A Comprehensive Workflow for General-Purpose Neural Modeling with Highly Configurable Neuromorphic Hardware Systems

In this paper we present a methodological framework that meets novel requirements emerging from upcoming types of accelerated and highly configurable neuromorphic hardware systems. We describe in detail a device with 45 million programmable and dynamic synapses that is currently under development, and we sketch the conceptual challenges that arise from taking this platform into operation. More specifically, we aim at the establishment of this neuromorphic system as a flexible and neuroscientifically valuable modeling tool that can be used by non-hardware-experts. We consider various functional aspects to be crucial for this purpose, and we introduce a consistent workflow with detailed descriptions of all involved modules that implement the suggested steps: The integration of the hardware interface into the simulator-independent model description language PyNN; a fully automated translation between the PyNN domain and appropriate hardware configurations; an executable specification of the future neuromorphic system that can be seamlessly integrated into this biology-to-hardware mapping process as a test bench for all software layers and possible hardware design modifications; an evaluation scheme that deploys models from a dedicated benchmark library, compares the results generated by virtual or prototype hardware devices with reference software simulations and analyzes the differences. The integration of these components into one hardware-software workflow provides an ecosystem for ongoing preparative studies that support the hardware design process and represents the basis for the maturity of the model-to-hardware mapping software. The functionality and flexibility of the latter is proven with a variety of experimental results

Deriving a mutation index of carcinogenicity using protein structure and protein interfaces

With the advent of Next Generation Sequencing the identification of mutations in the genomes of healthy and diseased tissues has become commonplace. While much progress has been made to elucidate the aetiology of disease processes in cancer, the contributions to disease that many individual mutations make remain to be characterised and their downstream consequences on cancer phenotypes remain to be understood. Missense mutations commonly occur in cancers and their consequences remain challenging to predict. However, this knowledge is becoming more vital, for both assessing disease progression and for stratifying drug treatment regimes. Coupled with structural data, comprehensive genomic databases of mutations such as the 1000 Genomes project and COSMIC give an opportunity to investigate general principles of how cancer mutations disrupt proteins and their interactions at the molecular and network level. We describe a comprehensive comparison of cancer and neutral missense mutations; by combining features derived from structural and interface properties we have developed a carcinogenicity predictor, InCa (Index of Carcinogenicity). Upon comparison with other methods, we observe that InCa can predict mutations that might not be detected by other methods. We also discuss general limitations shared by all predictors that attempt to predict driver mutations and discuss how this could impact high-throughput predictions. A web interface to a server implementation is publicly available at http://inca.icr.ac.uk/

A general reaction-diffusion model of acidity in cancer invasion

We model the metabolism and behaviour of a developing cancer tumour in the context of its microenvironment, with the aim of elucidating the consequences of altered energy metabolism. Of particular interest is the Warburg Effect, a widespread preference in tumours for cytosolic glycolysis rather than oxidative phosphorylation for glucose breakdown, as yet incompletely understood. We examine a candidate explanation for the prevalence of the Warburg Effect in tumours, the acid-mediated invasion hypothesis, by generalising a canonical non-linear reaction–diffusion model of acid-mediated tumour invasion to consider additional biological features of potential importance. We apply both numerical methods and a non-standard asymptotic analysis in a travelling wave framework to obtain an explicit understanding of the range of tumour behaviours produced by the model and how fundamental parameters govern the speed and shape of invading tumour waves. Comparison with conclusions drawn under the original system—a special case of our generalised system—allows us to comment on the structural stability and predictive power of the modelling framework

High JC virus load in tongue carcinomas may be a risk factor for tongue tumorigenesis

The John Cunningham virus (JCV) asymptomatically infects a large proportion (~90%) of the population worldwide but may be activated in immunodeficient patients, resulting in progressive multifocal leukoencephalopathy. Recent reports demonstrated its oncogenic role in malignancies. In this paper, the presence of JCV-targeting T antigen was investigated in tongue carcinoma (TC, n = 39), dysplastic tongue epithelium (DTE, n = 15) and glossitis (n = 15) using real-time polymerase chain reaction (PCR) and in situ PCR and immunohistochemistry, and JCV copies were analyzed with the clinicopathological parameters of TCs. The results demonstrated that glossitis and DTEs had significantly lower copies of JCV (410.5 ± 44.3 and 658.3 ± 53.3 copies/μg DNA respectively) than TCs (981.5 ± 14.0, p  < 0.05). When they were divided into three groups with 0–200 copies/μg DNA (low), 201–1,000 (moderate) and more than 1001 (high), TCs showed 3 (7.6%) in the low group, 21 (53.8%) in the moderate group and 15 (38.4%) in the high group and glossitis showed 11 (73.3%) in the low group, 0 (0%) in the moderate group and 4 (26.6%) in the high group. The DTEs occupied an intermediate position between them (p < 0.001). In situ PCR demonstrated that the nuclei of TC and DTE cells are sporadically T-antigen positive but not in nasal turbinate epithelial cells. Immunohistochemistry for T-antigen protein revealed four positive cases only in TCs. The existence of JCV T-antigen DNA was not associated with the clinicopathological variables of TCs. In conclusion, the presence of JCV may be a risk factor of tongue carcinogenesis

Combination of p53AIP1 and survivin expression is a powerful prognostic marker in non-small cell lung cancer

<p>Abstract</p> <p>Background</p> <p>p53AIP1 is a potential mediator of apoptosis depending on p53, which is mutated in many kinds of carcinoma. High survivin expression in non-small cell lung cancer is related with poor prognosis. To investigate the role of these genes in non-small cell lung cancer, we compared the relationship between p53AIP1 or survivin gene expression and the clinicopathological status of lung cancer.</p> <p>Materials and methods</p> <p>Forty-seven samples from non-small cell lung cancer patients were obtained between 1997 and 2003. For quantitative evaluation of RNA expression by PCR, we used Taqman PCR methods.</p> <p>Results</p> <p>Although no correlation between p53AIP1 or survivin gene expression and clinicopathological factors was found, the relationship between survivin gene expression and nodal status was significant (p = 0.03). Overall survival in the p53AIP1-negative group was significantly worse than in the positive group (p = 0.04); however, although survivin expression was not a prognostic factor, the combination of p53AIP1 and survivin was a significant prognostic predictor (p = 0.04). In the multivariate cox proportional hazard model, the combination was an independent predictor of overall survival (p53AIP1 (+) survivin (+), HR 0.21, 95%CI = [0.01–1.66]; p53AIP1 (+) survivin (-), HR 0.01, 95%CI = [0.002–0.28]; p53AIP1 (-) survivin (-), HR 0.01, 95%CI = [0.002–3.1], against p53AIP1 (-) survivin (+), p = 0.03).</p> <p>Conclusion</p> <p>These data suggest that the combination of p53AIP1 and survivin gene expression may be a powerful tool to stratify subgroups with better or worse prognosis from the variable non-small cell lung cancer population.</p

Statistical Analysis of Molecular Signal Recording

A molecular device that records time-varying signals would enable new approaches in neuroscience. We have recently proposed such a device, termed a “molecular ticker tape”, in which an engineered DNA polymerase (DNAP) writes time-varying signals into DNA in the form of nucleotide misincorporation patterns. Here, we define a theoretical framework quantifying the expected capabilities of molecular ticker tapes as a function of experimental parameters. We present a decoding algorithm for estimating time-dependent input signals, and DNAP kinetic parameters, directly from misincorporation rates as determined by sequencing. We explore the requirements for accurate signal decoding, particularly the constraints on (1) the polymerase biochemical parameters, and (2) the amplitude, temporal resolution, and duration of the time-varying input signals. Our results suggest that molecular recording devices with kinetic properties similar to natural polymerases could be used to perform experiments in which neural activity is compared across several experimental conditions, and that devices engineered by combining favorable biochemical properties from multiple known polymerases could potentially measure faster phenomena such as slow synchronization of neuronal oscillations. Sophisticated engineering of DNAPs is likely required to achieve molecular recording of neuronal activity with single-spike temporal resolution over experimentally relevant timescales.United States. Defense Advanced Research Projects Agency. Living Foundries ProgramGoogle (Firm)New York Stem Cell Foundation. Robertson Neuroscience Investigator AwardNational Institutes of Health (U.S.) (EUREKA Award 1R01NS075421)National Institutes of Health (U.S.) (Transformative R01 1R01GM104948)National Institutes of Health (U.S.) (Single Cell Grant 1 R01 EY023173)National Institutes of Health (U.S.) (Grant 1R01DA029639)National Institutes of Health (U.S.) (Grant 1R01NS067199)National Science Foundation (U.S.) (CAREER Award CBET 1053233)National Science Foundation (U.S.) (Grant EFRI0835878)National Science Foundation (U.S.) (Grant DMS1042134)Paul G. Allen Family Foundation (Distinguished Investigator in Neuroscience Award

Local staging of rectal cancer: the current role of MRI

With the advent of powerful gradient coil systems and high-resolution surface coils, magnetic resonance imaging (MRI) has recently extended its role in the staging of rectal cancer. MRI is superior to endorectal ultrasound, the most widely used staging modality in patients with rectal tumors, in that it visualizes not only the intestinal wall but also the surrounding pelvic anatomy. The crucial advantage of MRI is not that it enables exact T-staging but precise evaluation of the topographic relationship of a tumor to the mesorectal fascia. This fascia is the most important anatomic landmark for the feasibility of total mesorectal excision, which has evolved into the standard operative procedure for the resection of cancer located in the middle or lower third of the rectum. MRI is currently the only imaging modality that is highly accurate in predicting whether or not it is likely that a tumor-free margin can be achieved and thus provides important information for planning of an effective therapeutic strategy, especially in patients with advanced rectal cancer

p53 Target Gene SMAR1 Is Dysregulated in Breast Cancer: Its Role in Cancer Cell Migration and Invasion

Tumor suppressor SMAR1 interacts and stabilizes p53 through phosphorylation at its serine-15 residue. We show that SMAR1 transcription is regulated by p53 through its response element present in the SMAR1 promoter. Upon Doxorubicin induced DNA damage, acetylated p53 is recruited on SMAR1 promoter that allows activation of its transcription. Once SMAR1 is induced, cell cycle arrest is observed that is correlated to increased phospho-ser-15-p53 and decreased p53 acetylation. Further we demonstrate that SMAR1 expression is drastically reduced during advancement of human breast cancer. This was correlated with defective p53 expression in breast cancer where acetylated p53 is sequestered into the heterochromatin region and become inaccessible to activate SMAR1 promoter. In a recent report we have shown that SMAR1 represses Cyclin D1 transcription through recruitment of HDAC1 dependent repressor complex at the MAR site of Cyclin D1 promoter. Here we show that downmodulation of SMAR1 in high grade breast carcinoma is correlated with upregulated Cyclin D1 expression. We also established that SMAR1 inhibits tumor cell migration and metastases through inhibition of TGFβ signaling and its downstream target genes including cutl1 and various focal adhesion molecules. Thus, we report that SMAR1 plays a central role in coordinating p53 and TGFβ pathways in human breast cancer

A genome-wide DNA methylation study in colorectal carcinoma

<p>Abstract</p> <p>Background</p> <p>We performed a genome-wide scan of 27,578 CpG loci covering 14,475 genes to identify differentially methylated loci (DML) in colorectal carcinoma (CRC).</p> <p>Methods</p> <p>We used Illumina's Infinium methylation assay in paired DNA samples extracted from 24 fresh frozen CRC tissues and their corresponding normal colon tissues from 24 consecutive diagnosed patients at a tertiary medical center.</p> <p>Results</p> <p>We found a total of 627 DML in CRC covering 513 genes, of which 535 are novel DML covering 465 genes. We also validated the Illumina Infinium methylation data for top-ranking genes by non-bisulfite conversion q-PCR-based methyl profiler assay in a subset of the same samples. We also carried out integration of genome-wide copy number and expression microarray along with methylation profiling to see the functional effect of methylation. Gene Set Enrichment Analysis (GSEA) showed that among the major "gene sets" that are hypermethylated in CRC are the sets: "inhibition of adenylate cyclase activity by G-protein signaling", "Rac guanyl-nucleotide exchange factor activity", "regulation of retinoic acid receptor signaling pathway" and "estrogen receptor activity". Two-level nested cross validation showed that DML-based predictive models may offer reasonable sensitivity (around 89%), specificity (around 95%), positive predictive value (around 95%) and negative predictive value (around 89%), suggesting that these markers may have potential clinical application.</p> <p>Conclusion</p> <p>Our genome-wide methylation study in CRC clearly supports most of the previous findings; additionally we found a large number of novel DML in CRC tissue. If confirmed in future studies, these findings may lead to identification of genomic markers for potential clinical application.</p