Winter and spring soil CO2 efflux along trans-Alaska pipeline, Alaska

3-year winter and spring soil CO2 efflux was conducted in several sites along the trans-Alaska pipeline, Alaska during winter and spring seasons of 2010 to 2012. During the spring, the snow was disappeared mostly fast in the surrounding of tree such as white spruce (Picea glauca) and black spruce (Picea mariana) in boreal forest of Alaska. On the other hand, in tundra, the snow-covered tussock tundra was firstly exposed due to the topography. In white spruce forest, 4-directional soil CO2 efflux is higher east, south, west, and north in turn. Soil temperature is a crucial role in determining soil CO2 efflux, indicating a exponential curve. The CO2 efflux is related to with and without snow cluster that formed by sublimation. However, the efflux has much lower relation to snow depth. In exposed soil in spring of 2011, the CO2 efflux is similar to the growing season CO2 efflux. 3-yr spring CO2 efflux corresponds to 22-46% of annual CO2 efflux along the trans-Alaska pipeline, Alaska during the spring seasons.This research was conducted under the IARC-JAXA Information System (IJIS) project with funding by the Japan Aerospace Exploration Agency (JAXA), and under the JAMSTEC-IARC Collaboration Study (JICS) with funding provided by the Japan Agency for Marine-Earth Science and Technology (JAMSTEC), through a grant to the International Arctic Research Center (IARC)

Stem Respiration of Black Spruce (Picea mariana), Interior Alaska

This stem respiration, that is equipped with a control system that consists of data-logger (CR10X), NDIR CO2 analyzer, and pump, a compressor, and seven stem chambers, was conducted in parallel with the flux-measurement of soil respiration in different-sized black spruce of 4.3 cm to 13.5 cm in DBH (diameter at breast height), interior Alaska during the growing season of 2007 to 2009. The average stem respirations were 0.011±0.005 mgCO2/m3/s (range 0.005±0.002 to 0.015±0.008 mgCO2/m3/s, CV 45%) in black spruce forests, which the DBH (Diameter at Breast Height) of black spruce ranges from 4.3 to 13.5 cm. The stem respiration in different-sized black spruce forest soils has temporally varied during the growing season of 2007-9. This suggests that the young black spruce has 3-fold higher metabolism than the old. Temperature is one of critical roles in determining stem respiration rate. Q10 values on air temperature and average stem respiration rates are 2.02 in 2007, 2.00 in 2008, and 2.37 in 2009 during the growing season, respectively. However, during the dormant season, measurement of stem respiration was failed and especially the diaphragm pump was damaged by input of the extremely cold air of 35 °C below the zero. Interestingly, the lagging effect of stem respiration on temperature and PAR (photosynthetically active radiation) was found during the clear sky, indicating lagging time of 1-2 hours on temperature and of 4-5 hours on PAR, respectively. Based on the Q10 equation on air temperature, annual variation of stem respiration rate was estimated, suggesting that the relationship between measured and simulated daily stem respiration was a good linear for the better understanding of interannual variation of stem respiration rates during 2007-9. The contribution of simulated monthly stem respiration to the ecosystem respiration (Re) by the eddy covariance method was 4.2±2.1 % in 2007, 2.5±0.9 % in 2008, and 5.7±4.3 % in 2009, respectively. The suggests that the higher contribution during 2009 may be due to much higher temperature in late winter and early spring.This research was conducted under the IARC-JAXA Information System (IJIS) project with funding by the Japan Aerospace Exploration Agency (JAXA), and under the JAMSTEC-IARC Collaboration Study (JICS) with funding provided by the Japan Agency for Marine-Earth Science and Technology (JAMSTEC), through a grant to the International Arctic Research Center (IARC)

Investigation of retrieved snow depth by microwave remote sensing with in-situ field data

AMSR-E/AMSR2 is provided the brightness temperature data with more channels, higher spatial resolution and frequent coverage. New snow algorism techniques of remote sensing for snow depth and snow-melting area can be carried out using these in-situ data. We have conducted snow survey from 2006 to now, which is mainly on March and occasionally on January and April/May when seasonal snow melts. The sites are located at an interval of ca. 32-km along the Dalton Highway (Fig. 1). Snow density, snow depth and temperature were measured in snow-pit wall observation at each site. Snow water equivalent (SWE) was calculated by multiplying snow-column snow density by snow depth. As the results, the response of SWE to snow depth showed a positively linear relationship (R2 > 0.90).This research was conducted under the IARC-JAXA Information System (IJIS project) with funding provided by the Japan Aerospace Exploration Agency (JAXA) under a grant to the International Arctic Research Center (IARC). These archived data is stored in ADS (Arctic Data archive System) of the NIPR (National Institute of Polar Research, Japan)

Effect of thaw depth on fluxes of CO2 and CH4 in manipulated Arctic coastal tundra of Barrow, Alaska

The manipulation treatment consisted of draining, controlling, and flooding treated sections by adjusting standing water. Inundation increased CH4 emission by a factor of 4.3 compared to non-flooded sections. This may be due to the decomposition of organic matter under a limited oxygen environment by saturated standing water. On the other hand, CO2 emission in the dry section was 3.9-fold higher than in others. CH4 emission tends to increase with deeper thaw depth, which strongly depends on the water table; however, CO2 emission is not related to thaw depth. Quotients of global warming potential (GWPCO2) (dry/control) and GWPCH4 (wet/control) increased by 464 and 148 %, respectively, and GWPCH4 (dry/control) declined by 66 %. This suggests that CO2 emission in a drained section is enhanced by soil and ecosystem respiration, and CH4 emission in a flooded area is likely stimulated under an anoxic environment by inundated standing water. The findings of this manipulation experiment during the autumn period demonstrate the different production processes of CO2 and CH4, as well as different global warming potentials, coupled with change in thaw depth. Thus the outcomes imply that the expansion of tundra lakes leads the enhancement of CH4 release, and the disappearance of the lakes causes the stimulated CO2 production in response to the Arctic climate change.This research was conducted under the JAMSTEC-IARC Collaboration Study with funding provided by the Japan Agency for Marine-Earth Science and Technology (JAMSTEC) under a grant to the International Arctic Research Center (IARC)

Deep learning-based classification with improved time resolution for physical activities of children

Background The proportion of overweight and obese people has increased tremendously in a short period, culminating in a worldwide trend of obesity that is reaching epidemic proportions. Overweight and obesity are serious issues, especially with regard to children. This is because obese children have twice the risk of becoming obese as adults, as compared to non-obese children. Nowadays, many methods for maintaining a caloric balance exist; however, these methods are not applicable to children. In this study, a new approach for helping children monitor their activities using a convolutional neural network (CNN) is proposed, which is applicable for real-time scenarios requiring high accuracy. Methods A total of 136 participants (86 boys and 50 girls), aged between 8.5 years and 12.5 years (mean 10.5, standard deviation 1.1), took part in this study. The participants performed various movement while wearing custom-made three-axis accelerometer modules around their waists. The data acquired by the accelerometer module was preprocessed by dividing them into small sets (128 sample points for 2.8 s). Approximately 183,600 data samples were used by the developed CNN for learning to classify ten physical activities : slow walking, fast walking, slow running, fast running, walking up the stairs, walking down the stairs, jumping rope, standing up, sitting down, and remaining still. Results The developed CNN classified the ten activities with an overall accuracy of 81.2%. When similar activities were merged, leading to seven merged activities, the CNN classified activities with an overall accuracy of 91.1%. Activity merging also improved performance indicators, for the maximum case of 66.4% in recall, 48.5% in precision, and 57.4% in f1 score . The developed CNN classifier was compared to conventional machine learning algorithms such as the support vector machine, decision tree, and k-nearest neighbor algorithms, and the proposed CNN classifier performed the best: CNN (81.2%) > SVM (64.8%) > DT (63.9%) > kNN (55.4%) (for ten activities); CNN (91.1%) > SVM (74.4%) > DT (73.2%) > kNN (65.3%) (for the merged seven activities). Discussion The developed algorithm distinguished physical activities with improved time resolution using short-time acceleration signals from the physical activities performed by children. This study involved algorithm development, participant recruitment, IRB approval, custom-design of a data acquisition module, and data collection. The self-selected moving speeds for walking and running (slow and fast) and the structure of staircase degraded the performance of the algorithm. However, after similar activities were merged, the effects caused by the self-selection of speed were reduced. The experimental results show that the proposed algorithm performed better than conventional algorithms. Owing to its simplicity, the proposed algorithm could be applied to real-time applicaitons

CO2 Flux from Tundra Lichen, Moss, and Tussock, Council, Alaska: Assessment of Spatial Representativeness

CO2 flux-measurement in dominant tundra vegetation on the Seward Peninsula of Alaska was examined for spatial representativeness, using a manual chamber system. In order to assess the representativeness of CO2 flux, a 40 m × 40 m (5-m interval; 81 total points) plot was used in June, August, and September of 2011. Average CO2 fluxes in lichen, moss, and tussock tundra were 3.4 ± 2.7, 4.5 ± 2.9, and 7.2 ± 5.7 mgCO2/m2/m during growing season, respectively, suggesting that tussock tundra is a significant CO2 source, especially considering the wide distribution of tussock tundra in the circumpolar region. Further, soil temperature, rather than soil moisture, held the key role in regulating CO2 flux at the study site: CO2 flux from tussock increased linearly as soil temperature increased, while the flux from lichen and moss followed soil temperature nearly exponentially, reflecting differences in surface area covered by the chamber system. Regarding sample size, the 81 total sampling points over June, August, and September satisfy an experimental average that falls within ±10% of full sample average, with a 95% confidence level. However, the number of sampling points for each variety of vegetation during each month must provide at least ±20%, with an 80% confidence level. In order to overcome the logistical constraints, we were required to identify the site’s characteristics with a manual chamber system over a 40 m × 40 m plot and to subsequently employ an automated chamber for spatiotemporal representativeness.This work was supported by the National Research Foundation of Korea Grant funded by the Korean Government (MEST) (NRF-C1ABA001-2011-0021063

Applying Topographic Classification, Based on the Hydrological Process, to Design Habitat Linkages for Climate Change

The use of biodiversity surrogates has been discussed in the context of designing habitat linkages to support the migration of species affected by climate change. Topography has been proposed as a useful surrogate in the coarse-filter approach, as the hydrological process caused by topography such as erosion and accumulation is the basis of ecological processes. However, some studies that have designed topographic linkages as habitat linkages, so far have focused much on the shape of the topography (morphometric topographic classification) with little emphasis on the hydrological processes (generic topographic classification) to find such topographic linkages. We aimed to understand whether generic classification was valid for designing these linkages. First, we evaluated whether topographic classification is more appropriate for describing actual (coniferous and deciduous) and potential (mammals and amphibians) habitat distributions. Second, we analyzed the difference in the linkages between the morphometric and generic topographic classifications. The results showed that the generic classification represented the actual distribution of the trees, but neither the morphometric nor the generic classification could represent the potential animal distributions adequately. Our study demonstrated that the topographic classes, according to the generic classification, were arranged successively according to the flow of water, nutrients, and sediment; therefore, it would be advantageous to secure linkages with a width of 1 km or more. In addition, the edge effect would be smaller than with the morphometric classification. Accordingly, we suggest that topographic characteristics, based on the hydrological process, are required to design topographic linkages for climate change

A new method for speaker adaptation using bilinear model

ABSTRACT In this paper, a novel method for speaker adaptation using bilinear model is proposed. Bilinear model can express both characteristics of speakers (style) and phonemes across speakers (content) independently in a training database. The mapping from each speaker and phoneme space to observation space is carried out using bilinear mapping matrix which is independent of speaker and phoneme space. We apply the bilinear model to speaker adaption. Using adaptation data from a new speaker, speaker-adapted model is built by estimating the style(speaker)-specific matrix. Experimental results showed that the proposed method outperformed eigenvoice and MLLR. In vocabulary-independent isolated word recognition for speaker adaptation, bilinear model reduced word error rate by about 38% and about 10% compared to eigenvoice and MLLR respectively using 50 words for adaptation

Protocadherin-7 contributes to maintenance of bone homeostasis through regulation of osteoclast multinucleation

Kim, H., Takegahara, N., Walsh, M. C., Ueda, J., Fujihara, Y., Ikawa, M., & Choi, Y. (2020). Protocadherin-7 contributes to maintenance of bone homeostasis through regulation of osteoclast multinucleation. BMB Reports, 53(9), 472-477. doi:10.5483/BMBRep.2020.53.9.05