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We consider locally isotropic Gaussian random fields on the $N$-dimensional Euclidean space for fixed $N$. Using the so called Gaussian Orthogonally Invariant matrices first studied by Mallows in 1961 which include the celebrated Gaussian Orthogonal Ensemble (GOE), we establish the Kac--Rice representation of expected number of critical points of non-isotropic Gaussian fields, complementing the isotropic case obatined by Cheng and Schwartzman in 2018. In the limit $N=\8$, we show that such a representation can be always given by GOE matrices, as conjectured by Auffinger and Zeng in 2020.

Large language models (LLMs) have shown surprisingly good performance in multilingual neural machine translation (MNMT) even when trained without parallel data. Yet, despite the fact that the amount of training data is gigantic, they still struggle with translating rare words, particularly for low-resource languages. Even worse, it is usually unrealistic to retrieve relevant demonstrations for in-context learning with low-resource languages on LLMs, which restricts the practical use of LLMs for translation -- how should we mitigate this problem? To this end, we present a novel method, CoD, which augments LLMs with prior knowledge with the chains of multilingual dictionaries for a subset of input words to elicit translation abilities for LLMs. Extensive experiments indicate that augmenting ChatGPT with CoD elicits large gains by up to 13x chrF++ points for MNMT (3.08 to 42.63 for English to Serbian written in Cyrillic script) on FLORES-200 full devtest set. We further demonstrate the importance of chaining the multilingual dictionaries, as well as the superiority of CoD to few-shot demonstration for low-resource languages.

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We introduce a frustratingly simple, super efficient and surprisingly effective decoding method, which we call Frustratingly Simple Decoding (FSD), for neural text generation. The idea behind FSD is straightforward: we build an anti-LM based on previously generated text and use this anti-LM to penalize future generation of what has been generated. The anti-LM can be implemented as simple as an n-gram language model or a vectorized variant. In this way, FSD introduces no extra model parameters and negligible computational overhead (FSD can be as fast as greedy search). Despite the simplicity, FSD is surprisingly effective; Experiments show that FSD can outperform the canonical methods to date (i.e., nucleus sampling) as well as several strong baselines that were proposed recently.

The overall objective of this study was to develop cost-effective treatment processes for 1,4-dioxane removal that were safe and easy to scale up. Degradation of 1,4-dioxane was conducted and compared for the first time by heterogeneous photocatalysis and a photo-Fenton-like process under cool white fluorescent light in mild conditions, using two types of commercial nanoparticles–titanium dioxide (TiO2) and nanoscale zero-valent iron (nZVI), respectively. Both types of nanoparticles removed >99.9% of 1,4-dioxane in a short period of time. Hydroxyl radicals (·OH), superoxide radicals (·O2-), and hydrogen peroxide (H2O2) were detected in both degradation processes; photogenerated holes (h+) were critical in the degradation of 1,4-dioxane by the photocatalytic process using TiO2. 1,4-Dioxane can be degraded at pH 7 in TiO2/light system and at pH 3 in nZVI/light system, and faster degradation of 1,4-dioxane at even higher concentration was achieved in the former system. Increase in light intensity accelerated 1,4-dioxane degradation, which followed first order kinetics in both systems. In wastewater effluent, the removal of 1,4-dioxane was slower than that in deionised water, which likely reflected the complex compositions of the wastewater effluent. Under combined UVA and visible light illumination, a two-stage degradation process was proposed for 1,4-dioxane for the first time by TiO2 nanoparticles; this study also demonstrated for the first time 1,4-dioxane degradation by the photo-Fenton-like process using nZVI. The cost-effective solutions using commercial nanoparticles under fluorescent light developed in this study can be potentially applied to treat water contaminated by high concentrations of 1,4-dioxane in large-scale.

Photon avalanching nanoparticles (ANPs) exhibit extremely nonlinear upconverted emission valuable for sub-diffraction imaging, nanoscale sensing, and optical computing. Avalanching has been demonstrated with Tm3+, Nd3+ or Pr3+-doped nanocrystals, but their emission is limited to 600 and 800 nm, restricting applications. Here, we utilize Gd3+-assisted energy migration to tune the emission wavelengths of Tm3+-sensitized ANPs and generate highly nonlinear emission of Eu3+, Tb3+, Ho3+, and Er3+ ions. The upconversion intensities of these spectrally discrete ANPs scale with the nonlinearity factor s = 10-17 under 1064 nm excitation at power densities as low as 6 kW/cm2. This strategy for imprinting avalanche behavior on remote emitters can be extended to fluorophores adjacent to ANPs, as we demonstrate with CdS/CdSe/CdS core/shell/shell quantum dots. ANPs with rationally designed energy transfer networks provide the means to transform conventional linear emitters into a highly nonlinear ones, expanding the use of photon avalanching in biological, chemical, and photonic applications., Comment: 13 pages, 5 figures

The dynamic request patterns of machine learning (ML) inference workloads have driven an increasing trend towards exploiting serverless computing for scalable ML model serving. However, today's serverless platforms lack efficient support for GPUs -- provisioning functions on GPUs incurs extremely high overhead, forcing them to keep long-running even when idling for reduced cold starts. This leads to significant resource waste to perform ML inference and hinders the pay-per-use billing for GPUs. In this paper, we present FaaSwap, a serverless platform enabling fine-grained, request-level GPU sharing for resource-efficient ML inference. FaaSwap leverages model swapping to support fast inference execution at low resource cost. It keeps models in a host which has a large amount of cheap memory and quickly swaps models to GPUs when requested, reducing per-function keep-alive cost and enabling efficient GPU sharing across much more functions. FaaSwap also supports swapping models between GPUs for load balancing and improved inference performance. In FaaSwap, we design sophisticated request scheduling and memory management algorithms that efficiently exploit model swapping to reduce GPU cost and meet latency service-level objectives (SLOs) for all inference functions. We have implemented and integrated FaaSwap into Alibaba Cloud Function Compute (FC), one of the world's largest commercial serverless platform. Evaluation results show that FaaSwap can achieve low-latency model swapping, efficiently share a GPU across hundreds of functions, and satisfy per-function latency SLOs at scale.

We investigate thermodynamics of general nonequilibrium processes stopped at stochastic times. We propose a systematic strategy for constructing fluctuation-theorem-like martingales for each thermodynamic functional, yielding a family of stopping-time fluctuation theorems. We derive second-law-like thermodynamic inequalities for the mean thermodynamic functional at stochastic stopping times, the bounds of which are stronger than the thermodynamic inequalities resulting from the traditional fluctuation theorems when the stopping time is reduced to a deterministic one. Numerical verification is carried out for three well-known thermodynamic functionals, namely, entropy production, free energy dissipation and dissipative work. These universal equalities and inequalities are valid for arbitrary stopping strategies, and thus provide a comprehensive framework with new insights into the fundamental principles governing nonequilibrium systems., Comment: 7 pages, 2 figures

The adjustment of urban spatial structure in the process of urbanization and suburbanization leads to the separation of work and residence, which further leads to long-distance commuting. While there has been a lot of research on long-distance commuting in Western countries, the relevant studies in China are not enough. In the Chinese context, some factors deserve special attention, namely the hukou system and occupation. However, few studies have focused on the individual and interaction effects of these two factors on long-distance commuting. This paper explores the commuting behavior of different socioeconomic groups in Xiamen, China. Heckman's sample selection model was applied to data from the 2015 Xiamen household travel survey to separate the effect of socioeconomic factors and that of residential selection. Results show that the continued suburbanization of the industry attracted substantial numbers of blue-collar workers to live in the outer districts (Haicang, Jimei, Tong'an, and Xiang'an), and thus blue-collar workers are less likely than pink- and white-collar workers to be long-distance commuters in the outer districts. Among residents of the outer district, pink-collar migrants and white-collar migrants are more likely to be long-distance commuters than their local counterparts, while blue-collar migrants (a coefficient of −0.153) are less likely to be long-distance commuters than blue-collar locals (a coefficient of −0.046). For people who live in the inner districts, blue-collar locals (a coefficient of 0.256) are more likely to be long-distance commuters than blue-collar migrants (a coefficient of −0.029). These results have practical significance for providing alternative housing for migrants in urban renewal.

Multiple criteria decision making (MCDM) problems in practice may simultaneously contain both redundant and incomplete information and are difficult to solve. This paper proposes a new decision-making approach based on soft set theory to solve MCDM problems with redundant and incomplete information. Firstly, we give an incomplete soft set a precise definition. After that, the binary relationships of objects in an incomplete soft set are analyzed and some operations on it are provided. Next, some definitions regarding the incomplete soft decision system are also given. Based on that, an algorithm to solve MCDM problems with redundant and incomplete information based on an incomplete soft set is presented and illustrated with a numerical example. The results show that our newly developed method can be directly used on the original redundant and incomplete data set. There is no need to transform an incomplete information system into a complete one, which may lead to bad decision-making due to information loss or some unreliable assumptions about the data generating mechanism. To demonstrate its practical applications, the proposed method is applied to a problem of regional food safety evaluation in Chongqing, China.

Maintaining a consistent persona is essential for building a human-like conversational model. However, the lack of attention to the partner makes the model more egocentric: they tend to show their persona by all means such as twisting the topic stiffly, pulling the conversation to their own interests regardless, and rambling their persona with little curiosity to the partner. In this work, we propose COSPLAY(COncept Set guided PersonaLized dialogue generation Across both partY personas) that considers both parties as a "team": expressing self-persona while keeping curiosity toward the partner, leading responses around mutual personas, and finding the common ground. Specifically, we first represent self-persona, partner persona and mutual dialogue all in the concept sets. Then, we propose the Concept Set framework with a suite of knowledge-enhanced operations to process them such as set algebras, set expansion, and set distance. Based on these operations as medium, we train the model by utilizing 1) concepts of both party personas, 2) concept relationship between them, and 3) their relationship to the future dialogue. Extensive experiments on a large public dataset, Persona-Chat, demonstrate that our model outperforms state-of-the-art baselines for generating less egocentric, more human-like, and higher quality responses in both automatic and human evaluations., Accepted by SIGIR 2022, 11 pages, 9 figures

The construction of China's large-scale high-speed rail (HSR) could exert a significant impact on the connectivity of urban networks in the long run. Through the dominance index of city nodes and the strength index of city links, the evolution of Chinese HSR networks has been explored based on frequency flow data from 2007 to 2017. Meanwhile, this paper uses a spatial econometric model to test the impact of the evolution of HSR networks on real estate investment agglomeration. Our examination shows that, as China's HSR network expands spatially, connectivity between cities improves, and the spatial structure of China's urban systems tends to be less hierarchical and polycentric. HSR networks promote the spatial agglomeration of real estate investment in cities connected by HSR. Although the influence of population scale, manufacturing scale, per capita GDP, and the degree of openness on real estate investment is greater than that of HSR network connection, the impact of the proportion of residential (commercial service) land and per capita GDP on real estate investment is less than that of HSR network connection. China's HSR network connection can expand the spatial agglomeration effect of site cities' real estate investment. The urban land use structure, such as the proportion of residential (commercial) land and the degree of openness, can promote a spatial spillover effect of real estate investment.

Parameter-efficient tuning aims to distill knowledge for downstream tasks by optimizing a few introduced parameters while freezing the pretrained language models (PLMs). Continuous prompt tuning which prepends a few trainable vectors to the embeddings of input is one of these methods and has drawn much attention due to its effectiveness and efficiency. This family of methods can be illustrated as exerting nonlinear transformations of hidden states inside PLMs. However, a natural question is ignored: can the hidden states be directly used for classification without changing them? In this paper, we aim to answer this question by proposing a simple tuning method which only introduces three trainable vectors. Firstly, we integrate all layers hidden states using the introduced vectors. And then, we input the integrated hidden state(s) to a task-specific linear classifier to predict categories. This scheme is similar to the way ELMo utilises hidden states except that they feed the hidden states to LSTM-based models. Although our proposed tuning scheme is simple, it achieves comparable performance with prompt tuning methods like P-tuning and P-tuning v2, verifying that original hidden states do contain useful information for classification tasks. Moreover, our method has an advantage over prompt tuning in terms of time and the number of parameters.

A highly efficient and multifunctional white-emitting hydrogel was fabricated using a facile copolymerization process by introducing a hydrophilic terpyridine-based chromophore into a polyacrylamide network. White emission was obtained by a simple procedure for adjusting the concentration of the chromophore. The CIE 1931 chromaticity coordinates of white emission with 360 nm excitation were (0.349, 0.331). Exploiting the specific sensitivities of the terpyridyl group, the white emission was also highly sensitive to pH and metal ions. We envision this photoluminescent hydrogel as a versatile material for chemical and environmental sensing.

A new type of hydrogel-coated paper sensor was developed for the semiquantitative and naked-eye detection of Cu2+ ions with high selectivity and good regenerability. The paper sensor showed a continuous color change from colorless to blue, resulting in semiquantitative naked-eye detection. Furthermore, similar to the classical pH test paper, a standard colorimetric card was used in the visual detection of Cu2+ ions by identifying the RGB value of the hydrogel-coated paper sensor. The application of the standard colorimetric card has greatly shortened the detection time and reduced the cost of detection, providing a new strategy for the naked-eye and semiquantitative detection of heavy-metal ions in water samples.

Fine-tuning pre-trained models has been ubiquitously proven to be effective in a wide range of NLP tasks. However, fine-tuning the whole model is parameter inefficient as it always yields an entirely new model for each task. Currently, many research works propose to only fine-tune a small portion of the parameters while keeping most of the parameters shared across different tasks. These methods achieve surprisingly good performance and are shown to be more stable than their corresponding fully fine-tuned counterparts. However, such kind of methods is still not well understood. Some natural questions arise: How does the parameter sparsity lead to promising performance? Why is the model more stable than the fully fine-tuned models? How to choose the tunable parameters? In this paper, we first categorize the existing methods into random approaches, rule-based approaches, and projection-based approaches based on how they choose which parameters to tune. Then, we show that all of the methods are actually sparse fine-tuned models and conduct a novel theoretical analysis of them. We indicate that the sparsity is actually imposing a regularization on the original model by controlling the upper bound of the stability. Such stability leads to better generalization capability which has been empirically observed in a lot of recent research works. Despite the effectiveness of sparsity grounded by our theory, it still remains an open problem of how to choose the tunable parameters. To better choose the tunable parameters, we propose a novel Second-order Approximation Method (SAM) which approximates the original problem with an analytically solvable optimization function. The tunable parameters are determined by directly optimizing the approximation function. The experimental results show that our proposed SAM model outperforms many strong baseline models and it also verifies our theoretical analysis.

Additional file 1. Supplementary Figures.

Graph learning models are critical tools for researchers to explore graph-structured data. To train a capable graph learning model, a conventional method uses sufficient training data to train a graph model on a single device. However, it is prohibitive to do so in real-world scenarios due to privacy concerns. Federated learning provides a feasible solution to address such limitations via introducing various privacy-preserving mechanisms, such as differential privacy on graph edges. Nevertheless, differential privacy in federated graph learning secures the classified information maintained in graphs. It degrades the performances of the graph learning models. In this paper, we investigate how to implement differential privacy on graph edges and observe the performances decreasing in the experiments. We also note that the differential privacy on graph edges introduces noises to perturb graph proximity, which is one of the graph augmentations in graph contrastive learning. Inspired by that, we propose to leverage the advantages of graph contrastive learning to alleviate the performance dropping caused by differential privacy. Extensive experiments are conducted with several representative graph models and widely-used datasets, showing that contrastive learning indeed alleviates the models' performance dropping caused by differential privacy.

Recently, transformers have shown promising performance in learning graph representations. However, there are still some challenges when applying transformers to real-world scenarios due to the fact that deep transformers are hard to train from scratch and the quadratic memory consumption w.r.t. the number of nodes. In this paper, we propose Graph Masked Autoencoders (GMAEs), a self-supervised transformer-based model for learning graph representations. To address the above two challenges, we adopt the masking mechanism and the asymmetric encoder-decoder design. Specifically, GMAE takes partially masked graphs as input, and reconstructs the features of the masked nodes. The encoder and decoder are asymmetric, where the encoder is a deep transformer and the decoder is a shallow transformer. The masking mechanism and the asymmetric design make GMAE a memory-efficient model compared with conventional transformers. We show that, when serving as a conventional self-supervised graph representation model, GMAE achieves state-of-the-art performance on both the graph classification task and the node classification task under common downstream evaluation protocols. We also show that, compared with training in an end-to-end manner from scratch, we can achieve comparable performance after pre-training and fine-tuning using GMAE while simplifying the training process.

The rapid advances in automation technologies, such as artificial intelligence (AI) and robotics, pose an increasing risk of automation for occupations, with a likely significant impact on the labour market. Recent social-economic studies suggest that nearly 50\% of occupations are at high risk of being automated in the next decade. However, the lack of granular data and empirically informed models have limited the accuracy of these studies and made it challenging to predict which jobs will be automated. In this paper, we study the automation risk of occupations by performing a classification task between automated and non-automated occupations. The available information is 910 occupations' task statements, skills and interactions categorised by Standard Occupational Classification (SOC). To fully utilize this information, we propose a graph-based semi-supervised classification method named \textbf{A}utomated \textbf{O}ccupation \textbf{C}lassification based on \textbf{G}raph \textbf{C}onvolutional \textbf{N}etworks (\textbf{AOC-GCN}) to identify the automated risk for occupations. This model integrates a heterogeneous graph to capture occupations' local and global contexts. The results show that our proposed method outperforms the baseline models by considering the information of both internal features of occupations and their external interactions. This study could help policymakers identify potential automated occupations and support individuals' decision-making before entering the job market., Comment: 15 pages, 6 figures

While most existing segmentation methods usually combined the powerful feature extraction capabilities of CNNs with Conditional Random Fields (CRFs) post-processing, the result always limited by the fault of CRFs . Due to the notoriously slow calculation speeds and poor efficiency of CRFs, in recent years, CRFs post-processing has been gradually eliminated. In this paper, an improved Generative Adversarial Networks (GANs) for image semantic segmentation task (semantic segmentation by GANs, Seg-GAN) is proposed to facilitate further segmentation research. In addition, we introduce Convolutional CRFs (ConvCRFs) as an effective improvement solution for the image semantic segmentation task. Towards the goal of differentiating the segmentation results from the ground truth distribution and improving the details of the output images, the proposed discriminator network is specially designed in a full convolutional manner combined with cascaded ConvCRFs. Besides, the adversarial loss aggressively encourages the output image to be close to the distribution of the ground truth. Our method not only learns an end-to-end mapping from input image to corresponding output image, but also learns a loss function to train this mapping. The experiments show that our method achieves better performance than state-of-the-art methods., arXiv admin note: text overlap with arXiv:1802.07934 by other authors

We report the color conversion performance of amber and red emitting quantum dots (QDs) on InGaN solid-state lighting (SSL) light emitting diode (LED) packages. Spherical quantum well (SQW) architectures (CdS/CdSe1-xSx/CdS) were prepared using a library of thio- and selenourea synthesis reagents and high throughput synthesis robotics. CdS/CdSe1-xSx QDs with narrow luminescence bands were coated with thick CdS shells (thickness = 1.6-7.5 nm) to achieve photoluminescence quantum yields (PLQY) up to 88% at amber and red emission wavelengths (λmax = 600-642 nm, FWHM < 45 nm). The photoluminescence from SQWs encapsulated in silicone and deposited on LED packages was monitored under accelerated aging conditions (oven temperature = 85 °C, relative humidity = 5-85%, blue optical power density = 3-45 W/cm2) by monitoring the red photon output over several hundred hours of continuous operation. The growth of a ZnS shell on the SQW surface increases the stability under long-term operation but also reduces the PLQY, especially of SQWs with thick CdS shells. The results illustrate that the outer ZnS shell layer is key to optimizing the PLQY and the long-term stability of QDs during operation on SSL packages.

Emphasis Selection is a newly proposed task which focuses on choosing words for emphasis in short sentences. Traditional methods only consider the sequence information of a sentence while ignoring the rich sentence structure and word relationship information. In this paper, we propose a new framework that considers sentence structure via a sentence structure graph and word relationship via a word similarity graph. The sentence structure graph is derived from the parse tree of a sentence. The word similarity graph allows nodes to share information with their neighbors since we argue that in emphasis selection, similar words are more likely to be emphasized together. Graph neural networks are employed to learn the representation of each node of these two graphs. Experimental results demonstrate that our framework can achieve superior performance., Comment: RANLP 2021

Two effective and one-step-synthesized organogelators based on D-gluconic acetal derivatives have been developed to show phase-selective gelation behaviours towards aromatic solvents from their biphasic mixtures with water. The dominant factors that drive gelation have been studied using FT-IR and temperature-dependent 1H NMR spectroscopy. Particularly, gelator GAA-2 in powder form could selectively congeal toluene, benzene and o-xylene at room temperature under mild stirring. Additionally, GAA-2 could gelate the aromatic solvents within 10 min and the recovery rate of the aromatic solvents could reach about 82% under a certain condition. The benefits of wide source availability, being easy to synthesize, and recyclable performance of the gelator make GAA-2 ideal for real-world remediation of aromatic solvents.

Photoluminescent poly(urethane-acrylate) (PUA) nanowires are designed and synthesized through copolymerization of a presynthesized-europium (Eu) complex with active vinyl groups and a vinyl functionalized PUA macromonomer matrix, initiated by azobisisobutyronitrile. This procedure provides a method to prepare PUA-Eu nanowires through in situ polymerization. Based on this, a series of PUA-Eu nanowires with diameters of 80-300 nm are successfully obtained in templates of anodized aluminum oxide by in situ polymerization. The obtained PUA-Eu nanowires display different morphologies such as sharp, round, and flat head by controlling the casting conditions. Furthermore, the PUA-Eu nanowires exhibit unique luminescence properties provided through Eu(III) elements, and the luminescence intensity significantly enhances with the increase in Eu complex concentration. PUA-Eu nanowires have longer fluorescence lifetimes than that of Eu complexes and PUA-Eu plates.

Unstructured Persona-oriented Dialogue Systems (UPDS) has been demonstrated effective in generating persona consistent responses by utilizing predefined natural language user persona descriptions (e.g., "I am a vegan"). However, the predefined user persona descriptions are usually short and limited to only a few descriptive words, which makes it hard to correlate them with the dialogues. As a result, existing methods either fail to use the persona description or use them improperly when generating persona consistent responses. To address this, we propose a neural topical expansion framework, namely Persona Exploration and Exploitation (PEE), which is able to extend the predefined user persona description with semantically correlated content before utilizing them to generate dialogue responses. PEE consists of two main modules: persona exploration and persona exploitation. The former learns to extend the predefined user persona description by mining and correlating with existing dialogue corpus using a variational auto-encoder (VAE) based topic model. The latter learns to generate persona consistent responses by utilizing the predefined and extended user persona description. In order to make persona exploitation learn to utilize user persona description more properly, we also introduce two persona-oriented loss functions: Persona-oriented Matching (P-Match) loss and Persona-oriented Bag-of-Words (P-BoWs) loss which respectively supervise persona selection in encoder and decoder. Experimental results show that our approach outperforms state-of-the-art baselines, in terms of both automatic and human evaluations., Comment: Accepted by ECAI 2020

2-Epi-jaspine B is an isomer of the natural product jaspine B and shows certain selectivity for SphK1 and potent antitumor activity. Based on the crystal structure of SphK1, we transformed the structure of 2-epi-jaspine B and modified the hydrophobic side chain to obtain a series of 2-epi-jaspine B analogs. The MTT assay was used to examine the antitumor activities of these analogs. We identified a novel 2-epi-jaspine B analog YHR17, which has potent antiproliferative activities for tested cell lines with IC50 values that ranged from 0.68 to 5.68 μM and inhibited the proliferation of the A375 cell line by affecting the cell cycle and apoptosis. Furthermore, YHR17 inhibited SphK1 with more than 125-fold selectivity over SphK2.

In order to deeply analyze the influencing factors and main sources of soil heavy metal pollution in Huangdao District of Qingdao, 35 soil samples collected from the electronic information area of Haier Industrial Park and Aokema Industrial Park in Huangdao District were selected as the research objects, and the significant biological toxicity of Cd, Cr, Cu, Hg, Ni, Pb, Zn, As in the soil samples were analyzed and the result shows that the variation coefficients of Cr and Ni were examined high, which indicated that the pollutants mainly came from the production and processing activities in the electronic information area. Besides, in accordance with the soil risk assessment level, the analysis of heavy metal pollution status and human health risks of the site in the electronic information area were accomplished and also alarmingly conveyed an obvious risk carcinogenic grade of Cr and As with the “hand-mouth exposure” as the transmission routine of the highest carcinogenic risk level, which urges us to be vigilant.

Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by synovitise, and its pathogenesis is complicated. Sphingosine-1-phosphate (S1P) is a lipid produced by sphingosine kinase 1 and 2 (SphK1/2), which participate in some of most-spread skeletal diseases such as rheumatoid arthritis or osteoarthritis. To explore the anti-inflammatory activity of 2-epi-jaspine B analogs as SphKs inhibitors, we used LPS-induced rheumatoid arthritis fibroblast-like synovial cells (HFLS-RA) as the research object to evaluate the anti-inflammatory activity of 16 2-epi-jaspine B analogs and the newly synthesized salt CHJ01. We found that 2-epi-jaspine B analog CHJ01 in hydrochloride salt form has excellent SphK1 inhibitory effect and better anti-RA effect. CHJ01 showed an anti-inflammatory effect similar to that of MTX in vitro, its IC50 value is 8.64 μM. Moreover, the anti-RA effect of CHJ01 was also studied by using a Complete Freund’s Adjuvant (CFA)-induced arthritis (AIA) in a rat mode. Pharmacological experiments show that CHJ01 can help to significantly improve the symptoms of rheumatoid arthritis by reducing the swelling volume, arthritis score, spleen index and the level of IL-1β, TNF-α, IL-6 of AIA rats. Therefore, CHJ01 holds high potential for the treatment of RA.

Some 1,2,5-trisubstituted benzimidazole fluorinated derivatives were designed and screened by molecular docking. Five compounds which obtained high scores were selected to synthesize. All the target products were cha-racterized by 1H NMR, 13C NMR and high resolution mass spectra(HRMS) and preliminarily screened for inhibitory activity against thrombin, among which three compounds(5a, 5c and 5e) were evaluated in vitro. The results showed that compounds 5a, 5c and 5e exhibited better anticoagulant activity than argatroban. Docking simulations demon-strated that these compounds may act as candidates for further studies on thrombin inhibitors.

T helper type 17 (Th17) cells and regulatory T (Treg) cells are two distinct T-cell subsets with opposite effects on immune functions. While Th17 cells are a key effector in the immune response and play critical roles in the development of autoimmunity and inflammation, Treg cells orchestrate the overall immune response and maintain peripheral immune tolerance by regulating the activity of the effector T cells. However, the developmental pathways for Th17 and Treg cells are reciprocally interconnected and there is a significant amount of plasticity between them. Disturbed Th17/Treg balance contributes to the development of autoimmune diseases, like experimental autoimmune encephalomyelitis and multiple sclerosis. MicroRNAs (miRNAs) are small non-coding RNA molecules that post-transcriptionally regulate gene expression. Recently, emerging evidence demonstrates that miRNAs play an important role in regulating the pathogenesis of autoimmune diseases through the modulation of Th17/Treg balance. This review will provide an overview of the dysregulated miRNAs and their functions in modulating the Th17/Treg balance in autoimmune diseases.

High-speed railway (HSR) has become a sustainable transport mode for inter-city travel, especially in China. As public transport (PT), the use of HSR involves access and egress to and from HSR stations. However, the literature focusing on the intra-city mode choice of HSR travelers is limited, especially regarding their differential socio-demographic and trip characteristics. This paper aims to fill that gap with an analysis of access/egress mode choice for business and leisure journeys in the Yangzi River Delta region. Using the HSR survey from Fudan University, we found that in China older and wealthier travelers have a strong preference for car use. For leisure travel, the explanatory power of the socio-demographic variables is much more influential in the egress than the access stage. With increasing access time, business travelers may be enticed to shift to a faster form of PT (e.g., subway rather than bus) in the access stage. With increasing line-haul time, only business travelers have a stronger preference for car use as their intra-city mode choice for business activities. A higher number of subway lines and diversity of land use around HSR stations is associated with less car use for business travelers in the egress stage.

Seven new cucurbitane glucosides, hemslepensides J-P ( 1 – 7 ), and two known compounds, 16,25- O -diacetyl-cucurbitane F ( 8 ) and 25- O -acetyl-23,24-dihydrocucurbitacin F ( 9 ), were isolated from the tubers of Hemsleya pengxianensis var. jinfushanensis . The structures of 1 – 7 were elucidated using infrared absorption spectroscopy, nuclear magnetic resonance spectroscopy and high-resolution electrospray ionization mass spectrometry. The treatment of HT29 cells, human colon cancer cells, with compounds 8 and 9 inhibited cell proliferation. Further study demonstrated that compounds 8 and 9 induced F-actin aggregation, G 2 /M phase cell cycle arrest and cell apoptosis in HT29 cells. In summary, the present study enriched the chemical composition research of H. pengxianensis , and suggested that the compounds 8 / 9 treatment may be a potentially useful therapeutic option for colon cancer.

In this article, we demonstrated that composition modulation of Ag_2Te nanowires can be achieved during the self-templated transformation of Te nanowires into Ag_2Te nanowires during solution phase synthesis, which provides a mean to tune the carrier density of the Ag_2Te nanowires. Both nearly stoichiometric and Ag-rich nanowires have been synthesized, which give rise to p-type and n-type Ag_2Te nanocomposites after hot press, respectively. The electrical and thermal properties of the two kinds of samples have been measured. Theoretical modeling based on the near-equilibrium Boltzmann transport equations has been used to understand the experimental results. We found that ZT of the heavily doped n-type sample reaches 0.55 at 400 K, which is the highest ZT value reported for Ag_2Te at the same temperature mainly due to the reduced thermal conductivity by the nanostructures. Theoretical analysis on the carrier transport shows that the power factor is also very well optimized in the doped Ag_2Te sample considering the reduced carrier mobility by the nanostructures.

Solution-synthesized thermoelectric nanostructured materials have the potential to have lower cost and higher performance than materials synthesized by solid-state methods. Herein we present the synthesis of ultrathin PbTe nanowires, which are compressed by spark plasma sintering at various temperatures in the range of 405-500 degrees C. The resulting discs possess grains with sizes of 5-30 mu m as well as grains with sizes on the order of the original 12 nm diameter PbTe nanowires. This micro- and nanostructure leads to a significantly reduced thermal conductivity compared to bulk PbTe. Careful electron transport analysis shows suppressed electrical conductivity due to increased short-range and ionized defect scatterings, while the Seebeck coefficient remains comparable to the bulk value. The PbTe nanowire samples are found unintentionally p-type doped to hole concentrations of 2.16-2.59 x 10(18) cm(-3). The maximum figure of merit achieved in the unintentionally doped spark plasma sintered PbTe nanowires is 0.33 at 350 K, which is among the highest reported for unintentionally doped PbTe at low temperatures.

A rational yet scalable solution phase method has been established, for the first time, to obtain n-type Bi2Te3 ultrathin nanowires with an average diameter of 8 nm in high yield (up to 93%). Thermoelectric properties of bulk pellets fabricated by compressing the nanowire powder through spark plasma sintering have been investigated. Compared to the current commercial n-type Bi2Te3-based bulk materials, our nanowire devices exhibit an enhanced ZT of 0.96 peaked at 380 K due to a significant reduction of thermal conductivity derived from phonon scattering at the nanoscale interfaces in the bulk pellets, which corresponds to a 13% enhancement compared to that of the best n-type commercial Bi2Te2.7Se0.3 single crystals (similar to 0.85)and is comparable to the best reported result of n-type Bi2Te2.7Se0.3 sample (ZT = 1.04) fabricated by the hot pressing of ball-milled powder. The uniformity and high yield of the nanowires provide a promising route to make significant contributions to the manufacture of nanotechnology-based thermoelectric power generation and solid-state cooling devices with superior performance in a reliable and a reproducible way.