Kendall transformation

Kendall transformation is a conversion of an ordered feature into a vector of pairwise order relations between individual values. This way, it preserves ranking of observations and represents it in a categorical form. Such transformation allows for generalisation of methods requiring strictly categorical input, especially in the limit of small number of observations, when discretisation becomes problematic. In particular, many approaches of information theory can be directly applied to Kendall-transformed continuous data without relying on differential entropy or any additional parameters. Moreover, by filtering information to this contained in ranking, Kendall transformation leads to a better robustness at a reasonable cost of dropping sophisticated interactions which are anyhow unlikely to be correctly estimated. In bivariate analysis, Kendall transformation can be related to popular non-parametric methods, showing the soundness of the approach. The paper also demonstrates its efficiency in multivariate problems, as well as provides an example analysis of a real-world data

Inferring causal molecular networks: empirical assessment through a community-based effort

Inferring molecular networks is a central challenge in computational biology. However, it has remained unclear whether causal, rather than merely correlational, relationships can be effectively inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge that focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results constitute the most comprehensive assessment of causal network inference in a mammalian setting carried out to date and suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess the causal validity of inferred molecular networks

Inferring causal molecular networks: empirical assessment through a community-based effort

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense

Prolonged Consumption of Sweetened Beverages Lastingly Deteriorates Cognitive Functions and Reward Processing in Mice

International audienceAbstract We investigated the detrimental effects of chronic consumption of sweet or sweetened beverages in mice. We report that consumption of beverages containing small amounts of sucrose during several weeks impaired reward systems. This is evidenced by robust changes in the activation pattern of prefrontal brain regions associated with abnormal risk-taking and delayed establishment of decision-making strategy. Supporting these findings, we find that chronic consumption of low doses of artificial sweeteners such as saccharin disrupts brain regions’ activity engaged in decision-making and reward processes. Consequently, this leads to the rapid development of inflexible decisions, particularly in a subset of vulnerable individuals. Our data also reveal that regular consumption, even at low doses, of sweet or sweeteners dramatically alters brain neurochemistry, i.e., dopamine content and turnover, and high cognitive functions, while sparing metabolic regulations. Our findings suggest that it would be relevant to focus on long-term consequences on the brain of sweet or sweetened beverages in humans, especially as they may go metabolically unnoticed

A tendency to worse course of multisystem inflammatory syndrome in children with obesity: MultiOrgan Inflammatory Syndromes COVID-19 related study

International audienceBackground A new disease entity called multisystem inflammatory syndrome in children (MIS-C) is a rare consequence of COVID-19 infection. The pathophysiology and risk factors of MIS-C are still unclear, and the clinical manifestation ranges from milder forms to cases needing intensive care unit treatment. Based on available data, obesity is linked to pro-inflammatory stimulation. Moreover, several studies showed that obesity could play a role in COVID-19 severity and its comorbidities among the adult and children’s populations. This study aimed to investigate the influence of overweightedness/obesity in childhood for the course of MIS-C in Poland. Methods This study presented data from the national MultiOrgan Inflammatory Syndromes COVID-19 Related Study (MOIS-CoR) collected between 4 March 2020 and 20 February 2021. Of the 371 patients that met the Polish MIS-C criteria, 306 were included for further analysis. Results Children who are obese (OB with body mass index (BMI) ≥95th percentile) and overweight (OV with BMI ≥85th percentile but <95th percentile) (28 and 49 patients, respectively) represented 25.1% (n=77) of all recruited patients. Complete recovery at the time of discharge presented in 93% of normal body weight (NW) participants and 90% of OV children (p>0.05). Among OB children, 76% recovered fully, which differed from the NW group (p=0.01). Calculated odds ratio (OR) of incomplete recovery for OB children was 4.2. Irrespective of body weight, there were no differences (p>0.05) in the length of hospitalization and the duration of symptoms (for OB, 13 and 16.5 days; for OV and NW, 10 and 14 days, respectively), as well as in the frequency of cardiovascular abnormalities, necessity of oxygen therapy (OB, 26.9%; OV, 23.9%; and NW, 20.7%), and intravenous immunoglobulin and glucocorticosteroid (GCS) treatment. Conclusion The higher risk of incomplete recovery and observed tendency toward a worsening course of MIS-C in patients with obesity suggest the need for further studies to confirm and understand our findings

Distinct characteristics of multisystem inflammatory syndrome in children in Poland

International audienceAbstract During the winter months of 2020/2021 a wave of multisystem inflammatory syndrome in children (MIS-C) emerged in Poland. We present the results of a nationwide register aiming to capture and characterise MIS-C with a focus on severity determinants. The first MIS-C wave in Poland was notably high, hence our analysis involved 274 children. The group was 62.8% boys, with a median age of 8.8 years. Besides one Asian, all were White. Overall, the disease course was not as severe as in previous reports, however. Pediatric intensive care treatment was required for merely 23 (8.4%) of children, who were older and exhibited a distinguished clinical picture at hospital admission. We have also identified sex-dependent differences; teenage boys more often had cardiac involvement (decreased ejection fraction in 25.9% vs. 14.7%) and fulfilled macrophage activation syndrome definition (31.0% vs. 15.2%). Among all boys, those hospitalized in pediatric intensive care unit were significantly older (median 11.2 vs. 9.1 years). Henceforth, while ethnicity and sex may affect MIS-C phenotype, management protocols might be not universally applicable, and should rather be adjusted to the specific population

Inferring causal molecular networks: empirical assessment through a community-based effort

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense