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Pollen grains are carriers of allergens, causing severe symptoms to hay fever sufferers. Due to annual variability in pollen levels, there is a need to develop a precise forecasting model that can accurately predict levels of annual airborne pollen for use by patients and medical personnel. In a previous study, we developed and tested a new pollen prediction algorithm based on past meteorological conditions in a flat region. In this study, we analyzed pollen samples (spring 1997–2016) and meteorological data (summer 1991–2015) from nine Swiss cities, using the algorithm to test its applicability in a mountainous region. However, we found variations in model applicability (r2 and RMSE) among the nine observation sites. Based on considerations of topographical influence on the spatial representativeness of airborne pollen data, we then investigated the relationships between these indices and topographical features at varying spatial ranges. The results suggested that the applicability of the algorithm to annual total airborne pollen forecasts can indicate the spatial representativeness of observed airborne pollen concentrations: r2 and RMSE reflected deviations in airborne pollen concentration caused by dispersal conditions at local and meso-scales, respectively. At higher-altitude monitoring sites, the local spatial representativeness of airborne pollen declined due to the influence of local topography-driven climate. On the other hand, dispersal conditions in the atmospheric boundary layer may deteriorate the meso-scale representativeness of airborne pollen at monitoring sites with higher terrain complexity. This study provides a reference approach to determining the spatial representativeness of airborne pollen data by applying the pollen forecast algorithm. Image 1 • Accurate methods for predicting annual pollen are essential for public health. • This study tested a new algorithm for annual airborne pollen in a mountainous area. • A clear relationship between model applicability and topography was found. • The results' accuracy was affected by local and meso-scale dispersal. • The method's applicability indicated the spatial representativeness of pollen data. [ABSTRACT FROM AUTHOR]

The birch tree (genus Betula L.) disperses airborne pollen annually from April to June, causing severe symptoms in pollinosis sufferers. Because of interannual variations in pollen levels, there is an urgent need to develop a forecasting model with greater precision in order to provide accurate information to patients and medical personnel regarding airborne pollen levels. We developed an algorithm for forecasting the total amount of airborne birch pollen. This equation suggested that the total amount of airborne pollen in a given season could be estimated using only the meteorological data from previous years. In order to discover potential predictive relationships, a data set including airborne pollen data from 1996 to 2015 and meteorological data from 1990 to 2014 was used to construct forecasting models. Statistical evaluation results were examined to select the optimal model, showing that forecasting models obtained the highest accuracy when using meteorological data from June and the best model performance was achieved using the average daily maximum air temperature and solar radiation of the previous five years. We also developed an extended model that included relative humidity, which demonstrated better predictive capability. These findings clarify that a model with greater predictive power can be constructed using the meteorological conditions from the previous five years. In order to assess this conclusion, the algorithm was tested by forecasting the total amount of airborne birch pollen in Japan with good results. [ABSTRACT FROM AUTHOR]

The rapid spread of influenza-associated H1N1 viruses has caused serious concern in recent years. Therefore, there is an urgent need for the development of automatic, point-of-care devices for rapid diagnosis of the influenza virus. Conventional approaches suffer from several critical issues; notably, they are time-consuming, labor-intensive, and are characterized by relatively low sensitivity. In this work, we present a new approach for fluorescence-based detection of the influenza A H1N1 virus using a sandwich-based aptamer assay that is automatically performed on an integrated microfluidic system. The entire detection process was shortened to 30 min using this chip-based system which is much faster than the conventional viral culture method. The limit of detection was significantly improved to 0.032 hemagglutination unit due to the high affinity and high specificity of the H1N1-specific aptamers. The results showed that the two-aptamer microfluidic system had about 10 3 times higher sensitivity than the conventional serological diagnosis. It was demonstrated that the developed microfluidic system may play as a powerful tool in the detection of the H1N1 virus. [ABSTRACT FROM AUTHOR]

The seasonal pollen index (SPI) is a continuing concern within the fields of aerobiology, ecology, botany, and epidemiology. The SPI of anemophilous trees, which varies substantially from year to year, reflects the flowering intensity. This intensity is regulated by two factors: weather conditions during flower formation and the inner resource for assimilation. A deterministic approach has to date been employed for predicting SPI, in which the forecast is made entirely by parameters. However, given the complexity of the masting mechanism (which has intrinsic stochastic properties), few attempts have been made to apply a stochastic model that considers the inter-annual SPI variation as a stochastic process. We propose a hidden Markov model that can integrate the stochastic process of mast flowering and the meteorological conditions influencing flower formation to predict the annual birch pollen concentration. In experiments conducted, the model was trained and validated by using data in Hokkaido, Japan covering 22 years. In the model, the hidden Markov sequence was assigned to represent the recurrence of mast years via a transition matrix, and the observation sequences were designated as meteorological conditions in the previous summer, which are governed by hidden states with emission distribution. The proposed model achieved accuracies of 83.3% in the training period and 75.0% in the test period. Thus, the proposed model can provide an alternative perspective toward the SPI forecast and probabilistic information of pollen levels as a useful reference for allergy stakeholders. Unlabelled Image • A hidden Markov model (HMM) is proposed to predict seasonal pollen index (SPI). • Mast flowering stochasticity is combined with flower formation influencing factors. • Mast years' recurrence is parameterized via a transition matrix. • Relation between meteorological factors and SPI defines via emission distribution. • Probability of the next pollen level becomes available by using stochastic model. [ABSTRACT FROM AUTHOR]

Sulfate reduction could go through dissimilatory sulfate reduction and anaerobic methane oxidation couple with sulfate reduction (AOM-SR) with pyrite the end product. While AOM-SR is an important process in oxidizing methane and limiting methane entering the ocean, there is limited information available regarding pyrite formation and preservation under methane dominated environment. The purpose of this study is to report pyrite formation and preservation at a methane dominated environment, the YuanAn Ridge, where methane seeps have been observed, and to evaluate how would that differ from typical anoxic environment. Pore water methane, sulfate, dissolved sulfide, barium, and sediment pyrite, barium/Al ratio and organic carbon in sediments were analyzed from sediments collected by piston cores on board the R/V Ocean Researcher I (OR-I) from the study environment. The results showed methane flux is controlling pyrite formation in this methane dominated environment. Pyrite concentration is linearly correlated with methane flux with exceptions to shallower sulfate methane transition zone (SMTZ) sites where methane could have vent directly to the overlying water and contribute less to the pyrite formation. The more methane entering the SMTZ, the more pyrite formed and preserved in the sulfate methane transition zone sediments. Authigenic pyrite from dissimilatory sulfate reduction is a small fraction of the pyrite found in the methane dominant and low in organic carbon environment, with majority of pyrite derived from AOM-SR. Large spatial variations on rate of sulfate reduction, pyrite and methane concentrations were observed in the studied area sediments. Depth of sulfate methane transition zone varied between 1 and 14 m and is a log function of methane flux. Pore water sulfate profiles displayed three different types, linear, concave up and down, indicating methane flux have varied in time. Pyrite burial efficiency is high, approximately 50% of sulfate entering the SMTZ were preserved in sediments as pyrite. This efficiency of sulfate reduction through AOM-SR is much higher than pyrite formation from dissimilatory sulfate reduction in normal marine sediments. The AOM-SR and pyrite formation occurred at depth within the SMTZ favor a higher degree of pyrite preservation. Time require for the pyrite formation is about 4400 years in the YAR sediments, based on diffusion model calculation of barium sulfate precipitation. [ABSTRACT FROM AUTHOR]

TaHf thin films were deposited by performing the cosputtering of Ta and Hf targets. The results indicated that TaHf alloy is suitable for use as a resistive layer for thin film chip resistors and a replacement for NiCr and TaN x alloys. Maintaining the Hf:Ta ratio at approximately 1:1 produced the highest resistivity of 5.7 × 10−4 Ωcm when compared with TaN x and NiCr alloy thin films. This ratio also yielded a smaller temperature coefficient of resistance of −330 ppm/°C compared with TaN x thin films. Furthermore, the temperature coefficient of resistance of most TaHf thin films could be adjusted closer to zero through annealing. The increased Hf content in TaHf alloy was attributed to its poorer thermal stability and anticorrosion property relative to pure Ta films. However, the anticorrosion property of TaHf thin films was still stronger than that of NiCr thin films. • TaHf is a promising alternative material to TaN and NiCr for thin film resistor applications. • TaHf thin film possesses a smaller TCR and higher resistivity compared to TaN. • Anticorrosion properties of TaHf are stronger than those of NiCr. • TaHf thin film achieves a near-zero TCR by annealing if its oxygen content is low. [ABSTRACT FROM AUTHOR]

Ta–N–Al thin films were produced by reactive sputtering with two different TaAl alloys (Al 10 at.% and 20 at.%) for use as resistive materials for thin film chip resistors. Changing the N 2 gas flow of the N 2 /Ar plasma and the Al ratio in TaAl alloy resulted in different microstructures and electrical properties of the Ta–N–Al thin film. The resistivity of the thin film sputtered by TaAl (Al 20 at.%) alloy was 1.23 times that sputtered by Ta under 20% N 2 /Ar. After deposition, rapid thermal processing was applied to anneal the thin films for 5 min at 600 °C, 650 °C, and 700 °C under N 2 (99.995%) atmosphere, causing the Ta 2 N and TaN crystalline phase formation. Al in Ta–N–Al was observed to hinder Ta 2 N and TaN crystalline phase formation during annealing. It is crucial to dope Al ith Ta–N appropriately to obtain a near-zero temperature coefficient of resistance (TCR) after annealing. The TCR of Ta–N–Al thin film, sputtered by TaAl (Al 20 at.%) alloy, was substantially affected by the annealing temperature. However, the thin film sputtered by TaAl (Al 10 at.%) alloy under 16% N 2 /Ar can achieve a TCR of −1.8 ppm/°C and 1.4 ppm/°C after annealing at 600 °C and 650 °C, respectively. • Ta-N-Al with adequate Al has a higher resistivity than Ta-N. • Get near-zero TCR of Ta-N-Al by annealing within a specific temperature range. • Al in Ta-N-Al hinders Ta 2 N and TaN grain growth during annealing. [ABSTRACT FROM AUTHOR]