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Liver and lung tissue damage caused by sepsis is still one of the causes of death. B-lymphocyte-induced maturation protein-1 (Blimp-1) has a protective role in inflammation-related disease. However, whether Blimp-1 can regulate cell pyroptosis and affect disease progression in sepsis is still unclear. Animal and cell models were established by the cecal ligation and puncture method and lipopolysaccharides (LPS)-induced RAW 264.7 cells, respectively, and the role of Blimp-1 in regulation inflammatory response and pyroptosis was verified. The changes of inflammation and pyroptosis in liver and lung tissues of septic mice were determined by the addition of TAK-242 (TLR4 inhibitor). Cell pyroptosis and the level of inflammation was detected after Blimp-1 knockdown and TAK-242 treatment in the cell model. The expression of Blimp-1 was continuously increased in a septic mice model. After treatment with TAK-242, the expression of Blimp-1, pyroptosis and inflammatory levels were reduced in mice. In the LPS-induced cell model, cell injury by knockout Blimp-1 was increased, and cell activity was restored after TAK-242 intervention. Overexpression of Blimp-1 relieved LPS-induced cellular inflammatory damage and pyroptosis. Our study had shown that Blimp-1 could improve septic damage by regulating the level of cellular inflammation and pyroptosis in sepsis. [ABSTRACT FROM AUTHOR]

The soil respiration (Rs) of forests is a major component of global Rs, yet few studies have focused on it. This study aimed to estimate global forest Rs and its changes at a resolution of 30 m via an artificial neural network (ANN) model. Five input candidates representing forest type, climatic, soil, and geographical information, as well as 1472 satisfactory forest Rs records, were used to build the ANN model and evaluate the model performance via a 10‐fold cross‐validation scheme. Global forest change data sets were used to accurately define the extent of forests and their changes, which was achievable because of the dynamic information and high resolution (30 m) of the data sets. The results indicate that the average annual global forest Rs from 2000 to 2020, as estimated by the optimal ANN model with an r2 value of 0.67 and a root‐mean‐square error of 252.6 g C m−2 yr−1, was 46.24 ± 5.86 Pg C yr−1. From 2001 to 2019, the average theoretical annual global forest Rs loss was 0.22 ± 0.06 Pg C yr−1 due to an average forest loss area of 23.4 million ha yr−1. In addition, the annual Rs theoretically increased by 0.75 Pg C in 2012 due to a global forest gain area of 80.5 million from 2001 to 2012. The presented data sets of global forest Rs and its changes can provide an accurate benchmark for discussing the carbon cycle and climate change at global to regional scales, even when operating over a small forest area (i.e., dozens of ha), which is a scale that has been ignored in other global Rs studies. Plain Language Summary: This paper discusses the soil respiration (written as Rs) of global forests, which has rarely been the focus of other Rs studies. Soil respiration is defined as the amount of Carbon Dioxide released from the soil surface into the atmosphere. The goal of this study was to estimate global forest Rs and its changes at a resolution of 30 m using the artificial intelligence model. The model had five input candidates representing forest type, climatic, soil, and geographical information. Additionally, 1472 satisfactory forest Rs records were used to build the model and evaluate its performance via a cross‐validation scheme. Global forest change data sets have a high 30 m‐resolution and provide dynamic information, and were thus chosen to accurately define the extent of forests and their changes in this research. This results show that the estimated average annual global forest Rs from 2000 to 2020 was 46.24 ± 5.86 quadrillion gram of carbon. The data sets produced by this study is able to act as an accurate benchmark used to discuss the carbon cycle and climate change at global to regional scales, and they can even make it possible to operate over a smaller forest area, such as dozens of ha. Key Points: 30‐m resolution global data sets of forest soil respiration and its changes from 2000 to 2020 are providedThe average annual global forest soil respiration from 2000 to 2020 was 46.24 ± 5.86 Pg C yr−1An accurate benchmark is provided for discussing the carbon cycle and climate change at global to regional scales, even over a small forest area (i.e., dozens of ha) [ABSTRACT FROM AUTHOR]

Growth and yield models are critically important for forest management planning. Biophysical factors such as light, temperature, soil water, and nutrient conditions are known to have major impacts on tree growth. However, it is difficult to incorporate these biophysical variables into growth and yield models due to large variation and complex nonlinear relationships between variables. In this study, artificial intelligence technology was used to develop individual-tree-based basal area (BA) and volume increment models. The models successfully account for the effects of incident solar radiation, growing degree days, and indices of soil water and nutrient availability on BA and volume increments of over 40 species at 5-year intervals. The models were developed using data from over 3000 permanent sample plots across the province of Nova Scotia, Canada. Model validation with independent field data produced model efficiencies of 0.38 and 0.60 for the predictions of BA and volume increments, respectively. The models are applicable to predict tree growth in mixed species, even- or uneven-aged forests in Nova Scotia but can easily be calibrated for other climatic and geographic regions. Artificial neural network models demonstrated better prediction accuracy than conventional regression-based approaches. Artificial intelligence techniques have considerable potential in forest growth and yield modelling. Resume: Les modeles de croissance et de production sont tres importants pour la planification de l'amenagement forestier. Des facteurs biophysiques comme la lumiere, la temperature, l'eau du sol et les conditions nutritives ont tous de gros impacts sur la croissance des arbres. Cependant, il est difficile d'introduire ces variables biophysiques dans les modeles de croissance et de production a cause de leur grande variation et des relations non lineaires complexes entre ces variables. Dans cette etude, nous avons utilise une technologie d'intelligence artificielle pour mettre au point des modeles de prevision de l'accroissement en surface terriere et en volume a l'echelle de l'arbre. Les modeles tiennent compte adequatement des effets du rayonnement solaire incident, des degres-jours de croissance et d'indices de disponibilite en eau et en nutriments sur l'accroissement en surface terriere et en volume de plus de 40 especes a intervalle de 5 ans. Les modeles ont ete mis au point a l'aide des donnees de plus de 3000 placettes echantillons permanentes couvrant toute la province de la Nouvelle-Ecosse, au Canada. L'evaluation des modeles a l'aide d'un fichier de donnees independantes a produit des valeurs d'efficacite de respectivement 0,38 et 0,60 pour les previsions d'accroissement en surface terriere et en volume. Les modeles peuvent etre utilises pour prevoir la croissance des arbres en peuplement mixte, dans les forets de structure equienne ou inequienne de la Nouvelle-Ecosse, mais peuvent etre facilement etalonnes pour d'autres regions climatiques et geographiques. La precision des previsions obtenue avec les modeles de reseaux neuronaux artificiels etait superieure a celle des approches conventionnelles fondees sur la regression. Les techniques d'intelligence artificielle ont un potentiel considerable dans le domaine de la modelisation de la croissance et de la production forestiere. [Traduit par la Redaction], Introduction Forest growth and yield (G&Y) models provide quantitative information about stand dynamics. They are effective in (i) updating forest inventories, (ii) predicting future forest G&Y, and (iii) preparing forest [...]

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The article presents a study which assesses the effectiveness of the flow diversion terraces (FDT) system in soil erosion control using a geographic information system (GIS) model. The study used methodologies like data collection at Black Brook Watershed (BBW) in New Brunswick, and determination of slope steepness and slope length factors using the digital elevation model (DEM). The study concluded that GIS model can be a useful tool for FDT effectiveness assessment.

Liver and lung tissue damage caused by sepsis is still one of the causes of death. B-lymphocyte-induced maturation protein-1 (Blimp-1) has a protective role in inflammation-related disease. However, whether Blimp-1 can regulate cell pyroptosis and affect disease progression in sepsis is still unclear. Animal and cell models were established by the cecal ligation and puncture method and lipopolysaccharides (LPS)-induced RAW 264.7 cells, respectively, and the role of Blimp-1 in regulation inflammatory response and pyroptosis was verified. The changes of inflammation and pyroptosis in liver and lung tissues of septic mice were determined by the addition of TAK-242 (TLR4 inhibitor). Cell pyroptosis and the level of inflammation was detected after Blimp-1 knockdown and TAK-242 treatment in the cell model. The expression of Blimp-1 was continuously increased in a septic mice model. After treatment with TAK-242, the expression of Blimp-1, pyroptosis and inflammatory levels were reduced in mice. In the LPS-induced cell model, cell injury by knockout Blimp-1 was increased, and cell activity was restored after TAK-242 intervention. Overexpression of Blimp-1 relieved LPS-induced cellular inflammatory damage and pyroptosis. Our study had shown that Blimp-1 could improve septic damage by regulating the level of cellular inflammation and pyroptosis in sepsis., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Soil conservation beneficial management practices (BMPs) are effective at controlling soil loss from farmlands and minimizing water pollution in agricultural watersheds. However, costs associated with implementing and maintaining these practices are high and often deter farmers from using them. Consequently, it is necessary to conduct cost-benefit analysis of BMP implementation to assist decision-makers with planning to provide the greatest level of environmental protection with limited resources and funding. The Soil and Water Assessment Tool (SWAT) was used to evaluate the efficacy of flow diversion terraces (FDT) in abating sediment yield at the outlet of Black Brook Watershed (BBW), northwestern New Brunswick. Different FDT-implementation scenarios were expressed as the ratio of land area protected by FDT to the total cultivated area. From this analysis, we found that average annual sediment yield decreased exponentially with increased FDT protection. When the proportion of FDT-protected areas was low, sediment reductions caused by FDT increased sharply with increasing use of FDT. Similarly, marginal sediment yield abatement costs (dollar per tonne of sediment reduction) increased exponentially with increasing proportion of FDT-protected area. The results indicated that increasing land protection with FDT from 6 to 50% would result in a reduction of about 2.1 tonne ha(-1) yr(-1) and costs of sediment reduction increased from $7 to $12 per tonne. Increasing FDT-protected cropland from 50 to 100%, a reduction of about 0.9 tonne of sediment ha(-1) yr(-1) would occur and the costs would increase from $12 to $53 per tonne of sediment yield reduction.

To improve the accuracy of automatic classification and identification of TM remote sensing images in forest area, an expert system for automatically classifying and identifying deciduous-conifer mixed forest was built up, based on the GIS technique, quantitative analysis on the internal relations between geographic factors such as DEM and slope aspect and environment factors like soil type, and qualitative analysis on the spectrum information and preclassification information of sensing images, aimed to build a classification knowledge system. Taking the TM remote sensing image of Wangqing Forest Bureau in Jilin Province as an example, the study showed that this expert system could obviously reduce the influence of mixed pixel and terrain shadow. The classification precision of this system was increased by 14.22%, compared with that of Iterative Self-Organizing Data Analysis Techniques Algorithm (ISODATA) unsupervised classification, and the Kappa index was 0.7556, which could help to classify needle, deciduous and mixed forests. Introducing GIS data into the expert system could also solve the problem that TM remote sensing image could not do, due to the loss of correct spectrum value in cloudy and shady area.