Model simplification by asymptotic order of magnitude reasoning

AbstractOne of the hardest problems in reasoning about a physical system is finding an approximate model that is mathematically tractable and yet captures the essence of the problem. This paper describes an implemented program AOM which automates a powerful simplification method. AOM is based on two domain-independent ideas: self-consistent approximations and asymptotic order of magnitude reasoning. The basic operation of AOM consists of five steps: (1) assign order of magnitude estimates to terms in the equations, (2) find maximal terms of each equation, i.e., terms that are not dominated by any other terms in the same equation, (3) consider all possible n-term dominant balance assumptions, (4) propagate the effects of the balance assumptions, and (5) remove partial models based on inconsistent balance assumptions. AOM also exploits constraints among equations and submodels. We demonstrate its power by showing how the program simplifies difficult fluid models described by coupled nonlinear partial differential equations with several parameters. We believe the derivation given by AOM is more systematic and easily understandable than those given in published papers

A Computational Model for the Acquisition and Use of Phonological Knowledge

Does knowledge of language consist of symbolic rules? How do children learn and use their linguistic knowledge? To elucidate these questions, we present a computational model that acquires phonological knowledge from a corpus of common English nouns and verbs. In our model the phonological knowledge is encapsulated as boolean constraints operating on classical linguistic representations of speech sounds in term of distinctive features. The learning algorithm compiles a corpus of words into increasingly sophisticated constraints. The algorithm is incremental, greedy, and fast. It yields one-shot learning of phonological constraints from a few examples. Our system exhibits behavior similar to that of young children learning phonological knowledge. As a bonus the constraints can be interpreted as classical linguistic rules. The computational model can be implemented by a surprisingly simple hardware mechanism. Our mechanism also sheds light on a fundamental AI question: How are signals related to symbols

Sparse Representations for Fast, One-Shot Learning

Humans rapidly and reliably learn many kinds of regularities and generalizations. We propose a novel model of fast learning that exploits the properties of sparse representations and the constraints imposed by a plausible hardware mechanism. To demonstrate our approach we describe a computational model of acquisition in the domain of morphophonology. We encapsulate phonological information as bidirectional boolean constraint relations operating on the classical linguistic representations of speech sounds in term of distinctive features. The performance model is described as a hardware mechanism that incrementally enforces the constraints. Phonological behavior arises from the action of this mechanism. Constraints are induced from a corpus of common English nouns and verbs. The induction algorithm compiles the corpus into increasingly sophisticated constraints. The algorithm yields one-shot learning from a few examples. Our model has been implemented as a computer program. The program exhibits phonological behavior similar to that of young children. As a bonus the constraints that are acquired can be interpreted as classical linguistic rules

Systematic computational identification of prognostic cytogenetic markers in neuroblastoma

Background: Neuroblastoma (NB) is the most common extracranial solid tumor found in children. The frequent gain/loss of many chromosome bands in tumor cells and absence of mutations found at diagnosis suggests that NB is a copy number-driven cancer. Despite the previous work, a systematic analysis that investigates the relationship between such frequent gain/loss of chromosome bands and patient prognosis has yet to be implemented. Methods: First, we analyzed two NB CNV datasets to select chromosomal bands with a high frequency of gain or loss. Second, we applied a computational approach to infer sample-specific CNVs for each chromosomal band selected in step 1 based on gene expression data. Third, we applied univariate Cox proportional hazards models to examine the association between the resulting inferred copy number values (iCNVs) and patient survival. Finally, we applied multivariate Cox proportional hazards models to select chromosomal bands that remained significantly associated with prognosis after adjusting for critical clinical variables, including age, stage, gender, and MYCN amplification status. Results: Here, we used a computational method to infer the copy number variations (CNVs) of sample-specific chromosome bands from NB patient gene expression profiles. The resulting inferred CNVs (iCNVs) were highly correlated with the experimentally determined CNVs, demonstrating CNVs can be accurately inferred from gene expression profiles. Using this iCNV metric, we identified 58 frequent gain/loss chromosome bands that were significantly associated with patient survival. Furthermore, we found that 7 chromosome bands were still significantly associated with patient survival even when clinical factors, such as MYCN status, were considered. Particularly, we found that the chromosome band chr11p14 has high potential as a novel candidate cytogenetic biomarker for clinical use. Conclusion: Our analysis resulted in a comprehensive list of prognostic chromosome bands supported by strong statistical evidence. In particular, the chr11p14 gain event provided additional prognostic value in addition to well-established clinical factors, including MYCN status, and thereby represents a novel candidate cytogenetic biomarker with high clinical potential. Additionally, this computational framework could be readily extended to other cancer types, such as leukemia

Antidepressant use and risk of self-harm among people aged 40 years or older: A population-based cohort and self-controlled case series study

Background: Studies on the association between antidepressants and self-harm in adults were mostly conducted over a decade ago and have inconsistent findings. We aimed to compare self-harm risks by antidepressant classes among people aged 40 years or older with depression. Methods: Individuals aged ≥40 years with depression who initiated antidepressant treatment between 2001 and 2015 were retrieved from the Hong Kong Clinical Data Analysis & Reporting system, and were followed up until December 31, 2016. We conducted self-controlled case series (SCCS) analyses to estimate the incidence rate ratio (IRR) of self-harm comparing the pre-exposure (90 days before the first antidepressant use), index exposure (the first antidepressant use), and subsequent exposure (subsequent antidepressant use) periods to nonexposed periods. We applied Cox proportional hazard regressions to estimate the hazard ratio (HR) of self-harm comparing five antidepressant classes (tricyclic and related antidepressant drugs [TCAs], selective serotonin reuptake inhibitors [SSRIs], noradrenergic and specific serotonergic antidepressants [NaSSAs], serotonin–norepinephrine reuptake inhibitors [SNRIs], and others). Findings: A total of 48,724 individuals were identified. SCCS analyses (N = 3,846) found that the increased self-harm risk occurred during the pre-exposure (IRR: 22.24; 95% CI, 20.25-24.42), index exposure (7.03; 6.34-7.80), and subsequent exposure periods (2.47; 2.18-2.79) compared to the unexposed period. Cohort analyses (N = 48,724) found an association of higher self-harm risks in short-term (one year) for NaSSAs vs. TCAs (HR, 2.13; 95% CI, 1.53-2.96), SNRIs vs. TCAs (1.64; 1.01-2.68), and NaSSAs vs. SSRIs (1.75; 1.29-2.36) in the 40-64 years group. The higher risk remained significant in long-term (> one year) for NaSSAs vs. TCAs (1.55; 1.26-1.91) and NaSSAs vs. SSRIs (1.53; 1.26-1.87). In the 65+ group, only short-term differences were observed (SSRIs vs. TCAs [1.31; 1.03-1.66], SNRIs vs. SSRIs [0.44; 0.22-0.87], and SNRIs vs. NaSSAs [0.43; 0.21-0.87]). Interpretation: Within-person comparisons did not suggest that antidepressant exposure is causally associated with an increased risk of self-harm in people with depression. Between-person comparisons revealed differences in self-harm risks between certain pairs of antidepressant classes. These findings may inform clinicians’ benefit-risk assessments when prescribing antidepressants

Cognitive Information Processing

Contains reports on two research projects.Center for Advanced Television StudiesDARPA/U.S. Navy - Office of Naval Research (Contract MDA-972-88-K-0008)DARPA/U.S. Navy - Office of Naval Research (Contract N00014-85-K-0213

SREBP1c-CRY1 signalling represses hepatic glucose production by promoting FOXO1 degradation during refeeding

SREBP1c is a key lipogenic transcription factor activated by insulin in the postprandial state. Although SREBP1c appears to be involved in suppression of hepatic gluconeogenesis, the molecular mechanism is not thoroughly understood. Here we show that CRY1 is activated by insulin-induced SREBP1c and decreases hepatic gluconeogenesis through FOXO1 degradation, at least, at specific circadian time points. SREBP1c−/− and CRY1−/− mice show higher blood glucose than wild-type (WT) mice in pyruvate tolerance tests, accompanied with enhanced expression of PEPCK and G6Pase genes. CRY1 promotes degradation of nuclear FOXO1 by promoting its binding to the ubiquitin E3 ligase MDM2. Although SREBP1c fails to upregulate CRY1 expression in db/db mice, overexpression of CRY1 attenuates hyperglycaemia through reduction of hepatic FOXO1 protein and gluconeogenic gene expression. These data suggest that insulin-activated SREBP1c downregulates gluconeogenesis through CRY1-mediated FOXO1 degradation and that dysregulation of hepatic SREBP1c-CRY1 signalling may contribute to hyperglycaemia in diabetic animals

Infant feeding practices in a multi-ethnic Asian cohort: the GUSTO study

10.3390/nu8050293Nutrients851-17GUSTO (Growing up towards Healthy Outcomes

Chronic adiponectin deficiency leads to Alzheimer’s disease-like cognitive impairments and pathologies through AMPK inactivation and cerebral insulin resistance in aged mice

(a) Immunoblotting analysis of IRβ in the hippocampus and frontal cortex of 18-month old wildtype and APN-KO mice. (b) Densitometric analysis of the ratio of IRβ. Mean ± S.E.M.; ***p < 0.001, n.s. statistically not significant; Scale bar: 100 μm. (JPG 30 kb

Inter-hemispheric EEG coherence analysis in Parkinson's disease : Assessing brain activity during emotion processing

Parkinson’s disease (PD) is not only characterized by its prominent motor symptoms but also associated with disturbances in cognitive and emotional functioning. The objective of the present study was to investigate the influence of emotion processing on inter-hemispheric electroencephalography (EEG) coherence in PD. Multimodal emotional stimuli (happiness, sadness, fear, anger, surprise, and disgust) were presented to 20 PD patients and 30 age-, education level-, and gender-matched healthy controls (HC) while EEG was recorded. Inter-hemispheric coherence was computed from seven homologous EEG electrode pairs (AF3–AF4, F7–F8, F3–F4, FC5–FC6, T7–T8, P7–P8, and O1–O2) for delta, theta, alpha, beta, and gamma frequency bands. In addition, subjective ratings were obtained for a representative of emotional stimuli. Interhemispherically, PD patients showed significantly lower coherence in theta, alpha, beta, and gamma frequency bands than HC during emotion processing. No significant changes were found in the delta frequency band coherence. We also found that PD patients were more impaired in recognizing negative emotions (sadness, fear, anger, and disgust) than relatively positive emotions (happiness and surprise). Behaviorally, PD patients did not show impairment in emotion recognition as measured by subjective ratings. These findings suggest that PD patients may have an impairment of inter-hemispheric functional connectivity (i.e., a decline in cortical connectivity) during emotion processing. This study may increase the awareness of EEG emotional response studies in clinical practice to uncover potential neurophysiologic abnormalities