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As globalization becomes more and more obvious, digital transformation has become a necessity for business growth. Innovation, as the core driving force for sustainable development, is always pursued by enterprises. However, the way digital transformation drives innovation in firms is still a subject of scholarly disagreement. As scholars’ understanding of digital transformation in academia was deepened and the measurement methods are similar, this paper will summarize the influence relationships of linear influence and inverted U-shaped influence in the current academia and outline the mediating and moderating roles affecting the two relationships. A well-calibrated degree of digitalization may contribute to improved innovation performance within enterprises. However, if the organization’s collaborative management capacity fails to align with the degree of digital advancement, it may lead to adverse effects. In addition, the internal characteristics and the external environment of enterprises, as well as macro policies, will also moderate the relationship. In future studies in this area, it is necessary to be wary of incomplete and unrepresentative descriptions in the data construction process.

This study aims to review the long-term subjective and objective efficacy of mandibular advancement devices (MAD) in the treatment of adult obstructive sleep apnea (OSA). Electronic databases such as PubMed, Embase, and Cochrane Library were searched. Randomized controlled trials (RCTs) and non-randomized self-controlled trials with a treatment duration of at least 1 year with MAD were included. The quality assessment and data extraction of the included studies were conducted in the meta-analysis. A total of 22 studies were included in this study, of which 20 (546 patients) were included in the meta-analysis. All the studies had some shortcomings, such as small sample sizes, unbalanced sex, and high dropout rates. The results suggested that long-term treatment of MAD can significantly reduce the Epworth sleepiness scale (ESS) by -3.99 (95%CI -5.93 to -2.04, p <0.0001, I 2 = 84%), and the apnea-hypopnea index (AHI) -16.77 (95%CI -20.80 to -12.74) events/h ( p <0.00001, I 2 = 97%). The efficacy remained statistically different in the severity (AHI<30 or >30 events/h) and treatment duration (duration <5y or >5y) subgroups. Long-term use of MAD could also significantly decrease blood pressure and improve the score of functional outcomes of sleep questionnaire (FOSQ). Moderate evidence suggested that the subjective and objective effect of MAD on adult OSA has long-term stability. Limited evidence suggests long-term use of MAD might improve comorbidities and healthcare. In clinical practice, regular follow-up is recommended.

Carbon monoxide (CO) is a key gaseous signaling molecule in living cells and organisms. This protocol illustrates the synthesis of a highly sensitive Nile Red (NR)-Pd-based fluorescent probe, NR-PdA, and its applications for detecting endogenous CO in tissue culture cells, ex vivo organs, and zebrafish embryos. In the NR-PdA synthesis process, 3-diethylamine phenol reacts with sodium nitrite in the acidic condition to afford 5-(diethylamino)-2-nitrosophenol hydrochloride (compound 1), which is further treated with 1-naphthalenol at a high temperature to provide the NR dye via a cyclization reaction. Finally, NR is reacted with palladium acetate to obtain the desired Pd-based fluorescent probe NR-PdA. NR-PdA possesses excellent two-photon excitation and near-IR emission properties, high stability, low background fluorescence, and a low detection limit. In addition to the chemical synthesis procedures, we provide step-by-step procedures for imaging endogenous CO in RAW 264.7 cells, mouse organs ex vivo, and live zebrafish embryos. The synthesis process for the probe requires â^¼4 d, and the biological imaging experiments take â^¼14 d.

Background Cassava ( Manihot esculenta Crantz) is widely planted in tropical and several subtropical regions in which drought, high temperatures, and other abiotic stresses occur. Metallothionein (MT) is a group of conjugated proteins with small molecular weight and rich in cysteine. These proteins play a substantial role in response to physiological stress through the regulation of reactive oxygen species (ROS). However, the biological functions of MT genes in cassava are unknown. Results A total of 10 MeMT genes were identified in the cassava genome. The MeMTs were divided into 3 groups (Types 2–4) based on the contents and distribution of Cys residues. The MeMTs exhibited tissue-specific expression and located on 7 chromosomes. The MeMT promoters contain some hormones regulatory and stresses responsiveness elements. MeMTs were upregulated under hydrogen peroxide (H 2 O 2 ) treatment and in respond to post-harvest physiological deterioration (PPD). The results were consistent with defense-responsive cis-acting elements in the MeMT promoters. Further, four of MeMTs were selected and silenced by using the virus-induced gene silencing (VIGS) method to evaluate their functional characterization. The results of gene-silenced cassava suggest that MeMTs are involved in oxidative stress resistance, as ROS scavengers. Conclusion We identified the 10 MeMT genes, and explore their evolutionary relationship, conserved motif, and tissue-specific expression. The expression profiles of MeMT s under three kinds of abiotic stresses (wounding, low-temperature, and H 2 O 2 ) and during PPD were analyzed. The tissue-specific expression and the response to abiotic stresses revealed the role of MT in plant growth and development. Furthermore, silenced expression of MeMTs in cassava leaves decreased its tolerance to ROS, consistent with its predicted role as ROS scavengers. In summary, our results suggest an important role of MeMTs in response to physiological stress as well as species adaptation via the regulation of ROS homeostasis.

To develop green finance and ensure the goal of carbon peaking and carbon neutrality, China set up the pilot zones for green finance reform and innovation in 2017. We empirically tested the policy effect of the pilot zones with data from 2010 to 2019 for prefecture-level cities in China. The study shows that the pilot zones have induced an effect on regional green technology innovation, reflected in the application and acquisition of both green invention patents and green utility patents, and the promotion effect is better for green utility patents than green invention patents, which is supported by the robustness test using PSM-DID. This study provides theoretical support and empirical evidence for evaluating the policy effects of the pilot zones and provides a reference for the differentiated formulation of green financial policies.

In intensive care unit (ICU) patients undergoing mechanical ventilation (MV), the occurrence of difficult weaning contributes to increased ventilator-related complications, prolonged hospitalization duration, and a significant rise in healthcare costs. Therefore, early identification of influencing factors and prediction of patients at risk of difficult weaning can facilitate early intervention and preventive measures. This study aimed to strengthen airway management for ICU patients by constructing a risk prediction model with comprehensive and individualized offline programs based on machine learning techniques. This study involved the collection of data from 487 patients undergoing MV in the ICU, with a total of 36 variables recorded. The dataset was divided into a training set (70% of the data) and a test set (30% of the data). Five machine learning models, namely logistic regression, random forest, support vector machine, light gradient boosting machine, and extreme gradient boosting, were compared to predict the risk of difficult weaning in ICU patients with MV. Significant influencing factors were identified based on the results of these models, and a risk prediction model for ICU patients with MV was established. When evaluating the models using AUC (Area under the Curve of ROC) and Accuracy as performance metrics, the Random Forest algorithm exhibited the best performance among the five machine learning algorithms. The area under the operating characteristic curve for the subjects was 0.805, with an accuracy of 0.748, recall (0.888), specificity (0.767) and F1 score (0.825). This study successfully developed a risk prediction model for ICU patients with MV using a machine learning algorithm. The Random Forest algorithm demonstrated the highest prediction performance. These findings can assist clinicians in accurately assessing the risk of difficult weaning in patients and formulating effective individualized treatment plans. Ultimately, this can help reduce the risk of difficult weaning and improve the quality of life for patients.

The rapid integration of complex sensors and electronic control units (ECUs) in autonomous vehicles significantly increases cybersecurity risks in vehicular networks. Although the Controller Area Network (CAN) is efficient, it lacks inherent security mechanisms and is vulnerable to various network attacks. The traditional Intrusion Detection System (IDS) makes it difficult to effectively deal with the dynamics and complexity of emerging threats. To solve these problems, a lightweight vehicular network intrusion detection framework based on Dynamic Feature Fusion Federated Learning (DFF-FL) is proposed. The proposed framework employs a two-stream architecture, including a transformer-augmented autoencoder for abstract feature extraction and a lightweight CNN-LSTM–Attention model for preserving temporal and local patterns. Compared with the traditional theoretical framework of the federated learning, DFF-FL first dynamically fuses the deep feature representation of each node through the transformer attention module to realize the fine-grained cross-node feature interaction in a heterogeneous data environment, thereby eliminating the performance degradation caused by the difference in feature distribution. Secondly, based on the final loss LAEX,X^ index of each node, an adaptive weight adjustment mechanism is used to make the nodes with excellent performance dominate the global model update, which significantly improves robustness against complex attacks. Experimental evaluation on the CAN-Hacking dataset shows that the proposed intrusion detection system achieves more than 99% F1 score with only 1.11 MB of memory and 81,863 trainable parameters, while maintaining low computational overheads and ensuring data privacy, which is very suitable for edge device deployment.

To explore the potential effects and the corresponding mechanisms of brain and muscle arnt-like protein-1 (BMAL1) on the progression of intervertebral disc degeneration (IVDD) in vitro studies. The expression of BMAL1, SIRT1 and PINK1 were evaluated by the method of siRNA/pcDNA in the immortalized nucleus pulposus (NP) cells. The expression of SIRT1/PGC-1α pathway was assessed. The characteristics of NP cell, containing the activity and density, the level of apoptosis, inflammatory response, reactive oxygen species (ROS), senescence, and mitophagy were evaluated. The overexpression of BMAL1 was achieved with the pcDNA3.1, the expression of SIRT1 and PGC-1α were increased, the inflammatory response, the ROS, the level of apoptosis and senescence were decreased, however, the level of mitophagy, the activity and density of NP cell were enhanced. The BMAL1 inhibites the progression of IVDD by activating the SIRT1/PGC-1α pathway in the vitro studies.

This study developed a prognostic model for patients with colon adenocarcinoma (COAD) based on glycosylation-associated genes. By analyzing TCGA-COAD data, 110 key genes were identified, and a prognostic model incorporating five glycosylation-related genes was constructed. The model exhibits good predictive performance and is significantly associated with clinical features such as age, N stage, M stage, and lymph node count. The prognostic genes are involved in various biological processes and pathways, influence T cell differentiation, and may contribute to CRC development. High-risk patients show a higher degree of immune cell infiltration. This model aids in the early diagnosis, prognosis assessment, and treatment planning for CRC, and offers a direction for further research.