Estimating the Temporal Epidemiological Trends of Tuberculosis Incidence by Using an Advanced Theta Method.

We aimed to assess the temporal epidemiological trends in tuberculosis (TB) by use of an advanced Theta method. The TB incidence data from Tianjin, Heilongjiang, Hubei, and Guangxi provinces in China, spanning January 2005 to December 2019, were extracted. We then constructed and compared various modeling approaches, including the seasonal autoregressive integrated moving average (SARIMA) model, the Theta model, the standard Theta model (STM), the dynamic optimized Theta model (DOTM), the dynamic standard Theta model (DSTM), and the optimized Theta model (OTM). During 2005-2019, these four provinces recorded a total of 2,068,399 TB cases. Analyses indicated that TB exhibited seasonality, with prominent peaks in spring and winter, and a slight downward trend was seen in incidence. In the Tianjin forecast, the OTM consistently demonstrated superior performance with the lowest values across metrics, including mean absolute deviation (0.159), mean absolute percentage error (7.032), root mean square error (0.21), mean error rate (0.068), and root mean square percentage error (0.093), compared with those of SARIMA (0.397, 16.654, 0.436, 0.169, and 0.179, respectively), Theta (0.166, 7.248, 0.231, 0.071, and 0.102, respectively), DOTM (0.169, 7.341, 0.234, 0.072, and 0.102, respectively), DSTM (0.169, 7.532, 0.203, 0.072, and 0.092, respectively), and STM (0.165, 7.218, 0.231, 0.070, and 0.101, respectively). Similar results were also observed in the other provinces, emphasizing the effectiveness of the OTM in estimating TB trends. Thus, the OTM may serve as a beneficial and effective tool for estimating the temporal epidemiological trends of TB.

Interruption time series analysis using autoregressive integrated moving average model: evaluating the impact of COVID-19 on the epidemic trend of gonorrhea in China.

BACKGROUND: Interrupted time series (ITS) analysis is a growing method for assessing intervention impacts on diseases. However, it remains unstudied how the COVID-19 outbreak impacts gonorrhea. This study aimed to evaluate the effect of COVID-19 on gonorrhea and predict gonorrhea epidemics using the ITS-autoregressive integrated moving average (ARIMA) model. METHODS: The number of gonorrhea cases reported in China from January 2005 to September 2022 was collected. Statistical descriptions were applied to indicate the overall epidemiological characteristics of the data, and then the ITS-ARIMA was established. Additionally, we compared the forecasting abilities of ITS-ARIMA with Bayesian structural time series (BSTS), and discussed the model selection process, transfer function, check model fitting, and interpretation of results. RESULT: During 2005-2022, the total cases of gonorrhea were 2,165,048, with an annual average incidence rate of 8.99 per 100,000 people. The highest incidence rate was 14.2 per 100,000 people in 2005 and the lowest was 6.9 per 100,000 people in 2012. The optimal model was ARIMA (0,1, (1,3)) (0,1,1)12 (Akaike's information criterion = 3293.93). When predicting the gonorrhea incidence, the mean absolute percentage error under the ARIMA (16.45%) was smaller than that under the BSTS (22.48%). The study found a 62.4% reduction in gonorrhea during the first-level response, a 46.47% reduction during the second-level response, and an increase of 3.6% during the third-level response. The final model estimated a step change of - 2171 (95% confidence interval [CI] - 3698 to - 644) cases and an impulse change of - 1359 (95% CI - 2381 to - 338) cases. Using the ITS-ARIMA to evaluate the effect of COVID-19 on gonorrhea, the gonorrhea incidence showed a temporary decline before rebounding to pre-COVID-19 levels in China. CONCLUSION: ITS analysis is a valuable tool for gauging intervention effectiveness, providing flexibility in modelling various impacts. The ITS-ARIMA model can adeptly explain potential trends, autocorrelation, and seasonality. Gonorrhea, marked by periodicity and seasonality, exhibited a downward trend under the influence of COVID-19 intervention. The ITS-ARIMA outperformed the BSTS, offering superior predictive capabilities for the gonorrhea incidence trend in China.

Impact of COVID-19 on epidemic trend of hepatitis C in Henan Province assessed by interrupted time series analysis.

OBJECTIVE: Hepatitis C presents a profound global health challenge. The impact of COVID-19 on hepatitis C, however, remain uncertain. This study aimed to ascertain the influence of COVID-19 on the hepatitis C epidemic trend in Henan Province. METHODS: We collated the number of monthly diagnosed cases in Henan Province from January 2013 to September 2022. Upon detailing the overarching epidemiological characteristics, the interrupted time series (ITS) analysis using autoregressive integrated moving average (ARIMA) models was employed to estimate the hepatitis C diagnosis rate pre and post the COVID-19 emergence. In addition, we also discussed the model selection process, test model fitting, and result interpretation. RESULTS: Between January 2013 and September 2022, a total of 267,968 hepatitis C cases were diagnosed. The yearly average diagnosis rate stood at 2.42/100,000 persons. While 2013 witnessed the peak diagnosis rate at 2.97/100,000 persons, 2020 reported the least at 1.7/100,000 persons. The monthly mean hepatitis C diagnosed numbers culminated in 2291 cases. The optimal ARIMA model chosen was ARIMA (0,1,1) (0,1,1)12 with AIC = 1459.58, AICc = 1460.19, and BIC = 1472.8; having coefficients MA1=-0.62 (t=-8.06, P < 0.001) and SMA1=-0.79 (t=-6.76, P < 0.001). The final model's projected step change was - 800.0 (95% confidence interval [CI] -1179.9 ~ -420.1, P < 0.05) and pulse change was 463.40 (95% CI 191.7 ~ 735.1, P < 0.05) per month. CONCLUSION: The measures undertaken to curtail COVID-19 led to a diminishing trend in the diagnosis rate of hepatitis C. The ARIMA model is a useful tool for evaluating the impact of large-scale interventions, because it can explain potential trends, autocorrelation, and seasonality, and allow for flexible modeling of different types of impacts.

Inequality in urban green space benefits: Combining street greenery and park greenery.

In this paper, we measured the amount of urban green space (UGS), defined here as park greenery and street greenery, in the Guangzhou Beltway region using remote sensing image data and the green view index (GVI) based on human visual images. We also evaluated the benefits of UGS comprehensively considering park greenery and street greenery within the Guangzhou Beltway region. We then calculated the urban green space score (UGSS) by assessing the amount of street greenery and park greenery and then juxtaposing the score with the population distribution of the region. The results show inequities in the spatial distribution of UGSS values within the Guangzhou Beltway region. The benefit score of street greenery is low. The service area of parks can't cover the whole study area. The comprehensive benefit score of UGS is composed of two parts, the park greenery score and the street greenery score, but the spatial distribution of UGSS values remains uneven. The UGS benefits enjoyed by one-half of the population of the study area are low, and the UGSS values of the more densely populated areas are not high.

Excitation-Dependent Circularly Polarized Luminescence from Helical Assemblies Based on Tartaric Acid-Derived Acylhydrazones.

Here, we combined the merits of emergent excitation-dependent (ExD) emission and circularly polarized luminescence to develop an excitation-dependent circularly polarized luminescence (ExD CPL) material showing unique features. A series of acylhydrazones based on a chiral tartaric skeleton was designed and found to self-assemble into helical nanostructures through non-covalent bonds. The helical assemblies showed ExD CPL due to the cooperation of chirality transfer and excited state intramolecular proton transfer (ESIPT). Remarkably, not only the emission wavelength could be tuned by the excitation wavelength but the handedness of CPL could be modulated in an inverted or ON/OFF manner as well, thus leading to the first example of an ExD inverted or ON/OFF switchable CPL system. Time-dependent density functional theory (TD-DFT) calculations were carried out to explain the inversion of ExD CPL. This work provided a new insight into the unprecedented handedness controllable ExD CPL, which showcased a new paradigm of the advanced CPL materials.

Dissymmetrical tails-regulated helical nanoarchitectonics of amphiphilic ornithines: nanotubes, bundles and twists.

How dissymmetrical tails (i.e. tails of different lengths) in one lipid molecule exert an impact on the structure and properties of the resulting assembly is an intriguing issue in both biological and material senses. However, the underlying mechanism that engenders such phenomena is still obscure, which prompted us to unmask it by exploring the self-assembly behaviours of artificial building blocks comprising dissymmetrical tails. Here, a series of Fmoc-protected ornithine lipids with dissymmetrical alkyl tails was designed and the dissymmetry of the two tails was found to hierarchically tune the self-assembled nanostructures from nanotubes to bundles and nanotwists. With the Fmoc-headgroup employed as a chromophorous probe, it was revealed that the alkyl chain dissymmetry controlled the interacting modes of van der Waals interactions between alkyl tails, π-π stacking between Fmoc motifs and hydrogen bonding formed by the three amide bonds in lipid bilayers. The counterbalance between those noncovalent interactions was responsible for such remarkable tuning ability towards self-assembly and emissive behaviours of the lipids, including circularly polarized light emission. This work provides insight into dissymmetrical tails-regulated biological structures and functions of natural lipids, and also sets up a novel strategy of rationally modulating chiral and emissive properties of supramolecular materials, i.e., tunable CPL materials, by exploitation of the tail dissymmetry.

Solvent-Modulated Chiral Self-Assembly: Selective Formation of Helical Nanotubes, Nanotwists, and Energy Transfer.

As the medium for self-assembly processes, solvents strongly influence the supramolecular assemblies via specific solute-solvent interactions, which may result in effective modulation of properties, self-assembled nanostructures, and functions through varying the solvent. Here, two kinds of pyridine-cyanostilbene functionalized chiral amphiphiles (l/d-PyPhG and l-PyG) were designed, and their self-assembly behaviors in different solvents were investigated. It was found that both amphiphiles formed gels in dimethyl sulfoxide (DMSO) and self-assembled into right-handed nanotwists, while they formed suspensions in ethanol consisting of left-handed nanotubes. Although the molecular chirality in the compounds remained unchanged in the two solvents, the nanoassemblies showed opposite handedness at the nanoscale together with opposite circular dichroism (CD) and circularly polarized luminescence (CPL) signals. Furthermore, when the amphiphiles were co-assembled with an achiral dye, it was found that efficient energy transfer took place in the systems composed of nanotubes rather than those composed of nanotwists. Therefore, by assembling molecules with the same molecular chirality in different solvents, a selective formation of helical nanotubes or nanotwists and the regulation of handedness as well as energy transfer efficiency were achieved.