Human-multimodal deep learning collaboration in 'precise' diagnosis of lupus erythematosus subtypes and similar skin diseases.

BACKGROUND: Lupus erythematosus (LE) is a spectrum of autoimmune diseases. Due to the complexity of cutaneous LE (CLE), clinical skin image-based artificial intelligence is still experiencing difficulties in distinguishing subtypes of LE. OBJECTIVES: We aim to develop a multimodal deep learning system (MMDLS) for human-AI collaboration in diagnosis of LE subtypes. METHODS: This is a multi-centre study based on 25 institutions across China to assist in diagnosis of LE subtypes, other eight similar skin diseases and healthy subjects. In total, 446 cases with 800 clinical skin images, 3786 multicolor-immunohistochemistry (multi-IHC) images and clinical data were collected, and EfficientNet-B3 and ResNet-18 were utilized in this study. RESULTS: In the multi-classification task, the overall performance of MMDLS on 13 skin conditions is much higher than single or dual modals (Sen = 0.8288, Spe = 0.9852, Pre = 0.8518, AUC = 0.9844). Further, the MMDLS-based diagnostic-support help improves the accuracy of dermatologists from 66.88% ± 6.94% to 81.25% ± 4.23% (p = 0.0004). CONCLUSIONS: These results highlight the benefit of human-MMDLS collaborated framework in telemedicine by assisting dermatologists and rheumatologists in the differential diagnosis of LE subtypes and similar skin diseases.

Research on the morphological structure of partial fracture healing process in diabetic mice based on synchrotron radiation phase-contrast imaging computed tomography and deep learning.

The prevalence of diabetes mellitus has exhibited a notable surge in recent years, thereby augmenting the susceptibility to fractures and impeding the process of fracture healing. The primary objective of this investigation is to employ synchrotron radiation phase-contrast imaging computed tomography (SR-PCI-CT) to examine the morphological and structural attributes of different types of callus in a murine model of diabetic partial fractures. Additionally, a deep learning image segmentation model was utilized to facilitate both qualitative and quantitative analysis of callus during various time intervals. A total of forty male Kunming mice, aged five weeks, were randomly allocated into two groups, each consisting of twenty mice, namely, simple fracture group (SF) and diabetic fracture group (DF). Mice in DF group were intraperitoneally injected 60 mg/kg 1 % streptozotocin(STZ) solution for 5 consecutive days, and the standard for modeling was that the fasting blood glucose level was ≥11.1 mmol /l one week after the last injection of STZ. The right tibias of all mice were observed to have oblique fractures that did not traverse the entire bone. At three, seven, ten and fourteen days after the fracture occurred, the fractured tibias were extracted for SR-PCI-CT imaging and histological analysis. Furthermore, a deep learning image segmentation model was devised to automatically detect, categorize and quantitatively examine different types of callus. Image J software was utilized to measure the grayscale values of different types of callus and perform quantitative analysis. The findings demonstrated that:1)SR-PCI-CT imaging effectively depicted the morphological attributes of different types of callus of fracture healing. The grayscale values of different types of callus were significantly different(P < 0.01).2)In comparison to the SF group, the DF group exhibited a significant reduction in the total amount of callus during the same period (P < 0.01). Additionally, the peak of cartilage callus in the hypertrophic phase was delayed.3)Histology provides the basis for training algorithms for deep learning image segmentation models. The deep-learning image segmentation models achieved accuracies of 0.69, 0.81 and 0.733 for reserve/proliferative cartilage, hypertrophic cartilage and mineralized cartilage, respectively, in the test set. The corresponding Dice values were 0.72, 0.83 and 0.76, respectively. In summary, SR-PCI-CT images are close to the histological level, and a variety of cartilage can be identified on synchrotron radiation CT images compared with histological examination, while artificial intelligence image segmentation model can realize automatic analysis and data generation through deep learning, and further determine the objectivity and accuracy of SR-PCI-CT in identifying various cartilage tissues. Therefore, this imaging technique combined with deep learning image segmentation model can effectively evaluate the effect of diabetes on the morphological and structural changes of callus during fracture healing in mice.

Minimum taxi fleet algorithm considering human spatiotemporal behaviors.

With the development of information technology, more and more travel data have provided great convenience for scholars to study the travel behavior of users. Planning user travel has increasingly attracted researchers' attention due to its great theoretical significance and practical value. In this study, we not only consider the minimum fleet size required to meet the urban travel needs but also consider the travel time and distance of the fleet. Based on the above reasons, we propose a travel scheduling solution that comprehensively considers time and space costs, namely, the Spatial-Temporal Hopcroft-Karp (STHK) algorithm. The analysis results show that the STHK algorithm not only significantly reduces the off-load time and off-load distance of the fleet travel by as much as 81% and 58% and retains the heterogeneous characteristics of human travel behavior. Our study indicates that the new planning algorithm provides the size of the fleet to meet the needs of urban travel and reduces the extra travel time and distance, thereby reducing energy consumption and reducing carbon dioxide emissions. Concurrently, the travel planning results also conform to the basic characteristics of human travel and have important theoretical significance and practical application value.

The shock, the coping, the resilience: smartphone application use reveals Covid-19 lockdown effects on human behaviors.

Human mobility restriction policies have been widely used to contain the coronavirus disease-19 (COVID-19). However, a critical question is how these policies affect individuals' behavioral and psychological well-being during and after confinement periods. Here, we analyze China's five most stringent city-level lockdowns in 2021, treating them as natural experiments that allow for examining behavioral changes in millions of people through smartphone application use. We made three fundamental observations. First, the use of physical and economic activity-related apps experienced a steep decline, yet apps that provide daily necessities maintained normal usage. Second, apps that fulfilled lower-level human needs, such as working, socializing, information seeking, and entertainment, saw an immediate and substantial increase in screen time. Those that satisfied higher-level needs, such as education, only attracted delayed attention. Third, human behaviors demonstrated resilience as most routines resumed after the lockdowns were lifted. Nonetheless, long-term lifestyle changes were observed, as significant numbers of people chose to continue working and learning online, becoming "digital residents." This study also demonstrates the capability of smartphone screen time analytics in the study of human behaviors. Supplementary Information: The online version contains supplementary material available at 10.1140/epjds/s13688-023-00391-9.

Identifying Important Nodes in Trip Networks and Investigating Their Determinants.

Describing travel patterns and identifying significant locations is a crucial area of research in transportation geography and social dynamics. Our study aims to contribute to this field by analyzing taxi trip data from Chengdu and New York City. Specifically, we investigate the probability density distribution of trip distance in each city, which enables us to construct long- and short-distance trip networks. To identify critical nodes within these networks, we employ the PageRank algorithm and categorize them using centrality and participation indices. Furthermore, we explore the factors that contribute to their influence and observe a clear hierarchical multi-centre structure in Chengdu's trip networks, while no such phenomenon is evident in New York City's. Our study provides insight into the impact of trip distance on important nodes within trip networks in both cities and serves as a reference for distinguishing between long and short taxi trips. Our findings also reveal substantial differences in network structures between the two cities, highlighting the nuanced relationship between network structure and socio-economic factors. Ultimately, our research sheds light on the underlying mechanisms shaping transportation networks in urban areas and offers valuable insights into urban planning and policy making.

Interferon-α regulates abnormally increased expression of RSAD2 in Th17 and Tfh cells in systemic lupus erythematosus patients.

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease that often involves abnormal activation of regulatory IFN genes and regulation of B cells by CD4+ T cells. Radical S-adenosyl methionine domain containing 2 (RSAD2) is a viral suppressor protein regulated by type I IFN, and it has been proven to play an important regulatory role in SLE. However, the mechanism by which RSAD2 participates in the pathogenesis of SLE is unclear. In this study, we observed higher expression levels of RSAD2 in CD4+ T-cell subsets from the peripheral blood of SLE patients than in those from healthy controls by bioinformatics analysis and validation experiments. We analyzed the expression of RSAD2 in CD4+ T cells of patients with SLE and other autoimmune diseases. In addition, we found that the expression of RSAD2 in CD4+ T cells might be regulated by IFN-α, and RSAD2 significantly affected the differentiation of Th17 cells and T follicular helper (Tfh) cells. Our findings underlined that RSAD2 may promote B-cell activation by promoting the differentiation of Th17 and Tfh cells in SLE patients, a process that is regulated by IFN-α.

Environmental impacts of 222Rn, Hg and CO2 emissions from the fault zones in the western margin of the Ordos block, China.

Investigating the emissions of soil gas including radon, mercury and carbon dioxide (222Rn, Hg and CO2) from the solid earth to the atmosphere through active fault zones is of great significance for accession of atmospheric environment. In this study, the concentrations and fluxes of 222Rn, Hg and CO2 were measured at the main active fault zones at the western margin of the Ordos block, China. The concentrations of 222Rn, Hg and CO2 were in the range of 0-60.1 kBq m-3, 3-81 ng m-3 and 0.04-9.23%, respectively, while the fluxes of 222Rn, Hg and CO2 are in the range of 1.99-306.99 mBq m-2 s-1, 0-15.12 ng m-2 h-1 and 0-37.91 g m-2d-1, respectively. Most of the major fault zones at the study area are CO2 risk-free regions (CO2 concentration in soil gas < 5%). However, the extend of 222Rn pollution at the fault zones of F1, F4, F5 and F9 (the fault number) and that of Hg pollution at the fault zones of F2, F4, F5 and F7 were higher than the pollution level of 1. The annual emission of Hg and CO2 from the western margin of the Ordos block was estimated to be 2.03 kg and 0.70 Mt, respectively. Comprehensive analyses indicated that the higher emission rates of soil gases from the active fault zones were related to the seismic activities. The results suggest that the earthquake activity is a dominant factor enhancing the emission of 222Rn, Hg and CO2 from the solid earth through active fault zones and, furthermore, resulting great impact on the atmospheric environment.

Interplay between oceanic subduction and continental collision in building continental crust.

Generation of continental crust in collision zones reflect the interplay between oceanic subduction and continental collision. The Gangdese continental crust in southern Tibet developed during subduction of the Neo-Tethyan oceanic slab in the Mesozoic prior to reworking during the India-Asia collision in the Cenozoic. Here we show that continental arc magmatism started with fractional crystallization to form cumulates and associated medium-K calc-alkaline suites. This was followed by a period commencing at ~70 Ma dominated by remelting of pre-existing lower crust, producing more potassic compositions. The increased importance of remelting coincides with an acceleration in the convergence rate between India and Asia leading to higher basaltic flow into the Asian lithosphere, followed by convergence deceleration due to slab breakoff, enabling high heat flow and melting of the base of the arc. This two-stage process of accumulation and remelting leads to the chemical maturation of juvenile continental crust in collision zones, strengthening crustal stratification.

Abnormal lower expression of GPR183 in peripheral blood T and B cell subsets of systemic lupus erythematosus patients.

G protein-coupled receptor 183 (GPR183) has been indicated to mediate the migration and localisation of immune cells in T cell-dependent antibody responses. Systemic lupus erythematosus (SLE) is a canonical autoimmune disease involving B cell-mediated tolerance destruction and excessive pathogenic autoantibody production, in which multiple GPCRs play a role. To date, there has been no systematic study regarding the expression of GPR183 in lymphocyte subsets of SLE patients. In this research, firstly, we observed the expression trends of GRP183 in various T and B cell subsets in human tonsil tissues. These lymphocyte subsets include CD4+, CD8+, naïve T, effector T, Tfh, activated Tfh, Th1, Th2, Th17, Treg, CD19+CD27-, CD19+CD27+, naïve B, germinal centre B, memory B, and plasma cells. Further, compared with healthy controls (HCs), GPR183 expression levels in above peripheral blood lymphocyte subsets of patients with SLE were reduced overall. The differential expression of GPR183 expression between inactive and active SLE patients indicates that GPR183 expression may be concerned with the disease activity of SLE. This was further confirmed through the strong negative correlation with SLEDAI score and positive correlation with serum complement protein C3, C4 and C1q levels. Further receiver operating characteristic (ROC) curve analysis revealed that GPR183 expression in circulating CD27-IgD+ B cells may be beneficial in distinguishing between inactive and active SLE patients. In addition, type I interferon stimulation could down-regulate the expression of GPR183 in peripheral blood T and B cell subsets. Aberrant expression of GPR183 may provide some novel insights into disease activity prediction and underlying pathogenesis of SLE.

Network approach reveals the spatiotemporal influence of traffic on air pollution under COVID-19.

Air pollution causes widespread environmental and health problems and severely hinders the quality of life of urban residents. Traffic is critical for human life, but its emissions are a major source of pollution, aggravating urban air pollution. However, the complex interaction between traffic emissions and air pollution in cities and regions has not yet been revealed. In particular, the spread of COVID-19 has led various cities and regions to implement different traffic restriction policies according to the local epidemic situation, which provides the possibility to explore the relationship between urban traffic and air pollution. Here, we explore the influence of traffic on air pollution by reconstructing a multi-layer complex network base on the traffic index and air quality index. We uncover that air quality in the Beijing-Tianjin-Hebei (BTH), Chengdu-Chongqing Economic Circle (CCS), and Central China (CC) regions is significantly influenced by the surrounding traffic conditions after the outbreak. Under different stages of the fight against the epidemic, the influence of traffic in some regions on air pollution reaches the maximum in stage 2 (also called Initial Progress in Containing the Virus). For the BTH and CC regions, the impact of traffic on air quality becomes bigger in the first two stages and then decreases, while for CC, a significant impact occurs in phase 3 among the other regions. For other regions in the country, however, the changes are not evident. Our presented network-based framework provides a new perspective in the field of transportation and environment and may be helpful in guiding the government to formulate air pollution mitigation and traffic restriction policies.

Skin CD4+ Trm cells distinguish acute cutaneous lupus erythematosus from localized discoid lupus erythematosus/subacute cutaneous lupus erythematosus and other skin diseases.

BACKGROUND: Although the contribution of aberrant CD4+ T cell signaling to systemic lupus erythematosus (SLE) is well established, its role in cutaneous lupus erythematosus (CLE) skin is largely unknown. Because the rate of systemic manifestations varies in each subtype, resident memory CD4+ T cells in lesions that are responsible for only skin-associated tissue responses may vary in each subtype. However, the role of CD4+ tissue-resident memory T (CD4+ Trm) cells in each CLE subtype remains unclear. OBJECTIVES: To analyze and compare CD4+ Trm cells and absent in melanoma 2 (AIM2) identified by smart RNA sequencing (Smart-seq) in CD4+ Trm cells from patients with acute CLE (ACLE), subacute CLE (SCLE), and localized discoid lupus erythematosus (localized DLE) lesions. METHODS: We performed Smart-seq to investigate differences in dermal CD4+ Trm cells between patients with ACLE and normal controls (NCs). Multicolor immunohistochemistry was utilized to measure the levels of AIM2 in CD4+ Trm cells present in the skin of 134 clinical patients, which included patients with localized DLE (n = 19), ACLE (n = 19), SCLE (n = 16), psoriasis (n = 12), rosacea (n = 17), lichen planus (n = 18), and annular granuloma (n = 15), as well as NCs (n = 18). RESULTS: The Smart-seq data showed higher AIM2 expression in skin CD4+ Trm cells from ACLE lesions than NCs (fold change >10, adjusted P < 0.05). AIM2 expression in CD4+ Trm cells did not vary according to age or sex. AIM2 expression in CD4+ Trm cells was significantly lower in patients with ACLE (6.38 ± 5.22) than localized DLE (179.41 ± 160.98, P < 0.0001) and SCLE (63.43 ± 62.27, P < 0.05). In an overall comparison of ACLE with localized DLE and SCLE, the receiver operating characteristic curve for AIM2 expression in CD4+ Trm cells had a sensitivity of 100.00% and a specificity of 82.86% at a cutoff value of 18.26. In a comparison of ACLE with localized DLE, the sensitivity was 89.47%, and the specificity was 100.00% at a cutoff value of 12.26. In a comparison of ACLE with SCLE, the sensitivity was 100.00%, and the specificity was 75.00% at a cutoff value of 18.26. CONCLUSIONS: The number of CD4+ Trm cells is increased in lesions of SCLE and localized DLE compared to ACLE, suggesting that CD4+ Trm cells may have a more crucial role in persistent lesions of SCLE and localized DLE. In addition, AIM2 expression in CD4+ Trm cells discriminates patients with ACLE from those with localized DLE and SCLE.

Comprehensive analysis of epigenetic modifications and immune-cell infiltration in tissues from patients with systemic lupus erythematosus.

Aim: To explore potential abnormal epigenetic modifications and immune-cell infiltration in tissues from systemic lupus erythematosus (SLE) patients. Materials & methods: To utilize bioinformatics analysis and 'wet lab' methods to identify and verify differentially expressed genes in multiple targeted organs in SLE. Results: Seven key genes, IFI44, IFI44L, IFIT1, IFIT3, PLSCR1, RSAD2 and OAS2, which are regulated by epigenetics and may be involved in the pathogenesis of SLE, are identified by combined long noncoding RNA-miRNA-mRNA network analysis and DNA methylation analysis. The results of quantitative reverse transcription PCR, immunohistochemistry and DNA methylation analysis confirmed the potential of these genes as biomarkers. Conclusion: This study reveals the potential mechanisms in SLE from epigenetic modifications and immune-cell infiltration, providing diagnostic biomarkers and therapeutic targets for SLE.

AIM2 deficiency in B cells ameliorates systemic lupus erythematosus by regulating Blimp-1-Bcl-6 axis-mediated B-cell differentiation.

Absent in melanoma 2 (AIM2) has been reported to be a component of inflammasomes in innate immune cells. Surprisingly, AIM2 is expressed by B cells, and higher AIM2 expression is observed in the B cells from lupus patients. To date, the inflammasome-independent function of AIM2 in B cells remains unclear. Here, we report increased expression of AIM2 in human tonsil memory and germinal center (GC) B cells and in memory B cells and plasma cells from the circulation and skin lesions of lupus patients. Conditional knockout of AIM2 in B cells reduces the CD19+ B-cell frequency in lymph nodes and spleens, and dampens KLH-induced IgG1-antibody production. In a pristane-induced mouse model of lupus, AIM2 deficiency in B cells attenuates lupus symptoms and reduces the frequency of GC B cells, T follicular helper (Tfh) cells, plasmablast cells, and plasma cells. Furthermore, the loss of AIM2 in human B cells leads to the increased expression of Blimp-1 and reduces the expression of Bcl-6. However, the silencing of Blimp-1 and Bcl-6 has no significant effect on AIM2 expression, indicating that AIM2 might be the upstream regulator for Blimp-1 and Bcl-6. In addition, IL-10 is found to upregulate AIM2 expression via DNA demethylation. Together, our findings reveal that AIM2 is highly expressed in the B cells of lupus patients and promotes B-cell differentiation by modulating the Bcl-6-Blimp-1 axis, providing a novel target for SLE treatment.

Personality first in emotion: a deep neural network based on electroencephalogram channel attention for cross-subject emotion recognition.

In recent years, more and more researchers have focused on emotion recognition methods based on electroencephalogram (EEG) signals. However, most studies only consider the spatio-temporal characteristics of EEG and the modelling based on this feature, without considering personality factors, let alone studying the potential correlation between different subjects. Considering the particularity of emotions, different individuals may have different subjective responses to the same physical stimulus. Therefore, emotion recognition methods based on EEG signals should tend to be personalized. This paper models the personalized EEG emotion recognition from the macro and micro levels. At the macro level, we use personality characteristics to classify the individuals' personalities from the perspective of 'birds of a feather flock together'. At the micro level, we employ deep learning models to extract the spatio-temporal feature information of EEG. To evaluate the effectiveness of our method, we conduct an EEG emotion recognition experiment on the ASCERTAIN dataset. Our experimental results demonstrate that the recognition accuracy of our proposed method is 72.4% and 75.9% on valence and arousal, respectively, which is 10.2% and 9.1% higher than that of no consideration of personalization.

An Enhanced Expression Level of CXCR3 on Tfh-like Cells from Lupus Skin Lesions Rather Than Lupus Peripheral Blood.

Systemic lupus erythematosus (SLE) is a systemic autoimmune disease, and the etiopathogenesis is unclear. Follicular helper T (Tfh) cells have been reported as an important pathogenic cell type in SLE. CXCR3 was reported to be decreased on lupus peripheral CD4+T cells. However, the expression level of CCR4, CCR6 and CXCR3 on Tfh-like cells in SLE peripheral blood and skin lesions is unknown. In this study, we detected CCR4, CCR6 and CXCR3 expression level on Tfh-like cells in the peripheral blood and skin lesions from SLE patients and normal controls (NCs). A decreased expression level of CXCR3 on Tfh-like cells was found in lupus peripheral blood. However, an increased CXCR3 expression was observed on total CD4+T and Tfh-like cells from lupus skin lesions. Moreover, we observed a higher expression level of CXCR3 in Tfh cells from human tonsils. These findings indicate that CXCR3 might help Tfh-like cells to migrate into the inflammatory sites.

Simulating two-phase taxi service process by random walk theory.

City taxi service systems have been empirically studied by a number of data-driven methods. However, their underlying mechanisms are hard to understand because the present mathematical models neglect to explain a (whole) taxi service process that includes a pair of on-load phase and off-load phase. In this paper, by analyzing a large amount of taxi servicing data from a large city in China, we observe that the taxi service process shows different temporal and spatial features according to the on-load phase and off-load phase. Moreover, our correlation analysis results demonstrate the lack of dependence between the on-load phase and the off-load phase. Hence, we introduce two independent random walk models based on the Langevin equation to describe the underlying mechanism and to understand the temporal and spatial features of the taxi service process. Our study attempts to formulate the mathematical framework for simulating the taxi service process and better understanding of its underlying mechanism.

A model for meme popularity growth in social networking systems based on biological principle and human interest dynamics.

We analyze five big data sets from a variety of online social networking (OSN) systems and find that the growth dynamics of meme popularity exhibit characteristically different behaviors. For example, there is linear growth associated with online recommendation and sharing platforms, a plateaued (or an "S"-shape) type of growth behavior in a web service devoted to helping users to collect bookmarks, and an exponential increase on the largest and most popular microblogging website in China. Does a universal mechanism with a common set of dynamical rules exist, which can explain these empirically observed, distinct growth behaviors? We provide an affirmative answer in this paper. In particular, inspired by biomimicry to take advantage of cell population growth dynamics in microbial ecology, we construct a base growth model for meme popularity in OSNs. We then take into account human factors by incorporating a general model of human interest dynamics into the base model. The final hybrid model contains a small number of free parameters that can be estimated purely from data. We demonstrate that our model is universal in the sense that, with a few parameters estimated from data, it can successfully predict the distinct meme growth dynamics. Our study represents a successful effort to exploit principles in biology to understand online social behaviors by incorporating the traditional microbial growth model into meme popularity. Our model can be used to gain insights into critical issues such as classification, robustness, optimization, and control of OSN systems.

Dual-induced multifractality in online viewing activity.

Although recent studies have found that the long-term correlations relating to the fat-tailed distribution of inter-event times exist in human activity and that these correlations indicate the presence of fractality, the property of fractality and its origin have not been analyzed. We use both detrended fluctuation analysis and multifractal detrended fluctuation analysis to analyze the time series in online viewing activity separating from Movielens and Netflix. We find long-term correlations at both the individual and communal levels and that the extent of correlation at the individual level is determined by the activity level. These long-term correlations also indicate that there is fractality in the pattern of online viewing. We first find a multifractality that results from the combined effect of the fat-tailed distribution of inter-event times (i.e., the times between successive viewing actions of individuals) and the long-term correlations in online viewing activity and verify this finding using three synthesized series. Therefore, it can be concluded that the multifractality in online viewing activity is caused by both the fat-tailed distribution of inter-event times and the long-term correlations and that this enlarges the generic property of human activity to include not just physical space but also cyberspace.

Platelet-to-lymphocyte ratio (PLR) and neutrophil-to-lymphocyte ratio (NLR) are associated with chronic hepatitis B virus (HBV) infection.

OBJECTIVE: This retrospective study aimed to investigate the associations between the platelet-to-lymphocyte ratio (PLR) and neutrophil-to-lymphocyte ratio (NLR) and disease severity in patients with chronic HBV infection-related liver disease (CHB). METHODS: Patients with CHB were retrospectively identified. Clinical data for 172 HBV-infected patients and 40 healthy controls were collected from the electronic patient medical record system database of our hospital. RESULTS: HBV-related-compensated-cirrhosis patients (HBV-CC patients) had a significantly lower mean PLR than did other patients (P<0.001). HBV-related-decompensated-cirrhosis patients (HBV-DC patients) had a significantly higher mean NLR than did any other patients (P<0.001). In the entire cohort of CHB patients, significant correlations were observed between the PLR and both serum HBV DNA (r=0.264, P<0.001) and serum HBeAg (r=0.240, P=0.002). The PLR was significantly correlated with serum HBV DNA in both HBV-CC patients (r=-0.116, P=0.044) and HBV-DC patients (r=0.456, P=0.008). In HBV-Active-Carriers patients (HBV-AC patients), the PLR was positively correlated with serum HBeAg level (r=0.321, P=0.023). In HBV-DC patients, the NLR was positively correlated with serum HBeAg level (r=0.372, P=0.033). In the logistic regression prediction model, a predictive probability cutoff of 0.392 had the highest sensitivity and specificity (sensitivity, 91.2%; specificity, 84.0%) in distinguishing between HBV-CC and HBV-AC patients. A NLR cutoff value of 2.94 had the highest sensitivity and specificity (sensitivity, 81.8%; specificity, 88.2%) in distinguishing between HBV-DC and HBV-CC patients. CONCLUSION: The PLR and NLR partially reflect the amounts of serum HBV DNA and serum HBeAg levels circulating in CHB patients. The logistic regression model including the PLR and age most accurately distinguished between HBV-CC and HBV-AC patients. The NLR may be useful for follow-up in HBV-CC patients to predict disease progression. In summary, the PLR and NLR provided a supplementary means for effectively managing chronic HBV infection and disease.

Bounds of memory strength for power-law series.

Many time series produced by complex systems are empirically found to follow power-law distributions with different exponents α. By permuting the independently drawn samples from a power-law distribution, we present nontrivial bounds on the memory strength (first-order autocorrelation) as a function of α, which are markedly different from the ordinary ±1 bounds for Gaussian or uniform distributions. When 1<α≤3, as α grows bigger, the upper bound increases from 0 to +1 while the lower bound remains 0; when α>3, the upper bound remains +1 while the lower bound descends below 0. Theoretical bounds agree well with numerical simulations. Based on the posts on Twitter, ratings of MovieLens, calling records of the mobile operator Orange, and the browsing behavior of Taobao, we find that empirical power-law-distributed data produced by human activities obey such constraints. The present findings explain some observed constraints in bursty time series and scale-free networks and challenge the validity of measures such as autocorrelation and assortativity coefficient in heterogeneous systems.