On the formal foundations of cash management systems

[EN] Cash management aims to find a balance between what is held in cash and what is allocated in other investments in exchange for a given return. Dealing with cash management systems with multiple accounts and different links between them is a complex task. Current cash management models provide analytic solutions without exploring the underlying structure of accounts and its main properties. There is a need for a formal definition of cash management systems. In this work, we introduce a formal approach to manage cash with multiple accounts based on graph theory. Our approach allows a formal reasoning on the relation between accounts in cash management systems. A critical part of this formal reasoning is the characterization of desirable and non-desirable cash management policies. Novel theoretical results guide cash managers in the analysis of complex cash management systems.This work is partially funded by projects Logistar (H2020-769142), AI4EU (H2020-825619) and 2017 SGR 172.Salas-Molina, F.; Rodriguez-Aguilar, JA.; Pla Santamaría, D.; Garcia-Bernabeu, A. (2021). On the formal foundations of cash management systems. Operational Research. 21(2):1081-1095. https://doi.org/10.1007/s12351-019-00464-6S10811095212Baccarin S (2009) Optimal impulse control for a multidimensional cash management system with generalized cost functions. Eur J Oper Res 196(1):198–206Bollobás B (2013) Modern graph theory, vol 184. Springer, BerlinBondy JA, Murty USR (1976) Graph theory with applications, vol 290. Macmillan, LondonChartrand G, Oellermann OR (1993) Applied and algorithmic graph theory, vol 993. McGraw-Hill, New YorkConstantinides GM, Richard SF (1978) Existence of optimal simple policies for discounted-cost inventory and cash management in continuous time. Oper Res 26(4):620–636da Costa Moraes MB, Nagano MS, Sobreiro VA (2015) Stochastic cash flow management models: a literature review since the 1980s. In: Guarnieri P (ed) Decision models in engineering and management. Springer, Berlin, pp 11–28de Avila Pacheco JV, Morabito R (2011) Application of network flow models for the cash management of an agribusiness company. Comput Ind Eng 61(3):848–857Golden B, Liberatore M, Lieberman C (1979) Models and solution techniques for cash flow management. Comput Oper Res 6(1):13–20Gormley FM, Meade N (2007) The utility of cash flow forecasts in the management of corporate cash balances. Eur J Oper Res 182(2):923–935Gregory G (1976) Cash flow models: a review. Omega 4(6):643–656Makridakis S, Wheelwright SC, Hyndman RJ (2008) Forecasting methods and applications. Wiley, New YorkRighetto GM, Morabito R, Alem D (2016) A robust optimization approach for cash flow management in stationery companies. Comput Ind Eng 99:137–152Salas-Molina F (2017) Risk-sensitive control of cash management systems. Oper Res. https://doi.org/10.1007/s12351-017-0371-0Salas-Molina F, Pla-Santamaria D, Rodriguez-Aguilar JA (2018) A multi-objective approach to the cash management problem. Ann Oper Res 267(1):515–529Srinivasan V, Kim YH (1986) Deterministic cash flow management: state of the art and research directions. Omega 14(2):145–166Valiente G (2013) Algorithms on trees and graphs. Springer, Berli

Search for a correlation between the UHECRs measured by the Pierre Auger Observatory and the Telescope Array and the neutrino candidate events from IceCube

We have conducted three searches for correlations between ultra-high energy cosmic rays detected by the Telescope Array and the Pierre Auger Observatory, and high-energy neutrino candidate events from IceCube. Two cross-correlation analyses with UHECRs are done: one with 39 cascades from the IceCube `high-energy starting events' sample and the other with 16 high-energy `track events'. The angular separation between the arrival directions of neutrinos and UHECRs is scanned over. The same events are also used in a separate search using a maximum likelihood approach, after the neutrino arrival directions are stacked. To estimate the significance we assume UHECR magnetic deflections to be inversely proportional to their energy, with values 3◦, 6◦ and 9◦ at 100 EeV to allow for the uncertainties on the magnetic field strength and UHECR charge. A similar analysis is performed on stacked UHECR arrival directions and the IceCube sample of through-going muon track events which were optimized for neutrino point-source searches

GWAS and meta-analysis identifies 49 genetic variants underlying critical COVID-19

Data availability: Downloadable summary data are available through the GenOMICC data site (https://genomicc.org/data). Summary statistics are available, but without the 23andMe summary statistics, except for the 10,000 most significant hits, for which full summary statistics are available. The full GWAS summary statistics for the 23andMe discovery dataset will be made available through 23andMe to qualified researchers under an agreement with 23andMe that protects the privacy of the 23andMe participants. For further information and to apply for access to the data, see the 23andMe website (https://research.23andMe.com/dataset-access/). All individual-level genotype and whole-genome sequencing data (for both academic and commercial uses) can be accessed through the UKRI/HDR UK Outbreak Data Analysis Platform (https://odap.ac.uk). A restricted dataset for a subset of GenOMICC participants is also available through the Genomics England data service. Monocyte RNA-seq data are available under the title ‘Monocyte gene expression data’ within the Oxford University Research Archives (https://doi.org/10.5287/ora-ko7q2nq66). Sequencing data will be made freely available to organizations and researchers to conduct research in accordance with the UK Policy Framework for Health and Social Care Research through a data access agreement. Sequencing data have been deposited at the European Genome–Phenome Archive (EGA), which is hosted by the EBI and the CRG, under accession number EGAS00001007111.Extended data figures and tables are available online at https://www.nature.com/articles/s41586-023-06034-3#Sec21 .Supplementary information is available online at https://www.nature.com/articles/s41586-023-06034-3#Sec22 .Code availability: Code to calculate the imputation of P values on the basis of SNPs in linkage disequilibrium is available at GitHub (https://github.com/baillielab/GenOMICC_GWAS).Acknowledgements: We thank the members of the Banco Nacional de ADN and the GRA@CE cohort group; and the research participants and employees of 23andMe for making this work possible. A full list of contributors who have provided data that were collated in the HGI project, including previous iterations, is available online (https://www.covid19hg.org/acknowledgements).Change history: 11 July 2023: A Correction to this paper has been published at: https://doi.org/10.1038/s41586-023-06383-z. -- In the version of this article initially published, the name of Ana Margarita Baldión-Elorza, of the SCOURGE Consortium, appeared incorrectly (as Ana María Baldion) and has now been amended in the HTML and PDF versions of the article.Copyright © The Author(s) 2023, Critical illness in COVID-19 is an extreme and clinically homogeneous disease phenotype that we have previously shown1 to be highly efficient for discovery of genetic associations2. Despite the advanced stage of illness at presentation, we have shown that host genetics in patients who are critically ill with COVID-19 can identify immunomodulatory therapies with strong beneficial effects in this group3. Here we analyse 24,202 cases of COVID-19 with critical illness comprising a combination of microarray genotype and whole-genome sequencing data from cases of critical illness in the international GenOMICC (11,440 cases) study, combined with other studies recruiting hospitalized patients with a strong focus on severe and critical disease: ISARIC4C (676 cases) and the SCOURGE consortium (5,934 cases). To put these results in the context of existing work, we conduct a meta-analysis of the new GenOMICC genome-wide association study (GWAS) results with previously published data. We find 49 genome-wide significant associations, of which 16 have not been reported previously. To investigate the therapeutic implications of these findings, we infer the structural consequences of protein-coding variants, and combine our GWAS results with gene expression data using a monocyte transcriptome-wide association study (TWAS) model, as well as gene and protein expression using Mendelian randomization. We identify potentially druggable targets in multiple systems, including inflammatory signalling (JAK1), monocyte–macrophage activation and endothelial permeability (PDE4A), immunometabolism (SLC2A5 and AK5), and host factors required for viral entry and replication (TMPRSS2 and RAB2A).GenOMICC was funded by Sepsis Research (the Fiona Elizabeth Agnew Trust), the Intensive Care Society, a Wellcome Trust Senior Research Fellowship (to J.K.B., 223164/Z/21/Z), the Department of Health and Social Care (DHSC), Illumina, LifeArc, the Medical Research Council, UKRI, a BBSRC Institute Program Support Grant to the Roslin Institute (BBS/E/D/20002172, BBS/E/D/10002070 and BBS/E/D/30002275) and UKRI grants MC_PC_20004, MC_PC_19025, MC_PC_1905 and MRNO2995X/1. A.D.B. acknowledges funding from the Wellcome PhD training fellowship for clinicians (204979/Z/16/Z), the Edinburgh Clinical Academic Track (ECAT) programme. This research is supported in part by the Data and Connectivity National Core Study, led by Health Data Research UK in partnership with the Office for National Statistics and funded by UK Research and Innovation (grant MC_PC_20029). Laboratory work was funded by a Wellcome Intermediate Clinical Fellowship to B.F. (201488/Z/16/Z). We acknowledge the staff at NHS Digital, Public Health England and the Intensive Care National Audit and Research Centre who provided clinical data on the participants; and the National Institute for Healthcare Research Clinical Research Network (NIHR CRN) and the Chief Scientist’s Office (Scotland), who facilitate recruitment into research studies in NHS hospitals, and to the global ISARIC and InFACT consortia. GenOMICC genotype controls were obtained using UK Biobank Resource under project 788 funded by Roslin Institute Strategic Programme Grants from the BBSRC (BBS/E/D/10002070 and BBS/E/D/30002275) and Health Data Research UK (HDR-9004 and HDR-9003). UK Biobank data were used in the GSMR analyses presented here under project 66982. The UK Biobank was established by the Wellcome Trust medical charity, Medical Research Council, Department of Health, Scottish Government and the Northwest Regional Development Agency. It has also had funding from the Welsh Assembly Government, British Heart Foundation and Diabetes UK. The work of L.K. was supported by an RCUK Innovation Fellowship from the National Productivity Investment Fund (MR/R026408/1). J.Y. is supported by the Westlake Education Foundation. SCOURGE is funded by the Instituto de Salud Carlos III (COV20_00622 to A.C., PI20/00876 to C.F.), European Union (ERDF) ‘A way of making Europe’, Fundación Amancio Ortega, Banco de Santander (to A.C.), Cabildo Insular de Tenerife (CGIEU0000219140 ‘Apuestas científicas del ITER para colaborar en la lucha contra la COVID-19’ to C.F.) and Fundación Canaria Instituto de Investigación Sanitaria de Canarias (PIFIISC20/57 to C.F.). We also acknowledge the contribution of the Centro National de Genotipado (CEGEN) and Centro de Supercomputación de Galicia (CESGA) for funding this project by providing supercomputing infrastructures. A.D.L. is a recipient of fellowships from the National Council for Scientific and Technological Development (CNPq)-Brazil (309173/2019-1 and 201527/2020-0)