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IntroductionIn Positron Emission Tomography (PET) imaging, the use of tracers increases radioactive exposure for longitudinal evaluations and in radiosensitive populations such as pediatrics. However, reducing injected PET activity potentially leads to an unfavorable compromise between radiation exposure and image quality, causing lower signal-to-noise ratios and degraded images. Deep learning-based denoising approaches can be employed to recover low count PET image signals: nonetheless, most of these methods rely on structural or anatomic guidance from magnetic resonance imaging (MRI) and fails to effectively preserve global spatial features in denoised PET images, without impacting signal-to-noise ratios.MethodsIn this study, we developed a novel PET only deep learning framework, the Self-SiMilARiTy-Aware Generative Adversarial Framework (SMART), which leverages Generative Adversarial Networks (GANs) and a self-similarity-aware attention mechanism for denoising [18F]-fluorodeoxyglucose (18F-FDG) PET images. This study employs a combination of prospective and retrospective datasets in its design. In total, 114 subjects were included in the study, comprising 34 patients who underwent 18F-Fluorodeoxyglucose PET (FDG) PET imaging for drug-resistant epilepsy, 10 patients for frontotemporal dementia indications, and 70 healthy volunteers. To effectively denoise PET images without anatomical details from MRI, a self-similarity attention mechanism (SSAB) was devised. which learned the distinctive structural and pathological features. These SSAB-enhanced features were subsequently applied to the SMART GAN algorithm and trained to denoise the low-count PET images using the standard dose PET image acquired from each individual participant as reference. The trained GAN algorithm was evaluated using image quality measures including structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), normalized root mean square (NRMSE), Fréchet inception distance (FID), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR).ResultsIn comparison to the standard-dose, SMART-PET had on average a SSIM of 0.984 ± 0.007, PSNR of 38.126 ± 2.631 dB, NRMSE of 0.091 ± 0.028, FID of 0.455 ± 0.065, SNR of 0.002 ± 0.001, and CNR of 0.011 ± 0.011. Regions of interest measurements obtained with datasets decimated down to 10% of the original counts, showed a deviation of less than 1.4% when compared to the ground-truth values.DiscussionIn general, SMART-PET shows promise in reducing noise in PET images and can synthesize diagnostic quality images with a 90% reduction in standard of care injected activity. These results make it a potential candidate for clinical applications in radiosensitive populations and for longitudinal neurological studies.

Snow avalanche is one of the major natural hazards in the mountain region, yet it has received less attention compared to other mountain hazards, such as landslides, floods, and droughts. After a comprehensive overview of snow avalanche hazards in Tibet area, the spatial distribution and main driving factors of snow avalanche hazards in the high mountain region in Tibet were presented in the study first. Snow avalanche-prone areas in Tibet were then mapped based on the snow cover distribution and DEM data and were validated against in situ observations. Results show that there are the highest frequencies of avalanche occurrences in the southeastern Nyainqentanglha Mountains and the southern slope of the Himalayas. In the interior of plateau, avalanche development is constrained due to less precipitation and much flatter terrain. The perennially snow avalanche-prone areas in Tibet account for 1.6% of the total area of the plateau, while it reaches 2.9% and 4.9% of the total area of Tibet in winter and spring, respectively. Snow avalanche hazards and fatalities appear to be increasing trends under global climate warming due to more human activities at higher altitudes. In addition to the continuous implementation of engineering prevention and control measures in pivotal regions in southeastern Tibet, such as in the Sichuan–Tibet highway and railway sections, enhancing monitoring, early warning, and forecasting services are crucial to prevent and mitigate avalanche hazards in the Tibetan high mountain regions, which has significant implications for other global high mountain areas.

Climate change has caused substantial shifts in species’ ranges and vegetation distributions in local areas of the Himalayas. However, the spatial patterns and dynamic changes of the vegetation lines in the Himalayas remain poorly understood due to the lack of comprehensive vegetation line dataset. This study developed a method to identify vegetation lines by combining the Canny edge detection algorithm with elevation parameters and produced comprehensive vegetation line datasets with 30 m resolution in the Himalayas. First, the Modified Soil-Adjusted Vegetation Index (MSAVI) was applied to indicate vegetation presence. The image was then smoothed by filling (or removing) small non-vegetated (or vegetated) patches scattered within vegetated (or unvegetated) areas. Subsequently, the Canny edge detection algorithm was applied to identify vegetation edge pixels, and elevation differences were utilized to determine the upper edges of the vegetation. Finally, Gaussian function-based thresholds were used across 24 sub-basins to determine the vegetation lines. Field surveys and visual interpretations demonstrated that this method can effectively and accurately identify vegetation lines in the Himalayas. The R2 was 0.99, 0.93, and 0.98, respectively, compared with the vegetation line verification points obtained through three different ways. The mean absolute errors were 11.07 m, 29.35 m, and 13.99 m, respectively. Across the Himalayas, vegetation line elevations ranged from 4125 m to 5423 m (5th to 95th percentile), showing a trend of increasing and then decreasing from southeast to northwest. This pattern closely parallels the physics-driven snowline. The method proposed in this study enhances the toolkit for identifying vegetation lines across mountainous regions. Additionally, it provides a foundation for evaluating the responses of mountain vegetation to climate change in the Himalayas.

Abstract Background Quantification of the cerebral metabolic rate of glucose (CMRGlu) by dynamic [18F]FDG PET requires invasive arterial sampling. Alternatives to using an arterial input function (AIF) include the simultaneous estimation (SIME) approach, which models the image-derived input function (IDIF) by a series of exponentials with coefficients obtained by fitting time activity curves (TACs) from multiple volumes-of-interest. A limitation of SIME is the assumption that the input function can be modelled accurately by a series of exponentials. Alternatively, we propose a SIME approach based on the two-tissue compartment model to extract a high signal-to-noise ratio (SNR) model-derived input function (MDIF) from the whole-brain TAC. The purpose of this study is to present the MDIF approach and its implementation in the analysis of animal and human data. Methods Simulations were performed to assess the accuracy of the MDIF approach. Animal experiments were conducted to compare derived MDIFs to measured AIFs (n = 5). Using dynamic [18F]FDG PET data from neurologically healthy volunteers (n = 18), the MDIF method was compared to the original SIME-IDIF. Lastly, the feasibility of extracting parametric images was investigated by implementing a variational Bayesian parameter estimation approach. Results Simulations demonstrated that the MDIF can be accurately extracted from a whole-brain TAC. Good agreement between MDIFs and measured AIFs was found in the animal experiments. Similarly, the MDIF-to-IDIF area-under-the-curve ratio from the human data was 1.02 ± 0.08, resulting in good agreement in grey matter CMRGlu: 24.5 ± 3.6 and 23.9 ± 3.2 mL/100 g/min for MDIF and IDIF, respectively. The MDIF method proved superior in characterizing the first pass of [18F]FDG. Groupwise parametric images obtained with the MDIF showed the expected spatial patterns. Conclusions A model-driven SIME method was proposed to derive high SNR input functions. Its potential was demonstrated by the good agreement between MDIFs and AIFs in animal experiments. In addition, CMRGlu estimates obtained in the human study agreed to literature values. The MDIF approach requires fewer fitting parameters than the original SIME method and has the advantage that it can model the shape of any input function. In turn, the high SNR of the MDIFs has the potential to facilitate the extraction of voxelwise parameters when combined with robust parameter estimation methods such as the variational Bayesian approach.

Abstract Spin-based sensors have attracted considerable attention owing to their high sensitivities. Herein, we developed a metallofullerene-based nano spin sensor to probe gas adsorption within porous organic frameworks. For this, spin-active metallofullerene, Sc3C2@C80, was selected and embedded into a nanopore of a pyrene-based covalent organic framework (Py-COF). Electron paramagnetic resonance (EPR) spectroscopy recorded the EPR signals of Sc3C2@C80 within Py-COF after adsorbing N2, CO, CH4, CO2, C3H6, and C3H8. Results indicated that the regularly changing EPR signals of embedded Sc3C2@C80 were associated with the gas adsorption performance of Py-COF. In contrast to traditional adsorption isotherm measurements, this implantable nano spin sensor could probe gas adsorption and desorption with in situ, real-time monitoring. The proposed nano spin sensor was also employed to probe the gas adsorption performance of a metal–organic framework (MOF-177), demonstrating its versatility. The nano spin sensor is thus applicable for quantum sensing and precision measurements.

Land cover products provide the key inputs for terrestrial change monitoring and modeling. Numerous land cover products have been generated in the past few decades, but their performance on the southeastern Tibetan Plateau remains unclear. This study analyzed 15 land cover products for consistency through compositional similarity and overlay analyses. Additionally, 1305 validation samples from four datasets were employed to construct confusion matrices to evaluate their accuracy. The results indicate the following: (1) Land cover products exhibit relatively high consistency in 62.92% of the region. (2) Land cover products are strongly influenced by terrain fluctuations, showing lower consistency at elevation below 200 m and instability in land cover classification with increasing elevation, particularly between 2800–4400 m and 4800–5400 m. (3) The accuracy for forest, water, and snow/ice is relatively high. However, there is a relatively lower accuracy for wetland and shrubland, necessitating more field samples for reference to improve classification. (4) The average values of the four validation datasets show that the overall accuracy of the 15 products ranges from 50.97% to 73.50%. For broad-scale studies with lower resolution requirements, the CGLS-LC100 product can be considered. For studies requiring a finer scale, a combination of multiple land cover products should be utilized. ESRI is recommended as a reference for built-up land, while FROM-GLC30 can be used for cropland, although misclassification issues should be noted. This study provides valuable insights for analyzing land cover types on plateaus to refine classification. It also offers guidance for selecting suitable land cover products for future research in this region.

Exploring the changes and drivers of open-surface water bodies is essential for the well-being of humanity in the face of global climate change. As one of the core areas in the Qinghai-Tibet Plateau (QTP), the headwater region of the Yangtze, Yellow, and Lancang Rivers is a water source for billions of people in Asia. However, previous studies failed to provide complete open-surface water data and watershed-level analysis for this region. Utilizing the Google Earth Engine platform and vegetation-water algorithm, we interpreted open-surface water bodies from all available Landsat surface reflectance data and generated annual 30-m water body frequency maps for 1987–2021, which is a complete open-surface water body dataset in the study area. Compared to the Joint Research Centre’s Global Surface Water dataset, the dataset in our study maintains consistency in 90.1 % of pixels and achieved correct classification in 74.6 % of pixels within the inconsistent areas. The perennial water body area exhibited an increase from 232,335 to 293,534 ha. Instead, the seasonal water body area notably declined from 223,809 to 182,003 ha. The Yangtze River headwater region shows an increase in perennial water bodies, with no significant variation in seasonal water bodies. The Yellow River headwater region experiences an increase in perennial water bodies but a decrease in seasonal water bodies. Remarkably, in the Lancang River headwater region, with lower water body density, both the perennial and seasonal water body areas decreased by 36.8 % and 48.8 %, respectively. The divergent changes in the headwater region of the Three Rivers are primarily attributed to spatial heterogeneity in the temperature effects on water bodies. Anticipated increases in the temperature are expected to amplify these differences. Our findings suggest that climate-driven open-surface water body changes are ongoing in this core area in the QTP, threatening the water security of densely populated East and Southeast Asia in the future.

Transboundary forests in the Himalayas are one of the world’s biodiversity hotspots, but exploring the patterns and influencing factors related to forest disturbance-recovery is still limited. In this study, we used a random forests model, a LandTrendr temporal segmentation algorithm and geographical detector to investigate the spatial and temporal evolution characteristics of forest disturbance and recovery and their influencing factors in the central Himalayas from 1995 to 2018. The results showed that forest disturbance was dominant (∼2.94 × 104 km2) and greater than recovery (∼0.56 × 104 km2). Both disturbance and recovery showed a significant decreasing trend (p

Abstract Background The purpose of this study was to assess the feasibility of using a minimally invasive simultaneous estimation method (SIME) to quantify the binding of the 18-kDa translocator protein tracer [18F]FEPPA. Arterial sampling was avoided by extracting an image-derived input function (IDIF) that was metabolite-corrected using venous blood samples. The possibility of reducing scan duration to 90 min from the recommended 2–3 h was investigated by assuming a uniform non-displaceable distribution volume (V ND) to simplify the SIME fitting. Results SIME was applied to retrospective data from healthy volunteers and was comprised of both high-affinity binders (HABs) and mixed-affinity binders (MABs). Estimates of global V ND and regional total distribution volume (V T) from SIME were not significantly different from values obtained using a two-tissue compartment model (2CTM). Regional V T estimates were greater for HABs compared to MABs for both the 2TCM and SIME, while the SIME estimates had lower inter-subject variability (41 ± 17% reduction). Binding potential (BPND) values calculated from regional V T and brain-wide V ND estimates were also greater for HABs, and reducing the scan time from 120 to 90 min had no significant effect on BPND. The feasibility of using venous metabolite correction was evaluated in a large animal model involving a simultaneous collection of arterial and venous samples. Strong linear correlations were found between venous and arterial measurements of the blood-to-plasma ratio and the remaining [18F]FEPPA fraction. Lastly, estimates of BPND and the specific distribution volume (i.e., V S = V T − V ND) from a separate group of healthy volunteers (90 min scan time, venous-scaled IDIFs) agreed with estimates from the retrospective data for both genotypes. Conclusions The results of this study demonstrate that accurate estimates of regional V T, BPND and V S can be obtained by applying SIME to [18F]FEPPA data. Furthermore, the application of SIME enabled the scan time to be reduced to 90 min, and the approach worked well with IDIFs that were scaled and metabolite-corrected using venous blood samples.

Marsh wetlands present a rich variety of ecosystem goods and services, including important carbon sinks, of great significance to changes in the global environment. However, seasonal responses of marsh wetlands remain understudied and poorly understood. Therefore, this study adapted the Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr) model using field and Landsat data during 1990–2017 to identify the seasonal and spatial variations in marsh wetlands in the headwaters of the Brahmaputra, Ganges, and Indus Rivers (HBGIR) and analyze the drivers of their spatiotemporal changes. The spatiotemporal change in the marsh wetlands was divided into the three stages of expansion (1990–1998), retreat (1998–2004), and stability (2004–2017). The marsh wetland area showed an increasing trend before 1998 and a stable and slightly increasing trend after 2004. The expansion area of the marsh wetlands was located in the reserves of Shiquanhe and Manasarovar and their surrounding areas. The marsh wetlands were subjected to the largest expansion in summer (114.45 km2), followed by spring (80.45 km2). The expansion of the marsh wetlands in summer was mainly driven by snow depth and temperature, with the strongest interaction factor of 0.6 between them. However, the retreat of the marsh wetlands was driven by the expansion of construction and cultivated lands in the HBGIR. Finally, the LandTrendr model modified in this study was useful for improving the change-detection accuracy of the marsh wetlands. This study provides an important theoretical basis for wetland protection and management for the Tibetan Plateau.

Abstract Background Uncertainties exist in the magnitude and outbreak of debris flow disasters, resulting in significant loss of lives and property to human society. Improved identification of debris flow susceptibility areas can help to predict the location and sphere of influence of debris flow disaster, thus accurately assessing the risk of debris flow disaster and reducing losses caused by such a disaster. The dry-hot valleys of Basu County in the Eastern Qinghai-Tibet Plateau are typical areas of high debris flow incidence, mapping of debris flow susceptibility identification and regional risk assessment is needed in this area. Results The parameters improved Flow-R model was first applied to identify debris flow susceptibility areas in Basu county using the digital elevation model, flow accumulation, slope, plan curvature, and land use data, followed by debris flow risk assessment. The Flow-R model can output high result accuracy of high-resolution susceptibility to debris flow identification on a regional scale with less data, and its accuracy value is 87.6%, indicating that the susceptibility to regional debris flow disaster is credible. This study provides a useful basis for effective prevention of regional debris flow disasters in the future, and provides a useful method for effectively identifying the debris flow susceptibility areas and assessing the related risk in large-scale areas. Conclusions (1) The debris flow susceptibility areas in Basu County covered 97.04 km2 (0.79% of the study area), distributed mainly in the Nujiang River Valley, Lengqu tributaries, and both sides of National Highway 318. (2) The debris flow susceptibility areas were dominant in zones characterized by an altitude range of 3000–4000 m, a plane curvature of − 2/100 m−1 to 1/100 m−1, and a low slope of 20°–40°. In addition, the susceptibility areas were dominant in the unused land and less prevalent in the water area. The highest and lowest susceptibility values were observed for cultivated and unused lands, respectively. (3) The debris flow risk in the study areas accounted for 0.82 km2 and revealed a distribution of high-risk debris flow along roads. The areas with a high debris flow risk were mainly distributed along the mainstream of the Nujiang River, which is the main future protected area.

Establishing an artificial grassland is a common measure employed to restore heavily degraded alpine grasslands for regional sustainability. The Three-River Headwaters Region in China has significant areas of black-soil-type grassland which is typified by heavy degradation; nearly 35% of the grassland regions in the Three-River Headwaters Region has degraded into this type. There are different plant community types of black-soil-type grasslands, however, it is not clear which restoration measures should be adopted for different kinds of black-soil-type grasslands. Here, we investigate the plant community characteristics and soil physicochemical properties of artificial grasslands, two types of black-soil-type grasslands, and native undegraded grassland in the Three-River Headwaters Region, then analyzed the direct and indirect interactions between the plant and soil properties by partial least squares path models (PLS-PM). Our results revealed that establishing artificial grassland significantly increased aboveground biomass and plant community coverage, and also decreased plant species richness and diversity and soil water content, soil organic carbon and total nitrogen in the 0-10 cm soil layer as compared with black-soil-type grasslands. Plant community diversity had a positive effect on plant community productivity, soil nutrient, and soil water content in native undegraded grassland. These results suggest that more management interventions are needed after establishing an artificial grassland, such as reducing dominant species in two types of black-soil-type grasslands, water regulation in the A. frigida-dominated meadow, diversifying plant species (i.e., Gramineae and sedges), and fertilizer addition.

Many major river systems in Asia, such as the Yangtze, Yarlung Zangbo, Indus, Ganges and Salween originate in the Tibetan mountains and snow cover in Tibet provides substantial water resources for these rivers, in addition to its weather-related and climatic significance. The high mountain terrain of Tibet is the main condition that snow cover exists and persists at mid–low altitudes. However, the relationships between snow cover and topographic factors of the plateau have not been fully addressed. In this study, the overall spatial distribution of snow cover and the impacts of topography (elevation, aspect and slope) on snow cover distribution in Tibet were analyzed based on the MODIS snow cover product and digital elevation model (DEM) using GIS spatial analysis techniques. The results showed that (1) snow cover in Tibet is spatially very uneven and is characterized by rich snow and high SCF (snow cover frequency) on Nyainqentanglha mountain and the surrounding high mountains, with less snow and a low SCF in the southern Tibetan valley and central part of northern Tibet. (2) Snow cover in Tibet has a strong elevation dependence and a higher SCF corresponds well with high mountain ranges. The mean SCF below 2000 m above sea level (m a.s.l) was less than 4%, while above 6000 m a.s.l, it reached 75%. (3) Intra-annual snow cover distribution below 4000 m a.s.l was characterized by unimodal patterns, while above 4000 m a.s.l, it was characterized by bimodal patterns. The lowest SCF below 6000 m a.s.l occurred in summer, while above 6000 m it occurred in winter. (4) The mountain slope and aspect affect snow cover distribution through changing radiation and energy balances in the mountain regions. The mean SCF generally increased with mountain slopes, with the highest on the north-facing aspect and the lowest on the south-facing aspect.

Snow cover plays a critical role in global energy and water cycles. Snow cover on the Tibetan Plateau (TP) provides vital water sources in western China and Himalayan regions, in addition to its weather and climate significance. The massive high mountain topography of the TP is the main condition for the presence and persistence of snow cover on the plateau at the mid-low latitudes of the Northern Hemisphere (NH). However, how the mountain topography controls snow-cover distribution on the TP remains largely unclear, and the relationship is not well quantified. Here, the spatial distribution and the topographic controls of snow cover on the TP are examined based on snow cover frequency (SCF) derived from MODIS snow cover product (MOD10A2 v005) and digital elevation model (DEM) data. The results show that snow cover on the TP is spatially unevenly distributed, and that it is characterized by rich snow and high SCF on the interior and the surrounding high mountain ranges, with less snow and low SCF in inland basins and river valleys. Snow cover on the TP presents elevation dependence: the higher the altitude, the higher the SCF, the longer the snow cover duration, and the more stable the intra-annual variation. The annual mean SCF below 3000 m above sea level (m a.s.l) is less than 4%, and it reaches 77% above 6000 m a.s.l. The intra-annual snow cover variation below 4000 m a.s.l features a unimodal distribution, while above 4000 m a.s.l it presents a bimodal distribution. The mean minimum SCF below 6000 m a.s.l occurs in summer, while above 6000 m a.s.l it occurs in winter. Due to differences in solar radiation and moisture condition caused by the mountain slope and aspect, the mean SCF generally increases with mountain slopes, and it is the highest on the north-facing aspect and the lowest on the south-facing aspect.

Background: Clinical diagnosis of frontotemporal dementia (FTD) remains a challenge due to the overlap of symptoms among FTD subtypes and with other psychiatric disorders. Perfusion imaging by arterial spin labeling (ASL) is a promising non-invasive alternative to established PET techniques; however, its sensitivity to imaging parameters can hinder its ability to detect perfusion abnormalities. Purpose: This study evaluated the similarity of regional hypoperfusion patterns detected by ASL relative to the gold standard for imaging perfusion, PET with radiolabeled water (15O-water). Methods and materials: Perfusion by single-delay pseudo continuous ASL (SD-pCASL), free-lunch Hadamard encoded pCASL (FL_TE-pCASL), and 15O-water data were acquired on a hybrid PET/MR scanner in 13 controls and 9 FTD patients. Cerebral blood flow (CBF) by 15O-water was quantified by a non-invasive approach (PMRFlow). Regional hypoperfusion was determined by comparing individual patients to the control group. This was performed using absolute (aCBF) and CBF normalized to whole-brain perfusion (rCBF). Agreement was assessed based on the fraction of overlapping voxels. Sensitivity and specificity of pCASL was estimated using hypoperfused regions of interest identified by 15O-water. Results: Region of interest (ROI) based perfusion measured by 15O-water strongly correlated with SD-pCASL (R = 0.85 ± 0.1) and FL_TE-pCASL (R = 0.81 ± 0.14). Good agreement in terms of regional hypoperfusion patterns was found between 15O-water and SD-pCASL (sensitivity = 70%, specificity = 78%) and between 15O-water and FL_TE-pCASL (sensitivity = 71%, specificity = 73%). However, SD-pCASL showed greater overlap (43.4 ± 21.3%) with 15O-water than FL_TE-pCASL (29.9 ± 21.3%). Although aCBF and rCBF showed no significant differences regarding spatial overlap and metrics of agreement with 15O-water, rCBF showed considerable variability across subtypes, indicating that care must be taken when selecting a reference region. Conclusions: This study demonstrates the potential of pCASL for assessing regional hypoperfusion related to FTD and supports its use as a cost-effective alternative to PET.

Background: Advances in the understanding of the pathophysiology of frontotemporal dementia (FTD) and related disorders, along with the development of novel candidate disease modifying treatments, have stimulated the need for tools to assess the efficacy of new therapies. While perfusion imaging by arterial spin labeling (ASL) is an attractive approach for longitudinal imaging biomarkers of neurodegeneration, sources of variability between sessions including arterial transit times (ATT) and fluctuations in resting perfusion can reduce its sensitivity. Establishing the magnitude of perfusion changes that can be reliably detected is necessary to delineate longitudinal perfusion changes related to disease processes from the effects of these sources of error. Purpose: To assess the feasibility of ASL for longitudinal monitoring of patients with FTD by quantifying between-session variability of perfusion on a voxel-by-voxel basis. Methods and materials: ASL data were collected in 13 healthy controls and 8 patients with FTD or progressive supra-nuclear palsy. Variability in cerebral blood flow (CBF) by single delay pseudo-continuous ASL (SD-pCASL) acquired one month apart were quantified by the coefficient of variation (CV) and intraclass correlation coefficient (ICC). Additionally, CBF by SD-pCASL and ATT by low-resolution multiple inversion time ASL (LowRes-pCASL) were compared to Hadamard encoded sequences which are able to simultaneously measure CBF and ATT with improved time-efficiency. Results: Agreement of grey-matter perfusion between sessions was found for both patients and controls (CV = 10.8% and 8.3% respectively) with good reliability for both groups (ICC > 0.6). Intensity normalization to remove day-to-day fluctuations in resting perfusion reduced the CV by 28%. Less than 5% of voxels had ATTs above the chosen post labelling delay (2 s), indicating that the ATT was not a significant source of error. Hadamard-encoded perfusion imaging yielded systematically higher CBF compared to SD-pCASL, but produced similar transit-time measurements. Power analysis revealed that SD-pCASL has the sensitivity to detect longitudinal changes as low as 10% with as few as 10 patient participants. Conclusion: With the appropriate labeling parameters, SD-pCASL is a promising approach for assessing longitudinal changes in CBF associated with FTD.

Wetlands not only affect the local hydrology and ecosystems, but also regulate the conditions of human-environment. However, the availability of accurate wetland data remains a key challenge in wetland research. This study attempts to address this problem through a novel mapping framework that is based on the Google Earth Engine (GEE), feature optimization, and the random forest (RF) model (GFORF). This framework was built to map high-accuracy wetland data on the headwaters of the Brahmaputra, Ganges, and Indus rivers (HBGIR) in the western Tibetan Plateau (TP). Four time periods were examined: 1990, 2000, 2010, and 2017. Our results showed that the overall accuracy for the acquired wetland data was 82.73%, 83.16%, 82.47%, and 88.14% in 1990, 2000, 2010, and 2017, respectively. Furthermore, the feature optimization results showed that the spectral indices feature was the main contributor to the accuracy of wetland mapping, with the highest value being 26.9%. The seasonal factors, surface reflectance, auxiliary data, and texture contributed 21.8%, 21.6%, 21.5%, and 8.1%, respectively. Combining the seasonal features and auxiliary data of distances to rivers significantly improved the mapping accuracy of the wetlands by approximately 14%, 24%, 11%, and 10% in 1990, 2000, 2010, and 2017, respectively. In addition, our analysis showed that the wetland areas in the HBGIR amounted to 5177.39 km2, accounting for 5.82% of the total area. Over the 30-year observation period, the overall consolidation of the wetlands was characterized by a slight expansionary phase, with an average increase of 0.16% per year from 1990 to 2017. As a result of the improvement in the accuracy of wetland mapping in alpine areas, the change dynamics of wetlands was revealed, which provides justification for implementing ongoing wetland ecological services and protection measures.

Climate change is a global issue. Its impacts are recognized at different scales ranging from global to regional to local. Climate change particularly changes in temperature and precipitation has been observed differently in different ecological regions in Nepal Himalaya. The study area comprises five villages of three ecological regions in the Gandaki River Basin (GRB) of Nepal. Based on the observed climate data of a 30-year period from 1990 to 2020, the changes in temperature and precipitation of each ecological region are analyzed using the Mann–Kendall trend test and Sen’s slope. The temperature trend was found to be increasing at the rate of 0.0254 °C per year (°C/a) between 1990 and 2020 in the Mountain region, by 0.0921 °C/a in the Hill region and 0.0042 °C/a in the Tarai region. The precipitation trend in the Mountain region is decreasing by −13.126 mm per year (mm/a), by −9.3998 mm/a in the Hill region and by −5.0247 mm/a in the Tarai region. Household questionnaire surveys, key informant interviews and focus group discussions were carried out to assess the perception of climate change and its impact. The farmers of the three ecological regions have perceived increasing temperature trends, but perceived variability in precipitation trends. Both snowfall and rainfall have varied. Snowfall has drastically decreased. Drought has increased. Extreme disaster events and impacts from such climate-induced events are experienced by 67.9% of respondents. A major impact of climate change is reported on cultivated crops with damage caused by increased insect and rodent pests. The impact of climate change is varied by ecological region. The comparative study of observed data and household data shows the need for a micro-level study so that a real situation can be captured and would be very much useful for policy formulation to combat climate change at a local scale.

Identifying the driving forces and mechanisms underlying vegetation change is critical to globally adapting to future climate change. However, this topic remains unclear for ecosystems within the Headwater Source Region of Brahmaputra and Indus (HSRBI), southwestern Tibetan Plateau. In this study, vegetation changes, in terms of the Normalized Difference Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS), were detected for breakpoints for the period of 2000–2017 by segment regression analysis. The effects of 14 potential environmental variables on vegetation dynamics were estimated and quantified via random forest modelling. Our results showed that monotonic increases (MI) and abrupt increases (AI) in NDVI accounted for 39.87% and 22.55% of vegetation change, respectively. In contrast, monotonic decreases (MD) and abrupt decreases (AD) only accounted for 19.38% and 7.65% of vegetation change, respectively. Our findings also confirmed that climate change was the most crucial driver of vegetation change in the HSRBI, and human disturbance was responsible for abrupt changes in vegetation. The primary MI pattern was affected by distance to the replenishment water source, which contributed to 21.36% of vegetation change. The distance to lakes was an essential factor associated with vegetation greening. AI was mainly affected by the interaction between human disturbance and climate change, which contributed to 27.12% and 26.89% of vegetation changes, respectively. Distance to residential areas was the most significant factor that linked the human disturbance to abrupt vegetation changes. AD and MD were inclined to be affected by the variation in climate variables. Finally, this study will help to identify multidimensional mechanisms of vegetation change and aid ecosystem stability in the headwater zones.

The photoelectrochemical properties of three metallofullerene-[12]cycloparaphenylene ([12]CPP) supramolecular complexes of Sc3N@C78⊂[12]CPP, Sc3N@C80⊂[12]CPP, and Sc2C2@C82⊂[12]CPP were studied. It was revealed that the photocurrent responses of these supramolecular complexes show enhancement compared with those of pristine metallofullerenes, indicating the efficient photocurrent generation and promoted charge carrier transport caused by the supramolecular interaction. The results show that Sc2C2@C82 and Sc2C2@C82⊂[12]CPP have the strongest photocurrents. Then, by comparing the photocurrent intensities of the metallofullerene-biphenyl derivates mixture and the metallofullerene⊂[12]CPP complexes, it was demonstrated that the host–guest interaction is the key factor promoting photocurrent enhancement. At the same time, by observing the microscopic morphologies of pristine fullerenes and supramolecular complexes, it was found that the construction of supramolecules helps to improve the morphology of metallofullerenes on FTO glass. Additionally, their electrical conductivity in optoelectronic devices was tested, respectively, indicating that the construction of supramolecules facilitates the transport of charge carriers. This work discloses the potential application of metallofullerene supramolecular complexes as photodetector and photoelectronic materials.

Land-cover change is a major cause of global ecosystem degradation, a severe threat to sustainable development and human welfare. In mountainous regions that cross national political boundaries, sensitive and fragile ecosystems are under complex disturbance pressures. Land-cover change may further exacerbate ecological risks in these regions. However, few studies have assessed the ecological risks in transboundary areas. This study focused on the Gandaki Basin (GRB), a typical transboundary region in the Himalayas. Based on the dynamic change in land cover, the landscape ecological risk index (ERI) model was constructed to assess the ecological risk in the GRB, revealing the evolution characteristics and spatial correlation of such a risk during the period 1990–2020. The results showed that all land cover types in the GRB have changed over the last 30 years. The interconversion of cropland and forestland was a distinctive feature in all periods. Overall, the medium and medium to low ecological risk level areas account for approximately 65% of the study area. The areas of high ecological risk were mainly distributed in the high elevation mountains of the northern Himalayas, while the low risk areas were located in the other mountains and hills of Nepal. In addition, the ecological risk in the Gandaki basin has shown a fluctuating trend of increasing over the past 30 years. However, there were different phases, with the order of ecological risk being 2020 > 2000 > 2010 > 1990. Ecological risks displayed positive spatial correlation and aggregation characteristics across periods. The high–high risk clusters were primarily located in the high and medium high ecological risk areas, while the low–low risk clusters were similar to low risk levels region. The findings provided the reference for ecosystem conservation and landscape management in transboundary areas.

The Mt. Qomolangma (Everest) National Nature Preserve (QNNP) is among the highest natural reserves in the world. Monitoring the spatiotemporal changes in the vegetation in this complex vertical ecosystem can provide references for decision makers to formulate and adapt strategies. Vegetation growth in the reserve and the factors driving it remains unclear, especially in the last decade. This study uses the normalized difference vegetation index (NDVI) in a linear regression model and the Breaks for Additive Seasonal and Trend (BFAST) algorithm to detect the spatiotemporal patterns of the variations in vegetation in the reserve since 2000. To identify the factors driving the variations in the NDVI, the partial correlation coefficient and multiple linear regression were used to quantify the impact of climatic factors, and the effects of time lag and time accumulation were also considered. We then calculated the NDVI variations in different zones of the reserve to examine the impact of conservation on the vegetation. The results show that in the past 19 years, the NDVI in the QNNP has exhibited a greening trend (slope = 0.0008/yr, p < 0.05), where the points reflecting the transition from browning to greening (17.61%) had a much higher ratio than those reflecting the transition from greening to browning (1.72%). Shift points were detected in 2010, following which the NDVI tendencies of all the vegetation types and the entire preserve increased. Considering the effects of time lag and time accumulation, climatic factors can explain 44.04% of the variation in vegetation. No climatic variable recorded a change around 2010. Considering the human impact, we found that vegetation in the core zone and the buffer zone had generally grown better than the vegetation in the test zone in terms of the tendency of growth, the rate of change, and the proportions of different types of variations and shifts. A policy-induced reduction in livestock after 2010 might explain the changes in vegetation in the QNNP.

In the original article [...]

Land use and land cover (LULC) changes are regarded as one of the key drivers of ecosystem services degradation, especially in mountain regions where they may provide various ecosystem services to local livelihoods and surrounding areas. Additionally, ecosystems and habitats extend across political boundaries, causing more difficulties for ecosystem conservation. LULC in the Kailash Sacred Landscape (KSL) has undergone obvious changes over the past four decades; however, the spatiotemporal changes of the LULC across the whole of the KSL are still unclear, as well as the effects of LULC changes on ecosystem service values (ESVs). Thus, in this study we analyzed LULC changes across the whole of the KSL between 2000 and 2015 using Google Earth Engine (GEE) and quantified their impacts on ESVs. The greatest loss in LULC was found in forest cover, which decreased from 5443.20 km2 in 2000 to 5003.37 km2 in 2015 and which mainly occurred in KSL-Nepal. Meanwhile, the largest growth was observed in grassland (increased by 548.46 km2), followed by cropland (increased by 346.90 km2), both of which mainly occurred in KSL-Nepal. Further analysis showed that the expansions of cropland were the major drivers of the forest cover change in the KSL. Furthermore, the conversion of cropland to shrub land indicated that farmland abandonment existed in the KSL during the study period. The observed forest degradation directly influenced the ESV changes in the KSL. The total ESVs in the KSL decreased from 36.53 × 108 USD y−1 in 2000 to 35.35 × 108 USD y−1 in 2015. Meanwhile, the ESVs of the forestry areas decreased by 1.34 × 108 USD y−1. This shows that the decrease of ESVs in forestry was the primary cause to the loss of total ESVs and also of the high elasticity. Our findings show that even small changes to the LULC, especially in forestry areas, are noteworthy as they could induce a strong ESV response.

Advanced developments have been achieved in urban human population estimation, however, there is still a considerable research gap for the mapping of remote rural populations. In this study, based on demographic data at the town-level, multi-temporal high-resolution remote sensing data, and local population-sensitive point-of-interest (POI) data, we tailored a random forest-based dasymetric approach to map population distribution on the Qinghai–Tibet Plateau (QTP) for 2000, 2010, and 2016 with a spatial resolution of 1000 m. We then analyzed the temporal and spatial change of this distribution. The results showed that the QTP has a sparse population distribution overall; in large areas of the northern QTP, the population density is zero, accounting for about 14% of the total area of the QTP. About half of the QTP showed a rapid increase in population density between 2000 and 2016, mainly located in the eastern and southern parts of Qinghai Province and the central-eastern parts of the Tibet Autonomous Region. Regarding the relative importance of variables in explaining population density, the variables “Distance to Temples” is the most important, followed by “Density of Villages” and “Elevation”. Furthermore, our new products exhibited higher accuracy compared with five recently released gridded population density datasets, namely WorldPop, Gridded Population of the World version 4, and three national gridded population datasets for China. Both the root-mean-square error (RMSE) and mean absolute error (MAE) for our products were about half of those of the compared products except for WorldPop. This study provides a reference for using fine-scale demographic count and local population-sensitive POIs to model changing population distribution in remote rural areas.

Magnetic resonance imaging (MRI) is a non-invasive imaging modality used in longitudinal cell tracking. Previous studies suggest that MagA, a putative iron transport protein from magnetotactic bacteria, is a useful gene-based magnetic resonance contrast agent. Hemagglutinin-tagged MagA was stably expressed in undifferentiated embryonic mouse teratocarcinoma, multipotent P19 cells to provide a suitable model for tracking these cells during differentiation. Western blot and immunocytochemistry confirmed the expression and membrane localization of MagA in P19 cells. Surprisingly, elemental iron analysis using inductively-coupled plasma mass spectrometry revealed significant iron uptake in both parental and MagA-expressing P19 cells, cultured in the presence of iron-supplemented medium. Withdrawal of this extracellular iron supplement revealed unexpected iron export activity in P19 cells, which MagA expression attenuated. The influence of iron supplementation on parental and MagA-expressing cells was not reflected by longitudinal relaxation rates. Measurement of transverse relaxation rates (R2* and R2) reflected changes in total cellular iron content but did not clearly distinguish MagA-expressing cells from the parental cell type, despite significant differences in the uptake and retention of total cellular iron. Unlike other cell types, the reversible component R2' (R2* ‒ R2) provided only a moderately strong correlation to amount of cellular iron, normalized to amount of protein. This is the first report to characterize MagA expression in a previously unrecognized iron exporting cell type. The interplay between contrast gene expression and systemic iron metabolism substantiates the potential for diverting cellular iron toward the formation of a novel iron compartment, however rudimentary when using a single magnetotactic bacterial gene expression system like magA. Since relatively few mammalian cells export iron, the P19 cell line provides a tractable model of ferroportin activity, suitable for magnetic resonance analysis of key iron-handling activities and their influence on gene-based MRI contrast.

Drought and vegetation conditions within the Damqu River Basin, part of the Yangtze River Source Region (YRSR), are assessed here using the standardized precipitation index (SPI), the standardized precipitation evapotranspiration index (SPEI), the normalized difference vegetation index (NDVI), and the leaf area index (LAI). We utilized Sen's method, least squares regression method, linear regression and Pearson's correlation analysis to study variations in drought and vegetation indices and the drought effect on vegetation between 1988 and 2015. Results reveal that droughts occurred at a 25% frequency over this period; SPI and SPEI analyses show that 1994, 1999, 2005, and 2010 were change points and that the basin was characterized by varying drought and humidity trends. Subsequent to 2010, both SPI and SPEI decreased within the basin, while 1995, 2000, 2004, and 2010 were change points for NDVI and LAI while the watershed exhibited variable trends in vegetation reduction and increase. The NDVI-annual values of 63.36% regions and the LAI-summer values of 68.39% areas within the basin were decreased during 1988-2015 and 2000-2015, respectively. Subsequent to 2010, both NDVI and LAI decreased within the basin and significant positive correlations at inter-annual and inter-summer time scales were seen in both drought and vegetation indices; drought has exerted a lag effect on vegetation as shown by significant positive correlations between annual SPI/SPEI values and following year NDVI/LAI values.

Phylogeographical studies have suggested that several plant species on the Tibetan Plateau (TP) underwent recolonization during the Quaternary and may have had distinct range dynamics in response to the last glacial. To further test this hypothesis and locate the possible historical dispersal routes, we selected 20 plant species from different parts of the TP and modeled their geographical distributions over four time periods using species distribution models (SDMs). Furthermore, we applied the least-cost path method together with SDMs and shared haplotypes to estimate their historical dispersal corridors. We identified three general scenarios of species distribution change during the late Quaternary: the 'contraction-expansion' scenario for species in the northeastern TP, the 'expansion-contraction' scenario for species in the southeast and the 'stable' scenario for widespread species. During the Quaternary, we identified that these species were likely to recolonize along the low-elevation valleys, huge mountain ranges and flat plateau platform (e.g. the Yarlung Zangbo Valley and the Himalaya). We inferred that Quaternary cyclic glaciations along with the various topographic and climatic conditions of the TP could have resulted in the diverse patterns of range shift and dispersal of Tibetan plant species. Finally, we believe that this study would provide valuable insights for the conservation of alpine species under future climate change.

Alpine vegetation on the Tibetan Plateau (TP) is known to be sensitive to both climate change and anthropogenic disturbance. However, the magnitude and patterns of alpine vegetation dynamics and the driving mechanisms behind their variation on the TP remains under debate. In this study, we used updated MODIS Collection 6 Normalized Difference Vegetation Index (NDVI) from the Terra satellite combined with linear regression and the Break for Additive Season and Trend model to reanalyze the spatiotemporal patterns of vegetation change on the TP during 2000–2015. We then quantified the responses of vegetation variation to climatic and anthropogenic factors by coupling climatic and human footprint datasets. Results show that growing season NDVI (GNDVI) values increased significantly overall (0.0011 year−1, p < 0.01) during 2000–2015 and that 70.37% of vegetated area on the TP (23.47% significantly with p < 0.05) exhibited greening trends with the exception of the southwest TP. However, vegetation greenness experienced trend shifts from greening to browning in half of the ecosystem zones occurred around 2010, likely induced by spatially heterogeneous temporal trends of climate variables. The vegetation changes in the northeastern and southwestern TP were water limited, the mid-eastern TP exhibited strong temperature responses, and the south of TP was driven by a combination of temperature and solar radiation. Furthermore, we found that, to some extent, anthropogenic disturbances offset climate-driven vegetation greening and aggravated vegetation browning induced by water deficit. These findings suggest that the impact of anthropogenic activities on vegetation change might not overwhelm that of climate change at the region scale.

Understanding farmland changes and their mechanisms is important for food security and sustainable development. This study assesses the farmland changes and their drivers within the Tumen River of China and the Tuman River within the Democratic People’s Republic of Korea (DPR Korea) from 1991 to 2016 (1991–2000, 2000–2010, and 2010–2016). Farmland surfaces in Tumen/Tuman River Basin (TRB) for each of the years were mapped from satellite imagery using an object-based image segmentation and a support vector machine (SVM) approach. A logistic regression was applied to discern the mechanisms underlying farmland changes. Results indicate that cultivated surfaces changes within the two regions were characterized by large differences during the three time periods. The decreases of cultivated surface of −15.55 km2 (i.e., 0.55% of total cultivated surface area in 2000) and −23.61 km2 (i.e., 0.83% of total cultivated surface area in 2016) occurred in China between 1991 and 2000 and between 2010 and 2016, respectively; while an increase of 30.98 km2 (i.e., 1.09% of total cultivated surface area in 2010) was seen between 2000 and 2010. Cultivated surfaces increased within DPR Korea side over the three time periods; a marked increase, in particular, was seen between 1991 and 2000 by 443.93 km2 (i.e., 23.43% of total cultivated surface area in 2000), while farmland increased by 140.87 km2 (i.e., 6.92% of total cultivated surface area in 2010) and 180.86 km2 (i.e., 1.78% of total cultivated surface area in 2016), respectively, between 2000 and 2010 and between 2010 and 2016. We also found that expansions and contractions in farmland within both regions of the TRB were mainly influenced by topographic, soil, climatic, and distance factors, which had different importance degrees. Among these significant forces, the temperatures in the two regions were paramount positive factors on farmland changes during 1991–2016 and slope in China and precipitation in DPR Korea were the paramount negative factors affecting farmland changes, respectively. Additionally, except for between 2000 and 2010 in DPR Korea TRB region, most of the factors significantly influencing the farmland changes revealed the same positive or negative effects in different periods, because of mountainous topography. This study allows enhancing understanding of the mechanisms underlying farmland changes in the TRB.

Himalayan glacier changes in the context of global climate change have attracted worldwide attention due to their profound cryo-hydrological ramifications. However, an integrated understanding of the debris-free and debris-covered glacier evolution and its interaction with glacial lake is still lacking. Using one case study in the Gyirong River Basin located in the central Himalayas, this paper applied archival Landsat imagery and an automated mapping method to understand how glaciers and glacial lakes interactively evolved between 1988 and 2015. Our analyses identified 467 glaciers in 1988, containing 435 debris-free and 32 debris-covered glaciers, with a total area of 614.09 ± 36.69 km2. These glaciers decreased by 16.45% in area from 1988 to 2015, with an accelerated retreat rate after 1994. Debris-free glaciers retreated faster than debris-covered glaciers. As a result of glacial downwasting, supraglacial debris coverage expanded upward by 17.79 km2 (24.44%). Concurrent with glacial retreat, glacial lakes increased in both number (+41) and area (+54.11%). Glacier-connected lakes likely accelerated the glacial retreat via thermal energy transmission and contributed to over 15% of the area loss in their connected glaciers. On the other hand, significant glacial retreats led to disconnections from their proglacial lakes, which appeared to stabilize the lake areas. Continuous expansions in the lakes connected with debris-covered glaciers, therefore, need additional attention due to their potential outbursts. In comparison with precipitation variation, temperature increase was the primary driver of such glacier and glacial lake changes. In addition, debris coverage, size, altitude, and connectivity with glacial lakes also affected the degree of glacial changes and resulted in the spatial heterogeneity of glacial wastage across the Gyirong River Basin.

Tissue engineering has brought new possibilities for the treatment of spinal cord injury. Two important components for tissue engineering of the spinal cord include a suitable cell source and scaffold. In our study, we investigated induced mouse embryonic fibroblasts (MEFs) directly reprogrammed into neural stem cells (iNSCs), as a cell source. Three-dimensional (3D) electrospun poly (lactide-co-glycolide)/polyethylene glycol (PLGA-PEG) nanofiber scaffolds were used for iNSCs adhesion and growth. Cell growth, survival and proliferation on the scaffolds were investigated. Scanning electron microscopy (SEM) and nuclei staining were used to assess cell growth on the scaffolds. Scaffolds with iNSCs were then transplanted into transected rat spinal cords. Two or 8 weeks following transplantation, immunofluorescence was performed to determine iNSC survival and differentiation within the scaffolds. Functional recovery was assessed using the Basso, Beattie, Bresnahan (BBB) Scale. Results indicated that iNSCs showed similar morphological features with wild-type neural stem cells (wt-NSCs), and expressed a variety of neural stem cell marker genes. Furthermore, iNSCs were shown to survive, with the ability to self-renew and undergo neural differentiation into neurons and glial cells within the 3D scaffolds in vivo. The iNSC-seeded scaffolds restored the continuity of the spinal cord and reduced cavity formation. Additionally, iNSC-seeded scaffolds contributed to functional recovery of the spinal cord. Therefore, PLGA-PEG scaffolds seeded with iNSCs may serve as promising supporting transplants for repairing spinal cord injury (SCI).

The regulation of gene expression through histone posttranslational modifications plays a crucial role in breast cancer progression. However, the molecular mechanisms underlying the contribution of histone modification to tumor initiation remain unclear. To gain a deeper understanding of the role of the histone modifier Enhancer of Zeste homology 2 (Ezh2) in the early stages of mammary tumor progression, we employed an inducible mammary organoid system bearing conditional Ezh2 alleles that faithfully recapitulates key events of luminal B breast cancer initiation. We showed that the loss of Ezh2 severely impairs oncogene-induced organoid growth, with Ezh2-deficient organoids maintaining a polarized epithelial phenotype. Transcriptomic profiling showed that Ezh2-deficient mammary epithelial cells up-regulated the expression of negative regulators of Wnt signaling and down-regulated genes involved in mTORC1 (mechanistic target of rapamycin complex 1) signaling. We identified Sfrp1, a Wnt signaling suppressor, as an Ezh2 target gene that is derepressed and expressed in Ezh2-deficient epithelium. Furthermore, an analysis of breast cancer data revealed that Sfrp1 expression was associated with favorable clinical outcomes in luminal B breast cancer patients. Finally, we confirmed that targeting Ezh2 impairs mTORC1 activity through an indirect mechanism that up-regulates the expression of the tumor suppressor Pten. These findings indicate that Ezh2 integrates the mTORC1 and Wnt signaling pathways during early mammary tumor progression, arguing that inhibiting Ezh2 or therapeutically targeting Ezh2-dependent programs could be beneficial for the treatment of early-stage luminal B breast cancer. [ABSTRACT FROM AUTHOR]