Hybrid intelligent deep kernel incremental extreme learning machine based on differential evolution and multiple population grey wolf optimization methods

Focussing on the problem that redundant nodes in the kernel incremental extreme learning machine (KI-ELM) which leads to ineffective iteration increase and reduce the learning efficiency, a novel improved hybrid intelligent deep kernel incremental extreme learning machine (HI-DKIELM) based on a hybrid intelligent algorithms and kernel incremental extreme learning machine is proposed. At first, hybrid intelligent algorithms are proposed based on differential evolution (DE) and multiple population grey wolf optimization (MPGWO) methods which used to optimize the hidden layer neuron parameters and then to determine the effective hidden layer neurons number. The learning efficiency of the algorithm is improved by reducing the network complexity. Then, we bring in the deep network structure to the kernel incremental extreme learning machine to extract the original input data layer by layer gradually. The experiment results show that the HI-DKIELM methods proposed in this paper with more compact network structure have higher prediction accuracy and better ability of generation compared with other ELM methods

Molecular subtypes predict the preferential site of distant metastasis in advanced breast cancer: a nationwide retrospective study

ObjectiveThis study aimed to explore possible associations between molecular subtypes and site of distant metastasis in advanced breast cancer (ABC).Methods3577 ABC patients were selected from 21 hospitals of seven geographic regions in China from 2012-2014. A questionnaire was designed to collect medical information regarding demographic characteristics, risk factors, molecular subtype, recurrence/metastasis information, and disease-free survival (DFS). The cancers were classified into Luminal A, Luminal B, HER2-enriched and Triple Negative subtypes. Chi-square test and multivariate Cox proportional hazard models were performed to explore the associations between molecular subtypes and distant metastasis sites.ResultsA total of 2393 cases with molecular subtypes information were finally examined. Patients with Luminal A (51.1%) and Luminal B (44.7%) were most prone to bone metastasis, whereas liver metastasis was more frequently observed in HER2-enriched ABC patients (29.1%).The cumulative recurrence and metastasis rates of ABC patients at 36 months of DFS were the most significant within molecular types, of which Triple Negative was the highest (82.7%), while that of Luminal A was the lowest (58.4%). In the adjusted Cox regression analysis, Luminal B, HER2-enriched and Triple Negative subtypes increased the risk of visceral metastasis by 23%, 46% and 87% respectively. In addition, Triple Negative patients had a higher probability of brain metastasis (HR 3.07, 95% CI: 1.04-9.07).ConclusionMolecular subtypes can predict the preferential sites of distant metastasis, emphasizing that these associations were of great help in choices for surveillance, developing appropriate screening and cancer management strategies for follow-up and personalized therapy in ABC patients

Corrigendum to: The TianQin project: current progress on science and technology

In the originally published version, this manuscript included an error related to indicating the corresponding author within the author list. This has now been corrected online to reflect the fact that author Jun Luo is the corresponding author of the article

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

Comparative effect of ciprofloxacin and moxifloxacin on the modulation of bile acid profiles and gut microbiota in rats

Fluoroquinolones are an important class of antimicrobial agents to manage infectious diseases. However, knowledge about how host bile acids are modified by fluoroquinolones is limited. We investigated and compared the impact of fluoroquinolones on circulating bile acid profiles and gut microbiota from in vivo studies. We administered ciprofloxacin (100 mg/kg/day) or moxifloxacin (40 mg/kg/day) orally to male Wistar rats for seven days. Fifteen bile acids (BAs) from the serum and large intestine were quantified by HPLC-MS/MS. The diversity of gut microbiota after ciprofloxacin and moxifloxacin treatment was analyzed using high-throughput, next-generation sequencing technology. The two fluoroquinolone-treated groups had different BA profiles. Ciprofloxacin significantly reduced the hydrophobicity index of the BA pool, reduced secondary BAs, and increased taurine-conjugated primary BAs in both the serum and large intestine as compared with moxifloxacin. Besides, ciprofloxacin treatment altered intestinal microbiota with a remarkable increase in Firmicutes to Bacteroidetes ratio, while moxifloxacin exerted no effect. What we found suggests that different fluoroquinolones have a distinct effect on the host BAs metabolism and intestinal bacteria, and therefore provide guidance on the selection of fluoroquinolones to treat infectious diseases

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

International audienceCore-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

The core-collapse supernova (CCSN) is considered one of the most energetic astrophysical events in the universe. The early and prompt detection of neutrinos before (pre-SN) and during the supernova (SN) burst presents a unique opportunity for multi-messenger observations of CCSN events. In this study, we describe the monitoring concept and present the sensitivity of the system to pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), a 20 kton liquid scintillator detector currently under construction in South China. The real-time monitoring system is designed to ensure both prompt alert speed and comprehensive coverage of progenitor stars. It incorporates prompt monitors on the electronic board as well as online monitors at the data acquisition stage. Assuming a false alert rate of 1 per year, this monitoring system exhibits sensitivity to pre-SN neutrinos up to a distance of approximately 1.6 (0.9) kiloparsecs and SN neutrinos up to about 370 (360) kiloparsecs for a progenitor mass of 30 solar masses, considering both normal and inverted mass ordering scenarios. The pointing ability of the CCSN is evaluated by analyzing the accumulated event anisotropy of inverse beta decay interactions from pre-SN or SN neutrinos. This, along with the early alert, can play a crucial role in facilitating follow-up multi-messenger observations of the next galactic or nearby extragalactic CCSN

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

International audienceCore-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

International audienceCore-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN