A note on minimal resolutions of vector-spread Borel ideals

We consider vector-spread Borel ideals. We show that these ideals have linear quotients and thereby we determine the graded Betti numbers and the bigraded Poincaré series. A characterization of the extremal Betti numbers of such a class of ideals is given. Finally, we classify all Cohen-Macaulay vector-spread Borel ideals

Integrated microRNA and proteome analysis of cancer datasets with MoPC

MicroRNAs (miRNAs) are small molecules that play an essential role in regulating gene expression by post-transcriptional gene silencing. Their study is crucial in revealing the fundamental processes underlying pathologies and, in particular, cancer. To date, most studies on miRNA regulation consider the effect of specific miRNAs on specific target mRNAs, providing wet-lab validation. However, few tools have been developed to explain the miRNAmediated regulation at the protein level. In this paper, the MoPC computational tool is presented, that relies on the partial correlation between mRNAs and proteins conditioned on the miRNA expression to predict miRNA-target interactions in multi-omic datasets. MoPC returns the list of significant miRNA-target interactions and plot the significant correlations on the heatmap in which the miRNAs and targets are ordered by the chromosomal location. The software was applied on three TCGA/CPTAC datasets (breast, glioblastoma, and lung cancer), returning enriched results in three independent targets databases

A Multi-Cloud Warm-Absorber Model for NGC 4051

A multi-cloud model is presented which explains the soft X-ray excess in NGC 4051 and, consistently, the optical line spectrum and the SED of the continuum. The clouds are heated and ionized by the photoionizing flux from the active center and by shocks. Diffuse radiation, partly absorbed throughout the clouds, nicely fits the bump in the soft X-ray domain, while bremsstrahlung radiation from the gaseous clouds contribute to the fit of the continuum SED. Debris of high density fragmented clouds are necessary to explain the absorption oxygen throats observed at 0.87 keV and 0.74 keV. The debris are heated by shocks of about 200-300 km/s. Low velocity (100 km/s)-density (100 cm-3) clouds contribute to the line and continuum spectra, as well as high velocity (1000 km/s)-density (8000 cm-3) clouds which are revealed by the FWHM of the line profiles. The SED in the IR is explained by reradiation of dust, however, the dust-to-gas ratio is not particularly high. Radio emission is well fitted by synchrotron radiation created at the shock front by Fermi mechanism.Comment: 19 pages + 3 figures PostScrip

Identifying the oncogenic potential of gene fusions exploiting miRNAs

It is estimated that oncogenic gene fusions cause about 20% of human cancer morbidity. Identifying potentially oncogenic gene fusions may improve affected patients’ diagnosis and treatment. Previous approaches to this issue included exploiting specific gene-related information, such as gene function and regulation. Here we propose a model that profits from the previous findings and includes the microRNAs in the oncogenic assessment. We present ChimerDriver, a tool to classify gene fusions as oncogenic or not oncogenic. ChimerDriver is based on a specifically designed neural network and trained on genetic and post-transcriptional information to obtain a reliable classification. The designed neural network integrates information related to transcription factors, gene ontologies, microRNAs and other detailed information related to the functions of the genes involved in the fusion and the gene fusion structure. As a result, the performances on the test set reached 0.83 f1-score and 96% recall. The comparison with state-of-the-art tools returned comparable or higher results. Moreover, ChimerDriver performed well in a real-world case where 21 out of 24 validated gene fusion samples were detected by the gene fusion detection tool Starfusion. ChimerDriver integrates transcriptional and post-transcriptional information in an ad-hoc designed neural network to effectively discriminate oncogenic gene fusions from passenger ones. ChimerDriver source code is freely available at https://github.com/martalovino/ChimerDriver

MiREx: mRNA levels prediction from gene sequence and miRNA target knowledge

Messenger RNA (mRNA) has an essential role in the protein production process. Predicting mRNA expression levels accurately is crucial for understanding gene regulation, and various models (statistical and neural network-based) have been developed for this purpose. A few models predict mRNA expression levels from the DNA sequence, exploiting the DNA sequence and gene features (e.g., number of exons/introns, gene length). Other models include information about long-range interaction molecules (i.e., enhancers/silencers) and transcriptional regulators as predictive features, such as transcription factors (TFs) and small RNAs (e.g., microRNAs - miRNAs). Recently, a convolutional neural network (CNN) model, called Xpresso, has been proposed for mRNA expression level prediction leveraging the promoter sequence and mRNAs’ half-life features (gene features). To push forward the mRNA level prediction, we present miREx, a CNN-based tool that includes information about miRNA targets and expression levels in the model. Indeed, each miRNA can target specific genes, and the model exploits this information to guide the learning process. In detail, not all miRNAs are included, only a selected subset with the highest impact on the model. MiREx has been evaluated on four cancer primary sites from the genomics data commons (GDC) database: lung, kidney, breast, and corpus uteri. Results show that mRNA level prediction benefits from selected miRNA targets and expression information. Future model developments could include other transcriptional regulators or be trained with proteomics data to infer protein levels

A Novel Gaussian Extrapolation Approach for 2D Gel Electrophoresis Saturated Protein Spots

Analysis of images obtained from two-dimensional gel electrophoresis (2D-GE) is a topic of utmost importance in bioinformatics research, since commercial and academic software available currently has proven to be neither completely effective nor fully automatic, often requiring manual revision and refinement of computer generated matches. In this work, we present an effective technique for the detection and the reconstruction of over-saturated protein spots. Firstly, the algorithm reveals overexposed areas, where spots may be truncated, and plateau regions caused by smeared and overlapping spots. Next, it reconstructs the correct distribution of pixel values in these overexposed areas and plateau regions, using a two-dimensional least-squares fitting based on a generalized Gaussian distribution. Pixel correction in saturated and smeared spots allows more accurate quantification, providing more reliable image analysis results. The method is validated for processing highly exposed 2D-GE images, comparing reconstructed spots with the corresponding non-saturated image, demonstrating that the algorithm enables correct spot quantification

Metaplastic carcinoma with extensive dendritic cell differentiation: a previously unrecognised type of triple-negative breast cancer

The case of a metaplastic carcinoma of the breast exhibiting dendritic cell differentiation is described. The clinico-pathologic and immunohistochemical features are reported, together with the differential diagnosis

Correction to: inflammation is a target of medical treatment for lower urinary tract symptoms associated with benign prostatic hyperplasia

The article “Inflammation is a target of medical treatment for lower urinary tract symptoms associated with benign prostatic hyperplasia”, written by Cosimo De Nunzio, Andrea Salonia, Mauro Gacci and Vincenzo Ficarra was originally published electronically on the publisher’s internet portal on 14 February 2020 without open access

Inflammation is a target of medical treatment for lower urinary tract symptoms associated with benign prostatic hyperplasia

Purpose: To review the role of a persistent prostatic inflammatory status (PIS) in the development and progression of benign prostatic hyperplasia (BPH) associated with lower urinary tract symptoms (LUTS) and which medical therapies approved for LUTS/BPH may reduce persistent PIS. Methods: Literature search in PubMed up to July 2019. Results: The cause of histologically defined persistent PIS or chronic prostatic inflammation is multifactorial. It is evident in many men with LUTS/BPH, particularly in older men and in men with a large prostate volume or more severe (storage) LUTS. Additionally, persistent PIS is associated with an increased risk of acute urinary retention and symptom worsening. Of medical therapies approved for LUTS/BPH, the current evidence for a reduction of persistent PIS is greatest for the hexanic extract of Serenoa repens (HESr). This treatment relieves LUTS to the same extent as α1-adrenoceptor antagonists and short-term 5α-reductase inhibitors. Limited evidence is available on the effect of other mainstream LUTS/BPH treatments on persistent PIS. Conclusions: Persistent PIS plays a central role in both the development and progression of LUTS/BPH. In men with LUTS/BPH who have a high chance of harbouring persistent PIS, HESr will not only improve LUTS, but also reduce (underlying) inflammation. Well-designed clinical studies, with a good level of evidence, are required to better evaluate the impact of BPH/LUTS medical therapies on persistent PIS