A contrastive rule for meta-learning

Meta-learning algorithms leverage regularities that are present on a set of tasks to speed up and improve the performance of a subsidiary learning process. Recent work on deep neural networks has shown that prior gradient-based learning of meta-parameters can greatly improve the efficiency of subsequent learning. Here, we present a biologically plausible meta-learning algorithm based on equilibrium propagation. Instead of explicitly differentiating the learning process, our contrastive meta-learning rule estimates meta-parameter gradients by executing the subsidiary process more than once. This avoids reversing the learning dynamics in time and computing second-order derivatives. In spite of this, and unlike previous first-order methods, our rule recovers an arbitrarily accurate meta-parameter update given enough compute. We establish theoretical bounds on its performance and present experiments on a set of standard benchmarks and neural network architectures

Using patient-collected clinical samples and sera to detect and quantify the severe acute respiratory syndrome coronavirus (SARS-CoV)

BACKGROUND: Severe acute respiratory syndrome (SARS) caused a large outbreak of pneumonia in Beijing, China, in 2003. Reverse transcriptase polymerase chain reaction (RT-PCR) was used to detect and quantify SARS-CoV in 934 sera and self-collected throat washes and fecal samples from 271 patients with laboratory-confirmed SARS managed at a single institution. RESULTS: SARS-CoV detection rates in sera were highest in the first 9 days of illness, whereas detection was highest in throat washes 5–14 days after onset of symptoms. The highest SARS-CoV RT-PCR rates (70.4–86.3%) and viral loads (log(10 )4.5–6.1) were seen in fecal samples collected 2–4 weeks after the onset of clinical illness. Fecal samples were frequently SARS-CoV RT-PCR positive beyond 40 days, and occasional sera still had SARS-CoV detected after 3 weeks of illness. CONCLUSION: In the context of an extensive outbreak with major pressure on hospital resources, patient self-collected samples are an alternative to nasopharyngeal aspirates for laboratory confirmation of SARS-CoV infection

Learning where to learn: Gradient sparsity in meta and continual learning

Finding neural network weights that generalize well from small datasets is difficult. A promising approach is to learn a weight initialization such that a small number of weight changes results in low generalization error. We show that this form of meta-learning can be improved by letting the learning algorithm decide which weights to change, i.e., by learning where to learn. We find that patterned sparsity emerges from this process, with the pattern of sparsity varying on a problem-by-problem basis. This selective sparsity results in better generalization and less interference in a range of few-shot and continual learning problems. Moreover, we find that sparse learning also emerges in a more expressive model where learning rates are meta-learned. Our results shed light on an ongoing debate on whether meta-learning can discover adaptable features and suggest that learning by sparse gradient descent is a powerful inductive bias for meta-learning systems

Learning where to learn: Gradient sparsity in meta and continual learning

Finding neural network weights that generalize well from small datasets is difficult. A promising approach is to learn a weight initialization such that a small number of weight changes results in low generalization error. We show that this form of meta-learning can be improved by letting the learning algorithm decide which weights to change, i.e., by learning where to learn. We find that patterned sparsity emerges from this process, with the pattern of sparsity varying on a problem-by-problem basis. This selective sparsity results in better generalization and less interference in a range of few-shot and continual learning problems. Moreover, we find that sparse learning also emerges in a more expressive model where learning rates are meta-learned. Our results shed light on an ongoing debate on whether meta-learning can discover adaptable features and suggest that learning by sparse gradient descent is a powerful inductive bias for meta-learning systems

Asteroseismology of massive stars with the TESS mission: the runaway Beta Cep pulsator PHL 346 = HN Aqr

We report an analysis of the first known Beta Cep pulsator observed by the TESS mission, the runaway star PHL 346 = HN Aqr. The star, previously known as a singly-periodic pulsator, has at least 34 oscillation modes excited, 12 of those in the g-mode domain and 22 p modes. Analysis of archival data implies that the amplitude and frequency of the dominant mode and the stellar radial velocity were variable over time. A binary nature would be inconsistent with the inferred ejection velocity from the Galactic disc of 420 km/s, which is too large to be survivable by a runaway binary system. A kinematic analysis of the star results in an age constraint (23 +- 1 Myr) that can be imposed on asteroseismic modelling and that can be used to remove degeneracies in the modelling process. Our attempts to match the excitation of the observed frequency spectrum resulted in pulsation models that were too young. Hence, asteroseismic studies of runaway pulsators can become vital not only in tracing the evolutionary history of such objects, but to understand the interior structure of massive stars in general. TESS is now opening up these stars for detailed asteroseismic investigation.Comment: accepted for ApJ

Estimates for quality of life loss due to Respiratory Syncytial Virus

BACKGROUND: In children aged <5 years in whom severe respiratory syncytial virus (RSV) episodes predominantly occur, there are currently no appropriate standardised instruments to estimate quality of life years (QALY) loss. OBJECTIVES: We estimated the age-specific QALY loss due to RSV by developing a regression model which predicts the QALY loss without the use of standardised instruments. METHODS: We conducted a surveillance study which targeted confirmed RSV episodes in children aged <5 years (confirmed cases) and their household members who experienced symptoms of RSV during the same time (suspected cases). All participants were asked to complete questions regarding their health during the infection, with the suspected cases additionally providing health-related quality of life (HR-QoL) loss estimates by completing EQ-5D-3L-Y or EQ-5D-3L instruments. We used the responses from the suspected cases to calibrate a regression model which estimates the HR-QoL and QALY loss due to infection. FINDINGS: For confirmed RSV cases in children under 5 years of age who sought health care, our model predicted a QALY loss per RSV episode of 3.823 × 10-3 (95% CI 0.492-12.766 × 10-3 ), compared with 3.024 × 10-3 (95% CI 0.329-10.098 × 10-3 ) for under fives who did not seek health care. Quality of life years loss per episode was less for older children and adults, estimated as 1.950 × 10-3 (0.185-9.578 × 10-3 ) and 1.543 × 10-3 (0.136-6.406 × 10-3 ) for those who seek or do not seek health care, respectively. CONCLUSION: Evaluations of potential RSV vaccination programmes should consider their impact across the whole population, not just young child children

Student-Designed Cross-Sectional Pandemic Knowledge Survey of 8th−12th Grade Students, Milwaukee, WI, April 2020

Background: The novel coronavirus and its effect on our society are unprecedented. Given the recent pandemic, numerous measures have been taken to protect our communities. We sought to understand our school community's knowledge and the measures that were taken by our school for our safety.Objective: Our objective was to describe the overall understanding and attitudes of 8–12th grade students from a single institution during the initial phase of the Wisconsin's Governor's stay-at-home order.Methods: A voluntary web-based survey was communicated to 8–12th grade students through their online school portal. Data were collected and analyzed using SurveyMonkey.Results: There was a 20.2% response rate. Answers regarding the coronavirus, spread, and response to the coronavirus pandemic showed a high level of understanding of the virus and the actions necessary to prevent its spread.Conclusion: Eight-twelfth grade students have a high level of understanding of the virus, its effects, and the safety measures implemented to protect society

The First Provenance Challenge

The first Provenance Challenge was set up in order to provide a forum for the community to help understand the capabilities of different provenance systems and the expressiveness of their provenance representations. To this end, a Functional Magnetic Resonance Imaging workflow was defined, which participants had to either simulate or run in order to produce some provenance representation, from which a set of identified queries had to be implemented and executed. Sixteen teams responded to the challenge, and submitted their inputs. In this paper, we present the challenge workflow and queries, and summarise the participants contributions

Validation of Novel Molecular Imaging Targets Identified by Functional Genomic mRNA Profiling to Detect Dysplasia in Barrett’s Esophagus

SIMPLE SUMMARY: Barrett’s esophagus (BE) is the precursor of esophageal adenocarcinoma (EAC). Dysplastic BE (DBE), including low-grade dysplasia (LGD) and high-grade dysplasia (HGD), shows a higher progression risk to EAC compared to non-dysplastic BE (NDBE). If LGD or HGD is detected, more intensive endoscopic surveillance or endoscopic treatment is recommended. This results in a significantly improved prognosis compared to EACs treated by surgery and/or chemoradiotherapy. However, the miss rates for detecting DBE by endoscopy remain high. Fluorescence molecular endoscopy (FME) can fill this gap by targeting the tumor-specific expression of proteins. This study aimed to identify target proteins suitable for FME. We identified SPARC, SULF1, PKCι, and DDR1 as promising imaging targets for FME to differentiate DBE from NDBE tissue. We are also the first to develop near-infrared fluorescent tracers, SULF1-800CW and SPARC-800CW, for the endoscopic imaging of DBE tissue. ABSTRACT: Barrett’s esophagus (BE) is the precursor of esophageal adenocarcinoma (EAC). Dysplastic BE (DBE) has a higher progression risk to EAC compared to non-dysplastic BE (NDBE). However, the miss rates for the endoscopic detection of DBE remain high. Fluorescence molecular endoscopy (FME) can detect DBE and mucosal EAC by highlighting the tumor-specific expression of proteins. This study aimed to identify target proteins suitable for FME. Publicly available RNA expression profiles of EAC and NDBE were corrected by functional genomic mRNA (FGmRNA) profiling. Following a class comparison between FGmRNA profiles of EAC and NDBE, predicted, significantly upregulated genes in EAC were prioritized by a literature search. Protein expression of prioritized genes was validated by immunohistochemistry (IHC) on DBE and NDBE tissues. Near-infrared fluorescent tracers targeting the proteins were developed and evaluated ex vivo on fresh human specimens. In total, 1976 overexpressed genes were identified in EAC (n = 64) compared to NDBE (n = 66) at RNA level. Prioritization and IHC validation revealed SPARC, SULF1, PKCι, and DDR1 (all p < 0.0001) as the most attractive imaging protein targets for DBE detection. Newly developed tracers SULF1-800CW and SPARC-800CW both showed higher fluorescence intensity in DBE tissue compared to paired non-dysplastic tissue. This study identified SPARC, SULF1, PKCι, and DDR1 as promising targets for FME to differentiate DBE from NDBE tissue, for which SULF1-800CW and SPARC-800CW were successfully ex vivo evaluated. Clinical studies should further validate these findings

Effects of Taurine-Magnesium Coordination Compound on Type 2 Short QT Syndrome: A Simulation Study

Short QT Syndrome (SQTS)is an identified genetic arrhythmogenic disease associated with abnormally abbreviated QT intervals and an increased susceptibility to malignant arrhythmia and sudden cardiac death (SCD). SQT2 variant (linked to slow delayed rectifier, IKs) of SQTS, results from a gain-of-function (V307L) in the KCNQ1 subunit of the IKschannel. Pro-arrhythmogenic effects of SQT2 have been well characterized, but less is known about the pharmacological treatment of SQT2. We find that taurine-magnesium coordination compound (TMCC)exerted anti-arrhythmic effects with low toxicity. Therefore, this study aimed to assess the potential effects of TMCC on SQT2. The channel-blocking effect of TMCC on IKsin healthy and SQT2 cells were incorporated into computer models ofhuman ventricular action potential (AP) and into one dimensional transmural tissue simulations. In the single-cell model, TMCC prolonged cell AP duration at 90% repolarization (APD90). In the one dimensionalintact model, TMCC prolonged the QT interval on the pseudo-ECGs. Thus, the present study provides evidence that TMCC can extend the repolarization period and APD90and QT interval, thereby representing a therapeutic candidate for arrhythmia in SQT2