Comparison of relapse rates, postoperative infections and operation time between BSSO and DO: a meta-analysis

Purpose: Differences in common complications and operation times suggest that complications after mandibular advancement surgery for Class II mandibular hypoplasia using bilateral sagittal split ramus osteotomy (BSSO) and distraction osteogenesis (DO) require further evaluation. The aim here is to compare relapse and postoperative infection incidences and operation times by meta-analysis to provide information for surgeons in selecting the appropriate surgical method and to inform patients about the complication risks of both.Method: A comprehensive search using Medline, PubMed, Web of Science, Cochrane Library, EBSCO, CQVIP, CBA, CNKI, and SinoMed and the Internet until February 2017 was performed. Only randomized controlled trials (RCTs), controlled clinical trials (CCTs), and retrospective studies (RS) were included. We performed study selection, data extraction, and risk of bias assessment and meta-analyses with fixed and random effects models based on statistical heterogeneity. Data were combined using Review Manager software.Results: In total, 388 articles were retrieved; 8 met our inclusion criteria: 4 RCTs, 1 CCT, and 3 RSs. Five of the included articles were analyzed regarding horizontal and vertical relapse. Although horizontal relapse was not significantly different between treatment options (P=0.65), vertical relapse was (P=0.03). Three and 2 studies were included in analyses of postoperative infections and of operation time; both showed significant differences between treatment options (P=0.0009 and P=0.006, respectively).Conclusion: This analysis revealed lower incidence rates of vertical relapse and postoperative infections after BSSO, with the operation time also being significantly shorter. More high-quality RCTs are needed for a more reliable and convincing conclusion

Observation-based global soil heterotrophic respiration indicates underestimated turnover and sequestration of soil carbon by terrestrial ecosystem models

This study is supported by National Natural Science Foundation of China (grant number: 41988101), National Key R&D Program of China (2019YFA0607304), National Natural Science Foundation of China (Grant number: 42022004 and 41901085) and the Second Tibetan Plateau Scientific Expedition and Research Program (2019QZKK0606).Peer reviewedPostprin

Circuit-Noise-Resilient Virtual Distillation

Quantum error mitigation (QEM) is crucial for near-term quantum devices, as noise inherently exists in physical quantum systems and undermines the accuracy of quantum algorithms. A typical purification-based QEM method, called Virtual Distillation (VD), aims to mitigate state preparation errors and achieve effective exponential suppression using multiple copies of the noisy state. However, imperfect VD circuit implementation may yield negative mitigation outcomes, potentially more severe than those achieved without QEM. To address this, we introduce Circuit-Noise-Resilient Virtual Distillation (CNR-VD). This method, featuring a calibration procedure that utilizes easily-prepared input states, refines the outcomes of VD when its circuit is contaminated by noise, seeking to recover the results of an ideally conducted VD circuit. Simulation results demonstrate that the CNR-VD estimator effectively reduces deviations induced by noise in the VD circuit, showcasing improvements in accuracy by an order of magnitude at most compared to the original VD. Meanwhile, CNR-VD elevates the gate noise threshold for VD, enabling positive effects even in the presence of higher noise levels. Furthermore, the strength of our work lies in its applicability beyond specific QEM algorithms, as the estimator can also be applied to generic Hadamard-Test circuits. The proposed CNR-VD significantly enhances the noise-resilience of VD, and thus is anticipated to elevate the performance of quantum algorithm implementations on near-term quantum devices

Evaluating the Resilience of Variational Quantum Algorithms to Leakage Noise

As we are entering the era of constructing practical quantum computers, suppressing the inevitable noise to accomplish reliable computational tasks will be the primary goal. Leakage noise, as the amplitude population leaking outside the qubit subspace, is a particularly damaging source of error that error correction approaches cannot handle. However, the impact of this noise on the performance of variational quantum algorithms (VQAs), a type of near-term quantum algorithms that is naturally resistant to a variety of noises, is yet unknown. Here, {we consider a typical scenario with the widely used hardware-efficient ansatz and the emergence of leakage in two-qubit gates}, observing that leakage noise generally reduces the expressive power of VQAs. Furthermore, we benchmark the influence of leakage noise on VQAs in real-world learning tasks. Results show that, both for data fitting and data classification, leakage noise generally has a negative impact on the training process and final outcomes. Our findings give strong evidence that VQAs are vulnerable to leakage noise in most cases, implying that leakage noise must be effectively suppressed in order to achieve practical quantum computing applications, whether for near-term quantum algorithms and long-term error-correcting quantum computing.Comment: Accepted by PR

Protocol for analyzing protein ensemble structures from chemical cross-links using DynaXL

Chemical cross-linking coupled with mass spectroscopy (CXMS) is a powerful technique for investigating protein structures. CXMS has been mostly used to characterize the predominant structure for a protein, whereas cross-links incompatible with a unique structure of a protein or a protein complex are often discarded. We have recently shown that the so-called over-length cross-links actually contain protein dynamics information. We have thus established a method called DynaXL, which allow us to extract the information from the over-length cross-links and to visualize protein ensemble structures. In this protocol, we present the detailed procedure for using DynaXL, which comprises five steps. They are identification of highly confident cross-links, delineation of protein domains/subunits, ensemble rigid-body refinement, and final validation/assessment. The DynaXL method is generally applicable for analyzing the ensemble structures of multi-domain proteins and protein-protein complexes, and is freely available at www.tanglab.org/resources

Active Learning on a Programmable Photonic Quantum Processor

Training a quantum machine learning model generally requires a large labeled dataset, which incurs high labeling and computational costs. To reduce such costs, a selective training strategy, called active learning (AL), chooses only a subset of the original dataset to learn while maintaining the trained model's performance. Here, we design and implement two AL-enpowered variational quantum classifiers, to investigate the potential applications and effectiveness of AL in quantum machine learning. Firstly, we build a programmable free-space photonic quantum processor, which enables the programmed implementation of various hybrid quantum-classical computing algorithms. Then, we code the designed variational quantum classifier with AL into the quantum processor, and execute comparative tests for the classifiers with and without the AL strategy. The results validate the great advantage of AL in quantum machine learning, as it saves at most $85\%$ labeling efforts and $91.6\%$ percent computational efforts compared to the training without AL on a data classification task. Our results inspire AL's further applications in large-scale quantum machine learning to drastically reduce training data and speed up training, underpinning the exploration of practical quantum advantages in quantum physics or real-world applications