Coherent feedback enhanced quantum-dense metrology in a lossy environment.

Quantum dense metrology (QDM) performs high-precision measurements by a two-mode entangled state created by an optical parametric amplifier (PA), where one mode is a meter beam and the other is a reference beam. In practical applications, the photon losses of meter beam are unavoidable, resulting in a degradation of the sensitivity. Here, we employ coherent feedback that feeds the reference beam back into the PA by a beam splitter to enhance the sensitivity in a lossy environment. The results show that the sensitivity is enhanced significantly by adjusting the splitting ratio of the beam splitter. This method may find its potential applications in QDM. Furthermore, such a strategy that two non-commuting observables are simultaneous measurements could provide a new way to individually control the noise-induced random drift in phase or amplitude of the light field, which would be significant for stabilizing the system and long-term precision measurement.

Metabolic signatures and potential biomarkers of sarcopenia in suburb-dwelling older Chinese: based on untargeted GC-MS and LC-MS.

BACKGROUND: Untargeted metabolomics can be used to expand our understanding of the pathogenesis of sarcopenia. However, the metabolic signatures of sarcopenia patients have not been thoroughly investigated. Herein, we explored metabolites associated with sarcopenia by untargeted gas chromatography (GC)/liquid chromatography (LC)-mass spectrometry (MS) and identified possible diagnostic markers. METHODS: Forty-eight elderly subjects with sarcopenia were age and sex matched with 48 elderly subjects without sarcopenia. We first used untargeted GC/LC-MS to analyze the plasma of these participants and then combined it with a large number of multivariate statistical analyses to analyze the data. Finally, based on a multidimensional analysis of the metabolites, the most critical metabolites were considered to be biomarkers of sarcopenia. RESULTS: According to variable importance in the project (VIP > 1) and the p-value of t-test (p < 0.05), a total of 55 metabolites by GC-MS and 85 metabolites by LC-MS were identified between sarcopenia subjects and normal controls, and these were mostly lipids and lipid-like molecules. Among the top 20 metabolites, seven phosphatidylcholines, seven lysophosphatidylcholines (LysoPCs), phosphatidylinositol, sphingomyelin, palmitamide, L-2-amino-3-oxobutanoic acid, and palmitic acid were downregulated in the sarcopenia group; only ethylamine was upregulated. Among that, three metabolites of LysoPC(17:0), L-2-amino-3-oxobutanoic acid, and palmitic acid showed very good prediction capacity with AUCs of 0.887 (95% CI = 0.817-0.957), 0.836 (95% CI = 0.751-0.921), and 0.805 (95% CI = 0.717-0.893), respectively. CONCLUSIONS: These findings show that metabonomic analysis has great potential to be applied to sarcopenia. The identified metabolites could be potential biomarkers and could be used to study sarcopenia pathomechanisms.

Neuroprotective effects of cordycepin inhibit glutamate-induced apoptosis in hippocampal neurons.

Glutamate is a neurotransmitter that can cause excitatory neurotoxicity when its extracellular concentration is too high, leading to disrupted calcium balance and increased production of reactive oxygen species (ROS). Cordycepin, a nucleoside adenosine derivative, has been shown to protect against excitatory neurotoxicity induced by glutamate. To investigate its potential neuroprotective effects, the present study employed fluorescence detection and spectrophotometry techniques to analyze primary hippocampal-cultured neurons. The results showed that glutamate toxicity reduced hippocampal neuron viability, increased ROS production, and increased intracellular calcium levels. Additionally, glutamate-induced cytotoxicity activated acetylcholinesterase and decreased glutathione levels. However, cordycepin inhibited glutamate-induced cell death, improved cell viability, reduced ROS production, and lowered Ca2+ levels. It also inhibited acetylcholinesterase activation and increased glutathione levels. This study suggests that cordycepin can protect against glutamate-induced neuronal injury in cell models, and this effect was inhibited by adenosine A1 receptor blockers, indicating that its neuroprotective effect is achieved through activation of the adenosine A1 receptor.

Expression and electrophysiological characteristics of VGSC during mouse myoblasts differentiation.

Voltage-gated sodium channels (VGSC) are essential for triggering and relaying action potentials (AP), which perform critical functions in a variety of physiological processes, such as controlling muscle contractions and facilitating the release of neurotransmitters. In this study, we used a mouse C2C12 cell differentiation model to study the molecular expression and channel dynamics of VGSC and to investigate the exact role of VGSC in the development of muscle regeneration. Immunofluorescence, Real-time quantitative polymerase chain reaction, Western blot, and whole-cell patch clamp were employed for this purpose in mouse myoblasts. The findings revealed an increase in intracellular sodium concentration, NaV1.4 gene expression, and protein expression with the progress of differentiation (days 0, 1, 3, 5 and 7). Furthermore, VGSC dynamics exhibit the following characteristics: ① The increase of sodium current (INa); ② The decrease in the activation threshold and the voltage trigger maximum of INa; ③ A positive shift in the steady-state inactivation curve; ④ The recovery of INa during repolarization is delayed, the activity-dependent decay rate of INa was accelerated, and the proportionate amount of the fraction of activated channels was reduced. Based on these results, it is postulated that the activation threshold of AP could be decreased, and the refractory period could be extended with the extension of differentiation duration, which may contribute to muscle contraction. Taken together, VGSC provides a theoretical and empirical basis for exploring potential targets for neuromuscular diseases and other therapeutic muscle regeneration dysfunctions.

NMR 1H, 13C, 15N backbone resonance assignments of wild-type human K-Ras and its oncogenic mutants G12D and G12C bound to GTP.

Human K-Ras protein, which is a member of the GTPase Ras family, hydrolyzes GTP to GDP and concomitantly converts from its active to its inactive state. It is a key oncoprotein, because several mutations, particularly those at residue position 12, occur with a high frequency in a wide range of human cancers. The K-Ras protein is therefore an important target for developing therapeutic anti-cancer agents. In this work we report the almost complete sequence-specific resonance assignments of wild-type and the oncogenic G12C and G12D mutants in the GTP-complexed active forms, including the functionally important Switch I and Switch II regions. These assignments serve as the basis for a comprehensive functional dynamics study of wild-type K-Ras and its G12 mutants.

Interferometry-Integrated Noise-Immune Quantum Memory.

A quantum memory with the performances of low noise, high efficiency, and high bandwidth is of crucial importance for developing practical quantum information technologies. However, the excess noises generated during the highly efficient processing of quantum information inevitably destroy quantum state. Here, we present a quantum memory with built-in excess-noise eraser by integrating a photon-correlated quantum interferometry in quantum memory, where the memory efficiency can be enhanced and the excess noises can be suppressed to the vacuum level via destructive interference. This quantum memory is demonstrated in a rubidium vapor cell with a 10-ns-long photonics signal. We observe ∼80% noise suppression, the write-in efficiency enhancement from 87% to 96.2% without and with interferometry, and the corresponding memory efficiency excluding the noises from 70% to 77%. The fidelity is 93.7% at the single-photon level, significantly exceeding the no-cloning limit. Such interferometry-integrated quantum memory, the first expansion of quantum interference techniques to quantum information processing, simultaneously enables low noise, high bandwidth, high efficiency, and easy operation.

Excited-state observation of active K-Ras reveals differential structural dynamics of wild-type versus oncogenic G12D and G12C mutants.

Despite the prominent role of the K-Ras protein in many different types of human cancer, major gaps in atomic-level information severely limit our understanding of its functions in health and disease. Here, we report the quantitative backbone structural dynamics of K-Ras by solution nuclear magnetic resonance spectroscopy of the active state of wild-type K-Ras bound to guanosine triphosphate (GTP) nucleotide and two of its oncogenic P-loop mutants, G12D and G12C, using a new nanoparticle-assisted spin relaxation method, relaxation dispersion and chemical exchange saturation transfer experiments covering the entire range of timescales from picoseconds to milliseconds. Our combined experiments allow detection and analysis of the functionally critical Switch I and Switch II regions, which have previously remained largely unobservable by X-ray crystallography and nuclear magnetic resonance spectroscopy. Our data reveal cooperative transitions of K-Ras·GTP to a highly dynamic excited state that closely resembles the partially disordered K-Ras·GDP state. These results advance our understanding of differential GTPase activities and signaling properties of the wild type versus mutants and may thus guide new strategies for the development of therapeutics.

The combined analysis of urine and blood metabolomics profiles provides an accurate prediction of the training and competitive status of Chinese professional swimmers.

Objective: The purpose of this study was to employ metabolomics for the analysis of urine metabolites in swimmers, with the aim of establishing models for assessing their athletic status and competitive potential. Furthermore, the study sought to compare the identification efficacy of multi-component (urine and blood) model versus single-component (urine or blood) models, in order to determine the optimal approach for evaluating training and competitive status. Methods: A total of 187 Chinese professional swimmers, comprising 103 elite and 84 sub-elite level athletes, were selected as subjects for this study. Urine samples were obtained from each participant and subjected to nuclear magnetic resonance (NMR) metabolomics analysis. Significant urine metabolites were screened through multivariable logistic regression analysis, and an identification model was established. Based on the previously established model of blood metabolites, this study compared the discriminative and predictive performance of three models: either urine or blood metabolites model and urine + blood metabolites model. Results: Among 39 urine metabolites, 10 were found to be significantly associated with the athletic status of swimmers (p < 0.05). Of these, levels of 2-KC, cis-aconitate, formate, and LAC were higher in elite swimmers compared to sub-elite athletes, while levels of 3-HIV, creatinine, 3-HIB, hippurate, pseudouridine, and trigonelline were lower in elite swimmers. Notably, 2-KC and 3-HIB exhibited the most substantial differences. An identification model was developed to estimate physical performance and athletic level of swimmers while adjusting for different covariates and including 2-KC and 3-HIB. The urine metabolites model showed an area under the curve (AUC) of 0.852 (95% CI: 0.793-0.912) for discrimination. Among the three identification models tested, the combination of urine and blood metabolites showed the highest performance than either urine or blood metabolites, with an AUC of 0.925 (95% CI: 0.888-0.963). Conclusion: The two urine metabolites, 2-KC and 3-HIV, can serve as significant urine metabolic markers to establish a discrimination model for identifying the athletic status and competitive potential of Chinese elite swimmers. Combining two screened urine metabolites with four metabolites reported exhibiting significant differences in blood resulted in improved predictive performance compared to using urine metabolites alone. These findings indicate that combining blood and urine metabolites has a greater potential for identifying and predicting the athletic status and competitive potential of Chinese professional swimmers.

Cordycepin inhibits myogenesis via activating the ERK1/2 MAPK signalling pathway in C2C12 cells.

Cordycepin (with a molecular formula of C10H13N5O3), a natural adenosine isolated from Cordyceps militaris, has an important regulatory effect on skeletal muscle remodelling and quality maintenance. The aim of this study was to investigate the effect of cordycepin on myoblast differentiation and explore the underlying molecular mechanisms of this effect. Our results showed that cordycepin inhibited myogenesis by downregulating myogenic differentiation (MyoD) and myogenin (MyoG), preserved undifferentiated reserve cell pools by upregulating myogenic factor 5 (Myf5) and retinoblastoma-like protein p130 (p130), and enhanced energy reserves by decreasing intracellular reactive oxygen species (ROS) and enhancing mitochondrial membrane potential, mitochondrial mass, and ATP content. The effect of cordycepin on myogenesis was associated with increased phosphorylation of extracellular signal-regulated kinase 1/2 (p-ERK1/2). PD98059 (a specific inhibitor of p-ERK1/2) attenuated the inhibitory effect of cordycepin on C2C12 differentiation. The present study reveals that cordycepin inhibits myogenesis through ERK1/2 MAPK signalling activation accompanied by an increase in skeletal muscle energy reserves and improving skeletal muscle oxidative stress, which may have implications for its further application for the prevention and treatment of degenerative muscle diseases caused by the depletion of depleted muscle stem cells.

Cordycepin protects islet ß-cells against glucotoxicity and lipotoxicity via modulating related proteins of ROS/JNK signaling pathway.

Type 2 diabetes mellitus (T2DM) is a common and multiple endocrine metabolic disease. When pancreatic ß cell in case of dysfunction, the synthesis and secretion of insulin are reduced. This study is to explore the effect of cordycepin (the molecular formula C10H13N5O3), a natural adenosine isolated from Cordyceps militaris, on high glucose/lipid-induced glucotoxicity and lipotoxicity in INS-1 cells. Our results showed that cordycepin improved cell viability, improved cell energy metabolism and promoted insulin synthesis and secretion. The mechanism may be related to that cordycepin reduces intracellular reactive oxygen species (ROS), increases ATP content in cells, causes membrane depolarization and balances the steady state of Ca2+ concentration, cordycepin inhibits cell apoptosis, which may be related to the downregulation of proteins level of c-Jun N-terminal kinases (JNK) phosphorylation, cytochrome c (Cyt-c), Cleaved Capase-3, the mRNA level of JNK, Cyt-c, Capase-3 and upregulation of proteins/mRNA level of pancreatic and duodenal homeobox factor-1 (PDX-1). These results suggest that cordycepin can inhibit cell apoptosis and protect cell number by downregulating ROS/JNK mitochondrial apoptosis pathway under high glucose/lipid environment, thereby improving the function of pancreatic islet cells, providing a theoretical basis for the related research on the prevention and control of cordycepin on T2DM.

Protection of Noise Squeezing in a Quantum Interferometer with Optimal Resource Allocation.

Interferometers are crucial for precision measurements, including gravitational waves, laser ranging, radar, and imaging. The phase sensitivity, the core parameter, can be quantum-enhanced to break the standard quantum limit (SQL) using quantum states. However, quantum states are highly fragile and quickly degrade with losses. We design and demonstrate a quantum interferometer utilizing a beam splitter with a variable splitting ratio to protect the quantum resource against environmental impacts. The optimal phase sensitivity can reach the quantum Cramér-Rao bound of the system. This quantum interferometer can greatly reduce the quantum source requirements in quantum measurements. In theory, with a 66.6% loss rate, the sensitivity can break the SQL using only a 6.0 dB squeezed quantum resource with the current interferometer rather than a 24 dB squeezed quantum resource with a conventional squeezing-vacuum-injected Mach-Zehnder interferometer. In experiments, when using a 2.0 dB squeezed vacuum state, the sensitivity enhancement remains at ∼1.6 dB via optimizing the first splitting ratio when the loss rate changes from 0% to 90%, indicating that the quantum resource is excellently protected with the existence of losses in practical applications. This strategy could open a way to retain quantum advantages for quantum information processing and quantum precision measurement in lossy environments.

Enhanced phase sensitivity in a Mach-Zehnder interferometer via photon recycling.

We propose an alternative scheme for phase estimation in a Mach-Zehnder interferometer (MZI) with photon recycling. It is demonstrated that with the same coherent-state input and homodyne detection, our proposal possesses a phase sensitivity beyond the traditional MZI. For instance, it can achieve an enhancement factor of ∼9.32 in the phase sensitivity compared with the conventional scheme even with a photon loss of 10% on the photon-recycled arm. From another point of view, the quantum Cramér-Rao bound (QCRB) is also investigated. It is found that our scheme is able to achieve a lower QCRB than the traditional one. Intriguingly, the QCRB of our scheme is dependent of the phase shift ϕ while the traditional scheme has a constant QCRB regardless of the phase shift. Finally, we present the underlying mechanisms behind the enhanced phase sensitivity. We believe that our results provide another angle from which to enhance the phase sensitivity in a MZI via photon recycling.

DJ-1 Molecular Chaperone Activity Depresses Tau Aggregation Propensity through Interaction with Monomers.

Tau aggregate-bearing lesions are pathological markers and potential mediators of tauopathic neurodegenerative diseases, including Alzheimer's disease. The molecular chaperone DJ-1 colocalizes with tau pathology in these disorders, but it has been unclear what functional link exists between them. In this study, we examined the consequences of tau/DJ-1 interaction as isolated proteins in vitro. When added to full-length 2N4R tau under aggregation-promoting conditions, DJ-1 inhibited both the rate and extent of filament formation in a concentration-dependent manner. Inhibitory activity was low affinity, did not require ATP, and was not affected by substituting oxidation incompetent missense mutation C106A for wild-type DJ-1. In contrast, missense mutations previously linked to familial Parkinson's disease and loss of α-synuclein chaperone activity, M26I and E64D, displayed diminished tau chaperone activity relative to wild-type DJ-1. Although DJ-1 directly bound the isolated microtubule-binding repeat region of tau protein, exposure of preformed tau seeds to DJ-1 did not diminish seeding activity in a biosensor cell model. These data reveal DJ-1 to be a holdase chaperone capable of engaging tau as a client in addition to α-synuclein. Our findings support a role for DJ-1 as part of an endogenous defense against the aggregation of these intrinsically disordered proteins.

Discovery of potential biomarkers for osteoporosis using LC/GC-MS metabolomic methods.

Purpose: For early diagnosis of osteoporosis (OP), plasma metabolomics of OP was studied by untargeted LC/GC-MS in a Chinese elderly population to find possible diagnostic biomarkers. Methods: A total of 379 Chinese community-dwelling older adults aged ≥65 years were recruited for this study. The BMD of the calcaneus was measured using quantitative ultrasound (QUS), and a T value ≤-2.5 was defined as OP. Twenty-nine men and 47 women with OP were screened, and 29 men and 36 women were matched according to age and BMI as normal controls using propensity matching. Plasma from these participants was first analyzed by untargeted LC/GC-MS, followed by FC and P values to screen for differential metabolites and heatmaps and box plots to differentiate metabolites between groups. Finally, metabolic pathway enrichment analysis of differential metabolites was performed based on KEGG, and pathways with P ≤ 0.05 were selected as enrichment pathways. Results: We screened metabolites with FC>1.2 or FC<1/1.2 and P<0.05 and found 33 differential metabolites in elderly men and 30 differential metabolites in elderly women that could be potential biomarkers for OP. 2-Aminomuconic acid semialdehyde (AUC=0.72, 95% CI 0.582-0.857, P=0.004) is highly likely to be a biomarker for screening OP in older men. Tetradecanedioic acid (AUC=0.70, 95% CI 0.575-0.818, P=0.004) is highly likely to be a biomarker for screening OP in older women. Conclusion: These findings can be applied to clinical work through further validation studies. This study also shows that metabolomic analysis has great potential for application in the early diagnosis and recurrence monitoring of OP in elderly individuals.

Fitting of TC model according to key parameters affecting Parkinson's state based on improved particle swarm optimization algorithm.

Biophysical models contain a large number of parameters, while the spiking characteristics of neurons are related to a few key parameters. For thalamic neurons, relay reliability is an important characteristic that affects Parkinson's state. This paper proposes a method to fit key parameters of the model based on the spiking characteristics of neurons, and improves the traditional particle swarm optimization algorithm. That is, a nonlinear concave function and a Logistic chaotic mapping are combined to adjust the inertia weight of particles to avoid the particle falling into a local optimum in the search process or appearing premature convergence. In this paper, three parameters that play an important role in Parkinson's state of the thalamic cell model are selected and fitted by the improved particle swarm optimization algorithm. Using the fitted parameters to reconstruct the neuron model can predict the spiking trajectories well, which verifies the effectiveness of the fitting method. By comparing the fitting results with other particle swarm optimization algorithms, it is shown that the proposed particle swarm optimization algorithm can better avoid local optima and converge to the optimal values quickly.

Sensing the performance enhancement via asymmetric gain optimization in the atom-light hybrid interferometer.

The SU (1,1)-type atom-light hybrid interferometer (SALHI) is a kind of interferometer that is sensitive to both the optical phase and atomic phase. However, the loss has been an unavoidable problem in practical applications and greatly limits the use of interferometers. Visibility is an important parameter to evaluate the performance of interferometers. Here, we experimentally demonstrate the mitigating effect of the loss on visibility of the SALHI via asymmetric gain optimization, where the maximum threshold of loss to visibility close to 100% is increased. Furthermore, we theoretically find that the optimal condition for the largest visibility is the same as that for the enhancement of signal-to-noise ratio (SNR) to the best value with the existence of the losses using the intensity detection, indicating that visibility can act as an experimental operational criterion for SNR improvement in practical applications. Improvement of the interference visibility means achievement of SNR enhancement. Our results provide a significant foundation for practical application of the SALHI in radar and ranging measurements.

Magnetic Resonance Imaging to Evaluate the Recovery Effects of Cerebral Nerve Function in Comprehensive Treatment of Poststroke Depression by Intelligent Algorithm-Based Hyperbaric Oxygen Therapy.

This research was aimed to discuss magnetic resonance imaging (MRI) evaluation of recovery effects of cerebral nerve function in comprehensive treatment of poststroke depression (PSD) by intelligence-based hyperbaric oxygen therapy. Low-rank matrix algorithm was adopted to denoise MRI images of patients with PSD, and mean square error (MSE) and peak signal-to-noise ratio (PSNR) were the evaluation indicators of the results of image denoising. 118 patients were randomly divided into the control group (administered escitalopram oxalate) and the research group (hyperbaric oxygen therapy was implemented based on the treatment in control group). National Institutes of Health Stroke Scale (NIHSS), Hamilton Depression Scale (HAMD), Pittsburgh Sleep Quality Index (PSQI), glial fibrillary acidic protein (GFAP), and changes of norepinephrine (NE) level of patients in two groups were compared before and after treatment. The value of MSE of MRI images processed by low-rank matrix algorithm was 92.39, which was higher than that calculated by nonlocal mean (NLM) algorithm (80.54). The PSNR value calculated by low-rank matrix algorithm was 25.35, which was lower than that calculated by NLM algorithm (29.07). In contrast, NIHSS score and HAMD score of the research group after treatment were lower than those of the control group, while PSQI score of the research group was higher than that of the control group. The level of GFAP of the research group was at 852.46 ± 94.47, which was significantly lower than that of the control group, reaching 948.53 ± 98.42. However, the level of NE of the research group was 1478.59 ± 99.85, which was higher than that of the control group (1061.80 ± 98.02). All the comparisons of above indicators had statistical meaning (P < 0.05). The low-rank matrix algorithm can help in clinical diagnosis and treatment to provide more accurate MRI images. In addition, hyperbaric oxygen comprehensive therapy can promote the recovery of neurological function in patients with poststroke depression and significantly improve the depressive state and sleep quality of patients.

Fundamental and practical aspects of machine learning for the peak picking of biomolecular NMR spectra.

Rapid progress in machine learning offers new opportunities for the automated analysis of multidimensional NMR spectra ranging from protein NMR to metabolomics applications. Most recently, it has been demonstrated how deep neural networks (DNN) designed for spectral peak picking are capable of deconvoluting highly crowded NMR spectra rivaling the facilities of human experts. Superior DNN-based peak picking is one of a series of critical steps during NMR spectral processing, analysis, and interpretation where machine learning is expected to have a major impact. In this perspective, we lay out some of the unique strengths as well as challenges of machine learning approaches in this new era of automated NMR spectral analysis. Such a discussion seems timely and should help define common goals for the NMR community, the sharing of software tools, standardization of protocols, and calibrate expectations. It will also help prepare for an NMR future where machine learning and artificial intelligence tools will be common place.

SU(2)-in-SU(1,1) Nested Interferometer for High Sensitivity, Loss-Tolerant Quantum Metrology.

We present experimental and theoretical results on a new interferometer topology that nests a SU(2) interferometer, e.g., a Mach-Zehnder or Michelson interferometer, inside a SU(1,1) interferometer, i.e., a Mach-Zehnder interferometer with parametric amplifiers in place of beam splitters. This SU(2)-in-SU(1,1) nested interferometer (SISNI) simultaneously achieves a high signal-to-noise ratio (SNR), sensitivity beyond the standard quantum limit (SQL) and tolerance to photon losses external to the interferometer, e.g., in detectors. We implement a SISNI using parametric amplification by four-wave mixing (FWM) in Rb vapor and a laser-fed Mach-Zehnder SU(2) interferometer. We observe path-length sensitivity with SNR 2.2 dB beyond the SQL at power levels (and thus SNR) 2 orders of magnitude beyond those of previous loss-tolerant interferometers. We find experimentally the optimal FWM gains and find agreement with a minimal quantum noise model for the FWM process. The results suggest ways to boost the in-practice sensitivity of high-power interferometers, e.g., gravitational wave interferometers, and may enable high-sensitivity, quantum-enhanced interferometry at wavelengths for which efficient detectors are not available.