Simple Statistical Analysis of the Impact of Some Nonidealities in Downstream VDSL with Linear Precoding

Abstract This paper considers a VDSL downstream system where crosstalk is compensated by linear precoding. Starting from a recently introduced mathematical model for FEXT channels, simple analytical methods are derived for evaluating the average bit rates achievable, taking into account three of the most important nonidealities. First, absolute and relative estimation errors in the crosstalk coefficients are discussed, and explicit formulas are obtained to express their impact. A simple approach is presented for computing the maximum line length where linear precoding overcomes the noncoordinate system. Then, the effect of out-of-domain crosstalk is analyzed. Finally, quantization errors in precoding coefficients are considered. We show that by the assumption of a midtread quantization law with different thresholds, a relatively small number of quantization bits is sufficient, thus reducing the implementation complexity. The presented formulas allow to quantify the impact of practical impairments and give a useful tool to design engineers and service providers to have a first estimation of the performance achievable in a specified scenario

Colorectal cancer development is affected by the ECM molecule EMILIN-2 hinging on macrophage polarization via the TLR-4/MyD88 pathway

Background Colorectal cancer is one of the most frequent and deadly tumors. Among the key regulators of CRC growth and progression, the microenvironment has emerged as a crucial player and as a possible route for the development of new therapeutic opportunities. More specifically, the extracellular matrix acts directly on cancer cells and indirectly affecting the behavior of stromal and inflammatory cells, as well as the bioavailability of growth factors. Among the ECM molecules, EMILIN-2 is frequently down-regulated by methylation in CRC and the purpose of this study was to verify the impact of EMILIN-2 loss in CRC development and its possible value as a prognostic biomarker. Methods The AOM/DSS CRC protocol was applied to Emilin-2 null and wild type mice. Tumor development was monitored by endoscopy, the molecular analyses performed by IHC, IF and WB and the immune subpopulations characterized by flow cytometry. Ex vivo cultures of monocyte/macrophages from the murine models were used to verify the molecular pathways. Publicly available datasets were exploited to determine the CRC patients' expression profile; Spearman's correlation analyses and Cox regression were applied to evaluate the association with the inflammatory response; the clinical outcome was predicted by Kaplan-Meier survival curves. Pearson correlation analyses were also applied to a cohort of patients enrolled in our Institute. Results In preclinical settings, loss of EMILIN-2 associated with an increased number of tumor lesions upon AOM/DSS treatment. In addition, in the early stages of the disease, the Emilin-2 knockout mice displayed a myeloid-derived suppressor cells-rich infiltrate. Instead, in the late stages, lack of EMILIN-2 associated with a decreased number of M1 macrophages, resulting in a higher percentage of the tumor-promoting M2 macrophages. Mechanistically, EMILIN-2 triggered the activation of the Toll-like Receptor 4/MyD88/NF-kappa B pathway, instrumental for the polarization of macrophages towards the M1 phenotype. Accordingly, dataset and immunofluorescence analyses indicated that low EMILIN-2 expression levels correlated with an increased M2/M1 ratio and with poor CRC patients' prognosis. Conclusions These novel results indicate that EMILIN-2 is a key regulator of the tumor-associated inflammatory environment and may represent a promising prognostic biomarker for CRC patients

Structural Insights into Alternate Aggregated Prion Protein Forms

The conversion of the cellular form of the prion protein (PrPC) to an abnormal, alternatively folded isoform (PrPSc) is the central event in prion diseases or transmissible spongiform encephalopathies. Recent studies have demonstrated de novo generation of murine prions from recombinant prion protein (recPrP) after inoculation into transgenic and wild-type mice. These so-called synthetic prions lead to novel prion diseases with unique neuropathological and biochemical features. Moreover, the use of recPrP in an amyloid seeding assay can specifically detect and amplify various strains of prions. We employed this assay in our experiments and analyzed in detail the morphology of aggregate structures produced under defined chemical constraints. Our results suggest that changes in the concentration of guanidine hydrochloride can lead to different kinetic traces in a typical thioflavin T(ThT) assay. Morphological and structural analysis of these aggregates by atomic force microscopy indicates a variation in the structure of the PrP molecular assemblies

Genomic structural variations affecting virulence during clonal expansion of Pseudomonas syringae pv. actinidiae biovar 3 in Europe

Pseudomonas syringae pv. actinidiae (Psa) biovar 3 caused pandemic bacterial canker of Actinidia chinensis and Actinidia deliciosa since 2008. In Europe, the disease spread rapidly in the kiwifruit cultivation areas from a single introduction. In this study, we investigated the genomic diversity of Psa biovar 3 strains during the primary clonal expansion in Europe using single molecule real-time (SMRT), Illumina and Sanger sequencing technologies. We recorded evidences of frequent mobilization and loss of transposon Tn6212, large chromosome inversions, and ectopic integration of IS sequences (remarkably ISPsy31, ISPsy36, and ISPsy37). While no phenotype change associated with Tn6212 mobilization could be detected, strains CRAFRU 12.29 and CRAFRU 12.50 did not elicit the hypersensitivity response (HR) on tobacco and eggplant leaves and were limited in their growth in kiwifruit leaves due to insertion of ISPsy31 and ISPsy36 in the hrpS and hrpR genes, respectively, interrupting the hrp cluster. Both strains had been isolated from symptomatic plants, suggesting coexistence of variant strains with reduced virulence together with virulent strains in mixed populations. The structural differences caused by rearrangements of self-genetic elements within European and New Zealand strains were comparable in number and type to those occurring among the European strains, in contrast with the significant difference in terms of nucleotide polymorphisms. We hypothesize a relaxation, during clonal expansion, of the selection limiting the accumulation of deleterious mutations associated with genome structural variation due to transposition of mobile elements. This consideration may be relevant when evaluating strategies to be adopted for epidemics management

A Pan-Cancer Approach to Predict Responsiveness to Immune Checkpoint Inhibitors by Machine Learning.

Immunotherapy by using immune checkpoint inhibitors (ICI) has dramatically improved the treatment options in various cancers, increasing survival rates for treated patients. Nevertheless, there are heterogeneous response rates to ICI among different cancer types, and even in the context of patients affected by a specific cancer. Thus, it becomes crucial to identify factors that predict the response to immunotherapeutic approaches. A comprehensive investigation of the mutational and immunological aspects of the tumor can be useful to obtain a robust prediction. By performing a pan-cancer analysis on gene expression data from the Cancer Genome Atlas (TCGA, 8055 cases and 29 cancer types), we set up and validated a machine learning approach to predict the potential for positive response to ICI. Support vector machines (SVM) and extreme gradient boosting (XGboost) models were developed with a 10×5-fold cross-validation schema on 80% of TCGA cases to predict ICI responsiveness defined by a score combining tumor mutational burden and TGF- β signaling. On the remaining 20% validation subset, our SVM model scored 0.88 accuracy and 0.27 Matthews Correlation Coefficient. The proposed machine learning approach could be useful to predict the putative response to ICI treatment by expression data of primary tumors

A Pan-Cancer Approach to Predict Responsiveness to Immune Checkpoint Inhibitors by Machine Learning

Immunotherapy by using immune checkpoint inhibitors (ICI) has dramatically improved the treatment options in various cancers, increasing survival rates for treated patients. Nevertheless, there are heterogeneous response rates to ICI among different cancer types, and even in the context of patients affected by a specific cancer. Thus, it becomes crucial to identify factors that predict the response to immunotherapeutic approaches. A comprehensive investigation of the mutational and immunological aspects of the tumor can be useful to obtain a robust prediction. By performing a pan-cancer analysis on gene expression data from the Cancer Genome Atlas (TCGA, 8055 cases and 29 cancer types), we set up and validated a machine learning approach to predict the potential for positive response to ICI. Support vector machines (SVM) and extreme gradient boosting (XGboost) models were developed with a 10x5-fold cross-validation schema on 80% of TCGA cases to predict ICI responsiveness defined by a score combining tumor mutational burden and TGF- β signaling. On the remaining 20% validation subset, our SVM model scored 0.88 accuracy and 0.27 Matthews Correlation Coefficient. The proposed machine learning approach could be useful to predict the putative response to ICI treatment by expression data of primary tumors