SIVALHIPPUS PTYCHODUS AND SIVALHIPPUS PLATYODUS (PERISSODACTYLA, MAMMALIA) FROM THE LATE MIOCENE OF CHINA

  Herein, the authors report on skulls, mandibles and postcranial specimens of two species of Chinese Sivalhippus, S. ptychodus and S. platyodus. We frame our description and analyses within the context of newly described characters of the cheek teeth of Hippotherium from the Pannonian C of the Vienna Basin, the oldest and most primitive Old World hipparions. Our report includes original skull, mandibular and limited postcranial material of Sivalhippus ptychodus and skulls and dentitions of Sivalhippus platyodus from the Paleontological Museum of Uppsala (PMU, Uppsala, Sweden), the American Museum of Natural History (AMNH, New York, USA) and the Licent Collection in Tianjin Natural History Museum (Tianjin, China). The skull, maxillary and mandibular material we attribute to Sivalhippus ptychodus and Sivalhippus platyodus exhibit some primitive features for Old World hipparions and synapamorphies of the face and dentition that unite it with the Sivalhippus clade. Our analysis shows that S. ptychodus and S. platyodus differ significantly from the Cormohipparion occidentale – Hippotherium primigenium clade. Species belonging to the Sivalhippus clade are found in IndoPakistan (S. nagriensis, S. theobaldi, S. perimensis and S. anwari), Libya and Kenya (S. turkanensis) and Uganda (S. macrodon). We hypothesize that the Sivalhippus clade originated in South Asia where it is earliest represented by Sivalhipus nagriensis, ca. 10.4 Ma and underwent range extension into Africa and China circa 9-7 Ma

Lack of VMP1 impairs hepatic lipoprotein secretion and promotes non-alcoholic steatohepatitis

BACKGROUND & AIMS: Vacuole membrane protein 1 (VMP1) is an endoplasmic reticulum (ER) transmembrane protein that regulates the formation of autophagosomes and lipid droplets. Recent evidence suggests that VMP1 plays a critical role in lipoprotein secretion in zebra fish and cultured cells. However, the pathophysiological roles and mechanisms by which VMP1 regulates lipoprotein secretion and lipid accumulation in non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH) are unknown. METHODS: Liver-specific and hepatocyte-specific Vmp1 knockout mice as well as Vmp1 knock-in mice were generated by crossing Vmp1 RESULTS: Hepatocyte-specific deletion of Vmp1 severely impaired VLDL secretion resulting in massive hepatic steatosis, hepatocyte death, inflammation and fibrosis, which are hallmarks of NASH. Mechanistically, loss of Vmp1 led to decreased hepatic levels of phosphatidylcholine and phosphatidylethanolamine as well as to changes in phospholipid composition. Deletion of Vmp1 in mouse liver also led to the accumulation of neutral lipids in the ER bilayer and impaired mitochondrial beta-oxidation. Overexpression of VMP1 ameliorated steatosis in diet-induced NASH by improving VLDL secretion. Importantly, we also showed that decreased liver VMP1 is associated with NAFLD/NASH in humans. CONCLUSIONS: Our results provide novel insights on the role of VMP1 in regulating hepatic phospholipid synthesis and lipoprotein secretion in the pathogenesis of NAFLD/NASH. LAY SUMMARY: Non-alcoholic fatty liver disease and its more severe form, non-alcoholic steatohepatitis, are associated with a build-up of fat in the liver (steatosis). However, the exact mechanisms that underly steatosis in patients are not completely understood. Herein, the authors identified that the lack of a protein called VMP1 impairs the secretion and metabolism of fats in the liver and could therefore contribute to the development and progression of non-alcoholic fatty liver disease

A New Time–Frequency Attention Tensor Network for Language Identification

In this paper, we aim to improve traditional DNN x-vector language identification (LID) performance by employing Wide Residual Networks (WRN) as a powerful feature extractor which we combine with a novel frequency attention network (F-ATN). Compared with conventional time attention, our method learns discriminative weights for different frequency bands to generate weighted means and standard deviations for utterance-level classification. This mechanism enables the architecture to direct attention to important frequency bands rather than important time frames, as in traditional time attention (T-ATN) methods. Furthermore, we then introduce a cross-layer frequency attention tensor network (CLF-ATN) which exploits information from different layers to recapture frame-level language characteristics that have been dropped by aggressive frequency pooling in lower layers. This effectively restores fine-grained discriminative language details. Finally, we explore the joint fusion of frame-level and frequency-band attention in a time-frequency attention network (TF-ATN). Experimental results show firstly that WRN can significantly outperform a traditional DNN x-vector implementation. Secondly, the proposed frequency attention method is more effective than time attention and thirdly that frequency-time score fusion can yield further improvement. Finally, extensive experiments on CLF-ATN demonstrate that it is able to improve discrimination by regaining dropped fine-grained frequency information, particularly for low dimension frequency features

Dynamic analysis of a bacterial resistance model with impulsive state feedback control

Bacterial resistance caused by prolonged administration of the same antibiotics exacerbates the threat of bacterial infection to human health. It is essential to optimize antibiotic treatment measures. In this paper, we formulate a simplified model of conversion between sensitive and resistant bacteria. Subsequently, impulsive state feedback control is introduced to reduce bacterial resistance to a low level. The global asymptotic stability of the positive equilibrium and the orbital stability of the order-1 periodic solution are proved by the Poincaré-Bendixson Theorem and the theory of the semi-continuous dynamical system, respectively. Finally, numerical simulations are performed to validate the accuracy of the theoretical findings

Burden of disease resulting from chronic mountain sickness among young Chinese male immigrants in Tibet

BACKGROUND: In young Chinese men of the highland immigrant population, chronic mountain sickness (CMS) is a major public health problem. The aim of this study was to measure the disease burden of CMS in this population. METHODS: We used disability-adjusted life years (DALYs) to estimate the disease burden of CMS. Disability weights were derived using the person trade-off methodology. CMS diagnoses, symptom severity, and individual characteristics were obtained from surveys collected in Tibet in 2009 and 2010. The DALYs of individual patients and the DALYs/1,000 were calculated. RESULTS: Disability weights were obtained for 21 CMS health stages. The results of the analyses of the two surveys were consistent with each other. At different altitudes, the CMS rates ranged from 2.1-37.4%; the individual DALYs of patients ranged from 0.13-0.33, and the DALYs/1,000 ranged from 3.60-52.78. The age, highland service years, blood pressure, heart rate, smoking rate, and proportion of the sample working in engineering or construction were significantly higher in the CMS group than in the non-CMS group (p < 0.05). These variables were also positively associated with the individual DALYs (p < 0.05). Among the symptoms, headaches caused the largest proportion of DALYs. CONCLUSION: The results show that CMS imposes a considerable burden on Chinese immigrants to Tibet. Immigrants with characteristics such as a higher residential altitude, more advanced age, longer highland service years, being a smoker, and working in engineering or construction were more likely to develop CMS and to increase the disease burden. Higher blood pressure and heart rate as a result of CMS were also positively associated with the disease burden. The authorities should pay attention to the highland disease burden and support the development and application of DALYs studies of CMS and other highland diseases

Improved Conditional Generative Adversarial Net Classification For Spoken Language Recognition

Recent research on generative adversarial nets (GAN) for language identification (LID) has shown promising results. In this paper, we further exploit the latent abilities of GAN networks to firstly combine them with deep neural network (DNN)-based i-vector approaches and then to improve the LID model using conditional generative adversarial net (cGAN) classification. First, phoneme dependent deep bottleneck features (DBF) combined with output posteriors of a pre-trained DNN for automatic speech recognition (ASR) are used to extract i-vectors in the normal way. These i-vectors are then classified using cGAN, and we show an effective method within the cGAN to optimize parameters by combining both language identification and verification signals as supervision. Results show firstly that cGAN methods can significantly outperform DBF DNN i-vector methods where 49-dimensional i-vectors are used, but not where 600-dimensional vectors are used. Secondly, training a cGAN discriminator network for direct classification has further benefit for low dimensional i-vectors as well as short utterances with high dimensional i-vectors. However, incorporating a dedicated discriminator network output layer for classification and optimizing both classification and verification loss brings benefits in all test cases