Objective: Chronic periodontal disease (CP) is a multifactorial infectious and inflammatory disease that occurs due to the challenge between the immune response of the host and specific periodontal bacteria, and that can lead to tooth loss due to damage inflicted to the supporting tissue. The current study investigates the genotypes of the GSTM1 and GSTT1 genes, along with the allelic frequency of the single nucleotide polymorphism [SNP; rs1695] in the GSTP1 gene and correlates them individually or in various combinations with the incidence of CP. Methods: A total of 203 clinically confirmed CP patients and 201 control subjects were enrolled from Multan and Dera Ghazi Khan Districts in Pakistan from April to July 2022. Multiplex Polymerase Chain Reaction (PCR) and tetra-primer amplification refractory mutation system–polymerase chain reaction (T-ARMS–PCR) approaches were applied to determine the genotypes of the studied GSTs. The association of rs1695 in GSTP1 with CP was studied both individually and in various combinations with GSTM1 and T1. Results: The absence of GSTM1, the presence of GSTT1 and the presence of the mutant allele (G) at rs1695 in GSTP1 were found to be significantly associated with CP. Patients aged between 10 and 30 years were more affected by CP. Conclusion: Our results indicate that the genotypes of the analyzed GSTs affect the levels of protection from oxidative stress and may therefore influence the disease progression in CP.

In real experimentation, we often face situations where observations are lost, ignored or unavailable due to some accident or high cost experiments. Missing observations can make the results of a response surface experiment quite misleading. Therefore minimaxloss designs for Augmented Box-Behnken Designs (ABBDs) and Augmented Fractional Box-Behnken Designs (AFBBDs) are formulated under a minimaxloss criterion. These minimaxloss designs are considered to be robust to one missing observation and the investigation has been made in this article. Then G-efficiencies and Relative D-efficiencies of minimaxloss ABBDs and AFBBDs have been discussed.

In an experiment, there are many situations when some observations are missed, ignored or unavailable due to some accidents or high cost experiments. A missing observation can make the results of a response surface model quite misleading. It can have an adverse effect on desirable properties of a design like orthogonality, rotatability and optimality. Literature exhibits that some studies have investigated the effects of missing observations on prediction capabilities of response surface designs like Central Composite designs. This work investigates the impact of a missing observation on the estimation and prediction capabilities as well as on the relative A, D and G-efficiencies of augmented Box-Behnken designs. It has been observed that precision of model parameter estimates as well as relative A, D and G-efficiencies are less affected by missing a center run, but more by missing other design points. Prediction capability is adversely affected if we try to predict the response at the same location where we have missed the design point, however, when we capture the overall situation throughout the design region, a missing observation has smaller effect on the prediction capabilities of augment Box-Behnken designs. Missing a center run has less effect than missing other design points on prediction capability of these designs.

Binomial regression is used as a generalized linear model (GLM) in natural sciences to identify the covariate structure that is responsible for outcomes. It is very important to assess the adequacy and effectiveness of any model before its implementation. In GLM context, this study explores the structure and usefulness of partial residual (PRES), augmented partial residual (APRES), and conditional expectation and residuals (CERES) plots for visualizing influence diagnostics as a function of selected predictors. Binomial regression is considered here with predictor transformation, and PRES, APRES, and CERES plots are constructed for diagnostics of outliers and multicollinearity. The efficacy of these plots for obtaining a good visual impression may be varied due to behaviour of response variable and allied link function with different covariates. The certain techniques are applied on the data of hindered internal rotational (HIR) treatment of chemical species to recognize patterns for efficient modelling. The power of the tests for different plots shows that APRES and CERES (L) endure maximum power for detection of outliers and multicollinearity. The results revealed that residuals plots are more effective as compared to the conventional methods and help the scientists to easily and effectively model the data for their diagnostics policies.

Goats play a vital role in the economy of common man. It acts as pivotal point in the uplift of socio-economic status of females. The goats are such delicate and fragile animals that encounter a lot of infectious and non-infectious diseases including viruses, bacteria and gastrointestinal parasites (GIP). The goat being a range animal is selective feeder. It needs a lot of managemental practices which safeguards its health. This chapter focuses on management, impact of gastrointestinal parasites, role of intestinal immunity, various breeds reared in Pakistan, role of plant based phytochemicals to treat against GIT parasites and various models to predict the status of health in animals.