Management Letter, Year Ended June 30, 1999

The intestinal microbiome is essential in humans to maintain physiological balance and nutrition metabolism. Laparoscopic cholecystectomy due to gallstone disease and cholecystitis can cause intestinal microbial dysbiosis, and following bile acid metabolism dysfunction, positions the patient at high risk of colorectal cancer. However, little is known regarding intestinal microbiota characteristics in post-cholecystectomy patients. Here, we compared the microbial composition of cholecystectomy patients with that of a healthy population. We determined that cholecystectomy eliminated aging-associated fecal commensal microbiota and further identified several bile acid metabolism-related bacteria as contributors of colorectal cancer incidence via elevation of secondary bile acids.Significance statementWe identified aging-associated fecal microbiota in a healthy population, which was lost in cholecystectomy patients. Absent intestinal bacteria, such as Bacteroides, were negatively related to secondary bile acids and may be a leading cause of colorectal cancer incidence in cholecystectomy patients. Our study provides novel insight into the connection between cholecystectomy-altered gut microbiota and colorectal carcinoma, which is of value for colorectal cancer diagnosis and management

TAO Conceptual Design Report: A Precision Measurement of the Reactor Antineutrino Spectrum with Sub-percent Energy Resolution

The Taishan Antineutrino Observatory (TAO, also known as JUNO-TAO) is a satellite experiment of the Jiangmen Underground Neutrino Observatory (JUNO). A ton-level liquid scintillator detector will be placed at about 30 m from a core of the Taishan Nuclear Power Plant. The reactor antineutrino spectrum will be measured with sub-percent energy resolution, to provide a reference spectrum for future reactor neutrino experiments, and to provide a benchmark measurement to test nuclear databases. A spherical acrylic vessel containing 2.8 ton gadolinium-doped liquid scintillator will be viewed by 10 m^2 Silicon Photomultipliers (SiPMs) of >50% photon detection efficiency with almost full coverage. The photoelectron yield is about 4500 per MeV, an order higher than any existing large-scale liquid scintillator detectors. The detector operates at -50 degree C to lower the dark noise of SiPMs to an acceptable level. The detector will measure about 2000 reactor antineutrinos per day, and is designed to be well shielded from cosmogenic backgrounds and ambient radioactivities to have about 10% background-to-signal ratio. The experiment is expected to start operation in 2022

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

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

Energy-Storage Optimization Strategy for Reducing Wind Power Fluctuation via Markov Prediction and PSO Method

Wind power penetration ratios of power grids have increased in recent years; thus, deteriorating power grid stability caused by wind power fluctuation has caused widespread concern. At present, configuring an energy storage system with corresponding capacity at the grid connection point of a large-scale wind farm is an effective solution that improves wind power dispatchability, suppresses potential fluctuations, and reduces power grid operation risks. Based on the traditional energy-storage battery dispatching scheme, in this study, a multi-objective hybrid optimization model for joint wind-farm and energy-storage operation is designed. The impact of two new aspects, the energy-storage battery output and wind-power future output, on the current energy storage operation are considered. Wind-power future output assessment is performed using a wind-power-based Markov prediction model. The particle swarm optimization algorithm is used to optimize the wind-storage grid-connected power in real time, to develop an optimal operation strategy for an energy storage battery. Simulations incorporating typical daily wind power data from a several-hundred-megawatt wind farm and rolling optimization of the energy storage output reveal that the proposed method can reduce the grid-connected wind power fluctuation, the probability of overcharge and over-discharge of the stored energy, and the energy storage dead time. For the same smoothing performance, the proposed method can reduce the energy storage capacity and improve the economic efficiency of the wind-storage joint operation

Geostationary Precipitation Estimates by PDF Matching Technique over the Asia-Pacific and Its Improvement by Incorporating with Surface Data

An Infrared (IR)-passive microwave (PMW) blended technique is developed to derive precipitation estimates over the Asia-Pacific domain through calibrating the temperature of brightness blackbody from the Japanese Himawari-8 satellite to precipitation derived from the combined PMW retrievals (currently MWCOMB2x) based on the probability density function (PDF)-matching concept. Called IRQPE, the technique is modified and fine-tuned to better represent the spatially rapidly changing cloud–precipitation relationship over the target region with PDF-matching tables established over a refined spatial resolution of 0.5° lat/lon grid. The evaluation of the IRQPE shows broadly comparable performance to that of the CMORPH2 in detecting rainfall systems of large and medium-scales at a resolution of 1.0° degree. Rainfall variations from the two datasets over El Niño-Southern Oscillation and the Madden Julian Oscillation active convective centers show well consistency of each other, suggesting usefulness of the IRQPE in climate applications. Two approaches for regional improvements are explored by establishing the PDF tables for a further refined spatial resolution and by replacing the PMW-based precipitation ‘truth’ fields with the surface gauge data to overcome the shortcoming of PMW-based retrievals in capturing orographic rainfall over the Taiwan area. The results show significant improvements. The rainfall patterns of revised the IRQPE at a resolution of 0.1° degree on above the 5-day timescale correlate well with the Taiwan official surface ground truth called the QPESUMS, which is a gridded set of gauge-corrected Radar quantitative precipitation estimations. The root mean square error of the revised IRQPE on estimating the Taiwan overall land rainfall is close to Radar-derived rainfall accumulations on a 30-day time-scale

SAO analysis as a tool for identifying partner in open model: An Application in the DSSCs sector: SAO analysis as a tool for open innovation

International audienceThis paper aims to show how the information contained in research documents can be used to identify a technological partner for open innovation process. More generally, the aim is to highlight the role that the SAO analyses can play in identifying R&D partners in open perspective. In the existing literature, a SAO analysis is often considered as a tool for forecasting technological trends. In this research, we overturn this approach considering SAO analysis as a tool for recognizing (a) partner(s) for open innovation management. In doing so, the method, based on SAO analysis, is proposed. Concretely, the originality of this approach consists in first, to build a SAO structure map that illustrates the key technologies and research challenges and second, the key organizations (firms or research institutes) with similar technologies as well the relations between them in order to encourage the open model. The exploratory study is on DSSCs sector