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Google Earth Engine (GEE) has empowered researchers to map and monitor vegetation cover through the utilization of satellite imagery and cloud-computing resources. This study employed such platform to evaluate deforestation and forest degradation (DFD) in the Southern Sierra Madre Mountain range, where historical and current evidence underscores ongoing degradation. This research endeavors to capitalize on GEE to generate land cover maps for the Southern Sierra Madre region, specifically in General Nakar, Quezon, and in Rodriguez, Rizal and to present the changes that occurred during three specific years: 2016, 2019, and 2022. Community mapping activities were also conducted in these municipalities to further validate the results from GEE. The findings indicate that forested lands predominantly encompassed both municipalities, albeit exhibiting a declining pattern for successive years. Analysis of land cover maps revealed that in General Nakar, most of the forest areas converted to shrublands are found in the barangays of Umiray and Pagsangahan while for Rodiguez, most of the forest areas converted to shrublands were found to be in Marikina Watershed Forest Reserve and in Puray. Community mapping unveiled that DFD factors such as kaingin, infrastructure extension, timber poaching, small-scale mining, charcoal making, and natural hazards are also present in these areas. With the availability of these openly available cloud-computing platforms, aided by inputs from members of the communities, efficient monitoring and implementation of interventions may be realized.

Abstract Angiotensin II receptor 1(AT1) antagonists are beneficial in focal ischemia/reperfusion (I/R). However, in cases of global I/R, such as cardiac arrest (CA), AT1 blocker's potential benefits are still unknown. Wistar male rats were allocated into four groups: Control group (CG)—animals submitted to CA by ventricular fibrillation induced by direct electrical stimulation for 3 min, and anoxia for 5 min; Group AT1 (GAT1)—animals subjected to CA and treated with 0.2 mg/kg of candesartan diluted in dimethylsulfoxide (DMSO) (0.1%); Vehicle Group (VG): animals subjected to CA and treated with 0.2 ml/kg of DMSO and Sham group (SG)—animals submitted to surgical interventions, without CA. Cardiopulmonary resuscitation consisted of group medications, chest compressions, ventilation, epinephrine (20 mcg/kg) and defibrillation. The animals were observed up to 4 h after spontaneous circulation (ROSC) return, and survival rates, hemodynamic variables, histopathology, and markers of tissue injury were analyzed. GAT1 group had a higher rate of ROSC (62.5% vs. 42.1%, p

The relation between drought severity and drought impacts is complex and relatively unexplored in the African continent. This study assesses the relation between reported drought impacts, drought indices, water scarcity and aridity across several counties in Kenya. The monthly bulletins of the National Drought Management Authority in Kenya provided drought impact data. A random forest (RF) model was used to explore which set of drought indices (standardized precipitation index, standardized precipitation evapotranspiration index, standardized soil moisture index and standardized streamflow index) best explains drought impacts on pasture, livestock deaths, milk production, crop losses, food insecurity, trekking distance for water and malnutrition. The findings of this study suggest a relation between drought severity and the frequency of drought impacts, whereby the latter also showed a positive relation with aridity. A relation between water scarcity and aridity was not found. The RF model revealed that every region, aggregated by aridity, had their own set of predictors for every impact category. Longer timescales (≥ 12 months) and the standardized streamflow index were strongly represented in the list of predictors, indicating the importance of hydrological drought to predict drought impact occurrences. This study highlights the potential of linking drought indices with text-based impact reports while acknowledging that the findings strongly depend on the availability of drought impact data. Moreover, it emphasizes the importance of considering spatial differences in aridity, water scarcity and socio-economic conditions within a region when exploring the relationships between drought impacts and indices.

Abstract Global, continental and regional maps of concentrations, stocks and fluxes of natural resources provide baseline data to assess how ecosystems respond to human disturbance and global warming. They are also used as input to numerous modelling efforts. But these maps suffer from multiple error sources and, hence, it is good practice to report estimates of the associated map uncertainty so that users can evaluate their fitness for use. We explain why quantification of uncertainty of spatial aggregates is more complex than uncertainty quantification at point support because it must account for spatial autocorrelation of the map errors. Unfortunately, this is not done in a number of recent high‐profile studies. We describe how spatial autocorrelation of map errors can be accounted for with block kriging, a method that requires geostatistical expertise. Next, we propose a new, model‐based approach that avoids the numerical complexity of block kriging and is feasible for large‐scale studies where maps are typically made using machine learning. Our approach relies on Monte Carlo integration to derive the uncertainty of the spatial average or total from point support prediction errors. We account for spatial autocorrelation of the map error by geostatistical modelling of the standardized map error. We show that the uncertainty strongly depends on the spatial autocorrelation of the map errors. In a first case study, we used block kriging to show that the uncertainty of the predicted topsoil organic carbon in France decreases when the support increases. In a second case study, we estimated the uncertainty of spatial aggregates of a machine learning map of the above‐ground biomass in Western Africa using Monte Carlo integration. We found that this uncertainty was small because of the weak spatial autocorrelation of the standardized map errors. We present a tool to get realistic estimates of the uncertainty of spatial averages and totals of natural resource maps. The method presented in this paper is essential for parties that need to evaluate whether differences in aggregated environmental variables or natural resources between regions or over time are statistically significant.

Soil organic carbon (SOC) is the largest terrestrial carbon pool. SOC is composed of a continuous set of compounds with different chemical compositions, origins, and susceptibilities to decomposition that are commonly separated into pools characterised by different responses to anthropogenic and environmental disturbance. Here we map the contribution of three SOC fractions to the total SOC content of Australia's soils. The three SOC fractions, mineral-associated organic carbon (MAOC), particulate organic carbon (POC), and pyrogenic organic carbon (PyOC), represent SOC composition with distinct turnover rates, chemistry, and pathway formation. Data for MAOC, POC, and PyOC were obtained with near- and mid-infrared spectral models calibrated with measured SOC fractions. We transformed the data using an isometric-log-ratio (ilr) transformation to account for the closed compositional nature of SOC fractions. The resulting back-transformed ilr components were mapped across Australia. SOC fraction stocks for 0–30 cm were derived with maps of total organic carbon concentration, bulk density, coarse fragments, and soil thickness. Mapping was done by a quantile regression forest fitted with the ilr-transformed data and a large set of environmental variables as predictors. The resulting maps along with the quantified uncertainty show the unique spatial pattern of SOC fractions in Australia. MAOC dominated the total SOC with an average of 59 % ± 17 %, whereas 28 % ± 17 % was PyOC and 13 % ± 11 % was POC. The allocation of total organic carbon (TOC) to the MAOC fractions increased with depth. SOC vulnerability (i.e. POC/[MAOC+PyOC]) was greater in areas with Mediterranean and temperate climates. TOC and the distribution among fractions were the most influential variables in SOC fraction uncertainty. Further, the diversity of climatic and pedological conditions suggests that different mechanisms will control SOC stabilisation and dynamics across the continent, as shown by the model covariates' importance metric. We estimated the total SOC stocks (0–30 cm) to be 13 Pg MAOC, 2 Pg POC, and 5 Pg PyOC, which is consistent with previous estimates. The maps of SOC fractions and their stocks can be used for modelling SOC dynamics and forecasting changes in SOC stocks as a response to land use change, management, and climate change.

Insights into the controlling factors of soil organic carbon (SOC) stock variation are necessary both for our scientific understanding of the terrestrial carbon balance and to support policies that intend to promote carbon storage in soils to mitigate climate change. In recent years, complex statistical and algorithmic tools from the field of machine learning have become popular for modelling and mapping SOC stocks over large areas. In this paper, we report on the development of a statistical method for interpreting complex models, which we implemented for the study of SOC stock variation. We fitted a random forest machine learning model with 2206 measurements of SOC stocks for the 0–50 cm depth interval from mainland France and used a set of environmental covariates as explanatory variables. We introduce Shapley values, a method from coalitional game theory, and use them to understand how environmental factors influence SOC stock prediction: what is the functional form of the association in the model between SOC stocks and environmental covariates, and how does the covariate importance vary locally from one location to another and between carbon-landscape zones? Results were validated both in light of the existing and well-described soil processes mediating soil carbon storage and with regards to previous studies in the same area. We found that vegetation and topography were overall the most important drivers of SOC stock variation in mainland France but that the set of most important covariates varied greatly among locations and carbon-landscape zones. In two spatial locations with equivalent SOC stocks, there was nearly an opposite pattern in the individual covariate contribution that yielded the prediction – in one case climate variables contributed positively, whereas in the second case climate variables contributed negatively – and this effect was mitigated by land use. We demonstrate that Shapley values are a methodological development that yield useful insights into the importance of factors controlling SOC stock variation in space. This may provide valuable information to understand whether complex empirical models are predicting a property of interest for the right reasons and to formulate hypotheses on the mechanisms driving the carbon sequestration potential of a soil.

The turnover time (τ) of global soil organic carbon is central to the functioning of terrestrial ecosystems. Yet our spatially-explicit understanding of depth-dependent variations and environmental controls of τ at a global scale remain incomplete. In this study, we combine multiple state-of-the-art observation-based datasets, including over ninety thousand geo-referenced soil profiles, the latest root observations distributed globally, and large amounts of satellite-derived environmental variables, to generate global maps of apparent τ in topsoil (0–0.3 m) and subsoil (0.3–1 m) layers with a spatial resolution of 30 arcsec (~1 km at the Equator). We show that subsoil τ (385203485 years [mean with a variation range from 2.5th to 97.5th percentile]) is over eight times longer than topsoil τ (1511137 years). The cross-validation shows that the fitted machine learning models effectively captured the variabilities in τ, with R 2 values of 0.87 and 0.70 for topsoil and subsoil τ mapping, respectively. The prediction uncertainties of the τ maps were quantified for better user applications. The environmental controls on top- and subsoil τ were investigated at global, biome, and local scales. Our analyses illustrate that how temperature, water availability, physio-chemical properties and depth exert jointly impacts on τ. The data-driven approaches allow us to identify their interactions, thereby enriching our comprehension of mechanisms driving nonlinear τ–environment relationships from global to local scales. The distributions of dominating factors of τ at local scales were mapped for identifying context-dependent controls on τ across different regions. We further reveal that the current Earth system models may underestimate τ by comparing model-derived maps with our observation-derived τ maps. The resulting maps with new insights demonstrated in this study facilitate the future modelling efforts of carbon cycle–climate feedbacks and supporting effective carbon management. The dataset is archived and freely available at https://doi.org/10.5281/zenodo.14560239 (Zhang, 2025). [ABSTRACT FROM AUTHOR]

Background: In Bayesian models including predictive processing, the magnitude and precision of pain expectancies are key determinants of perception. However, relatively few studies have directly tested whether this holds for pain, and results so far have been inconclusive. Here, we investigated expectancy effects on pain experiences and associated affective responses. Methods: In two studies, healthy participants (n = 30 in each) received painful electrical stimuli preceded by explicit pain predictions. In study 1, the magnitude of pain predictions and administered pain intensities were varied. In study 2, the magnitude and precision of pain predictions were varied, while administered pain intensity was kept constant. Experienced pain intensity was the primary outcome in both studies. Results: Pain experiences assimilated towards both under‐ and overpredictions of pain. In study 1, however, effects were small, if present at all, for non‐painful stimuli and effects were not necessarily larger with predictions of greater magnitude. In study 2, assimilation of pain experiences appeared regardless of precision level, while no significant effects on EMG eyeblink startle responses were observed. Moreover, under‐ and overpredictions caused disappointment and relief, respectively, with greater disappointment upon precise than imprecise predictions. Conclusions: The influence of pain predictions on pain might be more complex than assumed in simple instantiations of current theoretical frameworks, with no systematically stronger assimilation of pain experiences to larger and more precise predictions. Since overpredictions are associated with relief, but underpredictions with disappointment, these findings underline the importance of providing correct predictions when preparing for upcoming painful procedures. Significance Statement: Our work supports, challenges, and extends the application of Bayesian and predictive processing frameworks to the influence of pain predictions on pain. Under‐ and overpredictions of pain yielded assimilation of pain experiences, but assimilation was not systematically stronger with larger prediction errors or greater precision. Moreover, under‐ and overpredictions resulted in disappointment and relief, respectively. This research signifies the importance of establishing accurate predictions of pain in clinical practice. [ABSTRACT FROM AUTHOR]

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Introduction: Buddhist-derived meditation is a well-known phenomenological practice that enhances overall wellbeing of individuals. However, broad- and less-rigorous criteria in recruiting suitable meditation practitioners affect the reliability of results obtained through meditation research. Objectives: To develop an instrument to identify skilled meditators among meditation practitioners, in order to use it in scientific research including medical research conducted to understand the physiological, psychological, social, and environmental effects of meditation Methods: Extensive literature reviews, interviews and focus group discussions with experts in a number of related fields were utilized in developing the instrument. Judgmental validation of the generated items was ensured through establishing their face, content and consensual validity. Internal consistency reliability and factor structure of the scale were explored to analyse its psychometric properties. Cut-off marks were determined to develop a scoring system for the instrument. Results: Twenty-five items were categorized under five sections (A, B, C1, C2, D) that addressed six factors: duration of the meditation practice, details of the meditation practice, peripheral awareness, stable attention, and alertness and emotional stability. Three further qualitative items were added under section E. An exploratory factor analysis process resulted in a two-factor structure between the Likert-scale items as theoretically expected. Scores to identify ‘skilled meditators’ were determined as: Section C1>7 (fall-back score; 7-9, ideal score; 10-12), Section C2>14 (fall-back score; 14-16, ideal score; 17-20) and Section D>25 (fall-back score; 25-29, ideal score; 30-35). Conclusions & Recommendations: The overall findings reveal the UoC-IISM to be a valid and reliable tool to be used in identifying skilled meditators, among meditation practitioners, for scientific research in the Sri Lankan context. The instrument explores six factors through its 28 items and the importance of using a combination of these factors is discussed.

Reliable information on building stock and its vulnerability is important for understanding societal exposure to floods. Unfortunately, developing countries have less access to and availability of this information. Therefore, calculations for flood damage assessments have to use the scarce information available, often aggregated on a national or district level. This study aims to improve current assessments of flood damage by extracting individual building characteristics and estimate damage based on the buildings' vulnerability. We carry out an object-based image analysis (OBIA) of high-resolution (11 cm ground sample distance) unmanned aerial vehicle (UAV) imagery to outline building footprints. We then use a support vector machine learning algorithm to classify the delineated buildings. We combine this information with local depth–damage curves to estimate the economic damage for three villages affected by the 2019 January river floods in the southern Shire Basin in Malawi and compare this to a conventional, pixel-based approach using aggregated land use to denote exposure. The flood extent is obtained from satellite imagery (Sentinel-1) and corresponding water depths determined by combining this with elevation data. The results show that OBIA results in building footprints much closer to OpenStreetMap data, in which the pixel-based approach tends to overestimate. Correspondingly, the estimated total damage from the OBIA is lower (EUR 10 140) compared to the pixel-based approach (EUR 15 782). A sensitivity analysis illustrates that uncertainty in the derived damage curves is larger than in the hazard or exposure data. This research highlights the potential for detailed and local damage assessments using UAV imagery to determine exposure and vulnerability in flood damage and risk assessments in data-poor regions.

Objectives: Patients with prosthetic joint infections (PJIs) not suitable for curative surgery may benefit from suppressive antibiotic therapy (SAT). However, the usefulness of SAT in cases with a draining sinus has never been investigated. Methods: A multicentre, retrospective observational cohort study was performed in which patients with a PJI and a sinus tract were eligible for inclusion if managed conservatively and if sufficient follow-up data were available (i.e. at least 2 years). SAT was defined as a period of > 6 months of oral antibiotic therapy. Results: SAT was initiated in 63 of 72 (87.5 %) included patients. Implant retention during follow-up was the same in patients receiving SAT vs. no SAT (79.4 % vs. 88.9 %; p=0.68). In total, 27 % of patients using SAT experienced side effects. In addition, the occurrence of prosthetic loosening in initially fixed implants, the need for surgical debridement, or the occurrence of bacteremia during follow-up could not be fully prevented with the use of SAT, which still occurred in 42 %, 6.3 %, and 3.2 % of cases, respectively. However, the sinus tract tended to close more often (42 % vs. 13 %; p=0.14), and a higher resolution of pain was observed (35 % vs. 14 %; p=0.22) in patients receiving SAT. Conclusions: SAT is not able to fully prevent complications in patients with a draining sinus. However, it may be beneficial in a subset of patients, particularly in those with pain or the hindrance of a draining sinus. A future prospective study, including a higher number of patients not receiving SAT, is needed.

Introduction: Epidemiological studies that involve interpretation of chest radiographs (CXRs) suffer from inter-reader and intra-reader variability. Inter-reader and intra-reader variability hinder comparison of results from different studies or centres, which negatively affects efforts to track the burden of chest diseases or evaluate the efficacy of interventions such as vaccines. This study explores machine learning models that could standardize interpretation of CXR across studies and the utility of incorporating individual reader annotations when training models using CXR data sets annotated by multiple readers. Methods: Convolutional neural networks were used to classify CXRs from seven low to middle-income countries into five categories according to the World Health Organization's standardized methodology for interpreting paediatric CXRs. We compared models trained to predict the final/aggregate classification with models trained to predict how each reader would classify an image and then aggregate predictions for all readers using unweighted mean. Results: Incorporating individual reader's annotations during model training improved classification accuracy by 3.4% (multi-class accuracy 61% vs 59%). Model accuracy was higher for children above 12 months of age (68% vs 58%). The accuracy of the models in different countries ranged between 45% and 71%. Conclusions: Machine learning models can annotate CXRs in epidemiological studies reducing inter-reader and intra-reader variability. In addition, incorporating individual reader annotations can improve the performance of machine learning models trained using CXRs annotated by multiple readers.

BackgroundThe phosphoinositide 3-kinase (PI3K) signaling pathway is an interesting target in cancer treatment. The awareness of the proarrhythmic risk of PI3K inhibitors was raised because PI3K is also involved in regulating signaling toward cardiac ion channels. Canine cardiomyocytes treated with PI3K inhibitors show an increased action potential duration and reduced cardiac repolarizing currents. Now, the potential proarrhythmic effect of chronic treatment of PI3K/mTOR inhibitor GSK2126458 (omipalisib) was investigated in the atrioventricular (AV) block dog model.MethodsPurpose-bred Mongrel dogs received complete AV block by ablation of the bundle of His and their hearts were paced in the right ventricular apex at VDD-mode (RVA-VDD). In this way, sinus rhythm was maintained for 15 ± 1 days and thereby bradycardia-induced cardiac remodeling was prevented. Dogs received 1 mg/kg omipalisib once (n = 3) or twice (n = 10) a day via oral administration for 7 days. Under standardized conditions (anesthesia, bradycardia at 60 beats/min, and a dofetilide challenge), potential proarrhythmic effects of omipalisib were investigated.ResultsTwice daily dosing of omipalisib increased accumulative plasma levels compared to once daily dosing accompanied with adverse events. Omipalisib prolonged the QT interval at baseline and more strongly after the dofetilide challenge (490 ± 37 to 607 ± 48 ms). The arrhythmic outcome after omipalisib resulted in single ectopic beats in 30% of dogs perpetuating in multiple ectopic beats and TdP arrhythmia in 20% of dogs. Isolated ventricular cardiomyocytes from omipalisib-treated dogs showed a diminished IKs current density.ConclusionChronic treatment of PI3K/mTOR inhibitor omipalisib prolonged the QT interval in a preclinical model under standardized proarrhythmic conditions. Furthermore, this study showed that electrical remodeling induced by omipalisib had a mild proarrhythmic outcome.