A Novel Document Generation Process for Topic Detection based on Hierarchical Latent Tree Models

We propose a novel document generation process based on hierarchical latent tree models (HLTMs) learned from data. An HLTM has a layer of observed word variables at the bottom and multiple layers of latent variables on top. For each document, we first sample values for the latent variables layer by layer via logic sampling, then draw relative frequencies for the words conditioned on the values of the latent variables, and finally generate words for the document using the relative word frequencies. The motivation for the work is to take word counts into consideration with HLTMs. In comparison with LDA-based hierarchical document generation processes, the new process achieves drastically better model fit with much fewer parameters. It also yields more meaningful topics and topic hierarchies. It is the new state-of-the-art for the hierarchical topic detection

Reducing bias through directed acyclic graphs

<p>Abstract</p> <p>Background</p> <p>The objective of most biomedical research is to determine an unbiased estimate of effect for an exposure on an outcome, i.e. to make causal inferences about the exposure. Recent developments in epidemiology have shown that traditional methods of identifying confounding and adjusting for confounding may be inadequate.</p> <p>Discussion</p> <p>The traditional methods of adjusting for "potential confounders" may introduce conditional associations and bias rather than minimize it. Although previous published articles have discussed the role of the causal directed acyclic graph approach (DAGs) with respect to confounding, many clinical problems require complicated DAGs and therefore investigators may continue to use traditional practices because they do not have the tools necessary to properly use the DAG approach. The purpose of this manuscript is to demonstrate a simple 6-step approach to the use of DAGs, and also to explain why the method works from a conceptual point of view.</p> <p>Summary</p> <p>Using the simple 6-step DAG approach to confounding and selection bias discussed is likely to reduce the degree of bias for the effect estimate in the chosen statistical model.</p

Association between Diagnosed Anxiety and Depression and Exposure to Life Stressors during the COVID-19 Pandemic

Research suggests that mental health disorders heighten the risk of exposure to life stressors. Drawing on a sample of 754 adults from a survey distributed at six primary care clinics, we examine whether adults who reported ever being diagnosed with depression or anxiety were more likely to experience an employment disruption, a housing disruption, and/or report more COVID-19-related stressors during the COVID-19 pandemic. Individuals who reported ever being diagnosed with depression reported a greater burden (B=.75) of COVID-19-related stressors. Those who reported ever being diagnosed with anxiety had higher odds of experiencing an employment disruption (OR=1.90) and a housing disruption (OR=2.92) and reported about one (B=.97) additional COVID-19-related stressor. Our results suggest that the COVID-19 pandemic may have deepened existing mental health disparities by exposing those with a depression or anxiety diagnosis to additional life stressors

Succinic semialdehyde dehydrogenase deficiency: Lessons from mice and men

Succinic semialdehyde dehydrogenase (SSADH) deficiency, a disorder of GABA degradation with subsequent elevations in brain GABA and GHB, is a neurometabolic disorder with intellectual disability, epilepsy, hypotonia, ataxia, sleep disorders, and psychiatric disturbances. Neuroimaging reveals increased T2-weighted MRI signal usually affecting the globus pallidus, cerebellar dentate nucleus, and subthalamic nucleus, and often cerebral and cerebellar atrophy. EEG abnormalities are usually generalized spike-wave, consistent with a predilection for generalized epilepsy. The murine phenotype is characterized by failure-to-thrive, progressive ataxia, and a transition from generalized absence to tonic-clonic to ultimately fatal convulsive status epilepticus. Binding and electrophysiological studies demonstrate use-dependent downregulation of GABA(A) and (B) receptors in the mutant mouse. Translational human studies similarly reveal downregulation of GABAergic activity in patients, utilizing flumazenil-PET and transcranial magnetic stimulation for GABA(A) and (B) activity, respectively. Sleep studies reveal decreased stage REM with prolonged REM latencies and diminished percentage of stage REM. An ad libitum ketogenic diet was reported as effective in the mouse model, with unclear applicability to the human condition. Acute application of SGS–742, a GABA(B) antagonist, leads to improvement in epileptiform activity on electrocorticography. Promising mouse data using compounds available for clinical use, including taurine and SGS–742, form the framework for human trials

Graphical Markov models, unifying results and their interpretation

Graphical Markov models combine conditional independence constraints with graphical representations of stepwise data generating processes.The models started to be formulated about 40 years ago and vigorous development is ongoing. Longitudinal observational studies as well as intervention studies are best modeled via a subclass called regression graph models and, especially traceable regressions. Regression graphs include two types of undirected graph and directed acyclic graphs in ordered sequences of joint responses. Response components may correspond to discrete or continuous random variables and may depend exclusively on variables which have been generated earlier. These aspects are essential when causal hypothesis are the motivation for the planning of empirical studies. To turn the graphs into useful tools for tracing developmental pathways and for predicting structure in alternative models, the generated distributions have to mimic some properties of joint Gaussian distributions. Here, relevant results concerning these aspects are spelled out and illustrated by examples. With regression graph models, it becomes feasible, for the first time, to derive structural effects of (1) ignoring some of the variables, of (2) selecting subpopulations via fixed levels of some other variables or of (3) changing the order in which the variables might get generated. Thus, the most important future applications of these models will aim at the best possible integration of knowledge from related studies.Comment: 34 Pages, 11 figures, 1 tabl

Mid-infrared optical parametric amplifier using silicon nanophotonic waveguides

All-optical signal processing is envisioned as an approach to dramatically decrease power consumption and speed up performance of next-generation optical telecommunications networks. Nonlinear optical effects, such as four-wave mixing (FWM) and parametric gain, have long been explored to realize all-optical functions in glass fibers. An alternative approach is to employ nanoscale engineering of silicon waveguides to enhance the optical nonlinearities by up to five orders of magnitude, enabling integrated chip-scale all-optical signal processing. Previously, strong two-photon absorption (TPA) of the telecom-band pump has been a fundamental and unavoidable obstacle, limiting parametric gain to values on the order of a few dB. Here we demonstrate a silicon nanophotonic optical parametric amplifier exhibiting gain as large as 25.4 dB, by operating the pump in the mid-IR near one-half the band-gap energy (E~0.55eV, lambda~2200nm), at which parasitic TPA-related absorption vanishes. This gain is high enough to compensate all insertion losses, resulting in 13 dB net off-chip amplification. Furthermore, dispersion engineering dramatically increases the gain bandwidth to more than 220 nm, all realized using an ultra-compact 4 mm silicon chip. Beyond its significant relevance to all-optical signal processing, the broadband parametric gain also facilitates the simultaneous generation of multiple on-chip mid-IR sources through cascaded FWM, covering a 500 nm spectral range. Together, these results provide a foundation for the construction of silicon-based room-temperature mid-IR light sources including tunable chip-scale parametric oscillators, optical frequency combs, and supercontinuum generators

Dissociation Between the Growing Opioid Demands and Drug Policy Directions Among the U.S. Older Adults with Degenerative Joint Diseases

We aim to examine temporal trends of orthopedic operations and opioid-related hospital stays among seniors in the nation and states of Oregon and Washington where marijuana legalization was accepted earlier than any others. As aging society advances in the United States (U.S.), orthopedic operations and opioid-related hospital stays among seniors increase in the nation. A serial cross-sectional cohort study using the healthcare cost and utilization project fast stats from 2006 through 2015 measured annual rate per 100,000 populations of orthopedic operations by age groups (45–64 vs 65 and older) as well as annual rate per 100,000 populations of opioid-related hospital stays among 65 and older in the nation, Oregon and Washington states from 2008 through 2017. Orthopedic operations (knee arthroplasty, total or partial hip replacement, spinal fusion or laminectomy) and opioid-related hospital stays were measured. The compound annual growth rate (CAGR) was used to quantify temporal trends of orthopedic operations by age groups as well as opioid-related hospital stays and was tested by Rao–Scott correction of χ2 for categorical variables. The CAGR (4.06%) of orthopedic operations among age 65 and older increased (P...) (See full abstract in article

The identification of informative genes from multiple datasets with increasing complexity

Background In microarray data analysis, factors such as data quality, biological variation, and the increasingly multi-layered nature of more complex biological systems complicates the modelling of regulatory networks that can represent and capture the interactions among genes. We believe that the use of multiple datasets derived from related biological systems leads to more robust models. Therefore, we developed a novel framework for modelling regulatory networks that involves training and evaluation on independent datasets. Our approach includes the following steps: (1) ordering the datasets based on their level of noise and informativeness; (2) selection of a Bayesian classifier with an appropriate level of complexity by evaluation of predictive performance on independent data sets; (3) comparing the different gene selections and the influence of increasing the model complexity; (4) functional analysis of the informative genes. Results In this paper, we identify the most appropriate model complexity using cross-validation and independent test set validation for predicting gene expression in three published datasets related to myogenesis and muscle differentiation. Furthermore, we demonstrate that models trained on simpler datasets can be used to identify interactions among genes and select the most informative. We also show that these models can explain the myogenesis-related genes (genes of interest) significantly better than others (P < 0.004) since the improvement in their rankings is much more pronounced. Finally, after further evaluating our results on synthetic datasets, we show that our approach outperforms a concordance method by Lai et al. in identifying informative genes from multiple datasets with increasing complexity whilst additionally modelling the interaction between genes. Conclusions We show that Bayesian networks derived from simpler controlled systems have better performance than those trained on datasets from more complex biological systems. Further, we present that highly predictive and consistent genes, from the pool of differentially expressed genes, across independent datasets are more likely to be fundamentally involved in the biological process under study. We conclude that networks trained on simpler controlled systems, such as in vitro experiments, can be used to model and capture interactions among genes in more complex datasets, such as in vivo experiments, where these interactions would otherwise be concealed by a multitude of other ongoing events

A repurposing strategy for Hsp90 inhibitors demonstrates their potency against filarial nematodes

Novel drugs are required for the elimination of infections caused by filarial worms, as most commonly used drugs largely target the microfilariae or first stage larvae of these infections. Previous studies, conducted in vitro, have shown that inhibition of Hsp90 kills adult Brugia pahangi. As numerous small molecule inhibitors of Hsp90 have been developed for use in cancer chemotherapy, we tested the activity of several novel Hsp90 inhibitors in a fluorescence polarization assay and against microfilariae and adult worms of Brugia in vitro. The results from all three assays correlated reasonably well and one particular compound, NVP-AUY922, was shown to be particularly active, inhibiting Mf output from female worms at concentrations as low as 5.0 nanomolar after 6 days exposure to drug. NVP-AUY922 was also active on adult worms after a short 24 h exposure to drug. Based on these in vitro data, NVP-AUY922 was tested in vivo in a mouse model and was shown to significantly reduce the recovery of both adult worms and microfilariae. These studies provide proof of principle that the repurposing of currently available Hsp90 inhibitors may have potential for the development of novel agents with macrofilaricidal properties

Renormalization Group Study of the Intrinsic Finite Size Effect in 2D Superconductors

Vortices in a thin-film superconductor interact logarithmically out to a distance on the order of the two-dimensional (2D) magnetic penetration depth $\lambda_\perp$, at which point the interaction approaches a constant. Thus, because of the finite $\lambda_\perp$, the system exhibits what amounts to an {\it intrinsic} finite size effect. It is not described by the 2D Coulomb gas but rather by the 2D Yukawa gas (2DYG). To study the critical behavior of the 2DYG, we map the 2DYG to the massive sine-Gordon model and then perform a renormalization group study to derive the recursion relations and to verify that $\lambda_\perp$ is a relevant parameter. We solve the recursion relations to study important physical quantities for this system including the renormalized stiffness constant and the correlation length. We also address the effect of current on this system to explain why finite size effects are not more prevalent in experiments given that the 2D magnetic penetration depth is a relevant parameter.Comment: 8 pages inRevTex, 5 embedded EPS figure