Study on the Self-Repairing Effect of Nanoclay in Powder Coatings for Corrosion Protection

Powder coatings are a promising, solvent-free alternative to traditional liquid coatings due to the superior corrosion protection they provide. This study investigates the effects of incorporating montmorillonite-based nanoclay additives with different particle sizes into polyester/triglycidyl isocyanurate (polyester/TGIC) powder coatings. The objective is to enhance the corrosion-protective function of the coatings while addressing the limitations of commonly employed epoxy-based coating systems that exhibit inferior UV resistance. The anti-corrosive and surface qualities of the coatings were evaluated via neutral salt spray tests, electrochemical measurements, and surface analytical techniques. Results show that the nanoclay with a larger particle size of 18.38 µm (D50, V) exhibits a better barrier effect at a lower dosage of 4%, while a high dosage leads to severe defects in the coating film. Interestingly, the coating capacitance is found, via electrochemical impedance spectroscopy, to decrease during the immersion test, indicating a self-repairing capability of the nanoclay, arising from its swelling and expansion. Neutral salt spray tests suggest an optimal nanoclay dosage of 2%, with the smaller particle size (8.64 µm, D50, V) nanoclay providing protection for 1.5 times as many salt spray hours as the nanoclay with a larger particle size. Overall, incorporating montmorillonite-based nanoclay additives is suggested to be a cost-effective approach for significantly enhancing the anti-corrosive function of powder coatings, expanding their application to outdoor environments

Development and validation of a diagnostic model to differentiate spinal tuberculosis from pyogenic spondylitis by combining multiple machine learning algorithms

This study focused on the development and validation of a diagnostic model to differentiate between spinal tuberculosis (STB) and pyogenic spondylitis (PS). We analyzed a total of 387 confirmed cases, out of which 241 were diagnosed with STB and 146 were diagnosed with PS. These cases were randomly divided into a training group (n = 271) and a validation group (n = 116). Within the training group, four machine learning (ML) algorithms (least absolute shrinkage and selection operator [LASSO], logistic regression analysis, random forest, and support vector machine recursive feature elimination [SVM-RFE]) were employed to identify distinctive variables. These specific variables were then utilized to construct a diagnostic model. The model’s performance was subsequently assessed using the receiver operating characteristic (ROC) curves and the calibration curves. Finally, internal validation of the model was undertaken in the validation group. Our findings indicate that PS patients had an average platelet-to-neutrophil ratio (PNR) of 277.86, which was significantly higher than the STB patients’ average of 69.88. The average age of PS patients was 54.71 years, older than the 48 years recorded for STB patients. Notably, the neutrophil-to-lymphocyte ratio (NLR) was higher in PS patients at 6.15, compared to the 3.46 NLR in STB patients. Additionally, the platelet volume distribution width (PDW) in PS patients was 0.2, compared to 0.15 in STB patients. Conversely, the mean platelet volume (MPV) was lower in PS patients at an average of 4.41, whereas STB patients averaged 8.31. Hemoglobin (HGB) levels were lower in PS patients at an average of 113.31 compared to STB patients' average of 121.64. Furthermore, the average red blood cell (RBC) count was 4.26 in PS patients, which was less than the 4.58 average observed in STB patients. After evaluation, seven key factors were identified using the four ML algorithms, forming the basis of our diagnostic model. The training and validation groups yielded area under the curve (AUC) values of 0.841 and 0.83, respectively. The calibration curves demonstrated a high alignment between the nomogram-predicted values and the actual measurements. The decision curve indicated optimal model performance with a threshold set between 2% and 88%. In conclusion, our model offers healthcare practitioners a reliable tool to efficiently and precisely differentiate between STB and PS, thereby facilitating swift and accurate diagnoses

Pharmacological targeting of STK19 inhibits oncogenic NRAS driven melanomagenesis

黑色素瘤是由黑色素细胞恶性转化产生的恶性程度极高的皮肤癌，含有NRAS激活突变的黑色素瘤约占20-30%，但至今还未有靶向NRAS的有效黑色素瘤治疗方案。针对这一难题，波士顿大学医学中心崔儒涛教授、厦门大学生命科学学院邓贤明教授、复旦大学附属肿瘤医院王鹏教授组成的联合研究团队利用激酶组siRNA文库筛选发现新颖的丝/苏氨酸激酶STK19是NRAS的上游激活子，进一步分子机制研究揭示STK19通过磷酸化NRAS的89位丝氨酸（S89）促进了NRAS介导的黑色素细胞恶性转化。该研究揭示了一种经由新颖激酶STK19调控NRAS突变黑色素瘤的分子机制，验证了STK19有望作为NRAS介导的黑色素瘤的有效靶标，为发展新的黑色素瘤靶向药物提供了先导化合物，同时也为发展其它素有“癌基因之王---RAS”驱动的相关肿瘤靶向药物发展提供了新思路。该论文由波士顿大学医学中心、厦门大学生命科学学院、复旦大学附属肿瘤医院等单位合作完成，共同第一作者厦门大学生命科学学院博士生张婷负责了该系列化合物的设计、合成与优化，崔儒涛教授、邓贤明教授和王鹏教授为共同通讯作者。【Abstract】Activating mutations in NRAS account for 20-30% of melanoma, but despite decades of research and in contrast to BRAF, no effective anti-NRAS therapies have been forthcoming. Here we identify a previously uncharacterized serine/threonine kinase STK19 as a novel NRAS activator. STK19 phosphorylates NRAS to enhance its binding to its downstream effectors and promotes oncogenic NRAS-mediated melanocyte malignant transformation. A recurrent D89N substitution in STK19 whose alterations were identified in 25% of human melanomas represents a gain-of-function mutation that interacts better with NRAS to enhance melanocyte transformation. STK19 D89N knockin leads to skin hyperpigmentation and promotes NRAS Q61R -driven melanomagenesis in vivo. Finally, we developed ZT-12-037-01 (1a) as a specific STK19-targeted inhibitor and showed that it effectively blocks oncogenic NRAS-driven melanocyte malignant transformation and melanoma growth in vitro and in vivo. Together, our findings provide a new and viable therapeutic strategy for melanomas harboring NRAS mutations.We thank Drs. Norman Sharpless and David Fisher for kindly providing the loxP/STOP/loxP NRAS Q61R knockin (LSL-NRAS Q61R ) mice. We thank Dr. Anurag Singh for kindly sharing cell lines. We also thank Drs. X. Shirley Liu, Tao Wang, Wantao Chen, Dali Liu, Chunxiao Xu, Jianming Zhang and Junrong Zou for discussion and assistance. This work was supported by grants from Boston University (to R.C.), the National Key R&D Program and the National Natural Science Foundation of China (No. 2017YFA0504504, 2016YFA0502001, 81422045, U1405223 and 81661138005 to X.D.), the Fundamental Research Funds for the Central Universities of China (No. 20720160064 to X.D.), and the Program of Introducing Talents of Discipline to Universities (111 Project, B12001).该研究得到了科技部重点研发计划、国家自然科学基金委和中央高校基本科研业务费等的资助

Research on short-term joint optimization scheduling strategy for hydro-wind-solar hybrid systems considering uncertainty in renewable energy generation

Due to its randomness, intermittence, and volatility, the high-proportional integration of wind and solar power poses challenges to the safe and stable operation of power systems. Cascade hydropower stations have a high response speed, high adjustability, and stable output. This study proposed a hydro-wind-solar hybrid system and investigated its short-term optimal coordinated operation based on deep learning and a double-layer nesting algorithm. A stochastic complementary scheduling model was constructed to maximize the cascade energy storage. The particle swarm optimization algorithm-dynamic programming (PSO-DP) coupled with the inner and outer nesting optimization algorithm reduces the problem-solving complexity. The hybrid system was applied to a national comprehensive development base of renewable energy with integrated wind, solar, and hydropower in China. Studies have shown the following: The hydro-wind-solar hybrid system has a certain degree of scalability. The utilization of deep learning methods can fully consider the uncertainty of wind and solar. The internal and external nested optimization algorithm, which realizes the reasonable and efficient distribution of water and electricity, and improves the future power generation capacity of cascade hydropower stations, was used to solve the problem. This study provided a valuable reference for the large-scale utilization of other renewable energy sources worldwide

Modeling and Performance Analysis of Energy Efficiency Binary Power Control in MIMO-OFDM Wireless Communication Systems

The energy efficiency optimization of the binary power control scheme for MIMO-OFDM wireless communication systems is formulated, and then a global optimization solution of power allocation is derived. Furthermore, a new energy efficiency binary power control (EEBPC) algorithm is designed to improve the energy efficiency of MIMO-OFDM wireless communication systems. Simulation results show that the EEBPC algorithm has better energy efficiency and spectrum efficiency than the average power control algorithm in MIMO-OFDM wireless communication systems

Discovery of Novel Dihydrolipoamide S-Succinyltransferase Inhibitors Based on Fragment Virtual Screening

A series of new oxadiazole sulfone derivatives containing an amide moiety was synthesized based on fragment virtual screening to screen high-efficiency antibacterial agents for rice bacterial diseases. All target compounds showed greater bactericidal activity than commercial bactericides. 3-(4-fluorophenyl)-N-((5-(methylsulfonyl)-1,3,4-oxadiazol-2-yl)methyl)acrylamide (10) showed excellent antibacterial activity against Xanthomonas oryzae pv. oryzae and Xanthomonas oryzae pv. oryzicola, with EC50 values of 0.36 and 0.53 mg/L, respectively, which were superior to thiodiazole copper (113.38 and 131.54 mg/L) and bismerthiazol (83.07 and 105.90 mg/L). The protective activity of compound 10 against rice bacterial leaf blight and rice bacterial leaf streak was 43.2% and 53.6%, respectively, which was superior to that of JHXJZ (34.1% and 26.4%) and thiodiazole copper (33.0% and 30.2%). The curative activity of compound 10 against rice bacterial leaf blight and rice bacterial leaf streak was 44.5% and 51.7%, respectively, which was superior to that of JHXJZ (32.6% and 24.4%) and thiodiazole copper (27.1% and 28.6%). Moreover, compound 10 might inhibit the growth of Xanthomonas oryzae pv. oryzae and Xanthomonas oryzae pv. oryzicola by affecting the extracellular polysaccharides, destroying cell membranes, and inhibiting the enzyme activity of dihydrolipoamide S-succinyltransferase

Disaggregation Ability of Different Chelating Molecules on Copper Ion-Triggered Amyloid Fibers

Dysfunctional interaction of amyloid-β (Aβ) with excess metal ions is proved to be related to the etiology of Alzheimer’s disease (AD). Using metal-binding compounds to reverse metal-triggered Aβ aggregation has become one of the potential therapies for AD. In this study, the ability of a carboxylic acid gemini surfactant (SDUC), a widely used metal chelator (EDTA), and an antifungal drug clioquinol (CQ) in reversing the Cu²⁺-triggered Aβ(1–40) fibers have been systematically studied by using turbidity essay, BCA essay, atomic force microscopy, transmission electron microscopy, and isothermal titration microcalorimetry. The results show that the binding affinity of Cu²⁺ with CQ, SDUC, and EDTA is in the order of CQ > EDTA > SDUC, while the disaggregation ability to Cu²⁺-triggered Aβ(1–40) fibers is in the order of CQ > SDUC > EDTA. Therefore, the disaggregation ability of chelators to the Aβ(1–40) fibers does not only depend on the binding affinity of the chelators with Cu²⁺. Strong self-assembly ability of SDUC and π–π interaction of the conjugate group of CQ also contributes toward the disaggregation of the Cu²⁺-triggered Aβ(1–40) fibers and result in the formation of mixed small aggregates

ZnO Microfiltration Membranes for Desalination by a Vacuum Flow-Through Evaporation Method

ZnO was deposited on macroporous α-alumina membranes via atomic layer deposition (ALD) to improve water flux by increasing their hydrophilicity and reducing mass transfer resistance through membrane pore channels. The deposition of ZnO was systemically performed for 4–128 cycles of ALD at 170 °C. Analysis of membrane surface by contact angles (CA) measurements revealed that the hydrophilicity of the ZnO ALD membrane was enhanced with increasing the number of ALD cycles. It was observed that a vacuum-assisted ‘flow-through’ evaporation method had significantly higher efficacy in comparison to conventional desalination methods. By using the vacuum-assisted ‘flow-through’ technique, the water flux of the ZnO ALD membrane (~170 L m−2 h−1) was obtained, which is higher than uncoated pristine membranes (92 L m−2 h−1). It was also found that ZnO ALD membranes substantially improved water flux while keeping excellent salt rejection rate (>99.9%). Ultrasonic membrane cleaning had considerable effect on reducing the membrane fouling

Identification of spinal tuberculosis subphenotypes using routine clinical data: a study based on unsupervised machine learning

AbstractObjective The identification of spinal tuberculosis subphenotypes is an integral component of precision medicine. However, we lack proper study models to identify subphenotypes in patients with spinal tuberculosis. Here we identified possible subphenotypes of spinal tuberculosis and compared their clinical results.Methods A total of 422 patients with spinal tuberculosis who received surgical treatment were enrolled. Clustering analysis was performed using the K-means clustering algorithm and the routinely available clinical data collected from patients within 24 h after admission. Finally, the differences in clinical characteristics, surgical efficacy, and postoperative complications among the subphenotypes were compared.Results Two subphenotypes of spinal tuberculosis were identified. Laboratory examination results revealed that the levels of more than one inflammatory index in cluster 2 were higher than those in cluster 1. In terms of disease severity, Cluster 2 showed a higher Oswestry Disability Index (ODI), a higher visual analysis scale (VAS) score, and a lower Japanese Orthopedic Association (JOA) score. In addition, in terms of postoperative outcomes, cluster 2 patients were more prone to complications, especially wound infections, and had a longer hospital stay.Conclusion K-means clustering analysis based on conventional available clinical data can rapidly identify two subtypes of spinal tuberculosis with different clinical results. We believe this finding will help clinicians to rapidly and easily identify the subtypes of spinal tuberculosis at the bedside and become the cornerstone of individualized treatment strategies

Prediction model for spinal cord injury in spinal tuberculosis patients using multiple machine learning algorithms: a multicentric study

Abstract Spinal cord injury (SCI) is a prevalent and serious complication among patients with spinal tuberculosis (STB) that can lead to motor and sensory impairment and potentially paraplegia. This research aims to identify factors associated with SCI in STB patients and to develop a clinically significant predictive model. Clinical data from STB patients at a single hospital were collected and divided into training and validation sets. Univariate analysis was employed to screen clinical indicators in the training set. Multiple machine learning (ML) algorithms were utilized to establish predictive models. Model performance was evaluated and compared using receiver operating characteristic (ROC) curves, area under the curve (AUC), calibration curve analysis, decision curve analysis (DCA), and precision-recall (PR) curves. The optimal model was determined, and a prospective cohort from two other hospitals served as a testing set to assess its accuracy. Model interpretation and variable importance ranking were conducted using the DALEX R package. The model was deployed on the web by using the Shiny app. Ten clinical characteristics were utilized for the model. The random forest (RF) model emerged as the optimal choice based on the AUC, PRs, calibration curve analysis, and DCA, achieving a test set AUC of 0.816. Additionally, MONO was identified as the primary predictor of SCI in STB patients through variable importance ranking. The RF predictive model provides an efficient and swift approach for predicting SCI in STB patients