Rendimiento en los juegos panamericanos de Santiago 2023: predicción mediante método de suavización exponencial simple / Performance at the Santiago 2023 pan american games: prediction using a simple exponential smoothing method / Desempenho nos jogos pan-americanos de Santiago 2023: predição usando o método de suavização exponencial simples

El objetivo del presente trabajo es analizar el desempeño deportivo de la delegación chilena en los Juegos Panamericanos celebrados entre los años 1951 y 2023, haciendo uso de datos retrospectivos y proyectivos a través de series temporales de tiempo. Para esto se empleó un diseño cuantitativo, no experimental y longitudinal de tendencias y un método de suavización exponencial simple, que utiliza promedios históricos y que permite realizar una predicción o comportamiento futuro basado en una media ponderada de los valores actuales y de los pasados. A partir de los resultados obtenidos, fue posible concluir que, en las últimas décadas, la ubicación de Chile en el ranking de los Juegos Panamericanos se ha estabilizado en torno a un onceavo lugar, posición pronosticada para Santiago 2023. Manteniéndose condiciones similares, el desempeño deportivo general y específico no tendría un quiebre exponencial de la tendencia y los resultados no resultan favorables, específicamente en lo que respecta a la obtención de medallas de oro y la posición general de la delegación.

The objective of this paper is to analyze the sports performance of the Chilean delegation in the Pan American Games held between 1951 and 2023, using retrospective and projective data through time series. For this purpose, a quantitative, non-experimental and longitudinal design of trends and a simple exponential smoothing method was used, which uses historical averages and allows a prediction or future behavior based on a weighted average of current and past values. From the results obtained, it was possible to conclude that, in recent decades, Chile's position in the Pan American Games ranking has stabilized around eleventh place, a position predicted for Santiago 2023. Maintaining similar conditions, the general and specific sporting performance would not have an exponential break in the trend and the results are not favorable, specifically in terms of obtaining gold medals and the overall position of the delegation.

O objetivo deste artigo é analisar o desempenho esportivo da delegação chilena nos Jogos Pan-Americanos realizados entre 1951 e 2023, usando dados retrospectivos e projetivos por meio de séries temporais. Para isso, foi utilizado um desenho quantitativo, não experimental e longitudinal de tendências e um método de suavização exponencial simples, que utiliza médias históricas e permite uma previsão do comportamento futuro com base em uma média ponderada dos valores atuais e passados. Com base nos resultados obtidos, foi possível concluir que, nas últimas décadas, a posição do Chile no ranking dos Jogos Pan-Americanos se estabilizou em torno do 11º lugar, posição prevista para Santiago 2023. Mantendo-se condições semelhantes, o desempenho esportivo geral e específico não teria uma quebra exponencial na tendência e os resultados não são favoráveis, especificamente em termos de conquista de medalhas de ouro e posição geral da delegação.

Exponential modelling of heart rate recovery after a maximal exercise

Objectives: Heart rate recovery (HRR) after exercise is clinically important as a predictor of mortality. In addition, HRR is an indicator of cardiac autonomic activity, since increased vagal activity and diminished sympathetic activity return the heart rate to resting conditions after exercise. The previous attempts to model HRR using polynomial, first-order and second-order modelling have produced mixed results. In this study, we hypothesised that the double-exponential fit would model the HRR more accurately than the single-exponential fit as it would capture the activity of both autonomic arms responsible for heart rate decay and investigated the outcome of these two models on the HRR data following a maximal exercise. Materials and Methods: Exponential curve fitting was done on a set of previously published data from our laboratory. The HRR data were acquired from 40 male participants (19–38 years) after a maximal treadmill exercise. The normalised HRR data from a 5-min time window from maximal heart rate were fitted using single and double-exponential curves, to obtain, respectively, the time constants Tau and, Tau 1 and Tau 2. The goodness-of-fit of the model was assessed with Chi-square values computed for each participant data set with both models. Considering that Chi-square of zero is a perfect fit, and therefore, smaller Chi-square values indicate a better fit than larger values, we computed the difference in the Chi-square values (??2 ) between the models by subtracting the Chi-square value of the double-exponential fit from the Chi-square value of the singleexponential fit. This was based on the premise that if the calculated ??2 is positive, it would indicate a better fit with double-exponential than single-exponential decay model. The data are presented as mean ± standard deviation. Comparisons were made with Student’s t-test. Results: Data from four participants were excluded for technical reasons. The Tau of the single-exponential fit was 65.50 ± 12.13 s, while Tau 1 and Tau 2 of the double-exponential fit were 43.75 ± 18.96 s and 120.30 ± 91.32 s, respectively, the Tau 1 value being significantly lower than the Tau 2 value (P < 0.0001). Remarkably among the 36 participants, the difference in the Chi-square value was positive (127.2 ± 171.04) in 22 subjects and zero or marginally negative (?0.17 ± 0.31) in 14 subjects. Conclusion: Our results indicate that the double-exponential model fitted the HRR data better than the single-exponential model in almost two-thirds (61%) of our study population. In the remaining participants, the goodness-of-fit was nearly equivalent for both fits with no evidence of superior modelling with the singleexponential fit. Our data show that while the single-exponential fit is sufficient for modelling the HRR of 14 subjects, it was less efficient for fitting the data of most participants. In comparison, the double-exponential curve fit effectively modelled 100% of our study population. Given our findings, we conclude that the doubleexponential model is more inclusive and better represented the HRR data of our study population than the singleexponential model.

An Atrial Fibrillation Classification Method Study Based on BP Neural Network and SVM / 中国医疗器械杂志

Atrial fibrillation is a common arrhythmia, and its diagnosis is interfered by many factors. In order to achieve applicability in diagnosis and improve the level of automatic analysis of atrial fibrillation to the level of experts, the automatic detection of atrial fibrillation is very important. This study proposes an automatic detection algorithm for atrial fibrillation based on BP neural network (back propagation network) and support vector machine (SVM). The electrocardiogram (ECG) segments in the MIT-BIH atrial fibrillation database are divided into 10, 32, 64, and 128 heartbeats, respectively, and the Lorentz value, Shannon entropy, K-S test value and exponential moving average value are calculated. These four characteristic parameters are used as the input of SVM and BP neural network for classification and testing, and the label given by experts in the MIT-BIH atrial fibrillation database is used as the reference output. Among them, the use of atrial fibrillation in the MIT-BIH database, the first 18 cases of data are used as the training set, and the last 7 cases of data are used as the test set. The results show that the accuracy rate of 92% is obtained in the classification of 10 heartbeats, and the accuracy rate of 98% is obtained in the latter three categories. The sensitivity and specificity are both above 97.7%, which has certain applicability. Further validation and improvement in clinical ECG data will be done in next study.

Time Series Analysis of COVID-19 Data- A study from Northern India

The continuing new Coronavirus (COVID-19) pandemic has caused millions of infections and thousands of fatalities globally. Identification of potential infection cases and the rate of virus propagation is crucial for early healthcare service planning to prevent fatalities. The research community is faced with the analytical and difficult real-world task of accurately predicting the spread of COVID-19. We obtained COVID-19 temporal data from District Surveillance Officer IDSP, Dehradun cum District Nodal Officer- Covid-19 under CMO, Department of Medical Health and Family Welfare, Government of Uttarakhand State, India, for the period, March 17, 2020, to May 6, 2022, and applied single exponential method forecasting model to estimate the COVID-19 outbreak's future course. The root relative squared error, root mean square error, mean absolute percentage error, and mean absolute error were used to assess the model's effectiveness. According to our prediction, 5438 people are subjected to hospitalization by September 2022, assuming that COVID cases will increase in the future and take on a lethal variety, as was the case with the second wave. The outcomes of the forecasting can be utilized by the government to devise strategies to stop the virus's spread.

Sensitive detection of alkaline phosphatase based on terminal deoxynucleotidyl transferase and endonuclease Ⅳ-assisted exponential signal amplification / 药物分析学报

Alkaline phosphatase(ALP)is widely expressed in human tissues.ALP plays an important role in the dephosphorylation of proteins and nucleic acids.Therefore,quantitative analysis of ALP plays a vital role in disease diagnosis and the development of biological detection methods.Terminal deoxynucleotidyl transferase(TdT)catalyzes continuous polymerization of deoxynucleotide triphosphates at the 3'-OH end of single-stranded DNA in the absence of a template.In this study,we developed a highly sensitive and selective method based on TdT and endonuclease Ⅳ(Endo Ⅳ)to quantify ALP activity.After ALP hydrolyzes the 3'-PO4 end of the substrate and generates 3'-OH,TdT can effectively elongate the 3'-OH end with deoxynucleotide adenine triphosphate(dATP)and produce a poly A tail,which can be detected by the poly T probes.Endo Ⅳ digests the AP site in poly T probes to generate a fluorescent signal and a new 3'-OH end,leading to the generation of exponential fluorescence signal amplification.The substrate for TdT elongation was optimized,and a limit of detection of 4.3×10-3 U/L was achieved for ALP by the optimized substrate structure.This method can also detect ALP in the cell lysate of a single cell.This work has potential applications in disease diagnosis and biomedical detection.

Espectroscopia de impedancia eléctrica, una herramienta para aplicaciones biotecnológicas con Lactobacillus casei ATCC 393 / Electrical impedance spectroscopy, a tool for biotechnological applications with Lactobacillus casei ATCC 393

RESUMEN La aplicación de espectroscopia de impedancia eléctrica (EIE), es una técnica que se utiliza para monitorear, detectar y cuantificar microorganismos de interés biotecnológico, con la medición de parámetros eléctricos de respuesta rápida de un medio inoculado a temperatura y agitación constante mediante electrodos sumergidos. Realizando una comparación del modelo de crecimiento y el recuento en placa con los parámetros eléctricos de respuesta, se puede dar una correlación para romper la barrera tecnológica entre la microbiología clásica y los métodos rápidos de detección. La comparación de ambas técnicas fue realizada para determinar el máximo crecimiento de Lactobacillus casei (L. casei) ATCC 393. Se encontró que tras la inoculación, después de 24 h en condiciones microaerofílicas (37 °C), el máximo crecimiento microbiano fue registrado por medio de la EIE, mediante los parámetros -Z- (29,1057) y Deg-Deg0 (24,555°). En contraste con la técnica de conteo en placa, el crecimiento máximo se estimó a las 9 h. Los datos experimentales obtenidos mediante la EIE fueron ajustados por un circuito RC en serie, posteriormente, las curvas generadas fueron ajustadas a los modelos de crecimiento de Gompertz y Boltzman. Usando la técnica de EIE, la impedancia del medio resultó el parámetro más eficiente para la estimación del pico máximo exponencial de crecimiento de L. casei. Se demostró que la EIE constituye una alternativa para la detección rápida de la concentración microbiana en procesos de producción de biomasa para la elaboración de productos alimenticios probióticos.

ABSTRACT The application of electrical impedance spectroscopy (EIS) is a technique used to monitor, detect and quantify microorganisms of biotechnological interest, with the measurement of electrical parameters of rapid response of a medium inoculated at temperature and constant agitation by submerged electrodes. By making a comparison of the growth model and the plate count with the electrical response parameters, a correlation can be made to break the technological barrier between classical microbiology and rapid detection methods. The comparison of both techniques was performed to determine the maximum growth of Lactobacillus casei (L. casei) ATCC 393. It was found that after inoculation, after 24 h under microaerophilic conditions (37 °C), the maximum microbial growth was recorded by medium of the EIE, using the parameters -Z- (29,1057) and Deg-Deg0 (24,555°). In contrast to the plate count technique, maximum growth was estimated at 9 h. The experimental data obtained through the EIE were adjusted by a series RC circuit; later, the generated curves were adjusted to the growth models of Gompertz and Boltzman. Using the EIE technique, the impedance of the medium was the most efficient parameter for the estimation of the maximum exponential growth peak of L. casei. It was demonstrated that the EIE constitutes an alternative for the rapid detection of the microbial concentration in biomass production processes for the elaboration of probiotic food products.

Screening and Identification of Nucleic Acid Aptamers Specifically Binding to Human Lipocalin 6 / 中国生物化学与分子生物学报

It is an urgent and difficult task to establish a simple and efficient method for identifying and isolating sperm cells from mixed stains in forensic science. Nucleic acid aptamers targeting sperm cell-surface proteins can be used for the separation and purification of sperms from mixed stain samples. Human lipocalin 6 (hLCN6) is an epididymal secreted protein that binds to the head and tail of sperm cells and is associated with sperm maturation. Using the systematic evolution of ligands by the exponential enrichment (SELEX) technique, magnetic bead-bound hLCN6 was used as the target molecule to screen for aptamers with high affinity and specificity to hLCN6 from a random single-stranded DNA (ssDNA) library. Through 15 rounds of positive selection and 3 rounds of negative selection, 24 clones were selected and subjected to sequence analysis. Subsequently, 4 candidate aptamers were selected and further examined for their binding affinity and specificity by enzyme-linked oligonucleotide adsorption (ELONA) and cell binding assays. One aptamer (H2) against hLCN6 with a high affinity and specificity was isolated and investigated by dot blotting and immunofluorescence staining. The result revealed that the candidate aptamer H2 with a dissociation constant of (3. 21 ± 0. 75) nmol/ L was able to recognize and specifically bind to hLCN6. The aptamer H2 also showed high affinity and specificity to human sperms in vitro, which establishes the foundation for the separation of sperm cells from mixed stain based on nucleic acid-protein interactions and provides a new scheme.

Factors associated with red blood cell transfusion among hospitalized patients: a cross-sectional study / 浙江大学学报（英文版）（B辑：生物医学和生物技术）

Red blood cell (RBC) transfusion is a clinically effective therapy in anemia, for example in patients with malignancies (Shander et al., 2020), bleeding (Odutayo et al., 2017), and preoperative anemia (Padmanabhan et al., 2019). The past few decades have witnessed a shortage of blood for transfusion due to limited health insurance coverage for blood use and the rapid expansion of hospitals (Chen et al., 2011; Shi et al., 2014). Blood donation levels may easily be affected by general changes in the environment, policy, major events such as disasters, and public sentiment (Hu et al., 2019). Meanwhile, the transfusion of allogeneic RBC is a double-edged sword, increasing the possibility of infectious and immunological complications, and also leading to higher morbidity and mortality after transfusion (Frank et al., 2012). Considering that the continual shortfall has been increasingly prominent, identifying the factors associated with RBC transfusion could help blood transfusion departments to improve their supply of blood products as well as their inventory management (O'Donnell et al., 2018).

Evaluation of the validity of POSSUM and P-POSSUM score in predicting the risk of morbidity and mortality respectively in patients undergoing emergency laparotomy

Background: Aim of this study is to examine the efficacy of Physiological and operative severity score for the enumeration of mortality and morbidity (POSSUM) and Portsmouth predictor modification (P-POSSUM) equations in predicting morbidity and mortality in patients undergoing emergency laparotomy, to study the morbidity and mortality patterns in patients undergoing emergency laparotomy at Malla Reddy Institute of Medical Sciences, Hyderabad. Methods: The study was conducted for a period of 2 years from February 2018 to February 2020. 100 Patients undergoing emergency laparotomy were studied in the Department of General surgery MRIMS, Hyderabad. POSSUM and P-POSSUM scores are used to predict mortality and morbidity. The ratio of observed to expected deaths (O:E ratio) was calculated for each analysis. Results: The study included total 100 patients, 83 men and 17 women. Observed mortality rate was compared to mortality rate with POSSUM, the O:E ratio was 0.62, and there was no significant difference between the observed and predicted values (χ²=10.79, 9 degree of freedom (df) p=0.148). Observed morbidity rates were compared to morbidity rates predicted by POSSUM, there was no significant difference between the observed and predicted values (χ²=9.89, 9 df, p=0.195) and the overall O:E ratio was 0.91. P-POSSUM predicted mortality equally well when the linear method of analysis was used, with an O:E ratio of 0.65 and no significant difference between the observed and predicted values (χ²= 5.33, 9 df, p= 0.617).Conclusion: POSSUM and P-POSSUM scoring is an accurate predictor of mortality and morbidity following emergency laparotomy and is a valid means of assessing adequacy of care provided to the patient.

Current Research Status of Aptamer-mediated Liposome Targeted Delivery of Antitumor Drugs / 中国实验方剂学杂志

Tumor has become the second most serious disease that threatens human health and life. Treating with chemical drugs (referred to as chemotherapy) is the most basic treatment, but most chemotherapeutic drugs cause damage to normal tissues. It is a difficult problem in the field of biomedical research that how to deliver anti-tumor drugs more efficiently, increase the concentration of drugs in tumor tissues, enhance the anti-tumor effect, and decrease the drug distribution in normal tissues to weaken the damage to normal tissues. In order to achieve the goals of accurate delivery of anti-tumor drugs and synergism and attenuation, the researchers used systematic evolution of ligands by exponential enrichment technology (SELEX technology) to screen aptamers that can specifically target tumor markers or tumor cells, and designed the novel liposome targeting drug delivery system with aptamers as targeting molecules (ligands). This paper briefly introduced nucleic acid aptamer technology and common tumor markers, and reviewed the research advances on the antitumor effect of aptamer-liposome drug delivery system. It will provide references for the selection of appropriate tumor markers as targets and the application of aptamer technology in the research and development of high-efficiency and low-toxicity liposome targeting agents of anti-tumor traditional Chinese medicine. Meanwhile, it is of great significance for promoting the application of aptamer technology in targeted drug delivery systems.

Application of the exponential smoothing model and ARIMA model in prediction of the endemic situation of schistosomiasis in Hunan Province / 中国血吸虫病防治杂志

Objective To predict the changes in the prevalence of Schistosoma japonicum infections in humans and livestock in Hunan Province using the exponential smoothing model and the ARIMA model. Methods The data pertaining to S. japonicum infections in humans and livestock in Hunan Province from 1957 to 2015 were collected, and the exponential smoothing model and the ARIMA model were created using the software Eviews and PASW Statistics 18.0. In addition, the effectiveness of these two models for the prediction of S. japonicum infections in humans and livestock in Hunan Province from 2016 to 2018 was evaluated. Results The exponential smoothing model and the ARIMA model had a high goodness of fit for prediction of S. japonicum infections in humans and livestock in Hunan Province from 1957 to 2015. There was a linear trend in the prevalence of S. japonicum infections in humans and livestock in Hunan Province from 1957 to 2015. The prevalence of S. japonicum infections in humans predicted with the Brown’s linear trend and the prevalence of S. japonicum infections in livestock predicted with the Holt’s linear trend in Hunan Province from 2016 to 2018 fitted better the actual data than the ARIMA model; however, prediction of the ARIMA model indicated that the endemic situation of schistosomiasis remained at a low level in Hunan Province. Conclusion At a low epidemic level, development of highly sensitive tools for monitoring schistosomiasis is urgently needed in Hunan Province to fit the current endemic situation, and the schistosomiasis control measures should be intensified to consolidate the control achievements.

COVID-19 analysis and forecast based on Exponential Smoothing Model in Hubei Province / 公共卫生与预防医学

Objective To analyze and forecast the epidemic of the new coronavirus pneumonia (COVID-19) in Hubei Province. Methods Exponential Smoothing Model was applied to fit the tendency of the number of confirmed cases, discharged cases, death cases, severe cases and critical cases. Results The epidemic of COVID-19 in Hubei province has been gradually alleviated, the rapid remission period and slow remission period were occurred after February 18th and March 21st, respectively. The Exponential Smoothing Model was significantly fit well and the fitting values were basically consistent with the actual values. Predicted results indicated that the number of existing confirmed cases was expected to reduce to less than 1 000 on April 2nd, and was mainly consist of severe and critical cases. Conclusions The prevention and control measures of COVID-19 in Hubei province were effective, and the Exponential Smoothing Model was applicable to predicate the epidemic of COVID-19.

Parámetros poblacionales de Tuta absoluta (Lepidoptera: Gelechiidae) y pérdidas asociadas en tomate de invernadero / Population parameters of Tuta absoluta (Lepidoptera: Gelechiidae) and associated losses in greenhouse tomato

Introducción: El minador de hojas de tomate, Tuta absoluta (Meyrick), la plaga más peligrosa para la producción de tomate, Solanum lycopersicum L., en todo el mundo; puede causar pérdidas totales y es difícil de controlar. Por lo tanto, comprender la dinámica de sus poblaciones en el cultivo es una prioridad para mitigar el daño. Objetivo: Aquí, los parámetros de la población de T. absoluta fueron evaluados en tomate var. Sonero en invernadero, en tres sistemas de gestión (comercial, población máxima y control total). Métodos: Durante 25 semanas en La Unión, Antioquia, Colombia, se aplicaron modelos matemáticos para describir las poblaciones de larvas y adultos (machos) en el tiempo, en relación con los parámetros de producción y las pérdidas asociadas. Resultados: La tasa de crecimiento intrínseco fue de 0.4466 larvas por semana. Los modelos gaussianos y exponenciales describieron mejor a la población en el tiempo. Las pérdidas se asociaron con el peso y la cantidad de frutos comercializables, y también con el peso total de producción y la cantidad de racimos y frutos totales. Conclusión: Las larvas de T. absoluta tienen un crecimiento exponencial en invernadero ventilado, con una tasa de crecimiento intrínseca menor que la calculada por las tablas de vida en el laboratorio.

Introduction: The tomato leafminer, Tuta absoluta (Meyrick), the most threatening pest for tomato, Solanum lycopersicum L., production worldwide, may cause total losses, and is difficult to control. Thus, understanding its populations dynamic in the crop is a priority to mitigate damage. Objective: Herein, population parameters of T. absoluta were evaluated in tomato var. Sonero in the greenhouse, in three management systems (commercial, maximum population and total control). Methods: During 25 weeks in La Union, Antioquia, Colombia, mathematical models were applied to describe the populations of larvae and adults (males) in time, as related to production parameters and associated losses. Results: The intrinsic growth rate was 0.447 larvae per week. The Gaussian and exponential models described best the population over time. The losses were associated with weight and number of marketable fruits, and with total production weight, and number of clusters and total fruits. Conclusion: T. absoluta larvae have growth exponentially in a ventilated greenhouse, with a smaller intrinsic growth rate than that calculated by life tables in the laboratory.

Leaf count overdispersion in coffee seedlings / Superdispersão relacionado a contagem de folhas em mudas de cafeeiro

ABSTRACT: Coffee crops play an important role in Brazilian agriculture, with a high level of social and economic participation resulting from the jobs created in the supply chain and from the income obtained by producers and the revenue generated for the country from coffee bean export. In coffee plant growth, leaves have a determinant role in higher production; therefore, the leaf count per plant provides relevant information to producers for adequate crop management, such as foliar fertilizer applications. To describe count data, the Poisson model is the most commonly employed model; when count data show overdispersion, the negative binomial model has been determined to be more adequate. The objective of this study was to compare the fitness of the Poisson and negative binomial models to data on the leaf count per plant in coffee seedlings. Data were collected from an experiment with a randomized block design with 30 treatments and three replicates and four plants per plot. Data from only one treatment, in which the number of leaves was counted over time, were employed. The first count was conducted on 8 April 2016, and the other counts were performed 18, 32, 47, 62, 76, 95, 116, 133, and 153 days after the first evaluation, for a total of ten measurements. The fitness of the models was assessed based on deviance values and simulated envelopes for residuals. Results of fitness assessment indicated that the Poisson model was inadequate for describing the data due to overdispersion. The negative binomial model adequately fitted the observations and was indicated to describe the number of leaves of coffee plants. Based on the negative binomial model, the expected relative increase in the number of leaves was 0.9768% per day.

RESUMO: A cultura do café desempenha papel relevante na agricultura do Brasil, com expressiva participação social e econômica tanto pelos empregos gerados na cadeia produtiva, bem como pela renda obtida pelos produtores e pelas divisas geradas para o país na exportação do grão. No crescimento das plantas de café, as folhas desempenham papel decisivo para que tenha maior produção, portanto a contagem do número de folhas por planta fornece informações importantes aos produtores para o manejo adequado da cultura como, por exemplo, a aplicação de adubações foliares. Em geral, na descrição de dados obtidos por contagem, o modelo mais utilizado é o Poisson, sendo que quando os dados apresentam superdispersão, o modelo Binomial Negativo tem se mostrado mais adequado. O objetivo deste trabalho foi comparar o ajuste dos modelos de Poisson e Binomial Negativo em dados de contagens do número de folhas por planta em mudas do cafeeiro. Os dados foram obtidos de um experimento usando o delineamento em blocos casualizados com trinta tratamentos e três repetições com quatro plantas por parcela. Foram utilizados os dados de apenas um tratamento no qual foi feita a contagem do número de folhas ao longo do tempo. A primeira avaliação foi feita em 8 de abril de 2016 e as demais aos 18, 32, 47, 62, 76, 95, 116, 133 e 153 dias após a primeira avaliação, totalizando dez medidas. A adequação dos mesmos foi verificada com base nos valores da Deviance e no envelope simulado para os resíduos. Os resultados do ajuste indicaram que o modelo Poisson foi inadequado para descrição dos dados devido a superdispersão. O modelo Binomial Negativo se ajustou adequadamente e foi indicado para descrever o número de folhas das plantas do cafeeiro. Com base no modelo Binomial Negativo o aumento relativo esperado para o número de folhas foi de 0,9768% para cada dia.

Prediction and analysis of death of children under 5 years old in Lanzhou based on time series model / 中华疾病控制杂志

Objective To analyze the death trend of children under 5 years old in Lanzhou and establish the time series model to predict the mortality and incidence of children under 5 years old in Lanzhou in 2019. Methods Descriptive epidemiological method was used to analyze the mortality of children under 5 years old in Lanzhou from January 2010 to December 2018. SPSS 21.0 software was used to construct time series analysis model, selecting the best model and predict the mortality of children under 5 years old in Lanzhou in 2019. Results A total of 1 650 deaths of children under 5 years old were reported in Lanzhou from 2010 to 2018. The number of deaths reported by boys and girls was 871 and 774 respectively, with an average annual mortality rate of 6.23‰. In recent years, the overall mortality rate of children under 5 years old in Lanzhou had declined. The majority of deaths among children under 5 years old were neonates, accounting for 65.27%. Simple seasonal model was the best model by comparing different models. The model could well fit the monthly death cases of children under 5 years old in Lanzhou from 2010 to 2018. It is predicted that the total number of deaths of children under 5 years old in Lanzhou will be 140 in 2019, which is similar to the number of deaths in 2018. Conclusions The mortality rate of children under 5 years old in Lanzhou is decreasing year by year. Simple seasonal model can better reflect the mortality trend of children under 5 years old in Lanzhou and make short-term prediction.

Predictive study on school absences due to illness with seasonal exponential smoothing method / 中华疾病控制杂志

Objective To establish a suitable exponential smoothing prediction model for school absentees due to illness, to discuss its application value for predicting school absences due to illness, and to provide a basis for early warning of absence due to illness. Methods Numbers of schools absences by year and month due to illness in 30 primary schools from November 2015 to June 2017 were collected from symptom monitoring system of border county, southern Yunnan and Simple seasonal model, Winters addition model and Winters multiplication model were used to build simulation. The data of July 2017 to December 2017 were used for model validation. The three models were overall compared and evaluated through indicator analysis, statistical analysis and residual diagram analysis. The best model was selected to predict school absences due to illness from January 2018 to March 2018. Results Simple seasonal model, Winters addition model and Winters multiplication model were used to fit the variation trend of number of school absences due to illness in time series. The root mean square error (RMSE) of three models were 445.11, 420.99 and 258.75; R2adj were 0.72, 0.72 and 0.77; R2 were 0.92, 0.93 and 0.98; P values of Ljung-Box Q were 0.54, 0.43 and 0.21. As for prediction method linear trend, Alpha were 0.999, 1.000 and 0.298. The average relative error between predicted value and actual value was 9.62%, 21.90% and 7.52%. Conclusion Winters multiplication model has practical value to predict school absence due to illness and provide scientific basis for early identification of abnormal signals.

Application and comparison of residual autoregressive model and Holt’s two-parameter exponential smoothing model in infant mortality prediction in some countries along the Belt and Road Initiative / 中华疾病控制杂志

@# Objective To explore the application of residual autoregressive model and Holt's two-parameter exponential model in the prediction of infant mortality rate in some countries along the “Belt and Road” (China-Indo-China Peninsula Economic Corridor). Methods The time series data of infant mortality rate in Vietnam, Laos, Cambodia, Myanmar, Thailand, Singapore, Malaysia, and China for 1978-2013 were used as training set to fit residual autoregressive model and Holt's two-parameter exponential model. The 2014-2016 data was used as the validation set to compare the performance of model prediction. Results The akaike information criterion (AIC) value of the residual autoregressive model was superior to Holt's two-parameter exponential model. Both prediction models showed high accuracy, and most evaluation indicators (absolute error and relative error) of residual autoregressive prediction model were smaller than Holt's two-parameter exponential model. The residual autoregressive models of Laos, Myanmar and Cambodia were better than the Holt’s two-parameter exponential model for the infant mortality rate(IMR) prediction in different years. Conclusions The residual autoregressive model and Holt's two-parameter exponential model performed well in infant mortality rate prediction in some countries along the China-Indo-china Peninsula Economic Corridor. The residual autoregressive model has better fitting effect. The residual autoregressive model for infant mortality prediction is superior to the Holt two-parameter exponential model in most countries in most years.

Research on Improved Algorithm of DR Image Enhancement Based on Gauss-Laplacian Pyramid / 中国医疗器械杂志

OBJECTIVE@#In order to obtain more decision information from Digital Radiography(DR) images, an improved image enhancement algorithm is proposed based on the algorithm of Gauss-Laplacian pyramid.@*METHODS@#The original algorithm is improved on the basis of the human visual characteristics and better enhancements, the low frequency components of the image is histogram equalized to make the image gray scale more balanced, and the high frequency component is enhanced by a hierarchical exponential enhancement to make the details of the image clearer.@*RESULTS@#The improved algorithm improves the contrast of DR images in chest, pelvic and spine, and makes the image more layered and obtains good image enhancement effect.@*CONCLUSIONS@#The results show that the improved algorithm is superior to the traditional algorithm in terms of image enhancement.

SELEX screening of EpCAMGspecific nucleic acid aptamer targeting prostate cancer cells / 实用放射学杂志

Objective To investigate the methods of screening specific aptamers for (EpCAM)Gpositive prostate cancer (PCa)cells by cellGSELEX technique.Methods A random DNA library was designed to screen EpCAMGspecific DNA aptamers from human prostate cancer cells expressing EpCAM molecule by cellGSELEX technique.After 12 rounds of in vitro screening,DNA products were cloned and sequenced.Flow cytometry and cellular immunofluorescence were used to detect the specific binding ability of aptamers to target cells.Results Two aptamers of Ep1 and Ep2 were selected.Both of them could specifically bind to EpCAMGpositive cancer cells LNCap,PCG3 ,DU1 45 , and HEK293T cells transfected with target molecule.The binding rates of Ep1 were 61.0%,74.3%,5 9.1% and 60.3%.The binding rates of Ep2 were 65.1%,77.8%,54.2% and 58.3%.Neither of them could bind to HEK293T cells transfected with empty vector with the binding rate of 5.4% in Ep1 and 3.3% in Ep2,respectively.Flow cytometry analysis and confocal images indicated that the EpCAM aptamers could specifically recognize human PCa cells expressing EpCAM,but could not bind to EpCAMGnegative cells.Conclusion EpCAM aptamers derived from cellGSELEX technology can recognize and bind to EpCAMGpositive PCa cells specifically,which may provide new ideas for the specific diagnosis and targeted therapy of prostate cancer,and lay an experimental basis for the other specific diagnosis and treatment schemes of malignant tumors.

The comparison of the value of mono-exponential mode and diffusion kurtosis imaging mode in predicting the response to neoadjuvant chemotherapy for locally advanced breast carcinoma using diffusion-weighted imaging / 中华放射学杂志

Objective To compare the value of diffusion kurtosis imaging (DKI) mode and mono-exponential mode in predicting the response to neoadjuvant chemotherapy (NAC) for locally advanced breast carcinoma using DWI.Methods From January 1,2013 to December 31,2016,eighty patients with locally advanced breast carcinoma were enrolled into this prospective clinical study.The diagnosis was confirmed on the basis of histopathological results.The clinical stage stayed at Ⅱ or Ⅲ.The patients would receive breast-conserving surgery after NAC.All the patients underwent DWI examination by using both mono-exponential mode and DKI mode before chemotherapy was initiated.The parameters included ADC,mean diffusivity (MD) and mean kurtosis (MK).Within 1 to 3 days before or after MRI examination,the patients underwent aspiration biopsy,received 4 to 8 cycles of NAC and followed by surgery.According to histologic grading before NAC,the patients were classified into well-differentiated and poor-differentiated group.According to the comparison between pathological results acquired from biopsy before NAC and specimen acquired after surgery,the patients were classified into pathologic complete response (pCR) and pathologic non-complete response (non-pCR) according to treatment effect.The imaging parameters were compared between the pCR and the non-pCR group using t test.The predicting ability of two imaging modes was compared and analyzed with ROC analysis.The relationships between multiple imaging parameters,pathologic,clinical characteristics of tumor and treatment effect were analyzed using logistic multi-variate regression analysis,and further analyzed using Wald test.Results There were 30 cases of pCR and 50 cases of non-pCR.The ADC and MD values were lower in the pCR group than in the non-pCR group (P＜0.05).MK value was higher in the pCR group than in the non-pCR group (P＜0.05).ROC analysis showed that the area under ROC curve of ADC,MD and MK in predicting treatment effect were 0.732,0.866 and 0.683 respectively.Logistic regression analysis showed that,according to predicting ability,MD,ADC and MK successively were the independent predictors for the early response to chemotherapy.Conclusion Compared with mono-exponential mode,DKI mode can reflect the real micro-environment and water diffusion restriction within the tumor area more reliably and accurately,and is more suitable to serve as an imaging technique for predicting the response to NAC for locally advanced breast carcinoma.