UNASSIGNED: The Centre for Disease Control and Prevention in Yangquan, China, has taken a series of preventive and control measures in response to the increasing trend of Kala-Azar. In response, we propose a new model to more scientifically evaluate the effectiveness of these interventions.
UNASSIGNED: We obtained the incidence data of Kala-Azar from 2017 to 2021 from the Centre for Disease Control and Prevention (CDC) in Yangquan. We constructed Poisson segmented regression model, harmonic Poisson segmental regression model, and improved harmonic Poisson segmented regression model, and used the three models to explain the intervention effect, respectively. Finally, we selected the optimal model by comparing the fitting effects of the three models.
UNASSIGNED: The primary analysis showed an underlying upward trend of Kala-Azar before intervention [incidence rate ratio (IRR): 1.045, 95% confidence interval (CI): 1.027-1.063, p < 0.001]. In terms of long-term effects, the rise of Kala-Azar slowed down significantly after the intervention (IRR:0.960, 95%CI:0.927-0.995, p = 0.026), and the risk of Kala-Azar increased by 0.3% for each additional month after intervention (β1  + β3  = 0.003, IRR = 1.003). The results of the model fitting effect showed that the improved harmonic Poisson segmental regression model had the best fitting effect, and the values of MSE, MAE, and RMSE were the lowest, which were 0.017, 0.101, and 0.130, respectively.
UNASSIGNED: In the long term, the intervention measures taken by the Yangquan CDC can well curb the upward trend of Kala-Azar. The improved harmonic Poisson segmented regression model has higher fitting performance, which can provide a certain scientific reference for the evaluation of the intervention effect of seasonal infectious diseases.

Single-molecule surface-enhanced Raman spectroscopy (SM-SERS) holds great potential to revolutionize ultratrace quantitative analysis. However, achieving quantitative SM-SERS is challenging because of strong intensity fluctuation and blinking characteristics. In this study, we reveal the relation P = 1 - e-α between the statistical SERS probability P and the microscopic average molecule number α in SERS spectra, which lays the physical foundation for a statistical route to implement SM-SERS quantitation. Utilizing SERS probability calibration, we achieve quantitative SERS analysis with batch-to-batch robustness, extremely wide detection range of concentration covering 9 orders of magnitude, and ultralow detection limit far below the single-molecule level. These results indicate the physical feasibility of robust SERS quantitation through statistical route and certainly open a new avenue for implementing SERS as a practical analysis tool in various application scenarios.

To understand the transmissibility and spread of infectious diseases, epidemiologists turn to estimates of the instantaneous reproduction number. While many estimation approaches exist, their utility may be limited. Challenges of surveillance data collection, model assumptions that are unverifiable with data alone, and computationally inefficient frameworks are critical limitations for many existing approaches. We propose a discrete spline-based approach that solves a convex optimization problem-Poisson trend filtering-using the proximal Newton method. It produces a locally adaptive estimator for instantaneous reproduction number estimation with heterogeneous smoothness. Our methodology remains accurate even under some process misspecifications and is computationally efficient, even for large-scale data. The implementation is easily accessible in a lightweight R package rtestim.

The aim of this study was to develop a methodology for estimating cancer incidence in Brazil and its regions. Using data from population-based cancer registries (RCBP, acronym in Portuguese) and the Brazilian Mortality Information System (SIM, acronym in Portuguese), annual incidence/mortality (I/M) ratios were calculated by type of cancer, age group and sex in each RCBP. Poisson longitudinal multilevel models were applied to estimate the I/M ratios by region in 2018. The estimate of new cancer cases in 2018 was calculated by applying the estimated I/M ratios to the number of SIM-corrected deaths that occurred that year. North and Northeast concentrated the lowest I/M ratios. Pancreatic, lung, liver and esophageal cancers had the lowest I/M ratios, whereas the highest were estimated for thyroid, testicular, prostate and female breast cancers. For 2018, 506,462 new cancer cases were estimated in Brazil. Female breast and prostate were the two main types of cancer in all regions. In the North and Northeast, cervical and stomach cancers stood out. Differences in the I/M ratios between regions were observed and may be related to socioeconomic development and access to health services.
O objetivo deste estudo foi desenvolver metodologia para estimar a incidência de câncer no Brasil e regiões. A partir de dados dos registros de câncer de base populacional (RCBP) e do Sistema de Informações sobre Mortalidade (SIM) foram calculadas razões de incidência e mortalidade (I/M) anuais, tipo de câncer, faixa etária e sexo em cada RCBP. Para estimar as razões I/M por região em 2018, foram aplicados modelos multiníveis longitudinais de Poisson. A estimativa de casos novos de câncer, em 2018, foi calculada aplicando-se as razões I/M estimadas ao número de óbitos corrigidos do SIM ocorridos naquele ano. Norte e Nordeste concentraram as menores razões I/M. Os cânceres de pâncreas, pulmão, fígado e esôfago tiveram as menores razões I/M, enquanto as maiores razões I/M foram estimadas para câncer de tireoide, testículo, próstata e mama feminina. Para 2018, foram estimados 506.462 casos novos de câncer no Brasil. Mama feminina e próstata foram os dois principais tipos de câncer em todas as regiões. No Norte e no Nordeste, destacaram-se os cânceres do colo do útero e de estômago. Diferenças nas razões I/M entre as regiões foram observadas e podem estar relacionadas ao desenvolvimento socioeconômico e ao acesso a serviços de saúde.
El objetivo de este estudio fue desarrollar una metodología para estimar la incidencia de cáncer en Brasil y sus regiones. A partir de datos de los registros de cáncer de base poblacional (RCBP) y el Sistema de Informaciones de Mortalidad (SIM), se calcularon las tasas anuales de incidencia y mortalidad (I/M), tipo de cáncer, grupo de edad y sexo en cada RCBP. Para estimar las tasas de I/M por región en 2018, se aplicaron modelos multinivel longitudinales de Poisson. Los nuevos casos de cáncer en 2018 se estimaron mediante la aplicación de las tasas I/M que se esperan para el número de muertes corregidas de SIM que habían ocurrido ese año. Las regiones Norte y Nordeste concentraron las más bajas tasas de I/M. Los cánceres de páncreas, pulmón, hígado y esófago tuvieron las más bajas tasas de I/M, mientras que las más altas tasas de I/M se estimaron para los cánceres de tiroides, testículos, próstata y mama femenina. Para 2018, se estimaron 506.462 nuevos casos de cáncer en Brasil. La mama femenina y la próstata representaron técnicas de estimación y configuraron ser los tipos principales de cáncer en todas las regiones. En el Norte y el Nordeste se destacaron los cánceres de cuello uterino y estómago. Se observaron diferencias en las tasas de I/M entre regiones, las cuales pueden estar relacionadas con el desarrollo socioeconómico y el acceso a los servicios de salud.

In recent decades, pension reforms have been implemented to address the financial sustainability of social security systems, resulting in an increase in the retirement age. This adjustment has led to ongoing debates about the relationship between retirement and health. This study investigates the impact of time spent in retirement on the risk of cardiovascular disease (CVD) in Italy. It uses a comprehensive dataset that includes socioeconomic, health, and behavioural risk factors, which is linked to administrative hospitalisation and mortality registers. To address the potential endogeneity of retirement, we employ an instrumental variables approach embedded in a Poisson rate model. The results show that, on average, years spent in retirement have a beneficial effect on the risk of CVD for both men and women. Each additional year spent in retirement reduces the incidence of such diseases by about 17% for men and 29% for women. Stratified analyses and robustness tests show that the benefits of retirement appear to be more robust and pronounced in men and in certain groups, particularly men in manual occupations or with poor ergonomic conditions at work. These results highlight that delaying access to retirement may lead to an increased burden of CVD in the older population. In addition, the protective effect of retirement on the development of CVD among workers with poorer ergonomic conditions underlines the different impact of increasing the retirement age on different categories of workers and the need for targeted and differentiated policies to avoid hitting the more vulnerable.

Dimension reduction on neural activity paves a way for unsupervised neural decoding by dissociating the measurement of internal neural pattern reactivation from the measurement of external variable tuning. With assumptions only on the smoothness of latent dynamics and of internal tuning curves, the Poisson gaussian-process latent variable model (P-GPLVM; Wu et al., 2017) is a powerful tool to discover the low-dimensional latent structure for high-dimensional spike trains. However, when given novel neural data, the original model lacks a method to infer their latent trajectories in the learned latent space, limiting its ability for estimating the neural reactivation. Here, we extend the P-GPLVM to enable the latent variable inference of new data constrained by previously learned smoothness and mapping information. We also describe a principled approach for the constrained latent variable inference for temporally compressed patterns of activity, such as those found in population burst events during hippocampal sharp-wave ripples, as well as metrics for assessing the validity of neural pattern reactivation and inferring the encoded experience. Applying these approaches to hippocampal ensemble recordings during active maze exploration, we replicate the result that P-GPLVM learns a latent space encoding the animal\'s position. We further demonstrate that this latent space can differentiate one maze context from another. By inferring the latent variables of new neural data during running, certain neural patterns are observed to reactivate, in accordance with the similarity of experiences encoded by its nearby neural trajectories in the training data manifold. Finally, reactivation of neural patterns can be estimated for neural activity during population burst events as well, allowing the identification for replay events of versatile behaviors and more general experiences. Thus, our extension of the P-GPLVM framework for unsupervised analysis of neural activity can be used to answer critical questions related to scientific discovery.

BACKGROUND: Schistosomiasis is a neglected disease prevalent in tropical and sub-tropical areas of the world, especially in Africa. Detecting the presence of the disease is based on the detection of the parasites in the stool or urine of children and adults. In such studies, typically, data collected on schistosomiasis infection includes information on many negative individuals leading to a high zero inflation. Thus, in practice, counts data with excessive zeros are common. However, the purpose of this analysis is to apply statistical models to the count data and evaluate their performance and results.
METHODS: This is a secondary analysis of previously collected data. As part of a modelling process, a comparison of the Poisson regression, negative binomial regression and their associated zero inflated and hurdle models were used to determine which offered the best fit to the count data.
RESULTS: Overall, 94.1% of the study participants did not have any schistosomiasis eggs out of 1345 people tested, resulting in a high zero inflation. The performance of the negative binomial regression models (hurdle negative binomial (HNB), zero inflated negative binomial (ZINB) and the standard negative binomial) were better than the Poisson-based regression models (Poisson, zero inflated Poisson, hurdle Poisson). The best models were the ZINB and HNB and their performances were indistinguishable according to information-based criteria test values.
CONCLUSIONS: The zero-inflated negative binomial and hurdle negative binomial models were found to be the most satisfactory fit for modelling the over-dispersed zero inflated count data and are recommended for use in future statistical modelling analyses.

This study investigates the factors contributing to bicycle accidents, focusing on four types of bicycle lanes and other exposure and built environment characteristics of census blocks. Using Seoul as a case study, three years of bicycle accident spot data from 2018 to 2020 was collected, resulting in 1,330 bicycle accident spots and a total of 2,072 accidents. The geographically weighted Poisson regression (GWPR) model was used as a methodological approach to investigate the spatially varying relationships between the accident frequency and explanatory variables across the space, as opposed to the Poisson regression model. The results indicated that the GWPR model outperforms the global Poisson regression model in capturing unobserved spatial heterogeneity. For example, the value of deviance that determines the goodness of fit for a model was 0.244 for the Poisson regression model and 0.500 for the far better-fitting GWPR model. Further findings revealed that the factors affecting bicycle accidents have varying impacts depending on the location and distribution of accidents. For example, despite the presence of bicycle lanes, some census blocks, particularly in the northeast part of the city, still pose a risk for bicycle accidents. These findings can provide valuable insights for urban planners and policymakers in developing bicycle safety measures and regulations.

Droplet-based single-cell sequencing techniques rely on the fundamental assumption that each droplet encapsulates a single cell, enabling individual cell omics profiling. However, the inevitable issue of multiplets, where two or more cells are encapsulated within a single droplet, can lead to spurious cell type annotations and obscure true biological findings. The issue of multiplets is exacerbated in single-cell multiomics settings, where integrating cross-modality information for clustering can inadvertently promote the aggregation of multiplet clusters and increase the risk of erroneous cell type annotations. Here, we propose a compound Poisson model-based framework for multiplet detection in single-cell multiomics data. Leveraging experimental cell hashing results as the ground truth for multiplet status, we conducted trimodal DOGMA-seq experiments and generated 17 benchmarking datasets from two tissues, involving a total of 280,123 droplets. We demonstrated that the proposed method is an essential tool for integrating cross-modality multiplet signals, effectively eliminating multiplet clusters in single-cell multiomics data-a task at which the benchmarked single-omics methods proved inadequate.

UNASSIGNED: Chromosomal dicentrics and translocations are commonly employed as biomarkers to estimate radiation doses. The main goal of this article is to perform a comparative analysis of yields of both types of aberrations. The objective is to determine if there are relevant distinctions between both yields, allowing for a comprehensive assessment of their respective suitability and accuracy in the estimation of radiation doses.
UNASSIGNED: The analysis involved data from a partial-radiation simulation study with the calibration data obtained through two scoring methods: conventional and PAINT modified. Subsequently, a Bayesian bivariate zero-inflated Poisson model was employed to compare the posterior marginal density of the mean of dicentrics and translocations and assess the differences between them.
UNASSIGNED: When employing the conventional method of scoring, the findings indicate that there is no notable disparity between the yield of observed translocations and dicentrics. However, when utilizing the PAINT modified method, a notable discrepancy is observed for higher doses, indicating a relevant difference in the mean number of the two types of aberrations.
UNASSIGNED: The choice of scoring method significantly influences the analysis of radiation-induced aberrations, especially when distinguishing between complex and simple chromosomal formations. Further research and analysis are necessary to gain a deeper understanding of the factors and mechanisms impacting the formation of dicentrics and translocations.