BIC Values Research Articles

PARAMETRIC AND SEMI-PARAMETRIC SURVIVAL MODELS WITH APPLICATION TO HYPERTENSION DATA

Correlated survival data with possible censoring are frequently encountered in survival analysis.When there are dependencies among observed survival times, conventional Cox proportionalhazards model (CPHM) and Accelerated Failure Time (AFT) models that assumes independentresponses may not be appropriate. In this study, we compare the performance of parametric andsemi-parametric survival models with application to clinical data. Specifically, the AFT modeland the CPHM with and without Random effect were compared. Data on hypertension wascollected from Federal Medical Centre Keffi and General Hospital Nasarawa for the period offive years (2016 – 2020). The results from the analysis revealed that the Weibull AFT modelwith Gamma Random effect distribution had the least AIC and BIC values indicating that itoutperformed the other models considered in this study. Hence, the interpretation of the resultswas based on the most efficient model. Based on our results, it was found that hypertensionpatient that were giving drugs on the visit to the hospital has longer survival time compared tothose that were not giving drugs. Also, Hypertension patient with blood group AB and Obesedhave lesser survival time as compared to those with blood group o+ and normal weightrespectively. The study recommend that health expert can use the Weibull AFT model withGamma Random effect for predicting the risk factors of Hypertension especially when the dataare correlated.Keywords: AFT, CPHM, Hazard, Hypertension, Survival,

Chronogram or phylogram for ancestral state estimation? Model‐fit statistics indicate the branch lengths underlying a binary character's evolution

AbstractModern methods of ancestral state estimation (ASE) incorporate branch length information, and it has been demonstrated that ASEs are more accurate when conducted on the branch lengths most correlated with a character's evolution; however, a reliable method for choosing between alternate branch length sets for discrete characters has not yet been proposed.In this study, we simulate paired chronograms and phylograms, and generate binary characters that evolve in correlation with one of these. We then investigate (a) the effect of alternate branch lengths on ASE error and (b) whether phylogenetic signal statistics and/or model‐fit statistic can be used to select the branch lengths most correlated with a binary character.In agreement with previous studies, we find that ASEs are more accurate when conducted on the branch lengths most correlated with the character. Phylogenetic signal statistics show limited utility for selecting the correct branch lengths, but model‐fit statistics are found to be more accurate, with the correct branch lengths generally returning greater model‐fit (lower AICc and BIC values). Using this method to choose between alternate branch length sets is more accurate when tree and character properties are more favourable for model optimization, and when shape differences between alternate phylogenies are greater.Our results indicate that researchers conducting ASEs on discrete characters should carefully consider which branch lengths are appropriate, and, in the absence of other evidence, we suggest estimating model‐fit values over alternate branch length sets and evolutionary models and choosing the branch length/model combination that returns better model‐fit.

Forcasting Stock Price PT. Indonesian Telecomunication with ARCH-GARCH Model

This research discusses the modeling of time series using R software, focusing on forecasting the stock price of PT. Indonesian telecommunications with ARCH-GARCH model. The data used daily closing data on stock prices from January 6, 2020, to January 6, 2021 was obtained from the website www.finance.yahoo.com. The goal is to find out the best model arch-garch on PT. Indonesian telecommunications to find out the results of stock price forecasting the next day using the ARCH-GARCH model. The best model was ARIMA (2,1,3). The results of the ARCH-LM test showed the data contained heteroskedasticity effects or ARCH elements. The research models proposed in this study are ARCH (1) and ARCH-GARCH (1,1). The smallest AIC and BIC values of these two models are ARCH-GARCH (1,1) which is the best model for forecasting the stock price of PT. Indonesian telecommunications for the next 10 days. The study attempts to conduct stock price forecasting with the ARCH-GARCH model. The result of the forecasting of the share price of PT. Indonesian telecommunications from January 07, 2021 to January 20, 2021 respectively except for holidays is IDR 3374.884, IDR 3379.617,IDR 3378.305, IDR 3376.610, IDR 3380.050, IDR 3376.372, IDR 3379.071, IDR 3377.964, IDR 3377.515, IDR 3379.002. Forecasting results are close to factual data for forecasting the next 10 days so that they can be taken into consideration in investing by investors.

Population Genetic Structure of Ornatus Spiny Lobster (Panulirus Ornatus) in Indo-West Pacific Region

Panulinus ornatus is among the widest distributed spiny lobsters, the high-value seafood, and the major cultured species in several Indo-West Pacific countries. Five microsatellite markers were applied to investigate diversity, genetic differentiation, and population structure of ornate spiny lobster in Vietnam (VN), Australia (AU), and Sri Lanka (SR). P. ornatus population in AU exhibited highest genetic diversity, and SR population was the lowest one (number of effective allele; observed/expected heterozygous are 7.091±1.53; 0.787±0.11/0.819±0.05 and 6.568±1.88; 0.620±0.12/0.782±0.07, respectively). The FST value (0.006-0.012; P≤0.01) and DAPC analysis (BIC value =3) showed the disconnectivity between the three populations, while AMOVA test and clustering structure analysis showed low significant difference. Directional migration relative rates suggest that Vietnam population may act as a source for surrounding populations in the Indo-West Pacific. The present study provides data on the genetic characteristics and population structure of P. ornatus in the Indo-West Pacific.

PSIV-2 Dry Matter Intake in Beef Cows is Influenced by Weather Variables

Abstract Models that can predict dry matter intake (DMI) of cows will help in efficient allocation and management of feed resources. A study was undertaken to understand the relationship between weather variables and DMI of beef cows in the Northern Great Plains of North America. Data from 155 beef cows with 2,161 observations (cow - weeks) was utilized. The variables utilized for this study were BW (455 to 875 kg), DMI (9.03 to 27.69 kg/d), NEm intake (9.16 to 46.12 Mcal/d), ambient temperature (-18.9 to 23.9°C), range of temperature (4.54 to 13.82 °C), wind speed (2.29 to 5.39 m/s), solar radiation (30.97 to 292.61 W/m2) dew point (-21.4 to 19.2 °C), and 2-week lag (average of previous 2 weeks values) and monthly lag (average of previous 4 weeks values) of each weather variable. Residuals of DMI fitting week of the year (fixed), and treatment (random), were used to generate scatter plots to identify if linear relationships existed. BW and weather variables had a linear relationship with DMI, while NEm intake had both linear and quadratic relationships with DMI. For the model, MIXED linear regression of SAS was used using stepwise regression. Model fits were determined using P-values, AIC, and BIC values. Absolute ambient temperature and range of temperature were important (P < 0.05) weather predictors of DMI. Wind speed interacted (P < 0.05) with ambient temperature and range of temperature, and this accounted for additional variation in DMI of beef cows. These results improved our understanding of the relationship between weather variables and DMI in beef cows like what was previously reported in beef steers.

Big data ordination towards intensive care event count cases using fast computing GLLVMS

BackgroundIn heart data mining and machine learning, dimension reduction is needed to remove multicollinearity. Meanwhile, it has been proven to improve the interpretation of the parameter model. In addition, dimension reduction can also increase the time of computing in high dimensional data.MethodsIn this paper, we perform high dimensional ordination towards event counts in intensive care hospital for Emergency Department (ED 1), First Intensive Care Unit (ICU1), Second Intensive Care Unit (ICU2), Respiratory Care Intensive Care Unit (RICU), Surgical Intensive Care Unit (SICU), Subacute Respiratory Care Unit (RCC), Trauma and Neurosurgery Intensive Care Unit (TNCU), Neonatal Intensive Care Unit (NICU) which use the Generalized Linear Latent Variable Models (GLLVM’s).ResultsDuring the analysis, we measure the performance and calculate the time computing of GLLVM by employing variational approximation and Laplace approximation, and compare the different distributions, including Negative Binomial, Poisson, Gaussian, ZIP, and Tweedie, respectively. GLLVMs (Generalized Linear Latent Variable Models), an extended version of GLMs (Generalized Linear Models) with latent variables, have fast computing time. The major challenge in latent variable modelling is that the function fleft(varTheta right)=int fleft(uvarTheta right)hleft(uright)du is not trivial to solve since the marginal likelihood involves integration over the latent variable u.ConclusionsIn a nutshell, GLLVMs lead as the best performance reaching the variance of 98% comparing other methods. We get the best model negative binomial and Variational approximation, which provides the best accuracy by accuracy value of AIC, AICc, and BIC. In a nutshell, our best model is GLLVM-VA Negative Binomial with AIC 7144.07 and GLLVM-LA Negative Binomial with AIC 6955.922.

Predicting breeding values in Murrah buffaloes using random regression models

Various random regression models with different order of Legendre polynomials for permanent environmental and genetic effects were constructed to evaluate Murrah buffaloes under field condition. A total of 24,160 test-day (TD) milk yield records of 2,336 buffaloes belonging to six districts of Haryana state from 2014 to 2019 under CHRS Rohtak were considered for estimating the random covariate coefficients using REML algorithm. The R2 and BIC improved up to seventh order of the polynomials but decreased at 8th order. The lowest value of AIC was for 8th order of polynomial. The models with LPs of order 5 and above gave an improved fit of the lactation curve having high R2 value (>98%). However, the number of parameters increased from 13 for LP of order 3 to 73 for LP of order 8 indicating over parameterization of models. Best order of fit was 7th order having highest R2 (99.23%), lowest AIC and lowest BIC value. The lactation curves plotted from the ETDMYs obtained under RRMs using LP5 and LP7 were smoother as compared to the lactation curve obtained from arithmetic mean. The 301 day lactation yield under RRMs using LP of order 7 and 5 averaged 3470.42±2.76 kg and 3460.72±3.10 kg, respectively. The milk yield ranged from 3,456.38 kg in Bhiwani district to 3,480.86 kg in Sonipat district under LP7 model while it ranged from 3,426.35 kg in Jhajjar district to 3,503.95 kg in Bhiwani district under LP5 model. It was observed that less than 50% were common in top 1% of total buffaloes under the two RRMs. The RRM using LP of order 7, which provided more accurate breeding values, must be considered for selection of top ranking/ superior animals for breeding purpose.

Application of Parametric Shared Frailty Models to Analyze Time-to-Death of Gastric Cancer Patients.

Despite its declining incidence, gastric cancer (GC) is one of the world's leading malignancies and a major global health concern due to its high prevalence and fatality rate. Furthermore, it is the world's fourth most common cancer and the second leading cause of cancer death. Studying the determinants of time to death of gastric cancer patients will give clinicians more information to develop specific treatment plans, forecast prognosis, and track the progress of death cases. The application of the frailty model can help account for random variation in survival that may exist due to unobserved factors, as well as show the impact of latent factors on death risk. As a result, the purpose of this study was to assess the determinants of time to death of GC patients' by applying the parametric shared frailty models. The data for this study were obtained from gastric cancer patients admitted to the Tikur Anbesa Specialized Hospital, Addis Ababa, from January 1, 2015, to February 29, 2020. With the aim of coming up with an appropriate survival model that determines factors that affect the time to death of gastric cancer patients, various parametric shared frailty models were compared. In all of the frailty models, patient regions were used as a clustering variable. The current study implemented exponential, Weibull, log-logistic, and lognormal distributions for baseline hazard functions with gamma and inverse Gaussian's frailty distributions. The performance of all models was compared using the AIC and BIC criteria. R statistical software was used to conduct the analysis. A retrospective study was undertaken on a total of 407 gastric cancer patients under follow-up at Tikur Anbesa Specialized Hospital. Of all 407 GC patients, 56.3% died while the remaining 43.7% were censored. The patients' median time to death was 21.9months, with a maximum survival time of 49.6months. In the current study, the clustering effect was significant in modeling the time to death from gastric cancer. The Weibull model with inverse Gaussian frailty has the minimum AIC and BIC value among the candidate models compared. The dependency within the clusters for the Weibull-inverse Gaussian frailty model was [Formula: see text] (13.4%). According to the results of our best model (Weibull-inverse Gaussian), the sex of the patient, the smoking status, the tumor size, the treatment taken, the vascular invasion, and the disease stage was found to be statistically significant at an alpha = 0.05 significance level. Time to death of GC patient's data set was well described by the Weibull-inverse Gaussian shared frailty. Furthermore, Weibull baseline distribution best fits the GC data set as it enables proportional hazard and accelerated failure time model, for time to failure data. There is unobserved heterogeneity between clusters (patient regions), indicating the need to account for this clustering effect. In this study, survival time to death among GC patients was discovered to be small. Covariates like older age, being male, having higher (advanced) stage of GC disease (stage three and stage four), advanced tumor size, being smoker, infected by Helicobacter pylori, and existence of vascular invasion significantly accelerate the time to death of GC patients. In contrast, talking combination of more treatments prolongs the time to death of patients. To improve the health of patients, interventions should be taken based on significant prognostic factors, with special attention dedicated to patients with such factors to prevent GC death.

Osseointegration of Zirconia Implants after UV-Light or Cold Atmospheric Plasma Surface Treatment In Vivo.

The influence of UV light and non-thermal plasma on the osseointegration of yttria-stabilized zirconia implants (Y-TZP) comparing the two methods is unclear. The aim of this study was to show the influence of these methods on the osseointegration of dental zirconia implants in an animal model. A total of 54 implants were either untreated, treated with UV light (UV), or non-thermal oxygen plasma for 12 min and inserted into the parietal bones of six domestic pigs. The animals were sacrificed after a healing interval of two, four, and nine weeks. The degree of osseointegration was determined using histomorphometric determination of bone-to-implant contact values (BIC) and the bone-to-implant contact values within the retentive parts of the implants (BAFO). BIC values decreased in all groups after four weeks of healing and re-increased after nine weeks in all groups. BAFO increased significantly over time in all groups. However, there were no statistically significant differences in BIC and BAFO values between the control group and the test groups and over time. Clinical studies may follow to confirm the influence of cold plasma and UV light on the healing and survival of zirconia implants.

Abstract PR-18: Risk prediction model for high-grade squamous intraepithelial lesions in persons living with HIV in Puerto Rico

Abstract Background: Persons living with HIV (PLWH) with concurrent HR-HPV infection are vulnerable to the development of anal high grade squamous intraepithelial lesions (HSIL), which greatly increase their risk of anal cancer. Despite anal cancer shares biological similarities with cervical cancer, there is no evidence-based screening recommendations. Clinical studies are underway evaluating clinical performance of anal cancer screening and optimal anal HSIL management approach. Due to the lack of evidence-based screening strategies for anal HSIL; we proposed to construct and validate a risk prediction model for anal HSIL among PLWH. Methods: We used data from the Anal Neoplasia Clinic (ANC) of the UPR Comprehensive Cancer Center from May, 2016 to April, 2021. From 688 HIV+ patients evaluated in the clinic during this period, 397 (57.7%) had information on biopsy-confirmed anal HSIL and thus were included in the analysis. The primary outcome was histologically confirmed anal HSIL. Potential predictors included demographic characteristics (e.g. sex at birth, age, and marital status), clinical characteristics (e.g. years living with HIV, last CD4 Nadir count, last HIV viral load, HPV vaccine, previous abnormal anal cytology, anal symptoms, HR-HPV result), and lifestyle risk factors (e.g. lifetime tobacco use, age at first sexual intercourse, lifetime number of sexual partners, history of anal sex, and history of STIs). For the prediction analysis, univariate logistic regression models (ULRM) were used to assess potential predictors of HSIL. Then, a multivariate logistic regression model (MLRM) was used to predict HSIL. The inclusion criteria for the MLRM were: (a) known or hypothesized risk factors for HSIL according to the scientific literature, (b) p-values < 0.25 in the ULRM, and (c) lower BIC and AIC values based on our data. After computing the probability of HSIL, we categorized these probabilities in deciles, to set the scale boundaries. Receiver-operating curves and the Area under the Curve (AUC) were constructed to summarize the performance of the scoring system. Results: Median age of patients was 45.8±12.6 and 72.0% were men. 47.6% had biopsy-confirmed anal HSIL and 52.4% had a benign lesion or LSIL; 37.5% had HR-HPV infection. Sex at birth, marital status, previous anal cytology, HIV diagnosis before HAART introduction, BMI, age at fist sexual intercourse, and HR-HPV were associated (p<0.25) with having HSIL according to the ULRM. Age, smoking, lifetime anal sex and history of STDs were also included in the MLRM. The AUC for the risk factor prediction model was 0.64 (95%CI: 0.51 – 0.77). Conclusion: Our risk prediction model showed a close to acceptable discrimination capacity for HSIL among PLWA. Further analysis with a bigger sample size and the consideration of additional biomarkers may be necessary to improve the prediction results. The development of risk-prediction models could lead to expansion of translational studies to inform future strategies to optimize current anal cancer screening strategies among PLWA. Citation Format: Marievelisse Soto-Salgado, Erick Suárez, Ashish Deshmukh, Tariz Viera, Jeslie Ramos-Cartagena, Luis R Pericchi, Ana Patricia Ortiz. Risk prediction model for high-grade squamous intraepithelial lesions in persons living with HIV in Puerto Rico [abstract]. In: Proceedings of the AACR Virtual Conference: 14th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2021 Oct 6-8. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr PR-18.

Regression Analysis of Some Service Expenditures on The Nigeria Gross Domestic Product (GDP)

Between 1981 and 2021, this research work looked at the test for equality of regression models employing some service expenditures on the Nigerian GDP. This study has five particular objectives that were created and used. Relevant related literatures were reviewed based on the factors in the objectives. This study used secondary data acquired from the National Bureau of Statistics and the Central Bank of Nigeria statistical bulletin, which included 46 activity sectors. The data was analyzed using multiple regression models. MINITAB 16 and Microsoft Excel 2010 were used for all calculations. The series plot results for each service expenditures indicate an upward trend for the whole plot, implying a linear link between expenditures and Nigeria's total GDP. To find the significant parameters and test for model equality, the Durbin method of estimating a multiple regression model was used; this revealed that all of the models' parameters estimates were not significant at 5%, which appears biased. Model (1), the Military Government Period Model, was found to be the "best" of the three regression models, with the highest R2 and Adj. R2 values of 99.8%, the smallest AIC and BIC values of 170.356 and 174.134, and the highest F-value of 4494.669 determined. According to the findings, the government should devote a smaller amount of its budget to recurrent spending and focus more on capital spending, such as agriculture, education, and health, as it is the primary driver of economic growth.

Analyzing the Best Fitted Probabilistic Model for the Seasonal Rainfall Data in Khulna Region of Bangladesh

There are several pieces of research on the statistical modeling of rainfall data in Bangladesh. Since all the seasons of a year do not receive a similar amount of rainfall, hence one single statistical model might not be able to explain the pattern of rainfall at any season of a year. According to the climatologists, Bangladesh has four seasons which are Monsoon, Post-monsoon, Summer, and Winter based on the geographical characteristics of this country. This paper aims to determine the best-fitted probability distribution model for the monthly rainfall data of each particular season in the Khulna district of Bangladesh using the rainfall data of the Khulna region from 1951 to 2018. Very commonly used seven continuous distributions- Normal, Weibull, Gamma, Log-normal, Exponential, Cauchy, and Logistic distributions were used to model the data and to evaluate the performances of the distributions, three non-parametric goodness-of-fit tests were conducted, and AIC, BIC values were calculated. Parameters of the distributions were estimated by the maximum likelihood method. The best-fit result of each season was taken as the distribution with the lowest AIC and BIC values. Among the seven distributions, the Gamma distribution showed the best-fit results of the monthly rainfall data for the Monsoon, Post-Monsoon, and Winter Season, and the Weibull distribution showed the best-fit result for Summer Season.

575 Heart failure as the cancer for the heart: the prognostic role of the new TNM-like classification

Abstract Aims Heart failure (HF) is the pandemic of the third millennium accounting for the highest mortality rate among general population, second only to lung cancer. Beside heart, HF can affect lungs and peripheral organs, such as kidney, liver, brain, erythropoiesis, leading to multiorgan dysfunction. This is similar to spread of cancer. We proposed a new staging system of HF, named HLM, analogous to TNM classification used in oncology, which refers to heart damage (H), instead of T for tumour, lung involvement (L), instead of N for lymphnodes, and malfunction (M) of peripheral organs, instead of M for metastasis. The aim of this study was a comparison of HLM score with NYHA classes, ACC/AHA stages and HF classification by left ventricular ejection fraction (LVEF), to assess the most accurate prognosis tool for HF patients, in terms of a composite endpoint of all-cause death and hospitalization. Methods and results We performed a multicentre observational, prospective study of consecutive patients admitted for HF, or at risk for HF. All parameters for heart, lungs, and peripheral organ function were collected and examined. Each patient was classified according to HLM, NYHA, ACC/AHA scores and LVEF, at hospital admission and at discharge. The composite endpoint was all-cause death and rehospitalization; the secondary endpoints were all-cause death, cardiac death, and rehospitalization. Patients were followed up at 12 months. We enrolled 2152 patients. Among those, 1720 patients completed the 12-months follow-up. Comparing HLM with other nosologies, the area under the ROC curve (AUC) was greater for HLM score than NYHA, ACC/AHA and LVEF scores regarding the composite endpoint (HLM = 0.644; NYHA = 0.580; ACC/AHA = 0.572; EF = 0.572) and all-cause death (HLM = 0.713; NYHA = 0.596; ACC/AHA = 0.594; EF = 0.565). HLM score related AUC showed statistically significant differences compared to LVEF (P < 0.001), ACC-AHA (P < 0.001), and NYHA (P < 0.001) scores’ AUC, in terms of all-cause death and the composite of all-cause death and rehospitalization, at 12 months follow-up. Moreover, the AIC and BIC values to predict the composite of all-cause death and rehospitalization, all-cause death, cardiac death and rehospitalization rate at 12 months follow-up were always lower for HLM model compared with the others. Conclusions According to our results, HLM score has greater prognostic power compared to other nosologies, in terms of composite outcome, rehospitalization, and all-cause death, as well as all-cause death, cardiac death, and rehospitalization, at 12 months follow-up in HF patients. HLM score overcomes the cardiocentric view of HF and it addresses the pathophysiological mechanisms underlining heart abnormalities. Such a multivariable, holistic staging system may be used in HF patients, in order to improve clinical management and to reduce healthcare costs.

The interaction effect of severe weather and non-weather factors on freeway travel volume

The existing literature has confirmed that extreme weather such as wind, rain, and snow have a negative impact on cross-sectional traffic flow. However, travel activities with different destination regions, travel distances and vehicle types may have different responses to severe weather. We employ the multilevel mixed-effects negative binomial (MENB) model to explore the interaction effect of severe weather and non-weather factors on intercity origin-destination (OD) demand, based on the data from freeway toll stations in Shandong Province from March 2011 to February 2012. The MENB model is superior to the SNB model in that the former has both smaller AIC (456,645.4 < 4,586,877.2) and BIC (456,963.4 < 458,975.8) values and log-likelihood ratio tests. The results indicate that weather impact on freeway travel to the same metropolitan area is homogeneous, and the impact in different metropolitan areas is heterogeneous. Besides, there is an interaction effect between severe weather (strong wind, fog, heavy rain, snow) and travel distance on freeway OD volume. With increasing travel distance, the impact of both strong wind and heavy rain decreases gradually, while the impact of both fog and snow increases. In addition, heat (0.0351 > 0.0201), strong wind (0.0930 > 0.0454), and heavy rain (0.1245 > 0.1044) have a greater impact on passenger car volume than on truck volume, while fog (0.4340 < 0.4802) and snow (0.4383 < 0.4884) have a less significant impact on passenger car volume. The findings of this study provide some deep insights into the relationship between severe weather factors and intercity travel demand, which suggests that different strategies of travel demand management should be adopted for different travel distances and different vehicle types under various weather conditions.

Success story of controlling COVID-19 in East Asia: lessons for South Asia

The novel coronavirus is an issue of life and death. The main purpose of the study is to know the East Asian success story of controlling Covid-19 and identify which strategies could be a lesson for South Asia and to examine the influence of good governance on controlling COVID-19. Total daily cases of COVID-19 are collected from March 10 to June 15 for East Asian and March 4 to June 15 for South Asian countries. ARIMA forecasting, ADF test, stability test, and diagnostic tests are applied. The minimum value of AIC and BIC shows the appropriate model is ARIMA (0, 1, 1) for both regions. In the East and South Asian model, the coefficients of the constant term are -0.759451 and 198.0155, and coefficients of MA (1) are -0.715686 and -0.339701 respectively for both regions. It's significant at a 1% significance level and support our hypotheses that the total daily cases of COVID-19 decreasing into East Asia but increasing into South Asia and prove that the South Asia region has faced a lot of difficulties to tackle COVID-19 as most of the countries have not enough government capacity, weak institutions, limited resources, narrow government reaches to the vulnerable people and corruption compare to East Asian region and no actual strategies are yet noticeable from the governments of South Asia as a result transmission increases day by day. That is why; we think that South Asian countries could take lessons from East Asian countries as these countries are more successful to control COVID-19.

Pharmacogenomic Single Nucleotide Polymorphism (SNP) Variants As Predictors of Toxicity Phenotypes in the Treatment of Acute Childhood Leukemia

Introduction: Despite cure rates in acute lymphoblastic leukemia (ALL) exceeding 90% in clinical trials, morbidity due to drug toxicities is high. Genetic polymorphisms can influence gene expression and activity, impacting pharmacokinetics and causing inter-individual variation in drug levels, which contributes to toxicity if levels are high or relapse if levels are low. We hypothesize that pharmacogenomic testing will identify patient specific variations in genes involved in metabolism of cytotoxic agents. This knowledge will allow clinicians to optimize therapy by providing pharmacogenomics based biomarkers related to increased toxicities. Data has shown that treatment interruptions and omissions due to toxicities affect outcomes and morbidities in children with cancer.Objective: To correlate pharmacogenomic biomarkers with toxicity phenotypes in children receiving therapy for ALL.Methods: This cross-sectional study involved subjects at a tertiary academic center (Fig. 1A). Subjects aged 1 year to 26 years with ALL treated after May 2012 were eligible. A total of 75 patients treated between 2012 and 2020 were included. Pharmacogenomic testing was performed on peripheral blood. Genomic DNA was tested for 118 single-nucleotide polymorphisms (SNP) in 55 genes for transport and metabolism of cytarabine, vincristine, methotrexate, dauno/doxorubicin, and mercaptopurine/thioguanine were analyzed using the Sequenom-based genotyping that uses MALDI-TOF based chemistry. SNPs were tested using logistic regression models for association with toxicities in additive, dominant, and recessive modes of inheritance. CTCAE v4.0 was used for grading all toxicities during the first 100 days of therapy. For endocrine (endo) and neurological (neuro) toxicities, 25 patients exhibited between grade 1-3 toxicities. For gastrointestinal (GI) toxicities, 25 patients exhibited between grade 2-3 toxicities. For hematological (heme) toxicities, 11 patients exhibited between grade 2-4 toxicities. Odds ratio and 95% confidence interval were calculated for each test and SNPs with association P-value <0.05 were considered significant. To conduct multivariable SNP combinations analysis, SNPs with univariate association p-value <0.1 were selected for each toxicity, and then SNP combinations (up to 3 SNPs per model) were tested for association with each toxicity. The combination model with the 1000 permutation p-value <0.05 and lowest BIC value was selected to build a SNP score after considering the mode of inheritance and the direction of association with the toxicity for the individual genotypes. A SNP score was generated by summation of genotype scores for the individual SNPs passing the top model. Patients were further classified into either of the 3 following SNP score groups: >0, 0 or <0.Results: For a GI toxicity score derived from 3 SNPs (TYMS-rs151264360, FPGS-rs1544105, and GSTM5-rs3754446), patients with >0 score had 79% incidence of GI toxicity (N=67) as compared to 10% in patients with score of 0 and 8% in patients with score <0 (p=2.07E-05, Fig 1B). For the SNP score (AKR1C3-rs6621365, ABCB1-rs4148737, and CTPS1-rs12067645) generated for heme toxicity, higher SNP scores were associated with high toxicity (p=0.003, Figure 1C). For neurotoxicity, the 3 SNPs (DCTD-rs6829021, SCL28A3-rs17343066, and CTPS1-rs12067635) involved were all in cytarabine metabolic pathway and predicted greater neurotoxicity (56%) with a score of >0; no toxicity was observed in patients with neurotoxicity score of <0 (p=0.0005, Fig. 1D). For SNP endo toxicity score (AKR1C3-rs1937840, TYMS-rs2853539, and CTS-rs648743), we observed 91% incidence of endo toxicity in patients with toxicity score of >0 (p=4.7E-08, Fig. 1E). None of the patients with a score of <0 experienced endo toxicity.Discussion: We identified germline SNPs predictive of toxicity phenotypes in a cohort of 75 subjects with ALL. The results of our multivariable SNP combination analysis suggest susceptibility to chemotherapy-induced toxicities is likely multigenic in nature. Instead of a single SNP approach, identification of combinations of mutations in drug pathways increases the robustness of predicting a patient's response to chemotherapy. Our results provide promising SNP models that can help establish clinically relevant biomarkers allowing for individualization of cancer therapy to optimize treatment for each patient. [Display omitted] DisclosuresNo relevant conflicts of interest to declare.

Time series forecasting for the adobe software company’s stock prices using ARIMA (BOX-JENKIN’) model

The time series forecasting strategy, Auto-Regressive Integrated Moving Average (ARIMA) model, is applied on the time series data consisting of Adobe stock prices, in order to forecast the future prices for a period of one year. ARIMA model is used due to its simple and flexible implementation for short term predictions of future stock prices. In order to achieve stationarity, the time series data requires second-order differencing. The comparison and parameterization of the ARIMA model has been done using auto-correlation plot, partial auto-correlation plot and auto.arima() function provided in R (which automatically finds the best fitting model based on the AIC and BIC values). The ARIMA (0, 2, 1) (0, 0, 2) [12] is chosen as the best fitting model, with a very less MAPE (Mean Absolute Percentage Error) of 3.854958%.

Study on Spirulina platensis growth employing non-linear analysis of biomass kinetic models

Spirulina platensis has been considered a promising source of food supplement to combat malnutrition worldwide. Numerous investigations have stated its immune activity, ability to absorb CO2 during the growth period, and antioxidant potential. Well-known theoretical biomass kinetic model sheds are capable of qualitative analysis of the fast microalgae growth. In this regard, we considered eight popular biomass models: Monod, Haldane, Andrews & Noack, Teissier, Hinshelwood, Yano & Koga, Webb and, Aiba model comprising analytical investigation within the numerical simulation. Besides, in this study, we establish a new mathematical biomass growth model by merging the well-known Hinshelwood and Yano & Koga models. We explored the most suitable Spirulina growth model to minimize the overstated and understated growth trends in the assorted eight biomass kinetic models. Our findings show microalgae biomass growth and substrate diminishes along with time, and these results were compared with available experimental data. Results present a high value of R2(0.9862), a low value of RSS (0.0813), AIC (-9.7277), and BIC (-8.2148) implied significantly fitted with the investigated data for the growth of Spirulina platensis compared with popular eight studied models.

The whole day path planning problem incorporating mode chains modeling in the era of mobility as a service

The growing popularity of Mobility as a Service (MaaS) has led researchers to realize that it can be used for the optimal management of the transportation system of a city. This study is to solve the whole day multimodal path planning problem considering user-specific modal preference, which can be used on the recommended system of the MaaS platform. The mathematical formulations of the whole day multimodal shortest hyperpath problem are proposed while accounting for individual constraints, such as the time window, multicriteria, and park-and-ride demands, to develop customized travel schemes. The primary obstacle involves consideration of the travelers’ modal preferences to develop an optimal whole day journey. The dynamic discrete choice model (DDCM) that accounts for unobservable heterogeneity is proposed to characterize dynamic mode choice behavior and generate a set of feasible mode chains. The conditional choice probability estimator and expectation–maximization (EM) algorithm are used in conjunction to estimate the dynamic model. In this way, the posterior probability can be adapted to the conditional choice probability estimation. The label correcting concept is used to develop a user-constrained shortest hyperpath algorithm that can solve the multimodal shortest hyperpath problem and calculate the Pareto set of the whole day travel schemes according to the feasible mode chains and different constraints. The proposed techniques are verified by utilizing household travel survey data and multimodal network data in Nanjing (China). Two unobserved states of travelers, namely time- and cost-sensitive, are identified based on the BIC value. The adjusted rho-squared value, accuracies of predicted mode chains, along with the aggregate validation results, confirm the model’s effectiveness. The top two resulting hyperpaths are generated for a randomly chosen traveler on the basis of his or her preferences. This study is an important step toward promoting MaaS and improving the sales of MaaS bundles.

Effect of heat stress on age at first calving in Mehsana buffaloes under field progeny testing programme

he records of 7791 Mehsana buffaloes sired by 182 sires on age at first calving (AFC) spread over a period of 24 years (1989 to 2012), collected from Dudhsagar Research and Development Association (DURDA), Dudhsagar Dairy, Mehsana. Meteorological data of the region (where field progeny testing was undergoing), were collected from the center of Indian Meteorological Department (IMD), Pune to know the association of AFC with heat stress indicators. The least squares mean and heritability of AFC were estimated. Various heat stress models were screened for optimization (Based on value of R2, adjusted R2, AIC and BIC) and effect of heat stress was quantified. Depending on the estimate of AFC and THI, months of the year were classified in to various heat stress zones. Sires were evaluated (both by BLUP-SM and BLUP-AM) for AFC in different HSZ, in order to identify optimum sire for each of the zone. The least squares mean estimate for AFC was 1419.61±2.18 days. The heritability estimates were obtained as 0.14±0.02 (LSML), 0.775±0.003 (BLUP-SM) and 0.13±0.023 (BLUP-AM). THI 6 model (Yousef, 1985) was found to be the best suited model and it was observed that AFC was declined by 1.26 day with per unit rise in THI. Year was classified in to NHSZ (non heat stress zone – November to March), HSZ (heat stress zone- April to October) and CHSZ (critical heat stress zone- June to August). The average breeding values of Mehsana buffalo bulls evaluated for AFC by BLUP-SM and BLUP-AM in NHSZ, HSZ and in CHSZ were estimated to be 1389.36, 1380.37 and 1375.04 days and 1389.33, 1382.85 and 1375.53 days, respectively. It was indicated that the bulls having superior performance in NHSZ were not as better performer in HSZ and CHSZ. This warrants that evaluation of bulls may be done separately in each heat stress zone and suitable bull for breeding under each HSZ may be identified.