Akaike Information Research Articles

Projecting COVID-19 intensive care admissions for policy advice, the Netherlands, February 2020 to January 2021.

BackgroundModel projections of coronavirus disease 2019 (COVID-19) incidence help policymakers about decisions to implement or lift control measures. During the pandemic, policymakers in the Netherlands were informed on a weekly basis with short-term projections of COVID-19 intensive care unit (ICU) admissions.AimWe aimed at developing a model on ICU admissions and updating a procedure for informing policymakers.MethodThe projections were produced using an age-structured transmission model. A consistent, incremental update procedure integrating all new surveillance and hospital data was conducted weekly. First, up-to-date estimates for most parameter values were obtained through re-analysis of all data sources. Then, estimates were made for changes in the age-specific contact rates in response to policy changes. Finally, a piecewise constant transmission rate was estimated by fitting the model to reported daily ICU admissions, with a changepoint analysis guided by Akaike's Information Criterion.ResultsThe model and update procedure allowed us to make weekly projections. Most 3-week prediction intervals were accurate in covering the later observed numbers of ICU admissions. When projections were too high in March and August 2020 or too low in November 2020, the estimated effectiveness of the policy changes was adequately adapted in the changepoint analysis based on the natural accumulation of incoming data.ConclusionThe model incorporates basic epidemiological principles and most model parameters were estimated per data source. Therefore, it had potential to be adapted to a more complex epidemiological situation with the rise of new variants and the start of vaccination.

Evaluation of models for estimation of genetic parameters for post-weaning body measurements and their association with yearling weight in Nellore sheep.

The objective of this study was to obtain (co) variance components and genetic parameter estimates for post-weaning body measurements such as body length (BL), height at withers (HW), and chest girth (HG) recorded at six (SBL, SHW, and SHG), nine (NBL, NHW, and NHG) and twelve (YBL, YHW, and YHG) months of age along with yearling weight (YW) in Nellore sheep maintained at livestock research station, Palamaner, Andhra Pradesh, India and also the association among body measurements with YW was studied. Data on 2,076 Nellore sheep (descended from 75 sires and 522 dams) recorded between 2007 and 2016 (10 years) were utilized in the study. Lambing year, sex of lamb, season of lambing and parity of dam were included in the model as fixed effects and ewe weight was kept as a covariate. Analyses were conducted with six animal models with different combinations of direct and maternal genetic effects using restricted maximum likelihood procedure. Best model for each trait was determined based on Akaike's information criterion. Moderate estimates of direct heritability were obtained for the studied traits viz., BL (0.02 to 0.24), HW (0.31 to 0.49), and CG (0.08 to 0.35) and their corresponding maternal heritability estimates were in the range of 0.00 to 0.07 (BL), 0.13 to 0.17 (HW), and 0.07 to 0.13 (CG), respectively. Positive direct genetic and phenotypic correlations among the traits and they ranged from 0.07 (YBL-YW) to 0.99 (SBL-SHG, SHG-YW, and NBL-YBL) and 0.01 (SBL-YBL) to 0.99 (NBL-NHG), respectively. Further, the genetic correlations among all the body measurements and YW were positive and ranged from 0.07 (YW and YBL) to 0.99 (YW and SHG). There was a strong association of chest girth at six months with YW. Further, it is indicated that moderate improvement of post-weaning body measurements in Nellore sheep would be possible through selection.

Comparison of clinical outcomes of several risk stratification tools in newly diagnosed AML patients: A real-world evidence in our current therapeutic era.

AML classification tools have been developed to stratify the risk at AML diagnosis. There is a need to evaluate these tools in the current therapeutic era. In this retrospective study, we compared five classifiers: ELN 2017, ELN 2022, ALFA classifier, Papaemmanuil etal. classifier, and Lindsley etal. classifier, in a real-life cohort of 281 patients newly diagnosed for AML in Nice University Hospital. In our cohort median age was 68 years old, sex ratio was M/F 56%/44%, performance status was lower than 2 in 73.1% of patients, AML subtype was "De novo" in 71.5%, "secondary" in 22.4%, and "therapy-related" in 6.0% of patients. Intensive chemotherapy was used in 53.0% of patients, and non-intensive chemotherapy in 40.6% of patients. Molecular analysis was available in a large majority of patients and the main mutations found were NPM1 (22.7%), DNMT3A (17.4%), TP53 (13.1%), TET2 (12.4%), and FLT3-ITD (12.4%). In our findings, the comparison of overall survival between the three prognostic groups in the global cohort was statistically significant in all classifiers: ELN 2017 p < 0.0001, ELN 2022 p < 0.0001, ALFA classifier p < 0.0001, Papaemmanuil classifier p < 0.0001, Lindsley classifier p = 0.001. ELN 2017, ELN 2022, ALFA classifier, Papaemmanuil classifier, and Lindsley classifier were calculated respectively in 99%, 99%, 89%, 90%, and 89% of patients. Using Akaike's information criteria (AIC) to compare all five classifiers, ELN 2022 is the best classifier into younger and older patients and for prognosis.

Enhancing and comparing shrinkage prediction models for High-Strength Concrete with and without admixtures

This study aimed to improve and compare the parameterization of three prominent shrinkage prediction models—RILEM B4, MC 2010, and WITS—tailored specifically for High-Strength Concrete (HSC), both with and without the inclusion of admixtures. The dataset used for refining model parameters consisted of 220 experiments related to drying shrinkage and 342 experiments concerning autogenous shrinkage. Model performance evaluation involved various statistical metrics applied to the entire HSC dataset, subdatasets, and distinct time periods of shrinkage (0–99 days, 100–199 days, 200–499 days, and ≥500 days). The statistical indicators included Root Mean Square Error (RMSE), R-squared adjusted (R2 adj), Akaike’s Information Criterion (AIC), and the overall coefficient of variation (C.o.Vall). Modified models exhibited significantly improved predictions compared to the original models, with most predictions falling within ±20% of the measured shrinkages. For HSC drying shrinkage, the original model accuracy ranked as WITS, RILEM B4, and MC 2010. However, after parameter adjustments, WITS, MC 2010, and RILEM B4 were the best-performing models. Conversely, for HSC autogenous shrinkage predictions, the RILEM B4 model surpassed the MC 2010 model, demonstrating superior accuracy and reliability in forecasting this specific type of shrinkage behaviour within High-Strength Concrete.

Discontinuous, biphasic, ontogenetic shifts in the metabolic allometry of aquatic animals?

Several investigations in recent years have reported patterns of discontinuous, biphasic, loglinear variation in the metabolic allometry of aquatic animals. These putative shifts in pattern of allometry have been attributed to changes in the primary site for gas exchange from cutaneous to branchial as animals undergo ontogenetic changes in size, shape, and surface area. Because of the important implications of the earlier research with regard to both physiology and evolution, I re-examined data that purportedly support claims of discontinuous, biphasic allometry in oxygen consumption versus body size of American eels (Anguilla rostrata) and spiny lobsters (Sagmariasus verreauxi). I used ANCOVA to fit three different statistical models to each set of logarithmic transformations and then assessed the fits by Akaike's Information Criterion. The observations for both species were described better by a single straight line fitted to the full distribution than by a biphasic model. Eels, lobsters, and other aquatic animals undergo changes in shape and surface area as they grow, but such changes are not necessarily accompanied by changes in the pattern of metabolic allometry.

Development and validation of mortality prediction models for heart transplantation using nutrition-related indicators: a single-center study from China.

We sought to develop and validate a mortality prediction model for heart transplantation (HT) using nutrition-related indicators, which clinicians could use to identify patients at high risk of death after HT. The model was developed for and validated in adult participants in China who received HT between 1 January 2015 and 31 December 2020. 428 subjects were enrolled in the study and randomly divided into derivation and validation cohorts at a ratio of 7:3. The likelihood-ratio test based on Akaike information was used to select indicators and develop the prediction model. The performance of models was assessed and validated by area under the curve (AUC), C-index, calibration curves, net reclassification index, and integrated discrimination improvement. The mean (SD) age was 48.67 (12.33) years and mean (SD) nutritional risk index (NRI) was 100.47 (11.89) in the derivation cohort. Mortality after HT developed in 66 of 299 patients in the derivation cohort and 28 of 129 in the validation cohort. Age, NRI, serum creatine, and triglyceride were included in the full model. The AUC of this model was 0.76 and the C statistics was 0.72 (95% CI, 0.67-0.78) in the derivation cohort and 0.71 (95% CI, 0.62-0.81) in the validation cohort. The multivariable model improved integrated discrimination compared with the reduced model that included age and NRI (6.9%; 95% CI, 1.8%-15.1%) and the model which only included variable NRI (14.7%; 95% CI, 7.4%-26.2%) in the derivation cohort. Compared with the model that only included variable NRI, the full model improved categorical net reclassification index both in the derivation cohort (41.8%; 95% CI, 9.9%-58.8%) and validation cohort (60.7%; 95% CI, 9.0%-100.5%). The proposed model was able to predict mortality after HT and estimate individualized risk of postoperative death. Clinicians could use this model to identify patients at high risk of postoperative death before HT surgery, which would help with targeted preventative therapy to reduce the mortality risk.

Spatial Heterogeneity of b Values in Northeastern Tibetan Plateau and Its Interpretation.

The northeastern margin of the Tibetan Plateau (NE Tibetan Plateau) exhibits active geological structures and has experienced multiple strong earthquakes, with M ≥ 7, throughout history. Particularly noteworthy is the 1920 M81/2 earthquake in the Haiyuan region that occurred a century ago and is documented as one of the deadliest earthquakes. Consequently, analyzing seismic risks in the northeastern margin of the Tibetan Plateau holds significant importance. The b value, a crucial parameter for seismic activity, plays a pivotal role in seismic hazard analyses. This study calculates the spatial b values in this region based on earthquake catalogs since 1970. The study area encompasses several major active faults, and due to variations in b values across different fault types, traditional grid-search methods may introduce significant errors in calculating the spatial b value within complex fault systems. To address this, we employed the hierarchical space-time point-process (HIST-PPM) method proposed by Ogata. This method avoids partitioning earthquake samples, optimizes parameters using Akaike's Bayesian Information Criterion (ABIC) with entropy maximization, and theoretically allows for a higher spatial resolution and more accurate b value calculations. The results indicate a high spatial heterogeneity in b values within the study area. The northwestern and southeastern regions exhibit higher b values. Along the Haiyuan fault zone, the central rupture zone of the Haiyuan earthquake has relatively higher b values than other regions of this fault zone, which is possibly related to the sufficient release of stress during the main rupture of the Haiyuan earthquake. The b values vary from high in the west to low in the east along the Zhongwei fault. On the West Qinling fault zone, the epicenter of the recent Minxian-Zhangxian earthquake is associated with a low b value. In general, regions with low b values correspond well to areas with moderate-strong seismic events in the past 50 years. The spatial differences in b values may reflect variances in seismic hazards among fault zones and regions within the same fault zone.

Systematic development and validation of a predictive model for major postoperative complications in the Peri-operative Quality Improvement Project (PQIP) dataset.

Complications are common following major surgery and are associated with increased use of healthcare resources, disability and mortality. Continued reliance on mortality estimates risks harming patients and health systems, but existing tools for predicting complications are unwieldy and inaccurate. We aimed to systematically construct an accurate pre-operative model for predicting major postoperative complications; compare its performance against existing tools; and identify sources of inaccuracy in predictive models more generally. Complete patient records from the UK Peri-operative Quality Improvement Programme dataset were analysed. Major complications were defined as Clavien-Dindo grade ≥ 2 for novel models. In a 75% train:25% test split cohort, we developed a pipeline of increasingly complex models, prioritising pre-operative predictors using Least Absolute Shrinkage and Selection Operators (LASSO). We defined the best model in the training cohort by the lowest Akaike's information criterion, balancing accuracy and simplicity. Of the 24,983 included cases, 6389 (25.6%) patients developed major complications. Potentially modifiable risk factors (pain, reduced mobility and smoking) were retained. The best-performing model was highly complex, specifying individual hospital complication rates and 11 patient covariates. This novel model showed substantially superior performance over generic and specific prediction models and scores. We have developed a novel complications model with good internal accuracy, re-prioritised predictor variables and identified hospital-level variation as an important, but overlooked, source of inaccuracy in existing tools. The complexity of the best-performing model does, however, highlight the need for a step-change in clinical risk prediction to automate the delivery of informative risk estimates in clinical systems.

Present‐day Upper‐crustal Strain Rate Field in Southeastern Tibet and its Geodynamic Implications: Constraints from GPS Measurements with ABIC Method

AbstractThe Earth's surface kinematics and deformation are fundamental to understanding crustal evolution. An effective research approach is to estimate regional motion field and deformation fields based on modern geodetic networks. If the discrete observed velocity field is obtained, the velocity related fields, such as dilatation rate and maximum shear strain rate, can be estimated by applying varied mathematical approaches. This study applied Akaike's Bayesian Information Criterion (ABIC) method to calculate strain rate fields constrained by GPS observations in the southeast Tibetan Plateau. Comparison with results derived from other three methods revealed that our ABIC‐derived strain rate fields were more precise. The maximum shear strain rate highlighted the Xianshuihe–Xiaojiang fault system as the main boundary for the outward migration of material in southeastern Tibet, indicating rotation of eastern Tibet material around the eastern Himalaya rather than whole extrusion along a fixed channel. Additionally, distinct dilatation rate patterns in the northeast and southwest regions of the fault system were observed. The northeast region, represented by the Longmenshan area, exhibited negative dilatational anomalies; while the southwest region, represented by the Jinsha River area north of 29°N, displayed positive dilatational anomalies. This indicates compression in the former and extension in the latter. Combined with deep geophysical observations, we believe that the upper and lower crusts of the Jinsha River area north of 29°N are in an entire expanding state, probably caused by the escape‐drag effect of material. The presence of a large, low‐viscosity region south of 29°N may not enable the entire escape of the crust, but instead result in a differential escape of the lower crust faster than the upper crust.

Comparison of nonlinear functions to describe lactation curves for cumulative milk production in buffalo.

The aim of this study was to examine the suitability of different growth functions (linear, sinusoidal, Gompertz, Schumacher and Richards) to fit cumulative milk production data from buffalo cows. Cumulative milk production at each day in milk was calculated from two published datasets reporting (i) fortnightly test-day milk yield records of the first lactation of Murrah buffalo that had calved during 1977-2012 and (ii) the first lactation records of Jaffarabadi buffalo collected from history-cum-pedigree registers for each quinquennium between 1991 and 2010. Each function was fitted to the lactation curves using nonlinear regression procedures. The Richards and sinusoidal equations provided the smallest root mean square error values, Akaike's and Bayesian information criteria and, therefore, the best fit for the cumulative lactation curves for milk yield. The Richards equation appeared to provide the most accurate estimate of the cumulative milk production at peak milk yield. Sinusoidal and flexible classical growth functions are appropriate to describe cumulative milk production curves and estimate lactation traits in buffalo.

Predictive markers for infections after extracorporeal shockwave lithotripsy in patients with kidney stone based on a large prospective cohort.

Infections in patients with kidney stones after extracorporeal shockwave lithotripsy (SWL) is a common clinical issue. However, the associated factors are unclear. Therefore, we aim to develop and validate a predictive model for infections after SWL in patients with kidney stone. Between June 2020 and May 2022, consecutive kidney stone patients were enrolled. Of them, 553 patients comprised the development cohort. One hundred sixty-five patients comprised the validation cohort. The data were prospectively collected. The stepwise selection was applied using the likelihood ratio test with Akaike's information criterion as the stopping rule; A predictive model was constructed through multivariate logistic regression. The performance was evaluated regarding discrimination, calibration, and clinical usefulness. Predictors of infections after SWL in treating kidney stones included older age (OR = 1.026, p = 0.041), female (OR = 2.066, p = 0.039), higher BMI (OR = 1.072, p = 0.039), lower stone density (OR = 0.995, p < 0.001), and higher grade of hydronephrosis (OR = 5.148, p < 0.001). For the validation cohort, the model showed good discrimination with an area under the receiver operating characteristic curve of 0.839 (95% CI 0.736, 0.941) and good calibration. Decision curve analysis demonstrated that the model was also clinically useful. This study indicated that age, gender, BMI, stone density, and hydronephrosis grade were significant predictors of infections after SWL in treating kidney stones. It provided evidence in optimizing prevention and perioperative treatment strategies to reduce the risk of infection after SWL.

Enhancing early-stage cell culture process development efficiency using an integrated approach of high-throughput miniaturized bioreactors and definitive screening design

Rapidly expanding pipelines in biopharmaceutical industry and increasingly stringent requirements from regulatory agencies call for a more efficient and rational study design at early-stage cell culture process development. Recent developments in high-throughput bioreactor system and design of experiments (DoE) enable cell culture process development sector to meet such demand by integrating both technological advances. In this study, we compared varied forms of fractional factorial designs (FFDs) and definitive screening design (DSD) covering key process parameters such as temperature shift target, temperature shift timing, feed strategy, pH, dissolved oxygen, harvest time and additives in Ambr® 15 bioreactors to better understand the initial cell culture process and thus optimize it. Results showed that DSD was more effective and efficient in detecting both main and quadratic effects comparing to FFDs. Comparison of varied available model development strategies revealed that two-stage forward regression combined with corrected Akaike's information criterion (AICc) and least absolute shrinkage and selection operator (LASSO) regression combined with self-validated ensembled models (SVEM) offered superior predictive capability. Moreover, key DoE regularities was evaluated for their validity. In conclusion, DSD is a suitable experimental design for the early-stage cell culture process development if quadratic effects might exist. Statistical models derived based on DSD-based dataset could be applied for predictive optimization of antibody producing bioprocess while regularities such as effect heredity and effect hierarchy might be relaxed considering higher-order interaction effects are more likely to existed for a cell-based biological system.

How enoxaparin underdosing and sex contribute to achieving therapeutic anti-Xa levels.

Anti-Xa serves as a clinical surrogate for assessing the efficacy and bleeding risk in patients treated with enoxaparin for thromboembolic events. Evidence from the literature and empirical observations suggest that patients are underdosed in clinical practice to avoid bleeding complications. This study aimed to investigate such underdosing of enoxaparin and its potential impact on achieving therapeutic anti-Xa levels. This multicentric, retrospective, observational study included patients with acute ischemic stroke due to atrial fibrillation. All patients received enoxaparin in the therapeutic setting with subsequent anti-Xa measurements. The one-sample, one-tailed Wilcoxon signed-rank test was used to identify a significant difference between the doses administered and the recommended daily dose. Logistic regression model analysis was performed to identify additional predictors affecting achievement of the therapeutic anti-Xa target range. Stepwise forward-backward selection with Akaike's information criterion as metric was applied to refine the logistic regression model. A total of 145 patients from the university hospitals of St. Pölten and Tulln in Lower Austria were included. The median daily enoxaparin dose administered was 1.23mg/kg, resulting in an overall target range achievement rate of 66%. As compared to recommended therapeutic doses, significant underdosing of enoxaparin was evident in both participating centers (p < 0.001). The calculated threshold dose to achieve the therapeutic target range with a 90% probability was 1.5mg/kg enoxaparin daily. Female sex was found to be a strong independent predictor of achieving a therapeutic target range (OR 9.44; 95% CI 3.40-30.05, p < 0.001). Despite the underdosing observed in both centers, therapeutic anti-Xa levels were achieved with lower than recommended doses of enoxaparin, and women required even lower doses than men. These findings warrant further confirmation by prospective studies.

Modeling COVID-19 cases using NB-INGARCH and ARIMA models: A case study in Iligan City, Philippines

Modeling COVID-19 cases using count data approach has been scarce in the Philippine setting. This study compares the NB-INGARCH with the traditional ARIMA in modeling daily COVID-19 cases in Iligan City (August 14, 2020 – October 31, 2021). We employ the maximum likelihood estimation method and compare the models using the Akaike's information criterion (AIC). Empirical results reveal that the NB-INGARCH(7,0) outperforms ARIMA(2,1,3) in terms of forecast evaluation measures. However, the results show that rainfall and air pressure have no significant effects on the cases. We conclude that the NB-INGARCH model is a viable alternative approach to modeling count time series.

Social Cognitive Theory Variables as Correlates of Physical Activity Behavior Among Hispanics With Multiple Sclerosis

ObjectiveTo examine the association between social cognitive theory (SCT) variables and physical activity (PA) behavior in Hispanic adults with multiple sclerosis (MS). DesignObservational, survey study. SettingGeneral community. ParticipantsInclusion criteria: (a) age 18 years or older, (b) self-identify as Hispanic or Latino, (c) diagnosis of MS, and (d) English or Spanish as primary language. Two hundred forty-three individuals opened the survey, 24 individuals did not meet inclusion criteria, and 66 individuals were excluded due to not completing any of the SCT variable measures (N=153). InterventionsNot applicable. Main Outcome Measure(s)PA was measured using the Godin Leisure Time Exercise Questionnaire (GLTEQ) total and Health Contribution Scores (HCS). SCT variables included standard measures of exercise self-efficacy, planning, goal setting, outcome expectations, social support, and functional impairment. ResultsThe final sample included 153 participants with a mean (SD) age of 43 (11) years, 78% identified as women, 57% were employed, and 63% had a college degree or higher. The mean (SD) MS disease duration was 11 (9) years, 74% of participants reported relapsing remitting MS, and median (interquartile range) Patient Determined Disease Steps (PDDS) score was 3.0 (3.0). Significant correlations were observed between all SCT variables and both GLTEQ total and GLTEQ HCS scores (P<.001). Forward stepwise regression with Akaike's Information Criterion (AICC) identified PDDS, age, exercise planning, exercise self-efficacy, and exercise goal setting as the most appropriate covariates and SCT variables to include in a multivariable model examining independent contributions on GLTEQ HCS scores. The multivariable, linear regression analysis indicated that PDDS (Standardized B=-.280, P<.001), exercise planning (Standardized B=.214, P<.05), and exercise self-efficacy (Standardized B=.183, P<.05) explained 42.4% of variance in GLTEQ HCS scores. ConclusionsThis study provides a contribution to the literature by identifying SCT variables that correlate with PA behavior among Hispanics with MS that may guide the creation of culturally appropriate PA programs.

Prevalence and Predictors of Adverse Events Associated With Dipeptidyl Peptidase-4 (DPP-4) Inhibitors in Type 2 Diabetic Patients: A Cross-sectional Study.

Dipeptidyl peptidase-4 (DPP-4) inhibitors are oral hypoglycemic agents widely prescribed in India despite safety concerns. However, studies focused on their safety profile are scarce, especially in South India. To evaluate the prevalence and predictors of adverse events (AEs) with DPP-4 inhibitors in patients with type 2 diabetes mellitus (T2DM). This retrospective cross-sectional study analyzed data from medical records of T2DM patients prescribed DPP-4 inhibitors admitted to the medicine department from 2019 to 2021 at a South Indian tertiary care hospital. The causality of AEs was assessed using the WHO-Uppsala Monitoring Centre (WHO-UMC) criteria and the Naranjo scale, and severity using the Modified Hartwig and Seigel scale. We applied a Generalized model with a binary response and logit-link function to understand the factors that best explain the AE. The best-fit models were chosen based on least Akaike's information criterion and highest PseudoR 2 and presented the odds ratio (OR) with a 95% confidence interval. The analyses were performed in R software version 4.2.1. Among the 796 patients included in the study, 26% experienced AEs. A total of 212 AEs were observed, and Saxagliptin-associated AEs were the most prevalent (66.6%). Hepatic AEs were predominant (37.7%), followed by gastrointestinal events (16.5%) and electrolyte imbalances (12.3%). Most AEs were possible based on WHO-UMC criteria (78.7%) and the Naranjo scale (86.7%), with 58% being of moderate severity and 42% mild. In the multivariate analysis, aspartate transaminase [OR: 1.013 (0.006-1.020)], alkaline phosphatase [OR: 1.004 (1.001-1.007)] and patients already on DPP-4 inhibitors [OR 1.191(1.012-1.366)] were significant predictors for AEs with DPP-4 inhibitors. The study highlighted a high prevalence of AEs with DPP-4 inhibitors and identified significant predictors of these AEs. These findings underscore the necessity of vigilant monitoring and risk assessment while prescribing DPP-4 inhibitors to the Indian population.

Forecasting of neonatal mortality trend at a special new-born care unit in Odisha, India: A time-series analysis

New born mortality is a public health problem in the state of Odisha. Newborn mortality is a dynamic process and variations in mortality are observed temporally and seasonally, and also across health facilities. Prior knowledge of mortality burden can enable health system’s readiness in terms of resources allocation and timely intervention, thereby improving the chances of survival of sick newborns admitted in the hospitals. Hence, this study aimed to examine temporal trends of newborn mortality in a Special Newborn Care Unit of Saheed Laxman Nayak Medical College and Hospital (SLNMCH) in Odisha and forecast a short-term monthly projection.The Box-Jenkins approach was used to fit a seasonal autoregressive integrated moving average (SARIMA) model to the monthly recorded mortality among the hospitalized new borns in the SNCU during 2016-2020. The best-fit model for forecasting was found based on the Akaike Information Criterion.The time-series analysis revealed a modest upward trend in newborn mortality rate among SNCU admitted newborns, with peaks in the late winter and late summer months. The seasonal ARIMA (0,1,1)(1,1,1)12 model offered the best fit for time-series data. This model predicted the monthly percentage of mortality in SNCU admitted newborns in the range of 9% to 35% with respective 95% confidence interval for two years period (2021-2022).SARIMA models are useful for monitoring newborn mortality and provide an estimate of temporal trends and seasonality. The models are helpful for predicting occurrence of mortality in the SNCU of SLNMCH and could be useful for developing early warning systems. It may help in early detection, timely treatment, and prevention of serious complications in admitted sick newborns.

Impact of heat stress on genetic evaluation of oocyte and embryo production in Gir dairy cattle.

Identifying and selecting genotypes tolerant to heat stress might improve reproductive traits in dairy cattle, including oocyte and embryo production. The temperature-humidity index (THI) was used, via random regression models, to investigate the impact of heat stress on genetic parameters and breeding values of oocyte and embryo production in Gir dairy cattle. We evaluated records of total oocytes (TO), viable oocytes (VO), cleaved embryos (CE), and viable embryos (VE) from dairy Gir donors. Twenty-four models were tested, considering age at ovum pick-up (AOPU) and THI means as a regressor in the genetic evaluation. We computed THI in eight periods, from 0 to 112days before ovum pick-up, which were adjusted by different orders of Legendre polynomials (second, third, and fourth). The best-fit model according to Akaike's information criterion (AIC) and Model Posterior Probabilities (MPP) considered Legendre polynomials of third order and THI means of 112days for TO, fourth order and 56days for VO, second order and 28days for CE, and second order and 42days for VE, respectively. The heritability (h2) estimates across AOPU and THI scales ranged from 0.34 to 0.62 for TO, 0.31 to 0.58 for VO, 0.26 to 0.39 for CE, and 0.15 to 0.26 for VE, respectively. The fraction of the phenotypic variance explained by the permanent environment in different AOPU and THI scales ranged from 0.03 to 0.25 for TO, 0.05 to 0.26 for VO, 0.09 to 0.36 for CE, and 0.15 to 0.27 for VE, respectively. Spearman's rank correlation between the estimated breeding values in different AOPU and THI scale from the top 5% sires and females ranged from 0.18 to 0.90 for TO, 0.31 to 0.95 for VO, 0.14 to 0.85 for CE, and 0.47 to 0.94 for VE, respectively. The h2estimates for all evaluated traits varied from moderate to high magnitude across AOPU and THI scales, indicating that genetic selection can result in rapid genetic progress for the evaluated traits. There was a reranking among the best animals in different AOPU and THI. It is possible to select dairy Gir cattle tolerant to heat stress to improve oocyte and embryo production.

Height-for-age and weight-for-age growth charts for Pakistani infants under six months: derived from a novel case selection method using multiple indicator cluster survey data

BackgroundIn the past two decades, there has been a growing recognition of the need to establish indigenous standards or reference growth charts, particularly following the WHO multicenter growth study in 2006. The availability of accurate and reliable growth charts is crucial for monitoring child health. The choice of an appropriate model for constructing growth charts depends on various data characteristics, including the distribution’s tails and peak. While Pakistan has reported some reference growth charts, there is a notable absence of indigenous charts for children under two years of age, especially for infants aged 0–6 months who are exclusively breastfed. Additionally, acquiring data poses a significant challenge, particularly for low-income countries, as it demands substantial resources such as finances, time, and expertise. The Multiple Indicator Cluster Survey (MICS) constitutes a large-scale national survey conducted periodically in low-income countries under the auspices of UNICEF. In this study, we propose methods for generating selection variables utilizing the “Novel Case Selection Method,“ as previously published. Further our approach enables to select and fit appropriate model to the MICS data, selected, and to develop the standard growth charts.MethodsOut of the 11,478 children under 6 months of age included in MICS-6 (Pakistan), 3,655 children (1,831 males and 1,824 females) met the specified criteria and were selected using the “Novel Case Selection Method”. The sample was distributed across provinces as follows: 841 (23.0%) from KPK, 1,464 (40.1%) from Punjab, 819 (22.4%) from Sindh, and 531 (14.5%) from Balochistan. This sample encompassed both rural (76.4%) and urban (23.6%) populations. Following data cleaning and outlier removal, a total of 3,540 records for weight (1,768 males and 1,772 females) and 3,515 records for height (1,759 males and 1,756 females) were ultimately available for the development of standard charts. The Bayesian Information Criterion (BIC) was employed to determine the optimal degrees of freedom for L, M, and S using RefCurv_0.4.2. Three families within the gamlss class—namely, Box Cox Cole and Green (BCCG), Box Cox T (BCT), and Box Cox Power Exponential (BCPE)—were applied, each with three smoothing techniques: penalized splines (ps), cubic splines (cs), and polynomial splines (poly). The best-fitted model was selected from these nine combinations based on the Akaike Information Criteria.ResultsThe Novel Case Selection Method yielded 3655 cases as per criteria. After cleaning the data, this method lead to selection of 3540 children for “weight for age” (W/A) and 3515 children for “height for age” (H/A). The “BCPE” family and “ps” as smoothing method proved to be best on AIC for all four curves, i.e. the W/A male, W/A female, H/A male, and H/A female. The optimum selected degrees of freedom for the curve “W/A”, for both genders were (M = 1, L = 0, S = 0). The optimum degrees of freedom for H/A male were again (M = 1, L = 0, S = 0), but for females the selected degrees of freedom were (M = 1, L = 1, S = 1). The indigenous fitted standard curves for Pakistan were on lower trajectory in comparison to WHO standards.ConclusionThis study uses the Novel Case Selection Method with introduced algorithms to construct tailored growth charts for lower and middle-income countries. Leveraging extensive MICS data, the methodology ensures representative national samples. The resulting charts hold practical value and await validation from established data sources, offering valuable tools for policy makers and clinicians in diverse global contexts.

Repairable System Analysis Using the Discrete Weibull Distribution

In many practical circumstances, a repair can be performed when a system fails to restore to a condition before a failure. This type of repair is known as minimal repair and one of the most used models is the power law process (PLP). It is common to consider equipment failure time as a continuous variable in this model, assuming a high degree of accuracy in the measurement tool. However, in practical situations, failures are usually observed and recorded as integer numbers of time units, such as the number of days and hours, indicating a discrete process. In this study, we used a discrete Weibull distribution instead of a continuous Weibull distribution. Since both Weibull models have similar complexities, some benefits are observed in the usage of the discrete model, such as a lower standard deviation of the parameter related to the system deterioration and also a lower Akaike's information criterion. For illustrative purposes, the use of a discrete Weibull distribution was applied to a dataset related to the failures of concrete mixer trucks. Moreover, an approach using a Markov chain with discrete states to obtain the average number of failures and the respective optimum maintenance policy was included. Additionally, six cases (found in the literature) fitted by the PLP model were reanalyzed and compared under the discrete model perspective.