Multifractal and Network Analysis of Phase Transition

Many models and real complex systems possess critical thresholds at which the systems shift from one sate to another. The discovery of the early warnings of the systems in the vicinity of critical point are of great importance to estimate how far a system is from a critical threshold. Multifractal Detrended Fluctuation analysis (MF-DFA) and visibility graph method have been employed to investigate the fluctuation and geometrical structures of magnetization time series of two-dimensional Ising model around critical point. The Hurst exponent has been confirmed to be a good indicator of phase transition. Increase of the multifractality of the time series have been observed from generalized Hurst exponents and singularity spectrum. Both Long-term correlation and broad probability density function are identified to be the sources of multifractality of time series near critical regime. Heterogeneous nature of the networks constructed from magnetization time series have validated the fractal properties of magnetization time series from complex network perspective. Evolution of the topology quantities such as clustering coefficient, average degree, average shortest path length, density, assortativity and heterogeneity serve as early warnings of phase transition. Those methods and results can provide new insights about analysis of phase transition problems and can be used as early warnings for various complex systems.Comment: 23 pages, 11 figure

Synergy of Database Techniques and Machine Learning Models for String Similarity Search and Join

Peer reviewe

Study of phase transition of Potts model with DANN

A transfer learning method, domain adversarial neural network (DANN), is introduced to study the phase transition of two-dimensional q-state Potts model. With the DANN, we only need to choose a few labeled configurations automatically as input data, then the critical points can be obtained after training the algorithm. By an additional iterative process, the critical points can be captured to comparable accuracy to Monte Carlo simulations as we demonstrate it for q = 3, 5, 7 and 10. The type of phase transition (first or second-order) is also determined at the same time. Meanwhile, for the second-order phase transition at q = 3, we can calculate the critical exponent $\nu$ by data collapse. Furthermore, compared with the traditional supervised learning, the DANN is of higher accuracy with lower cost.Comment: 25 pages, 23 figure

Boosting the Quality of Approximate String Matching by Synonyms

A string similarity measure quantifies the similarity between two text strings for approximate string matching or comparison. For example, the strings ``\textsf{Sam}'' and ``\textsf{Samuel}'' can be considered to be similar. Most existing work that computes the similarity of two strings only considers syntactic similarities, e.g., number of common words or \qgrams. While this is indeed an indicator of similarity, there are many important cases where syntactically different strings can represent the same real-world object. For example, ``\textsf{Bill}'' is a short form of ``\textsf{William}''; and ``\textsf{Database Management Systems}'' can be abbreviated as ``\textsf{DBMS}''. Given a collection of predefined synonyms, the purpose of this article is to explore such existing knowledge to effectively evaluate the similarity between two strings and efficiently perform similarity searches and joins, thereby boosting the quality of approximate string matching. In particular, we first present an expansion-based framework to measure string similarities efficiently while considering synonyms. We then study efficient algorithms for similarity searches and joins by proposing two novel indexes, called SI-tree and QP-tree, which combine signature filtering and length filtering strategies. In order to improve the efficiency of our algorithms, we develop an estimator to estimate the size of candidates to enable an online selection of signature filters. This estimator provides strong low-error, high-confidence guarantees while requiring only logarithmic space and time costs, thus making our method attractive both in theory and in practice. Finally, the experimental results from a comprehensive study of the algorithms with three real datasets verify the effectiveness and efficiency of our approaches.Peer reviewe

Ginsenoside Rh1 Improves the Effect of Dexamethasone on Autoantibodies Production and Lymphoproliferation in MRL/lpr Mice

Ginsenoside Rh1 is able to upregulate glucocorticoid receptor (GR) level, suggesting Rh1 may improve glucocorticoid efficacy in hormone-dependent diseases. Therefore, we investigated whether Rh1 could enhance the effect of dexamethasone (Dex) in the treatment of MRL/lpr mice. MRL/lpr mice were treated with vehicle, Dex, Rh1, or Dex + Rh1 for 4 weeks. Dex significantly reduced the proteinuria and anti-dsDNA and anti-ANA autoantibodies. The levels of proteinuria and anti-dsDNA and anti-ANA autoantibodies were further decreased in Dex + Rh1 group. Dex, Rh1, or Dex + Rh1 did not alter the proportion of CD4+ splenic lymphocytes, whereas the proportion of CD8+ splenic lymphocytes was significantly increased in Dex and Dex + Rh1 groups. Dex + Rh1 significantly decreased the ratio of CD4+/CD8+ splenic lymphocytes compared with control. Con A-induced CD4+ splenic lymphocytes proliferation was increased in Dex-treated mice and was inhibited in Dex + Rh1-treated mice. Th1 cytokine IFN-γ mRNA was suppressed and Th2 cytokine IL-4 mRNA was increased by Dex. The effect of Dex on IFN-γ and IL-4 mRNA was enhanced by Rh1. In conclusion, our data suggest that Rh1 may enhance the effect of Dex in the treatment of MRL/lpr mice through regulating CD4+ T cells activation and Th1/Th2 balance

RNA sequencing identifies common pathways between cigarette smoke exposure and replicative senescence in human airway epithelia

Abstract Background Aging is affected by genetic and environmental factors, and cigarette smoking is strongly associated with accumulation of senescent cells. In this study, we wanted to identify genes that may potentially be beneficial for cell survival in response to cigarette smoke and thereby may contribute to development of cellular senescence. Results Primary human bronchial epithelial cells from five healthy donors were cultured, treated with or without 1.5% cigarette smoke extract (CSE) for 24 h or were passaged into replicative senescence. Transcriptome changes were monitored using RNA-seq in CSE and non-CSE exposed cells and those passaged into replicative senescence. We found that, among 1534 genes differentially regulated during senescence and 599 after CSE exposure, 243 were altered in both conditions, representing strong enrichment. Pathways and gene sets overrepresented in both conditions belonged to cellular processes that regulate reactive oxygen species, proteasome degradation, and NF-κB signaling. Conclusions Our results offer insights into gene expression responses during cellular aging and cigarette smoke exposure, and identify potential molecular pathways that are altered by cigarette smoke and may also promote airway epithelial cell senescence

Cortex phellodendri

Cortex phellodendri is used to reduce fever and remove dampness and toxin. Berberine is an active ingredient of C. phellodendri. Berberine from Argemone ochroleuca can relax airway smooth muscle (ASM); however, whether the nonberberine component of C. phellodendri has similar relaxant action was unclear. An n-butyl alcohol extract of C. phellodendri (NBAECP, nonberberine component) was prepared, which completely inhibits high K+- and acetylcholine- (ACH-) induced precontraction of airway smooth muscle in tracheal rings and lung slices from control and asthmatic mice, respectively. The contraction induced by high K+ was also blocked by nifedipine, a selective blocker of L-type Ca2+ channels. The ACH-induced contraction was partially inhibited by nifedipine and pyrazole 3, an inhibitor of TRPC3 and STIM/Orai channels. Taken together, our data demonstrate that NBAECP can relax ASM by inhibiting L-type Ca2+ channels and TRPC3 and/or STIM/Orai channels, suggesting that NBAECP could be developed to a new drug for relieving bronchospasm

Aggregation-Induced Emission (AIE), Life and Health

Light has profoundly impacted modern medicine and healthcare, with numerous luminescent agents and imaging techniques currently being used to assess health and treat diseases. As an emerging concept in luminescence, aggregation-induced emission (AIE) has shown great potential in biological applications due to its advantages in terms of brightness, biocompatibility, photostability, and positive correlation with concentration. This review provides a comprehensive summary of AIE luminogens applied in imaging of biological structure and dynamic physiological processes, disease diagnosis and treatment, and detection and monitoring of specific analytes, followed by representative works. Discussions on critical issues and perspectives on future directions are also included. This review aims to stimulate the interest of researchers from different fields, including chemistry, biology, materials science, medicine, etc., thus promoting the development of AIE in the fields of life and health

The Role of Deubiquitinating Enzymes in Acute Lung Injury and Acute Respiratory Distress Syndrome

Acute lung injury and acute respiratory distress syndrome (ALI/ARDS) are characterized by an inflammatory response, alveolar edema, and hypoxemia. ARDS occurs most often in the settings of pneumonia, sepsis, aspiration of gastric contents, or severe trauma. The prevalence of ARDS is approximately 10% in patients of intensive care. There is no effective remedy with mortality high at 30–40%. Most functional proteins are dynamic and stringently governed by ubiquitin proteasomal degradation. Protein ubiquitination is reversible, the covalently attached monoubiquitin or polyubiquitin moieties within the targeted protein can be removed by a group of enzymes called deubiquitinating enzymes (DUBs). Deubiquitination plays an important role in the pathobiology of ALI/ARDS as it regulates proteins critical in engagement of the alveolo-capillary barrier and in the inflammatory response. In this review, we provide an overview of how DUBs emerge in pathogen-induced pulmonary inflammation and related aspects in ALI/ARDS. Better understanding of deubiquitination-relatedsignaling may lead to novel therapeutic approaches by targeting specific elements of the deubiquitination pathways

Grassland Phenology’s Sensitivity to Extreme Climate Indices in the Sichuan Province, Western China

Depending on the vegetation type, extreme climate and drought events have a greater impact on the end of the season (EOS) and start of the season (SOS). This study investigated the spatial and temporal distribution characteristics of grassland phenology and its responses to seasonal and extreme climate changes in Sichuan Province from 2001 to 2020. Based on the data from 38 meteorological stations in Sichuan Province, this study calculated the 15 extreme climate indices recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI). The results showed that SOS was concentrated in mid-March to mid-May (80–140 d), and 61.83% of the area showed a significant advancing trend, with a rate of 0–1.5 d/a. The EOS was concentrated between 270–330 d, from late September to late November, and 71.32% showed a delayed trend. SOS was strongly influenced by the diurnal temperature range (DTR), yearly maximum consecutive five-day precipitation (RX5), and the temperature vegetation dryness index (TVDI), while EOS was most influenced by the yearly minimum daily temperature (TNN), yearly mean temperature (TEMP_MEAN), and TVDI. The RX5 day index showed an overall positive sensitivity coefficient for SOS. TNN index showed a positive sensitivity coefficient for EOS. TVDI showed positive and negative sensitivities for SOS and EOS, respectively. This suggests that extreme climate change, if it causes an increase in vegetation SOS, may also cause an increase in vegetation EOS. This research can provide a scientific basis for developing regional vegetation restoration and disaster prediction strategies in Sichuan Province