Search Results (8)

Semantic segmentation of three-dimensional (3D) plant point clouds at the stem-leaf level is foundational and indispensable for high-throughput tomato phenotyping systems. However, existing semantic segmentation methods often suffer from issues such as low precision and slow inference speed. To address these challenges, we propose an innovative encoding-decoding structure, incorporating voxel sparse convolution (SpConv) and attention-based feature fusion (VSCAFF) to enhance semantic segmentation of the point clouds of high-resolution tomato seedling images. Tomato seedling point clouds from the Pheno4D dataset labeled into semantic classes of ‘leaf’, ‘stem’, and ‘soil’ are applied for the semantic segmentation. In order to reduce the number of parameters so as to further improve the inference speed, the SpConv module is designed to function through the residual concatenation of the skeleton convolution kernel and the regular convolution kernel. The feature fusion module based on the attention mechanism is designed by giving the corresponding attention weights to the voxel diffusion features and the point features in order to avoid the ambiguity of points with different semantics having the same characteristics caused by the diffusion module, in addition to suppressing noise. Finally, to solve model training class bias caused by the uneven distribution of point cloud classes, the composite loss function of Lovász-Softmax and weighted cross-entropy is introduced to supervise the model training and improve its performance. The results show that mIoU of VSCAFF is 86.96%, which outperformed the performance of PointNet, PointNet++, and DGCNN, respectively. IoU of VSCAFF achieves 99.63% in the soil class, 64.47% in the stem class, and 96.72% in the leaf class. The time delay of 35ms in inference speed is better than PointNet++ and DGCNN. The results demonstrate that VSCAFF has high performance and inference speed for semantic segmentation of high-resolution tomato point clouds, and can provide technical support for the high-throughput automatic phenotypic analysis of tomato plants. Full article

Several psychosocial, sleep/circadian, and cardiometabolic disorders have intricately interconnected pathologies involving melatonin disruption. Therefore, we hypothesize that melatonin could be a therapeutic target for treating potential comorbid diseases associated with this triad of psychosocial-sleep/circadian-cardiometabolic disorders. We investigated melatonin’s target prediction and tractability for this triad of disorders. The melatonin’s target prediction for the proposed psychosocial-sleep/circadian-cardiometabolic disorder triad was investigated using databases from Europe PMC, ChEMBL, Open Targets Genetics, Phenodigm, and PheWAS. The association scores for melatonin receptors MT1 and MT2 with this disorder triad were explored for evidence of target–disease predictions. The potential of melatonin as a tractable target in managing the disorder triad was investigated using supervised machine learning to identify melatonin activities in cardiovascular, neuronal, and metabolic assays at the cell, tissue, and organism levels in a curated ChEMBL database. Target–disease visualization was done by graphs created using “igraph” library-based scripts and displayed using the Gephi ForceAtlas algorithm. The combined Europe PMC (data type: text mining), ChEMBL (data type: drugs), Open Targets Genetics Portal (data type: genetic associations), PhenoDigm (data type: animal models), and PheWAS (data type: genetic associations) databases yielded types and varying levels of evidence for melatonin-disease triad correlations. Of the investigated databases, 235 association scores of melatonin receptors with the targeted diseases were greater than 0.2; to classify the evidence per disease class: 37% listed psychosocial disorders, 9% sleep/circadian disorders, and 54% cardiometabolic disorders. Using supervised machine learning, 546 cardiovascular, neuronal, or metabolic experimental assays with predicted or measured melatonin activity scores were identified in the ChEMBL curated database. Of 248 registered trials, 144 phase I to IV trials for melatonin or agonists have been completed, of which 33.3% were for psychosocial disorders, 59.7% were for sleep/circadian disorders, and 6.9% were for cardiometabolic disorders. Melatonin’s druggability was evidenced by evaluating target prediction and tractability for the triad of psychosocial-sleep/circadian-cardiometabolic disorders. While melatonin research and development in sleep/circadian and psychosocial disorders is more advanced, as evidenced by melatonin association scores, substantial evidence on melatonin discovery in cardiovascular and metabolic disorders supports continued R&D in cardiometabolic disorders, as evidenced by melatonin activity scores. A multiplatform analysis provided an integrative assessment of the target–disease investigations that may justify further translational research. Full article

Taphrinomycotina is the smallest subphylum of the phylum Ascomycota. It is an assemblage of distantly related early diverging lineages of the phylum, comprising organisms with divergent morphology and ecology; however, phylogenomic analyses support its monophyly. In this study, we report the isolation of a yeast strain, which could not be assigned to any of the currently recognised five classes of Taphrinomycotina. The strain of the novel budding species was recovered from extra virgin olive oil and characterised phenotypically by standard methods. The ultrastructure of the cell wall was investigated by transmission electron microscopy. Comparisons of barcoding DNA sequences indicated that the investigated strain is not closely related to any known organism. Tentative phylogenetic placement was achieved by maximum-likelihood analysis of the D1/D2 domain of the nuclear LSU rRNA gene. The genome of the investigated strain was sequenced, assembled, and annotated. Phylogenomic analyses placed it next to the fission Schizosaccharomyces species. To accommodate the novel species, Novakomyces olei, a novel genus Novakomyces, a novel family Novakomycetaceae, a novel order Novakomycetales, and a novel class Novakomycetes is proposed as well. Functional analysis of genes missing in N. olei in comparison to Schizosaccharomyces pombe revealed that they are biased towards biosynthesis of complex organic molecules, regulation of mRNA, and the electron transport chain. Correlating the genome content and physiology among species of Taphrinomycotina revealed some discordance between pheno- and genotype. N. olei produced ascospores in axenic culture preceded by conjugation between two cells. We confirmed that N. olei is a primary homothallic species lacking genes for different mating types. Full article

Phytosociology is a reference method to classify vegetation that relies on field data. Its classification in hierarchical vegetation units, from plant associations to class level, hierarchically reflects the floristic similarity between different sites on different spatial scales. The development of remotely sensed multispectral platforms as satellites enormously contributes to the detection and mapping of vegetation on all scales. However, the integration between phytosociology and remotely sensed data is rather difficult and little practiced despite being a goal for the modern science of vegetation. In this study, we demonstrate how normalized difference vegetation index (NDVI) time series with functional principal component analysis (FPCA) could support the analyses of phytosociologists. The approach supports the recognition and characterization of forest plant communities identified on the ground by the phytosociological approach by using NDVI time series that encode phenological behaviors. The methodology was evaluated in two study areas of central Italy, and it could characterize and discriminate six different forest plant associations that have similar dominant tree species but distinct specific composition: three dominated by black hornbeam (Ostrya carpinifolia) and three dominated by holm oak (Quercus ilex). The methodology was also able to optimize the ground data collection of unexplored areas (from a phytosociological point of view) by using a phenoclustering approach. The obtained results confirmed that by using remote sensing, it is possible to separate and distinguish plant communities in an objective/instrumental way, thus overcoming the subjectivity intrinsic to the phytosociological method. In particular, FPCA functional components (NDVI seasonalities) were significantly correlated with vegetation abundance data variation (Mantel r = 0.76, p < 0.001). Full article

We describe a polar coordinate transformation of vegetation index profiles which permits a broad-scale comparison of location-specific phenological variability influenced by climate, topography, land use, and other factors. We apply statistical data reduction techniques to identify fundamental dimensions of phenological variability and to classify phenological types with intuitive ecological interpretation. Remote sensing-based land surface phenology can reveal ecologically meaningful vegetational diversity and dynamics across broad landscapes. Land surface phenology is inherently complex at regional to continental scales, varying with latitude, elevation, and multiple biophysical factors. Quantifying phenological change across ecological gradients at these scales is a potentially powerful way to monitor ecological development, disturbance, and diversity. Polar coordinate transformation was applied to Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) time series spanning 2000-2018 across North America. In a first step, 46 NDVI values per year were reduced to 11 intuitive annual metrics, such as the midpoint of the growing season and degree of seasonality, measured relative to location-specific annual phenological cycles. Second, factor analysis further reduced these metrics to fundamental phenology dimensions corresponding to annual timing, productivity, and seasonality. The factor analysis explained over 95% of the variability in the metrics and represented a more than ten-fold reduction in data volume from the original time series. In a final step, phenological classes (‘phenoclasses’) based on the statistical clustering of the factor data, were computed to describe the phenological state of each pixel during each year, which facilitated the tracking of year-to-year dynamics. Collectively the phenology metrics, factors, and phenoclasses provide a system for characterizing land surface phenology and for monitoring phenological change that is indicative of ecological gradients, development, disturbance, and other aspects of landscape-scale diversity and dynamics. Full article

Observations of vegetation phenology at regional-to-global scales provide important information regarding seasonal variation in the fluxes of energy, carbon, and water between the biosphere and the atmosphere. Numerous algorithms have been developed to estimate phenological transition dates using time series of remotely sensed spectral vegetation indices. A key challenge, however, is that different algorithms provide inconsistent results. This study provides a comprehensive comparison of start of season (SOS) and end of season (EOS) phenological transition dates estimated from 500 m MODIS data based on two widely used sources of such data: the TIMESAT program and the MODIS Global Land Cover Dynamics (MLCD) product. Specifically, we evaluate the impact of land cover class, criteria used to identify SOS and EOS, and fitting algorithm (local versus global) on the transition dates estimated from time series of MODIS enhanced vegetation index (EVI). Satellite-derived transition dates from each source are compared against each other and against SOS and EOS dates estimated from PhenoCams distributed across the Northeastern United States and Canada. Our results show that TIMESAT and MLCD SOS transition dates are generally highly correlated (r = 0.51-0.97), except in Central Canada where correlation coefficients are as low as 0.25. Relative to SOS, EOS comparison shows lower agreement and higher magnitude of deviations. SOS and EOS dates are impacted by noise arising from snow and cloud contamination, and there is low agreement among results from TIMESAT, the MLCD product, and PhenoCams in vegetation types with low seasonal EVI amplitude or with irregular EVI time series. In deciduous forests, SOS dates from the MLCD product and TIMESAT agree closely with SOS dates from PhenoCams, with correlations as high as 0.76. Overall, our results suggest that TIMESAT is well-suited for local-to-regional scale studies because of its ability to tune algorithm parameters, which makes it more flexible than the MLCD product. At large spatial scales, where local tuning is not feasible, the MLCD product provides a readily available data set based on a globally consistent approach that provides SOS and EOS dates that are comparable to results from TIMESAT. Full article

The aim of this study was to investigate the presence of class 1 integrons in a collection of Shiga toxin-producing Escherichia coli (STEC) from different origins and to characterize pheno- and genotypically the antimicrobial resistance associated to them. A collection of 649 isolates were screened for the class 1 integrase gene (intI1) by Polymerase chain reaction The variable region of class 1 integrons was amplified and sequenced. Positive strains were evaluated for the presence of antimicrobial resistance genes with microarray and for antimicrobial susceptibility by the disk diffusion method. Seven out of 649 STEC strains some to serogroups, O26, O103 and O130 isolated from cattle, chicken burger, farm environment and pigs were identified as positive for intl1. Different arrangements of gene cassettes were detected in the variable region of class 1 integron: dfrA16, aadA23 and dfrA1-aadA1. In almost all strains, phenotypic resistance to streptomycin, tetracycline, trimethoprim/sulfamethoxazole, and sulfisoxazole was observed. Microarray analyses showed that most of the isolates carried four or more antimicrobial resistance markers and STEC strains were categorized as Multridrug-resistant. Although antimicrobials are not usually used in the treatment of STEC infections, the presence of Multridrug-resistant in isolates collected from farm and food represents a risk for animal and human health. Full article

This study introduces a new geographic framework, phenological classification, for the conterminous United States based on Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time-series data and a digital elevation model. The resulting pheno-class map is comprised of 40 pheno-classes, each having unique phenological and topographic characteristics. Cross-comparison of the pheno-classes with the 2001 National Land Cover Database indicates that the new map contains additional phenological and climate information. The pheno-class framework may be a suitable basis for the development of an Advanced Very High Resolution Radiometer (AVHRR)-MODIS NDVI translation algorithm and for various biogeographic studies. Full article