Investigation into PRS-precoded, constant-envelope, continuous-phase digital modulation schemes

Bibliography: leaves 78-79.Partial response signaling ( PRS) has been used successfully to improve the spectral properties of Pulse Amplitude Modulated (PAM) digital transmission systems. This thesis investigation studied the effect of PRS on frequency- and phase-modulated carrier systems, in particular on their spectral performance and their maintenance of constant envelope

Personalised, image-guided, noninvasive brain stimulation in gliomas: Rationale, challenges and opportunities

Malignant brain tumours are among the most aggressive human cancers, and despite intensive efforts made over the last decades, patients’ survival has scarcely improved. Recently, high-grade gliomas (HGG) have been found to be electrically integrated with healthy brain tissue, a communication that facilitates tumour mitosis and invasion. This link to neuronal activity has provided new insights into HGG pathophysiology and opened prospects for therapeutic interventions based on electrical modulation of neural and synaptic activity in the proximity of tumour cells, which could potentially slow tumour growth. Noninvasive brain stimulation (NiBS), a group of techniques used in research and clinical settings to safely modulate brain activity and plasticity via electromagnetic or electrical stimulation, represents an appealing class of interventions to characterise and target the electrical properties of tumour-neuron interactions. Beyond neuronal activity, NiBS may also modulate function of a range of substrates and dynamics that locally interacts with HGG (e.g., vascular architecture, perfusion and blood-brain barrier permeability). Here we discuss emerging applications of NiBS in patients with brain tumours, covering potential mechanisms of action at both cellular, regional, network and whole-brain levels, also offering a conceptual roadmap for future research to prolong survival or promote wellbeing via personalised NiBS interventions

Tumor BOLD connectivity profile correlates with glioma patients' survival

Background: Presence of residual neurovascular activity within glioma lesions have been recently demonstrated via functional MRI (fMRI) along with active electrical synapses between glioma cells and healthy neurons that influence survival. In this study, we aimed to investigate whether gliomas demonstrate synchronized neurovascular activity with the rest of the brain, by measuring Blood Oxygen Level Dependent (BOLD) signal synchronization, that is, functional connectivity (FC), while also testing whether the strength of such connectivity might predict patients' overall survival (OS). Methods: Resting-state fMRI scans of patients who underwent pre-surgical brain mapping were analyzed (total sample, n = 54; newly diagnosed patients, n = 18; recurrent glioma group, n = 36). A seed-To-voxel analysis was conducted to estimate the FC signal profile of the tumor mass. A regression model was then built to investigate the potential correlation between tumor FC and individual OS. Finally, an unsupervised, cross-validated clustering analysis was performed including tumor FC and clinical OS predictors (e.g., Karnofsky Performance Status-KPS-score, tumor volume, and genetic profile) to verify the performance of tumor FC in predicting OS with respect to validated radiological, demographic, genetic and clinical prognostic factors. Results: In both newly diagnosed and recurrent glioma patients a significant pattern of BOLD synchronization between the solid tumor and distant brain regions was found. Crucially, glioma-brain FC positively correlated with variance in individual survival in both newly diagnosed glioma group (r = 0.90-0.96; P <. 001; R2 = 81-92%) and in the recurrent glioma group (r = 0.72; P <. 001; R2 = 52%), outperforming standard clinical, radiological and genetic predictors. Conclusions: Results suggest glioma's synchronization with distant brain regions should be further explored as a possible diagnostic and prognostic biomarker

Enhancement of semantic integration reasoning by tRNS

The right hemisphere is involved with the integrative processes necessary to achieve global coherence during reasoning and discourse processing. Specifically, the right temporal lobe has been proven to facilitate the processing of distant associate relationships, such as generating novel ideas. Previous studies showed a specific swing of alpha and gamma oscillatory activity over the right parieto-occipital lobe and the right anterior temporal lobe respectively, when people solve semantic problems with a specific strategy, i.e., insight problem-solving. In this study, we investigated the specificity of the right parietal and temporal lobes for semantic integration using transcranial Random Noise Stimulation (tRNS). We administered a set of pure semantics (i.e., Compound Remote Associates [CRA]) and visuo-semantic problems (i.e., Rebus Puzzles) to a sample of 31 healthy volunteers. Behavioral results showed that tRNS stimulation over the right temporal lobe enhances CRA accuracy (+12%), while stimulation on the right parietal lobe causes a decrease of response time on the same task (−2,100 ms). No effects were detected for Rebus Puzzles. Our findings corroborate the involvement of the right temporal and parietal lobes when solving purely semantic problems but not when they involve visuo-semantic material, also providing causal evidence for their postulated different roles in the semantic integration process and promoting tRNS as a candidate tool to boost verbal reasoning in humans

Genome-level analyses of Mycobacterium bovis lineages reveal the role of SNPs and antisense transcription in differential gene expression

BACKGROUND: Bovine tuberculosis (bTB) is a disease with major implications for animal welfare and productivity, as well as having the potential for zoonotic transmission. In Great Britain (GB) alone, controlling bTB costs in the region of £100 million annually, with the current control scheme seemingly unable to stop the inexorable spread of infection. One aspect that may be driving the epidemic is evolution of the causative pathogen, Mycobacterium bovis. To understand the underlying genetic changes that may be responsible for this evolution, we performed a comprehensive genome-level analyses of 4 M. bovis strains that encompass the main molecular types of the pathogen circulating in GB. RESULTS: We have used a combination of genome sequencing, transcriptome analyses, and recombinant DNA technology to define genetic differences across the major M. bovis lineages circulating in GB that may give rise to phenotypic differences of practical importance. The genomes of three M. bovis field isolates were sequenced using Illumina sequencing technology and strain specific differences in gene expression were measured during in vitro growth and in ex vivo bovine alveolar macrophages using a whole genome amplicon microarray and a whole genome tiled oligonucleotide microarray. SNP/small base pair insertion and deletions and gene expression data were overlaid onto the genomic sequence of the fully sequenced strain of M. bovis 2122/97 to link observed strain specific genomic differences with differences in RNA expression. CONCLUSIONS: We show that while these strains show extensive similarities in their genetic make-up and gene expression profiles, they exhibit distinct expression of a subset of genes. We provide genomic, transcriptomic and functional data to show that synonymous point mutations (sSNPs) on the coding strand can lead to the expression of antisense transcripts on the opposing strand, a finding with implications for how we define a 'silent’ nucleotide change. Furthermore, we show that transcriptomic data based solely on amplicon arrays can generate spurious results in terms of gene expression profiles due to hybridisation of antisense transcripts. Overall our data suggest that subtle genetic differences, such as sSNPS, may have important consequences for gene expression and subsequent phenotype

Mycobacterium tuberculosis Responds to Chloride and pH as Synergistic Cues to the Immune Status of its Host Cell

PubMed ID: 23592993This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Global analyses of TetR family transcriptional regulators in mycobacteria indicates conservation across species and diversity in regulated functions

BACKGROUND: Mycobacteria inhabit diverse niches and display high metabolic versatility. They can colonise both humans and animals and are also able to survive in the environment. In order to succeed, response to environmental cues via transcriptional regulation is required. In this study we focused on the TetR family of transcriptional regulators (TFTRs) in mycobacteria. RESULTS: We used InterPro to classify the entire complement of transcriptional regulators in 10 mycobacterial species and these analyses showed that TFTRs are the most abundant family of regulators in all species. We identified those TFTRs that are conserved across all species analysed and those that are unique to the pathogens included in the analysis. We examined genomic contexts of 663 of the conserved TFTRs and observed that the majority of TFTRs are separated by 200 bp or less from divergently oriented genes. Analyses of divergent genes indicated that the TFTRs control diverse biochemical functions not limited to efflux pumps. TFTRs typically bind to palindromic motifs and we identified 11 highly significant novel motifs in the upstream regions of divergently oriented TFTRs. The C-terminal ligand binding domain from the TFTR complement in M. tuberculosis showed great diversity in amino acid sequence but with an overall architecture common to other TFTRs. CONCLUSION: This study suggests that mycobacteria depend on TFTRs for the transcriptional control of a number of metabolic functions yet the physiological role of the majority of these regulators remain unknown. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12864-015-1696-9) contains supplementary material, which is available to authorized users

Rapid, Label-Free Detection of Brain Tumors with Stimulated Raman Scattering Microscopy

Surgery is an essential component in the treatment of brain tumors. However, delineating tumor from normal brain remains a major challenge. We describe the use of stimulated Raman scattering (SRS) microscopy for differentiating healthy human and mouse brain tissue from tumor-infiltrated brain based on histoarchitectural and biochemical differences. Unlike traditional histopathology, SRS is a label-free technique that can be rapidly performed in situ. SRS microscopy was able to differentiate tumor from nonneoplastic tissue in an infiltrative human glioblastoma xenograft mouse model based on their different Raman spectra. We further demonstrated a correlation between SRS and hematoxylin and eosin microscopy for detection of glioma infiltration (κ = 0.98). Finally, we applied SRS microscopy in vivo in mice during surgery to reveal tumor margins that were undetectable under standard operative conditions. By providing rapid intraoperative assessment of brain tissue, SRS microscopy may ultimately improve the safety and accuracy of surgeries where tumor boundaries are visually indistinct.Chemistry and Chemical Biolog

Deep Learning for Detection and Localization of B-Lines in Lung Ultrasound

Lung ultrasound (LUS) is an important imaging modality used by emergency physicians to assess pulmonary congestion at the patient bedside. B-line artifacts in LUS videos are key findings associated with pulmonary congestion. Not only can the interpretation of LUS be challenging for novice operators, but visual quantification of B-lines remains subject to observer variability. In this work, we investigate the strengths and weaknesses of multiple deep learning approaches for automated B-line detection and localization in LUS videos. We curate and publish, BEDLUS, a new ultrasound dataset comprising 1,419 videos from 113 patients with a total of 15,755 expert-annotated B-lines. Based on this dataset, we present a benchmark of established deep learning methods applied to the task of B-line detection. To pave the way for interpretable quantification of B-lines, we propose a novel "single-point" approach to B-line localization using only the point of origin. Our results show that (a) the area under the receiver operating characteristic curve ranges from 0.864 to 0.955 for the benchmarked detection methods, (b) within this range, the best performance is achieved by models that leverage multiple successive frames as input, and (c) the proposed single-point approach for B-line localization reaches an F1-score of 0.65, performing on par with the inter-observer agreement. The dataset and developed methods can facilitate further biomedical research on automated interpretation of lung ultrasound with the potential to expand the clinical utility.Comment: 10 pages, 4 figure