Browse Articles

Development and validation of nomograms for predicting pentafecta outcomes before and after robot-assisted radical prostatectomy: a retrospective study

Pressure treatment enables white-light emission in Zn-IPA MOF via asymmetrical metal-ligand chelate coordination

Association between air pollution and lifestyle with the risk of developing mild cognitive impairment and dementia in individuals with cardiometabolic diseases

Abstract Lifestyle factors and ambient air pollution are linked to dementia and CMDs, yet few studies have investigated their impact on dementia risk in CMDs patients at the same time. The Cox proportional hazards model was used to evaluate the influence of lifestyle and ambient air pollution on the dementia risk of the CMDs population among 438,681 participants in the UK Biobank. It is found that the risk of developing mild cognitive impairment and dementia in the population seems to increase with the increase in the number of CMDs. There appears to be a statistically significant association between high levels of ambient air pollution, unhealthy lifestyles, and a higher risk of developing mild cognitive impairment and dementia in the CMDs population. It is found that a healthy lifestyle may have an effect modifier role in the association between ambient air pollution and the risk of mild cognitive impairment and the development of dementia in patients with CMDs. Therefore, maybe people with CMDs can lessen the impact of ambient air pollution on their risk of developing mild cognitive impairment and dementia by improving their lifestyle.

Morphological and functional convergence of visual projection neurons from diverse neurogenic origins in Drosophila

Hardware-efficient preparation of architecture-specific graph states on near-term quantum computers

Abstract Highly entangled quantum states are an ingredient in numerous applications in quantum computing. However, preparing these highly entangled quantum states on currently available quantum computers at high fidelity is limited by ubiquitous errors. Besides improving the underlying technology of a quantum computer, the scale and fidelity of these entangled states in near-term quantum computers can be improved by specialized compilation methods. In this work, the compilation of quantum circuits for the preparation of highly entangled architecture-specific graph states is addressed by defining and solving a formal model, i.e., a form of discrete constraint optimization. Our model incorporates information about gate cancellations, gate commutations, and accurate gate timing to determine an optimized graph state preparation circuit. Up to now, these aspects have only been considered independently of each other, typically applied to arbitrary quantum circuits. We quantify the quality of a generated state by performing stabilizer measurements and determining its fidelity. We show that our new method reduces the error when preparing a seven-qubit graph state by 3.5x on average compared to the state-of-the-art Qiskit solution. For a linear eight-qubit graph state, the error is reduced by 6.4x on average. The presented results highlight the ability of our approach to prepare higher fidelity or larger-scale graph states on gate-based quantum computing hardware.

Guaranteed efficient energy estimation of quantum many-body Hamiltonians using ShadowGrouping

Abstract Estimation of the energy of quantum many-body systems is a paradigmatic task in various research fields. In particular, efficient energy estimation may be crucial in achieving a quantum advantage for a practically relevant problem. For instance, the measurement effort poses a critical bottleneck for variational quantum algorithms. We aim to find the optimal strategy with single-qubit measurements that yields the highest provable accuracy given a total measurement budget. As a central tool, we establish tail bounds for empirical estimators of the energy. They are helpful for identifying measurement settings that improve the energy estimate the most. This task constitutes an NP-hard problem. However, we are able to circumvent this bottleneck and use the tail bounds to develop a practical, efficient estimation strategy, which we call ShadowGrouping. As the name indicates, it combines shadow estimation methods with grouping strategies for Pauli strings. In numerical experiments, we demonstrate that ShadowGrouping improves upon state-of-the-art methods in estimating the electronic ground-state energies of various small molecules, both in provable and practical accuracy benchmarks. Hence, this work provides a promising way, e.g., to tackle the measurement bottleneck associated with quantum many-body Hamiltonians.

Environmental influence and species occurrence of yellowjacket drones in an invaded area

Organocatalytic Activation of Alkynes Enabled Remote Control of Atroposelectivity via Vinylidene <i>para</i>-Quinone Methides

Cortical arealization of interneurons defines shared and distinct molecular programs in developing human and macaque brains

Establishment and internal validation of a model to predict the efficacy of Adalimumab in Crohn’s disease

Illuminating the impact of N-terminal acetylation: from protein to physiology

Research on the impact of land use and land cover changes on local meteorological conditions and surface ozone in the north China plain from 2001 to 2020

Silicate-Confined Hydrogen on Nanoscale Zerovalent Iron for Efficient Defluorination Reactions

Two-layer homolog network approach for PFAS nontarget screening and retrospective data mining

Abstract The rapid increase of novel per- and polyfluoroalkyl substances (PFAS) raises concerns, while their identification remains challenging. Here, we develop a two-layer homolog network approach for PFAS nontarget screening using mass spectrometry. The first layer constructs networks between homologs, with evaluation showing that it filters 94% of false candidates. The second layer builds a network between classes to expedite the identification of PFAS. We detected 94 PFAS in twelve waterproof products and two related industrial sludges, including 36 novel PFAS not previously reported in any sample. A local dataset is constructed for retrospective analysis by re-analyzing our previous samples, revealing fifteen novel PFAS in samples collected in 2005. The retrieval of the public database MassIVE uncovers novel PFAS in samples from seven countries. Here, we reveal the historic and global presence of novel PFAS, providing guidance for the management and policy-making concerning persistent chemicals.

Elucidating the role of pyrimidine metabolism in prostate cancer and its therapeutic implications

Structure of transmembrane AMPA receptor regulatory protein subunit γ2

Abnormality detection in nailfold capillary images using deep learning with EfficientNet and cascade transfer learning

Structural basis of phosphate export by human XPR1

Unloading damage evolution and rockburst risk assessment of Xuefengshan No.1 tunnel by combining multiple approaches

Training data diversity enhances the basecalling of novel RNA modification-induced nanopore sequencing readouts