Features and stability analysis of non-Schwarzschild black hole in quadratic gravity

Black holes are found to exist in gravitational theories with the presence of quadratic curvature terms and behave differently from the Schwarzschild solution. We present an exhaustive analysis for determining the quasinormal modes of a test scalar field propagating in a new class of black hole backgrounds in the case of pure Einstein-Weyl gravity. Our result shows that the field decay of quasinormal modes in such a non-Schwarzschild black hole behaves similarly to the Schwarzschild one, but the decay slope becomes much smoother due to the appearance of the Weyl tensor square in the background theory. We also analyze the frequencies of the quasinormal modes in order to characterize the properties of new back holes, and thus, if these modes can be the source of gravitational waves, the underlying theories may be testable in future gravitational wave experiments. We briefly comment on the issue of quantum (in)stability in this theory at linear order.Comment: 18 pages, 4 figures, 1 table, several references added, version published on JHE

Compilation for Quantum Computing on Chiplets

Chiplet architecture is an emerging architecture for quantum computing that could significantly increase qubit resources with its great scalability and modularity. However, as the computing scale increases, communication between qubits would become a more severe bottleneck due to the long routing distances. In this paper, we trade ancillary qubits for program concurrency by proposing a multi-entry communication highway mechanism, and building a compilation framework to efficiently manage and utilize the highway resources. Our evaluation shows that this framework significantly outperforms the baseline approach in both the circuit depth and the number of operations on some typical quantum benchmarks, leading to a more efficient and less error-prone compilation of quantum programs

Optimal Synthesis of Stabilizer Codes via MaxSAT

Quantum Error Correction (QEC) codes are crucial for achieving fault-tolerant quantum computing in the long term. However, efficiently implementing these codes on hardware poses significant challenges, including hardware connectivity matching, efficient circuit scheduling, and fault-tolerance enforcement. In this study, we present an optimal synthesizer that stitches generic stabilizer codes onto diverse hardware structures via MaxSAT. Our evaluation demonstrates (1) the capability of our approach to be applied for various codes and devices and (2) the consistently better efficiency than the best prior heuristic approaches that only target specific QEC codes. By bridging the gap between high-level QEC code design and low-level hardware constraints, this work paves the way toward achieving long-term fault-tolerant quantum computing goals

Direct prediction of antimicrobial resistance in Pseudomonas aeruginosa by metagenomic next-generation sequencing

ObjectivePseudomonas aeruginosa has strong drug resistance and can tolerate a variety of antibiotics, which is a major problem in the management of antibiotic-resistant infections. Direct prediction of multi-drug resistance (MDR) resistance phenotypes of P. aeruginosa isolates and clinical samples by genotype is helpful for timely antibiotic treatment.MethodsIn the study, whole genome sequencing (WGS) data of 494 P. aeruginosa isolates were used to screen key anti-microbial resistance (AMR)-associated genes related to imipenem (IPM), meropenem (MEM), piperacillin/tazobactam (TZP), and levofloxacin (LVFX) resistance in P. aeruginosa by comparing genes with copy number differences between resistance and sensitive strains. Subsequently, for the direct prediction of the resistance of P. aeruginosa to four antibiotics by the AMR-associated features screened, we collected 74 P. aeruginosa positive sputum samples to sequence by metagenomics next-generation sequencing (mNGS), of which 1 sample with low quality was eliminated. Then, we constructed the resistance prediction model.ResultsWe identified 93, 88, 80, 140 AMR-associated features for IPM, MEM, TZP, and LVFX resistance in P. aeruginosa. The relative abundance of AMR-associated genes was obtained by matching mNGS and WGS data. The top 20 features with importance degree for IPM, MEM, TZP, and LVFX resistance were used to model, respectively. Then, we used the random forest algorithm to construct resistance prediction models of P. aeruginosa, in which the areas under the curves of the IPM, MEM, TZP, and LVFX resistance prediction models were all greater than 0.8, suggesting these resistance prediction models had good performance.ConclusionIn summary, mNGS can predict the resistance of P. aeruginosa by directly detecting AMR-associated genes, which provides a reference for rapid clinical detection of drug resistance of pathogenic bacteria

In vitro phosphorylation as tool for modification of silk and keratin fibrous materials

An overview is given of the recent work on in vitro enzymatic phosphorylation of silk fibroin and human hair keratin. Opposing to many chemical "conventional" approaches, enzymatic phosphorylation is in fact a mild reaction and the treatment falls within "green chemistry" approach. Silk and keratin are not phosphorylated in vivo, but in vitro. This enzyme-driven modification is a major technological breakthrough. Harsh chemical chemicals are avoided, and mild conditions make enzymatic phosphorylation a real "green chemistry" approach. The current communication presents a novel approach stating that enzyme phosphorylation may be used as a tool to modify the surface charge of biocompatible materials such as keratin and silk

Inferring plant–plant interactions using remote sensing

Rapid technological advancements and increasing data availability have improved the capacity to monitor and evaluate Earth's ecology via remote sensing. However, remote sensing is notoriously ‘blind’ to fine-scale ecological processes such as interactions among plants, which encompass a central topic in ecology. Here, we discuss how remote sensing technologies can help infer plant–plant interactions and their roles in shaping plant-based systems at individual, community and landscape levels. At each of these levels, we outline the key attributes of ecosystems that emerge as a product of plant–plant interactions and could possibly be detected by remote sensing data. We review the theoretical bases, approaches and prospects of how inference of plant–plant interactions can be assessed remotely. At the individual level, we illustrate how close-range remote sensing tools can help to infer plant–plant interactions, especially in experimental settings. At the community level, we use forests to illustrate how remotely sensed community structure can be used to infer dominant interactions as a fundamental force in shaping plant communities. At the landscape level, we highlight how remotely sensed attributes of vegetation states and spatial vegetation patterns can be used to assess the role of local plant–plant interactions in shaping landscape ecological systems. Synthesis. Remote sensing extends the domain of plant ecology to broader and finer spatial scales, assisting to scale ecological patterns and search for generic rules. Robust remote sensing approaches are likely to extend our understanding of how plant–plant interactions shape ecological processes across scales—from individuals to landscapes. Combining these approaches with theories, models, experiments, data-driven approaches and data analysis algorithms will firmly embed remote sensing techniques into ecological context and open new pathways to better understand biotic interactions

OnePerc: A Randomness-aware Compiler for Photonic Quantum Computing

The photonic platform holds great promise for quantum computing. Nevertheless, the intrinsic probabilistic characteristics of its native fusion operations introduces substantial randomness into the computing process, posing significant challenges to achieving scalability and efficiency in program execution. In this paper, we introduce a randomness-aware compilation framework designed to concurrently achieve scalability and efficiency. Our approach leverages an innovative combination of offline and online optimization passes, with a novel intermediate representation serving as a crucial bridge between them. Through a comprehensive evaluation, we demonstrate that this framework significantly outperforms the most efficient baseline compiler in a scalable manner, opening up new possibilities for realizing scalable photonic quantum computing

Identifying important microbial biomarkers for the diagnosis of colon cancer using a random forest approach

Colon cancer is one of the most common cancers, with 30–50 % of patients returning or metastasizing within 5 years of treatment. Increasingly, researchers have highlighted the influence of microbes on cancer malignant activity, while no studies have explored the relationship between colon cancer and the microbes in tumors. Here, we used tissue and blood samples from 67 colon cancer patients to identify pathogenic microorganisms associated with the diagnosis and prediction of colon cancer and evaluate the predictive performance of each pathogenic marker and its combination based on the next-generation sequencing data by using random forest algorithms. The results showed that we constructed a database of 13,187 pathogenic microorganisms associated with human disease and identified 2 pathogenic microorganisms (Synthetic.construct_32630 and Dicrocoelium.dendriticum_57078) associated with colon cancer diagnosis, and the constructed diagnostic prediction model performed well for tumor tissue samples and blood samples. In summary, for the first time, we provide new molecular markers for the diagnosis of colon cancer based on the expression of pathogenic microorganisms in order to provide a reference for improving the effective screening rate of colon cancer in clinical practice and ameliorating the personalized treatment of colon cancer patients

Heterogeneity of work alienation and its relationship with job embeddedness among Chinese nurses: a cross-sectional study using latent profile analysis

Abstract Objective To identify the distinct profiles of work alienation among Chinese nurses, examine the demographic factors associated with profile memberships, and then explore the relationship between latent categories of work alienation and job embeddedness. Methods A cross-sectional survey of 523 nurses was conducted from July to August 2023. Latent profile analysis (LPA) was performed to identify distinct profiles of nurses based on three aspects: powerlessness, helplessness, and meaningfulness. A multinomial logistic regression analysis was conducted to explore the predictors of profile membership. Hierarchical regression analysis was carried out to examine the association between profile memberships and job embeddedness. Results Three subgroups of work alienation of nurses were identified: 23.1%, 57.8%, and 19.1% in the low work alienation group (profile 1), the moderate work alienation group (profile 3), and the high work alienation group (profile 2), respectively. Nurses with college degrees were more likely to be grouped into moderate work alienation. Nurses who did not work night shifts were more likely to have low or moderate levels of work alienation. Nurses earning 2,000–3,000 and 3,001–5,000 yuan per month were likely to be in the low work alienation group. The different categories of work alienation significantly predicted job embeddedness among nurses (ΔR 2 = 0.103, p < 0.001). Conclusions Work alienation has an important impact on clinical nurses’ job embeddedness. Nursing managers should pay attention to the differences in individual work alienation status and adopt reasonable management strategies to improve the level of job embeddedness, ensure the quality of care, and reduce nursing turnover