Bioinspired construction of petal-like regenerated PVDF/cellulose fibers for efficient fog harvesting.

Water collection from atmospheric fog was deemed to be an efficient and sustainable strategy to defuse the freshwater scarcity crisis. Fog harvesting and trapping fibers, therefore, has aroused extensive interest due to their ease of preparation, weave, and use. However, the traditional fibers used in fog collector usually have a low fog collection capacity and efficiency because of their unreasonable morphology and structure design. Herein, we proposed a simple process to construct advanced fibers using a one-step wet spinning of hydrophobic polyvinylidene fluoride (PVDF) and hydrophilic cellulose mixture fiber for fog harvesting. The as-prepared fibers featured a petaloid structure and surface hydrophobic gradient, thus facilitating fog deposition, water droplet formation, and drainage. The unique longitudinal groove structure above enabled the hybrid fiber to achieve an excellent fog collection efficiency of 2750.26 mg/cm2/h per monofilament, which outstripped most of other fiber materials. When woven these fibers were in a longitudinal array network with an interval of 1 mm, and the fog collection efficiency can maintain at 10.30 L/m2/h. Therefore, this work provided a new strategy for further exploration of effective fog collection by cellulose-based fiber materials.

Cross-level transformation of creativity from entrepreneurs to organizations.

With the intensification of competition in the business environment, organizational creativity is increasingly becoming crucial for organizations to build competitive advantages and promote organizational development. For innovative enterprises, their entrepreneurs largely determine the development orientation of the enterprise. They are one of the most critical factors determining the level of corporate innovation, but there need to be more effective creativity transformation path to pursue innovation development. The findings in this study show that entrepreneurial individual creativity has a significant positive effect on organizational creativity, platform leadership mediates the path of creativity transformation across hierarchical levels, and organizational culture has positive moderating effect between platform leadership and organizational creativity. The study results explain the transformation mechanism of creativity from the entrepreneur's perspective, expand the potential transformation path of organizational creativity, and are instructive for enhancing organizational creativity.

Relationship between low relative muscle mass and aortic regional morphological changes in adults underwent contrast CT scans for cancer diagnostics.

OBJECTIVES: Low muscle mass is related to cardiovascular risk factors. This study aimed to investigate whether relative low muscle mass is related to the diameter and tortuosity of the aorta. METHODS: We performed a cross-sectional study of 208 adults without known cardiovascular disease who underwent Computed Tomography (CT) enhanced scan between 2020 and 2021. Skeletal muscle index (SMI) was estimated. The morphology of the aorta was measured by diameter and tortuosity using CT. We assessed the relationship between SMI and diameter and tortuosity of the aorta using Spearman correlation analysis and univariate and multivariate-adjusted regression models. RESULTS: Of all -individuals, 124 (59.6%) were male. The average age was 60.13 ± 16.33 years old. SMI was inversely associated with the diameter and tortuosity of the aorta (p < 0.05). Specifically, in a multivariable-adjusted model adjusting for potential confounders, a one-unit increase in the SMI was associated with a -13.56mm(95% confidence intervals (CI): -18.16 to -8.96, p < 0.001), -7.93 mm (95% CI: -10.85 to -5.02, p < 0.001), -8.01 mm (95% CI: -11.30 to -4.73, p < 0.001), -5.16 mm (95% CI: -7.57 to -2.75, p < 0.001) and -2.73 mm (95% CI: -5.18 to -0.27, p = 0.031) increase in L1-L5 diameter respectively, a -0.89 (95% CI: -1.14 to -0.64, p < 0.001) increase in the aorta tortuosity, a -0.48 (95% CI: -0.59 to -0.36, p < 0.001) increase in the descending thoracic aorta tortuosity, and a -0.44 (95% CI: -0.52 to -0.35, p < 0.001) increase in the abdominal aorta tortuosity. CONCLUSIONS: Relative muscle mass was negatively associated with the diameter and tortuosity of the aorta, suggesting muscle mass maintenance may play a role in preventing aortic morphological changes.

Cellulose acetate-based electrospun nanofiber aerogel with excellent resilience and hydrophobicity for efficient removal of drug residues and oil contaminations from wastewater.

Cellulose acetate (CA)-based electrospun nanofiber aerogel (ENA) has drawn extensive attention for wastewater remediation due to its unique separation, inherent porosity and biodegradability. However, the low mechanical strength, poor durability, and limited adsorption ability hinder its further applications. We herein propose using silane-modified ENA, namely T-CA@Si@ZIF-67 (T-ENA), with enhanced resilience, hydrophobicity, durability and hetero-catalysis to remediate a complex wastewater containing oil and drug residues. The robust T-ENA was fabricated by pre-doping tetraethyl orthosilicate (TEOS) and ligand in its spinning precursors, followed by in-situ anchoring of porous ZIF-67 on the electrospun nanofibers (ENFs) via seeding method before freeze-drying and thermal curing (T). Results show that the T-ENA displays enhanced mechanical stability/resilience and hydrophobicity without compromise of its high porosity (>98 %) and low density (10 mg/cm3) due to the silane cross-linking. As a result, the hydrophobic T-ENA shows over 99 % separation efficiency towards different oil-water solutions. Meanwhile, thanks to the enhanced adsorption-catalytic ability and the activation of peroxymonosulfate (PMS) from the porous ZIF-67, fast degradation of carbamazepine (CBZ) residue in the wastewater can be achieved within 20 min. This work might provide a novel strategy for developing CA aerogels to remove organic pollutants.

Glycosylation in breast cancer progression and mammary development: Molecular connections and malignant transformations.

INTRODUCTION: The cellular behavior in normal mammary gland development and the progression of breast cancer is like the relationship between an object and its mirror image: they may appear similar, but their essence is completely different. Breast cancer can be considered as temporal and spatial aberrations of normal development in mammary gland. Glycans have been shown to regulate key pathophysiological steps during mammary development and breast cancer progression, and the glycoproteins that play a key role in both processes can affect the normal differentiation and development of mammary cells, and even cause malignant transformation or accelerate tumorigenesis due to differences in their type and level of glycosylation. KEY FINDINGS: In this review, we summarize the roles of glycan alterations in essential cellular behaviors during breast cancer progression and mammary development, and also highlight the importance of key glycan-binding proteins such as epidermal growth factor receptor, transforming growth factor ß receptors and other proteins, which are pivotal in the modulation of cellular signaling in mammary gland. Our review takes an overall view of the molecular interplay, signal transduction and cellular behaviors in mammary gland development and breast cancer progression from a glycobiological perspective. SIGNIFICANCE: This review will give a better understanding of the similarities and differences in glycosylation between mammary gland development and breast cancer progression, laying the foundation for elucidating the key molecular mechanisms of glycobiology underlying the malignant transformation of mammary cells.

Mutation Detection and Functional Analysis of MSX1, PAX9, AXIN2, and BMP in Nonsyndromic Congenital Missing Teeth Based on Intelligent Image Detection.

Due to the complexity of clinical manifestations and the lack of standardized diagnostic criteria, it is still difficult to distinguish the etiological types of congenital edentulousness corresponding to genetic defects. This paper studies the application of deep learning image processing and digital image processing in medical images in detail and analyzes the functions of congenital edentulous hotspot genes. The cases in the control group and the study group were collected, and the gene mutations of direct sequence MSX1, PAX9, AXIN2, and BMP were analyzed, and new pathogens were found. The experimental results suggest that PAX9 and MSX1 genes may have a synergistic effect in nonsyndromic congenital edentulous patients. In severely missing teeth, the role of PAX9 may be greater than that of MSX1. The experimental results will help us lay the foundation for further understanding of the disease in the future.

The Usefulness of C-Reactive Protein to Albumin Ratio in the Prediction of Adverse Cardiovascular Events in Coronary Chronic Total Occlusion Undergoing Percutaneous Coronary Intervention.

Inflammation and nutrition as main factors can affect the prognosis of patients with chronic total coronary occlusion (CTO) undergoing percutaneous coronary intervention (PCI). The C-reactive protein to albumin ratio (CAR) can clarify the inflammation and nutrition status, which are highly related to clinical outcomes. This study aims to investigate the association between CAR and adverse cardiovascular events in patients with CTO undergoing PCI. For this study, 664 patients were divided into three groups based on the tertiles of CAR. The primary endpoint was all-cause mortality and the secondary endpoint was major adverse cardiovascular events (MACE). Over a median follow-up of 33.7 months, the primary endpoint occurred in 64 patients (9.6%) and the secondary endpoint occurred in 170 patients (25.6%). The patients with higher CAR represented a worse prognosis with all-cause death and cardiovascular death after the adjustment for the baseline risk factors. Adding the CAR values raised the predictive value for the incidence of the all-cause death and cardiovascular death but not MACE. The capacity of prognosis prediction was improved after the addition of the CAR value to the traditional prediction model.

Motivation, Social Emotion, and the Acceptance of Artificial Intelligence Virtual Assistants-Trust-Based Mediating Effects.

The complexity of the emotional presentation of users to Artificial Intelligence (AI) virtual assistants is mainly manifested in user motivation and social emotion, but the current research lacks an effective conversion path from emotion to acceptance. This paper innovatively cuts from the perspective of trust, establishes an AI virtual assistant acceptance model, conducts an empirical study based on the survey data from 240 questionnaires, and uses multilevel regression analysis and the bootstrap method to analyze the data. The results showed that functionality and social emotions had a significant effect on trust, where perceived humanity showed an inverted U relationship on trust, and trust mediated the relationship between both functionality and social emotions and acceptance. The findings explain the emotional complexity of users toward AI virtual assistants and extend the transformation path of technology acceptance from the trust perspective, which has implications for the development and design of AI applications.

Method for Essential Protein Prediction Based on a Novel Weighted Protein-Domain Interaction Network.

In recent years a number of calculative models based on protein-protein interaction (PPI) networks have been proposed successively. However, due to false positives, false negatives, and the incompleteness of PPI networks, there are still many challenges affecting the design of computational models with satisfactory predictive accuracy when inferring key proteins. This study proposes a prediction model called WPDINM for detecting key proteins based on a novel weighted protein-domain interaction (PDI) network. In WPDINM, a weighted PPI network is constructed first by combining the gene expression data of proteins with topological information extracted from the original PPI network. Simultaneously, a weighted domain-domain interaction (DDI) network is constructed based on the original PDI network. Next, through integrating the newly obtained weighted PPI network and weighted DDI network with the original PDI network, a weighted PDI network is further constructed. Then, based on topological features and biological information, including the subcellular localization and orthologous information of proteins, a novel PageRank-based iterative algorithm is designed and implemented on the newly constructed weighted PDI network to estimate the criticality of proteins. Finally, to assess the prediction performance of WPDINM, we compared it with 12 kinds of competitive measures. Experimental results show that WPDINM can achieve a predictive accuracy rate of 90.19, 81.96, 70.72, 62.04, 55.83, and 51.13% in the top 1%, top 5%, top 10%, top 15%, top 20%, and top 25% separately, which exceeds the prediction accuracy achieved by traditional state-of-the-art competing measures. Owing to the satisfactory identification effect, the WPDINM measure may contribute to the further development of key protein identification.

Prognostic value of malnutrition using geriatric nutritional risk index in patients with coronary chronic total occlusion after percutaneous coronary intervention.

BACKGROUND: Malnutrition is associated with poor prognosis in a wide range of chronic illnesses, however, the impact of malnutrition on long-term outcomes of patients at advanced stages of atherosclerosis, coronary chronic artery occlusion (CTO), is not known. AIMS: This study aims to investigate the relationship between malnutrition and adverse cardiovascular events in patients with CTO after percutaneous coronary intervention (PCI). METHODS: Baseline malnutrition risk was determined in 669 patients with CTO after PCI in this study. All patients were divided into 3 groups according to 3 categories of the geriatric nutritional risk index (GNRI): moderate to severe, GNRI of <92 (n = 70); low, GNRI of 92-98 (n = 197); and absence of risk, GNRI of ≥98 (n = 402). The primary endpoint was all-cause mortality and the secondary endpoint was major adverse cardiovascular events (MACE). RESULTS: Average age in this study was 65.32 ± 9.97 years old. More than one-third of patients were at risk of malnutrition (moderate to severe: 10.5%; low: 29.4%; and absence of risk: 60.1%). Over a median follow-up of 33 months, compared to those with absent risk for malnutrition, moderate to severe risk was associated with significantly increased risk for the all-cause death, cardiovascular death and MACE (hazard ratio [HR]: 2.90, 95% confidence interval [CI]: 1.43 to 5.87, P for trend = 0.002; HR: 3.72, 95% CI: 1.42 to 9.77, P for trend = 0.010; HR: 1.76, 95% CI: 1.02 to 3.03, P for trend = 0.040; respectively) after adjustment for baseline variables. Moreover, addition of the GNRI score significantly raised the predictive value for the all-cause death (0.383, p = 0.004 and 0.022, p = 0.011, NRI and IDI respectively), cardiovascular death (0.488, p < 0.001 and 0.013, p = 0.014, NRI and IDI respectively) and MACE (0.368, p = 0.004 and 0.014, p = 0.008, NRI and IDI respectively) as compared to traditional factors. CONCLUSIONS: Malnutrition assessed by the GNRI score on admission was an independent predictor for adverse cardiovascular events in CTO patients after PCI. Addition of the GNRI score to the existing risk prediction model significantly increased the predictive ability for cardiovascular events in CTO patients after PCI.

Identification of Anisomerous Motor Imagery EEG Signals Based on Complex Algorithms.

Motor imagery (MI) electroencephalograph (EEG) signals are widely applied in brain-computer interface (BCI). However, classified MI states are limited, and their classification accuracy rates are low because of the characteristics of nonlinearity and nonstationarity. This study proposes a novel MI pattern recognition system that is based on complex algorithms for classifying MI EEG signals. In electrooculogram (EOG) artifact preprocessing, band-pass filtering is performed to obtain the frequency band of MI-related signals, and then, canonical correlation analysis (CCA) combined with wavelet threshold denoising (WTD) is used for EOG artifact preprocessing. We propose a regularized common spatial pattern (R-CSP) algorithm for EEG feature extraction by incorporating the principle of generic learning. A new classifier combining the K-nearest neighbor (KNN) and support vector machine (SVM) approaches is used to classify four anisomerous states, namely, imaginary movements with the left hand, right foot, and right shoulder and the resting state. The highest classification accuracy rate is 92.5%, and the average classification accuracy rate is 87%. The proposed complex algorithm identification method can significantly improve the identification rate of the minority samples and the overall classification performance.