Identification of genes associated with multiple cancers via integrative analysis

<p>Abstract</p> <p>Background</p> <p>Advancement in gene profiling techniques makes it possible to measure expressions of thousands of genes and identify genes associated with development and progression of cancer. The identified cancer-associated genes can be used for diagnosis, prognosis prediction, and treatment selection. Most existing cancer microarray studies have been focusing on the identification of genes associated with a specific type of cancer. Recent biomedical studies suggest that different cancers may share common susceptibility genes. A comprehensive description of the associations between genes and cancers requires identification of not only multiple genes associated with a specific type of cancer but also genes associated with multiple cancers.</p> <p>Results</p> <p>In this article, we propose the Mc.TGD (Multi-cancer Threshold Gradient Descent), an integrative analysis approach capable of analyzing multiple microarray studies on different cancers. The Mc.TGD is the first regularized approach to conduct "two-dimensional" selection of genes with joint effects on cancer development. Simulation studies show that the Mc.TGD can more accurately identify genes associated with multiple cancers than meta analysis based on "one-dimensional" methods. As a byproduct, identification accuracy of genes associated with only one type of cancer may also be improved. We use the Mc.TGD to analyze seven microarray studies investigating development of seven different types of cancers. We identify one gene associated with six types of cancers and four genes associated with five types of cancers. In addition, we also identify 11, 9, 18, and 17 genes associated with 4 to 1 types of cancers, respectively. We evaluate prediction performance using a Leave-One-Out cross validation approach and find that only 4 (out of 570) subjects cannot be properly predicted.</p> <p>Conclusion</p> <p>The Mc.TGD can identify a short list of genes associated with one or multiple types of cancers. The identified genes are considerably different from those identified using meta analysis or analysis of marginal effects.</p

Microheated substrates for patterning cells and controlling development

Here, we seek to control cellular development by devising a means through which cells can be subjected to a microheated environment in standard culture conditions. Numerous techniques have been devised for controlling cellular function and development via manipulation of surface environmental cues at the micro- and nanoscale. It is well understood that temperature plays a significant role in the rate of cellular activities, migratory behavior (thermotaxis), and in some cases, protein expression. Yet, the effects and possible utilization of micrometer-scale temperature fields in cell cultures have not been explored. Toward this end, two types of thermally isolated microheated substrates were designed and fabricated, one with standard backside etching beneath a dielectric film and another with a combination of surface and bulk micromachining and backside etching. The substrates were characterized with infrared microscopy, finite element modeling, scanning electron microscopy, stylus profilometry, and electrothermal calibrations. Neuron culture studies were conducted on these substrates to 1) examine the feasibility of using a microheated environment to achieve patterned cell growth and 2) selectively accelerate neural development on regions less than 100$mu m$wide. Results show that attached neurons, grown on microheated regions set at 37$~^circ C$, extended processes substantially faster than those incubated at 25$~^circ C$on the same substrate. Further, unattached neurons were positioned precisely along the length of the heater filament (operating at 45$~^circ C$) using free convection currents. These preliminary findings indicate that microheated substrates may be used to direct cellular development spatially in a practical manner.$hfillhbox[1414]

Abrupt shifts in phenology and vegetation productivity under climate extremes

©2015. American Geophysical Union. All Rights Reserved. Amplification of the hydrologic cycle as a consequence of global warming is predicted to increase climate variability and the frequency and severity of droughts. Recent large-scale drought and flooding over numerous continents provide unique opportunities to understand ecosystem responses to climatic extremes. In this study, we investigated the impacts of the early 21st century extreme hydroclimatic variations in southeastern Australia on phenology and vegetation productivity using Moderate Resolution Imaging Spectroradiometer Enhanced Vegetation Index and Standardized Precipitation-Evapotranspiration Index. Results revealed dramatic impacts of drought and wet extremes on vegetation dynamics, with abrupt between year changes in phenology. Drought resulted in widespread reductions or collapse in the normal patterns of seasonality such that in many cases there was no detectable phenological cycle during drought years. Across the full range of biomes examined, we found semiarid ecosystems to exhibit the largest sensitivity to hydroclimatic variations, exceeding that of arid and humid ecosystems. This result demonstrated the vulnerability of semiarid ecosystems to climatic extremes and potential loss of ecosystem resilience with future mega-drought events. A skewed distribution of hydroclimatic sensitivity with aridity is of global biogeochemical significance because it suggests that current drying trends in semiarid regions will reduce hydroclimatic sensitivity and suppress the large carbon sink that has been reported during recent wet periods (e.g., 2011 La Niña)

Implicit Neural Deformation for Sparse-View Face Reconstruction

In this work, we present a new method for 3D face reconstruction from sparse-view RGB images. Unlike previous methods which are built upon 3D morphable models (3DMMs) with limited details, we leverage an implicit representation to encode rich geometric features. Our overall pipeline consists of two major components, including a geometry network, which learns a deformable neural signed distance function (SDF) as the 3D face representation, and a rendering network, which learns to render on-surface points of the neural SDF to match the input images via self-supervised optimization. To handle in-the-wild sparse-view input of the same target with different expressions at test time, we propose residual latent code to effectively expand the shape space of the learned implicit face representation as well as a novel view-switch loss to enforce consistency among different views. Our experimental results on several benchmark datasets demonstrate that our approach outperforms alternative baselines and achieves superior face reconstruction results compared to state-of-the-art methods.Comment: 10 pages, 6 figures, The 30th Pacific Conference on Computer Graphics and Applications. Pacific Graphics(PG) 202

HairStep: Transfer Synthetic to Real Using Strand and Depth Maps for Single-View 3D Hair Modeling

In this work, we tackle the challenging problem of learning-based single-view 3D hair modeling. Due to the great difficulty of collecting paired real image and 3D hair data, using synthetic data to provide prior knowledge for real domain becomes a leading solution. This unfortunately introduces the challenge of domain gap. Due to the inherent difficulty of realistic hair rendering, existing methods typically use orientation maps instead of hair images as input to bridge the gap. We firmly think an intermediate representation is essential, but we argue that orientation map using the dominant filtering-based methods is sensitive to uncertain noise and far from a competent representation. Thus, we first raise this issue up and propose a novel intermediate representation, termed as HairStep, which consists of a strand map and a depth map. It is found that HairStep not only provides sufficient information for accurate 3D hair modeling, but also is feasible to be inferred from real images. Specifically, we collect a dataset of 1,250 portrait images with two types of annotations. A learning framework is further designed to transfer real images to the strand map and depth map. It is noted that, an extra bonus of our new dataset is the first quantitative metric for 3D hair modeling. Our experiments show that HairStep narrows the domain gap between synthetic and real and achieves state-of-the-art performance on single-view 3D hair reconstruction.Comment: CVPR 2023 Highlight, project page: https://paulyzheng.github.io/research/hairstep

Therapeutic Effects of PPAR α

Peroxisome-proliferator activated receptor-alpha (PPARα) is a broadly expressed nuclear hormone receptor and is a transcription factor for diverse target genes possessing a PPAR response element (PPRE) in the promoter region. The PPRE is highly conserved, and PPARs thus regulate transcription of an extensive array of target genes involved in energy metabolism, vascular function, oxidative stress, inflammation, and many other biological processes. PPARα has potent protective effects against neuronal cell death and microvascular impairment, which have been attributed in part to its antioxidant and anti-inflammatory properties. Here we discuss PPARα’s effects in neurodegenerative and microvascular diseases and also recent clinical findings that identified therapeutic effects of a PPARα agonist in diabetic microvascular complications

Critical sound attenuation in a diluted Ising system

The field-theoretic description of dynamical critical effects of the influence of disorder on acoustic anomalies near the temperature of the second-order phase transition is considered for three-dimensional Ising-like systems. Calculations of the sound attenuation in pure and dilute Ising-like systems near the critical point are presented. The dynamical scaling function for the critical attenuation coefficient is calculated. The influence of quenched disorder on the asymptotic behaviour of the critical ultrasonic anomalies is discussed.Comment: 12 RevTeX pages, 4 figure

Stroke Severity and Comorbidity Index for Prediction of Mortality after Ischemic Stroke from the Virtual International Stroke Trials Archive-Acute Collaboration

M. Kaste on työryhmän VISTA-Acute Collaboration jäsen.Background: There is increasing interest in the use of administrative data (incorporating comorbidity index) and stroke severity score to predict ischemic stroke mortality. The aim of this study was to determine the optimal timing for the collection of stroke severity data and the minimum clinical dataset to be included in models of stroke mortality. To address these issues, we chose the Virtual International Stroke Trials Archive (VISTA), which contains National Institutes of Health Stroke Scale (NIHSS) on admission and at 24 hours, as well as outcome at 90 days. Methods: VISTA was searched for patients who had baseline and 24-hour NIHSS. Improvement in regression models was performed by the net reclassification improvement (NRI) method. Results: The clinical data among 5206 patients were mean age, 69 +/- 13; comorbidity index, 3.3 +/- .9; median NIHSS at baseline, 12 (interquartile range [IQR] 8-17); NIHSS at 24 hours, 9 (IQR 8-15); and death at 90 days in 15%. The baseline model consists of age, gender, and comorbidity index. Adding the baseline NIHSS to model 1 improved the NRI by 0.671 (95% confidence interval [CI] 0.595-0.747) [or 67.1% correct reclassification between model 1 and model 2]. Adding the 24 hour NIHSS term to model 1 (model 3) improved the NRI by 0.929 (95% CI 0.857-1.000) for model 3 versus model 1. Adding the variable thrombolysis to model 3 (model 4) improve NRI by 0.1 (95% CI 0.023-0.178) [model 4 versus model 3]. Conclusion: The optimal model for the prediction of mortality was achieved by adding the 24-hour NIHSS and thrombolysis to the baseline model.Peer reviewe

A spatio-temporal model for estimating the long-term effects of air pollution on respiratory hospital admissions in Greater London

It has long been known that air pollution is harmful to human health, as many epidemiological studies have been conducted into its effects. Collectively, these studies have investigated both the acute and chronic effects of pollution, with the latter typically based on individual level cohort designs that can be expensive to implement. As a result of the increasing availability of small-area statistics, ecological spatio-temporal study designs are also being used, with which a key statistical problem is allowing for residual spatio-temporal autocorrelation that remains after the covariate effects have been removed. We present a new model for estimating the effects of air pollution on human health, which allows for residual spatio-temporal autocorrelation, and a study into the long-term effects of air pollution on human health in Greater London, England. The individual and joint effects of different pollutants are explored, via the use of single pollutant models and multiple pollutant indices

The Rise and Fall of Passive Disk Galaxies: Morphological Evolution Along the Red Sequence Revealed by COSMOS

The increasing abundance of passive "red-sequence" galaxies since z=1-2 is mirrored by a coincident rise in the number of galaxies with spheroidal morphologies. In this paper, however, we show that in detail the correspondence between galaxy morphology and color is not perfect, providing insight into the physical origin of this evolution. Using the COSMOS survey, we study a significant population of red sequence galaxies with disk-like morphologies. These passive disks typically have Sa-Sb morphological types with large bulges, but they are not confined to dense environments. They represent nearly one-half of all red-sequence galaxies and dominate at lower masses (log Mstar < 10) where they are increasingly disk-dominated. As a function of time, the abundance of passive disks with log Mstar < 11 increases, but not as fast as red-sequence spheroidals in the same mass range. At higher mass, the passive disk population has declined since z~1, likely because they transform into spheroidals. We estimate that as much as 60% of galaxies transitioning onto the red sequence evolve through a passive disk phase. The origin of passive disks therefore has broad implications for understanding how star formation shuts down. Because passive disks tend to be more bulge-dominated than their star-forming counterparts, a simple fading of blue disks does not fully explain their origin. We explore several more sophisticated explanations, including environmental effects, internal stabilization, and disk regrowth during gas-rich mergers. While previous work has sought to explain color and morphological transformations with a single process, these observations open the way to new insight by highlighting the fact that galaxy evolution may actually proceed through several separate stages.Comment: 16 pages, Accepted version to appear in Ap