Neural-network-based curve fitting using totally positive rational bases

This paper proposes a method for learning the process of curve fitting through a general class of totally positive rational bases. The approximation is achieved by finding suitable weights and control points to fit the given set of data points using a neural network and a training algorithm, called AdaMax algorithm, which is a first-order gradient-based stochastic optimization. The neural network presented in this paper is novel and based on a recent generalization of rational curves which inherit geometric properties and algorithms of the traditional rational Bézier curves. The neural network has been applied to different kinds of datasets and it has been compared with the traditional least-squares method to test its performance. The obtained results show that our method can generate a satisfactory approximation

Deciphering the complex interplay between pancreatic cancer, diabetes mellitus subtypes and obesity/BMI through causal inference and mediation analyses.

OBJECTIVES: To characterise the association between type 2 diabetes mellitus (T2DM) subtypes (new-onset T2DM (NODM) or long-standing T2DM (LSDM)) and pancreatic cancer (PC) risk, to explore the direction of causation through Mendelian randomisation (MR) analysis and to assess the mediation role of body mass index (BMI). DESIGN: Information about T2DM and related factors was collected from 2018 PC cases and 1540 controls from the PanGenEU (European Study into Digestive Illnesses and Genetics) study. A subset of PC cases and controls had glycated haemoglobin, C-peptide and genotype data. Multivariate logistic regression models were applied to derive ORs and 95% CIs. T2DM and PC-related single nucleotide polymorphism (SNP) were used as instrumental variables (IVs) in bidirectional MR analysis to test for two-way causal associations between PC, NODM and LSDM. Indirect and direct effects of the BMI-T2DM-PC association were further explored using mediation analysis. RESULTS: T2DM was associated with an increased PC risk when compared with non-T2DM (OR=2.50; 95% CI: 2.05 to 3.05), the risk being greater for NODM (OR=6.39; 95% CI: 4.18 to 9.78) and insulin users (OR=3.69; 95% CI: 2.80 to 4.86). The causal association between T2DM (57-SNP IV) and PC was not statistically significant (ORLSDM=1.08, 95% CI: 0.86 to 1.29, ORNODM=1.06, 95% CI: 0.95 to 1.17). In contrast, there was a causal association between PC (40-SNP IV) and NODM (OR=2.85; 95% CI: 2.04 to 3.98), although genetic pleiotropy was present (MR-Egger: p value=0.03). Potential mediating effects of BMI (125-SNPs as IV), particularly in terms of weight loss, were evidenced on the NODM-PC association (indirect effect for BMI in previous years=0.55). CONCLUSION: Findings of this study do not support a causal effect of LSDM on PC, but suggest that PC causes NODM. The interplay between obesity, PC and T2DM is complex

Co-option of Neutrophil Fates by Tissue Environments.

Classically considered short-lived and purely defensive leukocytes, neutrophils are unique in their fast and moldable response to stimulation. This plastic behavior may underlie variable and even antagonistic functions during inflammation or cancer, yet the full spectrum of neutrophil properties as they enter healthy tissues remains unexplored. Using a new model to track neutrophil fates, we found short but variable lifetimes across multiple tissues. Through analysis of the receptor, transcriptional, and chromatin accessibility landscapes, we identify varying neutrophil states and assign non-canonical functions, including vascular repair and hematopoietic homeostasis. Accordingly, depletion of neutrophils compromised angiogenesis during early age, genotoxic injury, and viral infection, and impaired hematopoietic recovery after irradiation. Neutrophils acquired these properties in target tissues, a process that, in the lungs, occurred in CXCL12-rich areas and relied on CXCR4. Our results reveal that tissues co-opt neutrophils en route for elimination to induce programs that support their physiological demands.This study was supported byIntramural grants from the Severo Ochoa program (IGP-SO), a grant from Fundacio la Marato de TV3 (120/C/2015-20153032), grant SAF2015-65607-R fromMinisterio de Ciencia e Innovacion (MICINN) with co-funding by Fondo Eu-ropeo de Desarrollo Regional (FEDER), RTI2018-095497-B-I00 from MICINN,HR17_00527 from Fundacion La Caixa, and Transatlantic Network of Excel-lence (TNE-18CVD04) from the Leducq Foundation to A.H. I.B. is supportedby fellowship MSCA-IF-EF-748381 and EMBO short-term fellowship 8261.A.R.-P. is supported by a fellowship (BES-2016-076635) and J.A.N.-A. byfellowship SVP-2014-068595 from MICINN. R.O. is supported by ERC startinggrant 759532, Italian Telethon Foundation SR-Tiget grant award F04, ItalianMoH grant GR-201602362156, AIRC MFAG 20247, Cariplo Foundation grant2015-0990, and the EU Infect-ERA 126. C.S. is supported by the SFB 1123,project A07, as well as by the DZHK (German Centre for Cardiovascular Research) and the BMBF (German Ministry of Education and Research) grant81Z0600204. L.G.N. is supported by SIgN core funding from A*STAR. The CNIC is supported by the MICINN and the Pro-CNIC Foundation and is a Severo Ochoa Center of Excellence (MICINN award SEV-2015-0505). G.F.-C. issupported by the Spanish Ministerio de Ciencia e Innovacio ́n (grantPID2019-110895RB-100) and Junta de Comunidades de Castilla-La Mancha(grant SBPLY/19/180501/000211). C.R. received funding from the BoehingerIngelheim Foundation (consortium grant ‘‘Novel and Neglected CardiovascularRisk Factors’’) and German Federal Ministry of Education and Research(BMBF 01EO1503) and is a Fellow of the Gutenberg Research College (GFK)at the Johannes Gutenberg-University MainzS

Towards Applying River Formation Dynamics in Continuous Optimization Problems

River Formation Dynamics (RFD) is a metaheuristic that has been successfully used by different research groups to deal with a wide variety of discrete combinatorial optimization problems. However, no attempt has been done to adapt it to continuous optimization domains. In this paper we propose a first approach to obtain such objective, and we evaluate its usefulness by comparing RFD results against those obtained by other more mature metaheuristics for continuous domains. In particular, we compare with the results obtained by Particle Swarm Optimization, Artificial Bee Colony, Firefly Algorithm, and Social Spider Optimization

Pancreatic cancer and autoimmune diseases: An association sustained by computational and epidemiological case-control approaches

This is the peer reviewed version of the following article: Gomez‐Rubio, P. , Piñero, J. , Molina‐Montes, E. , Gutiérrez‐Sacristán, A. , Marquez, M. , Rava, M. , Michalski, C. W., Farré, A. , Molero, X. , Löhr, M. , Perea, J. , Greenhalf, W. , O'Rorke, M. , Tardón, A. , Gress, T. , Barberá, V. M., Crnogorac‐Jurcevic, T. , Muñoz‐Bellvís, L. , Domínguez‐Muñoz, E. , Balsells, J. , Costello, E. , Yu, J. , Iglesias, M. , Ilzarbe, L. , Kleeff, J. , Kong, B. , Mora, J. , Murray, L. , O'Driscoll, D. , Poves, I. , Lawlor, R. T., Ye, W. , Hidalgo, M. , Scarpa, A. , Sharp, L. , Carrato, A. , Real, F. X., Furlong, L. I., Malats, N. and , (2019), Pancreatic cancer and autoimmune diseases: An association sustained by computational and epidemiological case–control approaches. Int. J. Cancer. doi:10.1002/ijc.31866, which has been published in final form at https://doi.org/10.1002/ijc.31866. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.Acción Especial de Genómica, Spain. Grant Number: #GEN2001‐4748‐c05‐03 Swedish ALF. Grant Number: #SLL20130022 Cancer Focus Northern Ireland and Department for Employment and Learning EU H2020 Programme 2014‐2020. Grant Number: 634143 MedBioinformatics676559 Elixir‐Excelerate EU‐6FP Integrated Project. Grant Number: #018771‐MOLDIAG‐PACA EU‐FP7‐HEALTH. Grant Number: #256974‐EPC‐TM‐Net#259737‐CANCERALIA#602783‐ Cam‐Pac Italian Foundation for Cancer Research (FIRC) Italian Ministry of Health. Grant Number: FIMPCUP_J33G13000210001 Red Temática de Investigación Cooperativa en Cáncer, Spain. Grant Number: #RD12/0036/0050#RD12/0036/ 0073(#RD12/0036/0034 The work was partially supported by Fondo de Investigaciones Sanitarias (FIS), Instituto de Salud Carlos III‐FEDER, Spain. Grant Number: #PI0902102#PI11/01542#PI12/ 00815#PI12/01635#PI13/ 00082CP10/00524PI15/01573 World Cancer Research Fund. Grant Number: WCR #15‐039