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Rockburst is a highly destructive geological hazard that can cause casualties and equipment damage. To achieve high-accuracy discrimination of rockburst intensity, this article proposes an improved model that addresses the inefficient maximum membership principle used in traditional rockburst fuzzy evaluation models. The stress coefficient σθ/σc, brittleness coefficient σc/σt, and elastic deformation energy index Wetare selected as evaluation indicators for rockburst classification. Subjective and objective weights are obtained using the Delphi method and entropy weight method (EWM). Three types of membership function distribution forms are then used to obtain the membership degrees of each indicator to rockburst grades: trapezoidal membership function (TMF), normal membership function (NMF), and quadratic parabolic membership function (QPMF). Finally, six traditional models and six improved models are established using the maximum membership principle (MMP) and weighted average-maximum membership principle combination evaluation principle (WMP), respectively. Based on the analysis of 100 sets of rockburst field data, the accuracy, precision, recall, and F1-score of the improved evaluation model are increased by 11.3%, 0.097, 0.068, and 0.089, respectively, compared to the traditional model. The Delphi-NMF-WMP model is selected as the best model, with four performance indices reaching 97.0%, 0.979, 0.979, and 0.978. The best model is applied to evaluate the rockburst intensity of the Cangling Tunnel, Dongguashan Copper Mine, and Jiangbian Hydropower Station Diversion Tunnel, with evaluation results consistent with the actual situation, demonstrating the reliability and scientificity of the model.

Distance-based neural network clustering requires the intrinsic assumption that a particular neuron in the network represents a cluster centroid. However, not all these neurons can perfectly represent the training data; these neurons can only represent part of the training samples. This paper proposes an effective training data splitting method (TDSM) to find perfect representative neurons and improve the clustering results in a distance-based neutral network without changing the original network’s internal algorithm or the training data quality. The method allows a network with Nneurons to be enlarged to a new network with m×Nneurons. These neurons represent msubnetworks, and each subnetwork perfectly represents a part of the training set, where the clustering qualification indicators (the purity, normalized mutual information, and adjusted rand index measures) all equal 1. The results are statistically validated with attest, and we demonstrate that the TDSM performs better than the original clustering paradigm on some real datasets.

N, S co-doped active carbons (ACs) were synthesized and utilized as supports to prepare palladium/activated carbon (Pd/C) catalysts. Characterization of the as-prepared supports suggested that the variety of surface functional groups impacted Pd nanoparticles (NPs) with regard to particle size, chemical state, and dispersion. N, S co-doped activated carbon-supported Pd nanoparticles modified with 3 wt% sulfur with a reduced size distribution of approximately 1.81 nm exhibited substantially enhanced activity for the one-pot synthesis of meropenem and provided a higher meropenem yield compared to other Pd/C catalysts. The nitrogen and sulfur groups provided efficient anchoring sites to facilitate Pd NPs dispersion onto the AC surface, which facilitated electron transformation between the supports and the Pd nanoparticles, and contributed to forming relatively electron-deficient, ultra-low size Pd NPs, thereby improving the anti-poisoning performance of Pd/C catalysts, leading to high hydrogenation activity.

It aims to study the efficacy and safety of low-concentration Atropine combined with orthokeratology (OK) lens in delaying juvenile myopia. This is a prospective study, 172 adolescents aged 8 to 12 years who were admitted to the diopter department of Hengshui People Hospital from April 2021 to May 2022 were selected. According to the equivalent spherical diopter measured at the time of initial diagnosis, myopic patients were randomly divided into low myopia group (group A) and moderate myopia group (group B). At the same time, according to the different treatment methods, the patients were divided into the group wearing frame glasses alone (group c), the group wearing frame glasses with low-concentration Atropine (group d), the group wearing corneal shaping glasses alone at night (group e), and the group wearing corneal shaping glasses at night with low-concentration Atropine (group f). The control effect of myopia development and axial elongation in group f was better than that in groups d and e (P < .05). The effect of controlling myopia development and axial elongation in group f is with P > .05. The probability of postoperative adverse reactions in group f was lower and lower than that in the other groups. Low-concentration atropine combined with OK lens could effectively delay the development of juvenile myopia, and had a high safety. Low-concentration of Atropine would not have a significant impact on the basic tear secretion and tear film stability. Nightwear of OK lens also had no significant impact, but it would significantly reduce the tear film rupture time in the first 3 months, and at the same time, the tear film rupture time would be the same after 6 months as before treatment.

Low cost and high efficiency catalysts for the reductive amination of carbonyl compounds are critical to the utilization of renewable biomass. In this paper, a highly efficient N-doped porous carbon supported Ni catalyst was prepared by a template-assisted pyrolysis and impregnation method. In the presence of NH3and H2, the as-prepared catalyst could effectively catalyze the reductive amination of various carbonyl compounds into corresponding primary amines with high yield and excellent stability. Comprehensive characterization demonstrated that the reductive amination of furfural towards furfuryl amine was linked to the formation of Ni–Nxsites and the electronic interaction of N and Ni species on the N-doped carbon surface, which promoted the reductive amination of CO bonds and significantly reduced the activation energy in reductive ammonolysis of trimers and Schiff base intermediates. This work provided a new insight into rational design of reductive amination catalysts for primary amine production.

Chemotherapy-induced premature ovarian failure (POF) in women is currently clinically irreversible. Bone marrow mesenchymal stem cells (BMSCs) are a promising cellular therapeutic strategy for POF. However, the underlying mechanism governing the efficacy of BMSCs in treating POF has not been determined. In this study, we show that BMSC and BMSC-derived exosome transplantation can significantly recover the estrus cycle, increase the number of basal and sinus follicles in POF rats, increase estradiol (E2) and anti-Mullerian hormone (AMH) levels, and reduce follicle stimulating hormone (FSH) and luteinizing hormone (LH) levels in the serum. Furthermore, we demonstrate that BMSC-derived exosomes prevent ovarian follicular atresia in cyclophosphamide (CTX)-treated rats via the delivery of miR-144-5p, which can be transferred to cocultured CTX-damaged granulosa cells (GCs) to decrease GC apoptosis. A functional assay revealed that overexpression of miR-144-5p in BMSCs showed efficacy against CTX-induced POF, and the improvement in the repair was related to the inhibition of GC apoptosis by targeting PTEN. The opposite effect was exhibited when miR-144-5p was inhibited. Taken together, our experimental results provide new information regarding the potential of using exosomal miR-144-5p to treat ovarian failure.

Chemotherapy-induced premature ovarian failure (POF) in women is currently clinically irreversible. Bone marrow mesenchymal stem cells (BMSCs) are a promising cellular therapeutic strategy for POF. However, the underlying mechanism governing the efficacy of BMSCs in treating POF has not been determined. In this study, we show that BMSC and BMSC-derived exosome transplantation can significantly recover the estrus cycle, increase the number of basal and sinus follicles in POF rats, increase estradiol (E2) and anti-Mullerian hormone (AMH) levels, and reduce follicle stimulating hormone (FSH) and luteinizing hormone (LH) levels in the serum. Furthermore, we demonstrate that BMSC-derived exosomes prevent ovarian follicular atresia in cyclophosphamide (CTX)-treated rats via the delivery of miR-144-5p, which can be transferred to cocultured CTX-damaged granulosa cells (GCs) to decrease GC apoptosis. A functional assay revealed that overexpression of miR-144-5p in BMSCs showed efficacy against CTX-induced POF, and the improvement in the repair was related to the inhibition of GC apoptosis by targeting PTEN. The opposite effect was exhibited when miR-144-5p was inhibited. Taken together, our experimental results provide new information regarding the potential of using exosomal miR-144-5p to treat ovarian failure.

Preterm infants with low birthweight are at heightened risk of developmental sequelae, including neurological and cognitive dysfunction that can persist into adolescence or adulthood. In addition, preterm birth and low birthweight can provoke changes in endocrine and metabolic processes that likely impact brain health throughout development. However, few studies have examined associations among birthweight, pubertal endocrine process, long-term neurological and cognitive development.

Background: The dikaryotic stage dominates most of the life cycle in basidiomycetes, and each cell carries two different haploid nuclei. Accurate phasing of these two nuclear genomes and their interactions have long been of interest. Results: We combine PacBio HiFi reads, Nanopore ultra-long reads, and Hi-C data to generate a complete, high-quality asymmetric dikaryotic genome of Tremella fuciformisTr01, including Haplotypes A and B genomes. We assemble a meiotic haploid DBZ04 genome and detect three recombination events in these two haplotypes. We identify several chromosomal rearrangements that lead to differences in chromosome number, length, content, and sequence arrangement between these two haplotypes. Each nucleus contains a two-speed genome, harboring three accessory chromosomes and two accessory compartments that affect horizontal chromatin transfer between nuclei. We find few basidiospores are ejected from fruiting bodies of Tr01. Most monospore isolates sequenced belong to Tr01-Haplotype A genome architecture. More than one-third of monospore isolates carry one or two extra chromosomes including Chr12B and two new chromosomes ChrN1 and ChrN2. We hypothesize that homologous regions of seven sister chromatids pair into a large complex during meiosis, followed by inter-chromosomal recombination at physical contact sites and formation of new chromosomes. Conclusion: We assemble two haplotype genomes of T. fuciformisTr01 and provide the first overview of basidiomycetous genomes with discrete genomic architecture. Meiotic activities of asymmetric dikaryotic genomes result in formation of new chromosomes, aneuploidy of some daughter cells, and inviability of most other daughter cells. We propose a new approach for breeding of sporeless mushroom.

Informatics paradigms for brain and mental health research have seen significant advances in recent years. These developments can largely be attributed to the emergence of new technologies such as machine learning, deep learning, and artificial intelligence. Data-driven methods have the potential to support mental health care by providing more precise and personalised approaches to detection, diagnosis, and treatment of depression. In particular, precision psychiatry is an emerging field that utilises advanced computational techniques to achieve a more individualised approach to mental health care. This survey provides an overview of the ways in which artificial intelligence is currently being used to support precision psychiatry. Advanced algorithms are being used to support all phases of the treatment cycle. These systems have the potential to identify individuals suffering from mental health conditions, allowing them to receive the care they need and tailor treatments to individual patients who are mostly to benefit. Additionally, unsupervised learning techniques are breaking down existing discrete diagnostic categories and highlighting the vast disease heterogeneity observed within depression diagnoses. Artificial intelligence also provides the opportunity to shift towards evidence-based treatment prescription, moving away from existing methods based on group averages. However, our analysis suggests there are several limitations currently inhibiting the progress of data-driven paradigms in care. Significantly, none of the surveyed articles demonstrate empirically improved patient outcomes over existing methods. Furthermore, greater consideration needs to be given to uncertainty quantification, model validation, constructing interdisciplinary teams of researchers, improved access to diverse data and standardised definitions within the field. Empirical validation of computer algorithms via randomised control trials which demonstrate measurable improvement to patient outcomes are the next step in progressing models to clinical implementation.

A large number of microRNAs (miRNAs) have been previously demonstrated to be dysregulated in breast cancer (BC), and alterations in miRNA expression may affect the initiation and progression of BC. This study showed that miR-664 expression was obviously reduced in BC tissues and cell lines. Resumption of the expression of miR-664 attenuated the proliferation and invasion of BC cells. The molecular mechanisms underlying the inhibitory effects of BC cell proliferation and invasion by miR-664 were also studied. Insulin receptor substrate 1 (IRS1) was identified as a novel and direct target of miR-664. In addition, siRNA-mediated silencing of IRS1 expression mimicked the suppressive effects of miR-664 overexpression in BC cells. Rescue experiments demonstrated that recovered IRS1 expression partially antagonized the inhibition of proliferation and invasion of BC cells caused by miR-664 overexpression. Thus, miR-664 may serve as a tumor suppressor in BC by directly targeting IRS1. Moreover, miR-664 downregulation in BC may contribute to the occurrence and development of BC, suggesting that miR-664 may be a novel therapeutic target for patients with BC.

Cesium carbonate-catalyzed oxidation of substituted phenylsilanes (ArSiH3) in N,N-dimethylformamide (DMF) at room temperature for the efficient synthesis of polyhedral oligomeric silsesquioxanes (POSS) was described. This protocol allowed the rapid and selective access to several types of new POSS cages in modest to good yields under nonaqueous conditions. Depending on the bulkiness of the substituents on the phenyl rings, hexa- (T6), octa- (T8), and dodecaphenylsilsesquioxanes (T12) can be selectively obtained. With the more bulky 2-(2′,4′,6′-trimethylphenyl)phenyl group, the cyclic tetrasiloxane (D4) bearing four hydroxyl groups was isolated. Mechanism studies disclosed that the initial step involved the Cs2CO3-catalyzed hydrosilylation of DMF with a hydrosilane to generate a siloxymethylamine intermediate followed by the dehydrocarbonative cross-coupling of the hydrosilane with the siloxymethylamine.

Abstract

Breast cancer causes great threats to public health worldwide. The aim of this study was to investigate the correlation between Ki-67 expression and the hemodynamics of contrast-enhanced ultrasound (CEUS) in patients with infiltrative ductal carcinoma (IDC). CEUS was performed on 109 masses in 85 IDC cases before resection. Based on the immunohistochemical staining on the antigen Ki-67, the masses were divided into negative group, weakly positive group, positive group, and strong-positive group. Significant statistical differences were noticed in time to peak, arrive intensity, and peak intensity in the positive groups compared with the negative group. Compared with the positive groups, the negative group showed significant statistical differences in arrive intensity and peak intensity. The antigen Ki-67 was positively correlated with arrived intensity, intensity changes, and rising curve's slope. In contrast, it was negatively correlated with arrived time, time to peak, and continuous time. The hemodynamic parameters of CEUS were correlated with the expression of antigen Ki-67. On this basis, Ki-67 is an effective supplement to the diagnosis of IDC.

Topic modelling methods such as Latent Dirichlet Allocation (LDA) have been successfully applied to various fields, since these methods can effectively characterize document collections by using a mixture of semantically rich topics. So far, many models have been proposed. However, the existing models typically outperform on full analysis on the whole collection to find all topics but difficult to capture coherent and specifically meaningful topic representations. Furthermore, it is very challenging to incorporate user preferences into existing topic modelling methods to extract relevant topics. To address these problems, we develop a novel personalized Association-based Topic Selection (ATS) model, which can identify semantically valid and relevant topics from a set of raw topics based on the semantical relatedness between users’ preferences and the structured patterns captured in topics. The advantage of the proposed ATS model is that it enables an interactive topic modelling process driven by users’ specific interests. Based on three benchmark datasets, namely, RCV1, R8, and WT10G under the context of information filtering (IF) and information retrieval (IR), our rigorous experiments show that the proposed ATS model can effectively identify relevant topics with respect to users’ specific interests, and hence to improve the performance of IF and IR.

With the deepening of knowledge base research and application, question answering over knowledge base, also called KBQA, has recently received more and more attention from researchers. Most previous KBQA models focus on mapping the input query and the fact in KBs into an embedding format. Then the similarity between the query vector and the fact vector is computed eventually. Based on the similarity, each query can obtain an answer representing a tuple (subject, predicate, object) from the KBs. However, the information about each word in the input question will lose inevitably during the process. To retain as much original information as possible, we introduce an attention-based recurrent neural network model with interactive similarity matrixes. It can extract more comprehensive information from the hierarchical structure of words among queries and tuples stored in the knowledge base. This work makes three main contributions: (1) A neural network-based question-answering model for the knowledge base is proposed to handle single relation questions. (2) A attentive module is designed to obtain information from multiple aspects to represent queries and data, which contributes to avoiding losing potentially valuable information. (3) Similarity matrixes are introduced to obtain the interaction information between queries and data from the knowledge base. Experimental results show that our proposed model performs better on simple questions than state-of-the-art in several effectiveness measures.

•A framework of extreme value theory and autoregressive integrated moving average method is developed.•A Bayesian generalised extreme value model estimates the crash risk at signal cycle levels.•An autoregressive integrated moving average model is applied to time series of crash risk.•The framework can reasonably estimate future crash risk at signalised intersections.

•Hybrid models of machine learning and extreme value theory to estimate crash frequency from traffic conflicts.•Unsupervised anomaly detection techniques identify traffic conflict extremes for extreme value models.•The isolation Forest-based hybrid model showed precise crash estimates and narrower confidence intervals.•Generalised Pareto models performed better than Generalised extreme value models.•Bivariate hybrid models showed better crash estimation performance than univariate hybrid models.

This paper provides a review of paleoclimate modeling activities in China. Rather than attempt to cover all topics, we have chosen a few climatic intervals and events judged to be particularly informative to the international community. In historical climate simulations, changes in solar radiation and volcanic activity explain most parts of reconstructions over the last millennium prior to the industrial era, while atmospheric greenhouse gas concentrations play the most important role in the 20th century warming over China. There is a considerable model-data mismatch in the annual and boreal winter temperature change over China during the mid-Holocene [6000 years before present (ka BP)], while coupled models with an interactive ocean generally perform better than atmospheric models. For the Last Glacial Maximum (21 ka BP), climate models successfully reproduce the surface cooling trend over China but fail to reproduce its magnitude, with a better performance for coupled models. At that time, reconstructed vegetation and western Pacific sea surface temperatures could have significantly affected the East Asian climate, and environmental conditions on the Qinghai-Tibetan Plateau were most likely very different to the present day. During the late Marine Isotope Stage 3 (30–40 ka BP), orbital forcing and Northern Hemisphere glaciation, as well as vegetation change in China, were likely responsible for East Asian climate change. On the tectonic scale, the Qinghai-Tibetan Plateau uplift, the Tethys Sea retreat, and the South China Sea expansion played important roles in the formation of the East Asian monsoon-dominant environment pattern during the late Cenozoic.

To investigate the effects of the Sini San at different doses on each sleeping state [slow-wave sleep 1 (SWS1), slow-wave sleep 2 (SWS2), rapid-eye-movement (REM), wakefulness (W)] in insomnia rats and to identify its mode of acaction for improving sleep.

To investigate the sedative and hypnotic activity of paeoniflorin and freeze-dried Sini San powder on mice and provide a reliable method for determining the pharmacodynamic material basis of Sini San.

In the era of Web 2.0, huge volumes of consumer reviews are posted to the Internet every day. Manual approaches to detecting and analyzing fake reviews (i.e., spam) are not practical due to the problem of information overload. However, the design and development of automated methods of detecting fake reviews is a challenging research problem. The main reason is that fake reviews are specifically composed to mislead readers, so they may appear the same as legitimate reviews (i.e., ham). As a result, discriminatory features that would enable individual reviews to be classified as spam or ham may not be available. Guided by the design science research methodology, the main contribution of this study is the design and instantiation of novel computational models for detecting fake reviews. In particular, a novel text mining model is developed and integrated into a semantic language model for the detection of untruthful reviews. The models are then evaluated based on a real-world dataset collected from amazon.com. The results of our experiments confirm that the proposed models outperform other well-known baseline models in detecting fake reviews. To the best of our knowledge, the work discussed in this article represents the first successful attempt to apply text mining methods and semantic language models to the detection of fake consumer reviews. A managerial implication of our research is that firms can apply our design artifacts to monitor online consumer reviews to develop effective marketing or product design strategies based on genuine consumer feedback posted to the Internet.

The notion of semantic information granulation is explored to estimate the information specificity or generality of documents. Basically, a document is considered more specific than another document if it contains more cohesive domain-specific terminologies than that of the other one. We believe that the dimension of semantic granularity is an important supplement to the existing similarity-based and popularity-based measures for building effective document ranking functions. The main contributions of this paper is the illustration of the design and development of a fuzzy ontology based granular information retrieval (IR) system to improve the effectiveness of IR decision making for various domains. Based on the notion of semantic information granulation, a novel computational model is developed to estimate the semantic granularity of documents; these documents can then be ranked according to the information seekers' specific semantic granularity requirements. One main component of the proposed computational model is the fuzzy ontology mining mechanism which can automatically build domain-specific ontology for the estimation of semantic granularity of documents. Our TREC-based experiment reveals that the proposed fuzzy ontology based granular IR system outperforms a classical vector space based IR system in domain specific IR. Our research work opens the door to the applications of granular computing and fuzzy ontology mining methods to enhance domain specific IR decision making.

The notion of semantic information granulation is explored to estimate the information specificity or generality of documents. Basically, a document is considered more specific than another document if it contains more cohesive domain-specific terminologies than that of the other one. We believe that the dimension of semantic granularity is an important supplement to the existing similarity-based and popularity-based measures for building effective document ranking functions. The main contributions of this paper is the illustration of the design and development of a fuzzy ontology based granular information retrieval (IR) system to improve the effectiveness of IR decision making for various domains. Based on the notion of semantic information granulation, a novel computational model is developed to estimate the semantic granularity of documents; these documents can then be ranked according to the information seekers’ specific semantic granularity requirements. One main component of the proposed computational model is the fuzzy ontology mining mechanism which can automatically build domain-specific ontology for the estimation of semantic granularity of documents. Our TREC-based experiment reveals that the proposed fuzzy ontology based granular IR system outperforms a classical vector space based IR system in domain specific IR. Our research work opens the door to the applications of granular computing and fuzzy ontology mining methods to enhance domain specific IR decision making.

Satellite cells comprise a pool of quiescent stem cells that repair muscle damage, but the mechanisms enforcing their quiescence are poorly defined. In this issue of Cell Stem Cell, Boonsanay et al. (2016) show that the histone methyltransferase Suv4-20H1 maintains satellite cell quiescence by promoting a heterochromatic state through transcriptional repression of the myogenic master regulator MyoD.

Porous carbon materials are widely used in adsorption, supercapacitors, catalytic reactions, electrochemistry, and so on. However, the need to develop porous carbon materials with easy template removal and controllable pore size is urgent. This study provides an efficient strategy for the preparation of boron-doped phenolic porous carbon through in situ pyrolysis to form a template. Phenylboronic acid is used to cross-link novolac resin. The optimization of the pyrolysis conditions reveals that boron oxide (B2O3) is formed at 400 °C. The porous carbon material with a high specific surface area is obtained utilizing the hydrolysis characteristics of B2O3. After loading titanate, 5-hydroxymethylfurfural with a high yield (92%) is achieved at 20 °C in ethanol. The catalytic activity showed no significant decrease after utilization six times. The method is simple and easy to implement, providing an efficient catalyst for the conversion of biomass materials into biofuels.

To promise the quality of multidimensional association mining in real applications is a challenging research issue. The challenging issue is how to represent multidimensional association rules efficiently because of the complicated correlation between attributes. Multi-tier granule mining is one initiative in solving this challenge. This paper presents a granule mining oriented data warehousing model. It can divide attributes into tiers and discover granules for each tier from large multidimensional databases. In addition, it uses association mappings to generate association rules for describing the correlation between tiers. Experiments for the proposed model and the testing results are prosecuted.

Muscle stem cells (MuSCs) are a rare stem cell population that provides myofibers with a remarkable capacity to regenerate after tissue injury. Here, we have adapted the Cleavage Under Target and Tagmentation technology to the mapping of the chromatin landscape and transcription factor binding in 50,000 activated MuSCs isolated from injured mouse hindlimb muscles. We have applied this same approach to human CD34+hematopoietic stem and progenitor cells. This protocol could be adapted to any rare stem cell population.