Career Adaptability, Work Engagement, and Employee Well-Being Among Chinese Employees: The Role of Guanxi

The present study examined whether and how career adaptability predicts employee well-being (EWB) based on career construction theory. A three-wave questionnaire design was used to collect the data, and 338 employees participated in the study. The results suggest that career adaptability has a significant effect on work engagement, which, in turn, predicts EWB. In addition to developing a mediation model, we tested the effect of guanxi as a moderator on the former part of the model. Thus, a moderated-mediation model was constructed in this research. In addition to the finding of the mediating role of work engagement, the discussion of guanxi represents a more important novel aspect that draws attention to contextual factors that may shape how employees respond to career adaptability. The results revealed that the indirect effect of career adaptability on EWB through work engagement when guanxi is low is stronger than that when guanxi is high. Furthermore, we discuss the limitations of this study and the implications for future research on career adaptability and EWB

The interplay between thermodynamics and kinetics in the solid-state synthesis of layered oxides.

In the synthesis of inorganic materials, reactions often yield non-equilibrium kinetic byproducts instead of the thermodynamic equilibrium phase. Understanding the competition between thermodynamics and kinetics is a fundamental step towards the rational synthesis of target materials. Here, we use in situ synchrotron X-ray diffraction to investigate the multistage crystallization pathways of the important two-layer (P2) sodium oxides Na0.67MO2 (M = Co, Mn). We observe a series of fast non-equilibrium phase transformations through metastable three-layer O3, O3' and P3 phases before formation of the equilibrium two-layer P2 polymorph. We present a theoretical framework to rationalize the observed phase progression, demonstrating that even though P2 is the equilibrium phase, compositionally unconstrained reactions between powder precursors favour the formation of non-equilibrium three-layered intermediates. These insights can guide the choice of precursors and parameters employed in the solid-state synthesis of ceramic materials, and constitutes a step forward in unravelling the complex interplay between thermodynamics and kinetics during materials synthesis

Tag2Text: Guiding Vision-Language Model via Image Tagging

This paper presents Tag2Text, a vision language pre-training (VLP) framework, which introduces image tagging into vision-language models to guide the learning of visual-linguistic features. In contrast to prior works which utilize object tags either manually labeled or automatically detected with a limited detector, our approach utilizes tags parsed from its paired text to learn an image tagger and meanwhile provides guidance to vision-language models. Given that, Tag2Text can utilize large-scale annotation-free image tags in accordance with image-text pairs, and provides more diverse tag categories beyond objects. As a result, Tag2Text achieves a superior image tag recognition ability by exploiting fine-grained text information. Moreover, by leveraging tagging guidance, Tag2Text effectively enhances the performance of vision-language models on both generation-based and alignment-based tasks. Across a wide range of downstream benchmarks, Tag2Text achieves state-of-the-art or competitive results with similar model sizes and data scales, demonstrating the efficacy of the proposed tagging guidance

Open-Set Image Tagging with Multi-Grained Text Supervision

In this paper, we introduce the Recognize Anything Plus Model (RAM++), an open-set image tagging model effectively leveraging multi-grained text supervision. Previous approaches (e.g., CLIP) primarily utilize global text supervision paired with images, leading to sub-optimal performance in recognizing multiple individual semantic tags. In contrast, RAM++ seamlessly integrates individual tag supervision with global text supervision, all within a unified alignment framework. This integration not only ensures efficient recognition of predefined tag categories, but also enhances generalization capabilities for diverse open-set categories. Furthermore, RAM++ employs large language models (LLMs) to convert semantically constrained tag supervision into more expansive tag description supervision, thereby enriching the scope of open-set visual description concepts. Comprehensive evaluations on various image recognition benchmarks demonstrate RAM++ exceeds existing state-of-the-art (SOTA) open-set image tagging models on most aspects. Specifically, for predefined commonly used tag categories, RAM++ showcases 10.2 mAP and 15.4 mAP enhancements over CLIP on OpenImages and ImageNet. For open-set categories beyond predefined, RAM++ records improvements of 5.0 mAP and 6.4 mAP over CLIP and RAM respectively on OpenImages. For diverse human-object interaction phrases, RAM++ achieves 7.8 mAP and 4.7 mAP improvements on the HICO benchmark. Code, datasets and pre-trained models are available at \url{https://github.com/xinyu1205/recognize-anything}.Comment: Homepage: https://github.com/xinyu1205/recognize-anythin

A Highly Selective Colorimetric Sensor for Cysteine in Water Solution and Bovine Serum Albumin

A simple colorimetric sensor, 2-bromonaphthalene-1,4-dione, has been developed for the Cysteine detection. The sensor showed its best performance in a mixture of ethanol and HEPES (5 : 5, v/v) solution at pH of 7.0. The results of UV-vis and fluorescence indicated that 2-bromonaphthalene-1,4-dione was selective and sensitive for Cysteine detection without the interference of other amino acids (Cysteine, Alanine, Arginine, Aspartinie, Glutamine, Glycine, Histidine, Isoleucine, Leucine, Lysine, Methionine, Proline, Serine, Threonine, Phenylalanine, Valine, Tryptophan, and Hydroxyproline). 2-Bromonaphthalene-1,4-dione also showed binding ability for Cysteine in bovine serum albumin and could be used as a potential colorimetric sensor among eighteen kinds of natural amino acids. Importantly, the recognition of CySH could be observed by naked eye

Primed 3D injectable microniches enabling low-dosage cell therapy for critical limb ischemia

The promise of cell therapy for repair and restoration of damaged tissues or organs relies on administration of large dose of cells whose healing benefits are still limited and sometimes irreproducible due to uncontrollable cell loss and death at lesion sites. Using a large amount of therapeutic cells increases the costs for cell processing and the risks of side effects. Optimal cell delivery strategies are therefore in urgent need to enhance the specificity, efficacy, and reproducibility of cell therapy leading to minimized cell dosage and side effects. Here, we addressed this unmet need by developing injectable 3D microscale cellular niches (microniches) based on biodegradable gelatin microcryogels (GMs). The microniches are constituted by in vitro priming human adipose-derived mesenchymal stem cells (hMSCs) seeded within GMs resulting in tissue-like ensembles with enriched extracellular matrices and enhanced cell–cell interactions. The primed 3D microniches facilitated cell protection from mechanical insults during injection and in vivo cell retention, survival, and ultimate therapeutic functions in treatment of critical limb ischemia (CLI) in mouse models compared with free cell-based therapy. In particular, 3D microniche-based therapy with 10[superscript 5] hMSCs realized better ischemic limb salvage than treatment with 10[superscript 6] free-injected hMSCs, the minimum dosage with therapeutic effects for treating CLI in literature. To the best of our knowledge, this is the first convincing demonstration of injectable and primed cell delivery strategy realizing superior therapeutic efficacy for treating CLI with the lowest cell dosage in mouse models. This study offers a widely applicable cell delivery platform technology to boost the healing power of cell regenerative therapy.National Natural Science Foundation (China) (Grant 81171474)National Natural Science Foundation (China) (Grant 51273106)National Natural Science Foundation (China) (Grant 81227901)Beijing Municipal Natural Science Foundation (Grant 157142090)National Basic Research Program of China (973 Program) (Grant 2011CB707701

Machine Learning Methods in Real-World Studies of Cardiovascular Disease

Objective: Cardiovascular disease (CVD) is one of the leading causes of death worldwide, and answers are urgently needed regarding many aspects, particularly risk identification and prognosis prediction. Real-world studies with large numbers of observations provide an important basis for CVD research but are constrained by high dimensionality, and missing or unstructured data. Machine learning (ML) methods, including a variety of supervised and unsupervised algorithms, are useful for data governance, and are effective for high dimensional data analysis and imputation in real-world studies. This article reviews the theory, strengths and limitations, and applications of several commonly used ML methods in the CVD field, to provide a reference for further application. Methods: This article introduces the origin, purpose, theory, advantages and limitations, and applications of multiple commonly used ML algorithms, including hierarchical and k-means clustering, principal component analysis, random forest, support vector machine, and neural networks. An example uses a random forest on the Systolic Blood Pressure Intervention Trial (SPRINT) data to demonstrate the process and main results of ML application in CVD. Conclusion: ML methods are effective tools for producing real-world evidence to support clinical decisions and meet clinical needs. This review explains the principles of multiple ML methods in plain language, to provide a reference for further application. Future research is warranted to develop accurate ensemble learning methods for wide application in the medical field

The prognostic value of deep earlobe creases in patients with acute ischemic stroke

Background and purposeData on earlobe crease (ELC) among patients with acute ischemic stroke (AIS) are limited. Here, we determined the frequency and characteristics of ELC and the prognostic effect of ELC among AIS patients.MethodsA total of 936 patients with acute AIS were enrolled during the period between December 2018 and December 2019. The patients were divided into those without and with ELC, unilateral and bilateral ELC, and shallow and deep ELC, according to the photographs taken of the bilateral ears. Logistic regression models were used to estimate the effect of ELC, bilateral ELC, and deep ELC on poor functional outcomes at 90 days (a modified Rankin Scale score ≥2) in AIS patients.ResultsAmong the 936 AIS patients, there were 746 (79.7%) patients with ELC. Among patients with ELC, there were 156 (20.9%) patients with unilateral ELC and 590 (79.1%) with bilateral ELC and 476 (63.8%) patients with shallow ELC and 270 (36.2%) with deep ELC. After adjusting for age, sex, baseline NIHSS score, and other potential covariates, patients with deep ELC were associated with a 1.87-fold [odds ratio (OR) 1.87; 95% confidence interval (CI), 1.13–3.09] and 1.63-fold (OR 1.63; 95%CI, 1.14–2.34) increase in the risk of poor functional outcome at 90 days in comparison with those without ELC or shallow ELC.ConclusionELC was a common phenomenon, and eight out of ten AIS patients had ELC. Most patients had bilateral ELC, and more than one-third had deep ELC. Deep ELC was independently associated with an increased risk of poor functional outcome at 90 days