Choosing the Optimal Trigger Point for Analysis of Movements after Stroke Based on Magnetoencephalographic Recordings

The aim of this study was to select the optimal procedure for analysing motor fields (MF) and motor evoked fields (MEF) measured from brain injured patients. Behavioural pretests with patients have shown that most of them cannot stand measurements longer than 30 minutes and they also prefer to move the hand with rather short breaks between movements. Therefore, we were unable to measure the motor field (MF) optimally. Furthermore, we planned to use MEF to monitor cortical plasticity in a motor rehabilitation procedure. Classically, the MF analysis refers to rather long epochs around the movement onset (M-onset). We shortened the analysis epoch down to a range from 1000 milliseconds before until 500 milliseconds after M-onset to fulfil the needs of the patients. Additionally, we recorded the muscular activity (EMG) by surface electrodes on the extensor carpi ulnaris and flexor carpi ulnaris muscles. Magnetoencephalographic (MEG) data were recorded from 9 healthy subjects, who executed horizontally brisk extension and flexion in the right wrist. Significantly higher MF dipole strength was found in data based on EMG-onset than in M-onset based data. There was no difference in MEF I dipole strength between the two trigger latencies. In conclusion, we recommend averaging in respect to the EMG-onset for the analysis of both components MF as well as MEF

Backbone conformational flexibility of the lipid modified membrane anchor of the human N-Ras protein investigated by solid-state NMR and molecular dynamics simulation

AbstractThe lipid modified human N-Ras protein, implicated in human cancer development, is of particular interest due to its membrane anchor that determines the activity and subcellular location of the protein. Previous solid-state NMR investigations indicated that this membrane anchor is highly dynamic, which may be indicative of backbone conformational flexibility. This article aims to address if a dynamic exchange between three structural models exist that had been determined previously. We applied a combination of solid-state nuclear magnetic resonance (NMR) methods and replica exchange molecular dynamics (MD) simulations using a Ras peptide that represents the terminal seven amino acids of the human N-Ras protein. Analysis of correlations between the conformations of individual amino acids revealed that Cys 181 and Met 182 undergo collective conformational exchange. Two major structures constituting about 60% of all conformations could be identified. The two conformations found in the simulation are in rapid exchange, which gives rise to low backbone order parameters and nuclear spin relaxation as measured by experimental NMR methods. These parameters were also determined from two 300 ns conventional MD simulations, providing very good agreement with the experimental data

Layered Symbolic Security Analysis in DY⋆^\star

While cryptographic protocols are often analyzed in isolation, they are typically deployed within a stack of protocols, where each layer relies on the security guarantees provided by the protocol layer below it, and in turn provides its own security functionality to the layer above. Formally analyzing the whole stack in one go is infeasible even for semi-automated verification tools, and impossible for pen-and-paper proofs. The DY$^\star$ protocol verification framework offers a modular and scalable technique that can reason about large protocols, specified as a set of F$^\star$ modules. However, it does not support the compositional verification of layered protocols since it treats the global security invariants monolithically. In this paper, we extend DY$^\star$ with a new methodology that allows analysts to modularly analyze each layer in a way that compose to provide security for a protocol stack. Importantly, our technique allows a layer to be replaced by another implementation, without affecting the proofs of other layers. We demonstrate this methodology on two case studies. We also present a verified library of generic authenticated and confidential communication patterns that can be used in future protocol analyses and is of independent interest

Health Monitoring based on Dynamic Flexibility matrix: Theoretical Models versus in-situ Tests

The paper focuses on damage detection of civil engineering structures and especially on concrete bridges. A method for structural health monitoring based on vibrational measurements is presented and discussed. Experimentally identified modal parameters (eigenfrequencies, mode shapes and modal masses) of bridge structures are used to calculate the inverse stiffness matrix, the so-called flexibility matrix. By monitoring of the stiffness matrix, damage can easily be detected, quantified and localized by tracking changes of its individual elements. However, based on dynamic field measurements, the acquisition of the flexibility matrix instead of the stiffness matrix is often the only choice and hence more relevant for practice. But the flexibility-based quantification and localisation of damage are often possible but more difficult, as it depends on the type of support and the location of the damage. These issues are discussed and synthetized, that is an originality of this paper and is believed useful for engineers in the damage detection of different bridge structures. First the theoretical background is briefly repeated prior to the illustration of the differences between stiffness and flexibility matrix on analytical and numerical examples. Then the flexibility-based detection is demonstrated on two true bridges with real-time measurement data and the results are promising

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Integrated Genomic Analysis of the Ubiquitin Pathway across Cancer Types

Protein ubiquitination is a dynamic and reversibleprocess of adding single ubiquitin molecules orvarious ubiquitin chains to target proteins. Here,using multidimensional omic data of 9,125 tumorsamples across 33 cancer types from The CancerGenome Atlas, we perform comprehensive molecu-lar characterization of 929 ubiquitin-related genesand 95 deubiquitinase genes. Among them, we sys-tematically identify top somatic driver candidates,including mutatedFBXW7with cancer-type-specificpatterns and amplifiedMDM2showing a mutuallyexclusive pattern withBRAFmutations. Ubiquitinpathway genes tend to be upregulated in cancermediated by diverse mechanisms. By integratingpan-cancer multiomic data, we identify a group oftumor samples that exhibit worse prognosis. Thesesamples are consistently associated with the upre-gulation of cell-cycle and DNA repair pathways, char-acterized by mutatedTP53,MYC/TERTamplifica-tion, andAPC/PTENdeletion. Our analysishighlights the importance of the ubiquitin pathwayin cancer development and lays a foundation fordeveloping relevant therapeutic strategies