Improving the predictability of take-off times with Machine Learning : a case study for the Maastricht upper area control centre area of responsibility

The uncertainty of the take-off time is a major contribution to the loss of trajectory predictability. At present, the Estimated Take-Off Time (ETOT) for each individual flight is extracted from the Enhanced Traffic Flow Management System (ETFMS) messages, which are sent each time there is an event triggering a recalculation of the flight data by the Network Man- ager Operations Centre. However, aircraft do not always take- off at the ETOTs reported by the ETFMS due to several factors, including congestion and bad weather conditions at the departure airport, reactionary delays and air traffic flow management slot improvements. This paper presents two machine learning models that take into account several of these factors to improve the take- off time prediction of individual flights one hour before their estimated off-block time. Predictions performed by the model trained on three years of historical flight and weather data show a reduction on the take-off time prediction error of about 30% as compared to the ETOTs reported by the ETFMS.Peer ReviewedPostprint (published version

<i>Trypanosoma brucei rhodesiense</i> transmitted by a single tsetse fly bite in vervet monkeys as a model of human African trypanosomiasis

Sleeping sickness is caused by a species of trypanosome blood parasite that is transmitted by tsetse flies. To understand better how infection with this parasite leads to disease, we provide here the most detailed description yet of the course of infection and disease onset in vervet monkeys. One infected tsetse fly was allowed to feed on each host individual, and in all cases infections were successful. The characteristics of infection and disease were similar in all hosts, but the rate of progression varied considerably. Parasites were first detected in the blood 4-10 days after infection, showing that migration of parasites from the site of fly bite was very rapid. Anaemia was a key feature of disease, with a reduction in the numbers and average size of red blood cells and associated decline in numbers of platelets and white blood cells. One to six weeks after infection, parasites were observed in the cerebrospinal fluid (CSF), indicating that they had moved from the blood into the brain; this was associated with a white cell infiltration. This study shows that fly-transmitted infection in vervets accurately mimics human disease and provides a robust model to understand better how sleeping sickness develops

The Role of B-cells and IgM Antibodies in Parasitemia, Anemia, and VSG Switching in Trypanosoma brucei–Infected Mice

African trypanosomes are extracellular parasitic protozoa, predominantly transmitted by the bite of the haematophagic tsetse fly. The main mechanism considered to mediate parasitemia control in a mammalian host is the continuous interaction between antibodies and the parasite surface, covered by variant-specific surface glycoproteins. Early experimental studies have shown that B-cell responses can be strongly protective but are limited by their VSG-specificity. We have used B-cell (µMT) and IgM-deficient (IgM−/−) mice to investigate the role of B-cells and IgM antibodies in parasitemia control and the in vivo induction of trypanosomiasis-associated anemia. These infection studies revealed that that the initial setting of peak levels of parasitemia in Trypanosoma brucei–infected µMT and IgM−/− mice occurred independent of the presence of B-cells. However, B-cells helped to periodically reduce circulating parasites levels and were required for long term survival, while IgM antibodies played only a limited role in this process. Infection-associated anemia, hypothesized to be mediated by B-cell responses, was induced during infection in µMT mice as well as in IgM−/− mice, and as such occurred independently from the infection-induced host antibody response. Antigenic variation, the main immune evasion mechanism of African trypanosomes, occurred independently from host antibody responses against the parasite's ever-changing antigenic glycoprotein coat. Collectively, these results demonstrated that in murine experimental T. brucei trypanosomiasis, B-cells were crucial for periodic peak parasitemia clearance, whereas parasite-induced IgM antibodies played only a limited role in the outcome of the infection

Improving the predictability of take-off times with Machine Learning : a case study for the Maastricht upper area control centre area of responsibility

The uncertainty of the take-off time is a major contribution to the loss of trajectory predictability. At present, the Estimated Take-Off Time (ETOT) for each individual flight is extracted from the Enhanced Traffic Flow Management System (ETFMS) messages, which are sent each time there is an event triggering a recalculation of the flight data by the Network Man- ager Operations Centre. However, aircraft do not always take- off at the ETOTs reported by the ETFMS due to several factors, including congestion and bad weather conditions at the departure airport, reactionary delays and air traffic flow management slot improvements. This paper presents two machine learning models that take into account several of these factors to improve the take- off time prediction of individual flights one hour before their estimated off-block time. Predictions performed by the model trained on three years of historical flight and weather data show a reduction on the take-off time prediction error of about 30% as compared to the ETOTs reported by the ETFMS. Peer Reviewe

Improving the predictability of take-off times with Machine Learning : a case study for the Maastricht upper area control centre area of responsibility

The uncertainty of the take-off time is a major contribution to the loss of trajectory predictability. At present, the Estimated Take-Off Time (ETOT) for each individual flight is extracted from the Enhanced Traffic Flow Management System (ETFMS) messages, which are sent each time there is an event triggering a recalculation of the flight data by the Network Man- ager Operations Centre. However, aircraft do not always take- off at the ETOTs reported by the ETFMS due to several factors, including congestion and bad weather conditions at the departure airport, reactionary delays and air traffic flow management slot improvements. This paper presents two machine learning models that take into account several of these factors to improve the take- off time prediction of individual flights one hour before their estimated off-block time. Predictions performed by the model trained on three years of historical flight and weather data show a reduction on the take-off time prediction error of about 30% as compared to the ETOTs reported by the ETFMS.Peer Reviewe

IL-10 limits production of pathogenic TNF by M1 myeloid cells through induction of nuclear NF-κB p50 member in Trypanosoma congolense infection-resistant C57BL/6 mice.

A balance between parasite elimination and control of infection-associated pathogenicity is crucial for resistance to African trypanosomiasis. By producing TNF and NO, CD11b(+) myeloid cells with a classical activation status (M1) contribute to parasitemia control in experimental Trypanosoma congolense infection in resistant C57BL/6 mice. However, in these mice, IL-10 is required to regulate M1-associated inflammation, avoiding tissue/liver damage and ensuring prolonged survival. In an effort to dissect the mechanisms behind the anti-inflammatory activity of IL-10 in T. congolense-infected C57BL/6 mice, we show, using an antibody blocking the IL-10 receptor, that IL-10 impairs the accumulation and M1 activation of TNF/iNOS-producing CD11b(+) Ly6C(+) cells in the liver. Using infected IL-10(flox/flox) LysM-Cre(+/+) mice, we show that myeloid cell-derived IL-10 limits M1 activation of CD11b(+) Ly6C(+) cells specifically at the level of TNF production. Moreover, higher production of TNF in infected IL-10(flox/flox) LysM-Cre(+/+) mice is associated with reduced nuclear accumulation of the NF-κB p50 subunit in CD11b(+) M1 cells. Furthermore, in infected p50(-/-) mice, TNF production by CD11b(+) Ly6C(+) cells and liver injury increases. These data suggest that preferential nuclear accumulation of p50 represents an IL-10-dependent anti-inflammatory mechanism in M1-type CD11b(+) myeloid cells that regulates the production of pathogenic TNF during T. congolense infection in resistant C57BL/6 mice.Journal ArticleResearch Support, Non-U.S. Gov'tinfo:eu-repo/semantics/publishe