Climate change analysis for guinea conakry with homogenized daily dataset

A aquesta tesi, les dades diàries de temperatures màximes i mínimes i precipitació de 12 estacions meteorològiques guineanes han segut sotmeses a controls de qualitat utilitzant les rutines RClimdex-ExtraQC. Les quals contenen eines per una sèrie temporal única. Els valors identificats com potencialment erronis han segut examinats mitjançant decisió subjectiva, han segut validats, corregits o eliminats. Aquestes dades lliures de qualsevol registre sospitós han segut homogeneïtzades utilitzant l’arxiu de sortida HOMER de dades mensuals. Els índexs escollits d’ETCCDI i ET-CRSCI s’han calculat per a les dades de temperatura i precipitació anuals i estacionals utilitzant ET-CRSCI del paquet RClimPact. A més, les mitjanes anuals de Tx, Tn i precipitació nacional junt amb vuit índexs d’extrem han segut utilitzats per detectar teleconexions entre els índexs climàtics i l’índex SOI. Un anàlisis de tendències no paramètric s’ha dut a terme sobre tres períodes superposats que comencen al 1941, 1961 i 1971 i tots acaben al 2010. Els resultats indiquen diversos patrons espacials coherents amb l’escalfament, significatius identificats tant amb els índexs extrems de Tx com de Tn per a tots els períodes i estacions. Els índexs basats en dades de precipitació diària mostren patrons menys coherents però, en general, existeixen patrons de sequera significatius per a la majoria d’índexs relacionats amb la precipitació diària per als períodes 1941-2010 i 1961-2010, mentre que per al període 1971-2010 l’anàlisi suggereix canvis no significatius cap a condicions més humides. En general, no s’han produït canvis espacialment coherents amb esdeveniments extrems de precipitació per al conjunt de Guinea i període d’estudi, ja que aquests, s’han produït a escala local. Hi ha indicis de que les condicions humides estan relacionades amb la Niña. Les precipitacions durant el període 1971-2010 han segut entre un 13.6 % i un 27.75 % menors que durant el període 1941-1970.En esta tesis, datos diarios de temperaturas máximas y mínimas y precipitación de 12 estaciones guineanas han sido sometidos a controles de calidad utilizando las rutinas RClimdex-ExtraQC. Estas rutinas contienen herramientas para una serie temporal única. Los valores identificados como potencialmente erróneos han sido examinados y mediante decisión subjetiva, han sido validados, corregidos o eliminados. Estos datos libres de cualquier tipo de registro sospechoso han sido homogeneizados utilizando el archivo de salida HOMER de datos mensuales. Índices elegidos de ETCCDI y ET-CRSCI se han calculado para las temperaturas y precipitaciones anuales y estacionales utilizando ET-CRSCI del paquete R ClimPact. Además, promedios anuales de Tx, Tn y precipitación nacional y ocho índices de extremos han sido usados para detectar teleconexiones entre los índices climáticos y el índice SOI. Un análisis de tendencias no paramétrica se ha realizado sobre tres períodos superpuestos que empiezan en 1941, 1961 y 1971, y terminan todos en 2010. Los resultados indican diversos patrones espaciales coherentes con el calentamiento, significativos, identificados tanto en los índices de extremos de Tx como de Tn para todos los periodos y estaciones. Los índices basados en datos de precipitación diaria mostraron patrones menos coherentes pero, en general, existen patrones de sequía significativos en la mayoría de los índices relacionados con la precipitación diaria para los períodos 1941-2010 i 1961-2010, mientras que para el período 1971-2010 el análisis sugiere cambios no significativos hacia condiciones más húmedas. En general, no se han producido cambios espacialmente coherentes con eventos extremos de precipitación para el conjunto de Guinea y el período de estudio, ya que estos, se han producido a escala local. Hay indicios de que las condiciones más húmedas están asociadas con La Niña. Las precipitaciones durante el periodo 1971-2010 han sido entre un 13.6% y un 27.75% menores que durante el período 1941-1970.In this thesis, Guinea's 12 weather stations daily minimum and daily maximum temperatures and daily precipitation data have been carefully quality controlled using the RClimdex-ExtraQC routines. These routines contain suitable tools to quality control single time series. The values identified as potentially erroneous have been carefully scrutinized and a subjective decision has been made to validate, correct or set them to missing. The resulted dataset free of any kind of suspicious data record has been homogenized using HOMER monthly output file. Chosen indices from ETCCDI and ET-CRSCI indices have been calculated using ET-CRSCI ClimPact R package. Annual and seasonal temperatures and precipitations indices have also been computed. Additionally, eight nationwide averaged climate extremes indices, the nationwide averaged annual Tx mean and Tn mean and total precipitation indices have been use to detect teleconnection between climate indices and SOI indices. A nonparametric trend analysis is then performed for three overlapping periods, with different starting years 1941, 1961 and 1971 but all ending the same 2010 year. The result suggested that coherent spatial patterns of significant warming changes have emerged from both Tx and Tn related extremes indices for all periods and season. Indices based on daily precipitation data showed more mixed patterns of change but, in general, significant drying patterns have been seen in most of the 1941-2010 and 1961-2010 periods' daily precipitation related indices while over the period 1971-2010 analyses suggested non-significant changes towards wetter conditions. Overall, there have been no spatially coherent changes in extreme rainfall events across Guinea for the study period, but changes in extreme precipitation events have occurred on local scales. There are signs that wetter conditions are associated with the La Niña years. Rainfall during the 1971-2010 is on average some 13.6 % to 27.75 % lower than during the period 1941–1970

The duration of protection against clinical malaria provided by the combination of seasonal RTS,S/AS01E vaccination and seasonal malaria chemoprevention versus either intervention given alone

BACKGROUND: A recent trial of 5920 children in Burkina Faso and Mali showed that the combination of seasonal vaccination with the RTS,S/AS01E malaria vaccine (primary series and two seasonal boosters) and seasonal malaria chemoprevention (four monthly cycles per year) was markedly more effective than either intervention given alone in preventing clinical malaria, severe malaria, and deaths from malaria. METHODS: In order to help optimise the timing of these two interventions, trial data were reanalysed to estimate the duration of protection against clinical malaria provided by RTS,S/AS01E when deployed seasonally, by comparing the group who received the combination of SMC and RTS,S/AS01E with the group who received SMC alone. The duration of protection from SMC was also estimated comparing the combined intervention group with the group who received RTS,S/AS01E alone. Three methods were used: Piecewise Cox regression, Flexible parametric survival models and Smoothed Schoenfeld residuals from Cox models, stratifying on the study area and using robust standard errors to control for within-child clustering of multiple episodes. RESULTS: The overall protective efficacy from RTS,S/AS01E over 6 months was at least 60% following the primary series and the two seasonal booster doses and remained at a high level over the full malaria transmission season. Beyond 6 months, protective efficacy appeared to wane more rapidly, but the uncertainty around the estimates increases due to the lower number of cases during this period (coinciding with the onset of the dry season). Protection from SMC exceeded 90% in the first 2-3 weeks post-administration after several cycles, but was not 100%, even immediately post-administration. Efficacy begins to decline from approximately day 21 and then declines more sharply after day 28, indicating the importance of preserving the delivery interval for SMC cycles at a maximum of four weeks. CONCLUSIONS: The efficacy of both interventions was highest immediately post-administration. Understanding differences between these interventions in their peak efficacy and how rapidly efficacy declines over time will help to optimise the scheduling of SMC, malaria vaccination and the combination in areas of seasonal transmission with differing epidemiology, and using different vaccine delivery systems. TRIAL REGISTRATION: The RTS,S-SMC trial in which these data were collected was registered at clinicaltrials.gov: NCT03143218

Machine learning techniques applied to dimensionality reduction for digital predistortion linearizers

Over the past half century, the improvements in spacecraft technology have been primarily in the areas of microelectronics for on-board processing, high frequency electronic devices, and integrated circuits for communications and navigation, solar cells and batteries for on-board power generation and storage among many others. Despite the fact that energy-storage technologies have advanced dramatically over the past years, the power consumption of on-board communications, sensors and digital signal processing systems is of paramount importance in battery or solar powered systems such as small satellites, HAPs or UAVs (drones). There is multiple applications that involves the use of these systems, e.g., Earth observation applications, surveillance, broadcast communications, scientific research, etc. In wireless communications, the power amplifier is a critical subsystem in the transmitter chain. Not only because it is one of the most power hungry devices that accounts for most of the direct current power consumption, but also because it is the main source of nonlinear distortion in the transmitter. Amplitude and phase modulated communications signals presenting high peak-to-average power ratio have a negative impact in the transmitter's power efficiency, because the PA has to be operated at high power back-off levels to avoid introducing nonlinear distortion. Digital predistortion (DPD) linearization is the most common and spread solution to cope with power amplifiers (PA) inherent linearity versus efficiency trade-off. When considering wide bandwidth signals, such as Doherty PAs, envelope tracking PAs or outphasing transmitters, the number of parameters required in the DPD model to compensate for both nonlinearities and memory effects can be very high. This has a negative impact in the DPD ceofficients extraction, because increases the computational complexity and drives to over-fitting and uncertainty. However, by applying dimensionality reduction techniques we can both avoid the numerical ill-conditioning of the estimation and reduce the number of coefficients of the DPD function, which ultimately impacts the baseband processing computational complexity and power consumption. In this Project, several dimensionality reduction techniques will be described and compared in terms of model order reduction capabilities and evaluation performance. In particular, some of the machine learning techniques for dimensionality reduction will be studied

Evaluation of HIV-1 p24 antigenemia and level of CD8+CD38+ T cells as surrogate markers of HIV-1 RNA viral load in HIV-1-infected patients in Dakar

Not the final published versionAlternative, affordable, and simple assays to monitor antiretroviral therapy (ART) in resource-poor settings are needed. We have evaluated and compared a heat-denatured (HD) HIV p24 amplified enzyme-linked immunosorbent assay from Perkin-Elmer and CD38CD8 T-cell levels, determined by flow cytometry, for their capacity to predict viral load (VL) in HIV-1-infected patients from Senegal. Median fluorescence intensity (MFI) of CD38 expression on memory (CD45RO) CD8 T cells correlated better with RNA VL than HD p24 antigenemia (R = 0.576, P < 0.0001 vs R = 0.548, P < 0.0001). MFI of CD38 expression on memory CD8 T cells could predict detectable RNA VL (VL = 2.6 log10) with a sensitivity of 87% and a specificity of 74%. A comparable sensitivity (89%) could be reached for HD p24 assay, but only to predict RNA VL of more than 5 logs, which might lead to unacceptable delays in clinical decision making. The clinical use of the HD p24 assay to monitor ART in Senegal would require more comparative data about the kinetics of p24 antigen and HIV RNA in peripheral blood as well as further evaluation regarding its sensitivity toward subtype A and CRF02. MFI of CD38 expression on memory CD8 T cells appeared to be a better alternative to monitor ART in HIV-infected patients from Senegal

Additional file 1 of The duration of protection against clinical malaria provided by the combination of seasonal RTS,S/AS01E vaccination and seasonal malaria chemoprevention versus either intervention given alone

Additional file 1: Table S1. Number of clinical malaria episodes by time since vaccination in each year of the study, using 90-day periods (as used in the Piecewise Cox regression models). Table S2. Number of clinical malaria episodes by time since vaccination in each year of the study, using 60-day periods (not used in the Piecewise Cox regression models, but provided to show the declining incidence of malaria further into the dry season). Table S3. Number and percentage of children who were scheduled to receive SMC, received SMC, and received all daily SMC doses (full SMC) over the course of the study. Figure S1. Observed hazard function and Cumulative hazard function, and the fitted cumulative hazard and estimated protective efficacy from flexible parametric survival models used to estimate vaccine efficacy in each year of the study. Figure S2. Protective Efficacy of SMC in the first 21 days and first 30 days after SMC received, by cycle. Figure S3. Protective Efficacy by time since the final SMC cycle in each year. Figure S4. Comparison of the SMC protective efficacy profile obtained in this study with the profile estimated for an earlier placebo-controlled trial of SMC

Development of an Updated Global Land In Situ‐Based Data Set of Temperature and Precipitation Extremes: HadEX3

We present the second update to a data set of gridded land‐based temperature and precipitation extremes indices: HadEX3. This consists of 17 temperature and 12 precipitation indices derived from daily, in situ observations and recommended by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI). These indices have been calculated at around 7,000 locations for temperature and 17,000 for precipitation. The annual (and monthly) indices have been interpolated on a 1.875°×1.25° longitude‐latitude grid, covering 1901–2018. We show changes in these indices by examining ”global”‐average time series in comparison with previous observational data sets and also estimating the uncertainty resulting from the nonuniform distribution of meteorological stations. Both the short and long time scale behavior of HadEX3 agrees well with existing products. Changes in the temperature indices are widespread and consistent with global‐scale warming. The extremes related to daily minimum temperatures are changing faster than the maximum. Spatial changes in the linear trends of precipitation indices over 1950–2018 are less spatially coherent than those for temperature indices. Globally, there are more heavy precipitation events that are also more intense and contribute a greater fraction to the total. Some of the indices use a reference period for calculating exceedance thresholds. We present a comparison between using 1961–1990 and 1981–2010. The differences between the time series of the temperature indices observed over longer time scales are shown to be the result of the interaction of the reference period with a warming climate. The gridded netCDF files and, where possible, underlying station indices are available from www.metoffice.gov.uk/hadobs/hadex3 and www.climdex.org.Robert Dunn was supported by the Met Office Hadley Centre Climate Programme funded by BEIS and Defra (GA01101) and thanks Nick Rayner and Lizzie Good for helpful comments on the manuscript. Lisa Alexander is supported by the Australian Research Council (ARC) Grants DP160103439 and CE170100023. Markus Donat acknowledges funding by the Spanish Ministry for the Economy, Industry and Competitiveness Ramón y Cajal 2017 Grant Reference RYC‐2017‐22964. Mohd Noor'Arifin Bin Hj Yussof and Muhammad Khairul Izzat Bin Ibrahim thank the Brunei Darussalam Meteorological Department (BDMD). Ying Sun was supported by China funding agencies 2018YFA0605604 and 2018YFC1507702. Fatemeh Rahimzadeh and Mahbobeh Khoshkam thank I.R. of Iranian Meteorological Organization (IRIMO) and the Atmospheric Science and Meteorological Organization Research Center (ASMERC) for Data and also sharing their experiences, especially Abbas Rangbar. Jose Marengo was supported by the National Institute of Science and Technology for Climate Change Phase 2 under CNPq Grant 465501/2014‐1, FAPESP Grants 2014/50848‐9 and 2015/03804‐9, and the National Coordination for High Level Education and Training (CAPES) Grant 88887.136402‐00INCT. The team that worked on the data in West Africa received funding from the UK's National Environment Research Council (NERC)/Department for International Development DFID) Future Climate For Africa programme, under the AMMA‐2050 project (Grants NE/M020428/1 and NE/M019969/1). Data from Southeast Asia (excl. Indonesia) was supported by work on using ClimPACT2 during the Second Workshop on ASEAN Regional Climate Data, Analysis and Projections (ARCDAP‐2), 25–29 March 2019, Singapore, jointly funded by Meteorological Service Singapore and WMO through the Canada‐Climate Risk and Early Warning Systems (CREWS) initiative. This research was supported by Thai Meteorological Department (TMD) and Thailand Science Research and Innovation (TSRI) under Grant RDG6030003. Daily data for Mexico were provided by the Servicio Meteorológico Nacional (SMN) of Comisión Nacional del Agua (CONAGUA). We acknowledge the data providers in the ECA&D project (https://www.ecad.eu), the SACA&D project (https://saca-bmkg.knmi.nl), and the LACA&D project (https://ciifen.knmi.nl). We thank the three anonymous reviewers for their detailed comments which improved the manuscript.Peer ReviewedPostprint (published version

Development of an updated global land in situ‐based data set of temperature and precipitation extremes: HadEX3

We present the second update to a data set of gridded land‐based temperature and precipitation extremes indices: HadEX3. This consists of 17 temperature and 12 precipitation indices derived from daily, in situ observations and recommended by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI). These indices have been calculated at around 7,000 locations for temperature and 17,000 for precipitation. The annual (and monthly) indices have been interpolated on a 1.875°×1.25° longitude‐latitude grid, covering 1901–2018. We show changes in these indices by examining ”global”‐average time series in comparison with previous observational data sets and also estimating the uncertainty resulting from the nonuniform distribution of meteorological stations. Both the short and long time scale behavior of HadEX3 agrees well with existing products. Changes in the temperature indices are widespread and consistent with global‐scale warming. The extremes related to daily minimum temperatures are changing faster than the maximum. Spatial changes in the linear trends of precipitation indices over 1950–2018 are less spatially coherent than those for temperature indices. Globally, there are more heavy precipitation events that are also more intense and contribute a greater fraction to the total. Some of the indices use a reference period for calculating exceedance thresholds. We present a comparison between using 1961–1990 and 1981–2010. The differences between the time series of the temperature indices observed over longer time scales are shown to be the result of the interaction of the reference period with a warming climate. The gridded netCDF files and, where possible, underlying station indices are available from www.metoffice.gov.uk/hadobs/hadex3 and www.climdex.org.Robert Dunn was supported by the Met Office Hadley Centre Climate Programme funded by BEIS and Defra (GA01101) and thanks Nick Rayner and Lizzie Good for helpful comments on the manuscript. Lisa Alexander is supported by the Australian Research Council (ARC) Grants DP160103439 and CE170100023. Markus Donat acknowledges funding by the Spanish Ministry for the Economy, Industry and Competitiveness Ramón y Cajal 2017 Grant Reference RYC‐2017‐22964. Mohd Noor'Arifin Bin Hj Yussof and Muhammad Khairul Izzat Bin Ibrahim thank the Brunei Darussalam Meteorological Department (BDMD). Ying Sun was supported by China funding agencies 2018YFA0605604 and 2018YFC1507702. Fatemeh Rahimzadeh and Mahbobeh Khoshkam thank I.R. of Iranian Meteorological Organization (IRIMO) and the Atmospheric Science and Meteorological Organization Research Center (ASMERC) for Data and also sharing their experiences, especially Abbas Rangbar. Jose Marengo was supported by the National Institute of Science and Technology for Climate Change Phase 2 under CNPq Grant 465501/2014‐1, FAPESP Grants 2014/50848‐9 and 2015/03804‐9, and the National Coordination for High Level Education and Training (CAPES) Grant 88887.136402‐00INCT. The team that worked on the data in West Africa received funding from the UK's National Environment Research Council (NERC)/Department for International Development DFID) Future Climate For Africa programme, under the AMMA‐2050 project (Grants NE/M020428/1 and NE/M019969/1). Data from Southeast Asia (excl. Indonesia) was supported by work on using ClimPACT2 during the Second Workshop on ASEAN Regional Climate Data, Analysis and Projections (ARCDAP‐2), 25–29 March 2019, Singapore, jointly funded by Meteorological Service Singapore and WMO through the Canada‐Climate Risk and Early Warning Systems (CREWS) initiative. This research was supported by Thai Meteorological Department (TMD) and Thailand Science Research and Innovation (TSRI) under Grant RDG6030003. Daily data for Mexico were provided by the Servicio Meteorológico Nacional (SMN) of Comisión Nacional del Agua (CONAGUA). We acknowledge the data providers in the ECA&D project (https://www.ecad.eu), the SACA&D project (https://saca-bmkg.knmi.nl), and the LACA&D project (https://ciifen.knmi.nl). We thank the three anonymous reviewers for their detailed comments which improved the manuscript.Peer ReviewedPostprint (published version