Types d'orbites et dynamique minimale pour les applicationes continues de graphes

We define the type of a periodic orbit of a graph map. We consider the class of ‘train-track’ representatives, that is, those graph maps which minimize the topological entropy of the topological representatives of a given free group endomorphism. We prove that each type of periodic orbit realized by an efficient representative is also realised by any representative of the same free group endomorphism. Moreover, the number of periodic orbits of a given type is minimized by the efficient representatives

Effect of Air Injection on Nucleation Rates: An Approach from Induction Time Statistics

From disruption of the supersaturated solution to improved mass transfer in the crystallizing suspension, the introduction of a moving gas phase in a crystallizer could lead to improved rates of nucleation and crystal growth. In this work, saturated air has been injected to batch crystallizers to study the effects on formation of the first crystal and subsequent turbidity buildup. To account for the typically large sample-to-sample variation, nucleation rates were evaluated for a large number of replicates using probability distributions of induction times. The slope and the intercept of the distributions were studied independently, allowing the simultaneous determination of the mean induction time and a certain detection delay related to the rate of crystal growth after formation of the first nucleus. When saturated air was injected in aqueous glycine solutions, the average detection delay was reduced from 69 to 13 min, and the mean induction time decreased from 128 to 36 min. The effect on aqueous solutions of l-arginine was less apparent, with a detection delay reduction from 15 to 3 min, and no significant changes on the rate of primary nucleation. These results demonstrate the potential of this technique for reduction in nucleation induction time and improved mass deposition rates in crystallization operations

Characterization of a Multistage Continuous MSMPR Crystallization Process assisted by Image Analysis of Elongated Crystals

This work demonstrates how quantitative image analysis can assist in the characterization of continuous crystallization processes and in the proper selection of mathematical models for the early assessment of crystal quality. An active pharmaceutical ingredient presenting an elongated crystal habit was crystallized using two stirred tank crystallizers in series. With image analysis of the crystallization magma, the sources of crystal breakage in the crystallization cascade were identified, and the impact on crystal habit was evaluated quantitatively. As it is expected for particles presenting high aspect ratios, crystal breakage preferentially occurs in the smallest plane, perpendicular to the largest dimension. This phenomenon is hardly avoidable in downstream production, but it can be accounted for with a design approach based on the real crystal dimensions. The kinetic rate equations for nucleation and crystal growth were determined based on crystal width, from which a model for the accurate prediction of this dimension was applied. The predicted crystal size distribution is consistent through a moderate degree of crystal breakage during downstream processing

Essentiality of fatty acid synthase in the 2D to anchorage-independent growth transition in transforming cells

Upregulation of fatty acid synthase (FASN) is a common event in cancer, although its mechanistic and potential therapeutic roles are not completely understood. In this study, we establish a key role of FASN during transformation. FASN is required for eliciting the anaplerotic shift of the Krebs cycle observed in cancer cells. However, its main role is to consume acetyl-CoA, which unlocks isocitrate dehydrogenase (IDH)-dependent reductive carboxylation, producing the reductive power necessary to quench reactive oxygen species (ROS) originated during the switch from two-dimensional (2D) to three-dimensional (3D) growth (a necessary hallmark of cancer). Upregulation of FASN elicits the 2D-to-3D switch; however, FASN's synthetic product palmitate is dispensable for this process since cells satisfy their fatty acid requirements from the media. In vivo, genetic deletion or pharmacologic inhibition of FASN before oncogenic activation prevents tumor development and invasive growth. These results render FASN as a potential target for cancer prevention studies.M.Q.F. is a recipient of the following grants: FIS PI13/00430 and FIS PI16/00354 funded by the Instituto de Salud Carlos III (ISCIII) and co-funded by the European Regional Development Fund (ERDF) and AECC Scientific Foundation (Beca de Retorno 2010). R.C. is a recipient of the following grants: FIS PI11/00832 and FIS PI14/00726 funded by the Instituto de Salud Carlos III (ISCIII) and co-funded by the European Regional Development Fund (ERDF), II14/00009 and PIE15/00068 from the Ministerio de Sanidad, Spain. N.S.C. is a recipient of an NIH grant (5R35CA197532). O.Y.T. is a recipient of the grants BFU2014-57466 from the Ministerio de Economia y Competitividad (MINECO). J.P.B. is funded by MINECO (SAF2016-78114-R), Instituto de Salud Carlos III (RD12/0043/0021), Junta de Castilla y Leon (Escalera de Excelencia CLU-2017-03), Ayudas Equipos Investigacion Biomedicina 2017 Fundacion BBVA, and Fundacion Ramon Areces. This study was partially supported by the generous donations from Fundacion CRIS Contra el Cancer and AVON Spain. We thank Drs. Erwin Wagner and Nabil Djouder for their critical review of the paper.S

Lipidome characterisation and sex-specific differences in type 1 and type 2 diabetes mellitus

Background: In this study, we evaluated the lipidome alterations caused by type 1 diabetes (T1D) and type 2 diabetes (T2D), by determining lipids significantly associated with diabetes overall and in both sexes, and lipids associated with the glycaemic state. Methods: An untargeted lipidomic analysis was performed to measure the lipid profiles of 360 subjects (91 T1D, 91 T2D, 74 with prediabetes and 104 controls (CT)) without cardiovascular and/or chronic kidney disease. Ultra-high performance liquid chromatography-electrospray ionization mass spectrometry (UHPLC-ESI-MS) was conducted in two ion modes (positive and negative). We used multiple linear regression models to (1) assess the association between each lipid feature and each condition, (2) determine sex-specific differences related to diabetes, and (3) identify lipids associated with the glycaemic state by considering the prediabetes stage. The models were adjusted by sex, age, hypertension, dyslipidaemia, body mass index, glucose, smoking, systolic blood pressure, triglycerides, HDL cholesterol, LDL cholesterol, alternate Mediterranean diet score (aMED) and estimated glomerular filtration rate (eGFR); diabetes duration and glycated haemoglobin (HbA1c) were also included in the comparison between T1D and T2D. Results: A total of 54 unique lipid subspecies from 15 unique lipid classes were annotated. Lysophosphatidylcholines (LPC) and ceramides (Cer) showed opposite effects in subjects with T1D and subjects with T2D, LPCs being mainly up-regulated in T1D and down-regulated in T2D, and Cer being up-regulated in T2D and down-regulated in T1D. Also, Phosphatidylcholines were clearly down-regulated in subjects with T1D. Regarding sex-specific differences, ceramides and phosphatidylcholines exhibited important diabetes-associated differences due to sex. Concerning the glycaemic state, we found a gradual increase of a panel of 1-deoxyceramides from normoglycemia to prediabetes to T2D. Conclusions: Our findings revealed an extensive disruption of lipid metabolism in both T1D and T2D. Additionally, we found sex-specific lipidome changes associated with diabetes, and lipids associated with the glycaemic state that can be linked to previously described molecular mechanisms in diabetes

Federated learning enables big data for rare cancer boundary detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing