Extremely cold and hot temperatures increase the risk of ischaemic heart disease mortality: epidemiological evidence from China.

OBJECTIVE: To examine the effects of extremely cold and hot temperatures on ischaemic heart disease (IHD) mortality in five cities (Beijing, Tianjin, Shanghai, Wuhan and Guangzhou) in China; and to examine the time relationships between cold and hot temperatures and IHD mortality for each city. DESIGN: A negative binomial regression model combined with a distributed lag non-linear model was used to examine city-specific temperature effects on IHD mortality up to 20 lag days. A meta-analysis was used to pool the cold effects and hot effects across the five cities. PATIENTS: 16 559 IHD deaths were monitored by a sentinel surveillance system in five cities during 2004-2008. RESULTS: The relationships between temperature and IHD mortality were non-linear in all five cities. The minimum-mortality temperatures in northern cities were lower than in southern cities. In Beijing, Tianjin and Guangzhou, the effects of extremely cold temperatures were delayed, while Shanghai and Wuhan had immediate cold effects. The effects of extremely hot temperatures appeared immediately in all the cities except Wuhan. Meta-analysis showed that IHD mortality increased 48% at the 1st percentile of temperature (extremely cold temperature) compared with the 10th percentile, while IHD mortality increased 18% at the 99th percentile of temperature (extremely hot temperature) compared with the 90th percentile. CONCLUSIONS: Results indicate that both extremely cold and hot temperatures increase IHD mortality in China. Each city has its characteristics of heat effects on IHD mortality. The policy for response to climate change should consider local climate-IHD mortality relationships

Wasserstein Generative Regression

In this paper, we propose a new and unified approach for nonparametric regression and conditional distribution learning. Our approach simultaneously estimates a regression function and a conditional generator using a generative learning framework, where a conditional generator is a function that can generate samples from a conditional distribution. The main idea is to estimate a conditional generator that satisfies the constraint that it produces a good regression function estimator. We use deep neural networks to model the conditional generator. Our approach can handle problems with multivariate outcomes and covariates, and can be used to construct prediction intervals. We provide theoretical guarantees by deriving non-asymptotic error bounds and the distributional consistency of our approach under suitable assumptions. We also perform numerical experiments with simulated and real data to demonstrate the effectiveness and superiority of our approach over some existing approaches in various scenarios.Comment: 50 pages, including appendix. 5 figures and 6 tables in the main text. 1 figure and 7 tables in the appendi

Robust Decoding of Rich Dynamical Visual Scenes With Retinal Spikes

Sensory information transmitted to the brain activates neurons to create a series of coping behaviors. Understanding the mechanisms of neural computation and reverse engineering the brain to build intelligent machines requires establishing a robust relationship between stimuli and neural responses. Neural decoding aims to reconstruct the original stimuli that trigger neural responses. With the recent upsurge of artificial intelligence, neural decoding provides an insightful perspective for designing novel algorithms of brain-machine interface. For humans, vision is the dominant contributor to the interaction between the external environment and the brain. In this study, utilizing the retinal neural spike data collected over multi trials with visual stimuli of two movies with different levels of scene complexity, we used a neural network decoder to quantify the decoded visual stimuli with six different metrics for image quality assessment establishing comprehensive inspection of decoding. With the detailed and systematical study of the effect and single and multiple trials of data, different noise in spikes, and blurred images, our results provide an in-depth investigation of decoding dynamical visual scenes using retinal spikes. These results provide insights into the neural coding of visual scenes and services as a guideline for designing next-generation decoding algorithms of neuroprosthesis and other devices of brain-machine interface.</p

Modeling the fear effect in the predator-prey dynamics with an age structure in the predators

We incorporate the fear effect and the maturation period of predators into a diffusive predator-prey model. Local and global asymptotic stability for constant steady states as well as uniform persistence of the solution are obtained. Under some conditions, we also exclude the existence of spatially nonhomogeneous steady states and the steady state bifurcation bifurcating from the positive constant steady state. Hopf bifurcation analysis is carried out by using the maturation period of predators as a bifurcation parameter, and we show that global Hopf branches are bounded. Finally, we conduct numerical simulations to explore interesting spatial-temporal patterns

Comparative studies of the anti-thrombotic effects of saffron and HongHua based on network pharmacology

Purpose: To investigate the comparative anti-thrombotic effects of saffron and Honghua, and also to explore possible mechanisms in thrombosis based on network pharmacology. Methods: A network pharmacology model was used for bioactive components, targets and pathways for saffron and HongHua via Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), PharmMapper, Genecard, Uniprot and KEGG databases. In animal experiments, 72 rats were randomly divided into 9 groups: normal control group (NC), model control group (MC), crocetin groups (80, 40, 20 mg/kg), hydroxysafflor yellow A(HSYA) groups (80, 40, 20 mg/kg), and aspirin group (40 mg/kg). Using in vitro thrombosis models and an acute blood stasis model in vivo, the anti-thrombotic effects of these treatments on clotting time, hemorheology parameters, Thromboxane B2 (TXB2), plasmin activator inhibitor (PAI), protein C (PC), protein S (PS), and thrombinantithrombin complex (TAT) were determined and comparisons made for saffron and HongHua. Results: Five potential compounds, 16 anti-thrombotic targets and 27 pathways were predicted for saffron, while 22 compounds, 37 disease targets and 35 pathways were found for HongHua (p &lt; 0.05). Pharmacological experiments revealed that crocetin and HSYA had significant effects on thrombus length, thrombus wet/dry mass, whole blood viscosity (WBV), erythrocyte aggregation index (EAI), clotting time and D-dimer for the high and middle groups. Unlike HSYA, crocetin also had significant and dose-dependent effects on PAI, prothrombin fragment 1+2 (F1+2) and PS and had highly significant effects on TXB2 and TAT. Conclusion: This research provides a systematic, comprehensive and comparative analysis of component, target and anti-thrombotic pathways of saffron and HongHua based on network pharmacology, and also shows that saffron has more significant anti-thrombotic effect than HongHua. Keywords: Saffron; HongHua; Network pharmacology; Anti-thrombosis; Network mode

Cloning and Expression of Human Norovirus GI.5 and GII.4 P Proteins and Their Binding Characteristics with Histo-Blood Group Antigens-like Substances in Pacific Oysters

To clarify the binding characteristics of human norovirus (HuNoV) with histo-blood group antigens (HBGAs)-like substances from Pacific oysters, this study used an Escherichia coli expression system to clone and express HuNoV GI.5 and GII.4 P proteins, and analyzed the binding characteristics of HuNoV P proteins with salivary HBGAs and HBGAs-like substances from Pacific oysters using enzyme-linked immunosorbent assay. The results showed that HuNoV GII.4 exhibited good binding characteristics with blood type A, B, AB, and O salivary HBGAs, while GI.5 HuNoV exhibited weak binding characteristics with blood type B salivary HBGAs but had significant advantage in binding with type O salivary HBGAs. HuNoV GI.5 and GII.4 could be bioaccumulated in the gills, digestive gland, and mantle of Pacific oysters, with the highest bioaccumulation in the digestive gland. Both types of HuNoV were mainly bound to type A and H1 HBGAs-like substances; HuNoV GII.4 had different degrees of binding with type Lea, Leb, Lex, and Ley HBGAs-like substances, while HuNoV GI.5 had weak binding with type Leb HBGAs-like substances but significant advantage in binding with type H1 HBGAs-like substances. In summary, different types of HuNoV have different binding characteristics with HBGAs or HBGAs-like substances. Specifically, HuNoV GII.4 shows broad-spectrum binding characteristics whereas HuNoV GI.5 shows selective binding characteristics