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In this work, Ti 3 C 2 MXene, a novel two-dimensional nanosheet, was introduced to waterborne polyurethane (WPU) coatings to prepare a composite coating. First, MAX phase materials were in situ etched by HF acid and further intercalated by water molecules to obtain exfoliated single-layer MXene nanosheet. And then, composite coatings were prepared via solution-blending low addition (0–0.4 wt%) of MXene, self-prepared waterborne polyacrylate emulsion (PAE) and isocyanate hardener, applying on Q235 mild steel. Results of AFM, XRD SEM and SEM–EDS confirm that single-layer MXene nanosheets with large lateral-to-thickness ratio are successfully prepared and achieved homogenous distribution within WPU matrix. With 0.4 wt% MXene incorporated, the WPU/Ti 3 C 2 MXene composite coatings reach a lowest corrosion current of 2.143 × 10 –6  A/cm 2 , a decrease of one order of magnitude compared with blank WPU (1.599 × 10 –5  A/cm 2 ) and own an excellent UV-blocking property (almost block the whole UV light). Graphical abstract

Location fingerprinting using Received Signal Strength Indicators (RSSIs) has become a popular technique for indoor localization due to its use of existing Wi-Fi infrastructure and Wi-Fi-enabled devices. Artificial intelligence/machine learning techniques such as Deep Neural Networks (DNNs) have been adopted to make location fingerprinting more accurate and reliable for large-scale indoor localization applications. However, the success of DNNs for indoor localization depends on the availability of a large amount of pre-processed and labeled data for training, the collection of which could be time-consuming in large-scale indoor environments and even challenging during a pandemic situation like COVID-19. To address these issues in data collection, we investigate multi-dimensional RSSI data augmentation based on the Multi-Output Gaussian Process (MOGP), which, unlike the Single-Output Gaussian Process (SOGP), can exploit the correlation among the RSSIs from multiple access points in a single floor, neighboring floors, or a single building by collectively processing them. The feasibility of MOGP-based multi-dimensional RSSI data augmentation is demonstrated through experiments using the hierarchical indoor localization model based on a Recurrent Neural Network (RNN)—i.e., one of the state-of-the-art multi-building and multi-floor localization models—and the publicly available UJIIndoorLoc multi-building and multi-floor indoor localization database. The RNN model trained with the UJIIndoorLoc database augmented with the augmentation mode of “by a single building”, where an MOGP model is fitted based on the entire RSSI data of a building, outperforms the other two augmentation modes and results in the three-dimensional localization error of 8.42 m.

This retrospective study investigated the impact of preoperative comorbidities on postoperative complications and survival in 408 head and neck cancer (HNC) patients undergoing complete tumor resection for curative intent. The mean age was 62.5 ± 13.2 years; 58.6% were male, 32.4% had pT3-4 tumors, and 27.5% had pN1-3 disease. Comorbidities were present in 70.6%, primarily hypertension (36.8%), cardiac disease (24.5%), endocrine/metabolic diseases (21.6%), pulmonary diseases (13.2%), and cerebrovascular diseases (CVDs, 10.8%). The overall postoperative medical/surgical complications rate was 24.7% (medical: 8.1%, surgical: 18.4%). Patients with comorbidities had higher complication rates (28.1% overall, 9.4% medical, 20.5% surgical). CVDs (20.5% vs. 6.6%), cardiac disease (14.0% vs. 6.2%), and endocrine/metabolic diseases (13.6% vs. 6.6%) significantly increased medical complication risks. Multivariable analysis identified tumor located in oral cavity, ASA grade III–IV, prolonged operation (> 3 h), flap reconstruction, and tracheotomy as independent risk factors for complications. Survival analysis showed reduced overall survival in patients with higher ASA grades, cervical lymph node metastasis, or history of preoperative adjuvant therapy (radiotherapy, chemotherapy, concurrent chemoradiotherapy). The findings highlight that preoperative CVDs, cardiac disease, or endocrine/metabolic disorders elevate medical complication risks by 2–3 times, underscoring the need for thorough preoperative assessment to improve outcomes in HNC surgery.

Large-scale multi-building and multi-floor indoor localization has recently been the focus of intense research in indoor localization based on Wi-Fi fingerprinting. Although significant progress has been made in developing indoor localization algorithms, few studies are dedicated to the critical issues of using existing and constructing new Wi-Fi fingerprint databases, especially for large-scale multi-building and multi-floor indoor localization. In this paper, we first identify the challenges in using and constructing Wi-Fi fingerprint databases for large-scale multi-building and multi-floor indoor localization and then provide our recommendations for those challenges based on a case study of the UJIIndoorLoc database, which is the most popular publicly available Wi-Fi fingerprint multi-building and multi-floor database. Through the case study, we investigate its statistical characteristics with a focus on the three aspects of (1) the properties of detected wireless access points, (2) the number, distribution and quality of labels, and (3) the composition of the database records. We then identify potential issues and ways to address them using the UJIIndoorLoc database. Based on the results from the case study, we not only provide valuable insights on the use of existing databases but also give important directions for the design and construction of new databases for large-scale multi-building and multi-floor indoor localization in the future.

Building information modeling (BIM) has been widely applied throughout the entire lifecycle of projects in the architecture, engineering, and construction (AEC) industry. The errors in BIM models can lead to significant losses in engineering projects, thus leading to BIM-based model checking (BMC) technology garnering an increasing amount of attention. Despite numerous documents detailing the BMC process, there is a lack of systematic analysis and visualization of existing research. This study employs a combined approach of scientometric analysis and a critical review to survey articles on BMC published from 2008 to 2024 in the Web of Science (WOS) and Scopus databases. The scientific analysis objectively presents the status and evolution of this research field through quantitative data, including publications, authors, and references. Furthermore, the critical review is employed to analyze the content of the articles, summarizing the topics and challenges of current research. Finally, potential promising directions for future development are proposed.

Proteins exhibit remarkable molecular recognition by dynamically adjusting their conformations to selectively interact with ligands at specialized binding sites. To bind hydrated ligands, proteins leverage amino acid residues with similar water affinities as the substrate, minimizing the energy required to strip water molecules from the hydrophilic substrates. In synthetic receptor design, replicating this sophisticated adaptability remains a challenge, as most artificial receptors are optimized to bind desolvated substances. Here, we show that proline-based synthetic receptors can mimic the conformational dynamics of proteins to achieve selective binding of hydrophilic and amphiphilic fluoride substances in aqueous environments. This finding highlights the critical role of receptor flexibility and strategic hydrophilicity in enhancing ligand recognition and affinity in water. Moreover, it establishes a new framework for designing versatile synthetic receptors with tunable hydrophobicity and hydrophilicity profiles. Proteins dynamically adjust their conformations to interact with their ligands through binding sites that accommodate either amphiphilic or hydrophilic substrates, but most synthetic receptors are designed to bind desolvated substances. Here, the authors design proline-based receptors capable of binding hydrated and amphiphilic substances.

In a wireless sensor network (WSN), reducing the energy consumption of battery-powered sensor nodes is key to extending their operating duration before battery replacement is required. Message bundling can save on the energy consumption of sensor nodes by reducing the number of message transmissions. However, bundling a large number of messages could increase not only the end-to-end delays and message transmission intervals, but also the packet error rate (PER). End-to-end delays are critical in delay-sensitive applications, such as factory monitoring and disaster prevention. Message transmission intervals affect time synchronization accuracy when bundling includes synchronization messages, while an increased PER results in more message retransmissions and, thereby, consumes more energy. To address these issues, this paper proposes an optimal message bundling scheme based on an objective function for the total energy consumption of a WSN, which also takes into account the effects of packet retransmissions and, thereby, strikes the optimal balance between the number of bundled messages and the number of retransmissions given a link quality. The proposed optimal bundling is formulated as an integer nonlinear programming problem and solved using a self-adaptive global-best harmony search (SGHS) algorithm. The experimental results, based on the Cooja emulator of Contiki-NG, demonstrate that the proposed optimal bundling scheme saves up to 51.8% and 8.8% of the total energy consumption with respect to the baseline of no bundling and the state-of-the-art integer linear programming model, respectively.

In the United States, an estimated 38 million individuals (10% of the population) have type 2 diabetes mellitus (T2D), while approximately 97.6 million adults (38%) have prediabetes. Long noncoding RNAs (lncRNAs) are critical regulators of gene expression and metabolism. We were the first to demonstrate that lncRNA Growth Arrest-Specific Transcript 5 (GAS5 (human)/gas5 (mouse)) is decreased in the serum of T2D patients and established GAS5 as a biomarker for T2D diagnosis and onset prediction, now validated by other groups. We further demonstrated that GAS5 depletion impaired glucose uptake, decreased insulin receptor levels, and inhibited insulin signaling in human adipocytes, highlighting its potential as a therapeutic target in T2D. To address this, we developed NPC86, a small-molecule compound that stabilizes GAS5 by disrupting its interaction with UPF-1, an RNA helicase involved in nonsense-mediated decay (NMD) that regulates RNA stability. NPC86 increased GAS5 and insulin receptor (IR) levels, enhanced insulin signaling, and improved glucose uptake in vitro. In this study, we tested the efficacy of NPC86 in vivo in a diet-induced obese diabetic (DIOD) mouse model, and NPC86 treatment elevated gas5 levels, improved glucose tolerance, and enhanced insulin sensitivity, with no observed toxicity or weight changes. A transcriptomics analysis of adipose tissue revealed the upregulation of insulin signaling and metabolic pathways, including oxidative phosphorylation and glycolysis, while inflammatory pathways were downregulated. These findings highlight NPC86’s therapeutic potential in T2D.

Long-term chronic inflammation after Achilles tendon injury is critical for tendinopathy. Platelet-rich plasma (PRP) injection, which is a common method for treating tendinopathy, has positive effects on tendon repair. In addition, tendon-derived stem cells (TDSCs), which are stem cells located in tendons, play a major role in maintaining tissue homeostasis and postinjury repair. In this study, injectable gelatine methacryloyl (GelMA) microparticles containing PRP laden with TDSCs (PRP–TDSC–GM) were prepared by a projection-based 3D bioprinting technique. Our results showed that PRP–TDSC–GM could promote tendon differentiation in TDSCs and reduce the inflammatory response by downregulating the PI3K–AKT pathway, thus promoting the structural and functional repair of tendons in vivo. Graphical Abstract

Histamine (HIM) and tyramine (TYM) are among the most toxic biogenic amines (BAs) commonly found in various fermented soybean foods, yet the crucial BAs-producing strains are ignored. This study discussed and compared the effectiveness of two methods based on medium pH screening and target gene amplification for identifying HIM- and TYM-producing strains from two fermented soybean foods. The crucial strains responsible for HIM and TYM formation were identified and then characterized. It was found that the strains forming large amounts of total BAs promoted a high pH at the final medium, but there was no correlation between TYM/HIM formation and the pH value among the isolates. Furthermore, a large portion of isolates that produce TYM/HIM cannot be amplified. The hdc and tdc genes utilized reported universal pairs of primers, resulting in false negative results. Following two rounds of screening, most TYM/HIM-producing strains were found to belong to Bacillus. Bacillus cereus-HT-31-2 and Millerozyma farinosa-HT-42-1 were identified as crucial producers of TYM and HIM in soy sauce during the fermentation stage, while Proteus mirabilis-T-24-2 was found to be the key producer of TYM in thua nao. Moreover, the simulated medium was found to be beneficial for the formation of TYM/HIM by B. cereus-HT-31-2 and P. mirabilis-T-24-2, but not for M. farinosa-HT-42-1. The formation of TYM/HIM was not synchronized under different conditions. This study provides insights into the key strain responsible for the formation of HIM and TYM in fermented soybean foods.