Correction Factors of the Measurement Errors of the LAMOST-LRS Stellar Parameters

We aim to investigate the propriety of stellar parameter errors of the official data release of the LAMOST lowresolution spectroscopy(LRS)survey.We diagnose the errors of radial velocity(RV),atmospheric parameters([Fe/H],Teff,logg)andα-enhancement([α/M])for the latest data release version of DR7,including 6,079,235effective spectra of 4,546,803 stars.Based on the duplicate observational sample and comparing the deviation of multiple measurements to their given errors,we find that,in general,the error of[α/M]is largely underestimated,and the error of RV is slightly overestimated.We define a correction factor k to quantify these misestimations and correct the errors to be expressed as proper internal uncertainties.Using this self-calibration technique,we find that the k-factors significantly vary with the stellar spectral types and the spectral signal-to-noise ratio(S/N).Particularly,we reveal a strange but evident trend between k-factors and error themselves for all five stellar parameters.Larger errors tend to have smaller k-factor values,i.e.,they were more overestimated.After the correction,we recreate and quantify the tight correlations between S/N and errors,for all five parameters,while these correlations have dependence on spectral types.It also suggests that the parameter errors from each spectrum should be corrected individually.Finally,we provide the error correction factors of each derived parameter of each spectrum for the entire LAMOST-LRS DR7 and plan to update them for the later data releases.

Response of arbuscular mycorrhizal fungi and phosphorus solubilizing bacteria to remediation abandoned solid waste of coal mine

Coal is the vital resource of energy in China,but abandoned coal ash and gangue lead to the degradation of vegetation cover and reduce soil quality.Both arbuscular mycorrhizal fungi (AMF) and phosphate solubilizing bacteria (PSB) play a key role in biogeochemical cycle such as soil organic matter decomposition,nutrition release,and energy flow.To improve and reclamation the soil quality and ecological efficiency of the coal mining waste,we investigated the effects of an AMF strain (Glomus mosseae) and a PSB strain (Pantoesstewarti) on phytate mineralization and subsequent transfer to the host plant (Medicago sativa L.) using a two-compartment microcosm with a central 30 mm nylon mesh barrier.The results showed that significantly higher available P (AP),above ground biomass (AGB) and underground biomass (UGB) were in combined inoculation of AMF-PSB than other treatments in root and hyphae compartment.The microbial inoculum of the AMF or PSB had a significant influence on soil acid phosphatase activities (ACP).AMF-PSB enhanced phytate mineralization,improved plant biomass.AP and ACP positively influenced the AGB and UGB.AMF-PSB could be used as bioinoculant to enhance sustainable production of the plant in abandoned solid waste of coal mine.

Single capillary oximetry and tissue ultrastructural sensing by dual-band dual-scan inverse spectroscopic optical coherence tomography

Measuring capillary oxygenation and the surrounding ultrastructure can allow one to monitor a microvascular niche and better understand crucial biological mechanisms.However,capillary oximetry and pericapillary ultrastructure are challenging to measure in vivo.Here we demonstrate a novel optical imaging system,dual-band dual-scan inverse spectroscopic optical coherence tomography(D2-ISOCT),that,for the first time,can simultaneously obtain the following metrics in vivo using endogenous contrast:(1)capillary-level oxygen saturation and arteriolar-level blood flow rates,oxygen delivery rates,and oxygen metabolic rates;(2)spatial characteristics of tissue structures at length scales down to 30 nm;and(3)morphological images up to 2mm in depth.To illustrate the capabilities of D2-ISOCT,we monitored alterations to capillaries and the surrounding pericapillary tissue(tissue between the capillaries)in the healing response of a mouse ear wound model.The obtained microvascular and ultrastructural metrics corroborated well with each other,showing the promise of D2-ISOCT for becoming a powerful new non-invasive imaging tool.

Fe/Fe2O3 nanoparticles anchored on Fe-N-doped carbon nanosheets as bifunctional oxygen electrocatalysts for rechargeable zinc-air batteries

Electrocatalysts with high catalytic activity and stability play a key role in promising renewable energy technologies, such as fuel cells and metal-air batteries. Here, we report the synthesis of Fe/Fe203 nanoparticles anchored on Fe-N-doped carbon nanosheets （Fe/Fe2Og@Fe-N-C） using shrimp shell-derived N-doped carbon nanodots as carbon and nitrogen sources in the presence of FeCI3 by a simple pyrolysis approach. Fe/Fe203@Fe-N-C obtained at a pyrolysis temperature of 1,000 ℃ （Fe/Fe2OB@Fe-N-C-1000） possessed a mesoporous structure and high surface area of 747.3 m2-g-1. As an electrocatalyst, Fe/Fe203@Fe-N-C-1000 exhibited bifunctional electrocatalytic activities toward the oxygen reduction reaction （ORR） and oxygen evolution reaction （OER） in alkaline media, com- parable to that of commercial Pt/C for ORR and RuO2 for OER, respectively. The Zn-air battery test demonstrated that Fe/Fe2OB@Fe-N-C-1000 had a superior rechargeable performance and cycling stability as an air cathode material with an open drcuit voltage of 1.47 V （vs. Ag/AgCl） and a power density of 193 mW.cm-2 at a current density of 220 mA-cm-2. These performances were better than other commercial catalysts with an open circuit voltage of 1.36 V and a power density of 173 mW-cm^-2 at a current density of 220 mA.cm-2 （a mixture of commercial Pt/C and RuO2 with a mass ratio of 1：1 was used for the rechargeable Zn-air battery measurements）. This work will be helpful to design and develop low-cost and abundant bifunctional oxygen electrocatalysts for future metal-air batteries.

Deep spectral learning for label-free optical imaging oximetry with uncertainty quantification

Measurement of blood oxygen saturation(sO_(2))by optical imaging oximetry provides invaluable insight into local tissue functions and metabolism.Despite different embodiments and modalities,all label-free optical-imaging oximetry techniques utilize the same principle of sO_(2)-dependent spectral contrast from haemoglobin.Traditional approaches for quantifying sO_(2) often rely on analytical models that are fitted by the spectral measurements.These approaches in practice suffer from uncertainties due to biological variability,tissue geometry,light scattering,systemic spectral bias,and variations in the experimental conditions.Here,we propose a new data-driven approach,termed deep spectral learning(DSL),to achieve oximetry that is highly robust to experimental variations and,more importantly,able to provide uncertainty quantification for each sO_(2) prediction.To demonstrate the robustness and generalizability of DSL,we analyse data from two visible light optical coherence tomography(vis-OCT)setups across two separate in vivo experiments on rat retinas.Predictions made by DSL are highly adaptive to experimental variabilities as well as the depth-dependent backscattering spectra.Two neural-network-based models are tested and compared with the traditional least-squares fitting(LSF)method.The DSL-predicted sO_(2) shows significantly lower mean-square errors than those of the LSF.For the first time,we have demonstrated en face maps of retinal oximetry along with a pixel-wise confidence assessment.Our DSL overcomes several limitations of traditional approaches and provides a more flexible,robust,and reliable deep learning approach for in vivo non-invasive label-free optical oximetry.