Search

Satellite data are increasingly used to provide information to support forest monitoring and reporting at varying levels of detail and for a range of attributes and spatial extents. Forests are dynamic environments and benefit from regular assessments to capture status and changes both locally and over large areas. Satellite data can provide products relevant to forest science and management on a regular basis (e.g. annually) for land cover, disturbance (i.e. date, extent, severity, and type), forest recovery (e.g. quantification of return of trees following disturbance), and forest structure (e.g. volume, biomass, canopy cover, stand height), with products generated over large areas in a systematic, transparent, and repeatable fashion. While pixel-based outcomes are typical based upon satellite data inputs, many end users continue to require polygon-based forest inventory information. To meet this information need and have a spatial context for forest inventory attributes such as tree species assemblages, we present a new work-flow to produce a novel spatially explicit, stand-level satellite-based forest inventory (SBFI) in Canada applying image segmentation approaches to generate spatially unique forest stands (polygons), which are the fundamental spatial unit of management-level inventories. Thus, SBFI offers spatial context to aggregate and generalize other pixel-based forest data sets. Canada has developed a National Terrestrial Ecosystem Monitoring System (NTEMS) that utilizes medium spatial resolution imagery, chiefly from Landsat, to annually characterize Canada's forests at a pixel level from 1984 until present. These NTEMS datasets are used to populate SBFI polygons with information regarding status (e.g. current land cover type, dominant tree species, or total biomass) as well as information on dynamics (e.g. has this polygon been subject to change, when, by what, and if so, how is the forest recovering). Here, we outline the information drivers for forest monitoring, present a set of products aimed at meeting these information needs, and follow to demonstrate the SBFI concept over the 650-Mha extent of Canada's forest-dominated ecosystems. In so doing, the entirety of Canada's forest ecosystems (managed and unmanaged) were mapped using the same data, attributes, and temporal representation. Moreover, the use of polygons allows for the generation of attributes such as tree species composition, and total biomass and wood volume in a stand-scale format familiar to landscape managers and suitable for strategic planning. The data, methods, and outcomes presented here are portable to other regions and input data sources, and the national SBFI outcomes for Canada are available via open access. [ABSTRACT FROM AUTHOR]

Spatial information on forest composition is invaluable for achieving scientific, ecological, and management objectives and for monitoring multiple changes in forest ecosystems. The increased flow of optical satellite data provides new opportunities to improve tree species mapping. However, the accuracy of such maps is affected by training data, and in particular on the homogeneity of individual classes. Thus, we evaluated the effect of data homogeneity in tree species classification. We performed tree species classification by considering different ways to partition data into tree species classes. The class sets considered were (i) only mixed coniferous and mixed deciduous forest classes, (ii) single-species classes, (iii) single-species, mixed coniferous and mixed deciduous classes, and (iv) single-species, mixed coniferous and mixed deciduous classes and a true mixed class. Using data from the Swedish National Forest Inventory, we varied the threshold that defined dominating species. Tree species were classified for a study area in central Sweden using Sentinel-2 data and two classification approaches: Bayesian inference and random forest (RF). Images were selected by class separability and the most informative images based on variable selection with RF. The most informative images tended to be selected by both methods. However, in forests with tree species of similar spectral behaviour, image selection on the basis of class separability was found to be more reliable. More accurate classification results were achieved as the number of classes decreased and the threshold of plot purity increased. The Bayesian classification approach of only mixed coniferous and mixed deciduous classes gave the highest OA, always greater than 90%. When discriminating between pure plots of Birch (Betula spp.), Spruce (Picea abies), Scots pine (Pinus sylvestris) and Lodgepole pine (Pinus contorta), the best OA values were 84% for Bayesian and 80% for RF. In more complicated scenarios, RF resulted in higher overall accuracies (OA). [ABSTRACT FROM AUTHOR]

Accurate and fine-scale forest data are essential to improve natural resource management, particularly in the face of climate change. Here, we present SCANFI, the Spatialized CAnadian National Forest Inventory, which provides coherent, 30 m resolution 2020 wall-to-wall maps of forest attributes (land cover type, canopy height, crown closure, aboveground tree biomass, and main species composition). These maps were developed using the NFI photo-plot dataset, a systematic regular sample grid of photo-interpreted high-resolution imagery covering all of Canada's non-arctic landmass. SCANFI was produced using temporally harmonized summer and winter Landsat spectral imagery along with hundreds of tile-level regional models based on a multiresponse k-nearest neighbours and random forest imputation method. This tile-level approach revealed the importance of radiometric variables in predicting vegetation attributes, namely winter radiometry, as the large-scale climate gradients were controlled at the tile-level. SCANFI was validated with rigorous cross-validation analyses, which revealed robust model performance for structural attributes (biomass R2 = 0.76; crown closure R2 = 0.82; height R2 = 0.78) and tree species cover (e.g., Douglas fir R2 = 0.60). SCANFI attributes were also validated with several independent external products, ranging from ground plot-based tree species cover (e.g., black spruce R2 = 0.53) to satellite LiDAR height data products (e.g., crown closure R2 = 0.71). SCANFI total aboveground biomass trends also followed those published by other studies. The methodology presented herein can be used to map time series of these attributes, identify the original training points used to make any given prediction, as well as map additional variables associated with the NFI photo-plots that are challenging to map using traditional remote sensing approaches. [ABSTRACT FROM AUTHOR]

The majority of pathogenic mutations in the neurofibromatosis type I (NF1) gene reduce total neurofibromin protein expression through premature truncation or microdeletion, but it is less well understood how loss-of-function missense variants drive NF1 disease. We have found that patient variants in codons 844 to 848, which correlate with a severe phenotype, cause protein instability and exert an additional dominant-negative action whereby wild-type neurofibromin also becomes destabilized through protein dimerization. We have used our neurofibromin cryogenic electron microscopy structure to predict and validate other patient variants that act through a similar mechanism. This provides a foundation for understanding genotype-phenotype correlations and has important implications for patient counseling, disease management, and therapeutics.

This article shows how probability sampling and citizen science efforts can complement each other, using the USDA Forest Service's Forest Inventory and Analysis (FIA) program and the ongoing search by the National Register of Champion Trees (NRCT) for the largest specimen of each naturally occurring tree species in the United States as an example. We develop a ratio statistic (Z s ) that uses the difference in size of the largest tree of a species from each database to order the tree species according to the assumed ease with which a larger specimen than the current national champion might be found. Our results show ninety-two candidate species that have been recorded by FIA for which there is no national champion and sixty-five species for which a new champion should be easy to find. In a supplemental table, we show ninety-four species listed as observable by FIA in the NRCT but not recorded in the FIA sample. Study Implications: An interest in forests and forestry is always accompanied by an interest in trees, especially very big trees. Two very different ways of learning about trees are analyzed concurrently in a way that reveals their complementarity. The two efforts are the probability sample, conducted by the USDA Forest Service's Forest Inventory and Analysis (FIA) Program, and the citizen science effort known as the National Register of Champion Trees (NRCT). We develop a statistic that will help tree sleuths find champion trees and provide FIA practitioners with a quality control measure and an indication of which species would benefit from an increase in sample intensity. [ABSTRACT FROM AUTHOR]

Abstrak – Konstruksi Net Farm Income untuk Kesejahteraan Petani Gula Tujuan Utama - Penelitian ini mencoba untuk menyusun formula Net Farm Income (NFI) yang berpihak pada petani. Metode - Penelitian ini menggunakan metode Islamic Anthropological Assumptions (IAA). Informan penelitian ini adalah beberapa pihak yang terlibat dalam pengembangan pertanian tebu. Temuan Utama – Penelitian ini menunjukkan bahwa meskipun hegemoni pemegang kekuasaan sangat kuat, terdapat nilai persatuan, budaya dan religiusitas di kalangan petani. Di sisi lain, petani tetap berada pada posisi yang terpinggirkan meskipun menyumbang penyerapan terbesar tenaga kerja dan pasokan tebu. Selain itu, penelitian ini menangkap nilai budaya lokal yang diwujudkan dalam ijtihad (kesungguhan) dan syukur. Implikasi Teori dan Kebijakan - Kajian ini berinisiatif untuk mentransformasikan dan mendemonstrasikan dimensi lokalitas dan religiusitas ke dalam konsep baru NFI. Bagi pengambil kebijakan, konsep NFI baru ini dapat menjadi dasar pemikiran dan pertimbangan baru untuk restrukturisasi regulasi yang menyangkut kesejahteraan petani tebu. Kebaruan Penelitian - Konstruksi baru NFI mengubah makna kesejahteraan dalam akuntansi pertanian syariah. Abstract – Net Farm Income Construction for Sugar Farmers' Welfare Main Purpose - This study attempts to develop a Net Farm Income (NFI) formula that is pro-sugar farmers. Method - This study uses the Islamic Anthropological Assumptions (IAA) method. The informants are several parties involved in developing sugarcane farming. Main Findings – This study shows that although the hegemony of the power holders is very strong, there are values of unity, culture and religiosity among the farmers. On the other hand, they are marginalised even though they are the biggest contributor to employment and sugarcane suppliers. In addition, this research captures local cultural values embodied in “ijtihad” (seriousness) and gratitude. Theory and Practical Implications - This study takes the initiative to transform and demonstrate the NFI concept with locality and religiosity dimensions. For policymakers, this concept can become a basis for new thoughts and considerations for restructuring regulations concerning the welfare of sugar farmers. Novelty - The new construction of NFI changes the meaning of welfare in Islamic farm accounting.

National forest inventory (NFI) programs provide vital information on forest parameters’ status, trend, and change. Most NFI designs and estimation methods are tailored to estimate status over large areas but are not well suited to estimate trend and change, especially over small spatial areas and/or over short time periods (e.g. annual estimates). Fine-scale space-time indexed estimates are critical to a variety of environmental, ecological, and economic monitoring efforts. In the United States, for example, NFI data are used to estimate forest carbon status, trend, and change to support national, state, and local user group needs. Increasingly, these users seek finer spatial and temporal scale estimates to evaluate existing land use policies and management practices, and inform future activities. Here we propose a spatio-temporal Bayesian small area estimation modeling framework that delivers statistically valid estimates with complete uncertainty quantification for status, trend, and change. The framework accommodates a variety of space and time dependency structures, and we detail model configurations for different settings. The proposed framework is used to quantify forest carbon dynamics at an annual county-level across a 14 year period for the contiguous United States. Also, using an analysis of simulated data, we compare the proposed framework with traditional NFI estimators and offer computationally efficient algorithms, software, and data to reproduce results for benchmarking.

Background: Scarring or progressive fibrosis and cirrhosis develop over time as a result of chronic viral infection, which induces inflammation and tissue healing via deposition of extracellular matrix. There has been an increase in the sustained virological response (SVR) and the rate of eradication of HCV because of the effectiveness of directacting antiviral drugs (DAAs). Reduced hepatic fibrosis is associated with increased SVR rates. There are a variety of non-invasive fibrosis imaging, scoring, and marker methods: transient elastography, aminotransferase platelet ratio index (APRI), as well as fibrosis-4 (FIB-4) score. Objective: The purpose of this research was comparing the novel fibrosis index to APRI, (FIB-4) score and fibroscan in predicting the degree of hepatic fibrosis in Egyptian chronic HCV patients who were managed by DAAs. Patients and Methods: 100 Egyptians with chronic HCV infection participated in a our 3-month long prospective Cohort research using the IFN-free DAA combination of Sofosbuvir and Daclatasvir ± Ribavirin. Results: There was significant regression of fibrosis with DAAs treatment in all patients achieving SVR by fibroscan, APRI and fib4. Novel fibrosis index is reliable and good tool in estimation of liver fibrosis in correlation to fibroscan, with the cutoff value in prediction of hepatic fibrosis stage 4 was >3.1 and has sensitivity of 81.5% while the specificity was 74.1%. Conclusion Novel fibrosis index has been found to be good reliable marker for assessment of liver fibrosis with high accuracy of predicting f4 fibrosis stage. There was significant marked reduction of fibrosis degree by fibroscan, APRI and FIB4 after DAAs treatment. [ABSTRACT FROM AUTHOR]

The aim of this study was to analyze the accuracy of predictions of dominant height, mean height, basal area, and volume from the nationwide forest attribute map (SR16). The analysis took advantage of field observations from 33 different forest inventory projects across Norway used for validation. Forest attributes for more than 5000 plots were predicted using non-stratified and stratified models of SR16 and the predictions were compared against corresponding ground reference values. Finally, the effect of different factors that might have influenced the prediction errors were analyzed using partial least squared regression (PLSR) to determine under which conditions the SR16 is less apt. The overall results across all plots were adequate (RMSE of 10%, MD of 2% for dominant and mean height; RMSE of 28%, MD of 4% for basal area; RMSE of 31%, MD of 5% for volume). However, when the accuracy was assessed locally for each inventory project, large differences in accuracy were observed. The MD% values for some inventory projects were substantial (>30% for basal area and volume). The results showed that stratification did not necessarily improve the results and that factors related to the forest structure had the greatest impact on the PLSR analysis. [ABSTRACT FROM AUTHOR]

Abstract Background The Norwegian forest resource map (SR16) maps forest attributes by combining national forest inventory (NFI), airborne laser scanning (ALS) and other remotely sensed data. While the ALS data were acquired over a time interval of 10 years using various sensors and settings, the NFI data are continuously collected. Aims of this study were to analyze the effects of stratification on models linking remotely sensed and field data, and assess the accuracy overall and at the ALS project level. Materials and methods The model dataset consisted of 9203 NFI field plots and data from 367 ALS projects, covering 17 Mha and 2/3 of the productive forest in Norway. Mixed-effects regression models were used to account for differences among ALS projects. Two types of stratification were used to fit models: 1) stratification by the three main tree species groups spruce, pine and deciduous resulted in species-specific models that can utilize a satellite-based species map for improving predictions, and 2) stratification by species and maturity class resulted in stratum-specific models that can be used in forest management inventories where each stand regularly is visually stratified accordingly. Stratified models were compared to general models that were fit without stratifying the data. Results The species-specific models had relative root-mean-squared errors (RMSEs) of 35%, 34%, 31%, and 12% for volume, aboveground biomass, basal area, and Lorey’s height, respectively. These RMSEs were 2–7 percentage points (pp) smaller than those of general models. When validating using predicted species, RMSEs were 0–4 pp. smaller than those of general models. Models stratified by main species and maturity class further improved RMSEs compared to species-specific models by up to 1.8 pp. Using mixed-effects models over ordinary least squares models resulted in a decrease of RMSE for timber volume of 1.0–3.9 pp., depending on the main tree species. RMSEs for timber volume ranged between 19%–59% among individual ALS projects. Conclusions The stratification by tree species considerably improved models of forest structural variables. A further stratification by maturity class improved these models only moderately. The accuracy of the models utilized in SR16 were within the range reported from other ALS-based forest inventories, but local variations are apparent.

Abstract Background Forest carbon models are recognized as suitable tools for the reporting and verification of forest carbon stock and stock change, as well as for evaluating the forest management options to enhance the carbon sink provided by sustainable forestry. However, given their increased complexity and data availability, different models may simulate different estimates. Here, we compare carbon estimates for Romanian forests as simulated by two models (CBM and EFISCEN) that are often used for evaluating the mitigation options given the forest-management choices. Results The models, calibrated and parameterized with identical or harmonized data, derived from two successive national forest inventories, produced similar estimates of carbon accumulation in tree biomass. According to CBM simulations of carbon stocks in Romanian forests, by 2060, the merchantable standing stock volume will reach an average of 377 m3 ha−1, while the carbon stock in tree biomass will reach 76.5 tC ha−1. The EFISCEN simulations produced estimates that are about 5% and 10%, respectively, lower. In addition, 10% stronger biomass sink was simulated by CBM, whereby the difference reduced over time, amounting to only 3% toward 2060. Conclusions This model comparison provided valuable insights on both the conceptual and modelling algorithms, as well as how the quality of the input data may affect calibration and projections of the stock and stock change in the living biomass pool. In our judgement, both models performed well, providing internally consistent results. Therefore, we underline the importance of the input data quality and the need for further data sampling and model improvements, while the preference for one model or the other should be based on the availability and suitability of the required data, on preferred output variables and ease of use.

The full-scale Russian invasion of Ukraine in 2022 has had a dramatic impact on Ukrainian forestry and forest resources. In this situation, the ability of the National Forest Inventory (NFI) to update forest information at the state level is very limited due to the large territory which is inaccessible for field data collection. This study presents an approach for the combined use of different sources of field observations and remotely-sensed forest vegetation imagery to produce a time series (TS) to characterize forest conditions and changes across Ukraine. First, we created a forest cover map for 2023 using reference information obtained through visual interpretation of high-resolution imagery at NFI plot locations. Then, we used newly collected NFI data (2021–2023) in areas controlled by the government of Ukraine territories, and retrospective Forest Management Planning (FMP) data (2019–2020) as a reference to map dominant tree species and forest attributes. Finally, we used a model-assisted estimation procedure to obtain information on forest resources over Ukraine, including Russian-occupied territories. We used a random forest classifier to map forest cover and dominant tree species, and gradient nearest neighbor (GNN) imputation to predict continuous forest attributes. Accuracy of the forest map over ecoregions correlates with forest cover; thus, we obtained the highest accuracy (90–98 %) for ecozones with forest cover of more than 40 %, and the lowest (about 60–72 %) for ecozones with forest cover of 2–7 %. The overall accuracy of dominant species classification was 76.3±1.5 %, the accuracy of mapping pure coniferous forest was 87–92 %, while it ranged from 35 % to 85 % for mixed deciduous forest. We found that FMP data greatly contributed to mapping forest attributes in inaccessible areas. The GNN model performed better in predicting basal area and growing volume (R2 = 0.44–0.47), but we found that predicting mean age, diameter, and height with optical data was still challenging (R2 = 0.10–0.35). The study revealed that the total forest area in Ukraine is 11.2±0.2 million ha and the mean growing stock volume is 251±5 m3·ha−1. Of that amount, about 1.7 million ha could be potentially affected by the war (temporarily occupied, contaminated, damaged, etc.), storing about 377 ± 15 million m3 of growing volume. Within this area, we identified about 67 thousand ha with some degree of canopy disturbance. This study creates a framework for further monitoring of forest resources in Ukraine and provides effective tools for spatial assessment of war-induced forest damage. • Remote sensing-based framework for forest assessment was deployed over Ukraine. • Retrospective field data and Sentinel 2 time series were used to map forests. • Maps of forest cover and dominant tree species achieved over 90 % accuracy. • Forest attribute maps contribute to war damage assessment in Ukraine. • About 67,000 ha in war-affected areas showed some degree of tree canopy disturbance. [ABSTRACT FROM AUTHOR]

Forest fragmentation is a widespread phenomenon that directly or indirectly affects the processes that take place both in forest ecosystems and in their immediate surroundings. So far, many studies confirm its negative effects, especially on biodiversity. On the other hand, there are few studies that address the effects of forest fragmentation on the amount of accumulated biomass or carbon, as well as on the characteristics of wood resources in managed forests. Therefore, issues related to timber production, which are important from the point of view of multifunctional forest management, are omitted. The aim of our research was to add to the knowledge in this area. In particular, we focused on assessing the impact of forest fragmentation on wood resources based on an analysis of edge effects in forest patches (units formed by combining forest fragments characterized by structural connectivity). Vector data describing the topography of forest fragments in Poland and the results of the National Forest Inventory (NFI) from 2015–2019 were used as material for solving this problem. The results of our research showed that the effects of fragmentation on managed pine stands depend on the age of the stand and the fertility of the habitat. In young stands growing on barren or strongly barren habitats, growing stock volume turned out to be significantly higher in the edge zone. In older stands, especially on moderately fertile habitats, significantly higher resources were found in the interior zone of forest patches. Habitat quality also had a significant effect on the amount of carbon accumulated. In strongly barren habitats, higher carbon mass was found in edge zones, while in moderately fertile habitats, stands had higher carbon volume in the interior zone. Our results illustrate that forest fragmentation is a very complex process that can increase or reduce wood resources, depending on the age of the stand and the quality of the habitat. From the standpoint of measurable benefits, it was concluded that protection from the negative effects of fragmentation should focus primarily on older stands and more fertile habitats. [ABSTRACT FROM AUTHOR]

Stand-level growth and yield models are important tools that support forest managers and policymakers. We used recent data from the Norwegian National Forest Inventory to develop stand-level models, with components for dominant height, survival (number of survived trees), ingrowth (number of recruited trees), basal area, and total volume, that can predict long-term stand dynamics (i.e. 150 years) for the main species in Norway, namely Norway spruce (Picea abies (L.) Karst.), Scots pine (Pinus sylvestris L.), and birch (Betula pubescens Ehrh. and Betula pendula Roth). The data used represent the structurally heterogeneous forests found throughout Norway with a wide range of ages, tree size mixtures, and management intensities. This represents an important alternative to the use of dedicated and closely monitored long-term experiments established in single species even-aged forests for the purpose of building these stand-level models. Model examination by means of various fit statistics indicated that the models were unbiased, performed well within the data range and extrapolated to biologically plausible patterns. The proposed models have great potential to form the foundation for more sophisticated models, in which the influence of other factors such as natural disturbances, stand structure including species mixtures, and management practices can be included. [ABSTRACT FROM AUTHOR]

Abstract Background The aim of this study was to construct a nationwide stand age model by using National Forest Inventory (NFI) data and nationwide airborne laser scanning (ALS) data. In plantation forestry, age is usually known. While this is not the case in boreal managed forests, age is still seldom predicted in forest management inventories. Measuring age accurately in situ is also very laborious. On the other hand, tree age is one of the accurately measured sample tree attributes in NFI field data. Many countries also have a nationwide coverage of airborne laser scanning (ALS) data. In this study, we merged these data sources and constructed a nationwide, area-based model for stand age. Results While constructing the model, we omitted old forests from the data, since the correlation between ALS height metrics and stand age diminished at stands with age > 100 years. Additionally, the effect of growth conditions was considerable, so we also utilized different geographical and NFI variables such as site fertility and soil type in the modeling. The resultant nationwide model for the stand age of managed forests yielded a root mean square error (RMSE) of about 14 years. The model could be improved further by additional forest structure variables, but such information may not be available in practice. Conclusions The results showed that the prediction of stand age by ALS, geographical and NFI information was challenging, but still possible with moderate success. This study is an example of the joint use of NFI and nationwide ALS data and re-use of NFI data in research.

Background: The Norwegian forest resource map (SR16) maps forest attributes by combining national forest inventory (NFI), airborne laser scanning (ALS) and other remotely sensed data. While the ALS data were acquired over a time interval of 10 years using various sensors and settings, the NFI data are continuously collected. Aims of this study were to analyze the effects of stratification on models linking remotely sensed and field data, and assess the accuracy overall and at the ALS project level. Materials and methods: The model dataset consisted of 9203 NFI field plots and data from 367 ALS projects, covering 17 Mha and 2/3 of the productive forest in Norway. Mixed-effects regression models were used to account for differences among ALS projects. Two types of stratification were used to fit models: 1) stratification by the three main tree species groups spruce, pine and deciduous resulted in species-specific models that can utilize a satellite-based species map for improving predictions, and 2) stratification by species and maturity class resulted in stratum-specific models that can be used in forest management inventories where each stand regularly is visually stratified accordingly. Stratified models were compared to general models that were fit without stratifying the data. Results: The species-specific models had relative root-mean-squared errors (RMSEs) of 35%, 34%, 31%, and 12% for volume, aboveground biomass, basal area, and Lorey's height, respectively. These RMSEs were 2–7 percentage points (pp) smaller than those of general models. When validating using predicted species, RMSEs were 0–4 pp. smaller than those of general models. Models stratified by main species and maturity class further improved RMSEs compared to species-specific models by up to 1.8 pp. Using mixed-effects models over ordinary least squares models resulted in a decrease of RMSE for timber volume of 1.0–3.9 pp., depending on the main tree species. RMSEs for timber volume ranged between 19%–59% among individual ALS projects. Conclusions: The stratification by tree species considerably improved models of forest structural variables. A further stratification by maturity class improved these models only moderately. The accuracy of the models utilized in SR16 were within the range reported from other ALS-based forest inventories, but local variations are apparent. [ABSTRACT FROM AUTHOR]

Mongolia’s first Multipurpose National Forest Inventory, 2014-2017, was implemented by the Forest Research and Development Centre, in collaboration with international expertise and the country’s main forestry institutions, universities and research organisations.The long-term objective of the multipurpose NFI is to promote sustainable management of forestry resources in Mongolia, to enhance their social, economic and environmental functions.The NFI findings show that there are 11.3 million hectares of Boreal Forest in Mongolia. 9.5 million hectares are Stocked Boreal Forest Area, of which 69 percent is located outside of protected areas, 4 percent are designated for green-wood utilisation through forest enterprise concessions, and another 16 percent designated for fallen dead-wood collection through forest user group concessions. The non-protected stocked forests (i.e. production forest) have an average growing stock volume of 115 m3 per hectare, compared with an optimal growing stock volume of 237 m3 per hectare, and there is an additional 46.5 m3 of dead wood per hectare. The growing stock age distribution shows that 24 m3 per hectare are over 200 years (i.e. economically over-aged). The main tree species in stocked forest are Larix sibirica (81%), Pinus sibirica (7%), Betula platyphylla (6%) and Pinus sylvestris (5%), of which all, except for P. sibirica, are classified as legally harvestable tree species. Wild fire is the current main environmental factor decreasing the forest tree biomass.The NFI helped identifying priority areas for the forestry sector, and to guide the implementation of sustainable forest management at the local level. The main forest management challenges of Mongolia’s boreal forest will be to address that they are a) under-stocked (less than 50% of production potential), b) over-aged (31% of growing stock volume in stocked production forest is above optimal production age), and c) under-utilised (4% of forest area designated to green-wood utilisation).

Background: Forest carbon models are recognized as suitable tools for the reporting and verification of forest carbon stock and stock change, as well as for evaluating the forest management options to enhance the carbon sink provided by sustainable forestry. However, given their increased complexity and data availability, different models may simulate different estimates. Here, we compare carbon estimates for Romanian forests as simulated by two models (CBM and EFISCEN) that are often used for evaluating the mitigation options given the forest-management choices. Results: The models, calibrated and parameterized with identical or harmonized data, derived from two successive national forest inventories, produced similar estimates of carbon accumulation in tree biomass. According to CBM simulations of carbon stocks in Romanian forests, by 2060, the merchantable standing stock volume will reach an average of 377 m3 ha−1, while the carbon stock in tree biomass will reach 76.5 tC ha−1. The EFISCEN simulations produced estimates that are about 5% and 10%, respectively, lower. In addition, 10% stronger biomass sink was simulated by CBM, whereby the difference reduced over time, amounting to only 3% toward 2060. Conclusions: This model comparison provided valuable insights on both the conceptual and modelling algorithms, as well as how the quality of the input data may affect calibration and projections of the stock and stock change in the living biomass pool. In our judgement, both models performed well, providing internally consistent results. Therefore, we underline the importance of the input data quality and the need for further data sampling and model improvements, while the preference for one model or the other should be based on the availability and suitability of the required data, on preferred output variables and ease of use. [ABSTRACT FROM AUTHOR]

Forest fragmentation has a relevant impact on biodiversity. An interesting alternative to estimate these indices is to use sampling data. This study aims to estimate aggregation index (AI) and the degree of clumping of forested landscape based on AI. The assessment was conducted using different point distances, inventory regions and cardinal directions. For this purpose, a dataset from one five-year periods (2007–2011) of the Swedish National Forest Inventory (NFI) was used. The estimation of AI from field-based inventory can give us a general picture of the current status of forest landscape. The results also show that the estimated AI is a distance dependent function. The corresponding estimated variance of the index is smaller for longer distances. The obtained results indicate that the estimated variance depends on both sample size and pair point distances. Estimated AI showed different values in different cardinal directions. To compare two regions or a given region over time, a given point distance should be used. The main advantage of the applied procedure is that a range of AI values can be produced rather than a single number. Furthermore, in field-based inventory, the obtained results are more reliable, because one works implicitly with a single forest definition only. [ABSTRACT FROM AUTHOR]

This is a case of a pediatric patient with a history of neurofibromatosis I (NFI) presenting to the emergency department secondary to a suicide attempt via self-strangulation after being verbally and physically bullied at school. Upon hospital admission, the 10-year-old patient was found to have significant auditory and visual perceptual hallucinations in addition to suicidal ideations, for which psychiatry was consulted. The patient underwent magnetic resonance imaging (MRI) of the brain to evaluate for intracranial neurofibromas as a potential etiology of his behavior. There is evidence that the growth of neurofibromas in the brain can be associated with psychosis. His brain MRI was significant for multiple foci of non-enhancing lesions seen in the cerebellum, white matter, supratentorial white matter, and bilateral hippocampi that can be seen in NFI, highlighting a medical etiology for the patient's auditory and visual perceptual disturbances. The objective of this case report is to explore medical causes of psychosis including metabolic disorders, neurodegenerative diseases, metabolic disturbances, parathyroid diseases, genetic disorders (Fragile X, Prader-Willi, etc.), autoimmune disorders, multiple sclerosis, temporal lobe epilepsy, infections, and brain tumors., Competing Interests: Human subjects: Consent was obtained or waived by all participants in this study. Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work., (Copyright © 2024, Sanzone et al.)

Background The nuclear factor I (NFI) is a family of transcription factors consisting of four distinct but closely related genes, NFIA, NFIB, NFIC and NFIX, which are important in the development of various tissues and organs in mammals. Recent study results have shown that NFI family may play a critical role in the progression of various human tumors and have been identified as key tumor suppressors and oncogenes for many cancers. However, the expression levels and distinctive prognostic values of the NFI family remain poorly explored in most cancers. Materials and Methods In the present study, the differences in mRNA expression of the NFI family in various cancers were investigated using the Oncomine and TCGA databases, and the mRNA expression, genetic alteration and DNA methylation of the NFI family members in various cancers were examined using cBioPortal for Cancer Genomics. In addition, the prognostic significance of the NFI family was assessed in multiple cancers using the Kaplan–Meier plotter (KM plotter) and SurvExpress databases. Results The mRNA expression levels in the NFI family were significantly downregulated in most cancers compared with normal tissues and DNA hypermethylation might downregulate the NFI family expression. Although NFIX expression was not downregulated in kidney, colorectal and prostate cancers. Furthermore, NFIB expression was upregulated in gastric cancer. Further survival analyses based on the KM plotter and SurvExpress databases showed dysregulations of the NFI genes were significantly correlated with survival outcomes in breast, lung, and head and neck cancers. Decreased expression levels of NFIA, NFIB and NFIC were associated with poor overall survival (OS) in head and neck cancer. Low mRNA expression of NFIA and NFIB was significantly associated with OS and first progression in lung adenocarcinoma, but not in lung squamous cell carcinoma. In addition, potential correlations between NFI family members and survival outcomes were also observed in liver, esophageal, kidney and cervical cancer. Conclusion The results from the present study indicated certain members of the NFI family could be promising therapeutic targets and novel prognostic biomarkers for human cancers.

In a Europe shaped by centuries of forest management, the task of today's scientists in characterising, understanding and modelling natural forests is highly challenging. Although numerous forest reserves exist, most remain hardly comparable case studies. Contrarily, National Forest Inventories (NFIs) consist of systematically distributed sample plots with varying time since last intervention and provide representative data. These characteristics make NFIs a unique opportunity to investigate hidden natural forests.Here we propose using NFI plots free of human influence for >40 to >70 years ('latent reserves') to conduct large‐scale studies on near‐natural forests. We tested this original concept in Swiss forests. We characterised compositional and structural attributes of 'latent reserves' and compared them with those of managed forests to assess whether the former demonstrated more signs of naturalness than the latter. As an example of an application, we analysed the tree‐ and stand‐level factors affecting natural tree mortality in 'latent reserves'.Up to 15.3% of Swiss NFI plots fulfilled the criteria of 'latent reserves', and most of these plots were distributed at mid‐ to high elevations where accessibility and management opportunities are limited. 'Latent reserves' showed more signs of naturalness than managed forests—a higher proportion of broadleaves, higher mortality rates, higher stand density and more deadwood. However, their size structure and basal area did not differ from those of managed forests, most likely because of a lower site productivity. Although 'latent reserves' were transitioning towards a natural state, more time without management might be required for these forests to become fully detached from the effects of past management, especially at high elevations. Mortality analyses in 'latent reserves' showed that species‐specific tree mortality had a U‐shaped response to tree size, was negatively related to tree growth and was higher when competition was stronger.Synthesis. Our findings demonstrate the potential of 'latent reserves' to study near‐natural forests at the country level, and point to further opportunities for larger‐scale collaborations. Investigating 'latent reserves' represents a first step towards a deeper understanding of such forests using existing long‐term data and shows promise for further research in Europe. [ABSTRACT FROM AUTHOR]

It has been reported that high mobility group nucleosomal binding domain 2 (HMGN2) is a nucleus‐related protein that regulates gene transcription and plays a critical role in bacterial clearance. An elevated level of HMGN2 reduced integrin α5/β1 expression of human pulmonary epithelial A549 cells was demonstrated during Klebsiella pneumoniae infection, thus weakening bacterial adhesion and invasion. However, the mechanism by which HMGN2 regulates integrin expression remains unclear. This study found that a transcription factor‐nuclear factor I (NFI), which serves as the potential target of HMGN2 regulated integrin expression. The results showed that HMGN2 was able to promote NFIA and NFIB expression by increasing H3K27 acetylation of NFIA/B promoter regions. The integrin α5/β1 expression was significantly enhanced by knockdown of NFIA/B via a siRNA approach. Meanwhile, NFIA/B silence could also compromise the inhibition effect of HMGN2 on the integrin α5/β1 expression. Mechanistically, it was demonstrated that HMGN2 facilitated the recruitment of NFI on the promoter regions of integrin α5/β1 according to the chromatin immunoprecipitation assay. In addition, it was further demonstrated that the knockdown of NFIA/B induced more adhesion of Klebsiella pneumoniae on pulmonary epithelial A549 cells, which could be reversed by the application of an integrin inhibitor RGD. The results revealed a regulatory role of HMGN2 on the transcription level of integrin α5/β1, indicating a potential treatment strategy against Klebsiella pneumoniae‐induced infectious lung diseases. [ABSTRACT FROM AUTHOR]

Background: The aim of this study was to construct a nationwide stand age model by using National Forest Inventory (NFI) data and nationwide airborne laser scanning (ALS) data. In plantation forestry, age is usually known. While this is not the case in boreal managed forests, age is still seldom predicted in forest management inventories. Measuring age accurately in situ is also very laborious. On the other hand, tree age is one of the accurately measured sample tree attributes in NFI field data. Many countries also have a nationwide coverage of airborne laser scanning (ALS) data. In this study, we merged these data sources and constructed a nationwide, area-based model for stand age. Results: While constructing the model, we omitted old forests from the data, since the correlation between ALS height metrics and stand age diminished at stands with age > 100 years. Additionally, the effect of growth conditions was considerable, so we also utilized different geographical and NFI variables such as site fertility and soil type in the modeling. The resultant nationwide model for the stand age of managed forests yielded a root mean square error (RMSE) of about 14 years. The model could be improved further by additional forest structure variables, but such information may not be available in practice. Conclusions: The results showed that the prediction of stand age by ALS, geographical and NFI information was challenging, but still possible with moderate success. This study is an example of the joint use of NFI and nationwide ALS data and re-use of NFI data in research. [ABSTRACT FROM AUTHOR]

Regulation of the epigenome during in vivo specification of brain stem cells is still poorly understood. Here, we report DNA methylome analyses of directly sampled cortical neural stem and progenitor cells (NS/PCs) at different development stages, as well as those of terminally differentiated cortical neurons, astrocytes, and oligodendrocytes. We found that sequential specification of cortical NS/PCs is regulated by two successive waves of demethylation at early and late development stages, which are responsible for the establishment of neuron- and glia-specific low-methylated regions (LMRs), respectively. The regulatory role of demethylation of the gliogenic genes was substantiated by the enrichment of nuclear factor I (NFI)-binding sites. We provide evidence that de novo DNA methylation of neuron-specific LMRs establishes glia-specific epigenotypes, essentially by silencing neuronal genes. Our data highlight the in vivo implications of DNA methylation dynamics in shaping epigenomic features that confer the differentiation potential of NS/PCs sequentially during development.

Abstract Based on the use of tissue-cultured human corneal endothelial cells (HCECs), cell therapy is a very promising avenue in the treatment of corneal endothelial pathologies such as Fuchs' dystrophy, and post-surgical corneal edema. However, once in culture, HCECs rapidly lose their phenotypic and physiological characteristics, and are therefore unsuitable for the reconstruction of a functional endothelial monolayer. Expression of NFI, a transcription factor that can either function as an activator or a repressor of gene transcription, has never been examined in endothelial cells. The present study therefore aimed to determine the impact of a non-proliferating, lethally irradiated i3T3 feeder layer on the maintenance of HCEC's morphological characteristics, and both the expression and stability of Sp1 (a strong transcriptional activator) and NFI in such cells. The typical morphology of endothelial cells was best maintained when 8 × 103/cm2 HCECs were co-cultured in the presence of 2 × 104 cells/cm2 i3T3. HCECs were found to express both Sp1 and NFI in vitro. Also, the presence of i3T3 led to higher levels of Sp1 and NFI in HCECs, with a concomitant increase in their DNA binding levels (assessed by electrophoretic mobility shift assays (EMSA)). Specifically, i3T3 increased the expression of the NFIA, NFIB and NFIC isoforms, without a noticeable increase in their mRNAs (as revealed by gene profiling on microarray). Gene profiling analysis also identified a few feeder layer-dependent, differentially regulated genes whose protein products may contribute to improving the properties of HCECs in culture. Therefore, co-culturing HCECs with an i3T3 feeder layer clearly improves their morphological characteristics by maintaining stable levels of Sp1 and NFI in cell culture. Highlights • HCECs better maintain a typical compact morphology when co-cultured in the presence of i3T3. • HCECs expresses both the Sp1 and NFI transcription factors in vitro. • The presence of i3T3 increases the expression and DNA binding properties of Sp1 and NFI. • The increased expression and DNA binding of NFI is the consequence of increased expression of the NFIA, NFIB and NFIC isoforms. • Several feeder-layer dependent, differentially regulated genes that may contribute at improving the properties of HCECs were identified by gene profiling analysis. [ABSTRACT FROM AUTHOR]

Maps of aboveground forest biomass based on different input data and modelling approaches have been recently produced for Europe, opening up the possibility for several applications and products not obtainable by summary statistics. However, the accuracy assessment of the existing maps is limited by the lack of reference data consistent over the study region and representative of the maps cells. Here, we used harmonized forest biomass data for 26 European countries derived by National Forest Inventories using a common biomass definition and estimator to assess four biomass maps. The assessment was performed at regional, national and sub-national scales using harmonized statistics derived from almost half million ground plot measurements, and at pixel level using a subset of 22,166 plots covering most European forest types. The field plots were temporally aligned with the maps using growth rates and further screened using an innovative approach based on tree cover variability to remove the plots not representative of the map cells. The harmonized reference data showed that all maps tended to overestimate at low biomass (<100 Mg ha −1 ) and underestimate at medium – high biomass (>100 Mg ha −1 ), resulting in an overall negative bias (23–43 Mg ha −1 at national level) relative to the harmonized estimates. The maps relative errors ranged from 29% to 40% at national level and increased at higher resolutions, reaching 58–67% at pixel level. We also assessed the effect of the harmonization of the national statistics and report that the harmonized biomass values present significant differences compared to the national estimates for 14 countries, and provide a slightly higher stock (+3.8%) at European scale. We show that harmonized and representative reference data are essential to properly assess the accuracy of biomass maps, and we further identify the factors affecting the maps performance and provide indications for their improvements. [ABSTRACT FROM AUTHOR]

Growth, feed intake, feed efficiency, and carcass and meat quality characteristics of 136 Angus steers differing in genetic merit for post-weaning residual feed intake (RFIp) were measured over 251 days in a large commercial feedlot. The steers were evaluated in two groups, low (Low-RFI) and high (High-RFI) genetic RFIp, measured by estimated breeding values for RFIp (RFIp-EBV). The difference in RFIp-EBV between the Low- and High-RFI groups was 1.05 kg/day (–0.44 vs 0.61 kg/day; P < 0.05). The Low- and High-RFI steers were similar (P > 0.05) in age (445 vs 444 days) and weight (435 vs 429 kg) at induction, and at the end of the feeding period (705 vs 691 kg). Average daily gain (ADG) over 251 days had a small negative association with variation in RFIp-EBV (P < 0.05), reflecting a 3.6% greater ADG accompanying a difference of 1 kg/day in RFIp-EBV. Pen feed intake and feed conversion by the Low-RFI group were 10.4 kg/day and 9.3 kg/kg, and for the High-RFI group were 11.1 kg/day and 10.4 kg/kg, but without availability of individual animal feed-intake data it was not possible to test for significant differences. Carcass weight and dressing-percentage was similar for the Low- and High-RFI steers. High-RFI steers had a significantly (P < 0.05) greater depth of subcutaneous rib fat at induction and finished with 5 mm more (P < 0.05) fat at the 10/11th ribs on the carcass than the Low-RFI steers. Cross-sectional area of the eye-muscle and three measures of intramuscular or marbling fat did not differ (P > 0.05) between the Low- and High-RFI steers. Shear force was higher (P < 0.05) in meat samples aged for 1 day from the Low-RFI steers, but there was no difference (P > 0.05) from the High-RFI steers after 7 days of ageing. Compression values for meat samples aged for 1 day did not differ between the RFI groups but were higher in meat samples aged for 7 days from the Low-RFI steers. For these Angus steers, genetic superiority in RFI was associated phenotypically with superior weight gain, decreased rib fat depth, slightly less tender meat, and no compromise in marbling fat or other carcass and meat quality traits. [ABSTRACT FROM AUTHOR]

The nuclear factor I (NFI) transcription factors play important roles during normal development and have been associated with developmental abnormalities in humans. All four family members, NFIA, NFIB, NFIC and NFIX, have a homologous DNA binding domain and function by regulating cell proliferation and differentiation via the transcriptional control of their target genes. More recently, NFI genes have also been implicated in cancer based on genomic analyses and studies of animal models in a variety of tumours across multiple organ systems. However, the association between their functions in development and in cancer is not well described. In this review, we summarise the evidence suggesting a converging role for the NFI genes in development and cancer. Our review includes all cancer types in which the NFI genes are implicated, focusing predominantly on studies demonstrating their oncogenic or tumour-suppressive potential. We conclude by presenting the challenges impeding our understanding of NFI function in cancer biology, and demonstrate how a developmental perspective may contribute towards overcoming such hurdles. [ABSTRACT FROM AUTHOR]

Forests are an important component of the global carbon (C) cycle: they can capture and retain large amounts of C annually, depending on stand characteristics, climate, and disturbance regimes. With climate and disturbance regimes shifting, it is important to be able to accurately represent the corresponding changes in forest C dynamics with well-calibrated models. The Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3) is a widely used model for simulating C dynamics in managed forests at stand, regional, and national levels. Here, we use a Bayesian Markov Chain Monte Carlo (MCMC) technique to calibrate the parameters in the CBM-CFS3 by assimilating C stocks of six deadwood and soil pools estimated from data collected from 635 plots within the Canadian National Forest Inventory. Calibration led to most improvement in the simulation of C stocks in small and fine woody debris, reducing RMSE by 54.3%, followed by the snag stems (RMSE reduced by 23.2%), and coarse woody debris (13%). The calibrated parameters resulted in increased rates of C cycling in fine and coarse woody debris and the soil organic layer, distinct C dynamics in hardwood and softwood dominated stands, and increased temperature sensitivity of the C contained in the mineral soil. While parameter calibration considerably improved the simulation of the small and fine woody debris and snags stem pools, model representation of the branch snag, coarse woody debris, soil organic layer, and mineral soil pools were not substantially improved. Lack of substantial improvements in the calibrated model performance indicated the need for including additional processes in C dynamics simulation or a change in the modelling paradigm. We illustrate the potential need to include a lignin effect on deadwood decay and suggest further exploration of the effects of tree species, soil types, and mosses on performance of the CBM-CFS3. [ABSTRACT FROM AUTHOR]

Summary Regulation of the epigenome during in vivo specification of brain stem cells is still poorly understood. Here, we report DNA methylome analyses of directly sampled cortical neural stem and progenitor cells (NS/PCs) at different development stages, as well as those of terminally differentiated cortical neurons, astrocytes, and oligodendrocytes. We found that sequential specification of cortical NS/PCs is regulated by two successive waves of demethylation at early and late development stages, which are responsible for the establishment of neuron- and glia-specific low-methylated regions (LMRs), respectively. The regulatory role of demethylation of the gliogenic genes was substantiated by the enrichment of nuclear factor I (NFI)-binding sites. We provide evidence that de novo DNA methylation of neuron-specific LMRs establishes glia-specific epigenotypes, essentially by silencing neuronal genes. Our data highlight the in vivo implications of DNA methylation dynamics in shaping epigenomic features that confer the differentiation potential of NS/PCs sequentially during development. [ABSTRACT FROM AUTHOR]

National and international carbon reporting systems require information on forest carbon stocks. This information can be derived from national forest inventory data and remote sensing. Here we present the conceptual challenges in assessing forest resources across Europe by combining MODIS satellite versus terrestrial driven NPP estimates calculated from 13 national forest inventory (NFI) data covering 200.000 sampling plots. The results suggest that MODIS NPP predictions using local daily climate data and addressing stand desnsity effects, provide realistic forest productivity estimates. Ignoring these effects leads to an overestimation in the estimated carbon storage of European forests derived from satellite data. [ABSTRACT FROM AUTHOR]

Although wall-to-wall, accurate, and up-to-date forest composition maps at the stand level are a fundamental input for many applications, ranging from global environmental issues to local forest management planning, countrywide mapping approaches on the tree type level remain rare. This paper presents and validates an innovative remote sensing based approach for a countrywide mapping of broadleaved and coniferous trees in Switzerland with a spatial resolution of 3 m. The classification approach incorporates a random forest classifier, explanatory variables from multispectral aerial imagery and a Digital Terrain Model (DTM) from Airborne Laser Scanning (ALS) data, digitized training polygons and independent validation data from the National Forest Inventory (NFI). The methodological workflow was optimized for an area of 41,285 km² that is characterized by temperate forests within a complex topography. Whereas high model overall accuracies (0.99) and kappa (0.98) were achieved, the comparison of the tree type map with independent NFI data revealed significant deviations that are related to underestimations of broadleaved trees (median of -3.17%). Constraints of the tree type mapping approach are mostly related to the acquisition date and time of the imagery and the topographic (negative) effects on the prediction. A comparison with the most recent High Resolution Layers (HRL) forest 2012 from the European Environmental Agency revealed that the tree type map is superior regarding spatial resolution, level of detail and accuracy. The high-quality map achieved with the approach presented here is of great value for optimizing forest management and planning activities and is also an important information source for applications outside the forestry sector. [ABSTRACT FROM AUTHOR]

The paper shows difficulties in the evaluation of the forest status when considering forests with different height structure (uneven-aged forests). It is the main problem in categorizing such forests. Concerning the actual forest management conception in Slovakia, it is assumed that the area of forests with close-to-nature management system will increase. It leads to more structured forests, as for the tree species, age, height, as well as the spatial structure. The typical vertically homogeneous forests account for only 50% of all Slovak forests, according to results from the National Forest Inventory in Slovakia (NFI SR). During the processing of NFI data the stands were divided into 2 classes as follows: (i) vertically homogeneous stands, which are classified into nine growth stages; (ii) vertically heterogeneous stands, which are classified into two growth stages (lower and advanced), and stands in the process of regeneration. New approaches are not based on the visual subjective estimation of forest homogeneity, but on objective analyses of measured values from inventory plots (tree heights, diameters, and age). The paper shows a new approach to the classification of forest stands on an example of the variability of measured heights on inventory plots from NFI.

Identifying patients with fibrotic nonalcoholic steatohepatitis (NASH) is crucial in order to refer them to specialist care as fibrotic NASH represents one of the major inclusion criteria for clinical trials. The aim of this study is to report the prevalence of fibrotic NASH in the general US population across the spectrum of glucose tolerance and evaluate the performance of the recently proposed Fibrotic NASH Index (FNI). This is a cross-sectional study of US adults participating in the 2017–2020 cycles of the National Health and Nutrition Examination Survey. Participants with available data to calculate FNI (which is based on AST, HbA1c and HDL-cholesterol) and with a reliable vibration controlled transient elastography examination were included. We excluded participants with chronic viral hepatitis, significant alcohol consumption or other forms of liver disease. Probable fibrotic NASH was defined as a Fibroscan-AST (FAST) score ≥ 0.35. We included a total of 6268 participants. The overall prevalence of probable fibrotic NASH was 5.9 % (95 % CI 5.2–6.7) and it increased progressively from participants with normal glucose tolerance (3.7 %, 95 % CI 2–9-4.7) to those with diabetes (14.7 %, 95 % CI 12.1–17.8). The performance of FNI for probable fibrotic NASH was satisfactory in the overall population (area under the receiver operating characteristic curve (AUROC): 0.93, 95 % CI 0.92–0.94) and it maintained a good accuracy also in participants with diabetes (n = 1113, AUROC 0.89, 95 % CI 0.86–0.92). In all groups it outperformed Fibrosis-4. FNI is an easy and reliable test to screen for NASH and its performance is maintained in patients with diabetes, a condition that was shown to negatively influence the performance of several non-invasive scores. [ABSTRACT FROM AUTHOR]

A climate change mitigation mechanism for emissions reduction from reduced deforestation and forest degradation, plus forest conservation, sustainable management of forest, and enhancement of carbon stocks (REDD+), has received an international political support in the climate change negotiations. The mechanism will require, among others, an unprecedented technical capacity for monitoring, reporting and verification of carbon emissions from the forest sector. A functional monitoring, reporting and verification requires inventories of forest area, carbon stock and changes, both for the construction of forest reference emissions level and compiling the report on the actual emissions, which are essentially lacking in developing countries, particularly in Africa. The purpose of this essay is to contribute to a better understanding of the state and prospects of forest monitoring and reporting in the context of REDD+ in Africa. We argue that monitoring and reporting capacities in Africa fall short of the stringent requirements of the methodological guidance for monitoring, reporting and verification for REDD+, and this may weaken the prospects for successfully implementing REDD+ in the continent. We presented the challenges and prospects in the national forest inventory, remote sensing and reporting infrastructures. A North-South, South-South collaboration as well as governments own investments in monitoring, reporting and verification system could help Africa leapfrog in monitoring and reporting. These could be delivered through negotiations for the transfer of technology, technical capacities, and experiences that exist among developed countries that traditionally compile forest carbon reports in the context of the Kyoto protocol. [ABSTRACT FROM AUTHOR]

Forests provide critical ecosystem services that ensure the sustainability of the environment and society. To manage forests on large scales, spatially explicit gridded data that describes the characteristics of these forests over the entire study area are required. There have been multiple efforts to create such data on regional and global scales. This type of gridded spatially explicit data on forest characteristics are typically done by integrating terrestrial forest inventory (NFI) and satellite-based remotely sensed data. Many studies that incorporate remotely sensed data and forest inventory data often directly compare pixels to inventory plots. The standard resolution of 0.0083° is typically used to integrate these two types of data sets. There is an assumption that, when producing gridded data sets incorporating forest inventory data, the finer the resolution the better the information. This assumption may seem intuitive, however at this resolution, in Europe, each 0.0083° cell has on average 1 NFI plot, which results in a sample with 0 degrees of freedom that represents 0.02% of the cell area. In this study, we challenge this assumption and we quantify the optimal resolution with which to compare and combine remotely sensed and NFI data from the largest collated and harmonized NFI data set in Europe including 196,434 plots. We determined that aggregating data with an original resolution of 0.0083° to between 0.0664° and 0.266° (or × 8 to × 32) produces the best agreement between these two forest inventory and remotely sensed data sets, and the lowest standard error in NFI data, and maintains the majority of the local-level spatial heterogeneity. [ABSTRACT FROM AUTHOR]

Spatially explicit knowledge of aboveground biomass (AGB) in large areas is important for accurate carbon accounting and quantifying the effect of forest disturbance on the terrestrial carbon cycle. We estimated AGB from 1990 to 2011 in northern Guangdong, China, based on a spatially explicit dataset derived from six years of national forest inventory (NFI) plots, Landsat time series imagery (1986-2011) and Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radars (PALSAR) 25 m mosaic data (2007-2010). Four types of variables were derived for modeling and assessment. The random forest approach was used to seek the optimal variables for mapping and validation. The root mean square error (RMSE) of plot-level validation was between 6.44 and 39.49 (t/ha), the normalized root-mean-square error (NRMSE) was between 7.49% and 19.01% and mean absolute error (MAE) was between 5.06 and 23.84 t/ha. The highest coefficient of determination R² of 0.8 and the lowest NRMSE of 7.49% were reported in 2006. A clear increasing trend of mean AGB from the lowest value of 13.58 t/ha to the highest value of 66.25 t/ha was witnessed between 1988 and 2000, while after 2000 there was a fluctuating ascending change, with a peak mean AGB of 67.13 t/ha in 2004. By integrating AGB change with forest disturbance, the trend in disturbance area closely corresponded with the trend in AGB decrease. To determine the driving forces of these changes, the correlation analysis was adopted and exploratory factor analysis (EFA) method was used to find a factor rotation that maximizes this variance and represents the dominant factors of nine climate elements and nine human activities elements affecting the AGB dynamics. Overall, human activities contributed more to short-term AGB dynamics than climate data. Harvesting and human-induced fire in combination with rock desertification and global warming made a strong contribution to AGB changes. This study provides valuable information for the relationships between forest AGB and climate as well as forest disturbance in subtropical zones. [ABSTRACT FROM AUTHOR]

Net primary production (NPP) is an important ecological metric for studying forest ecosystems and their carbon sequestration, for assessing the potential supply of food or timber and quantifying the impacts of climate change on ecosystems. The global MODIS NPP dataset using the MOD17 algorithm provides valuable information for monitoring NPP at 1-km resolution. Since coarse-resolution global climate data are used, the global dataset may contain uncertainties for Europe. We used a 1-km daily gridded European climate data set with the MOD17 algorithm to create the regional NPP dataset MODIS EURO. For evaluation of this new dataset, we compare MODIS EURO with terrestrial driven NPP from analyzing and harmonizing forest inventory data (NFI) from 196,434 plots in 12 European countries as well as the global MODIS NPP dataset for the years 2000 to 2012. Comparing these three NPP datasets, we found that the global MODIS NPP dataset differs from NFI NPP by 26%, while MODIS EURO only differs by 7%. MODIS EURO also agrees with NFI NPP across scales (from continental, regional to country) and gradients (elevation, location, tree age, dominant species, etc.). The agreement is particularly good for elevation, dominant species or tree height. This suggests that using improved climate data allows the MOD17 algorithm to provide realistic NPP estimates for Europe. Local discrepancies between MODIS EURO and NFI NPP can be related to differences in stand density due to forest management and the national carbon estimation methods. With this study, we provide a consistent, temporally continuous and spatially explicit productivity dataset for the years 2000 to 2012 on a 1-km resolution, which can be used to assess climate change impacts on ecosystems or the potential biomass supply of the European forests for an increasing bio-based economy. MODIS EURO data are made freely available at ftp://palantir.boku.ac.at/Public/MODIS_EURO. [ABSTRACT FROM AUTHOR]

Background & Aims Knockout studies of the murine Nuclear Factor I-C ( NFI-C) transcription factor revealed abnormal skin wound healing and growth of its appendages, suggesting a role in controlling cell proliferation in adult regenerative processes. Liver regeneration following partial hepatectomy ( PH) is a well-established regenerative model whereby changes elicited in hepatocytes lead to their rapid and phased proliferation. Although NFI-C is highly expressed in the liver, no hepatic function was yet established for this transcription factor. This study aimed to determine whether NFI-C may play a role in hepatocyte proliferation and liver regeneration. Methods Liver regeneration and cell proliferation pathways following two-thirds PH were investigated in NFI-C knockout (ko) and wild-type (wt) mice. Results We show that the absence of NFI-C impaired hepatocyte proliferation because of plasminogen activator I (PAI-1) overexpression and the subsequent suppression of urokinase plasminogen activator ( uPA) activity and hepatocyte growth factor (HGF) signalling, a potent hepatocyte mitogen. This indicated that NFI-C first acts to promote hepatocyte proliferation at the onset of liver regeneration in wt mice. The subsequent transient down regulation of NFI-C, as can be explained by a self-regulatory feedback loop with transforming growth factor beta 1 (TGF-ß1), may limit the number of hepatocytes entering the first wave of cell division and/or prevent late initiations of mitosis. Conclusion NFI-C acts as a regulator of the phased hepatocyte proliferation during liver regeneration. [ABSTRACT FROM AUTHOR]