Search

[Objective] The relationships between community characteristics, species diversity, and soil physical and chemical properties of Carex duriuscula in Hulunbeier steppe were analyzed in order to provide a scientific reference for the protection and restoration of grassland ecosystems. [Methods] Ninety-five sample plots typifying the Hulunbeier C. duriuscula steppe were studied. The relationships between community characteristics of four community types and species diversity and soil physical and chemical properties were studied. [Results] ① With regard to the community characteristics, aboveground biomass, species richness, and Shannon-Wiener index were significantly different among the four community types. The community of C. duriuscula-annual and biennial herbs was the highest. With regard to the soil physical and chemical properties, total nitrogen and total phosphorus were the highest in the community of C. duriuscula-rhizome herbs. ② Soil organic carbon, total nitrogen, and total phosphorus were significantly positively correlated with above-ground biomass and below-ground biomass in the C. duriuscula-clump herbs community and the C. duriuscula-forbs community. ③ Soil organic carbon, total nitrogen, and total phosphorus were significantly negatively correlated with the Shannon-Wiener index, Simpson index, and Pielou index in the C. duriuscula-rhizome herbs community and the C. duriuscula-annual and biennial herbs community. [Conclusion] There were differences in community characteristics among the four community types in the C. duriuscula steppe. Soil organic carbon, total nitrogen, and total phosphorus were the main factors affecting the differences in community characteristics and species diversity.

Monitoring crop condition, soil properties, and mapping tillage activities can be used to assess land use, forecast crops, monitor seasonal changes, and contribute to the implementation of sustainable development policy. Agricultural maps can provide independent and objective estimates of the extent of crops in a given area or growing season, which can be used to support efforts to ensure food security in vulnerable areas. Satellite data can help detect and classify different types of soil. The evolution of satellite remote sensing technologies has transformed techniques for monitoring the Earth’s surface over the last several decades. The European Space Agency (ESA) and the European Union (EU) created the Copernicus program, which resulted in the European satellites Sentinel-1B (S1B) and Sentinel-2A (S2A), which allow the collection of multi-temporal, spatial, and highly repeatable data, providing an excellent opportunity for the study of land use, land cover, and change. The goal of this study is to map the land cover of Côte d’Ivoire’s West Central Soubre area (5°47′1′′ North, 6°35′38′′ West) between 2014 and 2020. The method is based on a combination of S1B and S2A imagery data, as well as three types of predictors: the biophysical indices Normalized Difference Vegetation Index “(NDVI)”, Modified Normalized Difference Water Index “(MNDWI)”, Normalized Difference Urbanization Index “(NDBI)”, and Normalized Difference Water Index “(NDWI)”, as well as spectral bands (B1, B11, B2, B3, B4, B6, B7, B8) and polarization coefficients VV. For the period 2014–2020, six land classifications have been established: Thick_Forest, Clear_Drill, Urban, Water, Palm_Oil, Bareland, and Cacao_Land. The Random Forest (RF) algorithm with 60 numberOfTrees was the primary categorization approach used in the Google Earth Engine (GEE) platform. The results show that the RF classification performed well, with outOfBagErrorEstimates of 0.0314 and 0.0498 for 2014 and 2020, respectively. The classification accuracy values for the kappa coefficients were above 95%: 96.42% in 2014 and 95.28% in 2020, with an overall accuracy of 96.97% in 2014 and 96 % in 2020. Furthermore, the User Accuracy (UA) and Producer Accuracy (PA) values for the classes were frequently above 80%, with the exception of the Bareland class in 2020, which achieved 79.20%. The backscatter coefficients of the S1B polarization variables had higher GINI significance in 2014: VH (70.80) compared to VH (50.37) in 2020; and VV (57.11) in 2014 compared to VV (46.17) in 2020. Polarization coefficients had higher values than the other spectral and biophysical variables of the three predictor variables. During the study period, the Thick_Forest (35.90% ± 1.17), Palm_Oil (57.59% ± 1.48), and Water (5.90% ± 0.47) classes experienced a regression in area, while the Clear_Drill (16.96% ± 0.80), Urban (2.32% ± 0.29), Bareland (83.54% ± 1.79), and Cacao_Land (35.14% ± 1.16) classes experienced an increase. The approach used is regarded as excellent based on the results obtained.

High-accuracy land use and land cover maps (LULC) are increasingly in demand for environmental management and decision-making. Despite the limitation, Machine learning classifiers (MLC) fill the gap in any complex issue related to LULC data accuracy. Visualizing land-cover information is critical in mitigating Côte d’Ivoire’s deforestation and land use planning using the Google Earth Engine (GEE) software. This paper estimates the probability of RF classification in South Western Côte d’Ivoire. Landsat 8 Surface Reflectance Tiers 1 (L8OLI/TIRS) data with a resolution of 30 mn for 2020 were used to classify the western and southwestern Forest areas of Côte d’Ivoire. The Random Forest (RF) learning classifier was calibrated using 80% training data and 20% testing data to assess GEE classification accuracy performance. The findings indicate that the Forest land class accounts for 39.48% of the entire study area, followed by the Bareland class, the Cultivated land class 21.28±0.90%, the Water class 1.94±0.27%, and the 0.96±0.60% Urban class respectively. The classification reliability test results show that 99.85%±1.95 is the overall training accuracy (OTA), and 99.81±1.95% for the training kappa (TK). The overall validation accuracy (VOA) is 94.02±1.90%, while 92.25±1.88% validation kappa (VK) and 92.45±1.88% RF Accuracy. The different coefficients classification accuracy results obtained from the RF confusion matrix indicate that each class has three good performances. This is due to the cultivated land samples lower spatial resolution and smaller sample numbers, resulting in a lower PA for this class than for the other classes. All had producer accuracy (PA) and user accuracy (UA) more than 90% using the L8OLI/TIRS data. Using the RF-based classification method integrated into the GEE provides an efficient and high scores accuracy for classifying land use and land cover in the study area.

Assessing and quantifying the current forest resources status underpins sustainable forest resources planning and management. To this end, this study was conducted to analyze the dynamics of land use cover change (LUCC) and explore their drivers at the central highlands of Dry Afromontane Chilimo-Gaji forest for the study period (1973-2015) under consideration. The result of the study indicated that landscape trends have occurred in Chilimo-Gaji forest over the last 43 years and five classes of LUCC namely shrub land, rural settlement, bare land and road, forest land and agricultural land were identified. The most commonly reported drivers of deforestation in the study area were expansion of agricultural land, rural settlements, population growth, insecurity of the tenure and rights over the land, timber production and fuel wood collection. Surprisingly, the increasing demand for agricultural land and human settlement for increasing human population underpinned by expansion of agricultural activities led to the clearing of forest land in the study area. The study reveals that the forest land cover type has lost 922.14 hectares (26.96 %) which were changed into other land cover types such as agriculture and settlement for the study period under consideration with an annually rate of 21.45 ha. However, deforestation rates showed declining trend between the time periods 2008-2015 as a result of introduction of Participatory Forest Management (PFM) schemes (1996) which involve the local community in management and sharing of the benefit obtained from the management. In order to promote sustainable forest resources management in the study area in the years to come, integrated land use planning and management and addressing key drivers of deforestation were recommended.

Monitoring crop condition, soil properties, and mapping tillage activities can be used to assess land use, forecast crops, monitor seasonal changes, and contribute to the implementation of sustainable development policy. Agricultural maps can provide independent and objective estimates of the extent of crops in a given area or growing season, which can be used to support efforts to ensure food security in vulnerable areas. Satellite data can help detect and classify different types of soil. The evolution of satellite remote sensing technologies has transformed techniques for monitoring the Earth's surface over the last several decades. The European Space Agency (ESA) and the European Union (EU) created the Copernicus program, which resulted in the European satellites Sentinel-1B (S1B) and Sentinel-2A (S2A), which allow the collection of multi-temporal, spatial, and highly repeatable data, providing an excellent opportunity for the study of land use, land cover, and change. The goal of this study is to map the land cover of Côte d'Ivoire's West Central Soubre area (5°47'1" North, 6°35'38" West) between 2014 and 2020. The method is based on a combination of S1B and S2A imagery data, as well as three types of predictors: the biophysical indices Normalized Difference Vegetation Index "(NDVI)", Modified Normalized Difference Water Index "(MNDWI)", Normalized Difference Urbanization Index "(NDBI)", and Normalized Difference Water Index "(NDWI)", as well as spectral bands (B1, B11, B2, B3, B4, B6, B7, B8) and polarization coefficients VV. For the period 2014-2020, six land classifications have been established: Thick_Forest, Clear_Drill, Urban, Water, Palm_Oil, Bareland, and Cacao_Land. The Random Forest (RF) algorithm with 60 numberOfTrees was the primary categorization approach used in the Google Earth Engine (GEE) platform. The results show that the RF classification performed well, with outOfBagErrorEstimates of 0.0314 and 0.0498 for 2014 and 2020, respectively. The classification accuracy values for the kappa coefficients were above 95%: 96.42% in 2014 and 95.28% in 2020, with an overall accuracy of 96.97% in 2014 and 96 % in 2020. Furthermore, the User Accuracy (UA) and Producer Accuracy (PA) values for the classes were frequently above 80%, with the exception of the Bareland class in 2020, which achieved 79.20%. The backscatter coefficients of the S1B polarization variables had higher GINI significance in 2014: VH (70.80) compared to VH (50.37) in 2020; and VV (57.11) in 2014 compared to VV (46.17) in 2020. Polarization coefficients had higher values than the other spectral and biophysical variables of the three predictor variables. During the study period, the Thick_Forest (35.90% ± 1.17), Palm_Oil (57.59% ± 1.48), and Water (5.90% ± 0.47) classes experienced a regression in area, while the Clear_Drill (16.96% ± 0.80), Urban (2.32% ± 0.29), Bareland (83.54% ± 1.79), and Cacao_Land (35.14% ± 1.16) classes experienced an increase. The approach used is regarded as excellent based on the results obtained. [ABSTRACT FROM AUTHOR]

Introduction Diabetic nephropathy (DN) is a severe microvascular complication in patients with diabetes and also a primary cause of end-stage renal disease (Ritz et al. 1999; Sun et al. 2013). [...], The purpose of this study was to investigate the therapeutic effects of hyperoside (Hyp) on glomerulosclerosis in diabetic nephropathy and its underlying mechanisms. Blood glucose, kidney mass, and renal function of mice were measured. Renal morphology was observed using hematoxylin and eosin, periodic acid--Schiffs, andMasson's trichrome stain. Fibronectin (FN) and collagen IV (COL IV) in kidney were determined by Western blot and immunohistochemical studies. Matrix metalloproteinases (MMP)-2 and -9 and tissue inhibitors of metalloproteinase (TIMP)-1 in renal tissues were detected on both the mRNA and protein levels. miRNA expression and artificial alterations by miRNA agomir transfection were evaluated to investigate the protective mechanism of Hyp in mesangial cells. Hyp effectively improved renal function and physiologic features of db/db mice. Hyp also ameliorated glomerulosclerosis by suppressing FN, COL IV, and TIMP-1 expressions and promoting MMP-9 and MMP-2 expressions. The change in MMP-9 mRNA expression was inconsistent with that in protein levels in kidney, indicating that there was a post-transcriptional regulation. Further exploration in vitro showed that miR-21 was downregulated by Hyp, increasing expression of its target, MMP-9. These results suggest that Hyp can ameliorate glomerulosclerosis in diabetic nephropathy by downregulating miR-21 to increase expression of its target, MMP-9. Key words: hyperoside, glomerulosclerosis, diabetic nephropathy, miR-21, MMP-9. Le but de cette etude etait d'examiner les effets therapeutiques de l'hyperoside (Hyp) sur la glomerulosclerose dans la nephropathie diabetique et les mecanismes sous-jacents. Le glucose sanguin, le poids des reins et la fonction renale des souris ont ete mesures. La morphologie renale a ete observee par des colorations a l'hematoxiline-eosine, a l'acide periodique-reactif de Schiff et au trichrome de Masson. Le contenu en fibronectine (FN) et en collagene IV (COL IV) du rein a ete determine par buvardage Western et par immunohistochimie. Les metalloproteases de la matrice (MMP)-2 et -9 et l'inhibiteur tissulaire des metalloproteases (TIMP-1) des tissus renaux ont ete detectes tant sur le plan de l'ARNm qu'au niveau proteique. L'expression de miARN et des modifications artificielles realisees au moyen de transfections de miARN agomir ont ete evaluees afin d'examiner le mecanisme protecteur de l'Hyp dans les cellules mesangiales. L'Hyp ameliorait efficacement la fonction et les caracteristiques physiologiques renales des souris db/db. L'Hyp ameliorait aussi la glomerulosclerose en supprimant l'expression de FN, COL IV et TIMP-1 et en stimulant l'expression de MMP-9 et MMP-2. Le changement d'expression de l'ARNm de MMP-9 ne concordait pas avec les niveaux proteiques dans le rein, indiquant qu'une regulation post-transcriptionnelle etait survenue. De plus amples explorations in vitro ont montre que miR-21 etait regule a la baisse par l'Hyp, augmentant l'expression de sa cible, MMP-9. Ces resultats suggerent que l'Hyp peut ameliorer la glomerulosclerose lors de la nephropathie diabetique en regulant a la baisse miR-21 permettant d'accroitre l'expression de sa cible, MMP-9. [Traduit par la Redaction] Mots-cles: hyperoside, glomerulosclerose, nephropathie diabetique, miR-21, MMP-9.

Almost all eukaryotes need oxygen to maintain regular physiological activities. When the organism is under hypoxic situation for a persistent or periodic, it will induce irreversible physiological disorders and even pathological results. Hypoxia is closely related to the pathogenesis of metabolic diseases, cancer, chronic heart disease and kidney disease, myocardial ischemia, as well as reproductive diseases like preeclampsia and endometriosis. Therefore, monitoring and treatment of hypoxia have important implications for the pathophysiology of human-related diseases. Carbon dots (CDs) are emerging nanomaterials developed after 2004 with excellent performance, and have broad application potential in variousdomains likeoptical, biomedicine, energy. Advanced hypoxia therapeutics should be integrated with monitoring and treatment, and CDs with excellent performance are good potential options when sensing is combined with various therapeutic methods. Some researchers have also begun to carry out research in related fields and achieved some results. This article aims to clarify the various applications of CDs in hypoxia-related fields in recent years, including hypoxia sensing and hypoxia tumor theranostics. Finally, the possible challenges and prospects for the application of CDs in hypoxia-related fields are discussed. Graphical Abstract [ABSTRACT FROM AUTHOR]

High-accuracy land use and land cover maps (LULC) are increasingly in demand for environmental management and decision-making. Despite the limitation, Machine learning classifiers (MLC) fill the gap in any complex issue related to LULC data accuracy. Visualizing land-cover information is critical in mitigating Côte d'Ivoire's deforestation and land use planning using the Google Earth Engine (GEE) software. This paper estimates the probability of RF classification in South Western Côte d'Ivoire. Landsat 8 Surface Reflectance Tiers 1 (L8OLI/TIRS) data with a resolution of 30 mn for 2020 were used to classify the western and southwestern Forest areas of Côte d'Ivoire. The Random Forest (RF) learning classifier was calibrated using 80% training data and 20% testing data to assess GEE classification accuracy performance. The findings indicate that the Forest land class accounts for 39.48% of the entire study area, followed by the Bareland class, the Cultivated land class 21.28±0.90%, the Water class 1.94±0.27%, and the 0.96±0.60% Urban class respectively. The classification reliability test results show that 99.85%±1.95 is the overall training accuracy (OTA), and 99.81±1.95% for the training kappa (TK). The overall validation accuracy (VOA) is 94.02±1.90%, while 92.25±1.88% validation kappa (VK) and 92.45±1.88% RF Accuracy. The different coefficients classification accuracy results obtained from the RF confusion matrix indicate that each class has three good performances. This is due to the cultivated land samples lower spatial resolution and smaller sample numbers, resulting in a lower PA for this class than for the other classes. All had producer accuracy (PA) and user accuracy (UA) more than 90% using the L8OLI/TIRS data. Using the RF-based classification method integrated into the GEE provides an efficient and high scores accuracy for classifying land use and land cover in the study area. [ABSTRACT FROM AUTHOR]