Hydrogen Therapy Reduces Oxidative Stress-associated Risks Following Acute and Chronic Exposure to High-altitude Environment

Low pressure,low oxygen concentration,and intense ultraviolet（UV）radiation in high-altitude environments,can cause oxidative stress which can trigger mountain sickness.A recent study demonstrated that hydrogen gas with a good permeability in biological membranes can treat various disorders by exerting its selective anti-oxidation and anti-inflammatory effects,indicating that hydrogen therapy plays a role in scavenging free radicals and in balancing oxidation and anti-oxidation systems of ceils. Therefore, we hypothesize that inhaling low-dose hydrogen or drinking hydrogen-saturated water is a novel and simple method to prevent and treat oxidative stress injury caused by low pressure, low oxygen concentration and intense UV radiation in plateaus, thus reducing the risk of mountain sickness.

Combining Landsat-8 spectral bands with ancillary variables for land cover classification in mountainous terrains of northern Pakistan

Landsat-8 spectral values have been used to map the earth’s surface information for decades.However,forest types and other land-use/land-cover(LULC)in the mountain terrains exist on different altitudes and climatic conditions.Hence,spectral information alone cannot be sufficient to accurately classify the forest types and other LULC,especially in high mountain complex.In this study,the suitability of Landsat-8 spectral bands and ancillary variables to discriminate forest types,and other LULC,using random forest(RF)classification algorithm for the Hindu Kush mountain ranges of northern Pakistan,was discussed.After prior-examination(multicollinearity)of spectral bands and ancillary variables,three out of six spectral bands and five out of eight ancillary variables were selected with threshold correlation coefficients r2<0.7.The selected datasets were stepwise stacked together and six Input Datasets(ID)were created.The first ID-1 includes only the Surface Reflectance(SR)of spectral bands,and then in each ID,the extra one ancillary variable including Normalized Difference Vegetation Index(NDVI),Normalized Difference Water Index(NDWI),Normalized Difference Snow Index(NDSI),Land Surface Temperature(LST),and Digital Elevation Model(DEM)was added.We found an overall accuracy(OA)=72.8%and kappa coefficient(KC)=61.9%for the classification of forest types,and other LULC classes by using the only SR bands of Landsat-8.The OA=81.5%and KC=73.7%was improved by the addition of NDVI,NDWI,and NDSI to the spectral bands of Landsat-8.However,the addition of LST and DEM further increased the OA,and Kappa coefficient(KC)by 87.5%and 82.6%,respectively.This indicates that ancillary variables play an important role in the classification,especially in the mountain terrain,and should be adopted in addition to spectral bands.The output of the study will be useful for the protection and conservation,analysis,climate change research,and other mountains forest-related management information.

Influence of environmental predispositions on temperate mountain forest damage at different spatial scales during alternating drought and flood periods:case study in Hruby Jeseník Mts.(Czech Republic)

Mountain forests are more prone to environmental predispositions(EPs)than submountain ones.While remote sensing of mountain forests enables instantaneous damage mapping,the investigation of the causes requires field data.However,a local field or regionally modeled environmental characteristics influence remote data evaluation differently.This study focused on the evaluation of EPs effects damaging mountain forests between various spatial resolutions during environmental change.The evaluation was divided into managed and natural forests in the Hruby Jeseník Mts.(Czech Republic;240-1491 m a.s.l.;50.082°N,17.231°E).Damage was assessed through the discrimination analysis of the normalised difference vegetation index(NDVI)by MODIS VI during alternating drought and flood periods 2003-2014.The local environmental influence was assessed using the discrimination function(DF)separability of forest damage in the training sets.The regional influence was assessed through map algebra estimated via the DF and a forest decline spatial model based on EPs from differences between risk growth conditions and biomass fuzzy sets.Management,EPs and soil influenced forest NDVI at different levels.The management afflicted the NDVI more than the EPs.The EPs afflicted the NDVI more than the soil groups.Strong winters and droughts had a greater influence on the NDVI than the flood events,with the winter of 2005/2006 inverting the DF direction,and the 2003 drought increasing differences in managed forest biomass and decreasing differences in natural forest biomasses.More than 50% of declining managed forests in the training sets occurred on Leptosols,Podzols and Histosols.On a regional scale,the soil influence was eliminated by multiple predispositions.The EPs influenced 96% of natural forest and 65% of managed forest,though managed forest damage was more evident.The mountain forest NDVI decline was dependent on both management and risk predispositions.