Effects of the Biomimetic Microstructure in Electrospun Fiber Sutures and Mechanical Tension on Tissue Repair.

Electrospun microfibers, designed to emulate the extracellular matrix (ECM), play a crucial role in regulating the cellular microenvironment for tissue repair. Understanding their mechanical influence and inherent biological interactions at the ECM interface, however, remains a complex challenge. This study delves into the role of mechanical cues in tissue repair by fabricating Col/PLCL microfibers with varying chemical compositions and alignments that mimic the structure of the ECM. Furthermore, we optimized these microfibers to create the Col/PLCL@PDO aligned suture, with a specific emphasis on mechanical tension in tissue repair. The result reveals that within fibers of identical chemical composition, fibroblast proliferation is more pronounced in aligned fibers than in unaligned ones. Moreover, cells on aligned fibers exhibit an increased aspect ratio. In vivo experiments demonstrated that as the tension increased to a certain level, cell proliferation augmented, cells assumed more elongated morphologies with distinct protrusions, and there was an elevated secretion of collagen III and tension suture, facilitating soft tissue repair. This research illuminates the structural and mechanical dynamics of electrospun fiber scaffolds; it will provide crucial insights for the advancement of precise and controllable tissue engineering materials.

First-Principles Study of Bimetallic Pairs Embedded on Graphene Co-Doped with N and O for N2 Electroreduction.

The electrocatalytic nitrogen reduction reaction (NRR) is considered a viable alternative to the Haber-Bosch process for ammonia synthesis, and the design of highly active and selective catalysts is crucial for the industrialization of the NRR. Dual-atom catalysts (DACs) with dual active sites offer flexible active sites and synergistic effects between atoms, providing more possibilities for the tuning of catalytic performance. In this study, we designed 48 graphene-based DACs with N4O2 coordination (MM'@N4O2-G) using density functional theory. Through a series of screening strategies, we explored the reaction mechanisms of the NRR for eight catalysts in depth and revealed the "acceptance-donation" mechanism between the active sites and the N2 molecules through electronic structure analysis. The study found that the limiting potential of the catalysts exhibited a volcano-shaped relationship with the d-band center of the active sites, indicating that the synergistic effect between the bimetallic components can regulate the d-band center position of the active metal M, thereby controlling the reaction activity. Furthermore, we investigated the selectivity of the eight DACs and identified five potential NRR catalysts. Among them, MoCo@N4O2-G showed the best NRR performance, with a limiting potential of -0.20 V. This study provides theoretical insights for the design and development of efficient NRR electrocatalysts.

A feasible strategy for designing cytochrome P450-mimic sandwich-like single-atom nanozymes toward electrochemical CO2 conversion.

Carbon dioxide electroreduction (CO2ER) presents a promising strategy for environmentally friendly CO2 utilization due to its low energy consumption. Single-atom nanozymes (SANs), amalgamating the benefits of single-atom catalysts and nanozymes, have become a hot topic in catalysis. Inspired by the intricate structure of cytochrome P450, we designed 81 sandwich-like SANs using Group-VIII transition metals (TMN4-S-TM'N4) and evaluated their performance in CO2ER using density functional theory (DFT). Our investigation revealed that most SANs display superior catalytic activity and improved specific product selectivity in comparison to the Cu (211) surface. Notably, IrN4-S-TMN4 (TM = Co, Rh, Pd) exhibited selective CO2 reduction to CO with remarkable limiting potentials (UL) of -0.11, -0.07, and -0.09 V, respectively, demonstrating potential as artificial CO dehydrogenases. Furthermore, RuN4-S-RuN4 exhibited formate dehydrogenase-like activity, resulting in selective production of HCOOH at a UL of -0.10 V. Machine learning analysis elucidated that the exceptional activity and selectivity of these SANs stemmed from precise modulation of electron density on sulfur atoms, achieved by varying transition metals in the subsurface. Our research not only identifies exceptional SANs for CO2ER but also provides insights into innovative methods for regulating non-bonding interactions and achieving sustainable CO2 conversion.

Antithermal Quenching Upconversion Luminescence via Suppressed Multiphonon Relaxation in Positive/Negative Thermal Expansion Core/Shell NaYF4:Yb/Ho@ScF3 Nanoparticles.

Thermal quenching (TQ) has been naturally entangling with luminescence since its discovery, and lattice vibration, which is characterized as multiphonon relaxation (MPR), plays a critical role. Considering that MPR may be suppressed under exterior pressure, we have designed a core/shell upconversion luminescence (UCL) system of α-NaYF4:Yb/Ln@ScF3 (Ln = Ho, Er, and Tm) with positive/negative thermal expansion behavior so that positive thermal expansion of the core will be restrained by negative thermal expansion of the shell when heated. This imposed pressure on the crystal lattice of the core suppresses MPR, reduces the amount of energy depleted by TQ, and eventually saves more energy for luminescing, so that anti-TQ or even thermally enhanced UCL is obtained.

Enhancing CO2 electroreduction performance through transition metal atom doping and strain engineering in γ-GeSe: a first-principles study.

The development of electrocatalysts that exhibit stability, high activity, and selectivity for CO2 reduction reactions (CO2RR) remains a significant challenge. Single-atom catalysts (SACs) hold promise in addressing this challenge due to their high atomic utilization efficiency. In this study, we explore the potential of monolayer γ-GeSe doped with transition metals, referred to as TM@γ-GeSe, for facilitating electrocatalytic CO2RR. Among the 26 TM@γ-GeSe SACs systematically designed, we have identified four stable transition metal catalysts (TM = Rh, Pd, Pt, and Au). Mechanistic investigations into the CO2RR pathways reveal exceptional electrocatalytic activity for Rh@γ-GeSe and Pd@γ-GeSe, with limiting potentials of -0.26 and -0.35 V, respectively. Particularly, Pd@γ-GeSe exhibits outstanding product selectivity toward formic acid. The introduction of strain engineering induces modifications in the catalytic activity and selectivity of Rh@γ-GeSe. Notably, a 1% tensile strain promotes formic acid as the preferred product, thereby improving the specific product selectivity of Rh@γ-GeSe. Conversely, compressive strain reduces CO2RR activity while enhancing the hydrogen evolution reaction, leading to a decrease in CO2RR selectivity. Furthermore, we use the work function as a descriptor to elucidate the underlying mechanism of strain tunability. We hope that our theoretical study will offer valuable insights for the design of catalysts based on γ-GeSe for electrocatalytic CO2RR.

New oxide structures clearing up the origin of two-dimensional electron gas in AlGaN/GaN heterostructures.

Control over the two-dimensional electron gas (2DEG) in AlGaN/GaN heterostructures is crucial for their practical applications in current semiconducting devices. However, the oxide surface structures inducing 2DEG are still ambiguous because oxide-stoichiometry (OS) matching structures possess occupied surface donor states at 1.0-1.8 eV below the conduction band minimum of bulk but are usually not available in energy than electron counting (EC) rule structures. In this work, a global optimization algorithm was introduced to explore the possible oxidation structures on GaN (0001) and AlN (0001) surfaces; the method was demonstrated to be available due to the fact that the reported oxidized structures were reproduced at each stoichiometry. Interestingly, the two similar oxide structures with close energy were found in each oxide-bilayer, which can be used to clarify the experimental observations of disordered surface oxide layers below 550 °C. Additionally, new stable oxidation structures with low surface energy were proposed. Interestingly, the new OS matching structures are proposed with remarkably lower energy than EC rule structures under cation-rich and oxygen-poor conditions, which is caused by the large formation enthalpy of Al2O3 and Ga2O3. Further electronic structure calculations demonstrate that the new OS structures possess highest occupied states above the half of the gap and are the origin of 2DEG in AlGaN/GaN heterostructures.

Rapid Thalidomide Racemization Is Related to Proton Tunneling Reactions via Water Bridges.

Quantum mechanical tunneling (QMT) can play an important role in light element-related chemical reactions; however, its influence on racemization is not fully understood. Herein, we demonstrate that the role of QMT is decisive for rapid racemization of the well-known thalidomide molecule in aqueous environments, increasing the reaction rate constants of the most likely racemization pathways by 87-149 times at approximately body temperature and achieving good agreement between theoretical calculations and experimental observations. In addition, the kinetic isotope effect values fit well with those of previous experiments. These results are attributed to enhanced tunneling probability due to the alteration of potential barriers for proton transfer reactions via water bridges. This work highlights the significance of the QMT effect in racemization and its potential impact on drug safety, providing a fundamental perspective for understanding chirality-related issues in biological systems.

Analysis of left-turn behaviors of non-motorized vehicles and vehicle-bicycle conflicts.

In order to further study the expansion characteristics of left-turning non-motorized vehicles at intersections and the relationship between expansion characteristics and vehicle-bicycle conflicts, the trajectory point data of left-turning non-motorized vehicles are extracted using video trajectory tracking technology, and construct the cubic curve expansion envelope equation with the highest fitting degree. For the purpose of quantifying the expansion degree of non-motor vehicles after starting, two intersections in Guangxi Zhuang Autonomous Region were selected for case analysis, and the numerical range of expansion degree of the intersection with a left-turn waiting area and the intersection without a left-turn waiting area was obtained. Study the mathematical relationship between the expansion degree and its influencing factors, and establish the multivariate nonlinear regression equation between the expansion degree and the left-turn non-motorized vehicle flow, the number of parallel non-motorized vehicles, and the left-turn green light time. Analyze the vehicle-bicycle conflicts caused by the expansion of left-turning non-motorized vehicles, determine the essential factors affecting the number of non-motorized vehicles, and establish the multiple linear regression equation between the number of non-motorized vehicles and the number of left-turning non-motorized vehicles, the expansion degree, and the number of parallel non-motorized vehicles, the results show that the model has high accuracy. By analyzing the expansion characteristics of left-turning non-motorized vehicles at intersections, the relationship between different influencing factors and the expansion degree is obtained. Then the vehicle-bicycle conflicts under the influence of expansion characteristics is analyzed, providing theoretical ideas for improving traffic efficiency and optimizing traffic organization at intersections.

A 106-year-old patient with unexpected COVID-19 pneumonia: A case report.

The fast spread of COVID-19, which was caused by SARS-CoV-2 infection, has posed a major challenge to public health systems around the world. Morbidity and mortality are higher in the elderly than in the young, due to a loss in immune function and more comorbidities. In this case, we describe a 106-year-old female patient, the oldest COVID-19 patient since 2019, who had not previously received the SARS-CoV-2 vaccine. Her clinical symptoms included cough and sputum production. Images of her chest CT showed double lung pneumonia, and laboratory tests revealed elevated serum KL-6 levels. She was mostly on oral medication during her hospitalization and recovered well. With the case, we discuss the risk factors and biomarkers correlated to COVID-19 severity. Following the COVID outbreak, it's vital to explore the possible risk factors that can help with disease risk stratification, identifying high-risk individuals, developing precise treatment regimens, and lowering mortality rates.

Clustering and dynamic recognition based auto-reservoir neural network: A wait-and-see approach for short-term park power load forecasting.

This paper proposes a novel clustering and dynamic recognition-based auto-reservoir neural network (CDbARNN) for short-term load forecasting (STLF) of industrial park microgrids. In CDbARNN, the available load sets are first decomposed into several clusters via K-means clustering. Then, by extracting characteristic information of the load series input to CDbARNN and the load curves belonging to each cluster center, a dynamic recognition technology is developed to identify which cluster of the input load series belongs to. After that, the input load series and the load curves of the cluster to which it belongs constitute a short-term high-dimensional matrix entered into the reservoir of CDbARNN. Finally, reservoir node numbers of CDbARNN which are used to match different clusters are optimized. Numerical experiments conducted on STLF of an actual industrial park microgrid indicate the dominating performance of the proposed approach through several cases and comparisons with other well-known deep learning methods.

COVID-19 disease identification network based on weakly supervised feature selection.

The coronavirus disease 2019 (COVID-19) outbreak has resulted in countless infections and deaths worldwide, posing increasing challenges for the health care system. The use of artificial intelligence to assist in diagnosis not only had a high accuracy rate but also saved time and effort in the sudden outbreak phase with the lack of doctors and medical equipment. This study aimed to propose a weakly supervised COVID-19 classification network (W-COVNet). This network was divided into three main modules: weakly supervised feature selection module (W-FS), deep learning bilinear feature fusion module (DBFF) and Grad-CAM++ based network visualization module (Grad-Ⅴ). The first module, W-FS, mainly removed redundant background features from computed tomography (CT) images, performed feature selection and retained core feature regions. The second module, DBFF, mainly used two symmetric networks to extract different features and thus obtain rich complementary features. The third module, Grad-Ⅴ, allowed the visualization of lesions in unlabeled images. A fivefold cross-validation experiment showed an average classification accuracy of 85.3%, and a comparison with seven advanced classification models showed that our proposed network had a better performance.

Rational Design of Cu-Doped Tetrahedron of Spinel Oxide for High-Performance Nitric Oxide Electrochemical Sensor.

The real-time detection of nitric oxide (NO) in living cells is essential to reveal its physiological processes. However, the popular electrochemical detection strategy is limited to the utilization of noble metals. The development of new detection candidates without noble metal species still maintaining excellent catalytic performance has become a big challenge. Herein, we propose a spinel oxide doped with heteroatom-Cu-doped Co3O4 (Cu-Co3O4) for the sensitive and selective detection of NO release from the living cells. The material is strategically designed with Cu occupying the tetrahedral (Td) center of Co3O4 through the formation of a Cu-O bond. The introduced Cu regulates the local coordination environment and optimizes the electronic structure of Co3O4, hybridizing with the N 2p orbital to enhance charge transfer. The CuTd site can well inhibit the current response to nitrite (NO2-), resulting in a high improvement in the electrochemical oxidation of NO. The selectivity of Cu-Co3O4 can be markedly improved by the pore size of the molecular sieve and the negative charge on the surface. The rapid transmission of electrons is due to the fact that Cu-Co3O4 can be uniformly and densely in situ grown on Ti foil. The rationally designed Cu-Co3O4 sensor displays excellent catalytic activity toward NO oxidation with a low limit of detection of 2.0 nM (S/N = 3) and high sensitivity of 1.9 µA nM-1 cm-2 in cell culture medium. The Cu-Co3O4 sensor also shows good biocompatibility to monitor the real-time NO release from living cells (human umbilical vein endothelial cells: HUVECs; macrophage: RAW 264.7 cells). It was found that a remarkable response to NO was obtained in different living cells when stimulated by l-arginine (l-Arg). Moreover, the developed biosensor could be used for real-time monitoring of NO released from macrophages polarized to a M1/M2 phenotype. This cheap and convenient doping strategy shows universality and can be used for sensor design of other Cu-doped transition metal materials. The Cu-Co3O4 sensor presents an excellent example through the design of proper materials to implement unique sensing requirements and sheds light on the promising strategy for electrochemical sensor fabrication.

Metal-Organic Frameworks for Breakthrough Separation of 2-Butene Isomers with High Dynamic Selectivity and Capacity.

Developing porous sorbents represents a potential energy-efficient way for industrial gas separation. However, a bottleneck for reducing the energy penalty is the trade-off between dynamic adsorption capacity and selectivity. Herein, we showed this problem can be overcome by modulating the kinetic and thermodynamic separation behaviours in metal-organic frameworks for sieving 2-butene geometric isomers, which are desired for upgrading the raffinates to higher value-added end products. We found that the iron-triazolate framework can realize the selective shape screening of 2-butene isomers assisted by electrostatic interactions at the pore apertures. Further introducing uncoordinated N binding sites by ligand substitution lowered the gas diffusion barrier and greatly boosted the dynamic separation performance. In breakthrough tests under ambient conditions, trans-2-C4 H8 can be efficiently separated from cis-2-C4 H8 with a record capacity of 2.10 mmol g-1 with high dynamic selectivity of 2.39.

CO2 electroreduction performance of PtS2 supported single transition metal atoms: a theoretical study.

Electrocatalytic CO2 reduction is a sustainable strategy to convert CO2 into valuable carbon products. Atomically dispersed single-atom catalysts (SACs) have great potential as effective electrocatalysts for the CO2 reduction reaction (CO2RR). Transition metal dichalcogenides (TMDs) are considered to be a kind of promising SAC supports. In this work, ten different 3d TM single atoms (TM = Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu and Zn) embedded in PtS2 with single S-vacancy (TM-PtS2) were designed by density functional theory (DFT) as candidate electrocatalysts for the CO2RR. Possible reaction pathways of CO2 reduction to different C1 products were systematically investigated. The results show that for all these TM-PtS2 SACs, higher selectivity was achieved for CO2 reduction to C1 products than for the competing hydrogen evolution. HCOOH is the most favorable reduction product on PtS2-Sv supported Sc, Ti, V, Cr, Mn, Fe and Cu SACs, while multiple C1 products are generated on Co-, Ni- and Zn-PtS2. In particular, it is found that Sc-, V-, Fe-, Co- and Cu-PtS2 exhibit higher electrocatalytic performance for the CO2RR than Cu(211). Therefore, these five SACs are promising CO2RR electrocatalysts.

Weakly supervised segmentation of COVID-19 infection with local lesion coherence on CT images.

At the end of 2019, a novel coronavirus, COVID-19, was ravaging the world, wreaking havoc on public health and the global economy. Today, although Reverse Transcription-Polymerase Chain Reaction (RT-PCR) is the gold standard for COVID-19 clinical diagnosis, it is a time-consuming and labor-intensive procedure. Simultaneously, an increasing number of individuals are seeking for better alternatives to RT-PCR. As a result, automated identification of COVID-19 lung infection in computed tomography (CT) images may help traditional diagnostic approaches in determining the severity of the disease. Unfortunately, a shortage of labeled training sets makes using AI deep learning algorithms to accurately segregate diseased regions in CT scan challenging. We design a simple and effective weakly supervised learning strategy for COVID-19 CT image segmentation to overcome the segmentation issue in the absence of adequate labeled data, namely LLC-Net. Unlike others weakly supervised work that uses a complex training procedure, our LLC-Net is relatively easy and repeatable. We propose a Local Self-Coherence Mechanism to accomplish label propagation based on lesion area labeling characteristics for weak labels that cannot offer comprehensive lesion areas, hence forecasting a more complete lesion area. Secondly, when the COVID-19 training samples are insufficient, the Scale Transform for Self-Correlation is designed to optimize the robustness of the model to ensure that the CT images are consistent in the prediction results from different angles. Finally, in order to constrain the segmentation accuracy of the lesion area, the Lesion Infection Edge Attention Module is used to improve the information expression ability of edge modeling. Experiments on public datasets demonstrate that our method is more effective than other weakly supervised methods and achieves a new state-of-the-art performance.

DAFLNet: Dual Asymmetric Feature Learning Network for COVID-19 Disease Diagnosis in X-Rays.

COVID-19 has become the largest public health event worldwide since its outbreak, and early detection is a prerequisite for effective treatment. Chest X-ray images have become an important basis for screening and monitoring the disease, and deep learning has shown great potential for this task. Many studies have proposed deep learning methods for automated diagnosis of COVID-19. Although these methods have achieved excellent performance in terms of detection, most have been evaluated using limited datasets and typically use a single deep learning network to extract features. To this end, the dual asymmetric feature learning network (DAFLNet) is proposed, which is divided into two modules, DAFFM and WDFM. DAFFM mainly comprises the backbone networks EfficientNetV2 and DenseNet for feature fusion. WDFM is mainly for weighted decision-level fusion and features a new pretrained network selection algorithm (PNSA) for determination of the optimal weights. Experiments on a large dataset were conducted using two schemes, DAFLNet-1 and DAFLNet-2, and both schemes outperformed eight state-of-the-art classification techniques in terms of classification performance. DAFLNet-1 achieved an average accuracy of up to 98.56% for the triple classification of COVID-19, pneumonia, and healthy images.

Genetic characteristics of Chinese patients with hemorrhagic cerebrovascular disease.

BACKGROUND: We investigate the clinical and genetic characteristics of hemorrhagic cerebrovascular disease in order to provide a new theoretical basis for the prevention and treatment of hereditary cerebrovascular disease. METHOD: Three hereditary cerebral hemorrhage cases were analyzed retrospectively. The patients' families were surveyed, the clinical characteristics summarized, and gene polymorphisms investigated. RESULTS: Among the three cases, two patients had familial cerebral cavernous hemangiomas, and genetic testing revealed a heterozygous mutation in the CCM1 gene, with a deletion of base (T) in exon 15 (c.1542delT). The last patient had hereditary cerebral hemorrhage with amyloidosis, Finnish type, and the proband, his mother, and his daughter were found to have a heterozygous G duplicate mutation at position 100 in exon 1 of the GSN gene (c.100dupG). CONCLUSIONS: Future screening for genetic mutations associated with a high-risk of hereditary cerebral hemorrhage can help identify individuals at risk for this condition and thereby reduce the occurrence and progression of the disease. Such screening will further enhance the precision in preventing and treating cerebrovascular diseases.

Deep feature fusion classification network (DFFCNet): Towards accurate diagnosis of COVID-19 using chest X-rays images.

The widespread of highly infectious disease, i.e., COVID-19, raises serious concerns regarding public health, and poses significant threats to the economy and society. In this study, an efficient method based on deep learning, deep feature fusion classification network (DFFCNet), is proposed to improve the overall diagnosis accuracy of the disease. The method is divided into two modules, deep feature fusion module (DFFM) and multi-disease classification module (MDCM). DFFM combines the advantages of different networks for feature fusion and MDCM uses support vector machine (SVM) as a classifier to improve the classification performance. Meanwhile, the spatial attention (SA) module and the channel attention (CA) module are introduced into the network to improve the feature extraction capability of the network. In addition, the multiple-way data augmentation (MDA) is performed on the images of chest X-ray images (CXRs), to improve the diversity of samples. Similarly, the utilized Grad-CAM++ is to make the features more intuitive, and the deep learning model more interpretable. On testing of a collection of publicly available datasets, results from experimentation reveal that the proposed method achieves 99.89% accuracy in a triple classification of COVID-19, pneumonia, and health X-ray images, there by outperforming the eight state-of-the-art classification techniques.

High electron affinity triggered by lithium coordination: quasi-chalcogen properties of Li2Sn8Be.

This work puts forward an unusual but rational strategy to design superatoms mimicking the properties of group via elements. A new dianion with closo-configuration, namely Li2Sn8Be2-, has been obtained by decorating endohedral Zintl ion Sn8Be4- with two Li ligands. Its neutral counterpart, namely Li2Sn8Be, exhibits a high electron affinity of 2.526 eV, which not only exceeds that of the Sn8Be cluster but is higher than those of chalcogen elements. Li2Sn8Be has the potential to form stable ionic compounds with lithium, calcium, and even superalkali and superalkali-earth-metal atoms, and has an oxidation state of -2 therein. Besides, compound analogues of CO, O22-, H2O2, and Li2O2 can also be obtained with Li2Sn8Be serving as the building block. The striking resemblance between Li2Sn8Be and oxygen-group elements not only qualifies it for membership of the superatom family, but further collaborates the theoretical framework of the "three-dimensional periodic table".