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The productivity of vegetation is influenced by both climate change and human activities. Understanding the specific contributions of these influencing factors is crucial for ecological conservation and regional sustainability. This study utilized a combination of multi-source data to examine the spatiotemporal patterns of Net Primary Productivity (NPP) in the Yellow River Basin (YRB), China from 1982 to 2020. Additionally, a scenario-based approach was employed to compare Potential NPP (PNPP) with Actual NPP (ANPP) to determine the relative roles of climatic and human factors in NPP changes. The PNPP was estimated using the Lund-Potsdam-Jena General Ecosystem Simulator (LPJ-GUESS) model, while ANPP was evaluated by the Carnegie-Ames-Stanford Approach (CASA) model using different NDVI data sources. Both model simulations revealed that significant greening occurring in the YRB, with a gradual decrease observed from southeast to northwest. According to the LPJ_GUESS model simulations, areas experiencing an increasing trend in NPP accounted for 86.82% of the YRB. When using GIMMS and MODIS NDVI data with CASA model simulations, areas showing an increasing trend in NPP accounted for 71.42% and 97.02%, respectively. Furthermore, both climatic conditions and human factors had positive effects on vegetation restoration; approximated 41.15% of restored vegetation areas were influenced by both climate variation and human activities, while around 31.93% were solely affected by climate variation. However, it was found that human activities served as the principal driving force of vegetation degradation within the YRB, impacting 26.35% of degraded areas solely due to human activities. Therefore, effective management strategies encompassing both human activities and climate change adaptation are imperative for facilitating vegetation restoration within this region. These findings will valuable for enhancing our understanding in NPP changes and its underlying factors, thereby contributing to improved ecological management and the pursuit of regional carbon neutrality in China.

Let λ= (λ1, ⋯, λn) be β-Jacobi ensembles with parameters p1, p2, nand β, with βvarying with n. Set γ=limn→∞np1and σ=limn→∞p1p2. Suppose that limn→∞lognβn=0and 0 ≤ (σγ< 1. We offer the large deviation for p1+p2p1max1≤i≤nλiwhen γ> 0 via the large deviation of the corresponding empirical measure and via a direct estimate, respectively, when γ= 0.

Cervical cancer seriously endangers the health of the female reproductive system and even risks women's life in severe cases. Optical coherence tomography (OCT) is a non-invasive, real-time, high-resolution imaging technology for cervical tissues. However, since the interpretation of cervical OCT images is a knowledge-intensive, time-consuming task, it is tough to acquire a large number of high-quality labeled images quickly, which is a big challenge for supervised learning. In this study, we introduce the vision Transformer (ViT) architecture, which has recently achieved impressive results in natural image analysis, into the classification task of cervical OCT images. Our work aims to develop a computer-aided diagnosis (CADx) approach based on a self-supervised ViT-based model to classify cervical OCT images effectively. We leverage masked autoencoders (MAE) to perform self-supervised pre-training on cervical OCT images, so the proposed classification model has a better transfer learning ability. In the fine-tuning process, the ViT-based classification model extracts multi-scale features from OCT images of different resolutions and fuses them with the cross-attention module. The ten-fold cross-validation results on an OCT image dataset from a multi-center clinical study of 733 patients in China indicate that our model achieved an AUC value of 0.9963 ± 0.0069 with a 95.89 ± 3.30% sensitivity and 98.23 ± 1.36 % specificity, outperforming some state-of-the-art classification models based on Transformers and convolutional neural networks (CNNs) in the binary classification task of detecting high-risk cervical diseases, including high-grade squamous intraepithelial lesion (HSIL) and cervical cancer. Furthermore, our model with the cross-shaped voting strategy achieved a sensitivity of 92.06% and specificity of 95.56% on an external validation dataset containing 288 three-dimensional (3D) OCT volumes from 118 Chinese patients in a different new hospital. This result met or exceeded the average of four medical experts who have used OCT for over one year. In addition to promising classification performance, our model has a remarkable ability to detect and visualize local lesions using the attention map of the standard ViT model, providing good interpretability for gynecologists to locate and diagnose possible cervical diseases.

Developing a three-dimensional (3D) in vitrotumor model with vasculature systems suitable for testing endovascular interventional therapies remains a challenge. Here we develop an orthotopic liver tumor spheroid model that captures the organ-level complexity of vasculature systems and the extracellular matrix to evaluate transcatheter arterial chemoembolization (TACE) treatment. The orthotopic tumor spheroids are derived by seeding HepG2 cell colonies with controlled size and location surrounding the portal triads in a decellularized rat liver matrix and are treated by clinically relevant drug-eluting beads embolized in a portal vein vasculature while maintaining dynamic physiological conditions with nutrient and oxygen supplies through the hepatic vein vasculature. The orthotopic tumor model exhibits strong drug retention inside the spheroids and embolization location-dependent cellular apoptosis responses in an analogous manner to in vivoconditions. Such a tumor spheroid model built in a decellularized scaffold containing organ-specific vasculatures, which closely resembles the unique tumor microenvironment, holds the promise to efficiently assess various diagnostic and therapeutic strategies for endovascular therapies.

We use tridiagonal models to study the limiting behavior of β-Laguerre and β-Jacobi ensembles, focusing on the limiting behavior of the extremal eigenvalues and the central limit theorem for the two ensembles. For the central limit theorem of β-Laguerre ensembles, we follow the idea in [1] while giving a modified version for the generalized case. Then we use the total variation distance between the two sorts of ensembles to obtain the limiting behavior of β-Jacobi ensembles.

Microsphere‐based embolic agents have gained prominence in transarterial embolization (TAE) treatment, a critical minimally invasive therapy widely applied for a variety of diseases such as hypervascular tumors and acute bleeding. However, the development of microspheres with long‐term, real‐time, and repeated X‐ray imaging as well as ultrasound imaging remains challenging. In this study, emulsion‐based dual‐modal imaging microbeads with a unique internal multi‐interface structure is developed for TAE treatment. The embolic microbeads are fabricated from a solidified oil‐in‐water (O/W) emulsion composed of crosslinked CaAlg‐based aqueous matrix and dispersed radiopaque iodinated oil (IO) droplets through a droplet‐based microfluidic fabrication method. The CaAlg‐IO microbeads exhibit superior X‐ray imaging visibility due to the incorporation of exceptionally high iodine level up to 221 mgI mL−1, excellent ultrasound imaging capability attributed to the multi‐interface structure of the O/W emulsion, great microcatheter deliverability thanks to their appropriate biomechanical properties and optimal microbead density, and extended drug release behavior owing to the biodegradation nature of the embolics. Such an embolic agent presents a promising emulsion‐based platform to utilize multi‐phased structures for improving endovascular embolization performance and assessment capabilities. Emulsion‐based dual‐modal imaging microbeads, which offer synergistic advantages of superior X‐ray imaging visibility due to high content of iodinated oil and excellent ultrasound imaging capability attributed to the unique multi‐interface structure of the emulsion, are developed for transarterial embolization treatment. This embolic system presents an effective strategy to leverage an emulsion‐based platform for enhancing embolization performance and assessment capabilities.

Transarterial chemoembolization (TACE) has emerged as the mainstay treatment for patients suffering from unresectable intermediate hepatocellular carcinoma and also holds the potential to treat other types of hypervascular cancers such as renal cell carcinoma. However, an in vitromodel for evaluating both embolic performance and drug-release kinetics of the TACE embolic agents is still lacking since the current models greatly simplified the in vivovascular systems as well as the extracellular matrices (ECM) in the organs. Here, we developed a decellularized organ model with preserved ECM and vasculatures as well as a translucent appearance to investigate chemoembolization performances of a clinically widely used embolic agent, i.e., a doxorubicin-loaded ethiodised oil (EO)-based emulsion. We, for the first time, utilized an ex vivomodel to evaluate the liquid-based embolic agent in two organs, i.e., liver and kidneys. We found that the EO-based emulsion with enhanced stability by incorporating an emulsifier, i.e., hydrogenated castor oil-40 (HCO), showed an enhanced occlusion level and presented sustained drug release in the ex vivoliver model, suggesting an advantageous therapeutic effect for TACE treatment of hepatocellular carcinoma. In contrast, we observed that drug-release burst happened when applying the same therapy in the kidney model even with the HCO emulsifier, which may be explained by the presence of the specific renal vasculature and calyceal systems, indicating an unfavorable effect in the renal tumor treatment. Such an ex vivomodel presents a promising template for chemoembolization evaluation before in vivoexperiments for the development of novel embolic agents.

Colloidal nanoparticles with anisotropic interactions are promising building blocks for the fabrication of complex functional materials. A challenge in the self-assembly of colloidal particles is the rational design of geometry and chemistry to program the formation of a desired target structure. We report an inverse design procedure integrating Langevin dynamics simulations and evolutionary algorithms to engineer anisotropic patchy colloidal clusters to spontaneously assemble into a cubic diamond lattice possessing a complete photonic band gap. The combination of a tetrahedral cluster geometry and optimized placement of a single type of anisotropic interaction patch results in a colloidal building block predicted to assemble a cubic diamond lattice with more than 82% yield. This design represents an experimentally viable colloidal building block capable of high-fidelity assembly of a cubic diamond lattice.

Transarterial chemoembolization (TACE), the mainstay treatment of unresectable primary liver cancer that primarily employs nondegradable drug-loaded embolic agents to achieve synergistic vascular embolization and locoregional chemotherapy effects, suffers from an inferior drug burst behavior lacking long-term drug release controllability that severely limits the TACE efficacy. Here we developed gelatin-based drug-eluting microembolics grafted with nanosized poly(acrylic acid) serving as a biodegradable ion-exchange platform that leverages a counterion condensation effect to achieve high-efficiency electrostatic drug loading with electropositive drugs such as doxorubicin (i.e., drug loading capacity >34 mg/mL, encapsulation efficiency >98%, and loading time <10 min) and an enzymatic surface-erosion degradation pattern (∼2 months) to offer sustained locoregional pharmacokinetics with long-lasting deep-tumor retention capability for TACE treatment. The microembolics demonstrated facile microcatheter deliverability in a healthy porcine liver embolization model, superior tumor-killing capacity in a rabbit VX2 liver cancer embolization model, and stabilized extravascular drug penetration depth (>3 mm for 3 months) in a rabbit ear embolization model. Importantly, the microembolics finally exhibited vessel remodeling-induced permanent embolization with minimal inflammation responses after complete degradation. Such a biodegradable ion-exchange drug carrier provides an effective and versatile strategy for enhancing long-term therapeutic responses of various local chemotherapy treatments.

This article describes how understanding human mobility behavior is of great significance for predicting a broad range of socioeconomic phenomena in contemporary society. Although many studies have been conducted to uncover behavioral patterns of intra-urban and inter-urban human mobility, a fundamental question remains unanswered: To what degree is human mobility behavior predictable in new cities—a person has never visited before? Location-based social networks with a large volume of check-in records provide an unprecedented opportunity to investigate cross-urban human mobility. The authors' empirical study on millions of records from Foursquare reveals the motives and behavioral patterns of non-natives in 59 cities across the United States. Inspired by the ideology of transfer learning, the authors also propose a machine learning model, which is designed based on the regularities that they found in this study, to predict cross-urban human whereabouts after non-natives move to new cities. The experimental results validate the effectiveness and efficiency of the proposed model, thus allowing us to predict and control activities driven by cross-urban human mobility, such as mobile recommendation, visual (personal) assistant, and epidemic prevention.

A highly N2‐regioselective functionalization of 2H‐1,2,3‐triazole and 1H‐1,2,3‐triazole was achieved via a para‐toluenesulfonic‐acid‐catalyzed olefin hydroamination. The reaction afforded the corresponding products with up to 92 % N2regioselectivity. N1, N2, N123, go! A highly N2‐regioselective TsOH‐catalyzed olefin hydroamination using NH‐1,2,3‐triazoles as the N‐nucleophilc reagents leading to N2‐alkyl‐1,2,3‐triazoles is reported. The reaction showed good to excellent N2‐selectivities.

Recruiting or recommending appropriate potential Wikipedia editors to edit a specific Wikipedia entry (or article) can play an important role in improving the quality and credibility of Wikipedia. According to empirical observations based on a small-scale dataset collected from Wikipedia, this paper proposes an Interest Prediction Factor Graph (IPFG) model, which is characterized by editor's social properties, hyperlinks between Wikipedia entries, the categories of an entry and other important features, to predict an editor's editing interest in types of Wikipedia entries. Furthermore, the paper suggests a parameter learning algorithm based on the gradient descent algorithm and the Loopy Sum-Product algorithm for factor graphs. An experiment on a Wikipedia dataset (with different frequencies of data collection) shows that the average prediction accuracy (F1 score) of the IPFG model for data collected quarterly could be up to 0.875, which is approximately 0.49 higher than that of a collaborative filtering approach. In addition, the paper analyzes how incomplete social properties and editing bursts affect the prediction accuracy of the IPFG model. The authors' results can provide insight into effective Wikipedia article tossing and can improve the quality of special entries that belong to specific categories by means of collective collaboration.

We consider the nearest-neighbor model on the finite tree Twith generator L. We obtain a twosided estimate of the spectral gap by factor 2. We also identify explicitly the Lipschitzian norm of the operator (–L)−1in propriate functional space. This leads to the identification of the best constant in the generalized Cheeger isoperimetric inequality on the tree, and to transportation-information inequalities.

Point-of-interest (POI) recommendation is a popular topic on location-based social networks (LBSNs). Geographical proximity, known as a unique feature of LBSNs, significantly affects user check-in behavior. However, most of prior studies characterize the geographical influence based on a universal or personalized distribution of geographic distance, leading to unsatisfactory recommendation results. In this paper, the personalized geographical influence in a two-dimensional geographical space is modeled using the data field method, and we propose a semi-supervised probabilistic model based on a factor graph model to integrate different factors such as the geographical influence. Moreover, a distributed learning algorithm is used to scale up our method to large-scale data sets. Experimental results based on the data sets from Foursquare and Gowalla show that our method outperforms other competing POI recommendation techniques.

Particulate embolic agents with calibrated sizes, which employ interventional procedures to achieve endovascular embolization, have recently attracted tremendous interest in therapeutic embolotherapies for a wide plethora of diseases. However, the particulate shape effect, which may play a critical role in embolization performances, has been rarely investigated. Here, polyvinyl alcohol (PVA)‐based shape‐anisotropic microembolics are developed using a facile droplet‐based microfluidic fabrication method via heat‐accelerated PVA‐glutaraldehyde crosslinking reaction at a mild temperature of 38 °C. Precise geometrical controls of the microembolics are achieved with a nearly capsule shape through regulating surfactant concentration and flow rate ratio between dispersed phase and continuous phase in the microfluidics. Two specific models are employed, i.e., in vitro decellularized rabbit liver embolization model and in vivo rabbit ear embolization model, to systematically evaluate the embolization behaviors of the nonspherical microembolics. Compared to microspheres of the same volume, the elongated microembolics demonstrated advantageous endovascular navigation capability, penetration depth and embolization stability due to their comparatively smaller radial diameter and their central cylindrical part providing larger contact area with distal vessels. Such nonspherical microembolics present a promising platform to apply shape anisotropy to achieve distinctive therapeutic effects for endovascular treatments. Shape‐anisotropic microembolics are developed through a facile droplet‐based microfluidic technique using a heat‐accelerated crosslinking reaction. The PVA‐based microembolics with regular elongated shapes demonstrate superior embolization properties in three critical aspects, i.e., endovascular navigation, penetration depth, and embolization stability. Such a nonspherical platform presents a new perspective to apply shape anisotropy to achieve distinctive therapeutic effects for endovascular treatments.

Shape‐Anisotropic Microembolics In article number 2102281, Qiongyu Guo and co‐workers develop anisotropically‐shaped particulate embolic agents with calibrated sizes using a facile droplet‐based microfluidic technique based on a heat‐accelerated crosslinking reaction. Compared to conventional spherical embolic particles, the capsule shape‐based nonspherical microembolics simultaneously enhance endovascular navigation, penetration depth, and embolization stability, demonstrating distinctive therapeutic effects of shape anisotropy for transcatheter embolotherapies.

AbstractGate underlap structure can be utilized to improve the immunity to short channel effects in MOSFET devices. In this work, gate underlap design scheme in cylindrical gate-all-around MOSFETs is explored based on an analytical model. This model takes into account the fringing field from gate electrode to underlap regions based on conformal mapping and a channel length transformation method. By solving Poisson equations in the underlap and channel regions and matching the boundary conditions, this model reproduces the channel potential profile in subthreshold operation region. Both symmetric and asymmetric underlap structures are covered. The developed model is verified extensively with TCAD simulations. A gate underlap design scheme is then provided based on this analytical model.

Abstract: In this paper, we consider birth-death processes on a tree T and we are interested when it is regular, recurrent and ergodic (strongly, exponentially). By constructing two corresponding birth death processes on ℤ+, we obtain computable conditions sufficient or necessary for that (in many cases, these two conditions coincide). With the help of these constructions, we give explicit upper and lower bounds for the Dirichlet eigenvalue λ 0. At last, some examples are investigated to justify our results.

A new scale transformation method is used in the solving of the Schrödinger equation. With this new method, the uniform grids used in the discretization in the conventional method are changed into nonuniform grids. Consequently, for some cases, the computing quantity is greatly reduced while maitaining the required accuracy. The calculation of the quantized inversion layer in metal-oxide-semiconductor (MOS) structure is used to demonstrate the efficiency of the new method.