Highly oriented hydrogels for tissue regeneration: design strategies, cellular mechanisms, and biomedical applications.

Many human tissues exhibit a highly oriented architecture that confers them with distinct mechanical properties, enabling adaptation to diverse and challenging environments. Hydrogels, with their water-rich "soft and wet" structure, have emerged as promising biomimetic materials in tissue engineering for repairing and replacing damaged tissues and organs. Highly oriented hydrogels can especially emulate the structural orientation found in human tissue, exhibiting unique physiological functions and properties absent in traditional homogeneous isotropic hydrogels. The design and preparation of highly oriented hydrogels involve strategies like including hydrogels with highly oriented nanofillers, polymer-chain networks, void channels, and microfabricated structures. Understanding the specific mechanism of action of how these highly oriented hydrogels affect cell behavior and their biological applications for repairing highly oriented tissues such as the cornea, skin, skeletal muscle, tendon, ligament, cartilage, bone, blood vessels, heart, etc., requires further exploration and generalization. Therefore, this review aims to fill that gap by focusing on the design strategy of highly oriented hydrogels and their application in the field of tissue engineering. Furthermore, we provide a detailed discussion on the application of highly oriented hydrogels in various tissues and organs and the mechanisms through which highly oriented structures influence cell behavior.

Arcyria similaris: A new myxomycete species from China.

A new myxomycete species, Arcyria similaris, was reported herein. The specimens were found and collected in the field on dead bark from Jingangtai National Geopark in Henan Province of China. This species has distinct and unique morphological characteristics, including dark grayish olive sporothecae that fade to smoke gray with age, shallow saucer-shaped cups with marked reticulations and thick papillae on the inner surface, a netted capillitium with many bulges, uniformly marked with low, dense, and irregular reticulations, and spores (8.0-)9.3-10.1(-10.9) µm in diameter, marked with sparse small warts and grouped prominent warts. Apart from a comprehensive morphological study, partial sequences of the nuclear 18S rDNA and elongation factor-1 alpha (EF-1α) genes were also provided in this study. This new species was described and illustrated morphologically. The specimens are deposited in the Herbarium of Fungi of Nanjing Normal University (HFNNU).

Synthesis of Liquid Hydrocarbon via Direct Hydrogenation of CO2 over FeCu-Based Bifunctional Catalyst Derived from Layered Double Hydroxides.

Here, we report a Na-promoted FeCu-based catalyst with excellent liquid hydrocarbon selectivity and catalytic activity. The physiochemical properties of the catalysts were comprehensively characterized by various characterization techniques. The characterization results indicate that the catalytic performance of the catalysts was closely related to the nature of the metal promoters. The Na-AlFeCu possessed the highest CO2 conversion due to enhanced CO2 adsorption of the catalysts by the introduction of Al species. The introduction of excess Mg promoter led to a strong methanation activity of the catalyst. Mn and Ga promoters exhibited high selectivity for light hydrocarbons due to their inhibition of iron carbides generation, resulting in a lack of chain growth capacity. The Na-ZnFeCu catalyst exhibited the optimal C5+ yield, owing to the fact that the Zn promoter improved the catalytic activity and liquid hydrocarbon selectivity by modulating the surface CO2 adsorption and carbide content. Carbon dioxide (CO2) hydrogenation to liquid fuel is considered a method for the utilization and conversion of CO2, whereas satisfactory activity and selectivity remains a challenge. This method provides a new idea for the catalytic hydrogenation of CO2 and from there the preparation of high-value-added products.

Mucosa-Inspired Electro-Responsive Lubricating Supramolecular-Covalent Hydrogel.

Enabling the living capability of secreting liquids dynamically triggered by external stimuli while maintaining the bulk frame is a significant challenge for mucosa-inspired hydrogels. A mucosa-inspired electro-responsive hydrogel is developed in this study using the synergy between electro-responsive silk fibroin supramolecular non-covalent networks and covalent polyacrylamide and polyvinyl alcohol polymer networks. The formed supramolecular-covalent hydrogel exhibits a partial gel-sol transition upon the application of an electric field, and the liquid layer on the hydrogel surface near the cathode is used to mimic the mucus-secreting capability to regulate lubrication. The electro-responsive lubricating process can operate under a safe voltage and exhibits good reversibility. It is also a universal strategy to construct an electro-responsive hydrogel by introducing an electro-responsive supramolecular network into the polymer network. This mucosa-inspired electro-responsive supramolecular-covalent hydrogel offers a promising method for designing soft actuators or robots that can regulate lubrication using an electric strategy.

Bioinspired Supramolecular Hydrogel from Design to Applications.

Nature offers a wealth of opportunities to solve scientific and technological issues based on its unique structures and function. The dynamic non-covalent interaction is considered to be the main base of living functions of creatures including humans, animals, and plants. Supramolecular hydrogels formed by non-covalent bonding interactions has become a unique platform for constructing promising materials for medicine, energy, electronic, and biological substitute. In this review, the self-assemble principle of supramolecular hydrogels is summarized. Next, the stimulation of external environment that triggers the assembly or disassembly of supramolecular hydrogels are recapitulated, including temperature, mechanics, light, pH, ions, etc. The main applications of bioinspired supramolecular hydrogels in terms of bionic objects including humans, animals, and plants are also described. Although so many efforts are done for revealing the synergized mechanism of the function and non-covalent interactions on the supramolecular hydrogel, the complexity and variability between stimulus and non-covalent bonding in the supramolecular system still require impeccable theories. As an outlook, the bioinspired supramolecular hydrogel is just beginning to exhibit its great potential in human life, offering significant opportunities in drug delivery and screening, implantable devices and substitutions, tissue engineering, micro-fluidic devices, and biosensors.

Elsinochrome A induces cell apoptosis and autophagy in photodynamic therapy.

Elsinochrome A (EA) is a perylene quinone natural photosensitizer, photosensitizer under light excitation generates reactive oxygen species (ROS) to induce apoptosis, so can be used for treating tumors, that is so-called photodynamic therapy (PDT). However, the molecular mechanism, especially related to apoptosis and autophagy, is still unclear. In this study, we aimed to explore the mechanism of EA-PDT-induced B16 cells apoptosis and autophagy. The action of EA-PDT on mitochondrial permeability transition pore (MPTP), mitochondrial membrane potential (MMP) and the mitochondrial function were researched by fluorescence technique and Extracellular Flux Analyzer. Illumina sequencing, tandem mass tags Quantitative Proteomics and Western Blot studied the mechanism at the gene and protein levels. The results indicated that EA-PDT had excellent phototoxicity in vitro. EA could bind to the mitochondria. EA-PDT for 5 min caused MPTP opening, MMP decreasing and abnormal mitochondrial function with a concentration-dependent characteristic. EA-PDT resulted in an increase intracellular ROS and the number of autophagosomes. Caspase2, caspase9 and tnf were upregulated, and bcl2, prkn, atg2, atg9 and atg10 were downregulated. Our results indicated that EA-PDT induced cell apoptosis and autophagy through the mediation of ROS/Atg/Parkin. This study can provide enlightenment for exploring potential targets of drug development for the PDT of melanoma.

Distribution, assembly, and interactions of soil microorganisms in the bright coniferous forest area of China's cold temperate zone.

The bright coniferous forest area in the cold temperate zone of China is a terrestrial ecosystem primarily dominated by low mountain Larix gmelinii trees. Limited information is available regarding the assembly mechanisms and interactions of microbial communities in the soil in this region. This study employed high-throughput techniques to obtain DNA from myxomycetes, bacteria, and fungi in the soil, evaluated their diversity in conjunction with environmental factors, associated them with the assembly process, and explored the potential interaction relationships between these microorganisms. The findings of our study showed that environmental factors had a more significant influence on the α and ß diversity of bacteria compared to myxomycetes and fungi. Microbial communities were influenced by environmental selection and geographical diffusion, although environmental selection appeared to have a more significant impact than geographical diffusion. Our study suggested that different microorganisms exhibited unique evolutionary patterns and may have different assembly modes within phylogenetic groups. Myxomycetes and fungi exhibited a similar assembly process that was mainly influenced by stochastic dispersal limitation and drift. In contrast, bacteria's assembly process was primarily influenced by stochastic drift and deterministic homogeneous selection. The community of myxomycetes and fungi is greatly influenced by spatial distribution and random events, while bacteria have a relatively stable population composition in specific regions and may also be subject to environmental constraints. Finally, this study revealed that Humicolopsis cephalosporioides, a fungus that exclusively resided in cold environments, may play a critical role as a keystone species in maintaining molecular ecological networks and was considered a core member of the microbiome.

Formate Additive for Efficient and Stable Methylammonium-Free Perovskite Solar Cells by Gas-Quenching.

In order to promote the commercialization of perovskite solar cells, gas-quenching is considered to be a promising technique for perovskite film fabrication. However, when handling with methylammonium-free (MA-free) perovskites, it is often difficult to obtain high-quality perovskite films by gas-quenching. Herein, formate additives are employed to regulate the crystallization of MA-free perovskite, and improve the quality of perovskite films. Different additives of formamidine formate (FAFO) and potassium formate (KFO) is compared to investigate the role of formate groups in the crystallization of perovskite films prepared by gas-quenching. The FAFO additive facilitates the perovskite crystallization in (001) orientation whereas KFO favors for (111) orientation. The MA-free device with addition of FAFO demonstrate a champion power conversion efficiency of 20.94 %, compared to that of 20.14 % for KFO devices. In addition, FAFO device also exhibits improved thermal stability in ambient condition without encapsulation, extending the T80 lifetime by 18 times compared to the pristine device.

Process Simulation of Diesel into Aromatics and Carbon Nanotubes: A Techno and Economic Analyses.

With the sustainable increase of renewable energy and the maturation of heavy vehicle market, diesel consumption would face a downward trend worldwide. Herein, we have proposed a new route for hydrocracking of light cycle oil (LCO) into aromatics and gasoline and the tandem conversion of C1-C5 hydrocarbons (byproducts) into carbon nanotubes (CNTs) and H2, and by combining the simulation with Aspen Plus software and the experimental study of C2-C5 conversion, we have built a transformation network including LCO to aromatics/gasoline, C2-C5 to CNTs and H2, the conversion of CH4 into CNTs and H2, and the cycle use of H2 with pressure swing adsorption. Mass balance, energy consumption, and economic analysis were discussed as a function of varying CNT yield and CH4 conversion. 50% of H2 required for hydrocracking of LCO can be supplied by the downstream chemical vapor deposition processes. This can greatly reduce the cost of high-priced hydrogen feedstock. If the sale price of CNTs exceeds 2170 CNY per ton, the entire process would break even for a process of dealing with 520,000 t/a LCO. These results imply the great potential of this route, considering the vast demand and the current high price of CNTs.

Potential application of novel cadmium-tolerant bacteria in bioremediation of Cd-contaminated soil.

With the increase in cadmium (Cd) release into the environment, it is necessary to find appropriate solutions to reduce soil Cd pollution. Microorganisms are a green and effective means for the remediation of Cd-contaminated soil. In this study, in a Cd-contaminated farmland, we screened and identified novel Cd-resistant strains, Paenarthrobactor nitroguajacolicus, Lysinibacillus fusiformis, Bacillus licheniformis, and Methyllobacium brachiatum, with minimum inhibitory concentrations of 100, 100, 50, and 50 mg/L, respectively, and added them each to pots containing Cd-contaminated rape plants to explore their remediation ability. The results showed that treatment with each of the four strains significantly increased the abundance of Nitrospirae, Firmicutes, Verrucomicrobia, and Patescibacterium in the rhizosphere soil of the plants. This led to changes in soil physical and chemical indices; pH; and available phosphorus, urease, and catalase activities, which were significantly negatively correlated with bioavailable Cd, reducing 28.74-58.82 % Cd enrichment to plants and 23.72-43.79 % Cd transport within plants, and reducing 5.52-10.68 % available cadmium in soil, effectively reducing the biotoxicity of Cd. Thus, this study suggests microbial remediation as a reliable option, forming a basis for the remediation of Cd-contaminated soil.

High Throughput Fabrication of Flexible Top-Driven Sensing Probe.

In this work, considering the current status of conservative and complicated traditional thrombosis treatment methods, a kind of flexible intelligent probe (FIP) with a top-driven sensing strategy is proposed to realize the expected function of thrombosis accurate localization in a liquid flow environment. After throughput fabrication, we find that the FIP has excellent electrical conductivity and mechanical properties. Notable, our FIP with the principle of piezo-resistive sensing has a quasi-linear sensitivity (approx. 0.325 L per minute) to flow sensing in the low flow velocity range (0-1 L per minute). Via the well-designed magnetically driven method, our FIP has a maximum deflection output force of 443.264 mN, a maximum deflection angle of 43°, and a maximum axial force of 54.176 mN. We demonstrate that the FIP is capable of completing the specified command actions relatively accurately and has a good response to real-time sensing feedback performance, which has broad application prospects in thrombus localization detection.

Imaging of Insect Hole in Living Tree Trunk Based on Joint Driven Algorithm of Electromagnetic Inverse Scattering.

Trunk pests have always been one of the most important species of tree pests. Trees eroded by trunk pests will be blocked in the transport of nutrients and water and will wither and die or be broken by strong winds. Most pests are social and distributed in the form of communities inside trees. However, it is difficult to know from the outside if a tree is infected inside. A new method for the non-invasive detecting of tree interiors is proposed to identify trees eroded by trunk pests. The method is based on electromagnetic inverse scattering. The scattered field data are obtained by an electromagnetic wave receiver. A Joint-Driven algorithm is proposed to realize the electromagnetic scattered data imaging to determine the extent and location of pest erosion of the trunk. This imaging method can effectively solve the problem of unclear imaging in the xylem of living trees due to the small area of the pest community. The Joint-Driven algorithm proposed by our group can achieve accurate imaging with a ratio of pest community radius to live tree radius equal to 1:60 under the condition of noise doping. The Joint-Driven algorithm proposed in this paper reduces the time cost and computational complexity of tree internal defect detection and improves the clarity and accuracy of tree internal defect inversion images.

Classroom Digital Teaching and College Students' Academic Burnout in the Post COVID-19 Era: A Cross-Sectional Study.

The continued development of digital technology and its overuse in teaching and learning in the post-epidemic era have brought about digital health risks, which are associated with academic burnout among college students. This study focused on the relationship between classroom digital teaching and students' academic burnout and designed the Classroom Burnout Inventory (CBI) and the Classroom Burnout Causes Inventory (CBCI) to conduct a cross-sectional survey of 206 Chinese university students. Correlations and regression analyses were conducted between key factors and burnout values through a path model of "Digital teaching-Teaching & learning process-Causes subjects-Burnout". The results of the study show that an inappropriate and excessive use of unintegrated digital teaching and learning technologies in the classroom was positively correlated with academic burnout among college students. Burnout levels and the three manifestations were not correlated with students' gender, grade, and major. In terms of causes, the academic burnout of college students was more correlated with their own personal reasons than with external factors such as teachers, universities, and environments. Integrating digital technology platforms, enhancing teacher leadership in the digital classroom, and strengthening peer support and students' psychological resilience are all meaningful explorations of academic burnout prevention strategies.

Yolo-Based Traffic Sign Recognition Algorithm.

With the rapid development of intelligent transportation, more and more vehicles are equipped with intelligent traffic sign recognition systems, which can reduce the potential safety hazards caused by human cognitive errors. Therefore, a more safe and reliable traffic sign recognition system is the demand of drivers, and it is also the research hotspot of current automobile manufacturers. However, the pictures taken by the actual driving car are inevitably distorted and blurred. In addition, there are external uncontrollable factors, such as the impact of bad weather, which make the research of traffic sign recognition system face many difficulties, and the practical application is far from mature. In order to solve the above challenges, this paper proposes a Yolo model for traffic sign recognition. Firstly, the traffic signs are roughly divided into several categories and then preprocessed according to the characteristics of various types of signs. The processed pictures are input into the optimized convolutional neural network to subdivide the categories to obtain the specific categories. Finally, the proposed recognition algorithm is tested with the data set based on the German traffic sign recognition standard and compared with other baseline algorithms. The results show that the algorithm greatly improves the running speed on the basis of ensuring a high classification accuracy and is more suitable for traffic sign recognition system.

Preoperative estimation of the survival of patients with unresectable hepatocellular carcinoma achieving complete response after conventional transcatheter arterial chemoembolization: assessments of clinical and LI-RADS MR features.

PURPOSE: To identify the associations of clinical and magnetic resonance (MR) features with overall survival (OS) in patients with unresectable hepatocellular carcinoma (HCC) achieving complete response (CR) after conventional transcatheter arterial chemoembolization (TACE) and to further develop an individual nomograph to estimate the survival probability. MATERIALS AND METHODS: A total of 112 patients with unresectable HCC treated with TACE as first-line treatment were retrospectively evaluated. Potential risk factors associated with OS were identified by univariate and multivariate Cox analyses. The survival model was developed by multivariate Cox proportional hazard model. The area under the receiver operating characteristic curve was calculated to assess the performance of each marker and of the whole model. Discrimination was performed using Kaplan-Meier curves, and the survival curves were compared by the log-rank test. A nomogram derived from the survival model was established. RESULTS: Multivariate Cox analyses indicated that nonsmooth tumor margin, peritumoral enhancement, fat sparing in solid mass, and Barcelona clinic liver cancer (BCLC) stage were independent risk indicators associated with OS. The survival model showed acceptable diagnostic power, with an area under the curve (AUC) of 0.687. Kaplan-Meier curves demonstrated that the model discriminated well, as the high-risk and low-risk groups had median survival times of 21.6 months and 34.8 months, respectively (log-rank test, P = 0.01). CONCLUSIONS: Nonsmooth tumor margin, peritumoral enhancement, fat sparing in solid mass, and BCLC stage were potential biomarkers to evaluate the survival with favorable performance and discriminate HCC patients with CR under conventional TACE treatment.

A Novel Deep Transfer Learning Method for Intelligent Fault Diagnosis Based on Variational Mode Decomposition and Efficient Channel Attention.

In recent years, deep learning has been applied to intelligent fault diagnosis and has achieved great success. However, the fault diagnosis method of deep learning assumes that the training dataset and the test dataset are obtained under the same operating conditions. This condition can hardly be met in real application scenarios. Additionally, signal preprocessing technology also has an important influence on intelligent fault diagnosis. How to effectively relate signal preprocessing to a transfer diagnostic model is a challenge. To solve the above problems, we propose a novel deep transfer learning method for intelligent fault diagnosis based on Variational Mode Decomposition (VMD) and Efficient Channel Attention (ECA). In the proposed method, the VMD adaptively matches the optimal center frequency and finite bandwidth of each mode to achieve effective separation of signals. To fuse the mode features more effectively after VMD decomposition, ECA is used to learn channel attention. The experimental results show that the proposed signal preprocessing and feature fusion module can increase the accuracy and generality of the transfer diagnostic model. Moreover, we comprehensively analyze and compare our method with state-of-the-art methods at different noise levels, and the results show that our proposed method has better robustness and generalization performance.

Fisheye Image Detection of Trees Using Improved YOLOX for Tree Height Estimation.

Tree height is an essential indicator in forestry research. This indicator is difficult to measure directly, as well as wind disturbance adds to the measurement difficulty. Therefore, tree height measurement has always been an issue that experts and scholars strive to improve. We propose a tree height measurement method based on tree fisheye images to improve the accuracy of tree height measurements. Our aim is to extract tree height extreme points in fisheye images by proposing an improved lightweight target detection network YOLOX-tiny. We added CBAM attention mechanism, transfer learning, and data enhancement methods to improve the recall rate, F1 score, AP, and other indicators of YOLOX-tiny. This study improves the detection performance of YOLOX-tiny. The use of deep learning can improve measurement efficiency while ensuring measurement accuracy and stability. The results showed that the highest relative error of tree measurements was 4.06% and the average relative error was 1.62%. The analysis showed that the method performed better at all stages than in previous studies.

Modulating inherent lewis acidity at the intergrowth interface of mortise-tenon zeolite catalyst.

The acid sites of zeolite are important local structures to control the products in the chemical conversion. However, it remains a great challenge to precisely design the structures of acid sites, since there are still lack the controllable methods to generate and identify them with a high resolution. Here, we use the lattice mismatch of the intergrown zeolite to enrich the inherent Lewis acid sites (LASs) at the interface of a mortise-tenon ZSM-5 catalyst (ZSM-5-MT) with a 90° intergrowth structure. ZSM-5-MT is formed by two perpendicular blocks that are atomically resolved by integrated differential phase contrast scanning transmission electron microscopy (iDPC-STEM). It can be revealed by various methods that novel framework-associated Al (AlFR) LASs are generated in ZSM-5-MT. Combining the iDPC-STEM results with other characterizations, we demonstrate that the partial missing of O atoms at interfaces results in the formation of inherent AlFR LASs in ZSM-5-MT. As a result, the ZSM-5-MT catalyst shows a higher selectivity of propylene and butene than the single-crystal ZSM-5 in the steady conversion of methanol. These results provide an efficient strategy to design the Lewis acidity in zeolite catalysts for tailored functions via interface engineering.

Enhanced production of cordycepic acid from Cordyceps cicadae isolated from a wild environment.

Cordyceps acid is an active component of Cordyceps cicadae and has a variety of medicinal uses, including anti-tumor effects, the prevention of cerebral hemorrhaging and myocardial infarction, and the inhibition of a wide range of bacteria. The objectives of this study were to identify C. cicadae fungi and optimize the culture conditions to obtain a high yield of cordycepic acid. First, a wild C. cicadae was identified by morphological observation and rDNA sequence analysis. Secondly, the optimal fermentation conditions were determined using a single-factor method, a Plackett-Burman design, and a Box-Behnken response surface. Finally, using the yield of fruit bodies and the content of cordyceps acid as indices, combined with a single-factor experiment and a response surface design, the best combination of conditions for cultivation was determined. The results showed that the best combination was as follows: sucrose 2%, tryptone 2%, KH2PO4 0.4%, MgSO4·7H2O 0.4%, an initial pH of the fermentation liquid of 7.0, 5% inoculum, fermentation for 4.5 d, a ratio of medium to liquid of 1:1.7, illumination intensity 150 Lux, illumination time 15 h per day, and 70% humidity. The content of cordycepic acid in the fruiting bodies developed in cultivation was 2.07-fold higher than that in the wild C. cicadae. This study provides a theoretical basis for the large-scale cultivation of C. cicadae with a high concentration of cordycepic acid.

Plant Disease Detection Using Generated Leaves Based on DoubleGAN.

Plant leaves can be used to effectively detect plant diseases. However, the number of images of unhealthy leaves collected from various plants is usually unbalanced. It is difficult to detect diseases using such an unbalanced dataset. We used DoubleGAN (a double generative adversarial network) to generate images of unhealthy plant leaves to balance such datasets. We proposed using DoubleGAN to generate high-resolution images of unhealthy leaves using fewer samples. DoubleGAN is divided into two stages. In stage 1, we used healthy leaves and unhealthy leaves as inputs. First, the healthy leaf images were used as inputs for the WGAN (Wasserstein generative adversarial network) to obtain the pretrained model. Then, unhealthy leaves were used for the pretrained model to generate 64*64 pixel images of unhealthy leaves. In stage 2, a superresolution generative adversarial network (SRGAN) was used to obtain corresponding 256*256 pixel images to expand the unbalanced dataset. Finally, compared with images generated by DCGAN (Deep convolution generative adversarial network). The dataset expanded with DoubleGAN, the generated images are clearer than DCGAN, and the accuracy of plant species and disease recognition reached 99.80 and 99.53 percent, respectively. The recognition results are better than those from the original dataset.