Inheritance patterns in citation networks reveal scientific memes

Memes are the cultural equivalent of genes that spread across human culture by means of imitation. What makes a meme and what distinguishes it from other forms of information, however, is still poorly understood. Our analysis of memes in the scientific literature reveals that they are governed by a surprisingly simple relationship between frequency of occurrence and the degree to which they propagate along the citation graph. We propose a simple formalization of this pattern and we validate it with data from close to 50 million publication records from the Web of Science, PubMed Central, and the American Physical Society. Evaluations relying on human annotators, citation network randomizations, and comparisons with several alternative approaches confirm that our formula is accurate and effective, without a dependence on linguistic or ontological knowledge and without the application of arbitrary thresholds or filters.Comment: 8 two-column pages, 5 figures; accepted for publication in Physical Review

Mycobacterial PIMs Inhibit Host Inflammatory Responses through CD14-Dependent and CD14-Independent Mechanisms

Mycobacteria develop strategies to evade the host immune system. Among them, mycobacterial LAM or PIMs inhibit the expression of pro-inflammatory cytokines by activated macrophages. Here, using synthetic PIM analogues, we analyzed the mode of action of PIM anti-inflammatory effects. Synthetic PIM1 isomer and PIM2 mimetic potently inhibit TNF and IL-12 p40 expression induced by TLR2 or TLR4 pathways, but not by TLR9, in murine macrophages. We show inhibition of LPS binding to TLR4/MD2/CD14 expressing HEK cells by PIM1 and PIM2 analogues. More specifically, the binding of LPS to CD14 was inhibited by PIM1 and PIM2 analogues. CD14 was dispensable for PIM1 and PIM2 analogues functional inhibition of TLR2 agonists induced TNF, as shown in CD14-deficient macrophages. The use of rough-LPS, that stimulates TLR4 pathway independently of CD14, allowed to discriminate between CD14-dependent and CD14-independent anti-inflammatory effects of PIMs on LPS-induced macrophage responses. PIM1 and PIM2 analogues inhibited LPS-induced TNF release by a CD14-dependent pathway, while IL-12 p40 inhibition was CD14-independent, suggesting that PIMs have multifold inhibitory effects on the TLR4 signalling pathway

Multimodal PET/CT tumour segmentation and prediction of progression-free survival using a full-scale UNet with attention

Segmentation of head and neck (H&N) tumours and prediction of patient outcome are crucial for patient’s disease diagnosis and treatment monitoring. Current developments of robust deep learning models are hindered by the lack of large multi-centre, multi-modal data with quality annotations. The MICCAI 2021 HEad and neCK TumOR (HECKTOR) segmentation and outcome prediction challenge creates a platform for comparing segmentation methods of the primary gross target volume on fluoro-deoxyglucose (FDG)-PET and Computed Tomography images and prediction of progression-free survival in H&N oropharyngeal cancer. For the segmentation task, we proposed a new network based on an encoder-decoder architecture with full inter- and intra-skip connections to take advantage of low-level and high-level semantics at full scales. Additionally, we used Conditional Random Fields as a post-processing step to refine the predicted segmentation maps. We trained multiple neural networks for tumor volume segmentation, and these segmentations were ensembled achieving an average Dice Similarity Coefficient of 0.75 in cross-validation, and 0.76 on the challenge testing data set. For prediction of patient progression free survival task, we propose a Cox proportional hazard regression combining clinical, radiomic, and deep learning features. Our survival prediction model achieved a concordance index of 0.82 in cross-validation, and 0.62 on the challenge testing data se

Multimodal PET/CT tumour segmentation and prediction of progression-free survival using a full-scale UNet with attention

Segmentation of head and neck (H&N) tumours and prediction of patient outcome are crucial for patient’s disease diagnosis and treatment monitoring. Current developments of robust deep learning models are hindered by the lack of large multi-centre, multi-modal data with quality annotations. The MICCAI 2021 HEad and neCK TumOR (HECKTOR) segmentation and outcome prediction challenge creates a platform for comparing segmentation methods of the primary gross target volume on fluoro-deoxyglucose (FDG)-PET and Computed Tomography images and prediction of progression-free survival in H&N oropharyngeal cancer. For the segmentation task, we proposed a new network based on an encoder-decoder architecture with full inter- and intra-skip connections to take advantage of low-level and high-level semantics at full scales. Additionally, we used Conditional Random Fields as a post-processing step to refine the predicted segmentation maps. We trained multiple neural networks for tumor volume segmentation, and these segmentations were ensembled achieving an average Dice Similarity Coefficient of 0.75 in cross-validation, and 0.76 on the challenge testing data set. For prediction of patient progression free survival task, we propose a Cox proportional hazard regression combining clinical, radiomic, and deep learning features. Our survival prediction model achieved a concordance index of 0.82 in cross-validation, and 0.62 on the challenge testing data se

Investigation of atovaquone-induced spatial changes in tumour hypoxia assessed by hypoxia PET/CT in non-small cell lung cancer patients

Background Tumour hypoxia promotes an aggressive tumour phenotype and enhances resistance to anticancer treatments. Following the recent observation that the mitochondrial inhibitor atovaquone increases tumour oxygenation in NSCLC, we sought to assess whether atovaquone affects tumour subregions differently depending on their level of hypoxia. Methods Patients with resectable NSCLC participated in the ATOM trial (NCT02628080). Cohort 1 (n = 15) received atovaquone treatment, whilst cohort 2 (n = 15) did not. Hypoxia-related metrics, including change in mean tumour-to-blood ratio, tumour hypoxic volume, and fraction of hypoxic voxels, were assessed using hypoxia PET imaging. Tumours were divided into four subregions or distance categories: edge, outer, inner, and centre, using MATLAB. Results Atovaquone-induced reduction in tumour hypoxia mostly occurred in the inner and outer tumour subregions, and to a lesser extent in the centre subregion. Atovaquone did not seem to act in the edge subregion, which was the only tumour subregion that was non-hypoxic at baseline. Notably, the most intensely hypoxic tumour voxels, and therefore the most radiobiologically resistant areas, were subject to the most pronounced decrease in hypoxia in the different subregions. Conclusions This study provides insights into the action of atovaquone in tumour subregions that help to better understand its role as a novel tumour radiosensitiser

Assessment of the Performance of the Aptima Bacterial Vaginosis Assay Over a 3-Month Period in a French Hospital

International audienceBacterial vaginosis (BV) is the most common cause of abnormal vaginal discharge. BV represents a dysbiosis with the acquisition of a diverse community of anaerobic bacteria and a reduction in lactobacilli burden. Our objective was to evaluate the Aptima BV assay kit for the diagnosis of BV. From May to August 2019, we enrolled outpatients and inpatients, including nonpregnant women above 18 with vaginosis symptoms, consulting at Nantes University hospital. The Aptima BV assay measures the loads of Gardnerella vaginalis, Atopobium vaginae, and Lactobacillus species in relation to overall bacterial load. The Aptima BV assay was compared to Nugent scoring (NS). A total of 456 women were enrolled, and 347 patients met the inclusion criteria with data available for the analysis. NS was used to classify the samples and 144 (41.5%) samples were classified as normal (NS = 0-3), 45 (13%) as BV (NS = 7-10), 38 (11%) presented an intermediate vaginal microbiota (3 < NS < 7), 79 (22.7%) had various bacteria (excluding vaginal flora), 29 (8.3%) had insufficient bacterial density, and 12 (3.5%) had a predominance of yeasts. The Aptima BV kit displayed a sensitivity of 91.1% and specificity of 94.4% with a positive predictive value (PPV) of 83.7% and a negative predictive (NPV) value of 97.1%. The results of this monocentric retrospective study show that Aptima BV kit has a good diagnostic correlation compared to standard of care for dysbiotic diagnosis cases. IMPORTANCE The possibility exists of the involvement of a new molecular test in the routine algorithm of bacterial vaginosis diagnosis in microbiology laboratories. This manuscript reports on our experience, and we propose an organization combining Nugent scoring and molecular testing, especially for intermediate Nugent scores