Median Slope Research Articles

Historical snow measurements in the central and southern Apennine Mountains: climatology, variability, and trend

Abstract. This work presents an analysis of historical snow precipitation data collected in the period 1951–2001 in central and southern Apennines (Italy), an area scarcely investigated so far. To pursue this aim, we used the monthly observations of the snow cover duration, number of days with snowfall and total height of new snow collected at 129 stations located between 288 and 1750 m above sea level. Such data have been manually digitised from the Hydrological Yearbooks of the Italian National Hydrological and Mareographic Service. The available dataset has been primarily analysed to build a reference climatology (related to the 1971–2000 period) for the considered Apennine region. More specifically, using a methodology based on principal component analysis and k-means clustering, we have identified different modes of spatial variability, mainly depending on the elevation, which reflect different climatic zones. Subsequently, focusing on the number of days with snowfall and snow cover duration on the ground, we have carried out a linear trend analysis, employing the Theil–Sen estimator and the Mann–Kendall test. An overall negative tendency has been found for both variables. For clusters including only stations above 1000 m above the sea level, a significant (at 90 % or 95 % confidence levels) decreasing trend has been found in the winter season (i.e. from December to February), with −3.2 [−6.0 to 0.0] d per 10 years for snow cover duration and −1.6 [−2.5 to −0.6] d per 10 years for number of days with snowfall. Moreover, in all considered seasons, a clear and direct relationship between the trend magnitude and elevation has emerged. In addition, using a cross-wavelet analysis, we found a close in-phase linkage on a decadal timescale between the investigated snow indicators and the Eastern Mediterranean teleconnection Pattern. For both snow cover duration and number of days with snowfall, such connection appears to be more relevant in the full (i.e. from November to April) and in the late (i.e. from February to April) seasons.

Automated vision-based bolt loosening detection and localization for operating hydraulic turbine rotors

Turbine rotors are susceptible to bolt loosening and detachment due to factors such as vibration, stress concentration, and material degradation during the prolonged operation of hydropower units. These issues can result in significant safety incidents and economic losses. This article presents a novel vision-based automatic detection method for identifying loose bolts in turbine rotors by analyzing the angle between marking lines on the top surfaces of studs and nuts. The proposed method integrates enhanced you only look once version 8 (YOLOv8) for object detection, DeepSORT for multitarget tracking, DeepLabV3+ for image segmentation, and Theil-Sen estimation for line fitting. This approach effectively locates loose bolts on the rotor and determines the loosening angle. Experiments were conducted during the commissioning of a large hydroelectric unit, yielding results that demonstrate an average loosening angle calculation error 0.633°. With a loosening threshold established at 2°, the method achieves a detection accuracy of 98.88%. The proposed method does not depend on the historical states of the bolts and provides precise identification of bolt loosening angle up to 180°, thereby making it suitable for application to operating turbine rotors.

The Impact of Tibial Slope on Anterior Cruciate Ligament Reconstruction Outcomes – University Hospital Study

Anterior cruciate ligament (ACL) injuries are common knee injuries that often require surgical reconstruction to restore knee function and stability. Despite advances in surgical techniques, the failure rate for ACL reconstruction remains approximately 15%, emphasizing the need to understand factors contributing to suboptimal outcomes. One factor that has garnered attention is the tibial slope, particularly the posterior tibial slope (PTS), which may influence ACL injury and graft failure. Biomechanical studies suggest that an increased PTS can elevate anterior tibial translation, potentially increasing stress on the ACL and raising the risk of both primary ACL injury and graft failure. However, the clinical significance of tibial slope in ACL reconstruction outcomes remains debated. This retrospective study investigates whether an increased PTS is associated with a higher likelihood of revision surgery or poor surgical outcomes. A total of 152 patients who underwent primary ACL reconstruction or revision surgery between 2018 and 2022 with at least two years of follow-up were included. Tibial slope measurements were obtained from lateral tibial radiographs, and patients were categorized into three groups based on post-surgical outcomes: Group A (successful return to prior sport levels), Group B (stable knee without return to sport), and Group C (requiring revision surgery). The Mann-Whitney U test was used to compare tibial slope measurements across groups. No significant differences were found in tibial slope between the groups (p > 0.05). Group A had a median tibial slope of 6.08°, Group B had 6.68°, and Group C had 5.85°. These findings suggest that tibial slope may not be a major factor influencing ACL reconstruction success or failure. Other factors, including surgical technique, rehabilitation, and patient-specific characteristics, may play more significant roles. Further research is needed to explore the relationship between tibial slope and ACL ...

Monitoring Spatial-Temporal Variability of Vegetation Coverage and Its Influencing Factors in the Yellow River Source Region from 2000 to 2020

As a vital conservation area for water sources in the Yellow River Basin, understanding the spatial-temporal dynamics of vegetation coverage is crucial, along with the factors that affect it, to ensure ecological preservation and sustainable development of the Yellow River Source Region (YRSR). In this paper, we utilized Landsat surface reflectance data from 2000 to 2020 using de-clouding and masking methods implementing the Google Earth Engine (GEE) cloud platform. We investigated spatial-temporal changes in vegetation coverage by combining the maximum value composite (MVC), the dimidiate pixel model (DPM), the Theil–Sen median slope, and the Mann–Kendall test. The influencing factors on vegetation coverage were quantitatively analyzed using a geographic detector, and future tendencies in vegetation coverage were predicted utilizing the Future Land Use Simulation (FLUS) model. The outcomes suggested the following: (1) On the temporal scale, vegetation coverage exhibited a general upward trend between 2000 and 2020, with the YRSR showing a yearly growth rate of 0.23% (p < 0.001). In comparison to 2000, the area designated as having extremely high vegetation coverage increased by 19.3% in 2020. (2) Spatially, the central and southeast regions have higher values of vegetation coverage, whereas the northwest has lower values. In the study area, 75.5% of the region demonstrated a significant improvement trend, primarily in Xinghai County, Zeku County, and Dari County in the south and the northern portion of the YRSR; conversely, a notable tendency of degradation was identified in 11.8% of the area, mostly in the southeastern areas of Qumalai County, Chenduo County, Shiqu County, and scattered areas in the southeastern region. (3) With an explanatory power of exceeding 45%, the three influencing factors that had the biggest effects on vegetation coverage were mean annual temperature, elevation, and mean annual precipitation. Mean annual precipitation has been shown to have a major impact on vegetation covering; the interconnections involving these factors have increased the explanatory power of vegetation coverage’s regional distribution. (4) Predictions for 2030 show that the vegetation coverage is trending upward in the YRSR, with a notable recovery trend in the northwestern region. This study supplies a theoretical foundation to formulate strategies to promote sustainable development and ecological environmental preservation in the YRSR.

Response of Carbon and Water Use Efficiency to Climate Change and Human Activities in Central Asia

Carbon use efficiency (CUE) and water use efficiency (WUE) are key metrics for quantifying the coupling between terrestrial ecosystem carbon and water cycles. The impacts of intensifying climate change and human activities on carbon and water fluxes in Central Asian vegetation remain unclear. In this study, the CUE and WUE in Central Asia from 2001 to 2022 were accurately estimated with the help of the Google Earth Engine (GEE) data platform; the Theil–Sen median slope estimation combined with the Manna–Kendall significance test and partial derivative analysis were used to investigate the CUE and WUE trends and their responses to climate change and human activities. CUE and WUE show overall declining trends with significant spatial variability. Among meteorological factors, vapor pressure deficit and temperature show the strongest correlation with CUE, while precipitation and temperature are most correlated with WUE. Compared to human activities, climate change has a greater impact on CUE and WUE, mainly exerting a negative influence. Human activities are the main drivers in regions with developed agriculture, such as oases, farmlands, and areas near rivers and lakes. This study provides scientific references for the optimization of water and soil resources and the integrated regional environmental management in Central Asia.

Excitation of the bottom-up pathways has no effect on remote muscle fatigue in healthy participants.

The aim of this study is to investigate the effect of induced excitation of the bottom-up pathways at the lateral elbow muscles on local muscle fatigue in the neck region in healthy participants. Eligible participants (n:55) were randomly allocated to an intervention group (n:28) or a control group (n:27). The fatigue of bilateral neck flexor(sternocleidomastoid) and extensor (upper trapezius) muscles was evaluated using surface electromyography, at baseline and immediately post-intervention during a neck flexor and extensor endurance test respectively. Excitation of the bottom-up pathways was performed at multiple lateral elbow muscles in the intervention group by using a temporal summation protocol of mechanical pain, and the pressure pain threshold was determined once in each of the multiple lateral elbow muscles in the control group. Linear mixed model analyses were performed for each outcome measure to evaluate changes over time and within- and between-group differences. No significant "group X time" interaction effects were detected for any of the outcome measures. Significant main effects for time was found for "amplitude over time" of the left upper trapezius (p:0.003) and right sternocleidomastoid muscle (p: 0.013), and for "amplitude changes" of the left upper trapezius muscle (p:0.021). Significant within-group changes were identified in some outcomes in the control group: increased "amplitude over time" of the right sternocleidomastoid muscle (p:0.024) and decreased "amplitude changes" of the left upper trapezius muscle (p:0.024), decreased "normalized median frequency slope over time" of the left UT (p: 0.013). There were no significant within-group changes in the intervention group. No significant between-group differences for any of the outcome measures were found. This study shows no effect of the induction of excitation of the bottom-up pathways at the lateral elbow muscles on the neck muscles' fatigue characteristics compared to a control intervention. Clinical Trial Number: NCT05146960. Date of Registration: December 7, 2021.

Effect of electrode size and distance to tissue on unipolar and bipolar voltage electrograms and their implications for a near-field cutoff

Characteristics of electrograms depend on the electrode design and distance to the electric source. Our aim was to assess the impact of electrode design and distance from the myocardial electric source on the unipolar and bipolar electrograms to deduce a far-field cut-off. We retrospectively analyzed left atrial electroanatomical maps of 25 patients acquired using an ablation catheter with a 4.5 mm tip-, mini- and 2 mm ring electrodes. The unipolar and bipolar electrograms were characterized based on peak-to-peak amplitude, signal duration, maximal slope, and relative power of the high frequency spectrum above 50 Hz (HF_rel). The unipolar electrograms of ring electrodes showed an increased amplitude (140%), slope (150%) and HF_rel (16% vs. 11%) compared to the tip- and mini-electrodes. The median amplitude, slope, and HF_rel for the ring electrodes followed a power-law decay with distance with a steep decline up to 4 mm. This near-field cut-off can be identified based on a HF_rel above 10% in unipolar electrograms. In conclusion, we observed a higher unipolar amplitude for small ring-electrodes compared to larger tip-electrodes. The rapid decay of the amplitude, slope, and HF_rel up to a distance of 4 mm is suggestive for near-field cut-off identified based on HF_rel above 50 Hz.Clinical Trial Registration: NCT04095559.

Vegetation and carbon sink response to water level changes in a seasonal lake wetland.

Water level fluctuations are among the main factors affecting the development of wetland vegetation communities, carbon sinks, and ecological processes. Hongze Lake is a typical seasonal lake wetland in the Huaihe River Basin. Its water levels have experienced substantial fluctuations because of climate change, as well as gate and dam regulations. In this study, long-term cloud-free remote sensing images of water body area, net plant productivity (NPP), gross primary productivity (GPP), and Fractional vegetation cover (FVC) of the wetlands of Hongze Lake were obtained from multiple satellites by Google Earth Engine (GEE) from 2006 to 2023. The trends in FVC were analyzed using a combined Theil-Sen estimator and Mann-Kendall (MK) test. Linear regression was employed to analyze the correlation between the area of water bodies and that of different degrees of FVC. Additionally, annual frequencies of various water levels were constructed to explore their association with GPP, NPP, and FVC.The results showed that water level fluctuations significantly influence the spatial and temporal patterns of wetland vegetation cover and carbon sinks, with a significant correlation (P<0.05) between water levels and vegetation distribution. Following extensive restoration efforts, the carbon sink capacity of the Hongze Lake wetland has increased. However, it is essential to consider the carbon sink capacity in areas with low vegetation cover, for the lakeshore zone with a higher inundation frequency and low vegetation cover had a lower carbon sink capacity. These findings provide a scientific basis for the establishment of carbon sink enhancement initiatives, restoration programs, and policies to improve the ecological value of wetland ecosystem conservation areas.

Robust Trend Analysis in Environmental Remote Sensing: A Case Study of Cork Oak Forest Decline

We introduce a novel methodological framework for robust trend analysis (RTA) using remote sensing data to enhance the accuracy and reliability of detecting significant environmental trends. Our approach sequentially integrates the Theil–Sen (TS) slope estimator, the Contextual Mann–Kendall (CMK) test, and the false discovery rate (FDR) control. This comprehensive method addresses common challenges in trend analysis, such as handling small, noisy datasets with outliers and issues related to spatial autocorrelation, cross-correlation, and multiple testing. We applied this RTA workflow to study tree cover trends in Los Alcornocales Natural Park (Southern Spain), Europe’s largest cork oak forest, analysing interannual changes in tree cover from 2000 to 2022 using Terra MODIS MOD44B data. Our results reveal that the TS estimator provides a robust measure of trend direction and magnitude, but its effectiveness is dramatically enhanced when combined with the CMK test. This combination highlights significant trends and effectively corrects for spatial autocorrelation and cross-correlation, ensuring that genuine environmental signals are distinguished from statistical noise. Unlike previous workflows, our approach incorporates the FDR control, which successfully filtered out 29.6% of false discoveries in the case study, resulting in a more stringent assessment of true environmental trends captured by multi-temporal remotely sensed data. In the case study, we found that approximately one-third of the area exhibits significant and statistically robust declines in tree cover, with these declines being geographically clustered. Importantly, these trends correspond with relevant changes in tree cover, emphasising the ability of RTA to detect relevant environmental changes. Overall, our findings underscore the crucial importance of combining these methods, as their synergy is essential for accurately identifying and confirming robust environmental trends. The proposed RTA framework has significant implications for environmental monitoring, modelling, and management.

A phase III, open-label clinical trial evaluating pegunigalsidase alfa administered every 4 weeks in adults with Fabry disease previously treated with other enzyme replacement therapies.

Pegunigalsidase alfa, a PEGylated α-galactosidase A enzyme replacement therapy (ERT) for Fabry disease, has a longer plasma half-life than other ERTs administered intravenously every 2 weeks (E2W). BRIGHT (NCT03180840) was a phase III, open-label study in adults with Fabry disease, previously treated with agalsidase alfa or beta E2W for ≥3 years, who switched to 2 mg/kg pegunigalsidase alfa every 4 weeks (E4W) for 52 weeks. Primary objective assessed safety, including number of treatment-emergent adverse events (TEAEs). Thirty patients were enrolled (24 males); 23 previously received agalsidase beta. Pegunigalsidase alfa plasma concentrations remained above the lower limit of quantification throughout the 4-week dosing interval. Thirty-three of 182 TEAEs (in 9 patients) were considered treatment-related; all were mild/moderate. No patients developed de novo anti-drug antibodies (ADAs). In the efficacy analysis (n = 29), median (inter-quartile range) eGFR change from baseline over 52 weeks was -1.9 (-5.9; 1.8) mL/min/1.73 m2 (n = 28; males [n = 22]: -2.4 [-5.2; 3.2]; females [n = 6]: -0.7 [-9.2; 2.0]). Overall, median eGFR slope was -1.9 (-8.3; 1.9) mL/min/1.73 m2/year (ADA-negative [n = 20]: -1.2 [-6.4; 2.6]; ADA-positive [n = 9]: -8.4 [-11.6; -1.0]). Lyso-Gb3 concentrations were low and stable in females, with a slight increase in males (9/24 ADA-positive). The BRIGHT study results suggest that 2 mg/kg pegunigalsidase alfa E4W is tolerated well in stable adult patients with Fabry disease. Due to the low number of patients in this study, more research is needed to demonstrate the effects of pegunigalsidase alfa given E4W. Further evidence, outside of this clinical trial, should be factored in for physicians to prolong the biweekly ERT intervals to E4W. TAKE-HOME MESSAGE: Treatment with 2 mg/kg pegunigalsidase alfa every 4 weeks could offer a new treatment option for patients with Fabry disease.

Spatiotemporal variation and GeoDetector analysis of NDVI at the northern foothills of the Yinshan Mountains in Inner Mongolia over the past 40 years

The study of spatiotemporal variation and driving forces of the normalized difference vegetation index (NDVI) is conducive to regional ecosystem protection and natural resource management. Based on the 1982–2022 GIMMS NDVI data and 26 influencing variables, by using the Theil-Sen median slope analysis, Mann-Kendall (M − K) test method and GeoDetector model, we analyzed the spatial and temporal characteristics of vegetation cover and the driving factors of its spatial differentiation in the northern foothills of the Yinshan Mountains in Inner Mongolia. The NDVI showed a significantly increasing trend during 1982–2022, with a growth rate of 0.0091 per decade. It is further predicted that future change in NDVI will continue the 1982–2022 trend, and sustainable improvement will dominate in the future; however, 17.69 % of vegetation will degrade, that is, NDVI will degrade instead of improvement. The spatial distribution of the NDVI in the northern foothills of the study area was generally characterized by high in the east and low in the west. Annual precipitation (Pre), evapotranspiration (Evp), relative humidity (Rhu) and sunshine hours (Ssd) had >70 % explanatory power (73.5, 79.9, 79.0, and 74.9 %, respectively). The explanatory power of edaphic factors was >30 %, whereas anthropogenic and topographic factors had little influence on the spatial variation of NDVI, with an explanatory power of <30 %. Thus, climatic factors were the dominant factors influencing the spatial variability of NDVI in the study area. The results of the interaction detector analysis showed nonlinear strengthening for any two factors, and the interaction between Rhu and barometric pressure had the highest explanatory power. There were optimal ranges or characteristics of each factor that promoted vegetation growth. This study investigated the differences in the explanatory power of different factors on the NDVI and the optimal range of individual factors to promote vegetation growth, which can provide a basis for the development of vegetation resource management programs.

Frequency-of-seeing curves (psychometric functions) for perimetric stimuli in age-related macular degeneration.

Frequency-of-seeing (FoS) curves (psychometric functions) for perimetric stimuli have been widely used in computer simulations of new visual field test procedures. FoS curves for age-related macular degeneration (AMD) are not available in the literature and are needed for the development of improved microperimetry test procedures, which are of particular interest for use as clinical trial endpoints. Data were refitted from a previous study to generate FoS curves for 20 participants with AMD, each tested at nine locations within the central 10°. Stimulus parameters, background luminance and dB scale were matched to the MAIA-2 microperimeter, and stimuli were presented in a method of constant stimuli to build up FoS curves over multiple runs. FoS curves were fitted with a modified cumulative Gaussian function. The relationship between sensitivity and slope of fitted FoS curves was modelled by robust linear regression, producing models both with and without an eccentricity parameter. FoS curves were satisfactorily fitted to data from 174 visual field locations in 20 participants (age 65-83 years, 11 female). Each curve was made up of a median of 243 (range 177-297) stimulus presentations over a median of 12 (range 9-32) levels. Median sensitivity was 25.5 dB (range 3.8-31.4 dB). The median slope (SD of fitted function) was 1.6 dB (range 0.5-8.5 dB). As in previous studies of other conditions, the slope of fitted FoS curves increased as sensitivity decreased (p < 0.001). FoS are provided for participants with AMD, as well as models of the relationship between sensitivity and slope. These fitted models and data may be useful for computer simulation studies of microperimetry procedures. Full details of the fitted curves are provided as supporting information.

Understanding overfitting in random forest for probability estimation: a visualization and simulation study

BackgroundRandom forests have become popular for clinical risk prediction modeling. In a case study on predicting ovarian malignancy, we observed training AUCs close to 1. Although this suggests overfitting, performance was competitive on test data. We aimed to understand the behavior of random forests for probability estimation by (1) visualizing data space in three real-world case studies and (2) a simulation study.MethodsFor the case studies, multinomial risk estimates were visualized using heatmaps in a 2-dimensional subspace. The simulation study included 48 logistic data-generating mechanisms (DGM), varying the predictor distribution, the number of predictors, the correlation between predictors, the true AUC, and the strength of true predictors. For each DGM, 1000 training datasets of size 200 or 4000 with binary outcomes were simulated, and random forest models were trained with minimum node size 2 or 20 using the ranger R package, resulting in 192 scenarios in total. Model performance was evaluated on large test datasets (N = 100,000).ResultsThe visualizations suggested that the model learned “spikes of probability” around events in the training set. A cluster of events created a bigger peak or plateau (signal), isolated events local peaks (noise). In the simulation study, median training AUCs were between 0.97 and 1 unless there were 4 binary predictors or 16 binary predictors with a minimum node size of 20. The median discrimination loss, i.e., the difference between the median test AUC and the true AUC, was 0.025 (range 0.00 to 0.13). Median training AUCs had Spearman correlations of around 0.70 with discrimination loss. Median test AUCs were higher with higher events per variable, higher minimum node size, and binary predictors. Median training calibration slopes were always above 1 and were not correlated with median test slopes across scenarios (Spearman correlation − 0.11). Median test slopes were higher with higher true AUC, higher minimum node size, and higher sample size.ConclusionsRandom forests learn local probability peaks that often yield near perfect training AUCs without strongly affecting AUCs on test data. When the aim is probability estimation, the simulation results go against the common recommendation to use fully grown trees in random forest models.

Temporal trends of seasonal pollen indexes in a region of Northern Italy (2001–2022)

Increasing trends in allergic respiratory diseases may be linked to climate changes affecting pollen levels. This study aimed to investigate the temporal trends in seasonal indexes of 9 allergenic pollen types in the Veneto Region, Northern Italy. Available daily data from 20 monitoring stations covered 2001–2022 for Corylaceae, Cupressaceae, Poaceae, Oleaceae, and Urticaceae (pollen families), and 2006–2022 for Alnus, Betula, Ambrosia, and Artemisia (pollen genera). The 95-percentage method was used to identify the start/end dates of pollen seasons annually, and the seasonal cumulative pollen concentration (Seasonal Pollen Integral, SPIn) was calculated. The non-parametric Theil-Sen median slope method was used to examine trends in pollen seasons’ start day, duration, and SPIn in the whole region and for climatic areas (alpine, subcontinental eastern, subcontinental western). The results showed a clear upward trend in SPIn for several pollens, with median slopes ranging from 326 (Alnus) to 1089 p/m3 per 10yrs (Corylaceae); exceptions were Artemisia, whose SPIn decreased over time (−26 p/m3 per 10yrs), and Ambrosia, Betula, and Urticaceae, which did not show a clear trend. For families, pollen seasons showed a trend for anticipation (from −4.7, Urticaceae, to −13.5 days/10yrs, Oleaceae) and extended duration (from 8.3, Cupressaceae, to 13.3 days/10yrs, Oleaceae). An evident heterogeneity across the climatic areas was seen for the SPIn, with the subcontinental western area exhibiting the highest loads and most prominent increases over time. In conclusion, increased levels and prolonged seasons for several pollen taxa observed in the Veneto Region, particularly in subcontinental areas, suggest increasing risks for individuals with pollen allergies. Addressing this issue requires measures of mitigation and adaptation to climate-driven changes in pollen exposure.

PM2.5 concentrations based on near-surface visibility in the Northern Hemisphere from 1959 to 2022

Abstract. Long-term PM2.5 data are essential for the atmospheric environment, human health, and climate change. PM2.5 measurements are sparsely distributed and of short duration. In this study, daily PM2.5 concentrations are estimated using a machine learning method for the period from 1959 to 2022 in the Northern Hemisphere based on near-surface atmospheric visibility. They are extracted from the Integrated Surface Database (ISD). Daily continuous monitored PM2.5 concentration is set as the target, and near-surface atmospheric visibility and other related variables are used as the inputs. A total of 80 % of the samples of each site are the training set, and 20 % are the testing set. The training result shows that the slope of linear regression with a 95 % confidence interval (CI) between the estimated PM2.5 concentration and the monitored PM2.5 concentration is 0.955 [0.955, 0.955], the coefficient of determination (R2) is 0.95, the root mean square error (RMSE) is 7.2 µg m−3, and the mean absolute error (MAE) is 3.2 µg m−3. The test result shows that the slope within a 95 % CI between the predicted PM2.5 concentration and the monitored PM2.5 concentration is 0.864 [0.863, 0.865], the R2 is 0.79, the RMSE is 14.8 µg m−3, and the MAE is 7.6 µg m−3. Compared with a global PM2.5 concentration dataset derived from a satellite aerosol optical depth product with 1 km resolution, the slopes of linear regression on the daily (monthly) scale are 0.817 (0.854) from 2000 to 2021, 0.758 (0.821) from 2000 to 2010, and 0.867 (0.879) from 2011 to 2022, indicating the accuracy of the model and the consistency of the estimated PM2.5 concentration on the temporal scale. The interannual trends and spatial patterns of PM2.5 concentration on the regional scale from 1959 to 2022 are analyzed using a generalized additive mixed model (GAMM), suitable for situations with an uneven spatial distribution of monitoring sites. The trend is the slope of the Theil–Sen estimator. In Canada, the trend is −0.10 µg m−3 per decade, and the PM2.5 concentration exhibits an east–high to west–low pattern. In the United States, the trend is −0.40 µg m−3 per decade, and PM2.5 concentration decreases significantly after 1992, with a trend of −1.39 µg m−3 per decade. The areas of high PM2.5 concentration are in the east and west, and the areas of low PM2.5 concentration are in the central and northern regions. In Europe, the trend is −1.55 µg m−3 per decade. High-concentration areas are distributed in eastern Europe, and the low-concentration areas are in northern and western Europe. In China, the trend is 2.09 µg m−3 per decade. High- concentration areas are distributed in northern China, and the low-concentration areas are distributed in southern China. The trend is 2.65 µg m−3 per decade up to 2011 and −22.23 µg m−3 per decade since 2012. In India, the trend is 0.92 µg m−3 per decade. The concentration exhibits a north–high to south–low pattern, with high-concentration areas distributed in northern India, such as the Ganges Plain and Thar Desert, and the low-concentration area in the Deccan Plateau. The trend is 1.41 µg m−3 per decade up to 2013 and −23.36 µg m−3 per decade from 2014. The variation in regional PM2.5 concentrations is closely related to the implementation of air quality laws and regulations. The daily site-scale PM2.5 concentration dataset from 1959 to 2022 in the Northern Hemisphere is available at the National Tibetan Plateau/Third Pole Environment Data Center (https://doi.org/10.11888/Atmos.tpdc.301127) (Hao et al., 2024).

Spatial and Temporal Changes in Habitat Quality and Driving Forces in Typical Loess Hill and Gully Areas：A Case Study of the Zuli River Basin

Loess hills and gully areas are one of the important ecological barriers in China, and the study of the spatial and temporal changes of its habitat quality and its driving force is of great significance to guaranteeing the ecological security of China and safeguarding the national ecological rights and interests. Taking the Zuli River Basin as an example, the spatiotemporal distribution of the remote-sensing ecological index （RSEI） from 2000 to 2020 was systematically investigated using the Google Earth Engine platform and Landsat remote-sensing data. Combined with the coefficient of variation CV, the Theil-Sen Median slope estimation, the Mann-Kendall test of significance, and the Hurst index, the spatial and temporal changes of habitat quality in the study area were analyzed over a period of 20 years, and the effects of six major driving factors on the spatial distribution of RSEI were investigated using the geodetector method. The results of the study showed that： ① From 2000 to 2020, the value of the RSEI showed a downward and then upward trend, with an average annual increase of 0.084 5·（10 a）-1. ② During the 20-year period, the habitat quality improvement area accounted for 92.06%, of which the significant improvement area accounted for 28.49%, and the improvement area was mainly in Huining County, whereas the habitat degradation area only accounted for 7.82%. The trend of future ecological conditions showed that 74.98% of the areas would show a trend of continuous improvement or future improvement, but there would still be a potential risk of ecological degradation in 23.48% of the areas in the future. ③ Climate factors such as precipitation were the key factors affecting the habitat quality in the Zu Li River Basin； the interaction between factors had a higher explanatory power than that of any single factor on the habitat quality, among which the interaction between the precipitation factor and the elevation factor had the strongest explanatory power. The interaction between the terracing factor and the environmental factor significantly increased the explanatory power of the spatial variance, which indicated that terracing played an important role in improving habitat quality. The results of this study can provide a scientific basis for the management and sustainable development of the ecological environment in the loess hills and gullies.

Saltwater intrusion of the Nandu River under the changing environment in China

Saltwater problems in the Nandu River have gradually intensified in recent years. The effect of runoff variability (RV) on saltwater intrusion has not yet been fully revealed. Long-term trends in runoff and sea level (SL) were analyzed using the Mann-Kendall test and Theil-Sen estimation, and salinity exceedance rates (Ps) were calculated based on MIKE 11 and daily runoff distributions. As RV increased, Ps decreased the most at 16.0 km (6.7 %) and increased the most at 23.2 km (5.3 %). SL increased by 0.4 m and Ps increased the most at 20.5 km (11.7 %). Constant Ps is projected to move downstream by the 2060s and 210 s, with maximum increases in Ps of 6.2 % and 10.1 %, respectively. The ratio of changes in Ps due to changes in RV and SL is about 0.85. Constant Ps sections can be used to assess the risk of saline intrusion in some of the world's estuaries.

Пэгунигалсидаза альфа – новый пегилированный рекомбинантный препарат альфа-галактозидазы для ферментозаместительной терапии болезни Фабри

Fabry disease (FD) is a rare hereditary lysosomal storage disease. Enzyme replacement therapy improves symptoms and prevents progression of target organs involvement in patients with FD. Pegunigalsidase alfa is a novel, pegylated recombinant alpha-galactosidase enzyme characterized by prolonged half-life and reduced immunogenicity. Repeated kidney biopsy in phase 1/2 clinical trial demonstrated GL3 clearance from renal capillary endothelial cells. Three phase 3 clinical trials (BRIDGE, BRIGHT, and BALANCE) have shown favorable efficacy and safety profile in more than 130 patients with FD. In the pivotal phase 3 trial (BALANCE), pegunigalsidase alfa was non-inferior to agalsidase beta based on rate of eGFR decline over 2 years (the difference between median eGFR slopes was -0.36 mL/ min/1.73 m2/year) and was associated with a lower rate of infusion-related reactions in adult patients with Fabry disease and progressive nephropathy

Investigating the Dynamic Change and Driving Force of Vegetation Carbon Sink in Taihang Mountain, China

Vegetation plays an important role in absorbing carbon dioxide and accelerating the achievement of carbon neutrality. As the ecological barrier of North China, the Taihang Mountains are pivotal to the ecological construction project of China. Nevertheless, the dynamic development of the vegetation carbon sink in the region and the impact factors on the sink have not been systematically evaluated. This study employed a comprehensive approach, utilising remote sensing technology and meteorological and topographic data, in conjunction with the net ecosystem productivity (NEP) estimation model to reveal the characteristics of vegetation carbon sinks in the Taihang Mountain, and then revealed the dynamics evolution of the NEP and the inter-annual trend by using Theil–Sen Median slope estimation, the Mann–Kendall test, and the coefficient of dissociation and analysed the driving roles of the influencing factors by using the parameter optimal geographic detector. Our findings suggest that the NEP in the Taihang Mountain area has a clear growth trend in time, the average value of NEP in the Taihang Mountain area is 289 gC-m−2-a−1 from 2000 to 2022, and the spatial distribution shows the characteristics of high in the northeast and low in the middle and west, with a gradual increase from the northeast to the southwest; the areas with high fluctuation of NEP are mainly distributed in the areas around some cities that are susceptible to the interference of natural or anthropogenic factors. The vegetation carbon sinks in the Taihang Mountains are influenced by a variety of natural factors, among which the explanatory power of each natural factor is as follows: DEM (0.174) &gt; temperature (0.148) &gt; precipitation (0.026) &gt; slope (0.017) &gt; slope direction (0.003). The natural factor DEM had the strongest explanatory power for NEP changes, and the two-by-two effects of the natural factors on vegetation carbon sinks were all significantly stronger than the effects of a single factor, in which the interaction between DEM and precipitation had the strongest explanatory power; distinguishing from climate change factors, the contribution of anthropogenic activities to NEP changes in more than 90% of the area of the Taihang Mountainous Region was more than 60%, and the driving force of anthropogenic factors on NEP changes in the Taihang Mountainous Region was significantly stronger than that of natural climate change. The contribution of anthropogenic factors to NEP changes in the Taihang Mountains was significantly stronger than that of natural climate change. The results of this study can not only provide a reference for carbon reduction and sink increase and ecological restoration projects in the Taihang Mountains but also benefit the research paradigm of vegetation carbon sequestration in other regions.

Development of 20-Talker Korean Sentence In Noise Test Lists

Purpose: Clinical speech recognition measures often present the sentences recorded with a single speaker and one rate of speech. Performances with single-talker recordings may not accurately represent the speech recognition abilities of the listeners in a multitalker communication situation. The present study aimed to construct and optimize the sentences recorded by 20 different talkers for sentence-in-noise recognition tests (20-talker Korean sentence-in-noise test, 20-talker K-SIN).Methods: Phases I and II were conducted in this study. In phase I (developmental phase), preliminary 720 sentences composed of 3 to 6 words were selected and recorded by twenty different talkers (10 male and 10 female). The recorded sentences were superimposed to generate a speech-shaped noise similar to the long-term average speech spectrum of the sentences. In phase II (optimization and formation of equally intelligible sentence lists), the psychometric functions of 30 normal-hearing listeners were obtained from the sentence-in-noise recognition scores at three different signal-to-noise ratios (SNRs) (-2, -4, and -7 dB). Based on these scores, the SNR required for 50% sentence intelligibility (SNR-50) and the slope at that point were derived from the psychometric function curves.Results: Before level adjustment, the median SNR-50 and slope were -4.12 dB SNR and 24.33%/dB over 720 sentences. Level adjustments were applied to homogenize the intelligibility of the sentences, resulting in 508 sentences remained. Out of 508 sentences, 320 sentences were used to construct 1 practice list and 15 test lists, wherein 20 sentences in each list were spoken by each of the 20 different talkers.Conclusion: The 20-talker K-SIN sentences can be used for high-variability speech-in-noise recognition test to better reflect the multitalker communication abilities of listeners.