Prediction Intervals in Nonlinear Regression

By treating the nonlinear model as if it were linear in the parameterization θ in the neighbourhood of the least squares estimate θ, we construct two-sided nominally-q-prediction intervals by applying the usual linear model theory. The derivation of the truncated series expansion of the expected coverage of the prediction intervals at a feasible value of the parameter vector is described. The quadratic approximation of the expected coverage is then obtained for a two-parameter nonlinear model. Finally we show how we may construct the prediction intervals when a certain type of nonlinear transformation of the parameter vector has been applied.     

A single-spring model predicts the majority of variance in impact force during a fall onto the outstretched hand

The objective of this study was to validate a single-spring model in predicting measured impact forces during an outstretched arm falling scenario. Using an integrated force plate, impact forces were assessed from 10 young adults (5 males; 5 females), falling from planted knees onto outstretched arms, from a random order of drop heights: 3, 5, 7, 10, 15, 20, and 25 cm. A single-spring model incorporating body mass, drop height plus the estimated linear stiffness of the upper extremity (hand, wrist and arm) was used to predict impact force on the hand. We used an analysis of variance linearity test to test the validity of using a linear stiffness coefficient in the model. We used linear regression to assess variance (R²) in experimental impact force predicted by the single-spring model. We derived optimum linear stiffness coefficients for male, female and sex-combined. Our results indicated that the association between experimental and predicted impact forces was linear (P < 0.05). Explain variance in experimental impact force was R² = 0.82 for sex-combined, R² = 0.88 for males and R² = 0.84 for females. Optimum stiffness coefficients were 7436 N/m for sex-combined, 8989 N/m for males and 4527 N/m for females. In conclusion, a linear spring coefficient used in the single-spring model proved valid for predicting impact forces from fall heights up to 25 cm. Results also suggest the use of sex-specific spring coefficients when estimating impact force using the single-spring model. This model may improve impact force to bone strength ratios (factor-of-risk) and prediction of forearm and wrist fracture.     

Involvement of INF-γ functional single nucleotide polymorphism +874 T/A (rs2430561) in breast cancer risk

According Global Cancer Statistics 2020 GLOBOCAN estimates female breast cancer was found as the most commonly diagnosed cancer, with an estimated 2.3 million new cases (11.7%), and the fourth leading cause (6.9%) of cancer death among women worldwide. Identification of new diagnostic marker sharply characterize the tumor feature is intensive need. The present work was performed to investigate the involvement of the INF-γ + 874 T/A gene polymorphism in different breast cancer prognostic factors. Polymorphism detection analysis was performed on 163 subjects from breast cancer patients, 79 with inflamed cells of breast patients and 144 controls. The gene polymorphism was detected using the amplification refractory mutation system- polymerase chain reaction method (ARMS-PCR). The distribution of INF-γ T + 874A gene polymorphism shows strong significant association between INF-γ + 874 T/A genotypes TT in BC patients (ORTT: 6.41 [95% CI = 2.72–15.1] P < 0.0001) as well as strong significant association regarding T allele (ORT: 1.99 [95% CI = 1.43–2.76] P < 0.0001) when compared to the healthy control. In ICB group the strong association was noted with INF-γ + 874 T/A genotypes AT genotype (ORAT: 2.28 [95% CI = 1.22–4.29] P = 0.007). From the different histological BC hormonal markers the human epidermal growth factor receptor 2 (HER2) was showing significant association in INF-γ + 874 T/A genotypes TT (P = 0.03) and recessive model (TT versus AA + AT P = 0.03). Concerning different BC prognostic models, the poor prognostic one of luminal B, (ER^+ve PR^+ve Her2^+ve) show significant association in the host INF-γ + 874 T/A genotype (TT, P = 0.03) and recessive model (TT versus AA + AT P = 0.02) when compared to the good prognostic hormonal status luminal A model, (ER^+ve PR^+ve Her2-ve). It seems that this is the first study that interested in correlate the INF-γ + 874 T/A gene polymorphisms in Egyptian BC patients. T allele, TT genotype and recessive model of the INF-γ + 874 T/A gene variants were documented as risk factors for BC pathogenesis. It may be used as practical biomarker to guide the BC carcinogenesis and risk process.     

AlphaFold and the amyloid landscape

Protein aggregation is a widespread phenomenon with important implications in many scientific areas. Although amyloid formation is typically considered as detrimental, functional amyloids that perform physiological roles have been identified in all kingdoms of life. Despite their functional and pathological relevance, the structural details of the majority of molecular species involved in the amyloidogenic process remains elusive. Here, we explore the application of AlphaFold, a highly accurate protein structure predictor, in the field of protein aggregation. While we envision a straightforward application of AlphaFold in assisting the design of globular proteins with improved solubility for biomedical and industrial purposes, the use of this algorithm for predicting the structure of aggregated species seems far from trivial. First, in amyloid diseases, the presence of multiple amyloid polymorphs and the heterogeneity of aggregation intermediates challenges the “one sequence, one structure” paradigm, inherent to sequence-based predictions. Second, aberrant aggregation is not the subject of positive selective pressure, precluding the use of evolutionary-based approaches, which are the core of the AlphaFold pipeline. Instead, amyloid polymorphism seems to be constrained by the need for a defined structure-activity relationship in functional amyloids. They may thus provide a starting point for the application of AlphaFold in the amyloid landscape.     

沙眼衣原体多形态膜蛋白D基因序列分析及其B细胞抗原表位预测

分析沙眼衣原体多形态膜蛋白D(PmpD)的基因序列并预测PmpD蛋白的B细胞抗原表位。在GenBank中检索沙眼衣原体不同血清型的PmpD基因序列,进行序列比对分析。以L2血清型PmpD基因序列为材料,采用Karplus-Schulz、Chou-Fasman和Gamier-Robson方案预测蛋白质的二级结构和柔性区;按Jamesonv-Wolf方案预测抗原指数,运用Kyte-Doolittle方案预测PmpD氨基酸的亲水性,利用Emini方案预测蛋白质的表面可及性。检索到20个沙眼衣原体不同菌株的PmpD基因序列,分析发现其核苷酸序列非常保守,一致性高达99.14%~100%;对预测结果综合分析,推测最有可能的B细胞表位位于PmpD N端的67~74、132~140、335~340、851~861、972~988及1091~1097。多参数方案综合预测PmpD蛋白的B细胞抗原表位,为进一步实验鉴定PmpD抗原表位及其多表位疫苗设计和研究奠定基础。