Methods for estimating supersaturation in antisolvent crystallization systems

The mole fraction and activity coefficient-dependent (MFAD) supersaturation expression is the least-assumptive, practical choice for calculating supersaturation in solvent mixtures. This paper reviews the basic thermodynamic derivation of the supersaturation expression, revisits common simplifying assumptions, and discusses the shortcomings of those assumptions for the design of industrial crystallization processes. A step-by-step methodology for estimating the activity-dependent supersaturation is provided with focus on ternary systems. This method requires only solubility data and thermal property data from a single differential scanning calorimetry (DSC) experiment. Two case studies are presented, where common simplifications to the MFAD supersaturation expression are evaluated: (1) for various levels of supersaturation of L-asparagine monohydrate in water–isopropanol mixtures and (2) for the dynamic and steady-state mixed-suspension, mixed-product removal (MSMPR) crystallization of a proprietary API in water–ethanol–tetrahydrofuran solvent mixtures. When compared to the MFAD supersaturation estimation, it becomes clear that errors in excess of 190% may be introduced in the estimation of the crystallization driving force by making unnecessary simplifications to the supersaturation expression. These errors can result in additional parameter regression errors – sometimes by nearly an order of magnitude – for nucleation and growth kinetic parameters, limiting the accurate simulation of dynamic and steady-state crystallization systems

Maximizing entropy of cycles on trees

In this paper we give a partial characterization of the periodic tree patterns of maximum entropy. More precisely, we prove that each periodic pattern with maximal entropy is irreducible and simplicial. Moreover, it is also maximodal in the sense that for every monotone representative of the pattern every periodic point is a "turning point"

Redundant roles of the phosphatidate phosphatase family in triacylglycerol synthesis in human adipocytes.

AIMS/HYPOTHESIS: In mammals, the evolutionary conserved family of Mg(2+)-dependent phosphatidate phosphatases (PAP1), involved in phospholipid and triacylglycerol synthesis, consists of lipin-1, lipin-2 and lipin-3. While mutations in the murine Lpin1 gene cause lipodystrophy and its knockdown in mouse 3T3-L1 cells impairs adipogenesis, deleterious mutations of human LPIN1 do not affect adipose tissue distribution. However, reduced LPIN1 and PAP1 activity has been described in participants with type 2 diabetes. We aimed to characterise the roles of all lipin family members in human adipose tissue and adipogenesis. METHODS: The expression of the lipin family was analysed in adipose tissue in a cross-sectional study. Moreover, the effects of lipin small interfering RNA (siRNA)-mediated depletion on in vitro human adipogenesis were assessed. RESULTS: Adipose tissue gene expression of the lipin family is altered in type 2 diabetes. Depletion of every lipin family member in a human Simpson-Golabi-Behmel syndrome (SGBS) pre-adipocyte cell line, alters expression levels of adipogenic transcription factors and lipid biosynthesis genes in early stages of differentiation. Lipin-1 knockdown alone causes a 95% depletion of PAP1 activity. Despite the reduced PAP1 activity and alterations in early adipogenesis, lipin-silenced cells differentiate and accumulate neutral lipids. Even combinatorial knockdown of lipins shows mild effects on triacylglycerol accumulation in mature adipocytes. CONCLUSIONS/INTERPRETATION: Overall, our data support the hypothesis of alternative pathways for triacylglycerol synthesis in human adipocytes under conditions of repressed lipin expression. We propose that induction of alternative lipid phosphate phosphatases, along with the inhibition of lipid hydrolysis, contributes to the maintenance of triacylglycerol content to near normal levels.This study was supported by research grants from the ‘Instituto de Salud Carlos III’ (ISCIII, Spanish Ministry of Economy and Competitiveness) (PI10/00967 and CP11/0 0021 to MM); the R. Barri Private Foundation (PV12142S to MM); the Medical Research Council (G0701446 to SS); and National Institutes of Health Grant (GM028140 to GMC). CIBER de Diabetes y Enfermedades Metabólicas asociadas (CB07708/0012) is an initiative of the ISCIII. MM acknowledges support from the ‘Miguel Servet’ tenure track programme (CP11/00021), from the Fondo de Investigación Sanitaria (FIS) co-financed by the European Regional Development Fund (ERDF), and supported by a Salvador de Madariaga Mobility fellowship from the Spanish Ministry of Education (PR2011-0584). AT is the recipient of a FI-DGR fellowship (9015-97318/2012) from the Agència de Gestió d’Ajuts Universitaris i de Recerca (AGAUR)This is the author accepted manuscript. It is currently under an indefinite embargo pending publication by Springer

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