A statistical learning based approach for parameter fine-tuning of metaheuristics

Metaheuristics are approximation methods used to solve combinatorial optimization problems. Their performance usually depends on a set of parameters that need to be adjusted. The selection of appropriate parameter values causes a loss of efficiency, as it requires time, and advanced analytical and problem-specific skills. This paper provides an overview of the principal approaches to tackle the Parameter Setting Problem, focusing on the statistical procedures employed so far by the scientific community. In addition, a novel methodology is proposed, which is tested using an already existing algorithm for solving the Multi-Depot Vehicle Routing Problem.Peer ReviewedPostprint (published version

A statistical learning based approach for parameter fine-tuning of metaheuristics

Metaheuristics are approximation methods used to solve combinatorial optimization problems. Their performance usually depends on a set of parameters that need to be adjusted. The selectionof appropriate parameter values causes a loss of efficiency, as it requires time, and advanced analytical and problem-specific skills. This paper provides an overview of the principal approaches to tackle the Parameter Setting Problem, focusing on the statistical procedures employed so far by the scientific community. In addition, a novel methodology is proposed, which is tested using an already existing algorithm for solving the Multi-Depot Vehicle Routing Problem

A statistical learning based approach for parameter fine-tuning of metaheuristics

Metaheuristics are approximation methods used to solve combinatorial optimization problems. Their performance usually depends on a set of parameters that need to be adjusted. The selectionof appropriate parameter values causes a loss of efficiency, as it requires time, and advanced analytical and problem-specific skills. This paper provides an overview of the principal approaches to tackle the Parameter Setting Problem, focusing on the statistical procedures employed so far by the scientific community. In addition, a novel methodology is proposed, which is tested using an already existing algorithm for solving the Multi-Depot Vehicle Routing Problem.Peer Reviewe

ERS statement on standardisation of cardiopulmonary exercise testing in chronic lung diseases

The objective of this document was to standardise published cardiopulmonary exercise testing (CPET) protocols for improved interpretation in clinical settings and multicentre research projects. This document: 1) summarises the protocols and procedures used in published studies focusing on incremental CPET in chronic lung conditions; 2) presents standard incremental protocols for CPET on a stationary cycle ergometer and a treadmill; and 3) provides patients’ perspectives on CPET obtained through an online survey supported by the European Lung Foundation. We systematically reviewed published studies obtained from EMBASE, Medline, Scopus, Web of Science and the Cochrane Library from inception to January 2017. Of 7914 identified studies, 595 studies with 26 523 subjects were included. The literature supports a test protocol with a resting phase lasting at least 3 min, a 3-min unloaded phase, and an 8- to 12-min incremental phase with work rate increased linearly at least every minute, followed by a recovery phase of at least 2–3 min. Patients responding to the survey (n=295) perceived CPET as highly beneficial for their diagnostic assessment and informed the Task Force consensus. Future research should focus on the individualised estimation of optimal work rate increments across different lung diseases, and the collection of robust normative data.The document facilitates standardisation of conducting, reporting and interpreting cardiopulmonary exercise tests in chronic lung diseases for comparison of reference data, multi-centre studies and assessment of interventional efficacy. http://bit.ly/31SXeB

T Cell Cancer Therapy Requires CD40-CD40L Activation of Tumor Necrosis Factor and Inducible Nitric-Oxide-Synthase-Producing Dendritic Cells

Effective cancer immunotherapy requires overcoming immunosuppressive tumor microenvironments. We\ua0found that local nitric oxide (NO) production by tumor-infiltrating myeloid cells is important for adoptively transferred CD8(+) cytotoxic T\ua0cells to destroy tumors. These myeloid cells are phenotypically similar to inducible nitric oxide synthase (NOS2)- and tumor necrosis factor (TNF)-producing dendritic cells (DC), or Tip-DCs. Depletion of immunosuppressive, colony stimulating factor 1 receptor (CSF-1R)-dependent arginase 1(+) myeloid cells enhanced NO-dependent tumor killing. Tumor elimination via NOS2 required the CD40-CD40L pathway. We also uncovered a strong correlation between survival of colorectal cancer patients and NOS2, CD40, and TNF expression in their tumors. Our results identify a network of pro-tumor factors that can be targeted to boost cancer immunotherapies

AIMSurv: First pan-European harmonized surveillance of Aedes invasive mosquito species of relevance for human vector-borne diseases

Human and animal vector-borne diseases, particularly mosquito-borne diseases, are emerging or re-emerging worldwide. Six Aedes invasive mosquito (AIM) species were introduced to Europe since the 1970s: Aedes aegypti, Ae. albopictus, Ae. japonicus, Ae. koreicus, Ae. atropalpus and Ae. triseriatus. Here, we report the results of AIMSurv2020, the first pan-European surveillance effort for AIMs. Implemented by 42 volunteer teams from 24 countries. And presented in the form of a dataset named “AIMSurv Aedes Invasive Mosquito species harmonized surveillance in Europe. AIM-COST Action. Project ID: CA17108”. AIMSurv2020 harmonizes field surveillance methodologies for sampling different AIMs life stages, frequency and minimum length of sampling period, and data reporting. Data include minimum requirements for sample types and recommended requirements for those teams with more resources. Data are published as a Darwin Core archive in the Global Biodiversity Information Facility- Spain, comprising a core file with 19,130 records (EventID) and an occurrences file with 19,743 records (OccurrenceID). AIM species recorded in AIMSurv2020 were Ae. albopictus, Ae. japonicus and Ae. koreicus, as well as native mosquito species