In this prospective study, a canine-adapted histological grading method was compared with histopathological and clinical characteristics and was evaluated as a prognostic indicator in canine mammary ...carcinomas (CMCs). Recruited dogs with at least 1 malignant mammary tumor (n = 65) were clinically evaluated, surgically treated, and followed up (minimum follow-up 28 months, maximum 38 months). Histopathological diagnoses were performed according to Goldschmidt et al (2011). Tumors were graded as grade I (29/65), grade II (19/65), and grade III (17/65). The tumor size, clinical stage, histological diagnosis, presence/absence of myoepithelial proliferation, and regional lymph node metastases at diagnosis were significantly associated with histological grade. The histological grade, age, clinical stage, tumor subtype group, and lymph node metastases at time of diagnosis were significantly associated with the development of recurrences and/or metastases, cancer-associated death, and survival times (disease-free survival and overall survival) in univariate analyses. A subdivision of clinical stage I (T1N0M0) into stages IA and IB was proposed in terms of prognosis. The clinical stage, histological grade, and spay status were selected as independent prognostic variables (multivariate analyses) with disease-free survival as the dependent variable. When overall survival was evaluated as a dependent variable, clinical stage and histological grade were selected as the independent covariates. This grading system is a useful prognostic tool, facilitates histological interpretation, and offers uniform criteria for veterinary pathologists. Comparative studies on CMCs performed in different countries should take into account possible changes in the prognoses due to different proportions of spayed females among the selected dog population.
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•Generic approach for the classification of karst systems hydrological functioning.•Typology consists of 6 classes accounting for 3 aspects of karst systems functioning.•Proposal of a ...new indicator of temporal variability of the hydrological functioning.•Classification is tested against 78 karst systems worldwide in various contexts.•Proposed methodology is relevant for scarce-data context.
Classification is a first-line tool for understanding the main characteristics of a natural system’s response. We propose a new classification of karst systems hydrological functioning that is based on spring discharge time series and takes profit of spring discharge databases to encompass the high diversity of karst hydrological functioning. It discriminates six different classes based on three relevant indicators of karst hydrological functioning.
A core dataset made of 10 karst systems was first considered for the set-up of the classification. The spring discharge time series were investigated according to recession curves, statistical and signal analyses to identify relevant indicators of hydrological functioning. The selection of the most relevant indicators and the proposal of the classification were based on multivariate analyses. The classification was then tested on spring discharge time series of 78 karst systems located worldwide.
All the systems homogeneously spread among the six proposed classes, which highlights the relevance of the approach and the representativeness of the various classes of hydrological functioning. Results from the proposed methodology were finally discussed to explore its limitations and define guidelines for its application.
Is vision necessary for the development of the categorical organization of the Ventral Occipito-Temporal Cortex (VOTC)? We used fMRI to characterize VOTC responses to eight categories presented ...acoustically in sighted and early blind individuals, and visually in a separate sighted group. We observed that VOTC reliably encodes sound categories in sighted and blind people using a representational structure and connectivity partially similar to the one found in vision. Sound categories were, however, more reliably encoded in the blind than the sighted group, using a representational format closer to the one found in vision. Crucially, VOTC in blind represents the categorical membership of sounds rather than their acoustic features. Our results suggest that sounds trigger categorical responses in the VOTC of congenitally blind and sighted people that partially match the topography and functional profile of the visual response, despite qualitative nuances in the categorical organization of VOTC between modalities and groups.
Brain tumors are one of the most dangerous, because the position of these are in the organ that governs all life processes. Moreover, a lot of brain tumor types were observed, but only one main ...diagnostic method was used – histopathology, for which preparation of sample was long. Consequently, a new, quicker diagnostic method is needed. In this paper, FT-Raman spectra of brain tissues were analyzed by Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), four different machine learning (ML) algorithms to show possibility of differentiating between glioblastoma G4 and meningiomas, as well as two different types of meningiomas (atypical and angiomatous). Obtained results showed that in meningiomas additional peak around 1503 cm−1 and higher level of amides was noticed in comparison with glioblastoma G4. In the case of meningiomas differentiation, in angiomatous meningiomas tissues lower level of lipids and polysaccharides were visible than in atypical meningiomas. Moreover, PCA analyses showed higher distinction between glioblastoma G4 and meningiomas in the FT-Raman range between 800 cm−1 and 1800 cm−1 and between two types of meningiomas in the range between 2700 cm−1 and 3000 cm−1. Decision trees showed, that the most important peaks to differentiate glioblastoma and meningiomas were at 1151 cm−1 and 2836 cm−1 while for angiomatous and atypical meningiomas – 1514 cm−1 and 2875 cm−1. Furthermore, the accuracy of obtained results for glioblastoma G4 and meningiomas was 88 %, while for meningiomas – 92 %. Consequently, obtained data showed possibility of using FT-Raman spectroscopy in diagnosis of different types of brain tumors.
FT-Raman spectroscopy in combination with multivariate analyses and machine learning methods as a novel tool for differentiation brain tumors showed, that Raman shifts at 1151 cm−1 and 2836 cm−1 could be used as spectroscopy marker for differentiate glioblastoma G4 and meningiomas, while peaks at 1514 cm−1 and 2875 cm−1 distinguish angiomatous and atypical meningiomas. Display omitted
•Additional peak at 1503 cm−1 in FT-Raman spectrum of meningiomas•CH vibrations are more characteristic for atypical meningiomas than for angiomatous one.•The accuracy was higher than 75 %.•Peaks at 1151 cm−1 and 2836 cm−1 could be used for differentiation glioblastoma G4 and meningiomas.•Peaks at 1514 cm−1 and 2875 cm−1 could be used for distinguishing atypical and angiomatous meningiomas.
Studies of structural brain connectivity have revealed many intriguing features of complex cortical networks. To advance integrative theories of cortical organization, an understanding is required of ...how connectivity interrelates with other aspects of brain structure. Recent studies have suggested that interareal connectivity may be related to a variety of macroscopic as well as microscopic architectonic features of cortical areas. However, it is unclear how these features are inter-dependent and which of them most strongly and fundamentally relate to structural corticocortical connectivity. Here, we systematically investigated the relation of a range of microscopic and macroscopic architectonic features of cortical organization, namely layer III pyramidal cell soma cross section, dendritic synapse count, dendritic synapse density and dendritic tree size as well as area neuron density, to multiple properties of cortical connectivity, using a comprehensive, up-to-date structural connectome of the primate brain. Importantly, relationships were investigated by multi-variate analyses to account for the interrelations of features. Of all considered factors, the classical architectonic parameter of neuron density most strongly and consistently related to essential features of cortical connectivity (existence and laminar patterns of projections, area degree), and in conjoint analyses largely abolished effects of cellular morphological features. These results confirm neuron density as a central architectonic indicator of the primate cerebral cortex that is closely related to essential aspects of brain connectivity and is also highly indicative of further features of the architectonic organization of cortical areas, such as the considered cellular morphological measures. Our findings integrate several aspects of cortical micro- and macroscopic organization, with implications for cortical development and function.
•Principles of structural corticocortical connections are not well understood.•We examined the association of connectivity with features of cortical architecture.•Considered were macroscopic and microscopic, cellular morphological features.•Multivariate analyses enabled untangling of contributions of non-orthogonal features.•Neuron density accounted most strongly and consistently for most connectivity properties.
Petroleum contamination is a significant contributor of elevated level of toxic heavy metals, which are of great concern to human health, due to their non-biodegradable nature. Agaye community has ...experienced frequent gasoline spills due to pipeline vandalisation, resulting in the contamination of soil and water sources. The concentrations of metals (Cd, Cr, Cu, Mn, Ni, Pb, V and Zn) in groundwater, surface-water and soil were determined from a total of 216 samples acquired bi-monthly for two years by Inductively Coupled Plasma-Optical Emission Spectrometry (ICP-OES) to evaluate the impact of oil spills. Multivariate analyses using principal component analysis (PCA) and cluster analysis (CA) were also used to study the interactions between metals and identify the possible sources of contamination. The concentrations of heavy metals in soil and water samples (surface and groundwater) were in decreasing order of Mn > Ni > Zn > Cu > V > Cr > Pb > Cd and Ni > Zn > V > Cu > Mn > Pb > Cr > Cd respectively. Ni concentration ranged from 0.42–8.05 mg kg−1 and 0.10–2.85 mg L−1 for soil and groundwater respectively. Ni and V were more enhanced (P < 0.05) in soil samples. This study showed that there was significant relationship between elevated levels of Cr, Cu, Ni and Zn and oil spillage, due to petroleum spills and that residents were vulnerable to and at greater risk of non-carcinogenic hazards if they consumed groundwater. Multivariate analyses showed significant anthropogenic intrusions of two diagnostic heavy metals (Ni and V) for petroleum contamination in the soils and water sources.
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•Oil spillage was significantly related to elevated levels of Cr, Cu, Ni and Zn.•There were significant anthropogenic intrusions of Ni and V for petroleum contamination in soils and water sources.•Consumption of water may cause non-carcinogenic hazards among populace in this area.
Genetic correlations between complex traits suggest that pleiotropic variants contribute to trait variation. Genome-wide association studies (GWAS) aim to uncover the genetic underpinnings of traits. ...Multivariate association testing and the meta-analysis of summary statistics from single-trait GWAS enable detecting variants associated with multiple phenotypes. In this study, we used array-derived genotypes and phenotypes for 24 reproduction, production, and conformation traits to explore differences between the two methods and used imputed sequence variant genotypes to fine-map six quantitative trait loci (QTL).
We considered genotypes at 44,733 SNPs for 5,753 pigs from the Swiss Large White breed that had deregressed breeding values for 24 traits. Single-trait association analyses revealed eleven QTL that affected 15 traits. Multi-trait association testing and the meta-analysis of the single-trait GWAS revealed between 3 and 6 QTL, respectively, in three groups of traits. The multi-trait methods revealed three loci that were not detected in the single-trait GWAS. Four QTL that were identified in the single-trait GWAS, remained undetected in the multi-trait analyses. To pinpoint candidate causal variants for the QTL, we imputed the array-derived genotypes to the sequence level using a sequenced reference panel consisting of 421 pigs. This approach provided genotypes at 16 million imputed sequence variants with a mean accuracy of imputation of 0.94. The fine-mapping of six QTL with imputed sequence variant genotypes revealed four previously proposed causal mutations among the top variants.
Our findings in a medium-size cohort of pigs suggest that multivariate association testing and the meta-analysis of summary statistics from single-trait GWAS provide very similar results. Although multi-trait association methods provide a useful overview of pleiotropic loci segregating in mapping populations, the investigation of single-trait association studies is still advised, as multi-trait methods may miss QTL that are uncovered in single-trait GWAS.
The purpose of the present study was to evaluate the principal component analysis (PCA) to guide technical assistance regarding several dairy farms’ issues, which includes improving microbiological ...quality and physical-chemical composition of raw refrigerated milk. Data of monthly analysis of fat, protein, lactose, dry defatted stratum, somatic cell count, total bacterial count, milk temperature of 8,101 samples of milk from expansion tanks and production of 78 farms located in the northern region of Minas Gerais, Brazil were processed. Descriptive statistical measures and Pearson correlation coefficient were estimated involving all evaluated traits during the dry and rainy seasons. In addition, multivariate analyses were performed using PCA. The results showed that two farm sites were negatively related to milk quality in both seasons. One farm stood out positively, being able to be used as a herd management model to drive technical assistance actions. Thus, PCA is efficient in simplifying large amounts of data, allowing simpler and faster technical herd management interpretation.
Research into the neural correlates of individual differences in imagery vividness point to an important role of the early visual cortex. However, there is also great fluctuation of vividness within ...individuals, such that only looking at differences between people necessarily obscures the picture. In this study, we show that variation in moment-to-moment experienced vividness of visual imagery, within human subjects, depends on the activity of a large network of brain areas, including frontal, parietal, and visual areas. Furthermore, using a novel multivariate analysis technique, we show that the neural overlap between imagery and perception in the entire visual system correlates with experienced imagery vividness. This shows that the neural basis of imagery vividness is much more complicated than studies of individual differences seemed to suggest.
Visual imagery is the ability to visualize objects that are not in our direct line of sight: something that is important for memory, spatial reasoning, and many other tasks. It is known that the better people are at visual imagery, the better they can perform these tasks. However, the neural correlates of moment-to-moment variation in visual imagery remain unclear. In this study, we show that the more the neural response during imagery is similar to the neural response during perception, the more vivid or perception-like the imagery experience is.
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