Body mass, Thermogenesis and energy metabolism in Tupaia belangeri during cold acclimation

In order to study the relationship between energy strategies and environmental temperature, basal metabolic rate (BMR), nonshivering thermogenesis (NST), the total protein contents, mitochondrial protein contents, state and state respiratory ability, cytochrome C oxidase activity Ⅲ Ⅳ of liver, heart, diaphragm, gastrocnemius and brown adipose tissue (BAT), serum leptin level and serum thyroid hormone levels were measured in tree shrews (Tupaia belangeri) during cold exposure (5±1oC) for 1 day, 7 days,14days,21 days. The results showed that body mass increased, BMR and NST increased, the change of liver mitochondrial protein content was more acutely than total protein. The mitochondrial protein content of heart and BAT were significantly increased during cold-exposed, however the skeletal muscle more moderate reaction. The state Ⅲ and state Ⅳ mitochondrial respiration of these tissues were enhanced significantly than the control. The cytochrome C oxidase activity with cold acclimation also significantly increased except the gastrocnemius. Liver, muscle, BAT, heart and other organs were concerned with thermoregulation during the thermal regulation process above cold-exposed. There is a negative correlation between leptin level and body mass. These results suggested that T. belangeri enhanced thermogenic capacity during cold acclimation, and leptin participated in the regulation of energy balance and body weight in T. belangeri

Effects of photoperiod on body mass, thermogenesis and body composition in Eothenomys miletus during cold exposure

Many small mammals respond to seasonal changes in photoperiod by altering body mass and adiposity. These animals may provide valuable models for understanding the regulation of energy balance. In present study, we examined the effect on body mass, rest metabolic rate, food intake and body composition in cold-acclimated (5 °C) in Eothenomys miletus by transferring them from a short (SD, 8h :16h L: D) to long day photoperiod (LD, 16h: 8h L:D). During the first 4 weeks of exposure to SD, E. miletus decreased body mass. After the next 4 weeks of exposure to LD, which the average difference between body masses of LD and SD voles was 4.76 g. This 14.74% increase in body mass reflected significant increases in absolute amounts of body components, including wet carcass mass, dry carcass mass and body fat mass. After correcting body composition and organ morphology data for the differences in body mass, only livers, kidney, and small intestine were enlarged due to photoperiod treatment during cold exposure. E. miletus increased RMR and energy intake exposure to LD, but maintained a stable level to SD after 28 days. Serum leptin levels were positively correlated with body mass, body fat mass, RMR as well as energy intake. All of the results indicated that E. miletus may provide an attractive novel animal model for investigation of the regulation of body mass and energy balance at organism levels. Leptin is potentially involved in the photoperiod induced body mass regulation and thermogenesis in E. miletus during cold exposure

Stock Volatility Prediction Based on Transformer Model Using Mixed-Frequency Data

With the increasing volume of high-frequency data in the information age, both challenges and opportunities arise in the prediction of stock volatility. On one hand, the outcome of prediction using tradition method combining stock technical and macroeconomic indicators still leaves room for improvement; on the other hand, macroeconomic indicators and peoples' search record on those search engines affecting their interested topics will intuitively have an impact on the stock volatility. For the convenience of assessment of the influence of these indicators, macroeconomic indicators and stock technical indicators are then grouped into objective factors, while Baidu search indices implying people's interested topics are defined as subjective factors. To align different frequency data, we introduce GARCH-MIDAS model. After mixing all the above data, we then feed them into Transformer model as part of the training data. Our experiments show that this model outperforms the baselines in terms of mean square error. The adaption of both types of data under Transformer model significantly reduces the mean square error from 1.00 to 0.86.Comment: Accepted by the 7th APWeb-WAIM International Joint Conference on Web and Big Data. (APWeb 2023

Implementation of Breast Cancer Risk Assessment Tool using SAS ® Yuqin Li, inVentiv Health Clinical, Indianapolis, IN

ABSTRACT In this paper, a SAS macro is developed to implement the breast cancer risk assessment (BCRA) tool designed by National Cancer Institute (NCI). The BCRA tool itself is based on a complex statistical model known as the Gail model. The Gail model provides an estimate of a woman's risk of developing invasive breast cancer over a specific period of time by utilizing an individual's demographic information and risk factors. Breast cancer risk factors considered in the Gail model include (1) the number of previous breast biopsies, (2) the presence of atypical hyperplasia in any previous breast biopsy specimen, (3) age at the start of menstruation, (4) age at the first live birth of a child, (5) the history of breast cancer among her first-degree relatives (mother, sisters, daughters), and (6) the individual's age and race. The statistical model calculates individualized invasive breast cancer risk in terms of probabilities based on both the relative risk and the baseline hazard rate. We converted the C++ source code available from the NCI website to a SAS© macro. Features of the macro include ease of implementation and integration through SAS© as well as flexibility in calculating the probabilistic breast cancer risk at any duration of time

Majorization–Minimization Based Direct Localization Using One-Bit Channel Measurements

Direct localization or direct position determination (DPD) can outperform the more traditional angle and delay estimation based approaches, yet being less used in practice due to the requirement of aggregating raw data or measurements to a single processing point. To reduce the network burden, this paper considers one-bit quantized channel response data, and proposes a majorization–minimization (MM) based one-bit DPD (MO-DPD) algorithm to localize an orthogonal frequency division multiplexing (OFDM) signal source. First, the one-bit DPD is formulated as a maximum likelihood (ML) estimation problem, which is then iteratively solved using the MM approach. The proposed MO-DPD avoids iteratively estimating any nuisance parameters, leading to high computational efficiency. The numerical results show that the MO-DPD outperforms the baseline one-bit ML solver in terms of computational load, while efficiently converging to one-bit Cramér-Rao lower bound (CRLB) over wide range of signal-to-noise ratios (SNRs). Furthermore, we show that no more than three iterations are required to achieve high accuracy.Peer reviewe

Multishelled NiO Hollow Spheres Decorated by Graphene Nanosheets as Anodes for Lithium-Ion Batteries with Improved Reversible Capacity and Cycling Stability

Graphene-based nanocomposites attract many attentions because of holding promise for many applications. In this work, multishelled NiO hollow spheres decorated by graphene nanosheets nanocomposite are successfully fabricated. The multishelled NiO microspheres are uniformly distributed on the surface of graphene, which is helpful for preventing aggregation of as-reduced graphene sheets. Furthermore, the NiO/graphene nanocomposite shows much higher electrochemical performance with a reversible capacity of 261.5 mAh g−1 at a current density of 200 mA g−1 after 100 cycles tripled compared with that of pristine multishelled NiO hollow spheres, implying the potential application in modern science and technology

Plant developmental stage drives the differentiation in ecological role of the maize microbiome

Background: Plants live with diverse microbial communities which profoundly affect multiple facets of host performance, but if and how host development impacts the assembly, functions and microbial interactions of crop microbiomes are poorly understood. Here we examined both bacterial and fungal communities across soils, epiphytic and endophytic niches of leaf and root, and plastic leaf of fake plant (representing environment-originating microbes) at three developmental stages of maize at two contrasting sites, and further explored the potential function of phylloplane microbiomes based on metagenomics. Results: Our results suggested that plant developmental stage had a much stronger influence on the microbial diversity, composition and interkingdom networks in plant compartments than in soils, with the strongest effect in the phylloplane. Phylloplane microbiomes were co-shaped by both plant growth and seasonal environmental factors, with the air (represented by fake plants) as its important source. Further, we found that bacterial communities in plant compartments were more strongly driven by deterministic processes at the early stage but a similar pattern was for fungal communities at the late stage. Moreover, bacterial taxa played a more important role in microbial interkingdom network and crop yield prediction at the early stage, while fungal taxa did so at the late stage. Metagenomic analyses further indicated that phylloplane microbiomes possessed higher functional diversity at the early stage than the late stage, with functional genes related to nutrient provision enriched at the early stage and N assimilation and C degradation enriched at the late stage. Coincidently, more abundant beneficial bacterial taxa like Actinobacteria, Burkholderiaceae and Rhizobiaceae in plant microbiomes were observed at the early stage, but more saprophytic fungi at the late stage. Conclusions: Our results suggest that host developmental stage profoundly influences plant microbiome assembly and functions, and the bacterial and fungal microbiomes take a differentiated ecological role at different stages of plant development. This study provides empirical evidence for host exerting strong effect on plant microbiomes by deterministic selection during plant growth and development. These findings have implications for the development of future tools to manipulate microbiome for sustainable increase in primary productivity