an accuracy comparable with that of human raters. • Deep learning can not only segment RC muscles currently available in CT images but also learn their pre-existing locations and shapes from invariant anatomical structures visible on CT sections. • Our automatic method is able to provide a rapid and reliable quantification of RC muscle atrophy and fatty infiltration from conventional shoulder CT scans. • The accuracy of our automatic quantitative technique is comparable with that of human raters

Agroforesterie et services écosystémiques en zone tropicale

Respectueux de l’environnement et garantissant une sécurité alimentaire soutenue par la diversification des productions et des revenus qu’ils procurent, les systèmes agroforestiers apparaissent comme un modèle prometteur d’agriculture durable dans les pays du Sud les plus vulnérables aux changements globaux. Cependant, ces systèmes agroforestiers ne peuvent être optimisés qu’à condition de mieux comprendre et de mieux maîtriser les facteurs de leurs productions. L’ouvrage présente un ensemble de connaissances récentes sur les mécanismes biophysiques et socio-économiques qui sous-tendent le fonctionnement et la dynamique des systèmes agroforestiers. Il concerne, d’une part les systèmes agroforestiers à base de cultures pérennes, telles que cacaoyers et caféiers, de régions tropicales humides en Amérique du Sud, en Afrique de l’Est et du Centre, d’autre part les parcs arborés et arbustifs à base de cultures vivrières, principalement de céréales, de la région semi-aride subsaharienne d’Afrique de l’Ouest. Il synthétise les dernières avancées acquises grâce à plusieurs projets associant le Cirad, l’IRD et leurs partenaires du Sud qui ont été conduits entre 2012 et 2016 dans ces régions. L’ensemble de ces projets s’articulent autour des dynamiques des systèmes agroforestiers et des compromis entre les services de production et les autres services socio-écosystémiques que ces systèmes fournissent

The SIB Swiss Institute of Bioinformatics' resources: focus on curated databases

The SIB Swiss Institute of Bioinformatics (www.isb-sib.ch) provides world-class bioinformatics databases, software tools, services and training to the international life science community in academia and industry. These solutions allow life scientists to turn the exponentially growing amount of data into knowledge. Here, we provide an overview of SIB's resources and competence areas, with a strong focus on curated databases and SIB's most popular and widely used resources. In particular, SIB's Bioinformatics resource portal ExPASy features over 150 resources, including UniProtKB/Swiss-Prot, ENZYME, PROSITE, neXtProt, STRING, UniCarbKB, SugarBindDB, SwissRegulon, EPD, arrayMap, Bgee, SWISS-MODEL Repository, OMA, OrthoDB and other databases, which are briefly described in this article

Range expansion theories could shed light on the spatial structure of intra-tumour heterogeneity

Many theoretical studies of range expansions focus on the dynamics of species' ranges or on causes and consequences of biological invasions. The similarities between biological range expansions and the dynamics of tumour growth have recently become more obvious, highlighting that tumours can be viewed as a population of abnormal cells expanding its range in the body of its host. Here, we discuss the potential of recent theoretical developments in the context of range expansions to shed light on intra-tumour heterogeneity, and to develop novel computational and statistical methods for studying the increasingly available genomic and phenotypic data from tumour cells. We review two spatial eco-evolutionary processes that could lead to a better understanding of the spatial structure of intra-tumour heterogeneity during the development of solid tumours: (1) the increase in dispersal abilities and (2) the accumulation of deleterious mutations at the front of expanding range edges. We first summarize theoretical and empirical evidences for each of these two phenomena and illustrate the eco-evolutionary dynamics of these processes using mathematical models. Secondly, we review evidences that these phenomena could also occur during the spatial expansion of a tumour within hosts. Finally, we discuss promising avenues for future research with the aim of synthesizing insights from clinical and theoretical studies of tumour development and evolutionary biology

The Role of Cooking for Hospital Food Service in Cancer Care-Units: Nutrition Is a Supportive Care While Cooking Appears to Be a Prescription

Abstract Cancer cachexia and treatment-induced side effects can contribute to deterioration in nutritional status in patients declining the quality of life and survival rates. Culinary practices may provide new strategies to minimize the symptoms. NEODIA (learning to live with a cancer on a day-to-day basis) is an observational program whose objective is to better understand the occurrence of treatment-related side effects as well as culinary and dietary habits in cancer patients followed in the Cancer Unit of the Beauvais City Hospital, France. First study step in 2010 has investigated culinary solutions to improve the quality of life of the patient at home. The present part tries in particular to answer the question if the culinary advising is transposable to the offer of restoration proposed by institutions of care in oncology. We first conducted an investigation on the determinants of quality of life in patients currently treated in a hospital service involving 41 people. In a second step, we are collecting information on the food supply itself, using advising of some patient's specially prepared to test meals as a translational research expertise. The results of our survey show that patient's remarks constitute real guidelines to adapt the practices in culinary production and healthy catering management. Based on these results, the consistency of frozen prepared meals has been analyzed regarding their potential to regulate the under nutritioninducing treatment-related side effects. Conducted by the patient's panel of the translational research group the study highlights the benefits of agro-food products and margins of progress. * Corresponding author. P. R. Pouillart et al. 35

Pitfalls in diagnosis of infiltrative lung disease by CT

The diagnosis of interstitial lung disease may be challenging, especially in atypical disease. Various factors must be considered when performing and reading a chest CT examination for interstitial lung disease, because each of them may represent a source of misinterpretation. Firstly, technical aspects must be mastered, including acquisition and reconstruction parameters as well as post-processing. Secondly, mistakes in interpretation related to the inaccurate description of predominant features, potentially leading to false-positive findings, as well as satisfaction of search must be avoided. In all cases, clinical context, coexisting chest abnormalities and previous examinations must be integrated into the analysis to suggest the most appropriate differential diagnosis

Comparison Between Magnetic Resonance Imaging and Computed Tomography in the Detection and Volumetric Assessment of Lung Nodules: A Prospective Study.

Computed tomography (CT) lung nodule assessment is routinely performed and appears very promising for lung cancer screening. However, the radiation exposure through time remains a concern. With the overall goal of an optimal management of indeterminate lung nodules, the objective of this prospective study was therefore to evaluate the potential of optimized ultra-short echo time (UTE) MRI for lung nodule detection and volumetric assessment. Eight (54.9 ± 13.2 years) patients with at least 1 non-calcified nodule ≥4 mm were included. UTE under high-frequency non-invasive ventilation (UTE-HF-NIV) and in free-breathing at tidal volume (UTE-FB) were investigated along with volumetric interpolated breath-hold examination at full inspiration (VIBE-BH). Three experienced readers assessed the detection rate of nodules ≥4 mm and ≥6 mm, and reported their location, 2D-measurements and solid/subsolid nature. Volumes were measured by two experienced readers. Subsequently, two readers assessed the detection and volume measurements of lung nodules ≥4mm in gold-standard CT images with soft and lung kernel reconstructions. Volumetry was performed with lesion management software (Carestream, Rochester, New York, USA). UTE-HF-NIV provided the highest detection rate for nodules ≥4 mm (n = 66) and ≥6 mm (n = 32) (35 and 50%, respectively). No dependencies were found between nodule detection and their location in the lung with UTE-HF-NIV (p > 0.4), such a dependency was observed for two readers with VIBE-BH (p = 0.002 and 0.03). Dependencies between the nodule's detection and their size were noticed among readers and techniques (p < 0.02). When comparing nodule volume measurements, an excellent concordance was observed between CT and UTE-HF-NIV, with an overestimation of 13.2% by UTE-HF-NIV, <25%-threshold used for nodule's growth, conversely to VIBE-BH that overestimated the nodule volume by 28.8%. UTE-HF-NIV is not ready to replace low-dose CT for lung nodule detection, but could be used for follow-up studies, alternating with CT, based on its volumetric accuracy