SpinLink: An interconnection system for the SpiNNaker biologically inspired multi-computer

SpiNNaker is a large-scale biologically-inspired multi-computer designed to model very heavily distributed problems, with the flagship application being the simulation of large neural networks. The project goal is to have one million processors included in a single machine, which consequently span many thousands of circuit boards. A computer of this scale imposes large communication requirements between these boards, and requires an extensible method of connecting to external equipment such as sensors, actuators and visualisation systems. This paper describes two systems that can address each of these problems.Firstly, SpinLink is a proposed method of connecting the SpiNNaker boards by using time-division multiplexing (TDM) to allow eight SpiNNaker links to run at maximum bandwidth between two boards. SpinLink will be deployed on Spartan-6 FPGAs and uses a locally generated clock that can be paused while the asynchronous links from SpiNNaker are sending data, thus ensuring a fast and glitch-free response. Secondly, SpiNNterceptor is a separate system, currently in the early stages of design, that will build upon SpinLink to address the important external I/O issues faced by SpiNNaker. Specifically, spare resources in the FPGAs will be used to implement the debugging and I/O interfacing features of SpiNNterceptor

Evaluation of different recall periods for the US National Cancer Institute’s Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE)

Aims—The U.S. National Cancer Institute recently developed the Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE). PRO-CTCAE is a library of questions for clinical trial participants to self-report symptomatic adverse events (e.g., nausea). The objective of this study is to inform evidence-based selection of a recall period when PRO-CTCAE is included in a trial. We evaluated differences between 1-week, 2-week, 3-week, and 4-week recall periods, using daily reporting as the reference. Methods—English-speaking patients with cancer receiving chemotherapy and/or radiotherapy were enrolled at four U.S. cancer centers and affiliated community clinics. Participants completed 27 PRO-CTCAE items electronically daily for 28 days, and then weekly over 4 weeks, using 1-week, 2-week, 3-week, and 4-week recall periods. For each recall period, mean differences, effect sizes, and intraclass correlation coefficients were calculated to evaluate agreement between the maximum of daily ratings and the corresponding ratings obtained using longer recall periods (e.g., maximum of daily scores over 7 days vs. 1-week recall). Analyses were repeated using the average of daily scores within each recall period rather than the maximum of daily scores. Results—127 subjects completed questionnaires (57% male; median age 57). The median of the 27 mean differences in scores on the PRO-CTCAE 5-point response scale comparing the maximum daily versus the longer recall period (and corresponding effect size), was −0.20 (−0.20) for 1-week recall; −0.36 (−0.31) for 2-week recall; −0.45 (−0.39) for 3-week recall; and −0.47 (−0.40) for 4-week recall. The median intraclass correlation across 27 items between the maximum of daily ratings and the corresponding longer recall ratings for 1-week recall was 0.70 (range: 0.54–0.82); 2-week recall: 0.74 (range: 0.58–0.83); 3-week recall: 0.72 (range: 0.61–0.84); and 4-week recall: 0.72 (range: 0.64–0.86). Similar results were observed for all analyses using the average of daily scores rather than the maximum of daily scores. Conclusions—1-week recall corresponds best to daily reporting. Although intraclass correlations remain stable over time, there are small but progressively larger differences between daily and longer recall periods at 2, 3, and 4 weeks, respectively. The preferred recall period for the PRO-CTCAE is the past 7 days, although investigators may opt for recall periods of 2, 3, or 4 weeks with an understanding that there may be some information loss

Assessing the relationship between molecular rejection and parenchymal injury in heart transplant biopsies

[Abstract] Background: The INTERHEART study (ClinicalTrials.gov #NCT02670408) used genome-wide microarrays to detect rejection in endomyocardial biopsies; however, many heart transplants with no rejection have late dysfunction and impaired survival. We used the microarray measurements to develop a molecular classification of parenchymal injury. Methods: In 1320 endomyocardial biopsies from 645 patients previously studied for rejection-associated transcripts, we measured the expression of 10 injury-induced transcript sets: 5 induced by recent injury; 2 reflecting macrophage infiltration; 2 normal heart transcript sets; and immunoglobulin transcripts, which correlate with time. We used archetypal clustering to assign injury groups. Results: Injury transcript sets correlated with impaired function. Archetypal clustering based on the expression of injury transcript sets assigned each biopsy to 1 of 5 injury groups: 87 Severe-injury, 221 Late-injury, and 3 with lesser degrees of injury, 376 No-injury, 526 Mild-injury, and 110 Moderate-injury. Severe-injury had extensive loss of normal transcripts (dedifferentiation) and increase in macrophage and injury-induced transcripts. Late-injury was characterized by high immunoglobulin transcript expression. In Severe- and Late-injury, function was depressed, and short-term graft failure was increased, even in hearts with no rejection. T cell-mediated rejection almost always had parenchymal injury, and 85% had Severe- or Late-injury. In contrast, early antibody-mediated rejection (AMR) had little injury, but late AMR often had the Late-injury state. Conclusions: Characterizing heart transplants for their injury state provides new understanding of dysfunction and outcomes and demonstrates the differential impact of T cell-mediated rejection versus AMR on the parenchyma. Slow deterioration from AMR emerges as a major contributor to late dysfunction

Validity and Reliability of the US National Cancer Institute’s Patient-Reported Outcomes Version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE)

Symptomatic adverse events (AEs) in cancer trials are currently reported by clinicians using the National Cancer Institute's (NCI) Common Terminology Criteria for Adverse Events (CTCAE). To integrate the patient perspective, the NCI developed a patient-reported outcomes version of the CTCAE (PRO-CTCAE) to capture symptomatic AEs directly from patients

American Gut: an Open Platform for Citizen Science Microbiome Research

McDonald D, Hyde E, Debelius JW, et al. American Gut: an Open Platform for Citizen Science Microbiome Research. mSystems. 2018;3(3):e00031-18

New genetic loci link adipose and insulin biology to body fat distribution.

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms

A communication infrastructure for a million processor machine

SpiNNaker (Spiking Neural Network architecture) is a massively parallel computing machine, comprising a million ARM9 cores. These are realised on 50000 chips, 20 cores/chip. While it could be classed as a MIMD machine, there is no unifying bus structure, and there is no attempt to maintain cross-system memory coherence. Inter-core communication is brokered by a fast message-passing system, built in and managed at the hardware level - thus there is an inevitable tension between speed and flexibility. The message passing infrastructure was designed to be fast and have a high bandwidth; a consequence of this design decision is that the effective data payload is only 32 bits/packet. Whilst this is ample for a wide range of applications, when the system is initialising, it is necessary to transport relatively large and sophisticated data structures across the system. This can be slow and cumbersome, and makes some form of internal self-organisation extremely attractive. This is described in outline here