Chinese cities show different trend toward carbon peak.

Understanding the disparities in carbon emission trend among cities is critical for achieving carbon peak goal. However, the status and trends of carbon peaking and reduction in various city types are still unclear. Therefore, this study classified 315 Chinese cities according to their economic and industrial structure by SOM-K-means, aiming to evaluate the trends and dynamic drivers of carbon peaking progress in different city types. The findings reveal a decline in carbon emissions in 110 cities (34.9 %) since 2020. Notably, all city types show potential for carbon reduction and achieving carbon peaking. Specifically, resource-based cities and high-end service cities have the most effect on reducing emissions, with 48.4 % and 42.1 % of the cities declining in carbon emissions. Energy-based and heavy industrial cities face heightened pressure to reduce carbon emissions. Additionally, in high-end service cities, energy efficiency and investment intensity contribute to emission reduction, while industrial structure adjustment decrease carbon emissions in resource-based cities. Furthermore, enhancing energy efficiency effects and R&D intensity are effective ways to significantly reduce carbon emissions in heavy industrial cities. We conclude that differentiating carbon reduction pathways for different cities should constitute be a breakthrough in achieving the goal of carbon peaking. These insights provide recommendations for cities that have yet to reach their carbon peak for both China and other developing countries.

A piezoelectric-driven microneedle platform for skin disease therapy.

With over a million cases detected each year, skin disease is a global public health problem that diminishes the quality of life due to its difficulty to eradicate, propensity for recurrence, and potential for post-treatment scarring. Photodynamic therapy (PDT) is a treatment with minimal invasiveness or scarring and few side effects, making it well tolerated by patients. However, this treatment requires further research and development to improve its effective clinical use. Here, a piezoelectric-driven microneedle (PDMN) platform that achieves high efficiency, safety, and non-invasiveness for enhanced PDT is proposed. This platform induces deep tissue cavitation, increasing the level of protoporphyrin IX and significantly enhancing drug penetration. A clinical trial involving 25 patients with skin disease was conducted to investigate the timeliness and efficacy of PDMN-assisted PDT (PDMN-PDT). Our findings suggested that PDMN-PDT boosted treatment effectiveness and reduced the required incubation time and drug concentration by 25% and 50%, respectively, without any anesthesia compared to traditional PDT. These findings suggest that PDMN-PDT is a safe and minimally invasive approach for skin disease treatment, which may improve the therapeutic efficacy of topical medications and enable translation for future clinical applications.

Low-Frequency Ultrasound Sensitive Piezo1 Channels Regulate Keloid-Related Characteristics of Fibroblasts.

Keloids are benign fibroproliferative tumors that severely diminish the quality of life due to discomfort, dysfunction, and disfigurement. Recently, ultrasound technology as a noninvasive adjuvant therapy is developed to optimize treatment protocols. However, the biophysical mechanisms have not yet been fully elucidated. Here, it is proposed that piezo-type mechanosensitive ion channel component 1 (Piezo1) plays an important role in low-frequency sonophoresis (LFS) induced mechanical transduction pathways that trigger downstream cellular signaling processes. It is demonstrated that patient-derived primary keloid fibroblasts (PKF), NIH 3T3, and HFF-1 cell migration are inhibited, and PKF apoptosis is significantly increased by LFS stimulation. And the effects of LFS is diminished by the application of GsMTx-4, the selective inhibitor of Piezo1, and the knockdown of Piezo1. More importantly, the effects of LFS can be imitated by Yoda1, an agonist of Piezo1 channels. Establishing a patient-derived xenograft keloid implantation mouse model further verified these results, as LFS significantly decreased the volume and weight of the keloids. Moreover, blocking the Piezo1 channel impaired the effectiveness of LFS treatment. These results suggest that LFS inhibits the malignant characteristics of keloids by activating the Piezo1 channel, thus providing a theoretical basis for improving the clinical treatment of keloids.

Acoustic hologram-induced virtual in vivo enhanced waveguide (AH-VIEW).

Optical imaging and phototherapy in deep tissues face notable challenges due to light scattering. We use encoded acoustic holograms to generate three-dimensional acoustic fields within the target medium, enabling instantaneous and robust modulation of the volumetric refractive index, thereby noninvasively controlling the trajectory of light. Through this approach, we achieved a remarkable 24.3% increase in tissue heating rate in vitro photothermal effect tests on porcine skin. In vivo photoacoustic imaging of mouse brain vasculature exhibits an improved signal-to-noise ratio through the intact scalp and skull. These findings demonstrate that our strategy can effectively suppress light scattering in complex biological tissues by inducing low-angle scattering, achieving an effective depth reaching the millimeter scale. The versatility of this strategy extends its potential applications to neuroscience, lithography, and additive manufacturing.

Non-invasive evaluation of transdermal drug delivery using 3-D transient triplet differential (TTD) photoacoustic imaging.

Transdermal drug delivery (TDD) is less invasive and avoids first-pass metabolism, making it an attractive method for treating various diseases such as diabetes and hypertension. However, current methods for evaluating TDD systems lack in vivo descriptions of drug distribution in the skin. In this study, we demonstrate the capability of the Transient Triplet Differential (TTD) method, a non-invasive and background-free photoacoustic imaging technique, for accurately mapping drug distribution and evaluating different TDD systems. Our findings show that the TTD method can provide high sensitivity and specificity for targeted drug extraction and visualize 3D drug distribution in the skin. Furthermore, in vivo experiments confirmed the potential of the TTD method in evaluating the clinical performance of TDD. It's predictable that the TTD method can be a reliable and non-invasive approach for evaluating TDD systems and offer valuable insights into TDD research and development.

Integrating machine learning with linguistic features: A universal method for extraction and normalization of temporal expressions in Chinese texts.

BACKGROUND AND OBJECTIVE: With the rapid development of information dissemination technology, the amount of events information contained in massive texts now far exceeds the intuitive cognition of humans, and it is hard to understand the progress of events in order of time. Temporal information runs through the whole process of beginning, proceeding, and ending of events, and plays an important role in many natural language processing applications, such as information extraction, question answering, and text summary. Accurately extracting temporal information from Chinese texts and automatically mapping the temporal expressions in natural language to the time axis are crucial to understanding the development of events and dynamic changes in them. METHODS: This study proposes a method integrating machine learning with linguistic features (IMLLF) for extraction and normalization of temporal expressions in Chinese texts to achieve the above objectives. Linguistic features are constructed by analyzing the expression rules of temporal information, and are combined with machine learning to map the natural language form of time onto a one-dimensional timeline. The web text dataset we build is divided into five parts for five-fold cross-validation, to compare the influence of different combinations of linguistic features and different methods. In the open medical dialog dataset, based on the training model obtained from the web text dataset, 200 disease descriptions are randomly selected each time for three rounds of experiments. RESULTS: The F1 of multi-feature fusion is 95.2%, which is better than the single-feature and double-feature combination. The results of experiments showed that the proposed IMLLF method can improve the accuracy of recognition of temporal information in Chinese to a greater extent than classical methods, with an F1-score of over 95% on the web text dataset and medical conversation dataset. In terms of the normalization of time expressions, the accuracy of the IMLLF method is higher than 93%. CONCLUSIONS: IMLLF has better results in extracting and normalizing time expressions on the web text dataset and the medical conversation dataset, which verifies the universality of IMLLF to identify and quantify temporal information. IMLLF method can accurately map the time information to the time axis, which is convenient for doctors to intuitively see when and what happened to the patient, and helps to make better medical decisions.

Clustering by measuring local direction centrality for data with heterogeneous density and weak connectivity.

Clustering is a powerful machine learning method for discovering similar patterns according to the proximity of elements in feature space. It is widely used in computer science, bioscience, geoscience, and economics. Although the state-of-the-art partition-based and connectivity-based clustering methods have been developed, weak connectivity and heterogeneous density in data impede their effectiveness. In this work, we propose a boundary-seeking Clustering algorithm using the local Direction Centrality (CDC). It adopts a density-independent metric based on the distribution of K-nearest neighbors (KNNs) to distinguish between internal and boundary points. The boundary points generate enclosed cages to bind the connections of internal points, thereby preventing cross-cluster connections and separating weakly-connected clusters. We demonstrate the validity of CDC by detecting complex structured clusters in challenging synthetic datasets, identifying cell types from single-cell RNA sequencing (scRNA-seq) and mass cytometry (CyTOF) data, recognizing speakers on voice corpuses, and testifying on various types of real-world benchmarks.

3D-Printed Integrated Ultrasonic Microneedle Array for Rapid Transdermal Drug Delivery.

Transdermal drug delivery (TDD) is an attractive alternative to oral and hypodermic injection drug administration, and is poised to increase its impact on medicine and pharmaceutical design. Microneedles (MNs) are a new minimally invasive TDD method widely used in medicine and cosmetology. MNs create a microscale channel from the stratum corneum to the dermis and enable drug delivery of hydrophilic and macromolecular into the skin. Although MNs allow different drugs to penetrate the stratum corneum, they cannot provide an extra driving force for drug transport in tissue. To overcome this limitation and achieve fast, controllable drug delivery, an integrated 3D-printed ultrasonic MN array (USMA) device consisting of hollow MNs and an ultrasonic transducer is proposed. The hollow MNs enable drug to penetrate the stratum corneum, and the ultrasound transmitted through the MNs provides the driving force for drug transportation in tissue. Using methylene blue and bovine serum albumin as model drugs, we tested the drug delivery performance of USMA on porcine skin; the results show that USMA significantly enhanced the delivery efficiency of model drugs. Besides, USMA obviously reduced MNs insertion force and tissue damage, which were well-tolerated and gentle. This study suggests that the integrated ultrasonic MN array has great potential for clinical drug delivery with high efficiency and lessening the suffering of patients.

Bipartite Network of Interest (BNOI): Extending Co-Word Network with Interest of Researchers Using Sensor Data and Corresponding Applications as an Example.

Traditional co-word networks do not discriminate keywords of researcher interest from general keywords. Co-word networks are therefore often too general to provide knowledge if interest to domain experts. Inspired by the recent work that uses an automatic method to identify the questions of interest to researchers like "problems" and "solutions", we try to answer a similar question "what sensors can be used for what kind of applications", which is great interest in sensor- related fields. By generalizing the specific questions as "questions of interest", we built a knowledge network considering researcher interest, called bipartite network of interest (BNOI). Different from a co-word approaches using accurate keywords from a list, BNOI uses classification models to find possible entities of interest. A total of nine feature extraction methods including N-grams, Word2Vec, BERT, etc. were used to extract features to train the classification models, including naïve Bayes (NB), support vector machines (SVM) and logistic regression (LR). In addition, a multi-feature fusion strategy and a voting principle (VP) method are applied to assemble the capability of the features and the classification models. Using the abstract text data of 350 remote sensing articles, features are extracted and the models trained. The experiment results show that after removing the biased words and using the ten-fold cross-validation method, the F-measure of "sensors" and "applications" are 93.2% and 85.5%, respectively. It is thus demonstrated that researcher questions of interest can be better answered by the constructed BNOI based on classification results, comparedwith the traditional co-word network approach.

Green and facile fabrication of silver nanoparticles using Konjac Glucomannan by photocatalytic strategy.

Green fabrication of highly stable silver nanoparticles (AgNPs) is vital for its development. Herein, AgNPs with enhanced stability were prepared in an environmentally friendly manner using aqueous mixture of 0.8 mM silver nitrate and 10.0 mg/mL Konjac glucomannan (KGM) through a photocatalytic strategy. AgNPs were fabricated in situ at room temperature, followed by exposure to ultraviolet light for 510 min, resulting in complete reduction of all silver ions to AgNPs-KGM. AgNPs were firmly capped at the hydroxyl and acetyl groups of KGM through the formation of AgO bonds, which promoted the stable dispersion of AgNP-KGM, as determined by TSI value of 0.29 using multiple light scattering. Furthermore, AgNPs-KGM possessed excellent antibacterial activity against Staphylococcus aureus and Escherichia coli, with MIC and MBC values of 5.11 and 10.23 µg/mL, and 10.23 and 20.46 µg/mL, respectively. The proposed protocol is facile and feasible for large-scale production, achieving the goal of green fabrication.

A hierarchical temporal attention-based LSTM encoder-decoder model for individual mobility prediction.

Prediction of individual mobility is crucial in human mobility related applications. Whereas, existing research on individual mobility prediction mainly focuses on next location prediction and short-term dependencies between traveling locations. Long-term location sequence prediction is of great importance for long-time traffic planning and location advertising, and long-term dependencies exist as individual mobility regularity typically occurs daily and weekly. This paper proposes a novel hierarchical temporal attention-based LSTM encoder-decoder model for individual location sequence prediction. The proposed hierarchical attention mechanism captures both long-term and short-term dependencies underlying in individual longitudinal trajectories, and uncovers frequential and periodical mobility patterns in an interpretable manner by incorporating the calendar cycle of individual travel regularities into location prediction. More specifically, the hierarchical attention consists of local temporal attention to identify highly related locations in each day, and global temporal attention to discern important travel regularities over a week. Experiments on individual trajectory datasets with varying degree of traveling uncertainty demonstrate that our method outperforms four baseline methods on three evaluation metrics. In addition, we explore the interpretability of the proposed model in understanding individual daily, and weekly mobility patterns by visualizing the temporal attention weights and frequent traveling patterns associated with locations.

Coupling cellular automata with area partitioning and spatiotemporal convolution for dynamic land use change simulation.

Urbanization processes have accelerated over recent decades, prompting efforts to model land use change (LUC) patterns for decision support and urban planning. Cellular automata (CA) are extensively employed given their simplicity, flexibility, and intuitiveness when simulating dynamic LUC. Previous research, however, has ignored the spatial heterogeneity among sub-regions, instead applying the same transition rules across entire regions; moreover, most existing methods extract neighborhood effects with only one data time slice, which is inconsistent with the nature of neighborhood interactions as a long-term process exhibiting obvious spatiotemporal dependency. Accordingly, we propose a hybrid cellular automata model coupling area partitioning and spatiotemporal neighborhood features learning, named PST-CA. We use a machine-learning-based partitioning strategy, self-organizing map (SOM), to divide entire regions into several homogeneous sub-regions, and further apply a spatiotemporal three-dimensional convolutional neural network (3D CNN) to extract the spatiotemporal neighborhood features. An artificial neural network (ANN) is then built to create a conversion probability map for each sub-region using both spatiotemporal neighborhood features and factors that drive the LUC. Finally, the dynamic simulation results of entire study area are generated by fusing these probability maps, constraints and stochastic factors. Land use data collected from 2000 to 2015 in Shanghai were selected to verify our proposed method. Four traditional models were implemented for comparison, including logistic regression (LR)-CA, support vector machine (SVM)-CA, random forest (RF)-CA and conventional ANN-CA. Results illustrate that the proposed PST-CA outperformed four traditional models, with overall accuracy increased by 4.66%~6.41%. Moreover, three distinctly different "coverage rate-growth rate" composite patterns of built-up areas are shown in the SOM partitioning results, which verifies SOM's ability to address spatial heterogeneity; while the optimal time steps in 3D CNN generally maintained a positive correlation with the growth rate of built-up areas, which implies longer temporal dependency should be captured for rapidly developing areas.

City-Wide Traffic Flow Forecasting Using a Deep Convolutional Neural Network.

City-wide traffic flow forecasting is a significant function of the Intelligent Transport System (ITS), which plays an important role in city traffic management and public travel safety. However, this remains a very challenging task that is affected by many complex factors, such as road network distribution and external factors (e.g., weather, accidents, and holidays). In this paper, we propose a deep-learning-based multi-branch model called TFFNet (Traffic Flow Forecasting Network) to forecast the short-term traffic status (flow) throughout a city. The model uses spatiotemporal traffic flow matrices and external factors as its input and then infers and outputs the future short-term traffic status (flow) of the whole road network. For modelling the spatial correlations of the traffic flows between current and adjacent road segments, we employ a multi-layer fully convolutional framework to perform cross-correlation calculation and extract the hierarchical spatial dependencies from local to global scales. Also, we extract the temporal closeness and periodicity of traffic flow from historical observations by constructing a high-dimensional tensor comprised of traffic flow matrices from three fragments of the time axis: recent time, near history, and distant history. External factors are also considered and trained with a fully connected neural network and then fused with the output of the main component of TFFNet. The multi-branch model is automatically trained to fit complex patterns hidden in the traffic flow matrices until reaching pre-defined convergent criteria via the back-propagation method. By constructing a rational model input and network architecture, TFFNet can capture spatial and temporal dependencies simultaneously from traffic flow matrices during model training and outperforms other typical traffic flow forecasting methods in the experimental dataset.

Fabrication of highly stable silver nanoparticles using polysaccharide-protein complexes from abalone viscera and antibacterial activity evaluation.

Silver nanoparticles (AgNPs) are eco-friendly antibacterial agents, yet their use is limited by their facile aggregation and precipitation. Therefore, the development of highly stable AgNPs is desirable. Herein, a polysaccharide-protein complex (PSP) was successfully obtained from viscera of abalone through a combination of enzymatic hydrolysis, membrane filtration, and gel permeation chromatography. Furthermore, highly stable AgNPs were successfully synthesized by using PSP as a reducing and capping agent in situ. AgNPs were firmly capped by PSP through the formation of AgO, AgN, and AgS bonds, as observed by Fourier transform infrared spectroscopy, X-ray photoelectron spectroscopy, and scanning transmission electron microscopy. Such capping of AgNPs by PSP contributed to the stable dispersion of PSP-AgNP composites at room temperature for 12 months, as evidenced by visual inspection and multiple light scattering. Furthermore, PSP-AgNPs were found to have an excellent antibacterial activity and biocompatibility. The proposed synthesis of AgNPs with high antibacterial activity, dispersibility, and biocompatibility will be of likely benefit in the field of life science and technology.

DCT-Based Preprocessing Approach for ICA in Hyperspectral Data Analysis.

The huge quantity of information and the high spectral resolution of hyperspectral imagery present a challenge when performing traditional processing techniques such as classification. Dimensionality and noise reduction improves both efficiency and accuracy, while retaining essential information. Among the many dimensionality reduction methods, Independent Component Analysis (ICA) is one of the most popular techniques. However, ICA is computationally costly, and given the absence of specific criteria for component selection, constrains its application in high-dimension data analysis. To overcome this limitation, we propose a novel approach that applies Discrete Cosine Transform (DCT) as preprocessing for ICA. Our method exploits the unique capacity of DCT to pack signal energy in few low-frequency coefficients, thus reducing noise and computation time. Subsequently, ICA is applied on this reduced data to make the output components as independent as possible for subsequent hyperspectral classification. To evaluate this novel approach, the reduced data using (1) ICA without preprocessing; (2) ICA with the commonly used preprocessing techniques which is Principal Component Analysis (PCA); and (3) ICA with DCT preprocessing are tested with Support Vector Machine (SVM) and K-Nearest Neighbor (K-NN) classifiers on two real hyperspectral datasets. Experimental results in both instances indicate that data after our proposed DCT preprocessing method combined with ICA yields superior hyperspectral classification accuracy.

Effects of miR-29a and miR-101a Expression on Myocardial Interstitial Collagen Generation After Aerobic Exercise in Myocardial-infarcted Rats.

BACKGROUND AND AIMS: Myocardial infarction (MI) is accompanied by increased collagen deposition, cell necrosis and angiogenesis in cardiac tissue, which results in reduced ventricular compliance. Both microRNA-29a (miR-29a) and microRNA-101a (miR-101a) target the mRNAs encoding collagens and other proteins involved in fibrosis. METHODS: We assessed the effects of intermittent aerobic exercise on the expression of cardiac miR-29a and miR-101a and following effects on the TGFß, fos, Smad2/3, COL1A1 and COL3A1 in MI model of rats. Intermittent aerobic exercise for MI rats was begun from the second week and ended at the ninth week postsurgery. Expressions of microRNAs (miRNAs) and fibrosis-associated genes were detected from the infarction adjacent region located in the left ventricle. The heart coefficient (HC = heart weight/body weight) and hemodynamics assay were used to evaluate cardiac function level. RESULTS: Intermittent aerobic exercise inhibited myocardial interstitial collagen deposition and significantly improved cardiac function of MI rats. The results of real-time PCR and Western blot indicate that intermittent aerobic exercise enhanced the expression of miR-29a and miR-101a and inhibited TGFß pathway in the MI rats. CONCLUSIONS: Our results suggest that controlled intermittent aerobic exercise can inhibit TGFß pathway via up-regulation to the expression of miR-29a and miR-101a and finally cause a reduced fibrosis and scar formation in cardiac tissue. We believe that controlled intermittent aerobic exercise is beneficial to the healing and discovery of damaged cardiac tissues and their function after MI.

Content-Based Discovery for Web Map Service using Support Vector Machine and User Relevance Feedback.

Many discovery methods for geographic information services have been proposed. There are approaches for finding and matching geographic information services, methods for constructing geographic information service classification schemes, and automatic geographic information discovery. Overall, the efficiency of the geographic information discovery keeps improving., There are however, still two problems in Web Map Service (WMS) discovery that must be solved. Mismatches between the graphic contents of a WMS and the semantic descriptions in the metadata make discovery difficult for human users. End-users and computers comprehend WMSs differently creating semantic gaps in human-computer interactions. To address these problems, we propose an improved query process for WMSs based on the graphic contents of WMS layers, combining Support Vector Machine (SVM) and user relevance feedback. Our experiments demonstrate that the proposed method can improve the accuracy and efficiency of WMS discovery.

Effect of molecular characteristics of Konjac glucomannan on gelling and rheological properties of Tilapia myofibrillar protein.

Konjac glucomannan (KGM) is an important gelling agent in composite gels. This study aimed to investigate the effects of KGM molecular characteristics (molecular weight, size and conformation) on gelling properties of Tilapia myofibrillar protein (TMP). In this work, TMP composite gels were prepared under neutral pH with varying KGM (native KGM, 10kGy-KGM, 20kGy-KGM, and 100kGy-KGM) of different molecular characteristics. Native KGM, 10kGy-KGM, and 20kGy-KGM exerted negative effect on gel strength or whiteness of TMP gels. Interestingly 100kGy-KGM improved gelling properties and whiteness of TMP gels. Such effects presented by varying KGM were attributed the physical filling behaviors and the interaction between KGM and TMP. These behaviors or interactions are resulted from different molecular size and conformation. Smaller molecular size (root-mean square radius, Rz 20.2nm) and approximated spherical conformation in 100kGy-KGM enhanced its interaction with TMP and maintained its compact and smooth structure, but the larger molecular size (Rz≥40.2nm) and random coil conformation in other KGMs inhibited part of actins from gelling and deteriorated the network structure. Our study provided principle knowledge to understand the structure-functions relationships of KGM-TMP composite gels. These results can be used to provide theoretical guidance for surimi gel processing.

ANXA2 enhances the progression of hepatocellular carcinoma via remodeling the cell motility associated structures.

Hepatocellular carcinoma (HCC) ranks as the fifth most common malignancy worldwide. The detailed mechanism of signal regulation for HCC progression is still not known, and the high motility of cancer cells is known as a core property for cancer progression maintenance. Annexin A2 (ANXA2), a calcium-dependent phospholipids binding protein is highly expressed in HCC. To study the roles the excessively expressed ANXA2 during the progression of HCC, we inhibited the ANXA2 expression in SMMC-7721 cells using RNAi, followed by the analysis of cell growth, apoptosis and cell motility. To explore the relationship between the cell behaviors and its structures, the microstructure changes were observed under fluorescence microscopy, laser scanning confocal microscopy and electron microscopy. Our findings demonstrated that down-regulation of ANXA2 results in decreased the cell proliferation and motility, enhanced apoptosis, suppressed cell pseudopodia/filopodia, inhibited expression of F-actin and ß-tubulin, and inhibited or depolymerized Lamin B. The cell contact inhibition was also analyzed in the paper. Take together, our results indicate that ANXA2 plays an important role to enhance the malignant behaviors of HCC cells, and the enhancement is closely based on its remodeling to cell structures.