Correlation between HPV Vaccination and Cervical Cancer Incidence in Southeast Asian Population

Human Papillomavirus (HPV) is the most common sexually transmitted disease of genital tract that may cause cervical cancer, the second most frequent type of cancer in South East Asia. By far, HPV vaccination is widely used for risk reduction; however, the rate of developing cervical cancer post-vaccination is still not well-studied. The aim of this study is to evaluate the association between HPV vaccination and development of cervical cancer in Southeast Asia. Analysis of data on HPV vaccination in Southeast Asia was performed, based on literature from 2010 to 2016 accessible in PubMed, Google Scholar, and ScienceDirect. Vaccination coverage rates and changes in cervical cancer incidence in particular countries were subjected to comparative analysis using Pearson’s correlation coefficient. The statistical analysis showed HPV vaccination coverage and cervical cancer incidence has negative correlation but not significant (r=-0.04, p>0.05). This might due to HPV vaccination introduction is still at early stage (<10 years of implementation). In addition, 5 out of 9 countries are running the vaccination program as pilot project rather than nationwide program. Other factors may also influence the incidence of cervical cancer such as: genetics and lifestyle factor, socioeconomic status as well as having many children. Nevertheless, follow up study is needed to assess effect of HPV vaccination introduction and coverage to cervical cancer incidences in Southeast Asian countries.The author would acknowledge Indonesia International Institute for Life Sciences (i3L) for funding the registration payment and support

Gas-phase endstation of electron, ion and coincidence spectroscopies for diluted samples at the FinEstBeAMS beamline of the MAXIV 1.5GeV storage ring

Since spring 2019 an experimental setup consisting of an electron spectrometer and an ion time-of-flight mass spectrometer for diluted samples has been available for users at the FinEstBeAMS beamline of the MAXIV Laboratory in Lund, Sweden. The setup enables users to study the interaction of atoms, molecules, (molecular) microclusters and nanoparticles with short-wavelength (vacuum ultraviolet and X-ray) synchrotron radiation and to follow the electron and nuclear dynamics induced by this interaction. Test measurements of N-2 and thiophene (C4H4S) molecules have demonstrated that the setup can be used for many-particle coincidence spectroscopy. The measurements of the Ar3p photoelectron spectra by linear horizontal and vertical polarization show that angle-resolved experiments can also be performed. The possibility to compare the electron spectroscopic results of diluted samples with solid targets in the case of Co2O3 and Fe2O3 at the Co and Fe L-2,L-3-absorption edges in the same experimental session is also demonstrated. Because the photon energy range of the FinEstBeAMS beamline extends from 4.4eV up to 1000eV, electron, ion and coincidence spectroscopy studies can be executed in a very broad photon energy range

Optimal Timing of Insecticide Fogging to Minimize Dengue Cases: Modeling Dengue Transmission among Various Seasonalities and Transmission Intensities

Dengue virus infection is a serious infectious disease transmitted by Aedes mosquitoes in the tropics and sub-tropics. Disease control often involves the use of insecticide fogging against mosquito vectors. However, the effectiveness of this method for reducing dengue cases, in addition to appropriate application procedures, is still debated. The previous mathematical simulation study reported that insecticide fogging reduces dengue cases most effectively when applied soon after the epidemic peak; however, the model did not take into account seasonality and population immunity, which strongly affect the epidemic pattern of dengue infection. Considering these important factors, we used a mathematical simulation model to explore the most effective time for insecticide fogging and to evaluate its impact on reducing dengue cases. Simulations were conducted with various lengths of the wet season and population immunity levels. We found that insecticide fogging substantially reduces dengue cases if conducted at an appropriate time. In contrast to the previously suggested application time during the peak of disease prevalence, the optimal timing is relatively early: between the beginning of the dengue season and the prevalence peak

Gap-filling eddy covariance methane fluxes : Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting halfhourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET).Peer reviewe