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Fulltext Characterization and comparative sequence analysis of the DNA mismatch repair MSH2 and MSH7 genes from tomato

Tam SM, Samipak S, Britt A, Chetelat RT

Genetica, 2009 Dec;137(3):341-54.
PMID: 19690966 DOI: 10.1007/s10709-009-9398-3

DNA mismatch repair proteins play an essential role in maintaining genomic integrity during replication and genetic recombination. We successfully isolated a full length MSH2 and partial MSH7 cDNAs from tomato, based on sequence similarity between MutS and plant MSH homologues. Semi-quantitative RT-PCR reveals higher levels of mRNA expression of both genes in young leaves and floral buds. Genetic mapping placed MSH2 and MSH7 on chromosomes 6 and 7, respectively, and indicates that these genes exist as single copies in the tomato genome. Analysis of protein sequences and phylogeny of the plant MSH gene family show that these proteins are evolutionarily conserved, and follow the classical model of asymmetric protein evolution. Genetic manipulation of the expression of these MSH genes in tomato will provide a potentially useful tool for modifying genetic recombination and hybrid fertility between wide crosses.

Matched MeSH terms: Forecasting
Modified rapid shallow breathing index adjusted with anthropometric parameters increases predictive power for extubation failure compared with the unmodified index in postcardiac surgery patients

Takaki S, Kadiman SB, Tahir SS, Ariff MH, Kurahashi K, Goto T

J Cardiothorac Vasc Anesth, 2015 Feb;29(1):64-8.
PMID: 25620140 DOI: 10.1053/j.jvca.2014.06.022

The aim of this study was to determine the best predictors of successful extubation after cardiac surgery, by modifying the rapid shallow breathing index (RSBI) based on patients' anthropometric parameters.

Matched MeSH terms: Forecasting
Fulltext An Early Warning System for Flood Detection Using Critical Slowing Down

Syed Musa SMS, Md Noorani MS, Abdul Razak F, Ismail M, Alias MA, Hussain SI

Int J Environ Res Public Health, 2020 Aug 24;17(17).
PMID: 32846870 DOI: 10.3390/ijerph17176131

The theory of critical slowing down (CSD) suggests an increasing pattern in the time series of CSD indicators near catastrophic events. This theory has been successfully used as a generic indicator of early warning signals in various fields, including climate research. In this paper, we present an application of CSD on water level data with the aim of producing an early warning signal for floods. To achieve this, we inspect the trend of CSD indicators using quantile estimation instead of using the standard method of Kendall's tau rank correlation, which we found is inconsistent for our data set. For our flood early warning system (FLEWS), quantile estimation is used to provide thresholds to extract the dates associated with significant increases on the time series of the CSD indicators. We apply CSD theory on water level data of Kelantan River and found that it is a reliable technique to produce a FLEWS as it demonstrates an increasing pattern near the flood events. We then apply quantile estimation on the time series of CSD indicators and we manage to establish an early warning signal for ten of the twelve flood events. The other two events are detected on the first day of the flood.

Matched MeSH terms: Forecasting
Fulltext BOO-ST and CBCEC: two novel hybrid machine learning methods aim to reduce the mortality of heart failure patients

Sutradhar A, Al Rafi M, Shamrat FMJM, Ghosh P, Das S, Islam MA, et al.

Sci Rep, 2023 Dec 18;13(1):22874.
PMID: 38129433 DOI: 10.1038/s41598-023-48486-7

Heart failure (HF) is a leading cause of mortality worldwide. Machine learning (ML) approaches have shown potential as an early detection tool for improving patient outcomes. Enhancing the effectiveness and clinical applicability of the ML model necessitates training an efficient classifier with a diverse set of high-quality datasets. Hence, we proposed two novel hybrid ML methods ((a) consisting of Boosting, SMOTE, and Tomek links (BOO-ST); (b) combining the best-performing conventional classifier with ensemble classifiers (CBCEC)) to serve as an efficient early warning system for HF mortality. The BOO-ST was introduced to tackle the challenge of class imbalance, while CBCEC was responsible for training the processed and selected features derived from the Feature Importance (FI) and Information Gain (IG) feature selection techniques. We also conducted an explicit and intuitive comprehension to explore the impact of potential characteristics correlating with the fatality cases of HF. The experimental results demonstrated the proposed classifier CBCEC showcases a significant accuracy of 93.67% in terms of providing the early forecasting of HF mortality. Therefore, we can reveal that our proposed aspects (BOO-ST and CBCEC) can be able to play a crucial role in preventing the death rate of HF and reducing stress in the healthcare sector.

Matched MeSH terms: Forecasting
A Horizon Scan of Emerging Issues for Global Conservation in 2019

Sutherland WJ, Broad S, Butchart SHM, Clarke SJ, Collins AM, Dicks LV, et al.

Trends Ecol Evol, 2019 01;34(1):83-94.
PMID: 30554808 DOI: 10.1016/j.tree.2018.11.001

We present the results of our tenth annual horizon scan. We identified 15 emerging priority topics that may have major positive or negative effects on the future conservation of global biodiversity, but currently have low awareness within the conservation community. We hope to increase research and policy attention on these areas, improving the capacity of the community to mitigate impacts of potentially negative issues, and maximise the benefits of issues that provide opportunities. Topics include advances in crop breeding, which may affect insects and land use; manipulations of natural water flows and weather systems on the Tibetan Plateau; release of carbon and mercury from melting polar ice and thawing permafrost; new funding schemes and regulations; and land-use changes across Indo-Malaysia.

Matched MeSH terms: Forecasting*
Prospective Advances in Circular RNA Investigation

Sulaiman SA, Abdul Murad NA, Mohamad Hanif EA, Abu N, Jamal R

Adv Exp Med Biol, 2018 9 28;1087:357-370.
PMID: 30259380 DOI: 10.1007/978-981-13-1426-1_28

circRNAs have emerged as one of the key regulators in many cellular mechanisms and pathogenesis of diseases. However, with the limited knowledge and current technologies for circRNA investigations, there are several challenges that need to be addressed for. These include challenges in understanding the regulation of circRNA biogenesis, experimental designs, and sample preparations to characterize the circRNAs in diseases as well as the bioinformatics pipelines and algorithms. In this chapter, we discussed the above challenges and possible strategies to overcome those limitations. We also addressed the differences between the existing applications and technologies to study the circRNAs in diseases. By addressing these challenges, further understanding of circRNAs roles and regulations as well as the discovery of novel circRNAs could be achieved.

Matched MeSH terms: Forecasting
Fulltext Comparison between VAR, GSTAR, FFNN-VAR and FFNN-GSTAR Models for forecasting oil production

Suhartono, Prastyo, Dedy Dwi, Kuswanto, Heri, Muhammad Hisyam Lee

MATEMATIKA, 2018;34(1):103-111.
MyJurnal

Monthly data about oil production at several drilling wells is an example of
spatio-temporal data. The aim of this research is to propose nonlinear spatio-temporal
model, i.e. Feedforward Neural Network - VectorAutoregressive (FFNN-VAR) and FFNN
- Generalized Space-Time Autoregressive (FFNN-GSTAR), and compare their forecast
accuracy to linearspatio-temporal model, i.e. VAR and GSTAR. These spatio-temporal
models are proposed and applied for forecasting monthly oil production data at three
drilling wells in East Java, Indonesia. There are 60 observations that be divided to two
parts, i.e. the first 50 observations for training data and the last 10 observations for
testing data. The results show that FFNN-GSTAR(11) and FFNN-VAR(1) as nonlinear
spatio-temporal models tend to give more accurate forecast than VAR(1) and GSTAR(11)
as linear spatio-temporal models. Moreover, further research about nonlinear spatiotemporal
models based on neural networks and GSTAR is needed for developing new
hybrid models that could improve the forecast accuracy.

Matched MeSH terms: Forecasting
Fulltext Trends in Prevalence of Tobacco Use by Sex and Socioeconomic Status in 22 Sub-Saharan African Countries, 2003-2019

Sreeramareddy CT, Acharya K

JAMA Netw Open, 2021 12 01;4(12):e2137820.
PMID: 34878548 DOI: 10.1001/jamanetworkopen.2021.37820

Importance: Tobacco companies have shifted their marketing and production to sub-Saharan African countries, which are in an early stage of the tobacco epidemic.
Objective: To estimate changes in the prevalence of current tobacco use and socioeconomic inequalities among male and female participants from 22 sub-Saharan African countries from 2003 to 2019.
Design, Setting, and Participants: Secondary data analyses were conducted of sequential Demographic and Health Surveys in 22 sub-Saharan African countries including male and female participants aged 15 to 49 years. The baseline surveys (2003-2011) and the most recent surveys (2011-2019) were pooled.
Exposures: Household wealth index and highest educational level were the markers of inequality.
Main Outcomes and Measures: Sex-specific absolute and relative changes in age-standardized prevalence of current tobacco use in each country and absolute and relative measures of inequality using pooled data.
Results: The survey samples included 428 197 individuals (303 232 female participants [70.8%]; mean [SD] age, 28.6 [9.8] years) in the baseline surveys and 493 032 participants (348 490 female participants [70.7%]; mean [SD] age, 28.5 [9.4] years) in the most recent surveys. Both sexes were educated up to primary (35.7%) or secondary school (40.0%). The prevalence of current tobacco use among male participants ranged from 6.1% (95% CI, 5.2%-6.9%) in Ghana to 38.3% (95% CI, 35.8%-40.8%) in Lesotho in the baseline surveys and from 4.5% (95% CI, 3.7%-5.3%) in Ghana to 46.0% (95% CI, 43.2%-48.9%) in Lesotho during the most recent surveys. The decrease in prevalence ranged from 1.5% (Ghana) to 9.6% (Sierra Leone). The World Health Organization target of a 30% decrease in smoking was achieved among male participants in 8 countries: Rwanda, Nigeria, Ethiopia, Benin, Liberia, Tanzania, Burundi, and Cameroon. For female participants, the number of countries having a prevalence of smoking less than 1% increased from 9 in baseline surveys to 16 in the most recent surveys. The World Health Organization target of a 30% decrease in smoking was achieved among female participants in 15 countries: Cameroon, Namibia, Mozambique, Mali, Liberia, Nigeria, Burundi, Tanzania, Malawi, Kenya, Rwanda, Zimbabwe, Ethiopia, Burkina Faso, and Zambia. For both sexes, the prevalence of tobacco use and the decrease in prevalence of tobacco use were higher among less-educated individuals and individuals with low income. In both groups, the magnitude of inequalities consistently decreased, and its direction remained the same. Absolute inequalities were 3-fold higher among male participants, while relative inequalities were nearly 2-fold higher among female participants.
Conclusions and Relevance: Contrary to a projected increase, tobacco use decreased in most sub-Saharan African countries. Persisting socioeconomic inequalities warrant the stricter implementation of tobacco control measures to reach less-educated individuals and individuals with low income.

Matched MeSH terms: Forecasting
Fulltext The Future of Clinical Trial Design in Oncology

Spreafico A, Hansen AR, Abdul Razak AR, Bedard PL, Siu LL

Cancer Discov, 2021 Apr;11(4):822-837.
PMID: 33811119 DOI: 10.1158/2159-8290.CD-20-1301

Clinical trials represent a fulcrum for oncology drug discovery and development to bring safe and effective medicines to patients in a timely manner. Clinical trials have shifted from traditional studies evaluating cytotoxic chemotherapy in largely histology-based populations to become adaptively designed and biomarker-driven evaluations of molecularly targeted agents and immune therapies in selected patient subsets. This review will discuss the scientific, methodological, practical, and patient-focused considerations to transform clinical trials. A call to action is proposed to establish the framework for next-generation clinical trials that strikes an optimal balance of operational efficiency, scientific impact, and value to patients. SIGNIFICANCE: The future of cancer clinical trials requires a framework that can efficiently transform scientific discoveries to clinical utility through applications of innovative technologies and dynamic design methodologies. Next-generation clinical trials will offer individualized strategies which ultimately contribute to globalized knowledge and collective learning, through the joint efforts of all key stakeholders including investigators and patients.

Matched MeSH terms: Forecasting
Fulltext The use of quantile regression to forecast higher than expected respiratory deaths in a daily time series: a study of New York City data 1987-2000

Soyiri IN, Reidpath DD

PLoS One, 2013;8(10):e78215.
PMID: 24147122 DOI: 10.1371/journal.pone.0078215

Forecasting higher than expected numbers of health events provides potentially valuable insights in its own right, and may contribute to health services management and syndromic surveillance. This study investigates the use of quantile regression to predict higher than expected respiratory deaths. Data taken from 70,830 deaths occurring in New York were used. Temporal, weather and air quality measures were fitted using quantile regression at the 90th-percentile with half the data (in-sample). Four QR models were fitted: an unconditional model predicting the 90th-percentile of deaths (Model 1), a seasonal/temporal (Model 2), a seasonal, temporal plus lags of weather and air quality (Model 3), and a seasonal, temporal model with 7-day moving averages of weather and air quality. Models were cross-validated with the out of sample data. Performance was measured as proportionate reduction in weighted sum of absolute deviations by a conditional, over unconditional models; i.e., the coefficient of determination (R1). The coefficient of determination showed an improvement over the unconditional model between 0.16 and 0.19. The greatest improvement in predictive and forecasting accuracy of daily mortality was associated with the inclusion of seasonal and temporal predictors (Model 2). No gains were made in the predictive models with the addition of weather and air quality predictors (Models 3 and 4). However, forecasting models that included weather and air quality predictors performed slightly better than the seasonal and temporal model alone (i.e., Model 3 > Model 4 > Model 2) This study provided a new approach to predict higher than expected numbers of respiratory related-deaths. The approach, while promising, has limitations and should be treated at this stage as a proof of concept.

Matched MeSH terms: Forecasting*
Forecasting asthma-related hospital admissions in London using negative binomial models

Soyiri IN, Reidpath DD, Sarran C

Chron Respir Dis, 2013 May;10(2):85-94.
PMID: 23620439 DOI: 10.1177/1479972313482847

Health forecasting can improve health service provision and individual patient outcomes. Environmental factors are known to impact chronic respiratory conditions such as asthma, but little is known about the extent to which these factors can be used for forecasting. Using weather, air quality and hospital asthma admissions, in London (2005-2006), two related negative binomial models were developed and compared with a naive seasonal model. In the first approach, predictive forecasting models were fitted with 7-day averages of each potential predictor, and then a subsequent multivariable model is constructed. In the second strategy, an exhaustive search of the best fitting models between possible combinations of lags (0-14 days) of all the environmental effects on asthma admission was conducted. Three models were considered: a base model (seasonal effects), contrasted with a 7-day average model and a selected lags model (weather and air quality effects). Season is the best predictor of asthma admissions. The 7-day average and seasonal models were trivial to implement. The selected lags model was computationally intensive, but of no real value over much more easily implemented models. Seasonal factors can predict daily hospital asthma admissions in London, and there is a little evidence that additional weather and air quality information would add to forecast accuracy.

Matched MeSH terms: Forecasting
Forecasting peak asthma admissions in London: an application of quantile regression models

Soyiri IN, Reidpath DD, Sarran C

Int J Biometeorol, 2013 Jul;57(4):569-78.
PMID: 22886344 DOI: 10.1007/s00484-012-0584-0

Asthma is a chronic condition of great public health concern globally. The associated morbidity, mortality and healthcare utilisation place an enormous burden on healthcare infrastructure and services. This study demonstrates a multistage quantile regression approach to predicting excess demand for health care services in the form of asthma daily admissions in London, using retrospective data from the Hospital Episode Statistics, weather and air quality. Trivariate quantile regression models (QRM) of asthma daily admissions were fitted to a 14-day range of lags of environmental factors, accounting for seasonality in a hold-in sample of the data. Representative lags were pooled to form multivariate predictive models, selected through a systematic backward stepwise reduction approach. Models were cross-validated using a hold-out sample of the data, and their respective root mean square error measures, sensitivity, specificity and predictive values compared. Two of the predictive models were able to detect extreme number of daily asthma admissions at sensitivity levels of 76 % and 62 %, as well as specificities of 66 % and 76 %. Their positive predictive values were slightly higher for the hold-out sample (29 % and 28 %) than for the hold-in model development sample (16 % and 18 %). QRMs can be used in multistage to select suitable variables to forecast extreme asthma events. The associations between asthma and environmental factors, including temperature, ozone and carbon monoxide can be exploited in predicting future events using QRMs.

Matched MeSH terms: Forecasting
Fulltext Humans as animal sentinels for forecasting asthma events: helping health services become more responsive

Soyiri IN, Reidpath DD

PLoS One, 2012;7(10):e47823.
PMID: 23118897 DOI: 10.1371/journal.pone.0047823

The concept of forecasting asthma using humans as animal sentinels is uncommon. This study explores the plausibility of predicting future asthma daily admissions using retrospective data in London (2005-2006). Negative binomial regressions were used in modeling; allowing the non-contiguous autoregressive components. Selected lags were based on partial autocorrelation function (PACF) plot with a maximum lag of 7 days. The model was contrasted with naïve historical and seasonal models. All models were cross validated. Mean daily asthma admission in 2005 was 27.9 and in 2006 it was 28.9. The lags 1, 2, 3, 6 and 7 were independently associated with daily asthma admissions based on their PACF plots. The lag model prediction of peak admissions were often slightly out of synchronization with the actual data, but the days of greater admissions were better matched than the days of lower admissions. A further investigation across various populations is necessary.

Matched MeSH terms: Forecasting
Fulltext An overview of health forecasting

Soyiri IN, Reidpath DD

Environ Health Prev Med, 2013 Jan;18(1):1-9.
PMID: 22949173 DOI: 10.1007/s12199-012-0294-6

Health forecasting is a novel area of forecasting, and a valuable tool for predicting future health events or situations such as demands for health services and healthcare needs. It facilitates preventive medicine and health care intervention strategies, by pre-informing health service providers to take appropriate mitigating actions to minimize risks and manage demand. Health forecasting requires reliable data, information and appropriate analytical tools for the prediction of specific health conditions or situations. There is no single approach to health forecasting, and so various methods have often been adopted to forecast aggregate or specific health conditions. Meanwhile, there are no defined health forecasting horizons (time frames) to match the choices of health forecasting methods/approaches that are often applied. The key principles of health forecasting have not also been adequately described to guide the process. This paper provides a brief introduction and theoretical analysis of health forecasting. It describes the key issues that are important for health forecasting, including: definitions, principles of health forecasting, and the properties of health data, which influence the choices of health forecasting methods. Other matters related to the value of health forecasting, and the general challenges associated with developing and using health forecasting services are discussed. This overview is a stimulus for further discussions on standardizing health forecasting approaches and methods that will facilitate health care and health services delivery.

Matched MeSH terms: Forecasting*
Fulltext Asia Cohort Consortium: challenges for collaborative research

Song M, Rolland B, Potter JD, Kang D

J Epidemiol, 2012;22(4):287-90.
PMID: 22672913 DOI: 10.2188/jea.je20120024

In this era of chronic diseases, large studies are essential in investigating genes, environment, and gene-environment interactions as disease causes, particularly when associations are important but not strong. Moreover, to allow expansion and generalization of the results, studies should be conducted in populations outside Western countries. Here, we briefly describe the Asia Cohort Consortium (ACC), a collaborative cancer cohort research project that was first proposed in 2004 and now involves more than 1 million healthy individuals across Asia. There are approximately 50 active members from Bangladesh, China, India, Japan, Korea, Malaysia, Singapore, Taiwan, Thailand, the United States, and elsewhere. To date, the work of the ACC includes 3 articles published in 2011 on the roles of body mass index, tobacco smoking, and alcohol consumption in mortality, diabetes, and cancer of the small intestine. Many challenges remain, including data harmonization, resolution of ethical and legal issues, establishment of protocols for biologic samples and transfer agreements, and funding procurement.

Matched MeSH terms: Forecasting
Fulltext Performance evaluation of a new hybrid multivariate meteorological model analysis: a simulation study

Siti Mariam Norrulashikin, Fadhilah Yusof, Kane, Ibrahim Lawal

MATEMATIKA, 2018;34(1):73-85.
MyJurnal

Simulation is used to measure the robustness and the efficiency of the forecasting
techniques performance over complex systems. A method for simulating multivariate
time series was presented in this study using vector autoregressive base-process. By
applying the methodology to the multivariable meteorological time series, a simulation
study was carried out to check for the model performance. MAPE and MAE performance
measurements were used and the results show that the proposed method that consider
persistency in volatility gives better performance and the accuracy error is six time smaller
than the normal hybrid model.

Matched MeSH terms: Forecasting
Reproduction research and health part II--Fetal health

Sinnathuray TA

Med J Malaysia, 1979 Dec;34(2):176-80.
PMID: 548724
Matched MeSH terms: Forecasting
Fulltext Forecasting daily confirmed COVID-19 cases in Malaysia using ARIMA models

Singh S, Murali Sundram B, Rajendran K, Boon Law K, Aris T, Ibrahim H, et al.

J Infect Dev Ctries, 2020 09 30;14(9):971-976.
PMID: 33031083 DOI: 10.3855/jidc.13116

INTRODUCTION: The novel coronavirus infection has become a global threat affecting almost every country in the world. As a result, it has become important to understand the disease trends in order to mitigate its effects. The aim of this study is firstly to develop a prediction model for daily confirmed COVID-19 cases based on several covariates, and secondly, to select the best prediction model based on a subset of these covariates.
METHODOLOGY: This study was conducted using daily confirmed cases of COVID-19 collected from the official Ministry of Health, Malaysia (MOH) and John Hopkins University websites. An Autoregressive Integrated Moving Average (ARIMA) model was fitted to the training data of observed cases from 22 January to 31 March 2020, and subsequently validated using data on cases from 1 April to 17 April 2020. The ARIMA model satisfactorily forecasted the daily confirmed COVID-19 cases from 18 April 2020 to 1 May 2020 (the testing phase).
RESULTS: The ARIMA (0,1,0) model produced the best fit to the observed data with a Mean Absolute Percentage Error (MAPE) value of 16.01 and a Bayes Information Criteria (BIC) value of 4.170. The forecasted values showed a downward trend of COVID-19 cases until 1 May 2020. Observed cases during the forecast period were accurately predicted and were placed within the prediction intervals generated by the fitted model.
CONCLUSIONS: This study finds that ARIMA models with optimally selected covariates are useful tools for monitoring and predicting trends of COVID-19 cases in Malaysia.

Matched MeSH terms: Forecasting
Fulltext HDTO-DeepAR: A novel hybrid approach to forecast surface water quality indicators

Singh RB, Patra KC, Pradhan B, Samantra A

J Environ Manage, 2024 Feb 14;352:120091.
PMID: 38228048 DOI: 10.1016/j.jenvman.2024.120091

Water is a vital resource supporting a broad spectrum of ecosystems and human activities. The quality of river water has declined in recent years due to the discharge of hazardous materials and toxins. Deep learning and machine learning have gained significant attention for analysing time-series data. However, these methods often suffer from high complexity and significant forecasting errors, primarily due to non-linear datasets and hyperparameter settings. To address these challenges, we have developed an innovative HDTO-DeepAR approach for predicting water quality indicators. This proposed approach is compared with standalone algorithms, including DeepAR, BiLSTM, GRU and XGBoost, using performance metrics such as MAE, MSE, MAPE, and NSE. The NSE of the hybrid approach ranges between 0.8 to 0.96. Given the value's proximity to 1, the model appears to be efficient. The PICP values (ranging from 95% to 98%) indicate that the model is highly reliable in forecasting water quality indicators. Experimental results reveal a close resemblance between the model's predictions and actual values, providing valuable insights for predicting future trends. The comparative study shows that the suggested model surpasses all existing, well-known models.

Matched MeSH terms: Forecasting
Identifying factors associated with diagnostic concordance/discordance in oral lichen planus

Siar CH, Lim JS, Tang SP, Chia HS, Loh YM, Ng KH

J Oral Maxillofac Surg, 2013 Oct;71(10):1688-93.
PMID: 23773425 DOI: 10.1016/j.joms.2013.04.026

To identify factors associated with concordance and discordance between clinical and histopathologic diagnoses of oral lichen planus lesions.

Matched MeSH terms: Forecasting

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