Cluster Tree Research Articles

Three de novo assembled wild cacao genomes from the Upper Amazon

Theobroma cacao, the chocolate tree, is indigenous to the Amazon basin, the greatest biodiversity hotspot on earth. Recent advancement in plant genomics highlights the importance of de novo sequencing of multiple reference genomes to capture the genome diversity present in different cacao populations. In this study, three high-quality chromosome-level genomes of wild cacao were constructed, de novo assembled with HiFi long reads sequencing, and scaffolded using a reference-free strategy. These genomes represent the three most important genetic clusters of cacao trees from the Upper Amazon region. The three wild cacao genomes were compared with two reference genomes of domesticated cacao. The five cacao genetic clusters were inferred to have diverged in the early and middle Pleistocene period, approximately 1.83–0.69 million years ago. The results shown here serve as an example of understanding how the Amazonian biodiversity was developed. The three wild cacao genomes provide valuable resources for studying genetic diversity and advancing genetic improvement of this species.

Phytochemical investigation and experimental evaluation of Panchavalkala for Lekhana Karma w.s.r. to Antihyperlipedemic Activity

Panchavalkala the barks of five trees i.e., Nyagrodha (Ficus benghalensis L.), Udumbara (Ficus racemosa L.), Ashwatha (Ficus religiosa L.), Plaksha (Ficus virens Aiton) and Parisha (Thespesia populnea (L.)Sol.ex Correa) are also known as Pancha Ksheeri Vrikshas in use since Vedic period. The plant samples were collected in Sharad Rutu (late autumn) from their natural habitat, extracted using ethanol and water and subjected for the phytochemical analysis. The study was aimed to evaluate Antihyperlipidemic effect (Lekhana Karma) of Panchavalkala Bark extracts individually and in combination (1:1:1:1:1) both in ethanolic and aqueous extracts on serum lipid profile in Cafeteria died (HFD) induced hyperlipidemia in SD rats. The animals were divided into fifteen groups of six animals each. The normal control group continued to be fed laboratory pellet chow ad libitum. The cafeteria diet-control group received the cafeteria diet in addition to the normal pellet diet (NPD). The remaining 13 groups were fed with the cafeteria diet and NPD along with standard control Atorvastatin 10mg/kg and the test drug p.o for 30 days. Treatment with the test substance PVK AE and PVK EE at doses of 200mg/kg body weight p.o. controlled and regulated the HFD induced weight gain, glucose and lipids in the blood when compared against high fat diet control (P< 0.05). PVK bark extracts are capable of exhibiting significant antihyperlipidemic activity in HFD induced SD rats. Taken together, this study strongly suggests that PVK bark extracts might be an efficient way for treatment of hyperlipidemia.

Land Cover Patterns of Urban Lots and Their Contribution to Ecological Functions

The green infrastructure of urban lots performs socio-ecological functions and provides several ecosystem services (ESs) in urban environments. By assessing the land cover patterns of such sites, one can deduce ecological functions and potential ESs. We represented the various land cover combinations of lots by mapping and classifying the vegetation quality of 2828 lots in the city of Belo Horizonte, Southeast Brazil. We performed cluster analysis of land cover with weighting according to ecological functions, potential for ES provision, and performance. Most lots (1024, 36.21%) were in the moderate vegetation quality class (trees/native vegetation between 25% and 50% or >50% herbaceous-shrubby vegetation), which included the largest plot of 383,300 m2 and a median plot size of 403 m2. A total of 244 (8.63%) lots were in the highest vegetation quality class (trees/native vegetation between >50% and 100%). The lots included diverse vegetation cover combinations of up to ten land cover types, with two dominant types: herbaceous-shrubby vegetation and tree clumps. Among the four land cover patterns obtained, those covered by tree clusters (1193 lots; 42.18%) had the highest ecological performance and the greatest potential for regulating and supporting ESs. This cluster had the highest average land cover of tree clumps (49%) and the highest averages for native vegetation formations (2–6%). Our study showed a variety of land cover patterns and an expressive percentage of lots with capabilities to provide ecological functions and ESs, which can support urban sustainability policies that have yet to be addressed.

Hierarchical Integration of UAS and Sentinel-2 Imagery for Spruce Bark Beetle Grey-Attack Detection by Vegetation Index Thresholding Approach

This study aimed to examine the efficiency of the vegetation index (VI) thresholding approach for mapping deadwood caused by spruce bark beetle outbreak. For this, the study used upscaling from individual dead spruce detection by unmanned aerial (UAS) imagery as reference data for continuous spruce deadwood mapping at a stand/landscape level by VI thresholding binary masks calculated from satellite Sentinel-2 imagery. The study found that the Normalized Difference Vegetation Index (NDVI) was most effective for distinguishing dead spruce from healthy trees, with an accuracy of 97% using UAS imagery. The study results showed that the NDVI minimises cloud and dominant tree shadows and illumination differences during UAS imagery acquisition, keeping the NDVI relatively stable over sunny and cloudy weather conditions. Like the UAS case, the NDVI calculated from Sentinel-2 (S2) imagery was the most reliable index for spruce deadwood cover mapping using a binary threshold mask at a landscape scale. Based on accuracy assessment, the summer leaf-on period (June–July) was found to be the most appropriate for spruce deadwood mapping by S2 imagery with an accuracy of 85% and a deadwood detection rate of 83% in dense, close-canopy mixed conifer forests. The study found that the spruce deadwood was successfully classified by S2 imagery when the spatial extent of the isolated dead tree cluster allocated at least 5–7 Sentinel-2 pixels.

Mapping pollen allergenicity from urban trees in Valencia: A tool for green infrastructure planning

Urban trees provide many benefits to citizens but also have associated disservices such as pollen allergenicity. Pollen allergies affect 40% of the European population, a problem that will be exacerbated with climate change by lengthening the pollen season. The allergenic characteristics of the urban trees and urban parks of the city of Valencia (Spain) have been studied. The Value of Potential Allergenicity (VPA) was calculated for all species. The most abundant allergenic trees with a very high VPA were the cypresses, followed by Platanus x hispanica and species of genera Morus, Acer and Fraxinus, with a high VPA. On the contrary, Citrus x aurantium, Melia azedarach, Washingtonia spp., Brachychiton spp. and Jacaranda mimosifolia were among the most abundant low allergenic trees. VPA was mapped for the city and a hot spot analysis was applied to identify areas of clustering of high and low VPA values. This geostatistical analysis provides a comprehensive representation of the VPA patterns which is very useful for urban green infrastructure planning. The Index of Urban Green Zone Allergenicity (IUGZA) was calculated for the main parks of the city. The subtropical and tropical flora component included many entomophilous species and the lowest share of high and very high allergenic trees in comparison with the Mediterranean and Temperate components. Overall, a diversification of tree species avoiding clusters of high VPA trees, and the prioritization of species with low VPA are good strategies to minimize allergy-related impacts of urban trees on human health.

Genetic diversity, population structure and marker-trait associations in Indian kale (Brassica oleracea L. gp. acephala) using cross-species microsatellite markers

Kale is known for its exceptional nourishing and functional benefits to human body. However, it is an understudied species from genomic as well as agronomic aspects. It is important to characterize niche kale germplasms around the world to systematically conserve and utilize its genetic variability, especially for commercial traits in the interest of growers, consumers and industry. With this view, genomic and phenotypic characterizations of 62 Kashmiri kale accessions including popular landraces were done to estimate and partition genetic diversity, understand trait relationships, develop population structure and divulge marker-trait associations of economic significance. Sixty-six cross species microsatellite (SSR) markers within Brassica genus amplified 269 alleles in the germplasm. Their polymorphic information content (PIC) ranged from 0.00078 to 0.953 with an average of 0.407. The population structure analysis and neighbour joining tree clustering categorized the germplasm into three sub-populations. AMOVA revealed more within-population variance (67.73 %) than among-populations (32.27 %) variance. The principal component analysis (PCA) involving 24 agronomical traits revealed seven PCs (PC1 to PC7) having Eigen values more than 1, which explained a cumulative variation of 69.21 %. Association mapping with respect to these 24 agronomical traits using mixed linear model and general linear model revealed six overlapping significant marker-trait relationships with five being significant at probability value of 0.001/0.0001. The highly significant associations of two SSRs with economically important traits (siliqua length and seed weight) significantly correlated/related with leaf yield and seed yield were revealed for their possible utilization in marker assisted breeding for higher leaf and seed yields.

The Impact of Cluster Fig Fruits Powder Addition on Nutritional and Organoleptic Properties of Some Healthy Food Products

Cluster fig, also known as goolar in Hindi, is a medicinal fruit abundant in India, offering a rich source of iron and other essential micronutrients. Wheat, a staple in the Indian diet, is energy-dense and provides valuable carbohydrates and proteins. Pearl Millet, the most commonly grown type of millet, is renowned for its high calorie, vitamin, and mineral content. Mung beans, part of the legume family, are calorie-dense and a great source of protein and polyunsaturated fatty acids. In this study, cluster fig powder, germinated wheat flour, germinated mung bean flour, germinated pearl millet, carrot powder, and beetroot powder were utilized for product development. Two products were developed with three treatments, varying the quantity of ingredients. Sensory and proximate analysis were conducted on the developed products, with each treatment having three different replications to minimize errors during analysis. Based on the sensory evaluation, T1 is considered the best among all treatments, with a moderate ratio of cluster fig contributing to its taste and flavor. The ash content varied across replications, with the control product having the highest ash content of 11.03%, likely due to experimental errors. Treatments T1, T2, and T3 had ash content values of 10.19%, 10.55%, and 10.89%, respectively. Organoleptic evaluation indicated that as the quantity of cluster fig increased, the taste became bitter, leading to decreased acceptability. Proximate analysis revealed that an increase in the quantity of cluster fig resulted in higher iron and zinc content in the products. This study highlights the organoleptic acceptability and nutritive & medicinal properties of cluster fig, providing insights into the appropriate quantities of cluster fig powder and other ingredients for various processed convenience healthy foods. The research emphasizes the use of cluster fig, a readily available fruit in India, which has not been previously utilized in convenience food products.

The Chinese Diaspora of the Russian Far East: History, Modernity, and Prospects

This article presents the results of the process of sporadic formation of the Chinese diaspora in the Russian Far East, revealing the most controversial elements of this process. They are the difficulty of defining the meaning of the concept of “Chinese diaspora” and the date of the origin of the diaspora in the Russian Far East; also, the discussion about the size of the Chinese population in the Far East at the boundaries of the eighteenth and nineteenth and twentieth-twenty- first centuries. The author conducts a cluster analysis of the regions of the Russian Far East regarding the functioning of the Chinese diaspora there using data on the area of the Russian Far East regions, population, GRP per capita, and the number of murders per 100,000 people. Each of these indicators characterises the key areas of development of the regions of the Russian Far East. Based on the results of cluster tree construction, the identification of three groups of subregions of the Far East and map zoning of the region, it is revealed that the geographical proximity of these sub-regions to the Chinese territory has a much stronger influence on the formation of the Chinese diaspora than the economic factor. Yakutia, which accounts for 42 percent of the Chinese diaspora in the Far East, can be singled out separately.

Exploring the Antimicrobial Efficacy of <i>Panchavalkala Kwatha</i> and <i>Arka</i>: A Comprehensive Study

Panchavalkala, a combination of five tree barks, is significant in Ayurvedic formulations as an individual ingredient and in composite preparations. These five barks are Vata (Ficus bengalensis L.), Udumbara (Ficus glomerata Roxb.), Ashwatha (Ficus religiosa L.), Parisha (Thespesia populanea Soland. ex-Correa.), and Plaksha (Ficus lacor Buch-Ham.). As per Ayurveda, Panchavalkala Kwatha has vrana ropana (wound healing) properties and is also known for its diverse properties encompassing antiseptic, anti-inflammatory, antioxidant, antibacterial, and antimicrobial attributes, rendering it valuable for wound purification and healing. This study aims to investigate the antimicrobial potential of Panchavalkala Kwatha (PVK) while validating its ancient Ayurvedic significance in wound healing. The study involves meticulously preparing Panchavalkala Kwatha and Panchavalkala Arka. Panchavalkala Kwatha is meticulously prepared by the guidelines stipulated in Sharangadhara Samhita, while Panchavalkala Arka (PVA) follows the principles outlined in Arka Prakasha. The antimicrobial activity of Panchavalkala Kwatha and Panchavalkala Arka was assessed using Escherichia coli and Staphylococcus aureus through the well-diffusion assay. Panchavalkala Kwatha exhibits a substantial zone of inhibition measuring 25 mm against E. coli and 28 mm against S. aureus. This antimicrobial efficacy is comparable to Ciprofloxacin’s, signifying potent antimicrobial activity. In contrast, Panchavalkala Arka shows no inhibitory effect on microbial growth. These results underscore the robust antibacterial activity of Panchavalkala Kwatha against both E. coli and S. aureus, while Panchavalkala Arka remains inert. This research furnishes primary evidence supporting the antibacterial properties of Panchavalkala Kwatha against E. coli and S. aureus. These findings suggest the potential utility of this Ayurvedic formulation in expediting wound healing processes.

Infrared: a declarative tree decomposition-powered framework for bioinformatics.

Many bioinformatics problems can be approached as optimization or controlled sampling tasks, and solved exactly and efficiently using Dynamic Programming (DP). However, such exact methods are typically tailored towards specific settings, complex to develop, and hard to implement and adapt to problem variations. We introduce the Infrared framework to overcome such hindrances for a large class of problems. Its underlying paradigm is tailored toward problems that can be declaratively formalized as sparse feature networks, a generalization of constraint networks. Classic Boolean constraints specify a search space, consisting of putative solutions whose evaluation is performed through a combination of features. Problems are then solved using generic cluster tree elimination algorithms over a tree decomposition of the feature network. Their overall complexities are linear on the number of variables, and only exponential in the treewidth of the feature network. For sparse feature networks, associated with low to moderate treewidths, these algorithms allow to find optimal solutions, or generate controlled samples, with practical empirical efficiency. Implementing these methods, the Infrared software allows Python programmers to rapidly develop exact optimization and sampling applications based on a tree decomposition-based efficient processing. Instead of directly coding specialized algorithms, problems are declaratively modeled as sets of variables over finite domains, whose dependencies are captured by constraints and functions. Such models are then automatically solved by generic DP algorithms. To illustrate the applicability of Infrared in bioinformatics and guide new users, we model and discuss variants of bioinformatics applications. We provide reimplementations and extensions of methods for RNA design, RNA sequence-structure alignment, parsimony-driven inference of ancestral traits in phylogenetic trees/networks, and design of coding sequences. Moreover, we demonstrate multidimensional Boltzmann sampling. These applications of the framework-together with our novel results-underline the practical relevance of Infrared. Remarkably, the achieved complexities are typically equivalent to the ones of specialized algorithms and implementations. Infrared is available at https://amibio.gitlabpages.inria.fr/Infrared with extensive documentation, including various usage examples and API reference; it can be installed using Conda or from source.

Congo Red Removal from Polluted Water using NaOH Treated Fallen Leaves of Ficus racemosa

Naoh treated biosorbent Ficus Racemosa Leaf Powder (NaFRLP) was prepared from the fallen leaves of the plant. FT-IR, SEM and BET analysis were done to characterize the biosorbent. The biosorbent was found to be mesoporous with an average pore size 18 nm. Batch adsorption equilibrium studies were conducted for the adsorption of congo red on NaFRLP as a function of adsorbent dosage, agitation speed, dye concentration, temperature and contact time. Batch adsorption studies revealed that with an increase in the time of adsorption, the percentage removal of congo red increases and with an increase in the concentration of dye solution, congo red removal decreases. Initial dye concentration of 100 mgL-1, agitation speed 200 rpm and adsorbent dosage 1gL-1 were the optimum conditions for the effective removal of congo red. The adsorption data well agreed with the Langmuir isotherm as indicated by the higher correlation coefficient (R2=0.962) value. Thermodynamic analysis of the batch adsorption studies indicated that all the processes studied were spontaneous with the congo red adsorption on NaFRLP being endothermic. Intra particle diffusion model was also tested. NaFRLP was found to be an effective adsorbent for the removal of congo red from the polluted water.

Aerial Biomass Estimation in the Cerrado Biome Using Canopy Height Data

Adaptations to climate change rely on understanding the dynamics of plant biomass stocks on the planet. The high levels of deforestation in Cerrado have transformed this biome into the second-largest Brazilian source of carbon emissions. The objective of this study was to develop a method to accurately estimate aboveground and total biomass values among shrublands, savannas, and forests located in the Cerrado biome using an allometric equation adjusted from canopy height obtained through optical and laser sensors. The results show similarity between the estimates employed by our method and the data found in the literature review for different phytophysiognomies in the Cerrado biome. Shrubland formations showed higher biomass estimation uncertainties due to the discontinuity of isolated trees and the lower canopy height when compared to more clustered tree canopies in savannas and taller canopies in forests. Aboveground biomass estimates are related to expansion factors, and specific maps were developed for each compartment by root, litter, and necromass. The sum of these compartments is presented in the aboveground and below forest biomass map. This study presents, for the first time, the mapping of total biomass in 10 m pixels of all regions of the Cerrado biome.

Object recognition using cognition based decision tree clustering in multi-level artificial neural network classifier and self similarity as feature criterion

Object recognition using cognition based decision tree clustering in multi-level artificial neural network classifier and self similarity as feature criterion

Geometric analysis of SARS-CoV-2 variants

SARS-CoV-2 as a severe respiratory disease has been prevalent around the world since its first discovery in 2019.As a single-stranded RNA virus, its high mutation rate makes its variants manifold and enables some of them to have high pathogenicity, such as Omicron variant, the most prevalent virus now. Research on the relationship of these SARS-CoV-2 variants, especially exploring their difference is a hot issue. In this study, we constructed a geometric space to represent all SARS-CoV-2 sequences of different variants. An alignment-free method: natural vector method was utilized to establish genome space. The genome space of SARS-CoV-2 was constructed based on the 24-dimensional natural vector and the appropriate metric was determined through performing phylogenetic analysises. Phylogenetic trees of different lineages constructed under the selected natural vector and metric coincided with the lineage naming standards, which means lineages with same alphabetical prefix cluster in phylogenetic trees. Furthermore, the relationships between the various GISAID clades as depicted by the natural graph primarily matched the description provided in the GISAID clade naming.The validity of our geometric space was demonstrated by these phylogenetic analysis results. So in this research, we constructed a geometry space for the genomes of the novel coronavirus SARS-CoV-2, which allows us to compare the different variants. Our geometric space is valuable for resolving the issues insides the virus.

A cluster-based disambiguation method using pose consistency verification for structure from motion

Structure from motion (SfM) recovers scene structures and camera poses based on feature matching, and faces challenges from ambiguous scenes. There are a large number of ambiguous scenes in real environment, which contain many duplicate structures and textures. The ambiguity leads to incorrect feature matches between images with similar appearance, and makes geometric misalignment in SfM. To address this problem, recent methods have focused on investigating the inconsistencies in feature topology among multi-view images. However, the feature topology is directly derived from 2D images. Thus, it is susceptible to feature occlusion caused by changes in perspective. Therefore, we propose a new method that disambiguates scenes using pose consistency rather than feature consistency. The pose consistency is conducted in 3D geometric space which is less sensitive to feature occlusion. Thus, the pose consistency is more robust than feature consistency. Our core motivation lies that the incorrect matches between ambiguous images will cause pose deviation from the global poses generated by correct matches. To detect this pose deviation, we first combine local and global information of the scene to generate the global reliable camera poses. The local information of each image is obtained by image clustering, and it strengthens the global information that is represented as the verified maximum spanning tree of clusters. Then, the global poses serve as the reference for further pose consistency verification. The global poses also enable us to perform both rotation and translation consistency verification for uncertain matches. During the pose consistency verification, the pose deviation calculated on image-level may be too small to be noticed. Thus, we propose to perform pose consistency verification at cluster-level instead of image-level to amplify the pose deviation. In the experiments, we compared our approach with several state-of-the-art methods, including COLMAP, Geodesic-SfM and TC-SfM, on both ambiguous and regular datasets. The results demonstrate that our approach achieves the best robustness, only our approach succeeds on all ambiguous image sequences (14/14). The quantitative evaluation results on image sequences with ground truth also show that our approach achieves the best accuracy (average RMSE of translation = 0.109, average RMSE of rotation = 0.827) among all methods. The source code of our approach is publicly available at https://github.com/gongyeted/MA-SfM.

GAS CHROMATOGRAPHY-MASS SPECTROMETRY CHEMICAL PROFILING AND ANTIOXIDATION ACTIVITY OF THE THAI HERBAL FORMULA “PIKAD TRISUKKHATI SAMUTHARN” EXTRACT

Pikad Trisukkhati Samutharn (PTS) is a medicinal formula used in Thai traditional medicine for treating severe fever, inflammation, and diarrhea, as well as for general health promotion. This herbal formula was made from the roots of three plants: Oroxylum indicum (L.) Kurz, Sesbania grandiflora (L.) Pers and Ficus racemosa L. The purpose of this study was to investigate the antioxidation activity and chemical profiling of PTS and its herbal components. The antioxidation activity of the extracts was determined using the DPPH free radical scavenging assay, as well as the Folin-Ciocalteu method, to investigate the total phenolic contents. The thiobarbituric acid reactive substance (TBAR) assay was used to test lipid peroxidation activity. The chemical profiling of PTS was demonstrated using gas chromatography–mass spectrometry (GC-MS) analysis. The results revealed that the total phenolic content of the ethanolic extract (79.57 ± 2.33 mg GAE/g extract) was greater than that of the aqueous extract (12.55 ± 2.83 mg GAE/g extract) of the PTS. The DPPH free radical scavenging activity of the PTS ethanolic extract (IC50 value 80.69 ± 0.40 mg/mL) was also stronger than that of the PTS aqueous extract (IC50 value 650.10 ± 98.20 mg/mL). Lipid peroxidation of the PTS ethanolic extract was moderate with 32.47 ± 0.71% inhibition. GC-MS revealed the presence of several phenolic compounds contained in the PTS ethanolic extract in which the identified compounds had previously been reported to have several pharmacological activities such as antioxidation, anti-inflammation, and antimicrobial. In conclusion, the antioxidation and chemical constituents of the PTS extract were demonstrated for the first time, and more pharmacological activities of PTS should be explored to confirm its traditional use.

A Novel Approach for Object Recognition Using Decision Tree Clustering by Incorporating Multi-Level BPNN Classifiers and Hybrid Texture Features

This work proposes a novel approach to object recognition, particularly for human faces, based on the principle of human cognition. The suggested approach can handle a dataset or problem with a large number of classes for classification more effectively. The model for the facial recognition-based object detection system was constructed using a combination of decision tree clustering based multi-level Backpropagation neural network classifier-TFMLBPNN-DTC and hybrid texture feature (ILMFD+GLCM) and applied on NS and ORL databases. This model produced the classification accuracy (±standard deviation) of 95.37 ±0.951877% and 90.83 ± 1.374369% for single input and 96.58 ±0.5604582% and 91.50 ± 2.850439% for group-based decision for NS and ORL database respectively. The better classification results encourage its application to other object recognition and classification issues. This work's basic idea also makes it easier to improve classification management for a wide range of classes.

Effects of Mineral Chinese Medicine Chloriti Lapis on Gut Microbiota of Small Intestine in PTZ-kindling Epileptic Rats

Background: Epilepsy is a neurological disease, and its clinical treatment presents a significant medical challenge. Nevertheless, Chloriti Lapis (CL), a traditional Chinese Mineral Medicine, has been employed for centuries to address neurological disorders and has shown notable effectiveness. Objectives: The objectives of this study are to further explore whether CL can modulate the abundance and diversity of the small intestinal flora in rats with epilepsy induced by pentylenetetrazol (PTZ) and to investigate the interrelationship among the flora. Materials and Methods: Operational taxonomic units (OTUs), α diversity, β diversity, hierarchical clustering tree, and principal component analysis (PCA) were utilized to analyze the small intestinal flora of PTZ-kindled epileptic rats. Results: The predominant bacterial groups in the small intestine mainly belonged to the phyla Firmicutes, Actinobacteria, Proteobacteria, and Bacteroidetes. The administration of CL was found to effectively increase the abundance and diversity of the small intestinal flora, modify its structural composition, reduce the relative abundance of PTZ-elevated microflora, and enhance the abundance of Streptococcus, Staphylococcus, and Gemella Berger. Conclusion: CL exhibits promising potential, reliability, and unique value in managing epilepsy through the regulation of intestinal flora.

Multi-year mapping of cropping systems in regions with smallholder farms from Sentinel-2 images in Google Earth engine

ABSTRACT Accurate acquisition of spatial and temporal distribution information for cropping systems is important for agricultural production and food security. The challenges of extracting information about cropping systems in regions with smallholder farms are considerable, given the varied crops, complex cropping patterns, and the fragmentation of cropland with frequent reclamation and abandonment. This study presents a specialized workflow to solve this problem for regions with smallholder farms, which utilizes field samples and Sentinel-2 data to extract cropping system information over multiple years. The workflow involves four steps: 1) processing Sentinel-2 data to simulate crop growth curves with the Savitzky‒Golay filter and computing feature variables for classification, including phenology indices, spectral bands, and time series of vegetation indices; 2) mapping annual croplands with one-class support vector machine; 3) mapping various cropping patterns, including single cropping, intercropping, double cropping, multiple harvest, and fallow by decision tree and K-means clustering; and 4) mapping crops with random forest where Jeffries-Matusita distance was used to select appropriate vegetation indices. The workflow was applied in the Hetao irrigation district in Inner Mongolia Autonomous Region, China from 2018 to 2021. The overall accuracies were 0.98, 0.96, and 0.97 for cropland, cropping patterns, and crop type mapping, respectively. The mapping results indicated that the study area has low cropping continuity and is dominated by single cropping patterns. Furthermore, the area of wheat cultivation has decreased, and vegetable cultivation has expanded. Overall, the proposed workflow facilitated the accurate acquisition of cropping system information in regions with smallholder farms and demonstrated the effectiveness of available Sentinel-2 imagery in classifying complex cropping patterns. The workflow is available on Google Earth Engine.

Immunomodulatory Role of Plants and Their Constituents on the Management of Metabolic Disorders: An Evidence-Based Review.

The relationship between the immune system and metabolic diseases is complex and increasingly recognized as critical to understanding conditions like obesity, diabetes, and cardiovascular diseases. Modulation of the immune system in patients with metabolic disorders can offer several potential benefits. While the salutary impact of plant-derived bioactive compounds on metabolic and immune functions is acknowledged, there is a paucity of comprehensive reviews on the multifaceted and synergistic mechanisms through which these effects are mediated. This review elucidates the therapeutic potential of phytochemical formulations in ameliorating metabolic disorders and delineates their mechanistic implications on relevant biomarkers and immune modulation. Our analysis reveals a predominance of plant species, including Boswellia serrata, Cinnamomum cassia, Citrus bergamia, Coffea arabica, Ficus racemosa, Momordica charantia, Morus Alba, and Trigonella foenum-graecum, that have undergone clinical evaluation and have been substantiated to confer both metabolic and immunological benefits. The phytoconstituents contained in these plants exert their effects through a range of mechanisms, such as improving glucose regulation, reducing inflammatory responses, and modulating immune system. As such, these findings hold considerable promise for clinical and therapeutic translation and necessitate further empirical validation through randomized controlled trials and mechanistic elucidations to affirm the safety and efficacy of herbal formulations.