Search

Sapce-borne gravitational wave antennas, such as LISA and LISA-like mission (Taiji and Tianqin), will offer novel perspectives for exploring our Universe while introduce new challenges, especially in data analysis. Aside from the known challenges like high parameter space dimension, superposition of large number of signals etc., gravitational wave detections in space would be more seriously affected by anomalies or non-stationarities in the science measurements. Considering the three types of foreseeable non-stationarities including data gaps, transients (glitches), and time-varying noise auto-correlations, which may come from routine maintenance or unexpected disturbances during science operations, we developed a deep learning model for accurate signal extractions confronted with such anomalous scenarios. Our model exhibits the same performance as the current state-of-the-art models do for the ideal and anomaly free scenario, while shows remarkable adaptability in extractions of coalescing massive black hole binary signal against all three types of non-stationarities and even their mixtures. This also provide new explorations into the robustness studies of deep learning models for data processing in space-borne gravitational wave missions.

Time Delay Interferometry (TDI) is an indispensable step in the whole data processing procedure of space-based gravitational wave detection, as it mitigates the overwhelming laser frequency noise, which would otherwise completely bury the gravitational wave signals. Knowledge on the inter-spacecraft optical paths (i.e. delays) is one of the key elements of TDI. Conventional method for inter-spacecraft ranging mainly relies on the pseudo-random noise (PRN) code signal modulated onto the lasers. To ensure the reliability and robustness of this ranging information, it would be highly beneficial to develop other methods which could serve as cross-validations or backups. This paper explores the practical implementation of an alternative data-driven approach – time delay interferometry ranging (TDIR) – as a ranging technique independent of the PRN signal. Distinguished from previous research, our TDIR algorithm significantly relaxes the stringent requirement for clock synchronization imposed by traditional TDI procedure. By framing TDIR as a Bayesian parameter estimation problem and employing a general polynomial parametrization, we demonstrate our algorithm with simulated data based on the numerical orbit of Taiji. In the presence of laser frequency noise and secondary noises, the estimated median values of delays are only 5.28 ns away from the ground truths, capable of suppressing laser frequency noise to the desired level. Additionally, we have also analyzed the requirements of mitigating optical bench noise and clock noise on TDIR, and presented an illustrative example for the impact of laser locking.

Abstract Background the prevalence of physical and multidimensional frailty and their prognostic impact on clinical outcomes in patients with atrial fibrillation (AF) is unclear. Objective to evaluated frailty in a cohort of patients with AF according to different criteria, and studied the prevalence and its prognostic impact on clinical outcomes. Methods in this multicenter prospective cohort, 197 inpatients ≥ 65 years old with AF were recruited from September 2018 to April 2019.We used Fried Frailty phenotype (Fried) to assess physical frailty, and comprehensive geriatric assessment-frailty index (CGA-FI) to assess multidimensional frailty. The primary outcome was a composite of all-cause mortality or rehospitalization. Results the prevalence of frailty was determined as 34.5% by Fried, 42.6% by CGA-FI. Malnutrition and ≥ 7 medications were independently associated with frailty. Kaplan-Meier survival curve showed that the presence of frailty by CGA-FI had significantly lower all-cause mortality or rehospitalization survival rate (log-rank P = 0.04) within 1 year. Multivariate Cox regression adjusted for age and sex showed that the frailty by CGA-FI was significantly associated with the risk of all-cause mortality or rehospitalization within 1 year (HR 1.79, 95% CI 1.10–2.90). However, those associations were absent with the physical frailty. After broader multivariate adjustment, those associations were no longer statistically significant for both types of frailty. Conclusions in older people with AF, Multidimensional frailty is more significantly associated with a composite of all-cause mortality or rehospitalization within 1 year than physical frailty, but these association are attenuated after multivariate adjustment. Clinical trial registration ChiCTR1800017204; date of registration: 07/18/2018.

Metal-polymer composite components are widely used in aerospace, automotive, and other industries as lightweight composite parts that can result in significant weight savings. Injection-molded direct joining (IMDJ) technology has a broad application prospect for achieving a close connection between metal and polymer without destroying the metal matrix. This study subjected the titanium alloy surface to sandblasting, anodizing, etching, and annealing, resulting in uniformly distributed claw-like nanostructures generated on the surface. This treatment also improved the surface roughness, exhibiting excellent wettability with a contact angle of only 10.7°. The injection-molded direct joining technique was employed to join Ti alloy and polyphenylene sulfide (PPS) composite components. Tensile testing revealed a bond strength of 33.5 MPa, and analysis of the peeled specimens' residual elements indicated that failure of the composite components occurred in the form of polymer cohesion damage, showcasing excellent bonding effects.

Abstract Coalescing supermassive black hole binaries (SMBHBs) are the primary source candidates for low frequency gravitational wave (GW) detections, which could bring us deep insights into galaxy evolutions over cosmic time and violent processes of spacetime dynamics. Promising candidates had been found based on optical and X-ray observations, which claims for new and ready-to-use GW detection approaches before the operations of space-borne antennas. We show that, satellite laser ranging (SLR) missions could serve as probes of coalescing SMBHBs through the GW-induced resonant effects. Lasting and characteristic imprints caused by such resonances in the residual distances or accelerations from SLR measurements are studied, and the detection SNR is analyzed with both the current and future improved ranging precisions. Within redshift $$z \sim 1$$ z ∼ 1 , the threshold SNR = 5 requires 1–2 years of accumulated data for the current precision and months of data for improved precision, which are workable for the data processing of SLR missions. Meanwhile, joint detections with multiple SLR missions could further improve the total SNR and the confidence level. Such a detection scheme could fulfill the requirement of a tentative SMBHB probe during the preparing stage of LISA and Taiji, and it requires no further investment to any new and advanced facilities. It is also worthwhile to look back and re-process the archived data from the past decades, in where resonant signals from SMBHBs might be hidden.

Post-translational modifications (PTMs) are crucial epigenetic mechanisms that regulate various cellular biological processes. The use of mass spectrometry (MS)-proteomics has led to the discovery of numerous novel types of protein PTMs, such as acetylation, crotonylation, 2-hydroxyisobutyrylation, β-hydroxybutyrylation, protein propionylation and butyrylation, succinylation, malonylation, lactylation, and histone methylation. In this review, we specifically highlight the molecular mechanisms and roles of various histone and some non-histone PTMs in renal diseases, including diabetic kidney disease. PTMs exhibit diverse effects on renal diseases, which can be either protective or detrimental, depending on the specific type of protein PTMs and their respective targets. Different PTMs activate various signaling pathways in diverse renal pathological conditions, which could provide novel insights for studying epigenetic mechanisms and developing potential therapeutic strategies for renal diseases.

Abstract We analyze the anisotropic factors present in the gravitational wave signal, such as the peculiar velocity of the observer and the inhomogeneous distribution of matter in the universe. We model the gravitational wave source as a standard siren, extract the anisotropic part of its luminosity distance $$d_L$$ d L , and obtain the Hubble parameter H(z) by direct calculation instead of integration. Finally, we derive the equation of state $$w_\mathrm{DE}(z)$$ w DE ( z ) for the dark energy by both model-dependent and model-independent methods, and further investigate the cosmological significance of the simulated H(z) measurements. The advantage of this approach is that it makes full use of the anisotropic part of the $$d_L$$ d L data, which directly gives the value of H(z) at certain redshifts. This approach is sensitive to the local features of H(z) and does not depend on the cosmological model.

BackgroundsGene polymorphisms are critical for variations in warfarin dose. To date, more than 70 CYP2C9 alleles have been identified. This study was designed to clarify the clinical significance of CYP2C9*non-3 variants to warfarin sensitivity in Chinese Han patients.MethodsThe entire CYP2C9 gene region was sequenced in 1,993 individuals, and clinical data and VKORC1 genotypes were collected from 986 patients with atrial fibrillation treated with warfarin. The SKAT-O method was used to analyze the effects of CYP2C9*non-3 variants on warfarin sensitivity.ResultsA total of 20 CYP2C9 variants were identified, of which four were novel. Carriers with CYP2C9*non-3 variants may have lower warfarin dose requirements, and similar to CYP2C9*3, CYP2C9*non-3 variants are clearly relevant to warfarin-sensitive and highly sensitive responders.ConclusionOur results showed that, besides CYP2C9*3, the series of CYP2C9*non-3 variants is an unignorable predictor for warfarin sensitivity in Chinese population. From a safety consideration, people carried such variants may need a preferred choice of NOACs when they started anticoagulation therapy.

Context Rivaroxaban and ticagrelor are two common drugs for the treatment of atrial fibrillation and acute coronary syndrome. However, the drug–drug interaction between them is still unknown. Objective To investigate the effects of ticagrelor on the pharmacokinetics of rivaroxaban in rats both in vivo and in vitro. Materials and methods A sensitive and reliable UPLC-MS/MS method was developed for the determination of rivaroxaban in rat plasma. Ten Sprague-Dawley rats were randomly divided into ticagrelor pre-treated group (10 mg/kg/day for 14 days) and control group. The pharmacokinetics of orally administered rivaroxaban (10 mg/kg, single dose) with or without ticagrelor pre-treatment was investigated with developed UPLC-MS/MS method. Additionally, Sprague-Dawley rat liver microsomes were also used to investigate the drug–drug interaction between these two drugs in vitro. Results The Cmax (221.34 ± 53.33 vs. 691.18 ± 238.31 ng/mL) and the AUC(0–t) (1060.97 ± 291.21 vs. 3483.03 ± 753.83 μg·h/L) of rivaroxaban increased significantly (p

The study aims to explore the roles of Massive Open Online Courses (MOOCs) based on deep learning in college students’ English grammar teaching. The data are collected using a survey. After the experimental data are analyzed, it is found that students have a low sense of happiness and satisfaction and are unwilling to practice oral English and learn language points in English learning. They think that college English learning only meets the needs of CET-4 and CET-6 and does not take it as the ultimate learning goal. After the necessity and problems in English grammar teaching are discussed, the advantages of flipped classrooms of MOOCs are discussed in English grammar teaching. A teaching platform is constructed to study the foreign language teaching mode under MOOCs, and classroom teaching is combined with the advantages of MOOCs following the principle of “teaching students according to their personalities” to improve the listening, speaking, reading, writing, and translation skills of foreign language majors. The results show that high-quality online teaching resources and the deep learning-based teaching environment can provide a variety of interactive tools, by which students can communicate with their peers and teachers online. Sharing open online communication, classroom discussion, and situational simulation can enhance teachers’ deep learning ability, like the ability to communication and transfer thoughts. Constructivism with interaction as the core can help students grasp new knowledge easily. Extensive communication and interaction are important ways for learning and thinking. The new model provides students with profound learning experience, expands the teaching resources of MOOCs around the world, and maximizes the interaction between online and offline teachers and students, making knowledge widely rooted in the campus and realizing the combination of online resources and campus classroom teaching. Students can learn the knowledge through autonomous learning and discussion before class, which greatly broadens the learning time and space. In the classroom and after class, the internalization and sublimation of knowledge are completed through group cooperation, inquiry learning, scenario simulation, display, and evaluation, promoting students to know about new knowledge and highlighting the dominant position of students.

Abstract Functional probes are a leading contender for the recognition and manipulation of nervous behavior and are characterized by substantial scientific and technological potential. Despite the recent development of functional neural probes, a flexible biocompatible probe unit that allows for long‐term simultaneous stimulation and signaling is still an important task. Here, a category of flexible tiny multimaterial fiber probes (90%), and effectively minimizes the impedance (by up to one order of magnitude) of the probe. This ability allows to realize long‐term (at least 10 weeks) simultaneous optical stimulation and neural recording at the single‐cell level in behaving mice with signal‐to‐noise ratio (SNR = 30 dB) that is more than 6 times that of the benchmark probe such as an all‐polymer fiber.

The wide application of big data not only provides convenience for the teaching between teachers and students, but also plays a great role in the education and training of college students. However, how to cultivate students' ability to learn English independently in the current era remains to be studied. Under the background of big data, it is very important for today's education to make effective use of this technological advantage to explore scientific and reasonable strategies, stimulate college students' attention to English autonomous learning, and explore the learning process. Investigating the value of specific big data technology is also an important consideration in this study. At the same time, graduate students who are not English majors usually have fewer English courses, and students' English level is also very different. Therefore, we should also pay attention to improving students' oral English ability and pay special attention to the cultivation of students' self-discipline and autonomous learning ability.