Phylogenomic analysis of Pseudomonas nitroreducens strains FY43 and FY47

Proper identification of strain is essential in understanding the ecology of a bacteria species. The classification of Pseudomonas nitroreducens is still being questioned and revised until now. The novel P. nitroreducens strains FY43 and FY47 used in this study have been reported to show a high level of tolerance to glyphosate. In this study, next-generation sequencing (NGS) and whole genome analysis were used to clarify the delineation of the species. Whole genome analysis showed that P. nitroreducens strains FY43 and FY47 shared high homology to five reference genomes of P. nitroreducens: strain B, Aramco J, NBRC 12694, DF05, and TX01. Phylogenomic and phylogenetic analysis (average nucleotide identity based on BLAST (ANIb), genome-to-genome distance (GGDC) analysis) showed that both P. nitroreducens strains FY43 and FY47 are Pseudomonas nitroreducens members. However, strains DF05 and TX01 were not correctly assigned at the species level for all the analyses. The P. nitroreducens strain DF05 and TX01 should be further investigated for their classification as the correct species classification is the prerequisite for future diversity studies.

New Hyperspectral Procedure to Discriminate Intertidal Macroalgae

The recent development and miniaturization of hyperspectral sensors embedded in drones has allowed the acquisition of hyperspectral images with high spectral and spatial resolution. The characteristics of both the embedded sensors and drones (viewing angle, flying altitude, resolution) create opportunities to consider the use of hyperspectral imagery to map and monitor macroalgae communities. In general, the overflight of the areas to be mapped is conconmittently associated accompanied with measurements carried out in the field to acquire the spectra of previously identified objects. An alternative to these simultaneous acquisitions is to use a hyperspectral library made up of pure spectra of the different species in place, that would spare field acquisition of spectra during each flight. However, the use of such a technique requires developed appropriate procedure for testing the level of species classification that can be achieved, as well as the reproducibility of the classification over time. This study presents a novel classification approach based on the use of reflectance spectra of macroalgae acquired in controlled conditions. This overall approach developed is based on both the use of the spectral angle mapper (SAM) algorithm applied on first derivative hyperspectral data. The efficiency of this approach has been tested on a hyperspectral library composed of 16 macroalgae species, and its temporal reproducibility has been tested on a monthly survey of the spectral response of different macro-algae species. In addition, the classification results obtained with this new approach were also compared to the results obtained through the use of the most recent and robust procedure published. The classification obtained shows that the developed approach allows to perfectly discriminate the different phyla, whatever the period. At the species level, the classification approach is less effective when the individuals studied belong to phylogenetically close species (i.e., Fucus spiralis and Fucus serratus).

Estimation of Individual Tree Biomass in Natural Secondary Forests Based on ALS Data and WorldView-3 Imagery

Individual-tree aboveground biomass (AGB) estimation can highlight the spatial distribution of AGB and is vital for precision forestry. Accurately estimating individual tree AGB is a requisite for accurate forest carbon stock assessment of natural secondary forests (NSFs). In this study, we investigated the performance of three machine learning and three ensemble learning algorithms in tree species classification based on airborne laser scanning (ALS) and WorldView-3 imagery, inversed the diameter at breast height (DBH) using an optimal tree height curve model, and mapped individual tree AGB for a site in northeast China using additive biomass equations, tree species, and inversed DBH. The results showed that the combination of ALS and WorldView-3 performed better than either single data source in tree species classification, and ensemble learning algorithms outperformed machine learning algorithms (except CNN). Seven tree species had satisfactory accuracy of individual tree AGB estimation, with R2 values ranging from 0.68 to 0.85 and RMSE ranging from 7.47 kg to 36.83kg. The average individual tree AGB was 125.32 kg and the forest AGB was 113.58 Mg/ha in the Maoershan study site in Heilongjiang Province, China. This study provides a way to classify tree species and estimate individual tree AGB of NSFs based on ALS data and WorldView-3 imagery.

Assessment of Deep Convolutional Neural Network Models for Species Identification of Forensically-Important Fly Maggots Based on Images of Posterior Spiracles

Forensic entomology is the branch of forensic science that is related to using arthropod specimens found in legal issues. Fly maggots are one of crucial pieces of evidence that can be used for estimating post-mortem intervals worldwide. However, the species-level identification of fly maggots is difficult, time consuming, and requires specialized taxonomic training. In this work, a novel method for the identification of different forensically-important fly species is proposed using convolutional neural networks (CNNs). The data used for the experiment were obtained from a digital camera connected to a compound microscope. We compared the performance of four widely used models that vary in complexity of architecture to evaluate tradeoffs in accuracy and speed for species classification including ResNet-101, Densenet161, Vgg19_bn, and AlexNet. In the validation step, all of the studied models provided 100% accuracy for identifying maggots of 4 species including Chrysomya megacephala (Diptera: Calliphoridae), Chrysomya (Achoetandrus) rufifacies (Diptera: Calliphoridae), Lucilia cuprina (Diptera: Calliphoridae), and Musca domestica (Diptera: Muscidae) based on images of posterior spiracles. However, AlexNet showed the fastest speed to process the identification model and presented a good balance between performance and speed. Therefore, the AlexNet model was selected for the testing step. The results of the confusion matrix of AlexNet showed that misclassification was found between C. megacephala and C. (Achoetandrus) rufifacies as well as between C. megacephala and L. cuprina. No misclassification was found for M. domestica. In addition, we created a web-application platform called thefly.ai to help users identify species of fly maggots in their own images using our classification model. The results from this study can be applied to identify further species by using other types of images. This model can also be used in the development of identification features in mobile applications. This study is a crucial step for integrating information from biology and AI-technology to develop a novel platform for use in forensic investigation.

Forensic Tools for Species Identification of Skeletal Remains: Metrics, Statistics, and OsteoID

Although nonhuman remains constitute a significant portion of forensic anthropological casework, the potential use of bone metrics to assess the human origin and to classify species of skeletal remains has not been thoroughly investigated. This study aimed to assess the utility of quantitative methods in distinguishing human from nonhuman remains and present additional resources for species identification. Over 50,000 measurements were compiled from humans and 27 nonhuman (mostly North American) species. Decision trees developed from the long bone data can differentiate human from nonhuman remains with over 90% accuracy (>98% accuracy for the human sample), even if all long bones are pooled. Stepwise discriminant function results were slightly lower (>87.4% overall accuracy). The quantitative models can be used to support visual identifications or preliminarily assess forensic significance at scenes. For species classification, bone-specific discriminant functions returned accuracies between 77.7% and 89.1%, but classification results varied highly across species. From the study data, we developed a web tool, OsteoID, for users who can input measurements and be shown photographs of potential bones/species to aid in visual identification. OsteoID also includes supplementary images (e.g., 3D scans), creating an additional resource for forensic anthropologists and others involved in skeletal species identification and comparative osteology.

Structural and Logical Model of Transport Maritime Functioning Based on Modeling Information Technology

The article presents the sectoral structure of cruise (maritime) tourism and identifies the factors influencing the level of demand and supply of cruise tourism products. The sources of the influence of the cruise industry on the economic growth of the state and the welfare of its citizens are also considered. On the basis of specific features of cruise tourism functioning and the peculiarities of creating a cruise tourism product, a model of the functioning of a cruise (maritime) tourism complex has been built. Representation of the relationship of tourist needs according to the hierarchy of needs and a species classification of cruise tourism and the industries involved in its development is also given. The model of indicators and the structural components described are built in an environment of geoinformation modeling.

Continental-scale Hyperspectral tree species classification in the National Ecological Observatory Network

Advances in remote sensing imagery and computer vision applications unlock the potential for developing algorithms to classify individual trees from remote sensing at unprecedented scales. However, most approaches to date focus on site-specific applications and a small number of taxonomic groups. This limitation makes it hard to evaluate whether these approaches generalize well across broader geographic areas and ecosystems. Leveraging field surveys and hyperspectral remote sensing data from the National Ecological Observatory Network (NEON), we developed a continental extent model for tree species classification that can be applied to the entire network including a wide range of US terrestrial ecosystems. We compared the performance of the generalized approach to models trained at each individual site, evaluating advantages and challenges posed by training species classifiers at the US scale. We evaluated the effect of geography, environmental, and ecological conditions on the accuracy and precision of species predictions. On average, the general model resulted in good overall classification accuracy (micro-F1 score), with better accuracy than site-specific classifiers (average individual tree level accuracy of 0.77 for the general model and 0.72 for site-specific models). Aggregating species to the genus-level increased accuracy to 0.83. Regions with more species exhibited lower classification accuracy. Trees were more likely to be confused with congeneric and co-occurring species and confusion was highest for trees with structural damage and in complex closed-canopy forests. The model produced accurate estimates of uncertainty, correctly identifying trees where confusion was likely. Using only data from NEON this single integrated classifier can make predictions for 20% of all tree species found in forest ecosystems across the US, suggesting the potential for broad scale general models for species classification from hyperspectral imaging.

New Techniques for Seed Shape Description in Silene Species

Seed shape in Silene species is often described by means of adjectives such as reniform, globose, and orbicular, but the application of seed shape for species classification requires quantification. A method for the description and quantification of seed shape consists in the comparison with geometric models. Geometric models based on mathematical equations were applied to characterize the general morphology of the seeds in 21 species of Silene. In addition to the previously described four models (M1 is the cardioid, and M2 to M4 are figures derived from it), we present four new geometric models (model 5–8). Models 5 and 6 are open cardioids that resemble M3, quite different from the flat models, M2 and M4. Models 7 and 8 were applied to those species not covered by models 2 to 6. Morphological measures were obtained to describe and characterize the dorsal view of the seeds. The analyses done on dorsal views revealed a notable morphological diversity and four groups were identified. A correlation was found between roundness of dorsal view and the geometric models based on lateral views, such that some of the groups defined by seed roundness are also characterized by the similarity to particular models. The usefulness of new morphological tools of seed morphology to taxonomy is discussed.