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We examine the impact of transparency on the convenience yield of AAA-rated asset-backed security (ABS) tranches. AAA tranches of ABS are commonly held by investors to manage financial liquidity and therefore enjoy a price premium beyond what is determined solely by the expected monetary payoff (i.e., convenience yield). The Securities and Exchange Commission (SEC) requires ABS issuers to provide monthly disclosures about the performance of the underlying individual loans for ABS issued after November 23, 2016 to improve transparency. We document that AAA tranches of ABS experience a significant increase in price volatility after the loan-level disclosure mandate. As a result, the use of these tranches as collateral for short-term funding and their convenience yield decrease significantly after the increased disclosure. Our collective evidence highlights an important unintended consequence of increased transparency in the ABS market; it diminishes the pledgeability of long-term safe assets. [ABSTRACT FROM AUTHOR]

BACKGROUND: This study aimed to review bicycle-related injuries during the COVID-19 pandemic to assist with reinforcement or implementation of new policies for injury prevention. METHODS: This is a retrospective descriptive analysis of injuries sustained during cycling for patients 18 years old and above who presented to Singapore General Hospital from January to June 2021. Medical records were reviewed and consolidated. Descriptive analyses were used to summarize patient characteristics, and differences in characteristics sub grouped by triage acuity and discharge status were analyzed. RESULTS: The study included 272 patients with a mean age of 43 years and a male predominance (71.7%). Most presented without referrals (88.2%) and were not conveyed by ambulances (70.6%). Based on acuity category, there were 24 (8.8%) Priority 1 (P1) patients with 7 trauma activations, 174 (64.0%) and 74 (27.2%) P2 and P3 patients respectively. The most common injuries were fractures (34.2%), followed by superficial abrasion/contusion (29.4%) and laceration/wound (19.1%). Thirteen (4.8%) patients experienced head injury and 85 patients (31.3%) were documented to be wearing a helmet. The majority occurred on the roads as traffic accidents (32.7%). Forty-two patients (15.4%) were admitted with a mean length of stay of 4.1 d and 17 (6.3%) undergone surgical procedures. Out of 214 (78.7%) discharged patients, no re-attendances or mortality were observed. In the subgroup analysis, higher acuity patients were generally older, with higher proportions of head injuries leading to admission. CONCLUSION: Our study highlights significant morbidities in bicycle-related injuries. There is also a high proportion of fractures in the young healthy male population. Injury prevention is paramount and we propose emphasizing helmet use and road user safety. [ABSTRACT FROM AUTHOR]

Multiclass probability estimation is the problem of estimating conditional probabilities of a data point belonging to a class given its covariate information. It has broad applications in statistical analysis and data science. Recently a class of weighted Support Vector Machines (wSVMs) has been developed to estimate class probabilities through ensemble learning for K-class problems (Wu et al., 2010; Wang et al., 2019), where K is the number of classes. The estimators are robust and achieve high accuracy for probability estimation, but their learning is implemented through pairwise coupling, which demands polynomial time in K. In this paper, we propose two new learning schemes, the baseline learning and the One-vs-All (OVA) learning, to further improve wSVMs in terms of computational efficiency and estimation accuracy. In particular, the baseline learning has optimal computational complexity in the sense that it is linear in K. Though not the most efficient in computation, the OVA is found to have the best estimation accuracy among all the procedures under comparison. The resulting estimators are distribution-free and shown to be consistent. We further conduct extensive numerical experiments to demonstrate their finite sample performance. [ABSTRACT FROM AUTHOR]

If the student does not feel the hot and cold difference between the two sides after two seconds, the teacher should ask the student to first disconnect the Peltier tile from the batteries and then check if there is any incorrect connection in the circuitry. To prevent students from taking the criticism personally, the teacher can ask students to present their classmates' work. The teacher needs to be cautious as it may get too hot for students to hold and should explicitly instruct students not to continuously hold the tile for more than five seconds while it is connected to the batteries. The teacher can ask students to think about how to improve the cell phone or iPad stands they have seen before and help students realize that small improvements can be inventions, too. [Extracted from the article]

Following two O121 Shiga toxin–producing Escherichia coli (STEC) outbreaks linked to wheat flour, this study was conducted to gain baseline information on the occurrence of bacterial pathogens and levels of indicator organisms in wheat flour in Canada. A total of 347 prepackaged wheat flour samples were analyzed for Salmonella species, STEC, Listeria monocytogenes, aerobic colony count (ACC), total coliforms, and Escherichia coli. Salmonella spp. and O157 STEC were not detected in any of the samples. L. monocytogenes was identified in two samples (0.6%) at levels below the limit of detection (,0.7 log CFU/g). Non-O157 STEC were isolated from six samples (1.7%) and were characterized for the presence of STEC virulence genes: stx1, stx2, and their subtypes, eae, hlyA, and aggR. One O103:H25 STEC isolate carried virulence genes (stx1a+eae) that are known to be capable of causing diarrhea and/or bloody diarrhea in humans. Of the five remaining non-O157 STEC isolates, four carried single stx2a or stx2c genes and were considered to have the potential of causing diarrhea. The remaining non-O157 STEC isolate (stx2), while not a priority non-O157 STEC, was not available for sequencing; thus, its potential to cause illness is unknown. ACC, total coliforms, and E. coli were detected (0.48 log CFU/g) in 98.8, 72.6, and 0.6% of the flour samples. The mean counts of ACC were greater in whole wheat flour compared with the other flour types tested (P , 0.001). The results of this study suggest that the occurrence of O157 STEC and Salmonella is low but that the occurrence of non-O157 STEC in wheat flour with the potential to cause human illness of diarrhea is relatively common. Therefore, the consumption of raw flour could increase the likelihood of STEC infections. Further research is merited for potential risk mitigation strategies within the food production system and with consumers. [ABSTRACT FROM AUTHOR]

The reactivation of quiescent cells to proliferate is fundamental to tissue repair and homeostasis in the body. Often referred to as the G0 state, quiescence is, however, not a uniform state but with graded depth. Shallow quiescent cells exhibit a higher tendency to revert to proliferation than deep quiescent cells, while deep quiescent cells are still fully reversible under physiological conditions, distinct from senescent cells. Cellular mechanisms underlying the control of quiescence depth and the connection between quiescence and senescence are poorly characterized, representing a missing link in our understanding of tissue homeostasis and regeneration. Here we measured transcriptome changes as rat embryonic fibroblasts moved from shallow to deep quiescence over time in the absence of growth signals. We found that lysosomal gene expression was significantly up-regulated in deep quiescence, and partially compensated for gradually reduced autophagy flux. Reducing lysosomal function drove cells progressively deeper into quiescence and eventually into a senescence-like irreversibly arrested state; increasing lysosomal function, by lowering oxidative stress, progressively pushed cells into shallower quiescence. That is, lysosomal function modulates graded quiescence depth between proliferation and senescence as a dimmer switch. Finally,we found that a gene-expression signature developed by comparing deep and shallow quiescence in fibroblasts can correctly classify a wide array of senescent and aging cell types in vitro and in vivo, suggesting that while quiescence is generally considered to protect cells from irreversible arrest of senescence, quiescence deepening likely represents a common transition path from cell proliferation to senescence, related to aging. [ABSTRACT FROM AUTHOR]

Dysregulated adult hippocampal neurogenesis occurs in many temporal lobe epilepsy (TLE) models. Most dentate granule cells (DGCs) generated in response to an epileptic insult develop features that promote increased excitability, including ectopic location, persistent hilar basal dendrites (HBDs), and mossy fiber sprouting. However, some appear to integrate normally and even exhibit reduced excitability compared to other DGCs. To examine the relationship between DGC birthdate, morphology, and network integration in a model of TLE, we retrovirally birthdated either early-born [EB; postnatal day (P)7] or adult-born (AB; P60) DGCs. Male rats underwent pilocarpine-induced status epilepticus (SE) or sham treatment at P56. Three to six months after SE or sham treatment, we used whole-cell patch-clamp and fluorescence microscopy to record spontaneous excitatory and inhibitory currents from birthdated DGCs. We found that both AB and EB populations of DGCs recorded from epileptic rats received increased excitatory input compared with age-matched controls. Interestingly, when AB populations were separated into normally integrated (normotopic) and aberrant (ectopic or HBDcontaining) subpopulations, only the aberrant populations exhibited a relative increase in excitatory input (amplitude, frequency, and charge transfer). The ratio of excitatory-to-inhibitory input was most dramatically upregulated for ectopically localized DGCs. These data provide definitive physiological evidence that aberrant integration of post-SE, AB DGCs contributes to increased synaptic drive and support the idea that ectopic DGCs serve as putative hub cells to promote seizures. [ABSTRACT FROM AUTHOR]

Reactivating quiescent cells to proliferate is critical to tissue repair and homoeostasis. Quiescence exit is highly noisy even for genetically identical cells under the same environmental conditions. Deregulation of quiescence exit is associated with many diseases, but cellular mechanisms underlying the noisy process of exiting quiescence are poorly understood. Here we show that the heterogeneity of quiescence exit reflects a memory of preceding cell growth at quiescence induction and immediate division history before quiescence entry, and that such a memory is reflected in cell size at a coarse scale. The deterministic memory effects of preceding cell cycle, coupled with the stochastic dynamics of an Rb-E2F bistable switch, jointly and quantitatively explain quiescence-exit heterogeneity. As such, quiescence can be defined as a distinct state outside of the cell cycle while displaying a sequential cell order reflecting preceding cell growth and division variations. [ABSTRACT FROM AUTHOR]

Background: Transcriptome analytic tools are commonly used across patient cohorts to develop drugs and predict clinical outcomes. However, as precision medicine pursues more accurate and individualized treatment decisions, these methods are not designed to address single-patient transcriptome analyses. We previously developed and validated the N-of-1-pathways framework using two methods, Wilcoxon and Mahalanobis Distance (MD), for personal transcriptome analysis derived from a pair of samples of a single patient. Although, both methods uncover concordantly dysregulated pathways, they are not designed to detect dysregulated pathways with up- and downregulated genes (bidirectional dysregulation) that are ubiquitous in biological systems. Results: We developed N-of-1-pathways MixEnrich, a mixture model followed by a gene set enrichment test, to uncover bidirectional and concordantly dysregulated pathways one patient at a time. We assess its accuracy in a comprehensive simulation study and in a RNA-Seq data analysis of head and neck squamous cell carcinomas (HNSCCs). In presence of bidirectionally dysregulated genes in the pathway or in presence of high background noise, MixEnrich substantially outperforms previous single-subject transcriptome analysis methods, both in the simulation study and the HNSCCs data analysis (ROC Curves; higher true positive rates; lower false positive rates). Bidirectional and concordant dysregulated pathways uncovered by MixEnrich in each patient largely overlapped with the quasi-gold standard compared to other single-subject and cohort-based transcriptome analyses. Conclusion: The greater performance of MixEnrich presents an advantage over previous methods to meet the promise of providing accurate personal transcriptome analysis to support precision medicine at point of care. [ABSTRACT FROM AUTHOR]

Sufficient dimension reduction is popular for reducing data dimensionality without stringent model assumptions. However, most existing methods may work poorly for binary classification. For example, sliced inverse regression (Li, 1991) can estimate at most one direction if the response is binary. In this paper we propose principal weighted support vector machines, a unified framework for linear and nonlinear sufficient dimension reduction in binary classification. Its asymptotic properties are studied, and an efficient computing algorithm is proposed. Numerical examples demonstrate its performance in binary classification. [ABSTRACT FROM AUTHOR]

Meta-analysis plays an important role in summarizing and synthesizing scientific evidence derived from multiple studies. With high-dimensional data, the incorporation of variable selection into meta-analysis improves model interpretation and prediction. Existing variable selection methods require direct access to raw data, which may not be available in practical situations. We propose a new approach, sparse meta-analysis (SMA), in which variable selection for meta-analysis is based solely on summary statistics and the effect sizes of each covariate are allowed to vary among studies. We show that the SMA enjoys the oracle property if the estimated covariance matrix of the parameter estimators from each study is available. We also show that our approach achieves selection consistency and estimation consistency even when summary statistics include only the variance estimators or no variance/covariance information at all. Simulation studies and applications to high-throughput genomics studies demonstrate the usefulness of our approach. [ABSTRACT FROM AUTHOR]

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Adult hippocampal neurogenesis is thought to be essential for learning and memory, and has been implicated in the pathogenesis of several disorders. Although recent studies have identified key factors regulating neuroprogenitor proliferation in the adult hippocampus, the mechanisms that control the migration and integration of adult-born neurons into circuits are largely unknown. Reelin is an extracellular matrix protein that is vital for neuronal development. Activation of the Reelin cascade leads to phosphorylation of Disabled-1, an adaptor protein required for Reelin signaling. Here we used transgenic mouse and retroviral reporters along with Reelin signaling gain-of-function and loss-of-function studies to show that the Reelin pathway regulates migration and dendritic development of adult-generated hippocampal neurons. Whereas overexpression of Reelin accelerated dendritic maturation, inactivation of the Reelin signaling pathway specifically in adult neuroprogenitor cells resulted in aberrant migration, decreased dendrite development, formation of ectopic dendrites in the hilus, and the establishment of aberrant circuits. Our findings support a cell-autonomous and critical role for the Reelin pathway in regulating dendritic development and the integration of adult-generated granule cells and point to this pathway as a key regulator of adult neurogenesis. Moreover, our data reveal a novel role of the Reelin cascade in adult brain function with potential implications for the pathogenesis of several neurological and psychiatric disorders. [ABSTRACT FROM AUTHOR]

We propose a double-penalized likelihood approach for simultaneous model selection and estimation in semiparametric mixed models for longitudinal data. Two types of penalties are jointly imposed on the ordinary log-likelihood: the roughness penalty on the nonparametric baseline function and a nonconcave shrinkage penalty on linear coefficients to achieve model sparsity. Compared to existing estimation equation based approaches, our procedure provides valid inference for data with missing at random, and will be more efficient if the specified model is correct. Another advantage of the new procedure is its easy computation for both regression components and variance parameters. We show that the double-penalized problem can be conveniently reformulated into a linear mixed model framework, so that existing software can be directly used to implement our method. For the purpose of model inference, we derive both frequentist and Bayesian variance estimation for estimated parametric and nonparametric components. Simulation is used to evaluate and compare the performance of our method to the existing ones. We then apply the new method to a real data set from a lactation study. [ABSTRACT FROM AUTHOR]

We propose a novel model selection method for a nonparametric extension of the Cox proportional hazard model, in the framework of smoothing splines ANOVA models. The method automates the model building and model selection processes simultaneously by penalizing the reproducing kernel Hilbert space norms. On the basis of a reformulation of the penalized partial likelihood, we propose an efficient algorithm to compute the estimate. The solution demonstrates great flexibility and easy interpretability in modeling relative risk functions for censored data. Adaptive choice of the smoothing parameter is discussed. Both simulations and a real example suggest that our proposal is a useful tool for multivariate function estimation and model selection in survival analysis. [ABSTRACT FROM AUTHOR]

We investigate the variable selection problem for Coxs proportional hazards model, and propose a unified model selection and estimation procedure with desired theoretical properties and computational convenience. The new method is based on a penalized log partial likelihood with the adaptively weighted L1 penalty on regression coefficients, providing what we call the adaptive Lasso estimator. The method incorporates different penalties for different coefficients: unimportant variables receive larger penalties than important ones, so that important variables tend to be retained in the selection process, whereas unimportant variables are more likely to be dropped. Theoretical properties, such as consistency and rate of convergence of the estimator, are studied. We also show that, with proper choice of regularization parameters, the proposed estimator has the oracle properties. The convex optimization nature of the method leads to an efficient algorithm. Both simulated and real examples show that the method performs competitively. [ABSTRACT FROM AUTHOR]