The progress of cardiac stem cell study

It summarized the recent results and clarified the kinds of cardiac stem cells. Then the paper overviews the method inducing stem cells into cardiomyocytes. It also shows the clinic works having been made about cardiac stem cells. Almost all clinic studies have a significative conclusion increasing ejection fraction of heart. Through that it discusses the modifying technology regulating stem cells. At last the article reveals the biological organ future of clinic transplantation

Glucagon-like peptide-1 receptor agonist versus basal insulin in type-2 diabetic patients: An efficacy and safety analysis

Purpose: To compare the effectiveness of glucagon-like peptide 1 receptor agonist with that of basal insulin in type 2 diabetes patients. Methods: Type-2 diabetes patients who were insensitive to metformin were treated with glucagon-like peptide 1 receptor agonist (GP cohort, n = 115) or basal insulin (BI cohort, n = 152) with metformin. Hemoglobin A1c (HbA1c) level and body weight were determined, and adverse effects also recorded. Results: After 16 weeks of treatment, glucagon-like peptide 1 receptor agonist did not significantly reduce HbA1c levels (7.45 ± 2.11 % vs. 7.01 ± 2.01, p = 0.107). In contrast, basal insulin significantly reduced the levels of HbA1c (7.91 ± 2.98 % vs. 7.13 ± 2.22 %, p = 0.010, q = 3.852). Glucagon-likepeptide 1 receptor agonist reduced the body weight of patients (65.25 ± 7.55 kg vs. 62.16 ± 6.15 kg, p = 0.0008, q = 5.121), unlike basal insulin (63.71 ± 6.15 vs. 62.65 ± 6.76 kg, p = 0.154). Conclusion: Glucagon-like peptide 1 receptor agonist and basal insulin + metformin produce identical effectiveness in the treatment of type-2 diabetic patients. Keywords: Glucagon-like peptide-1 receptor agonist, Glycemic control, Insulin, Metformin, Type-2 diabete

A comprehensive analysis of Fermi Gamma-Ray Burst Data: IV. Spectral lag and Its Relation to Ep Evolution

The spectral evolution and spectral lag behavior of 92 bright pulses from 84 gamma-ray bursts (GRBs) observed by the Fermi GBM telescope are studied. These pulses can be classified into hard-to-soft pulses (H2S, 64/92), H2S-dominated-tracking pulses (21/92), and other tracking pulses (7/92). We focus on the relationship between spectral evolution and spectral lags of H2S and H2S-dominated-tracking pulses. %in hard-to-soft pulses (H2S, 64/92) and H2S-dominating-tracking (21/92) pulses. The main trend of spectral evolution (lag behavior) is estimated with $\log E_p\propto k_E\log(t+t_0)$ (${\hat{\tau}} \propto k_{\hat{\tau}}\log E$), where $E_p$ is the peak photon energy in the radiation spectrum, $t+t_0$ is the observer time relative to the beginning of pulse $-t_0$, and ${\hat{\tau}}$ is the spectral lag of photons with energy $E$ with respect to the energy band $8$-$25$ keV. For H2S and H2S-dominated-tracking pulses, a weak correlation between $k_{{\hat{\tau}}}/W$ and $k_E$ is found, where $W$ is the pulse width. We also study the spectral lag behavior with peak time $t_{\rm p_E}$ of pulses for 30 well-shaped pulses and estimate the main trend of the spectral lag behavior with $\log t_{\rm p_E}\propto k_{t_p}\log E$. It is found that $k_{t_p}$ is correlated with $k_E$. We perform simulations under a phenomenological model of spectral evolution, and find that these correlations are reproduced. We then conclude that spectral lags are closely related to spectral evolution within the pulse. The most natural explanation of these observations is that the emission is from the electrons in the same fluid unit at an emission site moving away from the central engine, as expected in the models invoking magnetic dissipation in a moderately-high-$\sigma$ outflow.Comment: 58 pages, 11 figures, 3 tables. ApJ in pres

Pathological Evidence Exploration in Deep Retinal Image Diagnosis

Though deep learning has shown successful performance in classifying the label and severity stage of certain disease, most of them give few evidence on how to make prediction. Here, we propose to exploit the interpretability of deep learning application in medical diagnosis. Inspired by Koch's Postulates, a well-known strategy in medical research to identify the property of pathogen, we define a pathological descriptor that can be extracted from the activated neurons of a diabetic retinopathy detector. To visualize the symptom and feature encoded in this descriptor, we propose a GAN based method to synthesize pathological retinal image given the descriptor and a binary vessel segmentation. Besides, with this descriptor, we can arbitrarily manipulate the position and quantity of lesions. As verified by a panel of 5 licensed ophthalmologists, our synthesized images carry the symptoms that are directly related to diabetic retinopathy diagnosis. The panel survey also shows that our generated images is both qualitatively and quantitatively superior to existing methods.Comment: to appear in AAAI (2019). The first two authors contributed equally to the paper. Corresponding Author: Feng L

SAM-RL: Sensing-Aware Model-Based Reinforcement Learning via Differentiable Physics-Based Simulation and Rendering

Model-based reinforcement learning (MBRL) is recognized with the potential to be significantly more sample efficient than model-free RL. How an accurate model can be developed automatically and efficiently from raw sensory inputs (such as images), especially for complex environments and tasks, is a challenging problem that hinders the broad application of MBRL in the real world. In this work, we propose a sensing-aware model-based reinforcement learning system called SAM-RL. Leveraging the differentiable physics-based simulation and rendering, SAM-RL automatically updates the model by comparing rendered images with real raw images and produces the policy efficiently. With the sensing-aware learning pipeline, SAM-RL allows a robot to select an informative viewpoint to monitor the task process. We apply our framework to real-world experiments for accomplishing three manipulation tasks: robotic assembly, tool manipulation, and deformable object manipulation. We demonstrate the effectiveness of SAM-RL via extensive experiments. Supplemental materials and videos are available on our project webpage at https://sites.google.com/view/sam-rl.Comment: Submitted to IEEE International Conference on Robotics and Automation (ICRA) 202

Effects of taurine on male reproduction in rats of different ages

<p>Abstract</p> <p>Background</p> <p>It has been demonstrated that taurine is one of the most abundant free amino acids in the male reproductive system, and can be biosynthesized by male reproductive organs. But the effect of taurine on male reproduction is poorly understood.</p> <p>Methods</p> <p>Taurine and β-alanine (taurine transport inhibitor) were offered in water to male rats of different ages. The effects of taurine on reproductive hormones, testis marker enzymes, antioxidative ability and sperm quality were investigated.</p> <p>Results</p> <p>The levels of T and LH were obviously increased by taurine supplementation in rats of different ages, and the level of E was also significantly elevated in baby rats. The levels of SOD, ACP, SDH and NOS were obviously increased by taurine administration in adult rats, but the levels of AKP, AST, ALT and NO were significantly decreased. The levels of SOD, ACP, LDH, SDH, NOS, NO and GSH were significantly elevated by taurine administration in aged rats, but the levels of AST and ALT were significantly decreased. The motility of spermatozoa was obviously increased by taurine supplement in adult rats. The numbers and motility of spermatozoa, the rate of live spermatozoa were significantly increased by taurine supplement in aged rats.</p> <p>Conclusions</p> <p>The present study demonstrated that a taurine supplement could stimulate the secretion of LH and T, increase the levels of testicular marker enzymes, elevate testicular antioxidation and improve sperm quality. The results imply that taurine plays important roles in male reproduction especially in aged animals.</p

Learning to Decompose Visual Features with Latent Textual Prompts

Recent advances in pre-training vision-language models like CLIP have shown great potential in learning transferable visual representations. Nonetheless, for downstream inference, CLIP-like models suffer from either 1) degraded accuracy and robustness in the case of inaccurate text descriptions during retrieval-based inference (the challenge for zero-shot protocol); or 2) breaking the well-established vision-language alignment (the challenge for linear probing). To address them, we propose Decomposed Feature Prompting (DeFo). DeFo leverages a flexible number of learnable embeddings as textual input while maintaining the vision-language dual-model architecture, which enables the model to learn decomposed visual features with the help of feature-level textual prompts. We further use an additional linear layer to perform classification, allowing a scalable size of language inputs. Our empirical study shows DeFo's significance in improving the vision-language models. For example, DeFo obtains 73.2% test accuracy on ImageNet with a ResNet-50 backbone without tuning any pretrained weights of both the vision and language encoder, outperforming zero-shot CLIP by a large margin of 15.0%, and outperforming state-of-the-art vision-language prompt tuning method by 7.6%

A comparative study of Sm networks in Al-10 at.%Sm glass and associated crystalline phases

The Al–Sm system is selected as a model system to study the transition process from liquid and amorphous to crystalline states. In recent work, we have shown that, in addition to long-range translational periodicity, crystal structures display well-defined short-range local atomic packing motifs that transcends liquid, amorphous and crystalline states. In this paper, we investigate the longer range spatial packing of these short-range motifs by studying the interconnections of Sm–Sm networks in different amorphous and crystalline samples obtained from molecular dynamics simulations. In our analysis, we concentrate on Sm–Sm distances in the range ~5.0–7.2 Å, corresponding to Sm atoms in the second and third shells of Sm-centred clusters. We discover a number of empirical rules characterising the evolution of Sm networks from the liquid and amorphous states to associated metastable crystalline phases experimentally observed in the initial stages of devitrification of different amorphous samples. As direct simulation of glass formation is difficult because of the vast difference between experimental quench rates and what is achievable on the computer, we hope these rules will be helpful in building a better picture of structural evolution during glass formation as well as a more detailed description of phase selection and growth during devitrification