Explore the vast range of titles available.

Collective variable-based enhanced sampling methods have been widely used to study thermodynamic properties of complex systems. Efficiency and accuracy of these enhanced sampling methods are affected by two factors: constructing appropriate collective variables for enhanced sampling and generating accurate free energy surfaces. Recently, many machine learning techniques have been developed to improve the quality of collective variables and the accuracy of free energy surfaces. Although machine learning has achieved great successes in improving enhanced sampling methods, there are still many challenges and open questions. In this perspective, we shall review recent developments on integrating machine learning techniques and collective variable-based enhanced sampling approaches. We also discuss challenges and future research directions including generating kinetic information, exploring high-dimensional free energy surfaces, and efficiently sampling all-atom configurations. Graphic abstract

Bridge inspection and structural health monitoring represent the primary approaches to managing bridge resilience. Data acquired through inspection and monitoring activities provides an effective technical basis for the systematic implementation of bridge resilience control strategies. Yet, uninterrupted monitoring and diverse inspection campaigns have yielded an enormous volume of data, which directly imposes comprehensive and stringent challenges on data storage, transmission and processing. Consequently, data compression has become a research priority in the field of bridge resilience control. However, existing data compression algorithms are all general-purpose data processing techniques, which decouple the intrinsic physical relevance between monitoring data and bridge structural behaviors. To tackle this limitation, this study integrates domain knowledge, the time-series characteristics of bridge monitoring data, and bridge deterioration models into the design of a novel data compression algorithm. This approach addresses the issue of indiscriminate data compression inherent to conventional algorithms, thereby enabling efficient data compression while preserving critical bridge structural state information. By incorporating domain knowledge, the proposed method transforms raw monitoring data into data information with engineering attributes. based on these attributes, a set of interrelated monitoring data is further converted into a small subset of key data that is directly applicable to bridge resilience control practice. Leveraging the steady-state variation law of bridge operational performance, the dynamic structural characteristics of bridges are extracted from time-series monitoring data, which correspondingly reduces the storage demand of time-series datasets. For data sampling intervals interrupted by various types of system faults, a sparse data supplementation method is proposed. After data supplementation, the complete dataset is further refined by utilizing the inherent time-series characteristics of the monitoring data, which not only ensures data integrity but also further reduces the overall data volume. Simulation analyses demonstrate that the domain knowledge-based compression method achieves a data compression ratio of 75%. Moreover, the comprehensive compression ratio exceeds 92% after the synergistic processing of time-series feature extraction and sparse data supplementation, with a data fidelity rate of 95%. These performance metrics indicate that the proposed method can reduce the data storage costs and transmission bandwidth consumption associated with bridge resilience control by 75% to 92%. Meanwhile, the 95% feature retention accuracy satisfies the engineering precision requirements for bridge resilience control assessments, which effectively reconciles the inherent contradiction between data compression efficiency and structural evaluation accuracy.

The current study aimed to determine the safety profile of intra-articular-injected allogeneic adipose-derived mesenchymal stem cells (ADSCs) GXCPC1 in subjects with knee osteoarthritis (OA) and its preliminary efficacy outcome. The 3 + 3 phase I study was designed with two dose-escalation cohorts: low dose (6.7 × 106 GXCPC1, N = 5) and high dose (4 × 107 GXCPC1, N = 6). The primary endpoint was safety, which was evaluated by recording adverse events throughout the trial; the secondary endpoints included total, pain, stiffness, and function subscales of the Western Ontario and McMaster Universities Arthritis Index (WOMAC), Visual Analogue Scale (VAS) for pain, and 12-Item Short Form (SF-12) health survey questionnaire. The GXCPC1 treatment was found to be safe after 1 year of follow-up with no treatment-related severe adverse events observed. When compared to baseline, subjects in both the low- and high-dose cohorts demonstrated improving trends in pain and knee function after receiving GXCPC1 treatment. Generally, the net change in pain (95% confidence interval (CI) = −7.773 to −2.561t at 12 weeks compared to baseline) and knee function (95% CI = −24.297 to −10.036t at 12 weeks compared to baseline) was better in subjects receiving high-dose GXCPC1. Although this study included a limited number of subjects without a placebo arm, it showed that the intra-articular injection of ADSCs was safe and well-tolerated in subjects with therapeutic alternatives to treat knee OA. However, a larger scale study with an appropriate control would be necessary for clinical efficacy in the following study.

Most photoredox catalysts in current use are precious metal complexes or synthetically elaborate organic dyes, the cost of which can impede their application for large-scale industrial processes. We found that a combination of triphenylphosphine and sodium iodide under 456-nanometer irradiation by blue light–emitting diodes can catalyze the alkylation of silyl enol ethers by decarboxylative coupling with redox-active esters in the absence of transition metals. Deaminative alkylation using Katritzky’s N-alkylpyridinium salts and trifluoromethylation using Togni’s reagent are also demonstrated. Moreover, the phosphine/iodide-based photoredox system catalyzes Minisci-type alkylation of N-heterocycles and can operate in tandem with chiral phosphoric acids to achieve high enantioselectivity in this reaction.

Bridge life cycle management is a collaborative work process involving multiple groups, and finite element analysis is a popular method used in bridge management. The frequent establishment of finite element models by diverse groups directly reduces the efficiency and quality of bridge management. To address this issue, a large model of bridge model transformation was developed by integrating domain knowledge based on existing general large models, and data transformation and data recombination were proposed. Simulation investigation demonstrates that the proposed bridge modeling approach may generate finite element models that fulfill the various needs of participating organizations based on their specific criteria. The domain knowledge base supports the data transformation algorithm, which calls the Pangu Large Model’s scientific computing model to enable autonomous data translation, minimizing the amount of time spent manually participating in the modeling process. The seismic study of the bridge reveals that the finite element model generated using data recombination differs by 5.93% from the analytical results of the manually established finite element model. Overall, data transformation and recombination modeling methods have greatly increased bridge modeling efficiency, reducing the need for duplicate modeling and improving bridge management efficiency.

Tendon injuries cause prolonged disability and never recover completely. Current mechanistic understanding of tendon regeneration is limited. Here, we use single-cell transcriptomics to identify a tubulin polymerization-promoting protein family member 3-expressing ( Tppp3 + ) cell population as potential tendon stem cells. Through inducible lineage tracing, we demonstrate that these cells can generate new tenocytes and self-renew upon injury. A fraction of Tppp3 + cells expresses platelet-derived growth factor receptor alpha ( Pdfgra ). Ectopic platelet-derived growth factor-AA (PDGF-AA) protein induces new tenocyte production while inactivation of Pdgfra in Tppp3 + cells blocks tendon regeneration. These results support Tppp3 + Pdgfra + cells as tendon stem cells. Unexpectedly, Tppp3 − Pdgfra + fibro-adipogenic progenitors coexist in the tendon stem cell niche and give rise to fibrotic cells, revealing a clandestine origin of fibrotic scars in healing tendons. Our results explain why fibrosis occurs in injured tendons and present clinical challenges to enhance tendon regeneration without a concurrent increase in fibrosis by PDGF application. Using single-cell transcriptomics and in vivo injury models, Harvey et al. identify a Tppp3 + Pdgfra + stem cell population in the tendon sheath and demonstrate the role of PDGFRα signalling in regeneration and fibrosis.