Application-Specific Heterogeneous Network-on-Chip Design

Cataloged from PDF version of article.As a result of increasing communication demands, application-specific and scalable Network-on-Chips (NoCs) have emerged to connect processing cores and subsystems in Multiprocessor System-on-Chips. A challenge in application-specific NoC design is to find the right balance among different tradeoffs, such as communication latency, power consumption and chip area. We propose a novel approach that generates latency-aware heterogeneous NoC topology. Experimental results show that our approach improves the total communication latency up to 27% with modest power consumption. © 2013 The Author 2013. Published by Oxford University Press on behalf of The British Computer Society

Application-specific heterogeneous network-on-chip design

As a result of increasing communication demands, application-specific and scalable Network-on-Chips (NoCs) have emerged to connect processing cores and subsystems in Multiprocessor System-on-Chips. A challenge in application-specific NoC design is to find the right balance among different tradeoffs, such as communication latency, power consumption and chip area. We propose a novel approach that generates latency-aware heterogeneous NoC topology. Experimental results show that our approach improves the total communication latency up to 27% with modest power consumption. © 2013 The Author 2013. Published by Oxford University Press on behalf of The British Computer Society

Pitfalls in machine learning-based assessment of tumor-infiltrating lymphocytes in breast cancer: a report of the international immuno-oncology biomarker working group

The clinical significance of the tumor-immune interaction in breast cancer is now established, and tumor-infiltrating lymphocytes (TILs) have emerged as predictive and prognostic biomarkers for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2-negative) breast cancer and HER2-positive breast cancer. How computational assessments of TILs might complement manual TIL assessment in trial and daily practices is currently debated. Recent efforts to use machine learning (ML) to automatically evaluate TILs have shown promising results. We review state-of-the-art approaches and identify pitfalls and challenges of automated TIL evaluation by studying the root cause of ML discordances in comparison to manual TIL quantification. We categorize our findings into four main topics: (1) technical slide issues, (2) ML and image analysis aspects, (3) data challenges, and (4) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns or design choices in the computational implementation. To aid the adoption of ML for TIL assessment, we provide an in-depth discussion of ML and image analysis, including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial and routine clinical management of patients with triple-negative breast cancer. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland

Serum high-sensitivity C-reactive protein, amyloid-associated protein and N-terminal proBNP levels do not predict reversible myocardial ischaemia

Aim: The aim of this study was to detect any relationship between serum high-sensitivity C-reactive protein (hs-CRP), serum amyloid-associated protein (SAA) and N-terminal pro B-type natriuretic peptide (NT-proBNP) levels, and reversible myocardial ischaemia during cardiovascular exercise tests and to determine whether these biomarkers could predict transient myocardial ischaemia

Strain-enhanced stabilization and catalytic activity of metal nanoclusters on graphene

10.1021/jp105368jJournal of Physical Chemistry C1143916541-1654

A core outcome set for pre-eclampsia research: an international consensus development study

Objective: To develop a core outcome set for pre-eclampsia. Design: Consensus development study. Setting: International. Population: Two hundred and eight-one healthcare professionals, 41 researchers and 110 patients, representing 56 countries, participated. Methods: Modified Delphi method and Modified Nominal Group Technique. Results: A long-list of 116 potential core outcomes was developed by combining the outcomes reported in 79 pre-eclampsia trials with those derived from thematic analysis of 30 in-depth interviews of women with lived experience of pre-eclampsia. Forty-seven consensus outcomes were identified from the Delphi process following which 14 maternal and eight offspring core outcomes were agreed at the consensus development meeting. Maternal core outcomes: death, eclampsia, stroke, cortical blindness, retinal detachment, pulmonary oedema, acute kidney injury, liver haematoma or rupture, abruption, postpartum haemorrhage, raised liver enzymes, low platelets, admission to intensive care required, and intubation and ventilation. Offspring core outcomes: stillbirth, gestational age at delivery, birthweight, small-for-gestational-age, neonatal mortality, seizures, admission to neonatal unit required and respiratory support. Conclusions: The core outcome set for pre-eclampsia should underpin future randomised trials and systematic reviews. Such implementation should ensure that future research holds the necessary reach and relevance to inform clinical practice, enhance women's care and improve the outcomes of pregnant women and their babies. Tweetable abstract: 281 healthcare professionals, 41 researchers and 110 women have developed #preeclampsia core outcomes @HOPEoutcomes @jamesmnduffy. [Correction added on 29 June 2020, after first online publication: the order has been corrected.]