Accurate detection of influenza A virus by use of a novel cross-priming isothermal amplification-based point-of-care assay.

Influenza virus is known to cause respiratory tract infections of varying severity in individuals of all ages. The EasyNAT Rapid Flu assay is a newly developed in vitro diagnostic test that employs cross-priming isothermal amplification (CPA) to detect and differentiate influenza A and B viruses in human nasopharyngeal (NP) swabs. The aim of this study is to determine the performance characteristics of the EasyNAT Rapid Flu assay for rapid detection of influenza virus. The limit of detection (LOD) and cross-reactivity of the EasyNAT Rapid Flu assay were assessed. The clinical performance of the assay was evaluated using NP swab samples that were tested with real-time reverse-transcription polymerase chain reaction (RT-PCR) and Xpert Xpress Flu/RSV assay. The LOD for the detection of influenza A and B using the EasyNAT Rapid Flu assay was found to be 500 copies/mL. Furthermore, the assay exhibited no cross-reactivity with other common respiratory viruses tested. For the 114 NP swab samples tested for influenza A using both the EasyNAT Rapid Flu assay and real-time RT-PCR, the two assays demonstrated a high level of agreement (κ = 0.963, P < 0.001), with a positive percentage agreement (PPA) of 97.7% and a negative percentage agreement (NPA) of 98.6%. Similarly, for the 43 NP swab samples tested for influenza A and B using both the EasyNAT Rapid Flu assay and Xpert Xpress Flu/RSV assay, the two assays showed a high level of agreement (κ = 0.933, P < 0.001), with the overall rate of agreement (ORA) of 97.7% for influenza A and 100% for influenza B. The EasyNAT Rapid Flu assay demonstrates excellent performance in the detection of influenza A, highlighted by its strong agreement with RT-PCR-based assays.IMPORTANCEThe newly developed EasyNAT Rapid Flu assay is an innovative cross-priming isothermal amplification-based method designed for detecting influenza A and B viruses at point-of-care settings. This study aims to thoroughly assess the analytical and clinical performance of the assay, offering valuable insights into its potential advantages and limitations. The findings of this research hold significant implications for clinical practice.

Evaluating the impact of COVID-19 outbreak on hepatitis B and forecasting the epidemiological trend in mainland China: a causal analysis.

BACKGROUND: It is uncertain how COVID-19 outbreak influences the hepatitis B epidemics. This study aims to evaluate the effects on hepatitis B owing to the COVID-19 outbreak and forecast the hepatitis B epidemiological trend in mainland China to speed up the course of the "End viral hepatitis Strategy". METHODS: We estimated the causal impacts and created a forecast through adopting monthly notifications of hepatitis B each year from 2005 to 2020 in mainland China using the Bayesian structural time series (BSTS) method. RESULTS: The hepatitis B epidemics fluctuates irregularly during the period 2005-2007(APC = 8.7, P = 0.246) and 2015-2020(APC = 1.7, P = 0.290), and there is a downturn (APC=-3.2, 95% CI -5.2 to -1.2, P = 0.006) from 2007 to 2015 in mainland China. The COVID-19 outbreak was found to have a monthly average reduction on the hepatitis B epidemics of 26% (95% CI 18-35%) within the first three months in 2020,17% (95% CI 7.7-26%) within the first six months in 2020, and 10% (95% CI19-22%) all year as a result of the COVID-19 outbreak, (probability of causal effect = 96.591%, P = 0.034) and the forecasts showed an upward trend from 2021 to 2025 (annual percentage change = 4.18, 95% CI 4.0 to 4.3, P < 0.001). CONCLUSION: The COVID-19 has a positive effect on the decline of hepatitis B cases. And the potential of BSTS model to forecast the epidemiological trend of the hepatitis B can be applied in automatic public health policymaking in mainland China.

Clinical Value of Laboratory Biomarkers for the Diagnosis and Early Identification of Culture-Positive Sepsis in Neonates.

Background: Neonatal sepsis (NS) is an important cause of mortality and morbidity in newborn infants. However, early diagnosis of proven sepsis (culture-positive sepsis) is difficult. We aimed to define the best combination of biomarkers to diagnose the onset of neonatal sepsis, distinguish culture-positive neonatal sepsis and predict the time of confirmation of neonatal sepsis. Methods: This retrospective cohort study was conducted from January 2016 to December 2020. Clinical characteristics and laboratory results were collected from the electronic medical records. Hematology profiles and biochemical indices were obtained upon hospital admission. Multivariate logistic regression analysis was used to evaluate the risk factors and construct a nomogram. The performance of the nomogram was evaluated by receiver operating characteristic (ROC) curve and decision curve analysis (DCA). Multivariable linear regression was used to identify the association between admission-to-diagnosis interval (ADI) and correlated variables. Results: Overall, 148 infants with neonatal sepsis (67 culture positive sepsis and 81 culture negative sepsis) and 150 controls were included. C-reactive protein (CRP) (p<0.001), platelets (PLT) (p=0.011), urea nitrogen (BUN) (p=0.001) and conjugated bilirubin (BC) (p=0.007) were independent risk factors for neonatal sepsis. The diagnostic nomogram based on CRP, PLT, BUN and BC showed excellent diagnostic accuracy for neonatal sepsis (AUC=0.928). The nomogram based on red blood cell distribution width (RDW) and mean platelet volume (MPV) was efficient in distinguishing proven neonatal sepsis from clinical sepsis, with an AUC of 0.700 in the training group and 0.689 in the validation group. Decision curve analysis (DCA) showed that the nomogram had good clinical utility. Multivariable analysis revealed gestational age, CRP, and MPV were significantly associated with admission-to-diagnosis interval in culture-positive sepsis (p < 0.001). Conclusion: Different combinations biomarkers were performant to diagnose the onset of neonatal sepsis, distinguish culture-positive neonatal sepsis, predict the time of confirmation, and aid in individual therapy.

Epidemiology, genetic characteristics, and association with meteorological factors of human metapneumovirus infection in children in southern China: A 10-year retrospective study.

OBJECTIVES: This study aimed to determine the epidemiological and genetic features of human metapneumovirus (HMPV) infection in children in southern China, and the effect of meteorological factors on infection. METHODS: 14,817 children (≤14 years) with acute respiratory tract infections from 2010 to 2019 were examined for HMPV and other respiratory viruses by real-time quantitative polymerase chain reaction. Full-length F gene of 54 positive samples were sequenced and subjected to phylogenetic analysis. The correlation between the HMPV-positive rate and meteorological factors was analyzed by linear regression analysis. RESULTS: HMPV was detected in 524 (3.5%) children, who were mostly younger than 1 year. The seasonal peak of HMPV prevalence mainly occurred in spring. Respiratory syncytial virus was the most common virus coinfected with HMPV (5.3%). Phylogenetic analysis revealed that the sequenced HMPV strains belonged to four sublineages, including A2b (1.9%), A2c (31.5%), B1 (50.0%), and B2 (16.7%). After adjusting for all meteorological factors, sunshine duration was inversely correlated with the HMPV-positive rate. CONCLUSION: HMPV is an important respiratory pathogen that causes acute respiratory tract infections in children in southern China, particularly in children ≤5 years old. The prevalence peak of HMPV in this area appeared in spring, and the predominant subtype was B1. Meteorological factors, especially long sunshine duration, might decrease the HMPV prevalence.

Simultaneous enhancement of soil properties along with water-holding and restriction of Pb-Cd mobility in a soil-plant system by the addition of a phosphorus-modified biochar to the soil.

Soil quality deterioration and heavy metal contamination have greatly limited soil productivity in mining areas. As soil is a complex system with various properties and interactions, it is imperative to conduct a comprehensive investigation to understand the amendment's mechanisms at work in the soil in mining areas as well as effective ways to address its deteriorating quality. In this study, a potassium dihydrogen phosphate-modified maize straw-cow dung biochar (PBC) was applied as a soil amendment. Various physicochemical properties of the soil including organic matter, total nitrogen, available phosphorus, and pore characteristics were analyzed. This study also assessed soil-saturated water content and soil moisture characteristic curve. Lettuce biomass was measured and changes in various speciation of Pb and Cd in the soil, and the accumulation of Pb and Cd in lettuce were examined. Results showed that the addition of PBC increased soil organic matter, total nitrogen, and available phosphorus while reducing soil bulk density, it also increased soil porosity, saturated water content, and capillary water capacity. Soil structure analysis using CT scanning revealed that 3% PBC increased the macrospores volume fraction while 5% PBC made the pores more uniform. Lettuce biomass increased by 53.3%. 5% PBC resulted in a 56.79% and 38.30% reduction in Pb and a 44.56% and 16.60% reduction in Cd in roots and shoots of lettuce respectively. PBC facilitated the transformation of Pb and Cd from unstable fractions to stable fractions through complexation and precipitation. Overall, the addition of PBC effectively improved soil nutrients, porosity, and water-holding capacity, promoted plant growth, immobilized Pb and Cd, as well as reduced the bioavailability in contaminated-soil from mining areas. This study provides an effective strategy and a new perspective for the remediation of Pb-Cd-contaminated soils.

Host factor DUSP5 potently inhibits dengue virus infection by modulating cytoskeleton rearrangement.

Cytoskeleton has been reported to play an essential role in facilitating the viral life cycle. However, whether the host can exert its antiviral effects by modulating the cytoskeleton is not fully understood. In this study, we identified that host factor DUSP5 was upregulated after dengue virus (DENV) infection. In addition, we demonstrated that overexpression of DUSP5 remarkably inhibited DENV replication. Conversely, the depletion of DUSP5 led to an increase in viral replication. Moreover, DUSP5 was found to restrain viral entry into host cells by suppressing F-actin rearrangement via negatively regulating the ERK-MLCK-Myosin IIB signaling axis. Depletion of dephosphorylase activity of DUSP5 abolished its above inhibitory effects. Furthermore, we also revealed that DUSP5 exhibited broad-spectrum antiviral effects against DENV and Zika virus. Taken together, our studies identified DUSP5 as a key host defense factor against viral infection and uncovered an intriguing mechanism by which the host exerts its antiviral effects through targeting cytoskeleton rearrangement.

Preoperative alpha fetoprotein, total bilirubin, fibrinogen, albumin, and lymphocytes predict postoperative survival in hepatocellular carcinoma.

AIMS: Our study focused on exploring the feasible prognostic laboratory parameters of HCC and establishing a score model to estimate individualized overall survival (OS) in HCC after resection. METHODS: Four hundred and sixty-one patients with HCC who underwent hepatectomy between January 2010 and December 2017 was enrolled in this investigation. Cox proportional hazards model was conducted to analyze the prognostic value of laboratory parameters. The score model construction was based on the forest plot results. Overall survival was evaluated by Kaplan-Meier method and the log-rank test. The novel score model was validated in an external validation cohort from a different medical institution. RESULTS: We identified that alpha fetoprotein (AFP), total bilirubin (TB), fibrinogen (FIB), albumin (ALB), and lymphocyte (LY) were independent prognostic factors. High AFP, TB, FIB (HR > 1, p < 0.05), and low ALB, LY (HR < 1, p < 0.05) were associated with the survival of HCC. The novel score model of OS based on these five independent prognostic factors achieved high C-index of 0.773 (95% confidence interval [CI]: 0.738-0.808), which was significantly higher than those of the single five independent factors (0.572-0.738). The score model was validated in the external cohort whose C-index was 0.7268 (95% CI: 0.6744-0.7792). CONCLUSION: The novel score model we established was an easy-to-use tool which could enable individualized estimation of OS in patients with HCC who underwent curative hepatectomy.

Sulfoaluminate cement-modified straw biochar as a soil amendment to inhibit Pb-Cd mobility in the soil-romaine lettuce system.

Biochar is widely used to remediate soil polluted by potentially toxic elements (PTEs), while the effect of a new type of biochar, obtained from modified cement material, on the mobility of Pb and Cd in the soil-plant system is still unknown. In this study, soils doped with sulfoaluminate cement modified biochar (SBC) were characterized using a series of approaches including FTIR, XRD, and XPS, and combined with pot experiments to explore its synergistic effects on the speciation transformation, accumulation, and mobility of both Pb and Cd in a soil-romaine lettuce system in heavily contaminated soils containing 500 mg·kg-1-Pb and 3 mg·kg-1-Cd. The results showed that SBC effectively immobilized Pb and Cd in the soil and that this was achieved through cation exchange, complexation, and gel encapsulation. Moreover, SBC also changed the soil physicochemical properties and indirectly affected the speciation transformation of Pb and Cd. FTIR and XRD analyses revealed that the groups such as -OH, -COOH, SO42-, and SiO32-introduced by SBC stimulated the conversion from the soluble to the residual state of Pb. XPS analysis indicated that, the deviation of the C-O-C, C-OOH, and O-CO peak and the increased in area suggested that organic groups in the SBC were engaged in the immobilization mechanism of Pb and Cd. The transformation of residual Cd in other extractable fractions might be due to either enhanced soil reducibility or competitive adsorption with Pb. In 5% SBC soil, Pb was reduced by 27.69% and 64.84%, and Cd was reduced by 20.45% and 35.87% for shoots and roots of romaine lettuce, respectively. SBC showed a significantly positive correlation with SOM, while SOM showed a highly significantly negative correlation with both Pb and Cd in the roots. In summary, SBC can be strongly recommended as a green amendment to remediate Pb-Cd contaminated soil and to inhibit the mobility to plant.

Diagnostic and Prognostic Nomograms for Hepatocellular Carcinoma Based on PIVKA-II and Serum Biomarkers.

BACKGROUND: The aim of the present study was to develop an improved diagnostic and prognostic model for HBV-associated HCC by combining AFP with PIVKA-II and other potential serum/plasma protein biomarkers. METHODS: A total of 578 patients, including 352 patients with HBV-related HCC, 102 patients with HBV-associated liver cirrhosis (LC), 124 patients with chronic HBV, and 127 healthy subjects (HS), were enrolled in the study. The serum levels of AFP, PIVKA-II, and other laboratory parameters were collected. Univariate and multivariate logistic regression and Cox regression analyses were performed to identify independent diagnostic and prognostic factors, respectively. The diagnostic efficacy of the nomogram was evaluated using receiver operator curve (ROC) analysis and the prognostic performance was measured by Harrell's concordance index (C-index). RESULTS: AFP and PIVKA-II levels were significantly increased in HBV-related HCC, compared with those in HBV-associated LC and chronic HBV participants (p < 0.05 and p < 0.001, respectively). The diagnostic nomogram, which included age, gender, AFP, PIVKA-II, prothrombin time (PT), and total protein (TP), discriminated patients with HBV-HCC from those with HBV-LC or chronic HBV with an AUC of 0.970. In addition, based on the univariate and multivariate Cox regression analysis, PIVKA-II, γ-glutamyl transpeptidase, and albumin were found to be significantly associated with the prognosis of HBV-related HCC and were incorporated into a nomogram. The C-index of the nomogram for predicting 3-year survival in the training and validation groups was 0.75 and 0.78, respectively. The calibration curves for the probability of 3-year OS showed good agreement between the nomogram prediction and the actual observation in the training and the validation groups. Furthermore, the nomogram had a higher C-index (0.74) than that of the Child-Pugh grade (0.62), the albumin-bilirubin (ALBI) score (0.64), and Barcelona Clinic Liver Cancer (0.56) in all follow-up cases. CONCLUSION: Our study suggests that the nomograms based on AFP, PIVKA-II, and potential serum protein biomarkers showed a better performance in the diagnosis and prognosis of HCC, which may help to guide therapeutic strategies and assess the prognosis of HCC.

A novel visual illusion paradigm provides evidence for a general factor of illusion sensitivity and personality correlates.

Visual illusions are a gateway to understand how we construct our experience of reality. Unfortunately, important questions remain open, such as the hypothesis of a common factor underlying the sensitivity to different types of illusions, as well as of personality correlates of illusion sensitivity. In this study, we used a novel parametric framework for visual illusions to generate 10 different classic illusions (Delboeuf, Ebbinghaus, Rod and Frame, Vertical-Horizontal, Zöllner, White, Müller-Lyer, Ponzo, Poggendorff, Contrast) varying in strength, embedded in a perceptual discrimination task. We tested the objective effect of the illusions on errors and response times, and extracted participant-level performance scores (n=250) for each illusion. Our results provide evidence in favour of a general factor underlying the sensitivity to different illusions (labelled Factor i). Moreover, we report a positive link between illusion sensitivity and personality traits such as Agreeableness, Honesty-Humility, and negative relationships with Psychoticism, Antagonism, Disinhibition, and Negative Affect.

Transfer-printing-enabled GeSn flexible resonant-cavity-enhanced photodetectors with strain-amplified mid-infrared optical responses.

Mid-infrared (MIR) flexible photodetectors (FPDs) constitute an essential element for wearable applications, including health-care monitoring and biomedical detection. Compared with organic materials, inorganic semiconductors are promising candidates for FPDs owing to their superior performance as well as optoelectronic properties. Herein, for the first time, we present the use of transfer-printing techniques to enable a cost-effective, nontoxic GeSn MIR resonant-cavity-enhanced FPDs (RCE-FPDs) with strain-amplified optical responses. A narrow bandgap nontoxic GeSn nanomembrane was employed as the active layer, which was grown on a silicon-on-insulator substrate and then transfer-printed onto a polyethylene terephthalate (PET) substrate, eliminating the unwanted defects and residual compressive strain, to yield the MIR RCE-FPDs. In addition, a vertical cavity was created for the GeSn active layer to enhance the optical responsivity. Under bending conditions, significant tensile strain up to 0.274% was introduced into the GeSn active layer to effectively modulate the band structure, extend the photodetection in the MIR region, and substantially enhance the optical responsivity to 0.292 A W-1 at λ = 1770 nm, corresponding to an enhancement of 323% compared with the device under flat conditions. Moreover, theoretical simulations were performed to confirm the strain effect on the device performance. The results demonstrated high-performance, nontoxic MIR RCE-FPDs for applications in flexible photodetection.

A data-driven interpretable ensemble framework based on tree models for forecasting the occurrence of COVID-19 in the USA.

This prevalence of coronavirus disease 2019 (COVID-19) has become one of the most serious public health crises. Tree-based machine learning methods, with the advantages of high efficiency, and strong interpretability, have been widely used in predicting diseases. A data-driven interpretable ensemble framework based on tree models was designed to forecast daily new cases of COVID-19 in the USA and to determine the important factors related to COVID-19. Based on a hyperparametric optimization technique, we developed three machine learning algorithms based on decision trees, including random forest (RF), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), and three linear ensemble models were used to integrate these outcomes for better prediction accuracy. Finally, the SHapley Additive explanation (SHAP) value was used to obtain the feature importance ranking. Our outcomes demonstrated that, among the three basic machine learners, the prediction accuracy was the following in descending order: LightGBM, XGBoost, and RF. The optimized LAD ensemble was the most precise prediction model that reduced the prediction error of the best base learner (LightGBM) by approximately 3.111%, while vaccination, wearing masks, less mobility, and government interventions had positive effects on the control and prevention of COVID-19.

Epidemiological analysis and prediction of varicella in Liaoning, 2006-2021 / 中国热带医学

@#Abstract: Objective　By collecting and sorting the information of varicella cases reported in Liaoning Province from 2006 to 2021, the epidemiological characteristics were analyzed, and the monthly incidence data were predicted, so as to explore the prevention and control strategy of varicella disease in Liaoning Province. Methods　By collecting the characteristic information of varicella cases in Liaoning Province, epidemiological analysis was carried out on the regional, population, and temporal characteristics of varicella incidence. The monthly incidence data of varicella were fitted with Eviews software, seasonal ARIMA model was used for modeling, and models were selected according to SC and AIC. After modeling, the model was used to predict the incidence data in 2022. Results　The incidence rate of varicella in Liaoning Province has increased in recent years. The onset time was "bimodal distribution", with the main peak occurring from November to January of the next year and the secondary peak occurring from May to June. Since 2019, the onset age has shifted backward. From the original 0-<10 age group with the highest incidence rate, it shifted to the 10-<20 age group with the highest incidence rate. From 2006 to 2021, the incidence of varicella mainly concentrated in people aged 0 to <40 years old, and the incidence rate of the population over 40 years old showed a cliff-like decline. The incidence of chickenpox was higher in the central region of Liaoning Province, such as Shenyang, Dalian, Anshan and Panjin, and relatively low in Huludao, Jinzhou, Fuxin and Liaoyang. The distribution of the population was mainly students, followed by kindergartens and scattered children. ARIMA model of monthly incidence data was established by software as ARIMA (1, 0, 1) (1, 1, 1)12. Conclusions　The incidence rate of varicella in Liaoning Province has been rising in recent years. The incidence is obviously seasonal, and the age group of the affected population has moved backward. It is predicted that the incidence will continue to increase in 2022. The prevention and control of varicella should still be the current key work. In order to reduce the population incidence rate, two-dose vaccination strategies should be vigorously promoted the implementation of the, and the inclusion of varicella vaccine in the immunization program should be achieved as soon as possible.

Ring Finger Protein 215 Negatively Regulates Type I IFN Production via Blocking NF-κB p65 Activation.

Germline-encoded pattern recognition receptors (PRRs) recognize molecules frequently found in pathogens (pathogen-associated molecular patterns [PAMPs]) during viral infection. This process induces production of IFNs, leading to expression of IFN-stimulated genes to establish a cellular antiviral state against viral infection. However, aberrant activation of the IFN system may cause immunopathological damage and systemic autoimmune diseases such as systemic lupus erythematosus. Stringent control of IFN signaling activation is critical for maintaining homoeostasis of the immune system; yet, the mechanisms responsible for its precise regulation remain to be elucidated. In this study, we identified that ring finger protein 215 (RNF215), a zinc finger protein, was upregulated by viral infection in human macrophages. In addition, we demonstrated that RNF215 inhibited the production of type I IFNs at least in part via interacting with p65, a subunit of NF-κB, and repressed the accumulation of NF-κB in the promoter region of IFNB1. Moreover, we found that the expression of RNF215 negatively correlated with type I IFNs in patients with systemic lupus erythematosus, indicating that RNF215 plays an important role in the pathogenesis of autoimmune diseases. Collectively, our data identified RNF215 as a key negative regulator of type I IFNs and suggested RNF215 as a potential target for intervention in diseases with aberrant IFN production.

The characteristics of postprandial glycemic response patterns to white rice and glucose in healthy adults: Identifying subgroups by clustering analysis.

Objectives: Large interpersonal variability in postprandial glycemic response (PGR) to white rice has been reported, and differences in the PGR patterns during the oral glucose tolerance test (OGTT) have been documented. However, there is scant study on the PGR patterns of white rice. We examined the typical PGR patterns of white rice and glucose and the association between them. Materials and methods: We analyzed the data of 3-h PGRs to white rice (WR) and glucose (G) of 114 normoglycemic female subjects of similar age, weight status, and same ethnic group. Diverse glycemic parameters, based on the discrete blood glucose values, were calculated over 120 and 180 min. K-means clustering based on glycemic parameters calculated over 180 min was applied to identify subgroups and representative PGR patterns. Principal factor analysis based on the parameters used in the cluster analysis was applied to characterize PGR patterns. Simple correspondence analysis was performed on the clustering categories of WR and G. Results: More distinct differences were found in glycemic parameters calculated over 180 min compared with that calculated over 120 min, especially in the negative area under the curve and Nadir. We identified four distinct PGR patterns to WR (WR1, WR2, WR3, and WR4) and G (G1, G2, G3, and G4), respectively. There were significant differences among the patterns regard to postprandial hyperglycemia, hypoglycemic, and glycemic variability. The WR1 clusters had significantly lower glycemic index (59 ± 19), while no difference was found among the glycemic index based on the other three clusters. Each given G subgroup presented multiple patterns of PGR to WR, especially in the largest G subgroup (G1), and in subgroup with the greatest glycemic variability (G3). Conclusion: Multiple subgroups could be classified based on the PGR patterns to white rice and glucose even in seemingly homogeneous subjects. Extending the monitoring time to 180 min was conducive to more effective discrimination of PGR patterns. It may not be reliable to extrapolate the patterns of PGR to rice from that to glucose, suggesting a need of combining OGTT and meal tolerance test for individualized glycemic management.

The Structure of Chaos: An Empirical Comparison of Fractal Physiology Complexity Indices Using NeuroKit2.

Complexity quantification, through entropy, information theory and fractal dimension indices, is gaining a renewed traction in psychophsyiology, as new measures with promising qualities emerge from the computational and mathematical advances. Unfortunately, few studies compare the relationship and objective performance of the plethora of existing metrics, in turn hindering reproducibility, replicability, consistency, and clarity in the field. Using the NeuroKit2 Python software, we computed a list of 112 (predominantly used) complexity indices on signals varying in their characteristics (noise, length and frequency spectrum). We then systematically compared the indices by their computational weight, their representativeness of a multidimensional space of latent dimensions, and empirical proximity with other indices. Based on these considerations, we propose that a selection of 12 indices, together representing 85.97% of the total variance of all indices, might offer a parsimonious and complimentary choice in regards to the quantification of the complexity of time series. Our selection includes CWPEn, Line Length (LL), BubbEn, MSWPEn, MFDFA (Max), Hjorth Complexity, SVDEn, MFDFA (Width), MFDFA (Mean), MFDFA (Peak), MFDFA (Fluctuation), AttEn. Elements of consideration for alternative subsets are discussed, and data, analysis scripts and code for the figures are open-source.

Application of a data-driven XGBoost model for the prediction of COVID-19 in the USA: a time-series study.

OBJECTIVE: The COVID-19 outbreak was first reported in Wuhan, China, and has been acknowledged as a pandemic due to its rapid spread worldwide. Predicting the trend of COVID-19 is of great significance for its prevention. A comparison between the autoregressive integrated moving average (ARIMA) model and the eXtreme Gradient Boosting (XGBoost) model was conducted to determine which was more accurate for anticipating the occurrence of COVID-19 in the USA. DESIGN: Time-series study. SETTING: The USA was the setting for this study. MAIN OUTCOME MEASURES: Three accuracy metrics, mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE), were applied to evaluate the performance of the two models. RESULTS: In our study, for the training set and the validation set, the MAE, RMSE and MAPE of the XGBoost model were less than those of the ARIMA model. CONCLUSIONS: The XGBoost model can help improve prediction of COVID-19 cases in the USA over the ARIMA model.

High Emotional Similarity Will Enhance the Face Memory and Face-Context Associative Memory.

Previous research has explored how emotional valence (positive or negative) affected face-context associative memory, while little is known about how arousing stimuli that share the same valence but differ in emotionality are bound together and retained in memory. In this study, we manipulated the emotional similarity between the target face and the face associated with the context emotion (i.e., congruent, high similarity, and low similarity), and examined the effect of emotional similarity of negative emotion (i.e., disgust, anger, and fear) on face-context associative memory. Our results showed that the greater the emotional similarity between the faces, the better the face memory and face-context associative memory were. These findings suggest that the processing of facial expression and its associated context may benefit from taking into account the emotional similarity between the faces.

Elder people can be more susceptible to the association between short-term ambient air pollution and sleep disorder outpatient visits: a time-series study.

Sleep disorders attract increasing concerns. However, the evidence of the association between ambient air pollution and sleep disorders is limited. Therefore, our aim was to determine the association between short-term air pollution exposure and outpatient visits for sleep disorders in Xi'an, the largest city in Northwest China. Baseline outpatient data of daily sleep disorders between 2011 and 2013 were collected. Quasi-Poisson distribution was applied by adjusting the day of the week and weather conditions. A total of 49,282 sleep disorder outpatient visits were recorded. The most significant association between air pollutants and outpatient visits was observed on concurrent day: per 10 µg/m3 increase of NO2, SO2, and PM10 at lag 0 corresponded to increased outpatient sleep disorder visits at 0.22% (95% CI: 0.03%, 0.42%), 1.53% (95% CI: 0.53, 2.53%), and 2.57% (95% CI: 1.33%, 3.82%), respectively. As for gender-specific analysis, there was no statistically significant difference between males and females. The result of season-specific analysis showed no statistically significant difference between warm seasons and cool seasons, either. As for age-specific analysis, obvious associations were observed in 20-40 age group (NO2) and > 40 age group (PM10 and SO2), while no evident association was found for the young age group (< 20 years old). Conclusively, short-term exposure to air pollutants, especially gaseous air pollutants, might increase the risk of sleep disorders, and such association appears to be more obvious in elder people. We provide novel data that there may be age differences in the relationship between short-term air pollution exposure and sleep disorders.

The correlation between temperature and the incidence of COVID-19 in four first-tier cities of China: a time series study.

The COVID-19 outbreak emerged in Wuhan, China, and was declared a global pandemic in March 2020. This study aimed to explore the association of daily mean temperature with the daily counts of COVID-19 cases in Beijing, Shanghai, Guangzhou, and Shenzhen, China. Data on daily confirmed cases of COVID-19 and daily mean temperatures were retrieved from the 4 first-tier cities in China. Distributed lag nonlinear models (DLNMs) were used to assess the association between daily mean temperature and the daily cases of COVID-19 during the study period. After controlling for the imported risk index and long-term trends, the distributed lag nonlinear model showed that there were nonlinear and lag relationships. The daily cumulative relative risk decreased for every 1.0 °C change in temperature in Shanghai, Guangzhou, and Shenzhen. However, the cumulative relative risk increased with a daily mean temperature below - 3 °C in Beijing and then decreased. Moreover, the delayed effects of lower temperatures mostly occurred within 6-7 days of exposure. There was a negative correlation between the cumulative relative risk of COVID-19 incidence and temperature, especially when the temperature was higher than - 3 °C. The conclusions from this paper will help government and health regulators in these cities take prevention and protection measures to address the COVID-19 crisis and the possible collapse of the health system in the future.