Spatiotemporal expression of Pax genes in amphioxus: Insights into Pax-related organogenesis and evolution

The expression of four AmphiPax genes in 16 developmental stages and different organs in amphioxus (Branchiostoma belcheri) was investigated, finding those genes expressed throughout amphioxus life with temporal-specific (especially during embryogenesis and metamorphosis) and spatial-specific patterns. This study suggests that duplicated Pax genes in vertebrates might maintain most of their ancestral functions and also expand their expression patterns after the divergence of protochordates and vertebrates.National Natural Science Foundation of China [30830023]; National High Technology Research and Development Program of China [2008AA092602]; Specialized Research Fund for the Doctoral Program of Higher Education [20070384041

Plasma concentrations of osteopontin, but not thrombin-cleaved osteopontin, are associated with the presence and severity of nephropathy and coronary artery disease in patients with type 2 diabetes mellitus

<p>Abstract</p> <p>Background</p> <p>The aim of the present cross-sectional study was to assess possible associations between osteopontin (OPN), and thrombin-cleaved (N-half) OPN, and nephropathy and coronary artery disease (CAD) in patients with type 2 diabetes mellitus (T2DM).</p> <p>Methods</p> <p>Plasma levels of OPN, N-half OPN, and high-sensitivity C-reactive protein (hsCRP) were determined in 301 diabetic patients with (n = 226) or without (n = 75) angiographically documented CAD (luminal diameter narrowing >50%), as well as in 75 non-diabetic controls with normal angiography. The estimated glomerular filtration rate (eGFR) was calculated in all patients.</p> <p>Results</p> <p>Plasma levels of OPN and hsCRP were significantly higher in patients with T2DM compared with controls. In addition, there was a higher occurrence of moderate renal insufficiency and lower eGFR in patients with T2DM (all <it>P </it>< 0.01). T2DM patients in whom OPN levels were greater than the median value had higher serum creatinine levels, a greater prevalence of mild or moderate renal insufficiency, a higher incidence of CAD, and lower eGFR (all <it>P </it>< 0.05) than T2DM patients in whom OPN levels were the same as or lower than the median value. However, there were no differences in these parameters when patients were stratified according to plasma N-half OPN levels. Furthermore, there was a significant correlation between OPN, but not N-half OPN, and the severity of nephropathy and CAD in diabetes. After adjustment for potential confounders and treatments, multiple linear regression analysis demonstrated an independent association between OPN, but not N-half OPN, and eGFR. Multivariate logistic regression revealed that higher OPN levels conferred a fourfold greater risk of renal insufficiency and CAD in patients with T2DM.</p> <p>Conclusions</p> <p>The results of the present study demonstrate that there is an independent association between plasma levels of OPN, but not N-half OPN, and the presence and severity of nephropathy and CAD in diabetes.</p

Decoding of human identity by computer vision and neuronal vision

Extracting meaning from a dynamic and variable flow of incoming information is a major goal of both natural and artificial intelligence. Computer vision (CV) guided by deep learning (DL) has made significant strides in recognizing a specific identity despite highly variable attributes. This is the same challenge faced by the nervous system and partially addressed by the concept cells—neurons exhibiting selective firing in response to specific persons/places, described in the human medial temporal lobe (MTL) ⁠. Yet, access to neurons representing a particular concept is limited due to these neurons’ sparse coding. It is conceivable, however, that the information required for such decoding is present in relatively small neuronal populations. To evaluate how well neuronal populations encode identity information in natural settings, we recorded neuronal activity from multiple brain regions of nine neurosurgical epilepsy patients implanted with depth electrodes, while the subjects watched an episode of the TV series “24”. First, we devised a minimally supervised CV algorithm (with comparable performance against manually-labeled data) to detect the most prevalent characters (above 1% overall appearance) in each frame. Next, we implemented DL models that used the time-varying population neural data as inputs and decoded the visual presence of the four main characters throughout the episode. This methodology allowed us to compare “computer vision” with “neuronal vision”—footprints associated with each character present in the activity of a subset of neurons—and identify the brain regions that contributed to this decoding process. We then tested the DL models during a recognition memory task following movie viewing where subjects were asked to recognize clip segments from the presented episode. DL model activations were not only modulated by the presence of the corresponding characters but also by participants’ subjective memory of whether they had seen the clip segment, and by the associative strengths of the characters in the narrative plot. The described approach can offer novel ways to probe the representation of concepts in time-evolving dynamic behavioral tasks. Further, the results suggest that the information required to robustly decode concepts is present in the population activity of only tens of neurons even in brain regions beyond MTL

Real-world High-dimensional Data with Multimodal Distributions: Mode Discovery and Mode-preserving Generative Models

Recent advances in Artificial Intelligence (AI) have demonstrated extraordinary performances in tasks relating to our daily life activities. However, the “intelligence” of the machine is still far below our expectations, especially when it concerns the ability to model the observable world in a reliable and predictive fashion. This is a challenging task, as real world data is not only high dimensional but has distributions with multiple modes and different modalities. Identifying such hidden modes and developing succinct generative models that can generalize to unseen scenarios are difficult problems. If one can solve such versatile data modeling problems, then one could take effective actions to achieve desired goals, enabling AI to display true cognitive abilities.In this thesis, we model real world high-dimensional data as having an inherently low dimensional generative mechanism: a set of computational laws – indexed by a hierarchical mixture of contexts– project low-dimensional data into the observable high-dimensional multi-modal data sets. While the ideal goal of reverse engineering such a universal generative mechanism –purely based on data– is a seemingly impossible task, a spectrum of techniques are available for handling cases where partial information about the underlying contexts and modes are given. The objective of the thesis is to address the problem of discovering modes and characterizing them along this spectrum of prior information, and demonstrate the effectiveness of our techniques in several real world applications. For example, as a step towards discovering low-dimensional modes of high-dimensional data, we propose an efficient and novel algorithm called eNMF for the non-negative matrix factorization (NMF) problem, which is used widely for interpretable dimensionality reduction tasks. Then we address a problem to refine the modes in the Electroencephalogram (EEG) data where ground truth labels – as assumed in a strictly supervised approach – are not available and only partial and noisy labels can be inferred. A class of events in EEG data, referred to as High-frequency oscillations (HFOs), have been found to be a promising biomarker of the epileptogenic zone in the brains of patients with epilepsy. HFOs, however, can be generated by healthy tis- sues as well, and further differentiation of the HFOs into epileptogenic HFOs (eHFOs) and non-eHFOs is needed for more accurate localization of the problematic zone. Going further along the spectrum, we address the domain of creating generative models when only the high-level contexts are given and demonstrate that it facilitates the fine-level discovery of modes within the given contexts. In particular, we construct context-aware 3D human motion generation models that enable on-demand sampling of discovered and interpolated modes within different categories of action types, and customization of motion trajectories not present in the training data. Finally, we tackle the problem of creating generative models for scenarios where the observable data is created by an agent that interacts sequentially with real physical environments. We show how one can create generative models that can not only imitate diverse behavioral data, but can do so robustly while interacting with an external environment

Diverse Imitation Learning via Self-Organizing Generative Models

Imitation learning is the task of replicating expert policy from demonstrations, without access to a reward function. This task becomes particularly challenging when the expert exhibits a mixture of behaviors. Prior work has introduced latent variables to model variations of the expert policy. However, our experiments show that the existing works do not exhibit appropriate imitation of individual modes. To tackle this problem, we adopt an encoder-free generative model for behavior cloning (BC) to accurately distinguish and imitate different modes. Then, we integrate it with GAIL to make the learning robust towards compounding errors at unseen states. We show that our method significantly outperforms the state of the art across multiple experiments

Lipoprotein(a) is associated with left ventricular systolic dysfunction in a Chinese population of patients with hypertension and without coronary artery disease

Introduction: Data on relationship between lipoprotein(a) [Lp(a)] and non-ischemic heart dysfunction are limited. This study is aimed to assess the association between Lp(a) and left ventricular systolic dysfunction in a Chinese population of patients with hypertension and without coronary artery disease (CAD). Material and methods: This cross-sectional study included 1611 patients with hypertension and without CAD in China. The factors associated with left ventricular ejection fraction (LVEF) were evaluated using univariate and multivariate analysis. Results : A higher percentage of hypertensive patients with LVEF<50% were men, and had lower plasma high-density lipoprotein cholesterol, but higher plasma Lp(a), serum creatinine, and hemoglobin levels than those with LVEF ≥ 50% using univariate analysis. When participants were classified as four groups according to Lp(a) quartiles, LVEF was decreased with increased Lp(a) levels. The prevalence of LVEF < 50% was increased with Lp(a) quartiles. Multiple linear regression analysis indicated that plasma Lp(a) levels, man, and serum creatinine levels were independently correlated with LVEF in hypertensive patients. Multiple logistic regression analysis indicated that plasma Lp(a) levels (OR = 5.566, 95% CI: 1.745–17.758, p = 0.004) or Lp(a) quartiles (Quartile 4: OR = 3.234, 95% CI: 1.290–8.105, quartile 1 as reference, p = 0.012) was independently correlated with LVEF < 50% with adjustment for other potential confounders. Ordinal logistic regression analysis demonstrated that Lp(a) (OR = 5.760, 95% CI: 1.831–18.120, p = 0.003) was independently correlated with different LVEF categories (≥ 50%, 35–49%, and < 35%) in hypertensive patients. Conclusions : Left ventricular ejection fraction is decreased with increased plasma Lp(a) levels. Lipoprotein(a) is independently correlated with left ventricular systolic dysfunction in patients with hypertension and without CAD