Classification Of Species Research Articles

CLIP-FSSC: A transferable visual model for fish and shrimp species classification based on natural language supervision

Fish and shrimp species classification is a practical need in the field of aquaculture. Traditional classification method includes extracting modal features of the single image and training them on downstream datasets. However, this method has the disadvantage of requiring manual annotated image data and significant training time. To address these issues, this paper introduces a method named CLIP-FSSC (Contrastive Language–Image Pre-training for Fish and Shrimp Species Classification) for zero-shot prediction using a pre-trained model. The proposed method aims to classify fish and shrimp species in the field of aquaculture using a multimodal pre-trained model that utilizes semantic text description as an image supervision signal for transfer learning. In the downstream fish dataset, we use natural language labels for three types of fish - grass carp, common carp, and silver carp. We extract text category features using a transformer and compare the results obtained from three different CLIP-based backbones for the image modality - vision transformer, Resnet50, and Resnet101. We compare the performance of these models with previous methods that performed well. After performing zero-shot predictions on samples of the three types of fish, we achieve similar or even better classification accuracy than models trained on downstream fish datasets. Our experiment results show an accuracy of 98.77 %, and no new training process is required. This proves that using the semantic text modality as the label for the image modality can effectively classify fish species. To demonstrate the effectiveness of this method on other species in the field of aquaculture, we collected two sets of shrimp data - prawn and cambarus. Through zero-shot prediction, we achieve the highest classification accuracy of 92.00 % for these two types of shrimp datasets. Overall, our results demonstrate that using a multimodal pre-trained model with semantic text description as an image supervision signal for transfer learning can effectively classify fish and shrimp species with high accuracy, while reducing the need for manual annotation and training time.

Tree species classification based on PointNet++ deep learning and true- colour point cloud

ABSTRACT Accurate identification of tree species is the foundation of forest resource surveys and an important research field in forestry remote sensing. The introduction of the PointNet++ deep learning network to point cloud processing provides a new approach for tree species identification. This network can directly compute and train unordered point clouds, greatly reducing the manual selection and extraction process of feature data. However, how to establish a high-quality single tree point cloud dataset is a key issue to be solved. In this study, a 2.5-ha temperate coniferous broad-leaved mixed forest located in Mao’er Mountain Experimental Forest Farm in Heilongjiang Province, China, was investigated. The unmanned aerial vehicle (UAV) RGB image and LiDAR synchronous observation system were used to obtain the true-colour point clouds of the forest. Combining upsampling and downsampling methods, a dataset containing coordinates, normal vectors, RGB, and intensity information was constructed from the original point cloud. Comparative experiments were designed based on resampling algorithms, number of sampling points, feature information, and time cost to find the optimal feature information and processing methods for tree species classification. The results showed that the accuracy of tree species classification by using PointNet++ was greatly improved after adding RGB and point cloud intensity information. The classification accuracy was about 5% higher than that using only coordinate data sets. In addition, the combination of PU-Net downsampling network based on point cloud completion and geometric upsampling method achieved the highest classification accuracy (OA = 0.944) when the number of single trees point cloud was 3072. The algorithm also took a relatively shorter running time. This study demonstrated that the introduction of multi-feature information and the optimization of resampling method can provide new solutions for tree species classification based on the PointNet++ deep learning network.

A novel approach to coral species classification using deep learning and unsupervised feature extraction

ABSTRACT This study presents a novel methodology to enhance coral species classification in underwater images by integrating Tri Convolutional Deep Autoencoders (TRI-CDAE) and Sparse Deep Autoencoders (SPDAE). TRI-CDAE employs three parallel CDAEs trained on distinct color spaces (RGB, HSV, LUV) to capture diverse features. These extracted features are fused and refined using SPDAE, promoting sparsity and enhancing discriminative power. The refined features are then classified using a softmax classifier. Evaluation on four coral image datasets shows exceptional performance, with recall (96.5%), F1 score (97.4%), precision (97.2%), accuracy (98.5%), Cohen’s J (0.959), Jaccard Index (0.971), Cohen’s Kappa (0.961), and Matthews Correlation Coefficient (0.982).

Comparative Analysis of CNN Algorithms for Mushroom Classification with Proposed Lightweight CNN Model

The classification of mushroom species presents significant ecologic and health-related challenges; advancement in classification techniques is required to gain reliable identifications. This study aims to explain a methodology that was devised and evaluated in the development of a novel, lightweight Convolutional Neural Network (CNN) designed specifically for the task of mushroom classification. The paper provides a custom CNN model that is computationally cost-effective and capable of high-precision classification, fit for real-time usage. Hence, the proposed model was evaluated on this dataset of curated mushroom images with traditional classifiers and state-of-the-art CNN architectures, such as EfficientNet-B7, ResNet50, InceptionV3, and MobileNetV2. The custom model is depth-wise separations engineered in such a way that while they reduce the computational load, they don't compromise the effectiveness of the model. The custom model achieved a test score of 0.68, which is moderate compared to more established models such as EfficientNet-B7 or ResNet50. This approach helps the model function effectively even on platforms having low computational resources. A comprehensive evaluation reveals that a custom CNN has reasonable accuracy in the identification of different mushroom species vis-à-vis existing models, but also significantly lightens the classification process.

Deep transfer learning-based bird species classification using mel spectrogram images.

The classification of bird species is of significant importance in the field of ornithology, as it plays an important role in assessing and monitoring environmental dynamics, including habitat modifications, migratory behaviors, levels of pollution, and disease occurrences. Traditional methods of bird classification, such as visual identification, were time-intensive and required a high level of expertise. However, audio-based bird species classification is a promising approach that can be used to automate bird species identification. This study aims to establish an audio-based bird species classification system for 264 Eastern African bird species employing modified deep transfer learning. In particular, the pre-trained EfficientNet technique was utilized for the investigation. The study adapts the fine-tune model to learn the pertinent patterns from mel spectrogram images specific to this bird species classification task. The fine-tuned EfficientNet model combined with a type of Recurrent Neural Networks (RNNs) namely Gated Recurrent Unit (GRU) and Long short-term memory (LSTM). RNNs are employed to capture the temporal dependencies in audio signals, thereby enhancing bird species classification accuracy. The dataset utilized in this work contains nearly 17,000 bird sound recordings across a diverse range of species. The experiment was conducted with several combinations of EfficientNet and RNNs, and EfficientNet-B7 with GRU surpasses other experimental models with an accuracy of 84.03% and a macro-average precision score of 0.8342.

Assessing population status and influencing factors of alpine musk deer in patchy habitats: Implications for conservation strategies

Musk deer (Moschus spp.) are widely known for its musk and are globally endangered as a result of poaching, habitat loss, and fragmentation. To facilitate restoration of the alpine musk deer (M. chrysogaster) in existing areas, reliable estimates of population size and influencing factors are essential to assess effectiveness of conservation interventions, particularly for isolated musk deer populations in patches. In this paper, we used line transect and camera trap data collected from 2020 to 2022 in the Helan mountains to assess the population status and characterized the influencing factors of alpine musk deer. Our findings indicate that musk deer are patchily distributed in three areas of high elevation, with the population limited to less than 200 individuals, remaining below historic levels (compared to 1995). There was a significant positive correlation between the role of sympatric species on musk deer, with red fox (Vulpes vulpes), badger (Meles meles) and blue eared-pheasant (Crossoptilon auritum) having a significant effect on the out-modeled pathway. Variable but insignificant effects of anthropogenic activities were observed across all species categories, which could be due to mutual adaptation over time. Our results imply that the recovery of sensitive population in island-like habitats remains challenging and may be exacerbated by limited environment and resources. Our study underscores the necessity of establishing ecological corridors to enhance habitat connectivity for alpine musk deer, undertaking conservation genetics research on isolated populations, controlling stray dogs to maintain interspecific balance, and implementing electronic fencing to mitigate human disturbance and resource competition. This study provides basic information for understanding the population status of alpine musk deer and offers insights for conservation efforts.

Unveiling relevant acoustic features for bird species automatic classification

The alarming rates of biodiversity loss has draw attention of the scientific community in developing quantitative characterization methods of the state of life variety, ecosystem and habitats. One way to address such aspects involves acoustically monitoring the bird population, given its relation with the health and status of the ecosystem. Automated computational tools can be adopted to enhance the processing and interpretation of birdsongs. The performance and reliability of the system heavily depends on the input features extracted from the acoustic recordings. Numerous acoustical features for characterizing birdsongs are reported in the literature, however the determination of the most relevant ones remains elusive. Moreover, literature evidences a marked focus on classification or detection performance, providing limited details on the set of features that contribute to high performance. This study investigates the problem of discerning relevant acoustical features when dealing with the automatic classification of eight Colombian bird species. We adopt different audio signal processing techniques, namely temporal, spectral, cepstral and chroma analyses, to construct a heterogeneous set of features. Feature selection is implemented using principal components analysis, ReliefF feature ranking, and genetic algorithms. By considering nearest neighbors classifiers, support vector machines, neural networks and bayesian classifiers, 49 distinct machine learning models are tested. Selection schemes are fine-tuned looking for maximizing the classification performance. Our results demonstrate that effective machine learning classification of bird species can be achieved by refining a heterogeneous set of features through feature selection. Consistently, our work identifies Mel frequency Cepstral coefficients, spectral decrease, and spectral rolloff point as the most recurrent features across various feature selection schemes. By incorporating these features in a heterogenous subset totaling a minimum of 19 features, classification performances above 95% can be achieved using a nearest neighbor classifier with the Manhattan distance. These findings are particularly notable as they are derived from processing field-noise corrupted acoustic recordings. The outcomes of this study yield potential acoustical features suitable for the characterization of birdsongs. In addition, our methodological design may serve as a reference for extending the assessment of new features and machine learning models, thereby contributing to the development of effective and feasible conservation tools.

The representation theory of Brauer categories II: Curried algebra

A representation of \mathfrak{gl}(V)=V \otimes V^{\ast} is a linear map \mu \colon \mathfrak{gl}(V) \otimes M \rightarrow M satisfying a certain identity. By currying, giving a linear map \mu is equivalent to giving a linear map a \colon V \otimes M \rightarrow V \otimes M , and one can translate the condition for \mu to be a representation into a condition on a . This alternate formulation does not use the dual of V and makes sense for any object V in a tensor category \mathcal{C} . We call such objects representations of the curried general linear algebra on V . The currying process can be carried out for many algebras built out of a vector space and its dual, and we examine several cases in detail. We show that many well-known combinatorial categories are equivalent to the curried forms of familiar Lie algebras in the tensor category of linear species; for example, the titular Brauer category is the curried form of the symplectic Lie algebra. This perspective puts these categories in a new light, has some technical applications, and suggests new directions to explore.

Performance of convolutional neural network (CNN) and performance influencing factors for wood species classification of Lepidobalanus growing in Korea

This study aimed to investigate the performance and factors affecting the species classification of convolutional neural network (CNN) architecture using whole-part and earlywood-part cross-sectional datasets of six Korean Quercus species. The accuracy of species classification for each condition was analyzed using the datasets, data augmentation, and optimizers—stochastic gradient descent (SGD), adaptive moment estimation (Adam), and root mean square propagation (RMSProp)—based on a CNN architecture with three to four convolutional layers. The model trained with the augmented dataset yielded significantly superior results in terms of classification accuracy compared to the model trained with the non-augmented dataset. The augmented dataset was the only factor affecting classification accuracy in the final five epochs. In contrast, four factors in the entire epoch, such as the Adam and SGD optimizers and the earlywood-part and whole-part datasets, affected species classification. The arrangement of earlywood vessels, broad rays, and axial parenchyma was identified as a major influential factor in the CNN species classification using gradient-weighted class activation mapping (Grad-CAM) analysis. The augmented whole-part dataset with the Adam optimizer achieved the highest classification accuracy of 85.7% during the final five epochs of the test phase.

Complete chloroplast genome sequence and phylogenetic analysis of Populus maximowiczii Henry 1913 (Salicaceae Mirb.)

The classification and identification of species in Populus has remained a formidable challenge due to widespread interspecies hybridization. The complete chloroplast genome of Populus maximowiczii was obtained by Illumina high-throughput sequencing technology, with a typical quadripartite structure and 37.0% GC content. The chloroplast genome of P. maximowiczii was 156,892 in length, including a large single-copy region (LSC: 84,988 bp), a small single-copy region (SSC: 16,630 bp), and a pair of inverted repeats (IRs: each 27,637 bp in length). A total of 131 genes were annotated, including 86 protein-coding genes, 37 tRNAs, and 8 rRNAs. The phylogenetic analysis indicated that 43 species belonging to Populus were classified into monophyly, with P. cathayana being the closest relatives to P. maximowiczii. In conclusion, this study provides valuable insights into understanding the phylogeny of Populus.

The complete chloroplast genome of Pyankovia brachiata (amaranthaceae), an annual desert plant in China

Pyankovia brachiata (Pall.) Akhani & Roalson 2007, is an annual plant belonging to the genus Pyankovia, family Amaranthaceae, which is widely distributed in the inland deserts of Northwest China. P. brachiata was previously categorized under the genus Salsola in Salsoleae and has been a long-standing topic of debate. Therefore, the complete chloroplast genome of P. brachiata must be studied to provide a theoretical reference for species classification. In this study, we sequenced P. brachiata samples and determined the species’ complete chloroplast genome. The complete chloroplast genome was 149,922 bp in length, with one large single copy (LSC: 83,565 bp), one small single copy (SSC: 18,535 bp), and two inverted repeat regions (IRa and IRb, 23,911 bp each). It contains 132 genes, including 87 protein-coding, eight rRNA, and 37 tRNA genes. The phylogenetic position showed that P. brachiata has the closest relationship with Caroxylon passerinum (accession number: NC057191.1). This study will provide genetic information and be beneficial to understanding the systematic position of P. brachiata within the Amaranthaceae.

Use of whistles for acoustic classification of delphinids (odontoceti: Delphinidae) in the Western South Atlantic Ocean.

This study focuses on the acoustic classification of delphinid species at the southern continental slope of Brazil. Recordings were collected between 2013 and 2015 using towed arrays and were processed using a classifier to identify the species in the recordings. Using Raven Pro 1.6 software (Cornell Laboratory of Ornithology, Ithaca, NY), we analyzed whistles for species identification. The random forest algorithm in R facilitates classification analysis based on acoustic parameters, including low, high, delta, center, beginning, and ending frequencies, and duration. Evaluation metrics, such as correct and incorrect classification percentages, global accuracy, balanced accuracy, and p-values, were employed. Receiver operating characteristic curves and area-under-the-curve (AUC) values demonstrated well-fitting models (AUC ≥ 0.7) for species definition. Duration and delta frequency emerged as crucial parameters for classification, as indicated by the decrease in mean accuracy. Multivariate dispersion plots visualized the proximity between acoustic and visual match data and exclusively acoustic encounter (EAE) data. The EAE results classified as Delphinus delphis (n = 6), Stenella frontalis (n = 3), and Stenella longirostris (n = 2) provide valuable insights into the presence of these species between approximately 23° and 34° S in Brazil. This study demonstrates the effectiveness of acousting classification in discriminating delphinids through whistle parameters.

Statistical analysis for chemical compound based on several species of aquilaria essential oil

The paper examines the characterization of Aquilaria essential oils from different species, namely Aquilaria malaccensis, Aquilaria beccariana, Aquilaria crassna, and Aquilaria subintegra, renowned for agarwood production in Malaysia. Gas chromatography-mass spectrometry (GC-MS) and gas chromatography-flame ionization detector (GC-FID) were employed for extracting essential oil data, facilitating compound identification. Subsequently, a preliminary analysis focused on classifying significant chemical compounds in the samples. The study then utilized boxplot pre-processing for visualizing and interpreting data distribution. The statistical analyses were performed using MATLAB software version R2021b, considering two key parameters which are the peak area (%) of significant chemical compounds and the classification of Aquilaria species (A. beccariana, A. malaccensis, A. crassna, and A. subintegra) based on their chemical composition. The results, presented through boxplot analyses, demonstrated a clear representation of the parameters and their distribution in the data. This method not only confirmed the potential of boxplot analysis in statistical evaluation of significant compounds in Aquilaria essential oil but also suggested its applicability for further classification work.

Ginkgoites manchurica from the Lower Cretaceous Shahezi Formation of Liaoning, China, and its palaeoclimate implications

The extreme greenhouse climate during the period of oceanic anoxic events (OAEs) in the Cretaceous is recognized based on marine stratigraphy, whereas the palaeoclimate remains poorly understood based on the terrestrial record due to the limitations of quantitative methods. The nearest living equivalent method, such as using the stomatal index of Ginkgo as a proxy, provides insight into reconstructing the terrestrial palaeoclimate during OAEs. However, due to the lack of well-preserved fossils with epidermal features and applicable quantitative methods, there are still controversies regarding the classification and nomenclature of species in the genus Ginkgoites, which have been assigned to Baiera, Ginkgo, or Ginkgoites. Here, we report well-preserved Ginkgoites fossil leaves from the Lower Cretaceous Shahezi Formation in Changtu County, Liaoning Province, Northeast China. The fossil dry leaf mass per area (LMA) was estimated as a quantitative morphological and ecological proxy. The LMA results of 192–241 g/m2 for different specimens are very close, suggesting that these homonyms should be assigned to the same species. Therefore, combining the detailed epidermal features and a quantitative morphological proxy (LMA), we summarized and amended the previously described fossils into Ginkgoites manchurica. Moreover, using the stomatal index of G. manchurica, the palaeo-atmospheric CO2 concentration was calculated at 1270 ppmv using the Barclay SI-pCO2 curve during the middle to late Aptian, indicating a typical greenhouse climate, which may be associated with the OAE global temperature increase event.

Genome-based reclassification of Kineococcus aureolus Xu et al. 2017 as a later heterotypic synonym of Kineococcus terrestris Xu et al. 2017.

We aimed to elucidate the relationship between Kineococcus terrestris and Kineococcus aureolus through whole-genome-based analysis. The genome-derived 16S rRNA gene sequences of K. terrestris KCTC 39738T and K. aureolus KCTC 39739T shared a 100% similarity. Phylogenetic trees based on 16S rRNA gene and whole-genome sequences revealed that K. terrestris KCTC 39738T and K. aureolus KCTC 39739T formed a robust clade, indicating a close relationship between them. Genomic comparison showed that the two strains shared 99.1% average nucleotide identity, 92.0% digital DNA-DNA hybridization and 98.9% average amino acid identity values, all of which exceeded the recommended threshold values for species classification. Most phenotypic characteristics between the two species were almost identical. Based on the above evidence, we propose the reclassification of Kineococcus aureolus Xu et al. 2017 as a later heterotypic synonym of Kineococcus terrestris Xu et al. 2017. Since these two species were proposed in the same article, the principle of priority does not apply. Our proposal is supported by the fact that the nomenclatural authorities first described Kineococcus terrestris.

Viral metagenomics combined with non-targeted serum metabolomics reveals the role of enteroviruses in a mouse model of coronary heart disease

BackgroundCoronary heart disease (CHD) is a common cardiovascular disease that is associated with altered gut microbiota. Enteroviruses, an essential component of the gut microbiome, may play an important role in disease progression. However, the relationship between enteroviruses and CHD remains unclear. The development of high-throughput sequencing technologies has facilitated research on the interconnections between viruses and disease-related metabolites.Methods and resultsMice were fed a high-fat diet (CHD group) or chow diet (Sham group) for 12 weeks, and ligation of the left anterior descending coronary artery was performed at the end of week 8. After 4 weeks, all animals were euthanised. Subsequently, the animals were evaluated for basic haemato-biochemical parameters and cardiac function, and aorta staining was performed. Based on enteroviral metagenomics and serum UPLC–MS/MS metabolomics analyses, we evaluated the association between enteroviral groups and serum metabolites of CHD mouse model. A high-fat diet and coronary ligation enabled the establishment of the CHD mouse model. Notably, the enterovirus spectrum of the sham group was significantly different from that of the CHD group, with 24 viral communities of different family and species classification, such as Tsarbombavirus, Mingyongvirus, Claudivirus, and Firehammervirus, exhibiting significant differences. In addition, 731 Differential metabolites were detected in the serum of both groups of mice. Correlation network analysis revealed a close relationship between various metabolites related to lipid metabolism and different viruses, including Tsarbombavirus, Mingyongvirus, Claudivirus, and Firehammervirus.ConclusionsAn animal model of CHD, characterised by lipid disturbance and myocardial ischaemia, was established using a high-fat diet and ligation of the left anterior descending branch of the coronary artery. Tsarbombavirus, Firehammervirus, Mingyongvirus, and Claudivirus were associated with metabolites in the lipid metabolism pathway. The results indicate that Tsarbombavirus may be the main genus interacting with CHD-related metabolites in mice. Conclusively, the findings of our study provide novel insights into the potential relationship enterovirus groups and metabolites associated with CHD.

Comparison of Algorithms and Optimal Feature Combinations for Identifying Forest Type in Subtropical Forests Using GF-2 and UAV Multispectral Images

The composition and spatial distribution of tree species are pivotal for biodiversity conservation, ecosystem productivity, and carbon sequestration. However, the accurate classification of tree species in subtropical forests remains a formidable challenge due to their complex canopy structures and dense vegetation. This study addresses these challenges within the Jiangxi Lushan National Nature Reserve by leveraging high-resolution GF-2 remote sensing imagery and UAV multispectral images collected in 2018 and 2022. We extracted spectral, texture, vegetation indices, geometric, and topographic features to devise 12 classification schemes. Utilizing an object-oriented approach, we employed three machine learning algorithms—Random Forest (RF), k-Nearest Neighbor (KNN), and Classification and Regression Tree (CART)—to identify 12 forest types in these regions. Our findings indicate that all three algorithms were effective in identifying forest type in subtropical forests, and the optimal overall accuracy (OA) was more than 72%; RF outperformed KNN and CART; S12 based on feature selection was the optimal feature combination scheme; and the combination of RF and Scheme S12 (S12) yielded the highest classification accuracy, with OA and Kappa coefficients for 2018-RF-S12 of 90.33% and 0.82 and OA and Kappa coefficients for 2022-RF-S12 of 89.59% and 0.81. This study underscores the utility of combining multiple feature types and feature selection for enhanced forest type recognition, noting that topographic features significantly improved accuracy, whereas geometric features detracted from it. Altitude emerged as the most influential characteristic, alongside significant variables such as the Normalized Difference Greenness Index (NDVI) and the mean value of reflectance in the blue band of the GF-2 image (Mean_B). Species such as Masson pine, shrub, and moso bamboo were accurately classified, with the optimal F1-Scores surpassing 89.50%. Notably, a shift from single-species to mixed-species stands was observed over the study period, enhancing ecological diversity and stability. These results highlight the effectiveness of GF-2 imagery for refined, large-scale forest-type identification and dynamic diversity monitoring in complex subtropical forests.

Identification and Full-Length Sequence Analysis of a Novel Recombinant Goat Astrovirus Genotype in Guangxi, China.

Astroviruses are single-stranded, positive-sense RNA viruses capable of infecting humans as well as a wide range of mammalian and avian species, with a length of approximately 6.6-7.7 kb. In this study, 139 goat fecal samples collected from the Guangxi province were used for the RT-PCR detection, and two of these were positive for goat astrovirus, with a positivity rate of 1.44% (2/139). The complete genome sequence of an astrovirus strain and the partial genome sequence of a strain astrovirus, named GX WZ 2023 and GX HC 2023, were amplified and sequenced, and their sequence lengths were 6284 nt and 6213 nt, respectively. Among them, the capsid protein of goat astrovirus GX HC 2023 showed the highest amino acid identity of 95.9% with ovine astrovirus GX, which belonged to the MAstV-2 genotype. However, the closest relative of the GX WZ 2023 strain was found to be the caprine astrovirus Sichuan, with a nucleotide sequence identity of 76.8%. The ORF1ab nonstructural protein of this strain showed the highest amino acid identities of 89.2 and 95.8% with the ovine astrovirus S5.1 and caprine astrovirus G5.1 strains, respectively. However, its ORF2 capsid protein has 68.4% amino acid identity with the bovine astrovirus (BAstV) 16 2021 CHN strain and only 21.9-64% amino acid identity with all available strains of goat astrovirus. The GX WZ 2023 strain was recombined with the Chinese (BAstV 16 2021 CHN) and Japanese bovine strains (BAstV JPN 2015) in the ORF2 region. Therefore, the goat astrovirus GX WZ 2023 is proposed as a new member of the family goat astroviridae based on the species classification criteria of the International Committee on Taxonomy of Viruses. These findings enhance our understanding of the prevalence and genetic evolution of goat astrovirus and provide a scientific basis for future studies of these viruses in other animals.

Mapping tree species of wetlands using multispectral images of UAVs and machine learning: A case study of the Dong Rui Commune

Wetlands provide resources, regulate the environment, and stabilize shorelines; however, they are among the most vulnerable ecosystems in the world. The classification of mangrove species allows the determination of the habitat of each species, thereby serving as a basis for determining protection solutions and planning plans for mangrove conservation and restoration according to each environmental condition. We used Phantom 4 multispectral unmanned aerial vehicles (UAVs) to collect data from wetland areas in the Dong Rui Commune, which is one of the most diverse and valuable wetland ecosystems in northern Vietnam. A tree-species classification map was constructed through a combination of the object-based image analysis method and spectral reflectance values of each plant species, and the characteristic distributions of mangrove plants, including Bruguiera gymnorrhiza, Rhizophora stylosa, and Kandelia obovata, were determined with an overall accuracy of 91.11 % and a kappa coefficient (K) of 0.87. The overall accuracy for Rhizophora stylosa was the highest (94.23 %), followed by Kandelia obovata (93.61 %) and Bruguiera gymnorrhiza (85.50 %). An experiment was conducted to map plant taxonomy in the same area based only on a graph of spectral reflectance values at five single-spectral bands, and normalized difference vegetation index values were constructed, resulting in an overall accuracy of 78.22 % and a K of 0.67. The constructed map is useful for classifying, monitoring, and evaluating the structure of each group of mangroves, thereby serving as a basis for determining the distribution of each mangrove species according to natural conditions and contributing to the formulation of policies for afforestation and mangrove conservation in Dong Rui commune.

Gut microbiota affects the activation of STING pathway and thus participates in the progression of colorectal cancer

BackgroundMore and more studies showed that gut microbiota was closely related to the development of colorectal cancer (CRC). However, the specific pathway of gut microbiota regulating CRC development is still unknown.MethodsWe collected fecal samples from 14 CRC patients and 20 normal volunteers for 16 S sequencing analysis. At the same time, 14 CRC patients’ tumors and their adjacent tissues were collected for the detection of STING pathway related protein level. Mice were injected with azoxymethane (AOM) to establish an animal model of CRC, and antibiotics were given at the same time to evaluate the influence of gut microbiota on STING pathway and whether it was involved in regulating the tumor development of CRC mice.ResultsThe sequencing results showed that compared with the normal group, the gut microbiota gut microbiota of CRC patients changed significantly at different species classification levels. At the level of genus, Akkermansia, Ligilactobacillus and Subdoligranulum increased the most in CRC patients, while Bacteroides and Dialister decreased sharply. The expression of STING-related protein was significantly down-regulated in CRC tumor tissues. Antibiotic treatment of CRC mice can promote the development of tumor and inhibit the activation of STING pathway.ConclusionGut microbiota participates in CRC progress by mediating STING pathway activation.