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Bovine mastitis is one of the most important economic and health issues in dairy farms. Data collection during routine recording procedures and access to large datasets have shed the light on the possibility to use trained machine learning algorithms to predict the udder health status of cows. In this study, we compared eight different machine learning methods (Linear Discriminant Analysis, Generalized Linear Model with logit link function, Naïve Bayes, Classification and Regression Trees, k-Nearest Neighbors, Support Vector Machines, Random Forest and Neural Network) to predict udder health status of cows based on somatic cell counts. Prediction accuracies of all methods were above 75%. According to different metrics, Neural Network, Random Forest and linear methods had the best performance in predicting udder health classes at a given test-day (healthy or mastitic according to somatic cell count below or above a predefined threshold of 200,000 cells/mL) based on the cow’s milk traits recorded at previous test-day. Our findings suggest machine learning algorithms as a promising tool to improve decision making for farmers. Machine learning analysis would improve the surveillance methods and help farmers to identify in advance those cows that would possibly have high somatic cell count in the subsequent test-day.

The presence in nature of species showing drastic differences in lifespan and cancer incidence has recently increased the interest of the scientific community. In particular, the adaptations and the genomic features underlying the evolution of cancer-resistant and long-lived organisms have recently focused on transposable elements (TEs). In this study, we compared the content and dynamics of TE activity in the genomes of four rodent and six bat species exhibiting different lifespans and cancer susceptibility. Mouse, rat, and guinea pig genomes (short-lived and cancer-prone organisms) were compared with that of naked mole rat ( Heterocephalus glaber ) which is a cancer-resistant organism and the rodent with the longest lifespan. The long-lived bats of the genera Myotis , Rhinolophus , Pteropus and Rousettus were instead compared with Molossus molossus , which is one of the organisms with the shortest lifespan among the order Chiroptera. Despite previous hypotheses stating a substantial tolerance of TEs in bats, we found that long-lived bats and the naked mole rat share a marked decrease of non-LTR retrotransposons (LINEs and SINEs) accumulation in recent evolutionary times.

Transposable elements (TEs) play an essential role in shaping eukaryotic genomes and generating variability. Speciation and TE activity bursts could be strongly related in mammals, in which simple gradualistic models of differentiation do not account for the currently observed species variability. In order to test this hypothesis, we designed two parameters: the Density of insertion (DI) and the Relative rate of speciation (RRS). DI is the ratio between the number of TE insertions in a genome and its size, whereas the RRS is a conditional parameter designed to identify potential speciation bursts. Thus, by analyzing TE insertions in mammals, we defined the genomes as “hot” (high DI) and “cold” (low DI). Then, comparing TE activity among 29 taxonomical families of the whole Mammalia class, 16 intra-order pairs of mammalian species, and four superorders of Eutheria, we showed that taxa with high rates of speciation are associated with “hot” genomes, whereas taxa with low ones are associated with “cold” genomes. These results suggest a remarkable correlation between TE activity and speciation, also being consistent with patterns describing variable rates of differentiation and accounting for the different time frames of the speciation bursts.

Analyses of microRNA expression profiles have shown that many microRNAs are expressed aberrantly and correlate with tumorigenesis, progression, and prognosis of various haematological and solid tumours. We aimed to assess the relation between microRNA expression and progression and prognosis of gastric cancer. 353 gastric samples from two independent subsets of patients from Japan were analysed by microRNA microarray. MicroRNA expression patterns were compared between non-tumour mucosa and cancer samples, graded by diffuse and intestinal histological types and by progression-related factors (eg, depth of invasion, metastasis, and stage). Disease outcome was calculated by multivariable regression analysis to establish whether microRNAs are independent prognostic factors. In 160 paired samples of non-tumour mucosa and cancer, 22 microRNAs were upregulated and 13 were downregulated in gastric cancer; 292 (83%) samples were distinguished correctly by this signature. The two histological subtypes of gastric cancer showed different microRNA signatures: eight microRNAs were upregulated in diffuse-type and four in intestinal-type cancer. In the progression-related signature, miR-125b, miR-199a, and miR-100 were the most important microRNAs involved. Low expression of let-7g (hazard ratio 2·6 [95% CI 1·3–4·9]) and miR-433 (2·1 [1·1–3·9]) and high expression of miR-214 (2·4 [1·2–4·5]) were associated with unfavourable outcome in overall survival independent of clinical covariates, including depth of invasion, lymph-node metastasis, and stage. MicroRNAs are expressed differentially in gastric cancers, and histological subtypes are characterised by specific microRNA signatures. Unique microRNAs are associated with progression and prognosis of gastric cancer. National Cancer Institute.

Background Precision medicine is a medical approach that takes into account individual genetic variability and often requires Next Generation Sequencing data in order to predict new treatments. Here we present GMIEC, Genomic Modules Identification et Characterization for genomics medicine, an application that is able to identify specific drugs at the level of single patient integrating multi-omics data such as RNA-sequencing, copy-number variation, methylation, Chromatin Immuno-Precipitation and Exome/Whole Genome sequencing. It is also possible to include clinical data related to each patient. GMIEC has been developed as a web-based R-Shiny platform and gives as output a table easy to use and explore. Results We present GMIEC, a Shiny application for genomics medicine. The tool allows the users the integration of two or more multiple omics datasets (e.g. gene-expression, copy-number), at sample level, to identify groups of genes that share common genomic and corresponding drugs. We demonstrate the characteristics of our application by using it to analyze a prostate cancer data set. Conclusions GMIEC provides a simple interface for genomics medicine. GMIEC was develop with Shiny to provide an application that does not require advanced programming skills. GMIEC consists of three sub-application for the analysis (GMIEC-AN), the visualization (GMIEC-VIS) and the exploration of results (GMIEC-RES). GMIEC is an open source software and is available at https://github.com/guidmt/GMIEC-shiny

Peri-implantitis is an inflammatory disease affecting tissues surrounding dental implants. Although it represents a common complication of dental implant treatments, the underlying mechanisms have not yet been fully described. The aim of this study is to identify the role of titanium nanoparticles released form the implants on the chronic inflammation and bone lysis in the surrounding tissue. We analyzed the in vitro effect of titanium (Ti) particle exposure on mesenchymal stem cells (MSCs) and fibroblasts (FU), evaluating cell proliferation by MTT test and the generation of reactive oxygen species (ROS). Subsequently, in vivo analysis of peri-implant Ti particle distribution, histological, and molecular analyses were performed. Ti particles led to a time-dependent decrease in cell viability and increase in ROS production in both MSCs and FU. Tissue analyses revealed presence of oxidative stress, high extracellular and intracellular Ti levels and imbalanced bone turnover. High expression of ZFP467 and the presence of adipose-like tissue suggested dysregulation of the MSC population; alterations in vessel morphology were identified. The results suggest that Ti particles may induce the production of high ROS levels, recruiting abnormal quantity of neutrophils able to produce high level of metalloproteinase. This induces the degradation of collagen fibers. These events may influence MSC commitment, with an imbalance of bone regeneration.

Background Archaea and Bacteria are distinct domains of life that are adapted to a variety of ecological niches. Several genome-based methods have been developed for their accurate classification, yet many aspects of the specific genomic features that determine these differences are not fully understood. In this study, we used publicly available whole-genome sequences from bacteria ( N = 2546 ) and archaea ( N = 109 ). From these, a set of genomic features (nucleotide frequencies and proportions, coding sequences (CDS), non-coding, ribosomal and transfer RNA genes (ncRNA, rRNA, tRNA), Chargaff’s, topological entropy and Shannon’s entropy scores) was extracted and used as input data to develop machine learning models for the classification of archaea and bacteria. Results The classification accuracy ranged from 0.993 (Random Forest) to 0.998 (Neural Networks). Over the four models, only 11 examples were misclassified, especially those belonging to the minority class (Archaea). From variable importance, tRNA topological and Shannon’s entropy, nucleotide frequencies in tRNA, rRNA and ncRNA, CDS, tRNA and rRNA Chargaff’s scores have emerged as the top discriminating factors. In particular, tRNA entropy (both topological and Shannon’s) was the most important genomic feature for classification, pointing at the complex interactions between the genetic code, tRNAs and the translational machinery. Conclusions tRNA, rRNA and ncRNA genes emerged as the key genomic elements that underpin the classification of archaea and bacteria. In particular, higher nucleotide diversity was found in tRNA from bacteria compared to archaea. The analysis of the few classification errors reflects the complex phylogenetic relationships between bacteria, archaea and eukaryotes.

Insects entombed in copal, the sub-fossilized resin precursor of amber, represent a potential source of genetic data for extinct and extant, but endangered or elusive, species. Despite several studies demonstrated that it is not possible to recover endogenous DNA from insect inclusions, the preservation of biomolecules in fossilized resins samples is still under debate. In this study, we tested the possibility of obtaining endogenous ancient DNA (aDNA) molecules from insects preserved in copal, applying experimental protocols specifically designed for aDNA recovery. We were able to extract endogenous DNA molecules from one of the two samples analyzed, and to identify the taxonomic status of the specimen. Even if the sample was found well protected from external contaminants, the recovered DNA was low concentrated and extremely degraded, compared to the sample age. We conclude that it is possible to obtain genomic data from resin-entombed organisms, although we discourage aDNA analysis because of the destructive method of extraction protocols and the non-reproducibility of the results.

The accurate detection of behavioural changes represents a promising method of detecting the early onset of disease in dairy cows. This study assessed the performance of deep learning (DL) in classifying dairy cows’ behaviour from accelerometry data acquired by single sensors on the cows’ left flanks and compared the results with those obtained through classical machine learning (ML) from the same raw data. Twelve cows with a tri-axial accelerometer were observed for 136 ± 29 min each to detect five main behaviours: standing still, moving, feeding, ruminating and resting. For each 8 s time interval, 15 metrics were calculated, obtaining a dataset of 211,720 observation units and 15 columns. The entire dataset was randomly split into training (80%) and testing (20%) datasets. The DL accuracy, precision and sensitivity/recall were calculated and compared with the performance of classical ML models. The best predictive model was an 8-layer convolutional neural network (CNN) with an overall accuracy and F1 score equal to 0.96. The precision, sensitivity/recall and F1 score of single behaviours had the following ranges: 0.93–0.99. The CNN outperformed all the classical ML algorithms. The CNN used to monitor the cows’ conditions showed an overall high performance in successfully predicting multiple behaviours using a single accelerometer.

MicroRNAs (miRNAs) are short noncoding RNAs regulating gene expression that play roles in human diseases, including cancer. Each miRNA is predicted to regulate hundreds of transcripts, but only few have experimental validation. In chronic lymphocytic leukemia (CLL), the most common adult human leukemia, miR-15a and miR-16-1 are lost or down-regulated in the majority of cases. After our previous work indicating a tumor suppressor function of miR-15a/16-1 by targeting the BCL2 oncogene, here, we produced a high-throughput profiling of genes modulated by miR-15a/16-1 in a leukemic cell line model (MEG-01) and in primary CLL samples. By combining experimental and bioinformatics data, we identified a miR-15a/16-1-gene signature in leukemic cells. Among the components of the miR-15a/16-1 signature, we observed a statistically significant enrichment in AU-rich elements (AREs). By examining the Gene Ontology (GO) database, a significant enrichment in cancer genes (such as MCL1, BCL2, ETS1, or JUN) that directly or indirectly affect apoptosis and cell cycle was found.