64 research outputs found
Cooperative Spin Amplification
Quantum amplification is recognized as a key resource for precision
measurements. However, most conventional paradigms employ an ensemble of
independent particles that usually limit the performance of quantum
amplification in gain, spectral linewidth, etc. Here we demonstrate a new
signal amplification using cooperative 129Xe nuclear spins embedded within a
feedback circuit, where the noble-gas spin coherence time is enhanced by at
least one order of magnitude. Using such a technique, magnetic field can be
substantially pre-enhanced by more than three orders and is in situ readout
with an embedded 87Rb magnetometer. We realize an ultrahigh magnetic
sensitivity of 4.0 fT/Hz that surpasses the photon-shot noise and even
below the spin-projection noise of the embedded atomic magnetometer, allowing
for exciting applications including searches for dark matter with sensitivity
well beyond supernova constraints. Our findings extend the physics of quantum
amplification to cooperative spin systems and can be generalized to a wide
variety of existing sensors, enabling a new class of cooperative quantum
sensors.Comment: 7 pages, 4 figure
Enhanced quantum sensing with amplification and deamplification
Quantum sensing is a fundamental building block of modern technology that
employs quantum resources and creates new opportunities for precision
measurements. However, previous methods usually have a common assumption that
detection noise levels should be below the intrinsic sensitivity provided by
quantum resources. Here we report the first demonstration of Fano resonance
between coupled alkali-metal and noble gases through rapid spin-exchange
collisions. The Fano resonance gives rise to two intriguing phenomena: spin
amplification and deamplification, which serve as crucial resources for
enhanced sensing. Further we develop a novel scheme of quantum sensing enhanced
by amplification and deamplification, with relaxed requirements on the
detection noise. The coupled systems of alkali-metal and noble gases act as
amplifiers or de-amplifiers, enabling to extract small signals above the
detection noise before final detection. We demonstrate magnetic-field
measurement about 54 decibels below the photon-shot noise, which outperforms
the state-of-the-art squeezed-light technology and realizes femtotesla-level
sensitivity. Our work opens new avenues to applications in searches for
ultralight dark matter with sensitivity well beyond the supernova-observation
constraints.Comment: 7 pages, 4 figure
Automatic recognition of white blood cell images with memory efficient superpixel metric GNN: SMGNN
An automatic recognizing system of white blood cells can assist hematologists in the diagnosis of many diseases, where accuracy and efficiency are paramount for computer-based systems. In this paper, we presented a new image processing system to recognize the five types of white blood cells in peripheral blood with marked improvement in efficiency when juxtaposed against mainstream methods. The prevailing deep learning segmentation solutions often utilize millions of parameters to extract high-level image features and neglect the incorporation of prior domain knowledge, which consequently consumes substantial computational resources and increases the risk of overfitting, especially when limited medical image samples are available for training. To address these challenges, we proposed a novel memory-efficient strategy that exploits graph structures derived from the images. Specifically, we introduced a lightweight superpixel-based graph neural network (GNN) and broke new ground by introducing superpixel metric learning to segment nucleus and cytoplasm. Remarkably, our proposed segmentation model superpixel metric graph neural network (SMGNN) achieved state of the art segmentation performance while utilizing at most 10000X
less than the parameters compared to existing approaches. The subsequent segmentation-based cell type classification processes showed satisfactory results that such automatic recognizing algorithms are accurate and efficient to execeute in hematological laboratories. Our code is publicly available at https://github.com/jyh6681/SPXL-GNN
SAPPHIRE: Search for exotic parity-violation interactions with quantum spin amplifiers
Quantum sensing provides sensitive tabletop tools to search for exotic
spin-dependent interactions beyond the Standard Model, which has attracted
great attention in theories and experiments. Here we develop a technique based
on quantum Spin Amplifier for Particle PHysIcs REsearch (SAPPHIRE) to
resonantly search for exotic interactions, specifically parity-odd spin-spin
interactions. The present technique effectively amplifies the pseudomagnetic
field generated by exotic interactions by a factor of about 200 while being
insensitive to spurious external magnetic fields. Our studies, using such a
quantum amplification technique, open the doors to exploring the
parity-violation interactions mediated by Z' bosons in the challenging
parameter space (force range between 3 mm and 0.1 km) and set the most
stringent constraints on Z'-mediated electron-neutron couplings, significantly
improving previous limits by up to five orders of magnitude. Moreover, our
bounds on Z'-mediated couplings between nucleons reaches into a hitherto
unexplored parameter space (force range below 1 m), complementing the existing
astrophysical and laboratory studies.Comment: 8 pages, 5 figure
Robust estimation of bacterial cell count from optical density
Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data
Glycosaminoglycan from Apostichopus japonicus Improves Glucose Metabolism in the Liver of Insulin Resistant Mice
Holothurian glycosaminoglycan isolated from Apostichopus japonicus (named AHG) can suppress hepatic glucose production in insulin resistant hepatocytes, but its effects on glucose metabolism in vivo are unknown. The present study was conducted to investigate the effects of AHG on hyperglycemia in the liver of insulin resistant mice induced by a high-fat diet (HFD) for 12 weeks. The results demonstrated that AHG supplementation apparently reduced body weight, blood glucose level, and serum insulin content in a dose-dependent manner in HFD-fed mice. The protein levels and gene expression of gluconeogenesis rate-limiting enzymes G6Pase and PEPCK were remarkedly suppressed in the insulin resistant liver. In addition, although the total expression of IRS1, Akt, and AMPK in the insulin resistant liver was not affected by AHG supplementation, the phosphorylation of IRS1, Akt, and AMPK were clearly elevated by AHG treatment. These results suggest that AHG could be a promising natural marine product for the development of an antihyperglycemic agent
Industrial Carbon Emission Efficiency of Cities in the Pearl River Basin: Spatiotemporal Dynamics and Driving Forces
In the context of green and high-quality development, effectively enhancing industrial carbon emission efficiency is critical for reducing carbon emissions and achieving sustainable economic growth. This study explored this research area using three models: the super-efficient SBM model was used to measure the industrial carbon emission efficiency of 48 cities in the Pearl River Basin from 2009 to 2017; the exploratory spatiotemporal data analysis method was used to reveal the spatiotemporal interaction characteristics of industrial carbon emission efficiency; and the geographical detectors and geographically weighted regression model were employed to explore the influencing factors. The results are as follows: (1) The Pearl River Basin’s industrial carbon emission efficiency steadily increased from 2009 to 2017, with an average annual growth rate of 0.18 percent, but the industrial carbon emission efficiency of some sites remains low; (2) The local spatiotemporal pattern of industrial carbon emission efficiency is solitary and spatially dependent; (3) The spatial variation of industrial carbon emission efficiency is influenced by a number of factors, including the industrialization level, openness to the outside world, the science and technology level, energy consumption intensity, and productivity level, with the productivity level, industrialization level, and openness to the outside world being the most important. Among these factors, the productivity level, science and technology level, openness to the outside world, and industrialization level all have a positive correlation with industrial carbon emission efficiency, but energy consumption intensity has a negative correlation. This study provides an integrated framework using exploratory spatiotemporal analysis and geographically weighted regression to examine carbon emission efficiency among cities. It can serve as a technical support for carbon reduction policies in cities within the Pearl River Basin, as well as a reference for industrial carbon emission studies of other regions of the world
Absorption and Transport of Sea Cucumber Saponins from Apostichopus japonicus
The present study is focused on the intestinal absorption of sea cucumber saponins. We determined the pharmacokinetic characteristics and bioavailability of Echinoside A and Holotoxin A1; the findings indicated that the bioavailability of Holotoxin A1 was lower than Echinoside A. We inferred that the differences in chemical structure between compounds was a factor that explained their different characteristics of transport across the intestine. In order to confirm the absorption characteristics of Echinoside A and Holotoxin A1, we examined their transport across Caco-2 cell monolayer and effective permeability by single-pass intestinal perfusion. The results of Caco-2 cell model indicate that Echinoside A is transported by passive diffusion, and not influenced by the exocytosis of P-glycoprotein (P-gp, expressed in the apical side of Caco-2 monolayers as the classic inhibitor). The intestinal perfusion also demonstrated well the absorption of Echinoside A and poor absorption of Holotoxin A1, which matched up with the result of the Caco-2 cell model. The results demonstrated our conjecture and provides fundamental information on the relationship between the chemical structure of these sea cucumber saponins and their absorption characteristics, and we believe that our findings build a foundation for the further metabolism study of sea cucumber saponins and contribute to the further clinical research of saponins
Industrial Carbon Emission Efficiency of Cities in the Pearl River Basin: Spatiotemporal Dynamics and Driving Forces
In the context of green and high-quality development, effectively enhancing industrial carbon emission efficiency is critical for reducing carbon emissions and achieving sustainable economic growth. This study explored this research area using three models: the super-efficient SBM model was used to measure the industrial carbon emission efficiency of 48 cities in the Pearl River Basin from 2009 to 2017; the exploratory spatiotemporal data analysis method was used to reveal the spatiotemporal interaction characteristics of industrial carbon emission efficiency; and the geographical detectors and geographically weighted regression model were employed to explore the influencing factors. The results are as follows: (1) The Pearl River Basin’s industrial carbon emission efficiency steadily increased from 2009 to 2017, with an average annual growth rate of 0.18 percent, but the industrial carbon emission efficiency of some sites remains low; (2) The local spatiotemporal pattern of industrial carbon emission efficiency is solitary and spatially dependent; (3) The spatial variation of industrial carbon emission efficiency is influenced by a number of factors, including the industrialization level, openness to the outside world, the science and technology level, energy consumption intensity, and productivity level, with the productivity level, industrialization level, and openness to the outside world being the most important. Among these factors, the productivity level, science and technology level, openness to the outside world, and industrialization level all have a positive correlation with industrial carbon emission efficiency, but energy consumption intensity has a negative correlation. This study provides an integrated framework using exploratory spatiotemporal analysis and geographically weighted regression to examine carbon emission efficiency among cities. It can serve as a technical support for carbon reduction policies in cities within the Pearl River Basin, as well as a reference for industrial carbon emission studies of other regions of the world
- …