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Background: The effect of weekend admission and surgery on patient morbidity and mortality has been studied in many settings and has been shown to lead to worse outcomes. Several studies have sought to determine whether there is a weekend effect in kidney transplantation specifically, but a clear effect on outcomes and graft survival has not been established. Methods: We analyzed data from all deceased-donor organ procurements and cadaveric kidney transplants occurring during the 5-year period between Apr. 1, 2013, and Dec. 31, 2017, included in the database of the Trillium Gift of Life Network, Ontario&apos;s organ and tissue donation agency. Results: A total of 1116 deceased donor nephrectomies (DNs) and 1858 recipient procedures were performed in Ontario during the study period. The overall rate of after-hours DNs on weekdays was significantly greater than during working hours (23.1/30 d v. 15.4/30 d, p < 0.001). Donations after neurological determination of death were more frequent during weekday working hours (22.8/30 d) than after hours on weekdays (17.3/30 d, p < 0.001) or weekends (16.3/30 d, p < 0.001), whereas donations after cardiac death were significantly more frequent after hours on weekdays than during working hours (10.3/30 d v. 7.7/30 d, p = 0.021). On weekdays, mean warm ischemia time (WIT) was significantly longer after hours than during working hours (40.75 [+ or -] 12.26 min v. 38.52 [+ or -] 11.92 min, p = 0.017). Similarly, mean WIT was longer after hours than during working hours on weekends (40.23 [+ or -] 12.48 min v. 38.59 [+ or -] 11.91 min, p = 0.015). Conclusion: Kidney transplantations occurred more frequently after hours and were associated with increased WIT. Further study is needed across multiple Canadian centres to better understand the temporal patterns of kidney transplantation and implications for patients, providers, and health care systems. Contexte : Plusieurs tudes r alis es dans diff rents milieux ont r v l que la morbidit et la mortalit taient plus lev es chez la patient le quand les admissions et les chirurgies avaient lieu la fin de semaine. Certaines tudes ont voulu d terminer si un tel effet s&apos;observe dans le cas des transplantations r nales sp cifiquement; or, aucun impact clair sur l&apos;issue de l&apos;intervention ou la survie du greffon n&apos;a t confirm . M thodes : Nous avons analys les donn es relatives tous les pr l vements d&apos;organes effectu s sur des personnes d c d es et aux greffons r naux provenant de cadavres inscrits au registre du R seau Trillium pour le don de vie, l&apos;agence ontarienne charg e de coordonner les dons organes et de tissus, pendant la p riode de 5 ans allant du [1.sup.er] avril 2013 au 31 d cembre 2017. R sultats : En tout, 1116 n phrectomies sur des donneurs d c d s (NDD) et 1858 transplantations r nales ont t r alis es en Ontario pendant la dur e de l&apos; tude. Le taux global de NDD a t significativement plus lev apr s les heures normales en semaine que durant les heures normales (23,1/30 j c. 15,4/30 j, p < 0,001). Les dons apr s d termination de la mort neurologique ont t plus fr quents durant les heures normales en semaine (22,8/30 j), qu&apos;apr s les heures normales en semaine (17,3/30 j, p < 0,001) ou les fins de semaine (16,3/30 j, p < 0,001), tandis que les dons suivant la mort cardiaque ont t beaucoup plus fr quents apr s que durant les heures normales en semaine (10,3/30 j c. 7,7/30 j, p = 0,021). En semaine, la dur e moyenne de l&apos;isch mie chaude a t significativement plus longue apr s que durant les heures normales (40,75 [+ or -] 12,26 min c. 38,52 [+ or -] 11,92 min, p = 0,017). Et de m me, les fins de semaine, la dur e moyenne de l&apos;isch mie chaude a t plus longue apr s que durant les heures normales (40,23 [+ or -] 12,48 min c. 38,59 [+ or -] 11,91 min, p = 0,015). Conclusion : Les transplantations r nales ont plus souvent eu lieu apr s les heures normales et ont t associ es un temps d&apos;isch mie chaude plus long. Il faudra approfondir la recherche aupr s de plusieurs centres canadiens pour mieux comprendre les modes de coordination des tapes de la transplantation r nale et leurs r percussions sur la patient le, sur le corps m dical et les syst mes de sant ., Transplantation is the gold-standard treatment for end-stage renal disease (ESRD). (1) All transplantation options--living donor, donation after cardiac death (DCD), and donation after neurologic determination of death (NDD)--are associated with [...]

Educational psychology usually focuses on explaining phenomena. As a result, researchers seldom explore how well their models predict the outcomes they care about using best-practice approaches to predictive statistics. In this paper, we focus less on explanation and more on prediction, showing how both are important for advancing the field. We apply predictive models to the role of teachers on student engagement, i.e. the thoughts, attitudes, and behaviours, that translate motivation into progress. We integrate the suggestions from four prominent motivational theories (self-determination theory, achievement goal theory, growth mindset theory, and transformational leadership theory), and aim to identify those most critical behaviours for predicting changes in students' engagement in physical education. Students (N = 1324 all from year 7, 52% girls) from 17 low socio-economic status schools rated their teacher's demonstration of 71 behaviours in the middle of the school year. We also assessed students' engagement at the beginning and end of the year. We trained elastic-net regression models on 70% of the data and then assessed their predictive validity on the held-out data (30%). The models showed that teacher behaviours predicted 4.39% of the variance in students' change in engagement. Some behaviours that were most consistently associated with a positive change in engagement were being good role models ([beta] = 0.046), taking interest in students' lives outside of class ([beta] = 0.033), and allowing students to make choices ([beta] = 0.029). The influential behaviours did not neatly fit within any single motivational theory. These findings support arguments for integrating different theoretical approaches, and suggest practitioners may want to consider multiple theories when designing interventions. More generally, we argue that researchers in educational psychology should more frequently test how well their models not just explain, but "predict" the outcomes they care about.

Teachers' behavior is a key factor that influences students' motivation. Many theoretical models have tried to explain this influence, with one of the most thoroughly researched being self-determination theory (SDT). We used a Delphi method to create a classification of teacher behaviors consistent with SDT. This is useful because SDT-based interventions have been widely used to improve educational outcomes. However, these interventions contain many components. Reliably classifying and labeling those components is essential for implementation, reproducibility, and evidence synthesis. We used an international expert panel (N = 34) to develop this classification system. We started by identifying behaviors from existing literature, then refined labels, descriptions, and examples using the Delphi panel's input. Next, the panel of experts iteratively rated the relevance of each behavior to SDT, the psychological need that each behavior influenced, and its likely effect on motivation. To create a mutually exclusive and collectively exhaustive list of behaviors, experts nominated overlapping behaviors that were redundant, and suggested new ones missing from the classification. After three rounds, the expert panel agreed upon 57 teacher motivational behaviors (TMBs) that were consistent with SDT. For most behaviors (77%), experts reached consensus on both the most relevant psychological need and influence on motivation. Our classification system provides a comprehensive list of TMBs and consistent terminology in how those behaviors are labeled. Researchers and practitioners designing interventions could use these behaviors to design interventions, to reproduce interventions, to assess whether these behaviors moderate intervention effects, and could focus new research on areas where experts disagreed.

Cystic fibrosis (CF) is a lethal autosomal recessive disorder that afflicts more than 70,000 people. People with CF experience multi-organ dysfunction resulting from aberrant electrolyte transport across polarized epithelia due to mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) gene. CF-related lung disease is by far the most important determinant of morbidity and mortality. Here we report results from a multi-institute consortium in which single-cell transcriptomics were applied to define disease-related changes by comparing the proximal airway of CF donors (n = 19) undergoing transplantation for end-stage lung disease with that of previously healthy lung donors (n = 19). Disease-dependent differences observed include an overabundance of epithelial cells transitioning to specialized ciliated and secretory cell subsets coupled with an unexpected decrease in cycling basal cells. Our study yields a molecular atlas of the proximal airway epithelium that will provide insights for the development of new targeted therapies for CF airway disease.

Given a classical channel—a stochastic map from inputs to outputs—the input can often be transformed into an intermediate variable that is informationally smaller than the input. The new channel accurately simulates the original but at a smaller transmission rate. Here, we examine this procedure when the intermediate variable is a quantum state. We determine when and how well quantum simulations of classical channels may improve upon the minimal rates of classical simulation. This inverts Holevo’s original question of quantifying the capacity of quantum channels with classical resources: We determine the lowest-capacity quantum channel required to simulate a classical channel. We also show that this problem is equivalent to another, involving the local generation of a distribution from common entanglement.

Causal asymmetry is one of the great surprises in predictive modeling: The memory required to predict the future differs from the memory required to retrodict the past. There is a privileged temporal direction for modeling a stochastic process where memory costs are minimal. Models operating in the other direction incur an unavoidable memory overhead. Here, we show that this overhead can vanish when quantum models are allowed. Quantum models forced to run in the less-natural temporal direction not only surpass their optimal classical counterparts but also any classical model running in reverse time. This holds even when the memory overhead is unbounded, resulting in quantum models with unbounded memory advantage.

Understanding the generative mechanism of a natural system is a vital component of the scientific method. Here, we investigate one of the fundamental steps toward this goal by presenting the minimal generator of an arbitrary binary Markov process. This is a class of processes whose predictive model is well known. Surprisingly, the generative model requires three distinct topologies for different regions of parameter space. We show that a previously proposed generator for a particular set of binary Markov processes is, in fact, not minimal. Our results shed the first quantitative light on the relative (minimal) costs of prediction and generation. We find, for instance, that the difference between prediction and generation is maximized when the process is approximately independently, identically distributed.

Fronts propagating in two-dimensional advection-reaction-diffusion systems exhibit a rich topological structure. When the underlying fluid flow is periodic in space and time, the reaction front can lock to the driving frequency. We explain this mode-locking phenomenon using the so-called burning invariant manifolds (BIMs). In fact, the mode-locked profile is delineated by a BIM attached to a relative periodic orbit (RPO) of the front element dynamics. Changes in the type (and loss) of mode-locking can be understood in terms of local and global bifurcations of the RPOs and their BIMs. We illustrate these concepts numerically using a chain of alternating vortices in a channel geometry.

We introduce a quantum algorithm for memory-efficient biased sampling of rare events generated by classical memoryful stochastic processes. Two efficiency metrics are used to compare quantum and classical resources for rare-event sampling. For a fixed stochastic process, the first is the classical-to-quantum ratio of required memory. We show for two example processes that there exists an infinite number of rare-event classes for which the memory ratio for sampling is larger than r, for any large real number r. Then, for a sequence of processes each labeled by an integer size N, we compare how the classical and quantum required memories scale with N. In this setting, since both memories can diverge as N→, the efficiency metric tracks how fast they diverge. An extreme quantum memory advantage exists when the classical memory diverges in the limit N→, but the quantum memory has a finite bound. We then show that finite-state Markov processes and spin chains exhibit memory advantage for sampling of almost all of their rare-event classes.

Classical stochastic processes can be generated by quantum simulators instead of the more standard classical ones, such as hidden Markov models. One reason for using quantum simulators has recently come to the fore: they generally require less memory than their classical counterparts. Here, we examine this quantum advantage for strongly coupled spin systems-in particular, the Dyson one-dimensional Ising spin chain with variable interaction length. We find that the advantage scales with both interaction range and temperature, growing without bound as interaction range increases. In particular, simulating Dyson's original spin chain with the most memory-efficient classical algorithm known requires infinite memory, while a quantum simulator requires only finite memory. Thus, quantum systems can very efficiently simulate strongly coupled one-dimensional classical spin systems.

One of the most basic characterizations of the relationship between two random variables, X and Y, is the value of their mutual information. Unfortunately, calculating it analytically and estimating it empirically are often stymied by the extremely large dimension of the variables. One might hope to replace such a high-dimensional variable by a smaller one that preserves its relationship with the other. It is well known that either X (or Y) can be replaced by its minimal sufficient statistic about Y (or X) while preserving the mutual information. While intuitively reasonable, it is not obvious or straightforward that both variables can be replaced simultaneously. We demonstrate that this is in fact possible: the information X's minimal sufficient statistic preserves about Y is exactly the information that Y's minimal sufficient statistic preserves about X. We call this procedure information trimming. As an important corollary, we consider the case where one variable is a stochastic process' past and the other its future. In this case, the mutual information is the channel transmission rate between the channel's effective states. That is, the past-future mutual information (the excess entropy) is the amount of information about the future that can be predicted using the past. Translating our result about minimal sufficient statistics, this is equivalent to the mutual information between the forward- and reverse-time causal states of computational mechanics. We close by discussing multivariate extensions to this use of minimal sufficient statistics.

A system's perceived simplicity depends on whether it is represented classically or quantally. This is not so surprising, as classical and quantum physics are descriptive frameworks built on different assumptions that capture, emphasize, and express different properties and mechanisms. What is surprising is that, as we demonstrate, simplicity is ambiguous: the relative simplicity between two systems can change sign when moving between classical and quantum descriptions. Here, we associate simplicity with small model-memory. We see that the notions of absolute physical simplicity at best form a partial, not a total, order. This suggests that appeals to principles of physical simplicity, via Ockham's Razor or to the “elegance” of competing theories, may be fundamentally subjective. Recent rapid progress in quantum computation and quantum simulation suggest that the ambiguity of simplicity will strongly impact statistical inference and, in particular, model selection.

Little is known about the effectiveness of simulation in health professionals who are 'hands-off' practitioners such as, exercise physiologists. The aim of this study was to evaluate the effectiveness of using simulation, towards improving knowledge, confidence and experience in exercise physiology students. Fifty-two (M[subscript age]= 22 years) students participated in weekly learning activities involving role-play simulation of a client with a neurological health condition. Students completed a modified Participant Perception Indicator survey to evaluate knowledge, experience and confidence attributes and a Student Assessment of Learning Gains survey to evaluate the effectiveness of the class pedagogy. Students learning was assessed by written exams. Knowledge, experience and confidence all significantly improved after participation in simulation and client preparation and design and discussion of exercises contributed to learning. Simulation may be suitable in developing students self-reported knowledge, experience and confidence in working with neurological case for exercise physiologists.

Collaborative testing has been shown to enhance student performance compared to individual testing. It is suggested that collaborative testing promotes higher order thinking, but research has yet to explore this assumption directly. The aim of this study was to explore the benefits of collaborative testing on overall performance, as well as performance on higher order thinking questions. It was hypothesised that, compared to individual test results, students would perform better overall and on higher order thinking questions under collaborative testing conditions. It was expected that these differences would be equal when comparing students of different academic abilities (i.e. 'upper', 'middle' and 'lower' performers). Undergraduate students completed an individual followed by a collaborative test as part of summative assessment. Analyses revealed that with the exception of upper performers, students performed better overall on the collaborative test. Additionally, regardless of their academic abilities, students performed better on the higher order thinking questions under collaborative conditions. This improvement was equal across different academic abilities, suggesting that collaborative testing promotes higher order thinking even when taking into account previous academic achievement. The acceptability and application of collaborative testing is discussed.

PurposeDiscovery of SNPs that predict a patient's risk of docetaxel-induced neuropathy would enable treatment individualization to maximize efficacy and avoid unnecessary toxicity. The objectives of this analysis were to discover SNPs associated with docetaxel-induced neuropathy and mechanistically validate these associations in preclinical models of drug-induced neuropathy.Experimental designA genome-wide association study was conducted in metastatic castrate-resistant prostate cancer patients treated with docetaxel, prednisone and randomized to bevacizumab or placebo on CALGB 90401. SNPs were genotyped on the Illumina HumanHap610-Quad platform followed by rigorous quality control. The inference was conducted on the cumulative dose at occurrence of grade 3+ sensory neuropathy using a cause-specific hazard model that accounted for early treatment discontinuation. Genes with SNPs significantly associated with neuropathy were knocked down in cellular and mouse models of drug-induced neuropathy.ResultsA total of 498,081 SNPs were analyzed in 623 Caucasian patients, 50 (8%) of whom experienced grade 3+ neuropathy. The 1,000 SNPs most associated with neuropathy clustered in relevant pathways including neuropathic pain and axonal guidance. An SNP in VAC14 (rs875858) surpassed genome-wide significance (P = 2.12 × 10-8, adjusted P = 5.88 × 10-7). siRNA knockdown of VAC14 in stem cell-derived peripheral neuronal cells increased docetaxel sensitivity as measured by decreased neurite processes (P = 0.0015) and branches (P < 0.0001). Prior to docetaxel treatment, VAC14 heterozygous mice had greater nociceptive sensitivity than wild-type litter mate controls (P = 0.001).ConclusionsVAC14 should be prioritized for further validation of its potential role as a predictor of docetaxel-induced neuropathy and biomarker for treatment individualization. Clin Cancer Res; 22(19); 4890-900. ©2016 AACR.

The predictive information required for proper trajectory sampling of a stochastic process can be more efficiently transmitted via a quantum channel than a classical one. This recent discovery allows quantum information processing to drastically reduce the memory necessary to simulate complex classical stochastic processes. It also points to a new perspective on the intrinsic complexity that nature must employ in generating the processes we observe. The quantum advantage increases with codeword length: the length of process sequences used in constructing the quantum communication scheme. In analogy with the classical complexity measure, statistical complexity, we use this reduced communication cost as an entropic measure of state complexity in the quantum representation. Previously difficult to compute, the quantum advantage is expressed here in closed form using spectral decomposition. This allows for efficient numerical computation of the quantum-reduced state complexity at all encoding lengths, including infinite. Additionally, it makes clear how finite-codeword reduction in state complexity is controlled by the classical process's cryptic order, and it allows asymptotic analysis of infinite-cryptic-order processes.

A stochastic process' statistical complexity stands out as a fundamental property: the minimum information required to synchronize one process generator to another. How much information is required, though, when synchronizing over a quantum channel? Recent work demonstrated that representing causal similarity as quantum state-indistinguishability provides a quantum advantage. We generalize this to synchronization and offer a sequence of constructions that exploit extended causal structures, finding substantial increase of the quantum advantage. We demonstrate that maximum compression is determined by the process' cryptic order--a classical, topological property closely allied to Markov order, itself a measure of historical dependence. We introduce an efficient algorithm that computes the quantum advantage and close noting that the advantage comes at a cost-one trades off prediction for generation complexity.

In this article, the author expresses his concern about the current state of the profession and the changes in college admission processes. Paper has been replaced with computers, counseling has been replaced with marketing, and the emphasis on "fit" has been replaced by a preoccupation with prestige, or perceived prestige. These changes point to a growing feeling among families, guidance counselors, and the media, that colleges are running a racket, behaving conspiratorially, and even damaging society. The author contends that members of the college admissions profession must pay attention to public perception in order to avoid further tarnishing of their integrity.

This document is a personal account of a college admission professional that traveled to meet perspective students. Out of 44 schools that he visited, the final school left him with a powerful impression of what this process is for young people and those who make their living working with them. Most people agree that today?s college admission process is deeply flawed. Those who work in college and university settings shoulder a large portion of the blame. Emphasis is place on technology over people, marketing over fit, prestige over quality, rankings over reality, and the bottom line over the greater good. So many conflicting messages are sent to the marketplace about institutions and policies that people no longer know what to believe. Admission deans often face inordinate pressure from their presidents and trustees to grow application pools, improve quality, increase diversity, and reduce expenditures on financial aid. In short, upper level administration wants it all, so deans do what they must to retain their jobs. Parents and headmasters contribute to the chaos. Young people are victims when their parents call the shots on where they should apply and where they will enroll.

We consider two important time scales-the Markov and cryptic orders-that monitor how an observer synchronizes to a finitary stochastic process. We show how to compute these orders exactly and that they are most efficiently calculated from the ε-machine, a process's minimal unifilar model. Surprisingly, though the Markov order is a basic concept from stochastic process theory, it is not a probabilistic property of a process. Rather, it is a topological property and, moreover, it is not computable from any finite-state model other than the ε-machine. Via an exhaustive survey, we close by demonstrating that infinite Markov and infinite cryptic orders are a dominant feature in the space of finite-memory processes. We draw out the roles played in statistical mechanical spin systems by these two complementary length scales.