Search

Fan Zhang,1 Yi-Nuo Yang,2 Jin-Di Feng,2 Jing-Hui Zhao,1 Li Wan,1 Jing Che,1 Ying Yan,1 Ning-Ning Guo,1 Jia-Yu Zhang1 1Hair Medical Research Center, Department of Dermatology and Venereology, Beijing Jishuitan Hospital, Beijing, People’s Republic of China; 2School of Clinical Medicine, Peking University School of Medicine, Beijing, People’s Republic of ChinaCorrespondence: Fan Zhang, Hair Medical Research Center, Department of Dermatology and Venereology, Beijing Jishuitan Hospital, No. 31 of Xinjiekou East Street, Xicheng District, Beijing, 100035, People’s Republic of China, Tel +86 10 58516688, Email zhangfanzf63z@126.comObjective: To evaluate the effectiveness and safety of a combined treatment for moderate and severe androgenetic alopecia (AGA) involving the use of electric microneedles.Methods: A total of 83 patients with moderate to severe AGA in the Department of Dermatology at Beijing Jishuitan Hospital were included in this study. The male patients were administered finasteride orally and 5% minoxidil for external use, while the female patients were given spironolactone orally or Diane-35 and 2% minoxidil for external use. All the patients were then treated via electric microneedle therapy alongside the YUFA ®medical care package (Foshan, China) once a week for 1– 28 weeks. The seven-point method and root hair measurement using a hair mirror were adopted to evaluate the efficacy and any adverse reactions of the combined treatment.Results: Eleven patients were treated for 1– 3 weeks, 60 for 4– 12 weeks, and 12 for more than 12 weeks. The efficacy evaluation using the seven-point method for 12 weeks of treatment indicated a 100% response rate, specifically, a 42.1% mild improvement rate, a 38.6% moderate improvement rate, and a 19.3% marked improvement rate. Besides, the efficacy assessment was also completed with root hair count method and the number of hair roots measured at fixed points were 148.67± 11.15, 158.13± 5.11 and 169.75± 2.06 after treatment time at 16, 20 and 24 weeks, respectively. Of note, a statistical difference in the number of hair roots could be observed during the period of week 20–week 24 (P < 0.01).Conclusion: The combined treatment of moderate to severe AGA using the electric microneedle technique has a clear effect and can effectively increase the hair density. With a simple operation and mild side effects, the technique has wide application prospects.Keywords: electric microneedles, androgenic alopecia, combined therapy, efficacy observation

The bearing-shear connector (B-SC) is a newly developed connector that exhibits excellent shear behaviour and is easy to process. However, research on the application of B-SCs as substitutes for grouped studs in prefabricated steel–concrete composite beams is rare, and systematically studying their shear behaviour is necessary. Thus, a refined numerical model was developed to study the shear behaviour of the B-SCs. The numerical model, validated by push-out tests, was conducted to analyse the stress of the B-SCs and concrete slab during loading and to explore the failure mechanism of B-SCs. Then, a parametric study was performed to identify the key factors influencing the shear behaviour of the B-SCs. The concrete strength, and the thickness and the tensile strength of the shear plate were found to significantly influence the shear behaviour of B-SCs. According to the experiments and numerical analysis, calculation formulae for the ultimate shear resistance and slip modulus were proposed.

Task-oriented Dialogue (TOD) Systems aim to build dialogue systems that assist users in accomplishing specific goals, such as booking a hotel or a restaurant. Traditional TODs rely on domain-specific APIs/DBs or external factual knowledge to generate responses, which cannot accommodate subjective user requests (e.g., "Is the WIFI reliable?" or "Does the restaurant have a good atmosphere?"). To address this issue, we propose a novel task of subjective-knowledge-based TOD (SK-TOD). We also propose the first corresponding dataset, which contains subjective knowledge-seeking dialogue contexts and manually annotated responses grounded in subjective knowledge sources. When evaluated with existing TOD approaches, we find that this task poses new challenges such as aggregating diverse opinions from multiple knowledge snippets. We hope this task and dataset can promote further research on TOD and subjective content understanding. The code and the dataset are available at https://github.com/alexa/dstc11-track5.

Graph neural networks (GNNs), a type of neural network that can learn from graph-structured data and learn the representation of nodes through aggregating neighborhood information, have shown superior performance in various downstream tasks. However, it is known that the performance of GNNs degrades gradually as the number of layers increases. In this paper, we evaluate the expressive power of GNNs from the perspective of subgraph aggregation. We reveal the potential cause of performance degradation for traditional deep GNNs, i.e., aggregated subgraph overlap, and we theoretically illustrate the fact that previous residual-based GNNs exploit the aggregation results of 1 to $k$ hop subgraphs to improve the effectiveness. Further, we find that the utilization of different subgraphs by previous models is often inflexible. Based on this, we propose a sampling-based node-level residual module (SNR) that can achieve a more flexible utilization of different hops of subgraph aggregation by introducing node-level parameters sampled from a learnable distribution. Extensive experiments show that the performance of GNNs with our proposed SNR module outperform a comprehensive set of baselines.

IntroductionType 2 diabetes mellitus (T2DM) has a high incidence rate globally, increasing the burden of death, disability, and the economy worldwide. Previous studies have found that the compositions of oral and intestinal microbiota changed respectively in T2DM; whether the changes were associated or interacted between the two sites and whether there were some associations between T2DM and the ectopic colonization of oral microbiota in the gut still need to be identified.Research design and methodsWe performed a cross-sectional observational study; 183 diabetes and 74 controls were enrolled. We used high-throughput sequencing technology to detect the V3-V4 region of 16S rRNA in oral and stool samples. The Source Tracker method was used to identify the proportion of the intestinal microbiota that ectopic colonized from the oral cavity.ResultsThe oral marker bacteria of T2DM were found, such as Actinobacteria, Streptococcus, Rothia, and the intestinal marker bacteria were Bifidobacterium, Streptococcus, and Blautia at the genus level. Among them, Actinobacteria and Blautia played a vital role in different symbiotic relationships of oral and intestinal microbiota. The commonly distributed bacteria, such as Firmicutes, Bacteroidetes, and Actinobacteria, were found in both oral and intestine. Moreover, the relative abundance and composition of bacteria were different between the two sites. The glycine betaine degradation I pathway was the significantly up-regulated pathway in the oral and intestinal flora of T2DM. The main serum indexes related to oral and intestinal flora were inflammatory. The relative abundance of Proteobacteria in the intestine and the Spirochete in oral was positively correlated, and the correlation coefficient was the highest, was 0.240 (PConclusionThe dysbacteriosis exited in the oral and intestine simultaneously, and there were differences and connections in the flora composition at the two sites in T2DM. Ectopic colonization of oral flora in the intestine might relate to T2DM. Further, clarifying the oral-gut-transmitting bacteria can provide an essential reference for diagnosing and treating T2DM in the future.

Hepatocellular carcinoma (HCC) is a deadly cancer, with the dominant driver mutations such as p53 yet undruggable. There is urgent demand to search for novel agents and therapeutic targets for HCC treatment. Andrographolide (Andro, a clinical-available anti-inflammation phytochemical agent) could inhibit different types of cancer including HCC, but the molecular mechanisms involved are yet unclear. The present study thus determined how Andro mechanistically cause HCC cell death in both in vitro and in vivo models. Andro dose- and time-dependently induced HCC cell death but spared normal hepatocyte HL-7702. Furthermore, Andro caused ROS-mediated DNA damage, which was critical for cell death. Since p53 could induce G2/M cell cycle arrest, the p53-present tumour cells were found to be more resistant to Andro-induced cell death, compared to the p53-deficient cells. Notably, Andro-induced p62 aggregation promoted proteasomal degradation of RAD51 and 53BP1, two essential proteins involved in DNA damage repair. As a result, either silencing or knocking-out p62 promoted DNA damage repair and consequently protected HCC cells from Andro- induced cell death. Consistently, xenograft tumour developed by p62-knockout cells were more resistant to Andro treatment. In addition, p53 and p62 were independent with each other, since disrupting any one protein did not affect expression of the other protein. Taken together, the present study may shed light on repurposing Andro in the treatment of both p53-present and p53-deficient HCC.

Dare to be different: A sociolinguistic perspective on diasporic Chinese Protestant ministries of Chinese international students in the Netherlands in the 2010s Since the 2010s, the Netherlands has seen an increasing number of local and transnational migrant Chinese Protestant institutes operating across the country. Many of them organise or once organised particular religious services for Chinese international students in Dutch universities. These services meet with a mixed reception as they seek religious conversion, preach conservative social values, and have a significant impact on the student participants’ language practices. The Chinese student religious services in the Netherlands provide a window into thriving non-Western Christianity in the Western world, further cultural diversification of the Chinese diaspora, and the interplay between language and religion. However, the dynamics of the services and of the broader Dutch Chinese Protestant community in the 2010s are largely unknown to both Dutch society and academia. Thus, drawing on an ethnographic approach, this study aims to shed a light on the very religious activities and interpret their implications for disciplines in migrant studies, religious studies and sociolinguistics. With regard to non-western Christianity, there has been a proliferation of churches and adherents in the Dutch Chinese Protestant community; the migrant Chinese religious institutes exhibit hues of religious fundamentalism. In the meantime, somewhat uniquely, the local Dutch Chinese churches have a strong occupational background of the catering industry whereas the transnational religious institutes have several new features of religious fundamentalism. As an arrival infrastructure in the Dutch Chinese community, the Chinese student services function like cram schools in China and attract some student participants for religious as well as secular benefits. However, the services are discarded by the other because of various socio-cultural and socio-political differences between the service providers and the (former) student participants. In addition to religious conversion, the student religious services exert considerable influence upon the remaining student participants’ Mandarin Chinese speeches. The religiously influenced Chinese variety is based on the Chinese Union Version Bible and considered a by-product of the religious service providers’ fundamentalist doctrines. Taking the Chinese student religious services in the Netherlands as an example, this study appeals for further academic works on migrant Chinese Protestant communities, transnationalism, and the religiously influenced Mandarin Chinese variety given their potential impact on non-Western Christianity, the Western host societies, the Chinese diaspora and China.

This work focuses on in-context data augmentation for intent detection. Having found that augmentation via in-context prompting of large pre-trained language models (PLMs) alone does not improve performance, we introduce a novel approach based on PLMs and pointwise V-information (PVI), a metric that can measure the usefulness of a datapoint for training a model. Our method first fine-tunes a PLM on a small seed of training data and then synthesizes new datapoints - utterances that correspond to given intents. It then employs intent-aware filtering, based on PVI, to remove datapoints that are not helpful to the downstream intent classifier. Our method is thus able to leverage the expressive power of large language models to produce diverse training data. Empirical results demonstrate that our method can produce synthetic training data that achieve state-of-the-art performance on three challenging intent detection datasets under few-shot settings (1.28% absolute improvement in 5-shot and 1.18% absolute in 10-shot, on average) and perform on par with the state-of-the-art in full-shot settings (within 0.01% absolute, on average)., Accepted at EACL 2023

While large neural-based conversational models have become increasingly proficient dialogue agents, recent work has highlighted safety issues with these systems. For example, these systems can be goaded into generating toxic content, which often perpetuates social biases or stereotypes. We investigate a retrieval-based method for reducing bias and toxicity in responses from chatbots. It uses in-context learning to steer a model towards safer generations. Concretely, to generate a response to an unsafe dialogue context, we retrieve demonstrations of safe responses to similar dialogue contexts. We find our method performs competitively with strong baselines without requiring training. For instance, using automatic evaluation, we find our best fine-tuned baseline only generates safe responses to unsafe dialogue contexts from DiaSafety 4.04% more than our approach. Finally, we also propose a re-ranking procedure which can further improve response safeness.

As information filtering services, recommender systems have extremely enriched our daily life by providing personalized suggestions and facilitating people in decision-making, which makes them vital and indispensable to human society in the information era. However, as people become more dependent on them, recent studies show that recommender systems potentially own unintentional impacts on society and individuals because of their unfairness (e.g., gender discrimination in job recommendations). To develop trustworthy services, it is crucial to devise fairness-aware recommender systems that can mitigate these bias issues. In this survey, we summarise existing methodologies and practices of fairness in recommender systems. Firstly, we present concepts of fairness in different recommendation scenarios, comprehensively categorize current advances, and introduce typical methods to promote fairness in different stages of recommender systems. Next, after introducing datasets and evaluation metrics applied to assess the fairness of recommender systems, we will delve into the significant influence that fairness-aware recommender systems exert on real-world industrial applications. Subsequently, we highlight the connection between fairness and other principles of trustworthy recommender systems, aiming to consider trustworthiness principles holistically while advocating for fairness. Finally, we summarize this review, spotlighting promising opportunities in comprehending concepts, frameworks, the balance between accuracy and fairness, and the ties with trustworthiness, with the ultimate goal of fostering the development of fairness-aware recommender systems., Comment: 27 pages, 9 figures

It is well known that lipid membranes respond to a threshold transmembrane electric field through a reversible mechanism called electroporation, where hydrophilic water pores form across the membrane, an effect widely used in biological systems. The effect of such fields on interfacially-confined (stacked or supported) lipid membranes, on the other hand, which may strongly modulate interfacial properties, has not to our knowledge been previously studied. Motivated by recent surface forces experiments showing a striking effect of electric fields on lubrication by confined lipid bilayers, we carried out all-atom molecular dynamics simulations of such membranes under transverse electric fields. We find that in addition to electroporation, a new feature emerges of locally merged bilayers which act to bridge the confining interfaces. These features shed light on the remodelling of confined lipid membrane stacks by electric fields, and provides insight into how such fields may modulate frictional and more generally surface interactions in the presence of lipid-based boundary layers.

It was recently discovered that friction between surfaces bearing phosphatidylcholine (PC) lipid bilayers can be increased by two orders of magnitude or more via an externally-applied electric field, and that this increase is fully reversible when the field is switched off. While this striking effect holds promising application potential, its molecular origin remains unknown due to difficulty in experimentally probing confined membrane structure at a molecular level. Our earlier molecular dynamics simulations revealed the equilibrium electroporated structure of such confined lipid membranes under an electric field; here we extend this approach to study the associated sliding friction between two solid surfaces across such PC bilayers. We identify the enhanced friction in the field as arising from membrane undulations due to the electroporation; this leads to some dehydration at the lipid-water interfaces, leading to closer contact and thus increased attraction between the zwitterionic headgroups, which results in increased frictional dissipation between the bilayers as they slide past each other. Additionally, the electric field facilitates formation of lipid bridges spanning the intersurface gap; at the sliding velocities of the experiments, these bridges increase the friction by topologically-forcing the slip-plane to pass through the acyl tail-tail interface, associated with higher dissipation during sliding. Our results account quantitatively for the experimentally-observed electro-modulated friction with boundary lipid bilayers, and indicate more generally how they may affect interactions between contacting surfaces, where high local transverse fields may be ubiquitous.

Chain-of-Thought prompting (CoT) enables large-scale language models to solve complex reasoning problems by decomposing the problem and tackling it step-by-step. However, Chain-of-Thought is a greedy thinking process that requires the language model to come up with a starting point and generate the next step solely based on previous steps. This thinking process is different from how humans approach a complex problem e.g., we proactively raise sub-problems related to the original problem and recursively answer them. In this work, we propose Socratic Questioning, a divide-and-conquer fashion algorithm that simulates the self-questioning and recursive thinking process. Socratic Questioning is driven by a Self-Questioning module that employs a large-scale language model to propose sub-problems related to the original problem as intermediate steps and Socratic Questioning recursively backtracks and answers the sub-problems until reaches the original problem. We apply our proposed algorithm to the visual question-answering task as a case study and by evaluating it on three public benchmark datasets, we observe a significant performance improvement over all baselines on (almost) all datasets. In addition, the qualitative analysis clearly demonstrates the intermediate thinking steps elicited by Socratic Questioning are similar to the human's recursively thinking process of a complex reasoning problem., Comment: 15 pages, 12 figure, 2 algorithms

Phospholipid surface assemblies are crucial ingredients in reducing the boundary friction of articular cartilage in synovial joints such as hips and knees, of central importance to their homeostasis and to tissue-wear-related diseases such as osteoarthritis. From the point of view of biolubrication, the very large number of different lipids in joints begs the question of whether this is natural redundancy, or does this multiplicity confer any benefits, possibly through natural selection. Here we demonstrate that particular combinations of lipids present in joints may carry a clear benefit for their lubricating properties. Using progressively more complex mixtures of lipids representative of those in joints, and measuring their interactions using a uniquely-sensitive surface forces balance at physiologically-relevant salt concentrations and pressures, we show that different lipid combinations lead to very significant differences in their efficacy as boundary lubricants. This points to a clear synergy arising from the multiple lipid types in the lubricating layers, provides insight into the role of lipid type proliferation in synovial joints, of possibly evolutionary origins, and may suggest new treatment modalities for osteoarthritis. We identify parameters of lipid-based boundary layers that might contribute to improved boundary lubrication in the light of the present study. Finally, we describe a possible approach based on molecular dynamics (MD) to emulating such optimal lipid combinations, and provide proof-of-concept MD simulations to illustrate this approach.

Reference ranges for bone turnover markers (BTMs) are still lacking in the healthy Chinese population. To establish reference intervals for BTMs and to investigate the correlations between BTMs and bone mineral density (BMD) in Chinese older adults. A community-based cross-sectional study was conducted among 2511 Chinese subjects aged over 50 yrs residing in Zhenjiang, Southeast China. Reference intervals for BTMs (i.e. procollagen type I N-terminal propeptide, P1NP; β cross-linked C-terminal telopeptide of type I collagen, β-CTX) were calculated as the central 95% range of all measurements in Chinese older adults. The reference intervals of P1NP, β-CTX and P1NP/β-CTX were 15.8–119.9 ng/mL, 0.041–0.675 ng/mL and 49.9–1261.5 for females and 13.6–111.4 ng/mL, 0.038–0.627 ng/mL and 41.0–1269.1 for males, respectively. In the multiple linear regression analysis, only β-CTX was negatively associated with BMD after adjusting for age and body mass index (BMI) in both sex-stratified groups (all p < .05). This study established age- and sex-specific reference intervals for BTMs in a large sample of healthy Chinese participants ≥ 50 and < 80 years of age and explored the correlations between BTMs and BMD, which provides an effective reference for the assessment of bone turnover in the clinical practice of osteoporosis.

Dialogue state tracking (DST) is an important step in dialogue management to keep track of users' beliefs. Existing works fine-tune all language model (LM) parameters to tackle the DST task, which requires significant data and computing resources for training and hosting. The cost grows exponentially in the real-world deployment where dozens of fine-tuned LM are used for different domains and tasks. To reduce parameter size and better utilize cross-task shared information, we propose to use soft prompt token embeddings to learn task properties. Without tuning LM parameters, our method drastically reduces the number of parameters needed to less than 0.5% of prior works while achieves better low-resource DST performance., Comment: In the INTERSPEECH 2023, and the Second Workshop on Efficient Natural Language and Speech Processing (ENLSP) at NeurIPS 2022

Transient electric fields across cell bilayer membranes can lead to electroporation, as well as to cell fusion, and have been extensively studied. We find that transmembrane electric fields similar to those in cells can lead to a massive, reversible modulation--by up to 200-fold--of the interfacial energy dissipation between surfaces sliding across the lipid bilayer membranes. Atomistic simulations reveal that this arises from (fully reversible) electroporation of the interfacially-confined bilayers, and formation of bilayer bridges analogous to stalks preceding intermembrane fusion. These cell-membrane-mimicking effects topologically-force the slip to partially-revert from the low-dissipation, hydrated lipid-headgroups plane to the intra-bilayer, high-dissipation acyl tail interface. Our results demonstrate that lipid bilayers under transmembrane electric fields can have striking materials-modification properties, and shed new light on membrane hemifusion., Comment: Yu Zhang and Di Jin contributes equally to this work

Prunellae Spica (PS) is a commonly used medicinal herb in China. Secondary metabolites and saccharides are major bioactive components of PS. However, holistic quality consistency of commercial PS is ambiguous due to lack of comprehensive evaluation methods and reliable quality control markers.Integrating multiple chromatographic and chemometric methods to comprehensively evaluate the holistic quality of PS.Ultrahigh-performance liquid chromatography coupled with triple-quadrupole tandem mass spectrometry (UPLC-TQ-MS/MS) was applied to quantify 12 secondary metabolites of PS. High-performance liquid chromatography coupled with photodiode array/evaporative light scattering detection (HPLC-PDA/ELSD) and high-performance gel permeation chromatography (HPGPC) methods were used to characterise the saccharides. Multivariate statistical analysis was adopted to evaluate the quality consistency of commercial PS and explore the potential quality control markers.The contents of secondary metabolites and saccharides were significantly different among commercial PS. All samples could be classified into three groups with ferulic acid, protocatechualdehyde, gallic acid, ursolic acid/oleanolic acid, sucrose, p-coumaric acid, chlorogenic acid as the major contributing components responsible for the difference. The content of rosmarinic acid was correlated with that of betulinic acid, hyperposide, chlorogenic acid, rutin, caffeic acid, p-coumaric acid and glucose, whereas polysaccharides, ferulic acid, protocatechualdehyde and ursolic acid/oleanolic acid, quercetin, sucrose and majority monosaccharides were not.The holistic quality of commercial PS was inconsistent. Together with rosmarinic acid, ferulic acid, protocatechualdehyde, ursolic acid/oleanolic acid, polysaccharides and sucrose might be recommended as potential quality control markers for the holistic quality control of PS.

Atractylodis Macrocephalae Rhizoma (AMR) is one of commonly used medicinal and edible herbs in China. It is often sulfur-fumigated during post-harvest processing. Carbohydrates are important active components of AMR. However, it is unknown whether sulfur-fumigation would induce changes on carbohydrates. Here, carbohydrates including polysaccharides, oligosaccharides and free monosaccharides were comprehensively analyzed to characterize the quality changes of sulfur-fumigated AMR. Determination of both homemade sulfur-fumigated AMR samples and commercial samples from market revealed that sulfur-fumigation did not affect molecular weight distribution of polysaccharides, but altered polysaccharides content and its ratios of constituent monosaccharides, especially glucose (Glc) and fructose (Fru), as well as the contents of oligosaccharides DP2-10 and free monosaccharide Fru. Moreover, the variations enhanced with the increasing of residual SO

Prefix-tuning, or more generally continuous prompt tuning, has become an essential paradigm of parameter-efficient transfer learning. Using a large pre-trained language model (PLM), prefix-tuning can obtain strong performance by training only a small portion of parameters. In this paper, we propose to understand and further develop prefix-tuning through the kernel lens. Specifically, we make an analogy between \textit{prefixes} and \textit{inducing variables} in kernel methods and hypothesize that \textit{prefixes} serving as \textit{inducing variables} would improve their overall mechanism. From the kernel estimator perspective, we suggest a new variant of prefix-tuning -- \textit{inducer-tuning}, which shares the exact mechanism as prefix-tuning while leveraging the residual form found in adapter-tuning. This mitigates the initialization issue in prefix-tuning. Through comprehensive empirical experiments on natural language understanding and generation tasks, we demonstrate that inducer-tuning can close the performance gap between prefix-tuning and fine-tuning., Comment: To appear in EMNLP 2022. Code is available at https://github.com/ychen-stat-ml/kernel-adapters

Apocyni Veneti Folium (AVF) is a commonly used traditional Chinese medicinal herb for the treatment of hypertension. Chemical markers are crucial for the quality control of herbal medicines; however, the therapeutic components of AVF remain to be well elucidated.This study was intended to integrate serum pharmacochemistry and network pharmacology to identify chemical markers of AVF and establish an efficacy-related quality control method of AVF.Ultra-performance liquid chromatography quadrupole time-of-flight tandem mass spectrometry (UPLC-QTOF-MS/MS) was applied to identify the absorbed AVF constituents in rat serum. Network pharmacology was further used to identify anti-hypertension-related chemical markers. Subsequently, a quantitative method was established using UPLC with diode array detection (DAD) and applied for quality evaluation of commercial AVF samples.Thirteen prototype constituents were unequivocally or tentatively characterized in serum samples, among which quercetin, kaempferol, hyperoside, isoquercitrin, chlorogenic acid, cryptochlorogenic acid, and neochlorogenic acid were identified as dominant chemicals related to anti-hypertensive efficacy. The quantitative data showed that the total contents of seven marker components even showed 2-fold variation among 14 batches of commercial AVF samples with RSD values ranging from 12.15% to 75.61%. Hierarchical cluster analysis and heatmap analysis showed that 14 batches of commercial AVF samples could be divided into three main groups.The chemical markers obtained from this study could be applicable for efficacy-related quality control of AVF.

Cicadae Periostracum (CP) is a traditional Chinese medicinal herb derived from the slough that is molted from the nymph of the insect Cryptotympana pustulata Fabricius. Washing with water to remove residual silt is a primary processing method of CP that is recommended by the Chinese Pharmacopoeia, but how washing methods affect the quality and bioactivity of CP is unknown. In this study, the quality and bioactivity of non-washed CP (CP-NW), post-molting-washed CP (CP-WAT), and pre-molting-washed CP (CP-WBT) were comparatively investigated. The quality of these CP samples was evaluated in terms of the UPLC-QTOF-MS/MS-based chemical profiling and semi-quantification of 39 N-acetyldopamine oligomers (belonging to six chemical types), the HPLC-UV-based quantification of 17 amino acids, the ICP-MS-based quantification of four heavy metals, and the contents of ash; the bioactivities of the samples were compared regarding their anti-oxidant and anti-inflammatory activities. It was found that, compared with CP-NW, both CP-WBT and CP-WAT had significantly lower contents of ash and heavy metals. Moreover, compared with CP-WAT, CP-WBT contained lower levels of total ash, acid-insoluble ash, and heavy metals and higher contents of N-acetyldopamine oligomers and amino acids. It also had enhanced anti-oxidant and anti-inflammatory activities. A Spearman’s correlation analysis found that the contents of N-acetyldopamine oligomers and free amino acids were positively correlated with the anti-oxidant/-inflammatory activities of CP. All these results suggest that pre-molting washing can not only remove the residual silt but can also avoid the loss of the bioactive components and assure higher bioactivities. It is concluded that pre-molting washing could enhance the quality and bioactivity of CP and should be a superior alternative method for the primary processing of qualified CP.

Graph similarity learning refers to calculating the similarity score between two graphs, which is required in many realistic applications, such as visual tracking, graph classification, and collaborative filtering. As most of the existing graph neural networks yield effective graph representations of a single graph, little effort has been made for jointly learning two graph representations and calculating their similarity score. In addition, existing unsupervised graph similarity learning methods are mainly clustering-based, which ignores the valuable information embodied in graph pairs. To this end, we propose a contrastive graph matching network (CGMN) for self-supervised graph similarity learning in order to calculate the similarity between any two input graph objects. Specifically, we generate two augmented views for each graph in a pair respectively. Then, we employ two strategies, namely cross-view interaction and cross-graph interaction, for effective node representation learning. The former is resorted to strengthen the consistency of node representations in two views. The latter is utilized to identify node differences between different graphs. Finally, we transform node representations into graph-level representations via pooling operations for graph similarity computation. We have evaluated CGMN on eight real-world datasets, and the experiment results show that the proposed new approach is superior to the state-of-the-art methods in graph similarity learning downstream tasks., 7 pages, 5 figures

Our previous study found that Qiong-Yu-Gao (QYG), a traditional Chinese medicine formula derived from Rehmanniae Radix, Poria, and Ginseng Radix, has protective effects against cisplatin-induced acute kidney injury (AKI), but the underlying mechanisms remain unknown. In the present study, the potential role of gut microbiota in the nephroprotective effects of QYG was investigated. We found that QYG treatment significantly attenuated cisplatin-induced AKI and gut dysbiosis, altered the levels of bacterial metabolites, with short-chain fatty acids (SCFAs) such as acetic acid and butyric acid increasing and uremic toxins such as indoxyl sulfate and

Graph Neural Networks (GNNs) have gained great popularity in tackling various analytical tasks on graph-structured data (i.e., networks). Typical GNNs and their variants follow a message-passing manner that obtains network representations by the feature propagation process along network topology, which however ignore the rich textual semantics (e.g., local word-sequence) that exist in many real-world networks. Existing methods for text-rich networks integrate textual semantics by mainly utilizing internal information such as topics or phrases/words, which often suffer from an inability to comprehensively mine the text semantics, limiting the reciprocal guidance between network structure and text semantics. To address these problems, we propose a novel text-rich graph neural network with external knowledge (TeKo), in order to take full advantage of both structural and textual information within text-rich networks. Specifically, we first present a flexible heterogeneous semantic network that incorporates high-quality entities and interactions among documents and entities. We then introduce two types of external knowledge, that is, structured triplets and unstructured entity description, to gain a deeper insight into textual semantics. We further design a reciprocal convolutional mechanism for the constructed heterogeneous semantic network, enabling network structure and textual semantics to collaboratively enhance each other and learn high-level network representations. Extensive experimental results on four public text-rich networks as well as a large-scale e-commerce searching dataset illustrate the superior performance of TeKo over state-of-the-art baselines.

Steel bottom plates are applied as replacements for the concrete bottom plates in order to reduce the dead weight of the composite box girders with corrugated steel webs and steel bottom plates (CSWSB). Due to the change in the material, the previous analytical calculation methods of vertical deflection of composite box girders with corrugated steel webs (CSWs) cannot be directly applied to the improved composite box girders. The shear lag warpage displacement function was derived based on the shear deformation laws of the upper flange and the bottom plates of the improved composite box girders. The equations for the calculation of the shear deformation and the additional deflection due to the shear lag of continuous and simply supported composite box girders with CSWSB under concentrated and uniformly distribution loads were derived by considering the double effects of the shear lag and the shear deformations of the top and the bottom plates with different elastic moduli. The analytical solutions of the vertical deflection of the improved composite box girders include the theory of the bending deflection of elementary beams, shear deformation of CSWs, and the additional deflection caused by the shear lag. Based on the theoretical derivation, an analytical solution method was established and the obtained vertical deflection analytical solutions were compared with the finite element method (FEM) calculation results and the experimental values. The analytical equations of vertical deflection under the two supporting conditions and the two load cases have verified the analyses and the comparisons. Further, the additional deflections due to the shear lag and the shear deformation are found to be less than 2% and 34% of the total deflection values, respectively. Moreover, under uniform distributed load conditions, the deflection value was found to be higher than that of the under concentrated load condition. It was also found that the ratio of the deflection caused by the shear lag or the shear deformation to the total deflection decreased gradually with the increase in the span width ratio. When the value of the span width ratio of a single box and single chamber composite box girder with CSWSB was equal to or greater than 8, the deflections caused by the shear lag and the shear deformation could be ignored.

To improve the quality of pre-combustion cracking gas, the gliding arc discharge plasma is adopted in this paper. The influence of incoming flow rate, electrode gap distance and discharge frequency on cracking effect have been experimentally studied. The results show that through plasma cracking, the concentration of H2 increases while the concentration of CH4 and C2H4 decrease. With different fuel ratio conditions, the variation trend of C/H ratio as well as the mass flow of carbon and hydrogen atoms in the cracking products vary from each other. The cracking effect becomes weaker with the increase of incoming flow rate, while is better when the fuel ratio is larger. Given the flow rate of 18slpm and 24slpm, the best cracking effect can be acquired with the electrode gap of 1.5 mm. Besides, the effect of gliding arc discharge plasma cracking is weakened as the discharge frequency rises.

An efficient copper-catalyzed addition of arylboronic esters to (Boc)2O was developed. The reaction can be conducted under exceedingly mild conditions and is compatible with a variety of synthetically relevant functional groups. It therefore represents a useful alternative route for the synthesis of methyl arylcarboxylates. A preliminary mechanistic study indicated the involvement of an addition–elimination mechanism.

Surface-enhanced Raman scattering (SERS) sensing has always been considered as a kind of high-efficiency analysis technique in different areas. Herein, we report a AgNPs decorated 3D bionic silicon (Si) nanograss SERS substrate with higher sensitivity and specificity by green galvanic displacement. The Si nanograss arrays are directly grown on a Si substrate via catalyst-assisted vapor–liquid–solid (VLS) growth and subsequent plasma interaction. AgNPs were rapidly immobilized on Si nanograss arrays without any organic reagents, and avoiding the interference signal of additives. The AgNPs decorated 3D bionic silicon nanograss arrays not only possess a larger specific surface area (loading more reporter molecules), but also provide a potential distribution and arrangement for plentiful hot spots. Using Rhodamine 6G (R6G) as a probe molecule, the prepared SERS substrates exhibited great potential for high-sensitivity SERS sensing, and pushed the limit of detection (LOD) down to 0.1 pM. A higher Raman analytical enhancement factor (AEF, 3.3 × 107) was obtained, which was two magnitudes higher than our previous Ag micro–nano structures. Additionally, the practicality and reliability of our 3D bionic SERS substrates were confirmed by quantitative analysis of the spiked Sudan I in environmental water, with a wide linear range (from 10−10 M to 10−6 M) and low detection limit (0.1 nM).

Trust evaluation is critical for many applications such as cyber security, social communication and recommender systems. Users and trust relationships among them can be seen as a graph. Graph neural networks (GNNs) show their powerful ability for analyzing graph-structural data. Very recently, existing work attempted to introduce the attributes and asymmetry of edges into GNNs for trust evaluation, while failed to capture some essential properties (e.g., the propagative and composable nature) of trust graphs. In this work, we propose a new GNN based trust evaluation method named TrustGNN, which integrates smartly the propagative and composable nature of trust graphs into a GNN framework for better trust evaluation. Specifically, TrustGNN designs specific propagative patterns for different propagative processes of trust, and distinguishes the contribution of different propagative processes to create new trust. Thus, TrustGNN can learn comprehensive node embeddings and predict trust relationships based on these embeddings. Experiments on some widely-used real-world datasets indicate that TrustGNN significantly outperforms the state-of-the-art methods. We further perform analytical experiments to demonstrate the effectiveness of the key designs in TrustGNN.

Heterogeneous graph neural network (HGNN) is a very popular technique for the modeling and analysis of heterogeneous graphs. Most existing HGNN-based approaches are supervised or semi-supervised learning methods requiring graphs to be annotated, which is costly and time-consuming. Self-supervised contrastive learning has been proposed to address the problem of requiring annotated data by mining intrinsic information hidden within the given data. However, the existing contrastive learning methods are inadequate for heterogeneous graphs because they construct contrastive views only based on data perturbation or pre-defined structural properties (e.g., meta-path) in graph data while ignore the noises that may exist in both node attributes and graph topologies. We develop for the first time a novel and robust heterogeneous graph contrastive learning approach, namely HGCL, which introduces two views on respective guidance of node attributes and graph topologies and integrates and enhances them by reciprocally contrastive mechanism to better model heterogeneous graphs. In this new approach, we adopt distinct but most suitable attribute and topology fusion mechanisms in the two views, which are conducive to mining relevant information in attributes and topologies separately. We further use both attribute similarity and topological correlation to construct high-quality contrastive samples. Extensive experiments on three large real-world heterogeneous graphs demonstrate the superiority and robustness of HGCL over state-of-the-art methods.

This paper studies how social ties interact with bribery and corruption. In the laboratory, subjects are in triads where two ‘performers’ individually complete an objective real-effort task and an evaluator designates one of them as the winner of a monetary prize. In one treatment dimension, we vary whether performers can bribe the evaluator—where any bribe made is non-refundable, irrespective of the evaluator’s decision. A second treatment dimension varies the induced social ties between the evaluator and the performers. The experimental evidence suggests that both bribes and social ties may corrupt evaluators’ decisions. Bribes decrease the importance of performance in the decision. The effect of social ties is asymmetric. While performers’ bribes vary only little with their ties to the evaluator, evaluators exhibit favoritism based on social ties when bribes are not possible. This ‘social-tie-based’ corruption is, however, replaced by bribe-based corruption when bribes are possible. We argue that these results have concrete consequences for possible anti-corruption policies.

This study proposes two types of U-bar joints: rectangular joints with stainless steel ties and T-shaped joints. The mechanical properties of four joint specimens subjected to combined bending and shear forces were studied experimentally and numerically. The T-shaped joint details effectively increased the cracking load and controlled the development of interface cracks. The bearing capacity of the four joint details were similar. Furthermore, a formula to estimate the bearing capacities of the T-shaped U-bar lap joint specimen is proposed, and its accuracy is verified.

Ginseng is usually consumed as a dietary supplement for health care in the normal state or prescribed as a herbal medicine in pathologic conditions. Although metabolic studies of ginseng are commonly performed on healthy organisms, the metabolic characteristics in pathologic organisms remain unexplored. This study aimed to uncover the difference in intestinal metabolism of ginseng between normal and cyclophosphamide-induced immunosuppressed rats and further discuss the potential mechanisms involved. Twelve Sprague-Dawley rats (6–8 weeks old) were randomly divided into two groups: the normal group (NG) and immunosuppressed group (ISG). Rats in the NG and ISG groups were intraperitoneally administered normal saline and cyclophosphamide injections (40 mg/kg) on the 1st, 2nd, 3rd and 10th days; on the 12th day, all rats were intragastrically administered ginseng water extract (900 mg/kg). The difference in intestinal metabolism of ginseng was compared using an ultra-high-performance liquid chromatography coupled with quadruple time-of-flight mass spectrometry-based metabolomics approach, and the diversities of gut microbiota were analyzed by 16S rRNA gene sequencing between the two groups. The intestinal metabolomic characteristics of ginseng were significantly different between the normal and immunosuppressed rats, with the ginsenoside F2 (F2), 20S-ginsenoside Rg3 (20(S)-Rg3), pseudo-ginsenoside Rt5 (Pseudo-Rt5), ginsenoside Rd (Rd), ginsenoside Rh1 (Rh1), 20S-ginsenoside Rg1 (20(S)-Rg1), ginsenoside compound K (CK), ginsenoside Rg2 (Rg2) and 20S-panaxatriol (S-PPT) more abundant in immunosuppressed ones (P

Increasingly demanding performance standards and operating requirements are driving the growing interest on multi-field coupling technologies in gear transmission systems. Optimized factors of gear multi-field coupling technology are necessary to meet the high-speed and heavy-load capacity requirements of the gear transmission system and solve the problem of gear failure under high-speed and heavy-load working conditions. Numerous theoretical and experimental studies have been dedicated to gear multi-field coupling, especially on gear accurate modeling method and multi-field coupling simulation analysis method. The accurate model of the gear can provide a model basis for the thermal–fluid–solid multi-field coupled numerical simulation. The choice of gear multi-field coupling simulation method is the way to realize gear multi-field coupling simulation. In order to compile and categorize key investigations in an expansive field with substantial recent research, this work reviews factors of gear multi-field coupling simulation papers with focus on gear accurate modeling method and multi-field coupling simulation analysis method.

In this paper, we discuss the problem of QKD iterative information reconciliation based on LDPC codes. The aim is to correct high error rates in longer distances or in the condition of noisier environments. We use the methods of data reordering, data grouping, bit filling and error groups iterative reconciliation to improve the reconciliation capability and enhance reconciliation data security, and it can also reduce the impact of burst errors and retain more useful raw data. The simulation experiments have shown that the error-correcting ability of the iterative information reconciliation scheme proposed in this paper is beyond the results of traditional one-way scheme.