Denosumab, teriparatide and bisphosphonates for glucocorticoid-induced osteoporosis: a Bayesian network meta-analysis

Background: Several medications have been used for glucocorticoids-induced osteoporosis (GIO). However, the best therapeutic option for GIO is still controversial. A Bayesian network meta-analysis was conducted to compare the efficacy and safety of denosumab, teriparatide and bisphosphonates for patients with GIO.Methods: Relevant randomized controlled trials published in PubMed, Embase, Cochrane Library and ClinicalTrials.gov up to August 2023 were searched. The following efficiency and safety outcomes were extracted for comparison: bone mineral density (BMD) percentage changes in lumbar spine, femur neck and total hip, and incidences of adverse events (AEs), serious adverse events (SAEs), vertebrae and non-vertebrae fracture. Bayesian random effects models were used for multiple treatment comparisons.Results: 11 eligible RCTs involving 2,877 patients were identified. All the six medications including alendronate, risedronate, etidronate, zoledronate, teriparatide, and denosumab and were effective in increasing BMD. Teriparatide and denosumab were more effective in improving lumbar spine and femur neck BMD, and reducing vertebrae fracture. Alendronate and denosumab were more effective in improving total hip BMD. Alendronate and teriparatide had the lowest incidences of AEs and SAEs.Conclusion: Teriparatide denosumab and the bisphosphonates are all effective in improving BMD for GIO patients. Based on this network meta-analysis, teriparatide and denosumab have higher efficiency in improving lumbar spine and femur neck BMD, and reducing vertebrae fracture.Systematic Review Registration:10.17605/OSF.IO/2G8YA, identifier CRD42023456305

Realizing Non-Physical Actions through Hermitian-Preserving Map Exponentiation

Quantum mechanics features a variety of distinct properties such as coherence and entanglement, which could be explored to showcase potential advantages over classical counterparts in information processing. In general, legitimate quantum operations must adhere to principles of quantum mechanics, particularly the requirements of complete positivity and trace preservation. Nonetheless, non-physical maps, especially Hermitian-preserving maps, play a crucial role in quantum information science. To date, there exists no effective method for implementing these non-physical maps with quantum devices. In this work, we introduce the Hermitian-preserving map exponentiation algorithm, which can effectively realize the action of an arbitrary Hermitian-preserving map by encoding its output into a quantum process. We analyze the performances of this algorithm, including its sample complexity and robustness, and prove its optimality in certain cases. When combined with algorithms such as the Hadamard test and quantum phase estimation, it allows for the extraction of information and generation of states from outputs of Hermitian-preserving maps, enabling various applications. Utilizing positive but not completely positive maps, this algorithm provides exponential advantages in entanglement detection and quantification compared to protocols based on single-copy operations. In addition, it facilitates the recovery of noiseless quantum states from multiple copies of noisy states by implementing the inverse map of the corresponding noise channel, offering an intriguing approach to handling quantum errors. Our findings present a pathway for systematically and efficiently implementing non-physical actions with quantum devices, thereby boosting the exploration of potential quantum advantages across a wide range of information processing tasks.Comment: 34 pages, 10 figures, comments are welcom

How Robust is Google's Bard to Adversarial Image Attacks?

Multimodal Large Language Models (MLLMs) that integrate text and other modalities (especially vision) have achieved unprecedented performance in various multimodal tasks. However, due to the unsolved adversarial robustness problem of vision models, MLLMs can have more severe safety and security risks by introducing the vision inputs. In this work, we study the adversarial robustness of Google's Bard, a competitive chatbot to ChatGPT that released its multimodal capability recently, to better understand the vulnerabilities of commercial MLLMs. By attacking white-box surrogate vision encoders or MLLMs, the generated adversarial examples can mislead Bard to output wrong image descriptions with a 22% success rate based solely on the transferability. We show that the adversarial examples can also attack other MLLMs, e.g., a 26% attack success rate against Bing Chat and a 86% attack success rate against ERNIE bot. Moreover, we identify two defense mechanisms of Bard, including face detection and toxicity detection of images. We design corresponding attacks to evade these defenses, demonstrating that the current defenses of Bard are also vulnerable. We hope this work can deepen our understanding on the robustness of MLLMs and facilitate future research on defenses. Our code is available at https://github.com/thu-ml/Attack-Bard. Update: GPT-4V is available at October 2023. We further evaluate its robustness under the same set of adversarial examples, achieving a 45% attack success rate.Comment: Technical repor

Spin-glass ground state in a triangular-lattice compound YbZnGaO4_4

We report on comprehensive results identifying the ground state of a triangular-lattice structured YbZnGaO$_4$ to be spin glass, including no long-range magnetic order, prominent broad excitation continua, and absence of magnetic thermal conductivity. More crucially, from the ultralow-temperature a.c. susceptibility measurements, we unambiguously observe frequency-dependent peaks around 0.1 K, indicating the spin-glass ground state. We suggest this conclusion to hold also for its sister compound YbMgGaO$_4$, which is confirmed by the observation of spin freezing at low temperatures. We consider disorder and frustration to be the main driving force for the spin-glass phase.Comment: Version as accepted to PR

A tea bud segmentation, detection and picking point localization based on the MDY7-3PTB model

IntroductionThe identification and localization of tea picking points is a prerequisite for achieving automatic picking of famous tea. However, due to the similarity in color between tea buds and young leaves and old leaves, it is difficult for the human eye to accurately identify them.MethodsTo address the problem of segmentation, detection, and localization of tea picking points in the complex environment of mechanical picking of famous tea, this paper proposes a new model called the MDY7-3PTB model, which combines the high-precision segmentation capability of DeepLabv3+ and the rapid detection capability of YOLOv7. This model achieves the process of segmentation first, followed by detection and finally localization of tea buds, resulting in accurate identification of the tea bud picking point. This model replaced the DeepLabv3+ feature extraction network with the more lightweight MobileNetV2 network to improve the model computation speed. In addition, multiple attention mechanisms (CBAM) were fused into the feature extraction and ASPP modules to further optimize model performance. Moreover, to address the problem of class imbalance in the dataset, the Focal Loss function was used to correct data imbalance and improve segmentation, detection, and positioning accuracy.Results and discussionThe MDY7-3PTB model achieved a mean intersection over union (mIoU) of 86.61%, a mean pixel accuracy (mPA) of 93.01%, and a mean recall (mRecall) of 91.78% on the tea bud segmentation dataset, which performed better than usual segmentation models such as PSPNet, Unet, and DeeplabV3+. In terms of tea bud picking point recognition and positioning, the model achieved a mean average precision (mAP) of 93.52%, a weighted average of precision and recall (F1 score) of 93.17%, a precision of 97.27%, and a recall of 89.41%. This model showed significant improvements in all aspects compared to existing mainstream YOLO series detection models, with strong versatility and robustness. This method eliminates the influence of the background and directly detects the tea bud picking points with almost no missed detections, providing accurate two-dimensional coordinates for the tea bud picking points, with a positioning precision of 96.41%. This provides a strong theoretical basis for future tea bud picking

Effect of carbon fiber crystallite size on the formation of hafnium carbide coating and the mechanism of the reaction of hafnium with carbon fibers

The effect of carbon source crystallite size on the formation of hafnium carbide (HfC) coating was investigated via direct reaction of hafnium powders with mesophase pitch-based carbon fibers (CFs) heat-treated at various temperatures. X-ray diffraction, scanning electron microscopy and energy dispersive X-ray spectroscopy analyses reveal that uniform and dense HfC coatings are preferentially formed on CFs containing larger and more ordered graphite crystallites. The carbide synthesis temperature and the sizes of crystallites in the CFs have a remarkable influence on the integrity and thickness of the coatings. The formation the HfC coatings can be attributed to the surface diffusion of hafnium and the bi-directional diffusion of hafnium and carbon sources inside the HfC coating. The reaction of HfC coated carbon fibers with zirconium powders leads to the growth of ZrC on the HfC coating and this has been shown to occur by the diffusion of carbon from the carbon fiber core through the carbide coating to its surface