The Analytic Bootstrap in Fermionic CFTs

We apply the method of the large spin bootstrap to analyse fermionic conformal field theories with weakly broken higher spin symmetry. Through the study of correlators of composite operators, we find the anomalous dimensions and OPE coefficients in the Gross-Neveu model in $d=2+\varepsilon$ dimensions and the Gross-Neveu-Yukawa model in $d=4-\varepsilon$ dimensions, based only on crossing symmetry. Furthermore a non-trivial solution in the $d=2+\varepsilon$ expansion is found for a fermionic theory in which the fundamental field is not part of the spectrum. The results are perturbative in $\varepsilon$ and valid to all orders in the spin, reproducing known results for operator dimensions and providing some new results for operator dimensions and OPE coefficients.Comment: 42+3 pages, no figure

Taming the ϵ\epsilon-expansion with Large Spin Perturbation Theory

We apply analytic bootstrap techniques to the four-point correlator of fundamental fields in the Wilson-Fisher model. In an $\epsilon$-expansion crossing symmetry fixes the double discontinuity of the correlator in terms of CFT data at lower orders. Large spin perturbation theory, or equivalently the recently proposed Froissart-Gribov inversion integral, then allows one to reconstruct the CFT data of intermediate operators of any spin. We use this method to compute the anomalous dimensions and OPE coefficients of leading twist operators. To cubic order in $\epsilon$ the double discontinuity arises solely from the identity operator and the scalar bilinear operator, making the computation straightforward. At higher orders the double discontinuity receives contributions from infinite towers of higher spin operators. At fourth order, the structure of perturbation theory leads to a proposal in terms of functions of certain degree of transcendentality, which can then be fixed by symmetries. This leads to the full determination of the CFT data for leading twist operators to fourth order.Comment: 16 pages. v2: Added discussion on low spin spectru

Stacked Penalized Logistic Regression for Selecting Views in Multi-View Learning

In biomedical research, many different types of patient data can be collected, such as various types of omics data and medical imaging modalities. Applying multi-view learning to these different sources of information can increase the accuracy of medical classification models compared with single-view procedures. However, collecting biomedical data can be expensive and/or burdening for patients, so that it is important to reduce the amount of required data collection. It is therefore necessary to develop multi-view learning methods which can accurately identify those views that are most important for prediction. In recent years, several biomedical studies have used an approach known as multi-view stacking (MVS), where a model is trained on each view separately and the resulting predictions are combined through stacking. In these studies, MVS has been shown to increase classification accuracy. However, the MVS framework can also be used for selecting a subset of important views. To study the view selection potential of MVS, we develop a special case called stacked penalized logistic regression (StaPLR). Compared with existing view-selection methods, StaPLR can make use of faster optimization algorithms and is easily parallelized. We show that nonnegativity constraints on the parameters of the function which combines the views play an important role in preventing unimportant views from entering the model. We investigate the performance of StaPLR through simulations, and consider two real data examples. We compare the performance of StaPLR with an existing view selection method called the group lasso and observe that, in terms of view selection, StaPLR is often more conservative and has a consistently lower false positive rate.Comment: 26 pages, 9 figures. Accepted manuscrip

An alternative to diagrams for the critical O(N) model: dimensions and structure constants to order 1/N21/N^2

We apply the methods of modern analytic bootstrap to the critical $O(N)$ model in a $1/N$ expansion. At infinite $N$ the model possesses higher spin symmetry which is weakly broken as we turn on $1/N$. By studying consistency conditions for the correlator of four fundamental fields we derive the CFT-data for all the (broken) currents to order $1/N$, and the CFT-data for the non-singlet currents to order $1/N^2$. To order $1/N$ our results are in perfect agreement with those in the literature. To order $1/N^2$ we reproduce known results for anomalous dimensions and obtain a variety of new results for structure constants, including the global symmetry central charge $C_J$ to this order.Comment: 32 pages + appendices, 2 figures. v2: Improved presentation + new appendix considering mixed correlator. Version to appear in JHE

Analyzing hierarchical multi-view MRI data with StaPLR: An application to Alzheimer's disease classification

Multi-view data refers to a setting where features are divided into feature sets, for example because they correspond to different sources. Stacked penalized logistic regression (StaPLR) is a recently introduced method that can be used for classification and automatically selecting the views that are most important for prediction. We introduce an extension of this method to a setting where the data has a hierarchical multi-view structure. We also introduce a new view importance measure for StaPLR, which allows us to compare the importance of views at any level of the hierarchy. We apply our extended StaPLR algorithm to Alzheimer's disease classification where different MRI measures have been calculated from three scan types: structural MRI, diffusion-weighted MRI, and resting-state fMRI. StaPLR can identify which scan types and which derived MRI measures are most important for classification, and it outperforms elastic net regression in classification performance.Comment: 36 pages, 9 figures. Accepted manuscrip

Study protocol: NITric oxide during cardiopulmonary bypass to improve Recovery in Infants with Congenital heart defects (NITRIC trial): a randomised controlled trial

Introduction Congenital heart disease (CHD) is a major cause of infant mortality. Many infants with CHD require corrective surgery with most operations requiring cardiopulmonary bypass (CPB). CPB triggers a systemic inflammatory response which is associated with low cardiac output syndrome (LCOS), postoperative morbidity and mortality. Delivery of nitric oxide (NO) into CPB circuits can provide myocardial protection and reduce bypass-induced inflammation, leading to less LCOS and improved recovery. We hypothesised that using NO during CPB increases ventilator-free days (VFD) (the number of days patients spend alive and free from invasive mechanical ventilation up until day 28) compared with standard care. Here, we describe the NITRIC trial protocol. Methods and analysis The NITRIC trial is a randomised, double-blind, controlled, parallel-group, two-sided superiority trial to be conducted in six paediatric cardiac surgical centres. One thousand three-hundred and twenty infants <2 years of age undergoing cardiac surgery with CPB will be randomly assigned to NO at 20 ppm administered into the CPB oxygenator for the duration of CPB or standard care (no NO) in a 1:1 ratio with stratification by age (<6 and ≥6 weeks), single ventricle physiology (Y/N) and study centre. The primary outcome will be VFD to day 28. Secondary outcomes include a composite of LCOS, need for extracorporeal membrane oxygenation or death within 28 days of surgery; length of stay in intensive care and in hospital; and, healthcare costs. Analyses will be conducted on an intention-to-treat basis. Preplanned secondary analyses will investigate the impact of NO on host inflammatory profiles postsurgery. Ethics and dissemination The study has ethical approval (HREC/17/QRCH/43, dated 26 April 2017), is registered in the Australian New Zealand Clinical Trials Registry (ACTRN12617000821392) and commenced recruitment in July 2017. The primary manuscript will be submitted for publication in a peer-reviewed journal. Trial registration number ACTRN12617000821392.</p

A small dose of whey protein co-ingested with mixed-macronutrient breakfast and lunch meals improves postprandial glycemia and suppresses appetite in men with type 2 diabetes: a randomized controlled trial.

Background: Large doses of whey protein consumed as a preload before single high-glycemic load meals has been shown to improve postprandial glycemia in type 2 diabetes. It is unclear if this effect remains with smaller doses of whey co-ingested at consecutive mixed-macronutrient meals. Moreover, whether hydrolyzed whey offers further benefit under these conditions is unclear. Objective: The aim of this study was to investigate postprandial glycemic and appetite responses after small doses of intact and hydrolyzed whey protein co-ingested with mixed-nutrient breakfast and lunch meals in men with type 2 diabetes. Design: In a randomized, single-blind crossover design, 11 men with type 2 diabetes [mean ± SD age: 54.9 ± 2.3 y; glycated hemoglobin: 6.8% ± 0.3% (51.3 ± 3.4 mmol/mol)] attended the laboratory on 3 mornings and consumed 1) intact whey protein (15 g), 2) hydrolyzed whey protein (15 g), or 3) placebo (control) immediately before mixed-macronutrient breakfast and lunch meals, separated by 3 h. Blood samples were collected periodically and were processed for insulin, intact glucagon-like peptide 1 (GLP-1), gastric inhibitory polypeptide (GIP), leptin, peptide tyrosine tyrosine (PYY3-36), and amino acid concentrations. Interstitial glucose was measured during and for 24 h after each trial. Subjective appetite was assessed with the use of visual analog scales. Results: Total postprandial glycemia area under the curve was reduced by 13% ± 3% after breakfast following the intact whey protein when compared with control (P  0.05). Conclusions: The consumption of a small 15-g dose of intact whey protein immediately before consecutive mixed-macronutrient meals improves postprandial glycemia, stimulates insulin release, and increases satiety in men with type 2 diabetes. This trial was registered at www.clinicialtrials.gov as NCT02903199