Explore the vast range of titles available.

The purpose of this study was to explore piano performers' experiences of practicing Tai Chi and their perceptions of how it supports pain relief, mental regulation, and performance-related control. This qualitative descriptive study conducted semi-structured, in-depth interviews with five piano performers who had practised Tai Chi for at least 1 year. The interviews were audio-recorded, transcribed, and analyzed using thematic analysis with iterative coding, grouping, and clustering to derive core themes. The participants reported integrating Tai Chi into daily warm-ups, practice breaks and pedagogical routines. Perceived benefits included reduced muscle tension and pain, improved bodily coordination and tone control through principles such as \"lowering the shoulders and elbows,\" and enhanced focus and emotional regulation before performance. Several participants described faster recovery from fatigue and increased efficiency during practice. Tai Chi was described by participants as alleviating performance-related physical discomfort, supporting psychological calm, and improving aspects of pianistic control. While the findings are limited by the small sample, they suggest Tai Chi as a feasible adjunct for performers and teachers.

Large machine learning models are revolutionary technologies of artificial intelligence whose bottlenecks include huge computational expenses, power, and time used both in the pre-training and fine-tuning process. In this work, we show that fault-tolerant quantum computing could possibly provide provably efficient resolutions for generic (stochastic) gradient descent algorithms, scaling as O ( T 2 × polylog ( n ) ) , where n is the size of the models and T is the number of iterations in the training, as long as the models are both sufficiently dissipative and sparse, with small learning rates. Based on earlier efficient quantum algorithms for dissipative differential equations, we find and prove that similar algorithms work for (stochastic) gradient descent, the primary algorithm for machine learning. In practice, we benchmark instances of large machine learning models from 7 million to 103 million parameters. We find that, in the context of sparse training, a quantum enhancement is possible at the early stage of learning after model pruning, motivating a sparse parameter download and re-upload scheme. Our work shows solidly that fault-tolerant quantum algorithms could potentially contribute to most state-of-the-art, large-scale machine-learning problems. It is still unclear whether and how quantum computing might prove useful in solving known large-scale classical machine learning problems. Here, the authors show that variants of known quantum algorithms for solving differential equations can provide an advantage in solving some instances of stochastic gradient descent dynamics.

Novel diagnostic and prognostic biomarkers of cancers are needed to improve precision medicine. Circular RNAs act as important regulators in cancers at the transcriptional and posttranscriptional levels. The circular RNA circMAN1A2 is highly expressed in nasopharyngeal carcinoma according to our previous RNA sequencing data; however, the expression and functions of circMAN1A2 in cancers are still obscure. Therefore, in this study, we evaluated the expression of circMAN1A2 in the sera of patients with nasopharyngeal carcinoma and other malignant tumors and analyzed its correlations with clinical features and diagnostic values. The expression levels of circMAN1A2 were detected by quantitative real‐time PCR, and the correlations of clinical features with circMAN1A2 expression were analyzed by χ2 tests. Receiver operating characteristic curves were used to evaluate the clinical applications of circMAN1A2. The results showed that circMAN1A2 was upregulated in nasopharyngeal carcinoma, oral cancer, thyroid cancer, ovarian cancer, and lung cancer, with areas under the curves of 0.911, 0.779, 0.734, 0.694, and 0.645, respectively, indicating the good diagnostic value of circMAN1A2. Overall, our findings suggested that circMAN1A2 could be a serum biomarker for malignant tumors, providing important insights into diagnostic approaches for malignant tumors. Further studies are needed to elucidate the mechanisms of circMAN1A2 in the pathogenesis of cancer. We verified that circMAN1A2 was significantly upregulated in the sera of patients with NPC, oral cancer, thyroid cancer, ovarian cancer, and lung cancer and had good clinical diagnostic value. We speculate that circMAN1A2 could be a serum biomarker for malignant cancers and provide effective clues for the early diagnosis of malignant cancers.

Nonlinear differential equations model diverse phenomena but are notoriously difficult to solve. While there has been extensive previous work on efficient quantum algorithms for linear differential equations, the linearity of quantum mechanics has limited analogous progress for the nonlinear case. Despite this obstacle, we develop a quantum algorithm for dissipative quadratic n-dimensional ordinary differential equations. Assuming R < 1, where R is a parameter characterizing the ratio of the nonlinearity and forcing to the linear dissipation, this algorithm has complexity T²q poly(log T, log n, log 1/ϵ)ϵ where T is the evolution time, ϵ is the allowed error, and q measures decay of the solution. This is an exponential improvement over the best previous quantum algorithms, whose complexity is exponential in T. While exponential decay precludes efficiency, driven equations can avoid this issue despite the presence of dissipation. Our algorithm uses the method of Carleman linearization, for which we give a convergence theorem. This method maps a system of nonlinear differential equations to an infinite-dimensional system of linear differential equations, which we discretize, truncate, and solve using the forward Euler method and the quantum linear system algorithm. We also provide a lower bound on the worst-case complexity of quantum algorithms for general quadratic differe pntial equations, showing that the problem is intractable for R ≥ √2. Finally, we discuss potential applications, showing that the R < 1 condition can be satisfied in realistic epidemiological models and giving numerical evidence that the method may describe a model of fluid dynamics even for larger values of R.

Sepsis remains a leading cause of death worldwide, despite advances in critical care, and understanding of the pathophysiology and treatment strategies. No specific therapy or drugs are available for sepsis. Neutrophils play a critical role in controlling infection under normal conditions, and it is suggested that their migration and antimicrobial activity are impaired during sepsis which contribute to the dysregulation of immune responses. Recent studies further demonstrated that interruption or reversal of the impaired migration and antimicrobial function of neutrophils improves the outcome of sepsis in animal models. In this review, we provide an overview of the associated mediators and signal pathways involved which govern the survival, migration and antimicrobial function of neutrophils in sepsis, and discuss the potential of neutrophils as a target to specifically diagnose and/or predict the outcome of sepsis.

Background Sepsis is a leading cause of preventable death around the world. Population-based estimation of sepsis incidence is lacking in China. In this study, we aimed to estimate the population-based incidence and geographic variation of hospitalized sepsis in China. Methods We retrospectively identified hospitalized sepsis from the nationwide National Data Center for Medical Service (NDCMS) and the National Mortality Surveillance System (NMSS) by ICD-10 codes for the period from 2017 to 2019. In-hospital sepsis case fatality and mortality rate were calculated to extrapolate the national incidence of hospitalized sepsis. The geographic distribution of hospitalized sepsis incidence was examined using Global Moran's Index. Results We identified 9,455,279 patients with 10,682,625 implicit-coded sepsis admissions in NDCMS and 806,728 sepsis-related deaths in NMSS. We estimated that the annual standardized incidence of hospitalized sepsis was 328.25 (95% CI 315.41–341.09), 359.26 (95% CI 345.4–373.12) and 421.85 (95% CI 406.65–437.05) cases per 100,000 in 2017, 2018 and 2019, respectively. We observed 8.7% of the incidences occurred among neonates less than 1 year old, 11.7% among children aged 1–9 years, and 57.5% among elderly older than 65 years. Significant spatial autocorrelation for incidence of hospitalized sepsis was observed across China (Moran's Index 0.42, p  = 0.001; 0.45, p  = 0.001; 0.26, p  = 0.011 for 2017, 2018, 2019, respectively). Higher number of hospital bed supply and higher disposable income per capita were significantly associated with a higher incidence of hospitalized sepsis. Conclusion Our study showed a greater burden of sepsis hospitalizations than previous estimated. The geographical disparities suggested more efforts were needed in prevention of sepsis.

The Majorana fermion, which is its own antiparticle and obeys non-Abelian statistics, plays a critical role in topological quantum computing. It can be realized as a bound state at zero energy, called a Majorana zero mode (MZM), in the vortex core of a topological superconductor, or at the ends of a nanowire when both superconductivity and strong spin orbital coupling are present. A MZM can be detected as a zero-bias conductance peak (ZBCP) in tunneling spectroscopy. However, in practice, clean and robust MZMs have not been realized in the vortices of a superconductor because of contamination from impurity states or other closely packed Caroli–de Gennes-Matricon (CdGM) states, which hampers further manipulations of MZMs. Here, using scanning tunneling spectroscopy, we show that a ZBCP well separated from the other discrete CdGM states exists ubiquitously in the cores of free vortices in the defect-free regions of(Li0.84Fe0.16)OHFeSe, which has a superconducting transition temperature of 42 K. Moreover, a Dirac-cone-type surface state is observed by angle-resolved photoemission spectroscopy, and its topological nature is confirmed by band calculations. The observed ZBCP can naturally be attributed to a MZM arising from the chiral topological surface state of a bulk superconductor. Thus,(Li0.84Fe0.16)OHFeSeprovides an ideal platform for studying MZMs and topological quantum computing.

Enterprise human resources management plays an important role in enterprise development, and it is an important support for the resisting the risk of market competition. The quality of enterprise managers and whether the performance appraisal is reasonable and effective directly affect the operation status and overall benefits of enterprises. Therefore, in order to enhance the competitiveness and achieve the development goals of the enterprise, the enterprise needs a set of scientific performance appraisal systems for enterprise management personnel. By combining the characteristics and research status of enterprise managers’ performance evaluation, the shortcomings of the current popular enterprise managers’ performance evaluation system were analyzed and pointed out in this paper, and the optimization method of enterprise managers’ performance evaluation system based on KPI indicators was studied and put forward, which can provide a certain theoretical support for enterprises to improve their management level and market competitiveness.

Ocean warming with climate change is forcing marine organisms to shift their distributions polewards and phenology. In warm tropical seas, evolutionary adaptation by local species to warming will be crucial to avoid predicted desertification and reduction in diversity. However, little is known about the adaptation of phytoplankton in warm seas. Across the ocean, diatomic microalgae are the main primary producers in cold waters; they also contribute to tropical communities where they play a necessary role in the biological pump. Here we show that four species of diatoms isolated from the tropical Red Sea adapted to warming conditions (30 °C) after 200–600 generations by using various thermal strategies. Two of the warming adapted species increased their optimal growth temperature ( T opt ) and maximum growth rate. The other two diatoms did not increase T opt and growth, but shifted from specialist to generalist increasing their maximum critical thermal limit. Our data show that tropical diatoms can adapt to warming, although trade offs on photosynthetic efficiency, high irradiance stress, and lower growth rate could alter their competitive fitness. Our findings suggest that adaptive responses to warming among phytoplankton could help to arrest the sharp decline in diversity resulting from climate change that is predicted for tropical waters.

Immunotherapy, as a powerful strategy for cancer treatment, has achieved tremendous efficacy in clinical trials. Despite these advancements, there is much to do in terms of enhancing therapeutic benefits and decreasing the side effects of cancer immunotherapy. Advanced nanobiomaterials, including liposomes, polymers, and silica, play a vital role in the codelivery of drugs and immunomodulators. These nanobiomaterial-based delivery systems could effectively promote antitumor immune responses and simultaneously reduce toxic adverse effects. Furthermore, nanobiomaterials may also combine with each other or with traditional drugs via different mechanisms, thus giving rise to more accurate and efficient tumor treatment. Here, an overview of the latest advancement in these nanobiomaterials used for cancer immunotherapy is given, describing outstanding systems, including lipid-based nanoparticles, polymer-based scaffolds or micelles, inorganic nanosystems, and others.