Variance Stabilization Research Articles

Confidence intervals of difference and ratio between two means of delta-Birnbaum-Saunders distributions

During the dry season, the northern region of Thailand consistently grapples with compromised air quality, notably an excessive level of particulate matter with a diameter of less than 2.5 microns (PM2.5), which poses implications for public health. Wind speed emerges as the primary factor that diminishes the concentration of PM2.5, making the evaluation of wind speed changes crucial in understanding and potentially mitigating the impact of air pollution on public health in the region. The wind speed data contain zero and positive values. This can be estimated through the confidence intervals of the difference and ratio between two means of delta-Birnbaum-Saunders distributions using various methods, including the generalized confidence interval (GCI), bootstrap confidence interval (BCI), and generalized fiducial confidence interval (GFCI). These methods rely on variance stabilizing transformations (VST), the Wilson and Hannig methods. According to the simulation, for small sample sizes, the GCI based on the VST and Hannig was the most effective method in terms of coverage probabilities and average lengths. For medium sample sizes, it was found that the performance of GFCI based on the VST method was superior to other methods. Additionally, when confronted with a large sample size, the BCI based on the VST method becomes relevant and will be introduced. To demonstrate the efficacy of our proposed methods, we applied them to wind speed data gathered from Chiang Mai Airport station in Thailand from January to February in both 2022 and 2023.

Application of univariate, multivariate, and mixed models to the stability analysis of Ethiopian tetraploid wheat cultivars under irrigation condition

AbstractThe testing of durum wheat (Triticum turgidum subsp. durum) varieties in different irrigated environments is critical for determining the stability of their performance and adaptation. In this study, 12 popular and newly developed durum wheat varieties were examined in eight irrigated locations with the purpose of investigating genotype–environment interaction and their effect on Ethiopian tetraploid wheat yield stability across diverse environments. The field experiment has two replications with a randomized complete block design. Multivariate (analysis of variance, additive main effect and multiplicative interaction [AMMI], and genetics, genetics × environment [GGE] biplot) and univariate (bi [regression coefficient], S2d [deviation from regression], σi2 [Shukla's stability variance], Wi2 [Wricke's ecovalence], YSi [yield stability statistic], and CVi [coefficient of variance]) analysis methods were used to identify stable varieties. The AMMI analysis showed that the genetic × environmental interaction was highly significant (p > 0.01), while the genotype and environment variation were not significant. The first two principal component axes (IPCA1 and IPCA2) were highly significant (p > 0.01) and contributed 79% of the total GE interaction. Univariate statistical models indicate that Bulala is a stable, high‐yielding variety that can adapt to various environments. GGE biplot analysis revealed that the eight test environments were clustered into three mega‐environments, ranked Bulala as the most stable variety across diverse environments. The results of the combined mean analysis, multivariate and univariate models revealed that Bulala is a high yielder (3.46 tons ha−1) and stable variety across all test environments, while Mukiye variety has a high yield (3.43 tons ha−1) but is not as stable or adaptive to multiple locations. As a result, Bulala was recommended for further demonstration and popularization in test locations and places with similar agroecologies under irrigation.

Stability and plasticity in personality: A meta‐analytic investigation of their influence on cultural intelligence and five forms of job performance

AbstractThis meta‐analysis clarifies and tests the structural relationships among the Big Five personality traits, cultural intelligence (CQ), and an expanded criterion domain of job performance. Positioning CQ in the nomological network of personality traits is timely because research has demonstrated that CQ predicts a range of work‐related outcomes in today's multicultural workplace. Drawing on the Cybernetic Big Five Theory (CB5T), we conduct a meta‐analytic investigation (n = 24,552; k = 109) of the metatraits of stability (shared variance of conscientiousness, agreeableness, and emotional stability) and plasticity (shared variance of extraversion and openness) on CQ and five job performance outcomes (task performance, affiliative citizenship performance, change‐oriented citizenship performance, adaptive performance, and creative performance). Our findings show that CQ is a stronger mediator of the plasticity metatrait (than stability) on job performance, supporting our hypotheses. We also discover intriguing suppressor effects, suggesting that the importance of the plasticity metatrait on job performance could be underestimated in existing Big Five meta‐analyses.

Performance metrics for multi-step forecasting measuring win-loss, seasonal variance and forecast stability: an empirical study

This paper addresses the evaluation of multi-step point forecasting models. Currently, deep learning models for multi-step forecasting are evaluated on datasets by selecting one error metric that is aggregated across the time series and the forecast horizon. This approach hides insights that would otherwise be useful for practitioners when evaluating and selecting forecasting models. We propose four novel metrics to provide additional insights when evaluating models: 1) a win-loss metric that shows how models perform across time series in the dataset , allowing the practitioner to check whether the model is superior for all series or just a subset of series. 2) a variance weighted metric that accounts for differences in variance across the seasonal period. It can be used to evaluate models for seasonal datasets such as rush hour traffic prediction, where it is desirable to select the model that performs best during the periods of high uncertainty. 3) a delta horizon metric measuring how much models update their forecast for a period in the future over the forecast horizon. Less change to the forecast means more stability over time and is desirable for most forecasting applications. 4) decomposed errors that relate the forecasting error to trend, seasonality, and noise. Decomposing the errors allows the practitioners to identify for which components the model is making more errors and adjust the model accordingly. To show the applicability of the proposed metrics, we implement four deep learning architectures and conduct experiments on five benchmark datasets. We highlight several use cases for the proposed metrics and discuss the applicability in light of the empirical results.

Adjunctive Statistical Standardization of Adjuvant Estrogen Receptor and Progesterone Receptor in Canadian Cancer Trials Group MA.27 Postmenopausal Breast Cancer Trial of Exemestane Versus Anastrozole.

ASCO/College of American Pathologists guidelines recommend reporting estrogen receptor (ER) and progesterone receptor (PgR) as positive with (1%-100%) staining. Statistically standardized quantitated positivity could indicate differential associations of positivity with breast cancer outcomes. MA.27 (ClinicalTrials.gov identifier: NCT00066573) was a phase III adjuvant trial of exemestane versus anastrozole in postmenopausal women with early-stage breast cancer. Immunochemistry ER and PgR HSCORE and % positivity (%+) were centrally assessed by machine image quantitation and statistically standardized to mean 0 and standard deviation (SD) 1 after Box-Cox variance stabilization transformations of square for ER; for PgR, (1) natural logarithm (0.1 added to 0 HSCOREs and 0%+) and (2) square root. Our primary end point was MA.27 distant disease-free survival (DDFS) at a median 4.1-year follow-up, and secondary end point was event-free survival (EFS). Univariate survival with cut points at SDs about a mean of 0 (≤-1; (-1, 0]; (0, 1]; >1) was described with Kaplan-Meier plots and examined with Wilcoxon (Peto-Prentice) test statistic. Adjusted Cox multivariable regressions had two-sided Wald tests and nominal significance P < .05. Of 7,576 women accrued, 3,048 women's tumors had machine-quantitated image analysis results: 2,900 (95%) for ER, 2,726 (89%) for PgR, and 2,582 (85% of 3,048) with both ER and PgR. Higher statistically standardized ER and PgR HSCORE and %+ were associated with better univariate DDFS and EFS (P < .001). In multivariable assessments, ER HSCORE and %+ were not significantly associated (P = .52-.88) with DDFS in models with PgR, whereas higher PgR HSCORE and %+ were significantly associated with better DDFS (P = .001) in models with ER. Adjunctive statistical standardization differentiated quantitated levels of ER and PgR. Patients with higher ER- and PgR-standardized units had superior DDFS compared with those with HSCOREs and %+ ≤-1.

Modeling and Analysis of Monkeypox Outbreak Using a New Time Series Ensemble Technique

The coronavirus pandemic has raised concerns about the emergence of other viral infections, such as monkeypox, which has become a significant hazard to public health. Thus, this work proposes a novel time series ensemble technique for analyzing and forecasting the spread of monkeypox in the four highly infected countries with the monkeypox virus. This approach involved processing the first cumulative confirmed case time series to address variance stabilization, normalization, stationarity, and a nonlinear secular trend component. After that, five single time series models and three proposed ensemble models are used to estimate the filtered confirmed case time series. The accuracy of the models is evaluated using typical accuracy mean errors, graphical evaluation, and an equal forecasting accuracy statistical test. Based on the results, it is found that the proposed time series ensemble forecasting approach is an efficient and accurate way to forecast the cumulative confirmed cases for the top four countries in the world and the entire world. Using the best ensemble model, a forecast is made for the next 28 days (four weeks), which will help understand the spread of the disease and the associated risks. This information can prevent further spread and enable timely and effective treatment. Furthermore, the developed novel time series ensemble approach can be used to forecast other diseases in the future.

Feature selection followed by a novel residuals-based normalization that includes variance stabilization simplifies and improves single-cell gene expression analysis

Normalization is a crucial step in the analysis of single-cell RNA-sequencing (scRNA-seq) counts data. Its principal objectives are reduction of systematic biases primarily introduced through technical sources and transformation of counts to make them more amenable for the application of established statistical frameworks. In the standard workflows, normalization is followed by feature selection to identify highly variable genes (HVGs) that capture most of the biologically meaningful variation across the cells. Here, we make the case for a revised workflow by proposing a simple feature selection method and showing that we can perform feature selection before normalization by relying on observed counts. We highlight that the feature selection step can be used to not only select HVGs but to also identify stable genes. We further propose a novel variance stabilization transformation inclusive residuals-based normalization method that in fact relies on the stable genes to inform the reduction of systematic biases. We demonstrate significant improvements in downstream clustering analyses through the application of our proposed methods on biological truth-known as well as simulated counts datasets. We have implemented this novel workflow for analyzing high-throughput scRNA-seq data in an R package called Piccolo.

O-279 The lower reproductive tract microbiota composition alters upon ovarian stimulation and is associated with, but not accurately predictive of reproductive outcomes following ART

Abstract Study question Are ovarian stimulation (OS) and ART success associated with the microbiota of the lower reproductive tract (LRT), i.e. vagina and cervix? Summary answer Estrogen rise during OS has a pivotal role in the LRT microbiota variation. The LRT microbiota pre-OS, although associated, may not accurately predict ART success. What is known already OS is a key component to enhance success rates in ART. OS triggers a significant rise in serum estrogen levels, which are known to influence the vaginal microbiota. The role of the female LRT microbiota in ART success is increasingly recognized, yet the impact of OS, particularly the estrogen rise, on these microbial communities and how the change may influence ART success remains largely unknown. Additionally, finding putative biomarkers within LRT microbiota could aid in predicting ART success. Understanding this relationship could refine and personalize fertility treatments, but robust evidence is needed to guide microbiota-based clinical decisions in ART. Study design, size, duration Vaginal and cervical swabs were collected longitudinally from patients undergoing ART and this at baseline (pre-OS) and at oocyte retrieval (post-OS). We gathered a final dataset comprising 275 vaginal samples at baseline and 240 samples at oocyte retrieval. Additionally, we defined a prospective cohort of n = 228x2 samples corresponding to patients for which both pre- and post-OS samples were available. The main results as detected within the vaginal samples were confirmed in the cervical samples. Participants/materials, setting, methods Bacterial DNA from −80 °C preserved swabs was extracted in a Tecan Nucleic Acid Extraction Platform, using the QIAamp DNA-Stool-Mini-Kit. Samples for paired-end Illumina MiSeq were constructed using a two-step PCR amplicon approach targeting the V4 region of the 16S rRNA. Then, LotuS, DADA2, the RDP classifier trainset 16 and variance stabilisation (poscounts, DESeq2) were used for read de-multiplexing, pre-processing, taxonomy assignment and transformation, respectively, as recommended for zero-inflated compositional data. Main results and the role of chance First, a confounder analysis revealed that both the menstrual phase and intercourse correlated with the LRT’s baseline microbiota composition (multivariate distance-based redundancy analysis [dbRDA], AdjP&amp;lt;0.05, N = 275). Next, in the paired cohort, we observed that the OS-associated rise in estrogen was correlated with confounder-independent compositional (confounder-adjusted dbRDA, AdjP=0.002, R2=1.14%, N = 228x2) and microbial-feature changes (Wilcoxon test, AdjP&amp;lt;0.05, Ndelta=228) in the LRT. Interestingly, post-OS we noted a decrease in Lactobacillus proportions compared to baseline (in participants sampled at either the follicular or luteal phase of the menstrual cycle). Additionally, post-OS microbiota composition was not associated with the live birth rate (LBR, following the first embryo transfer of the cycle) or the cumulative LBR (CLBR, following all embryo transfers of the cycle), nor with the total number of high-quality (HQ) embryos available (dbRDA, AdjP&amp;gt;0.05, N = 240). Notably, pre-OS microbiota communities and LRT Anaerococcus and Peptoniphilus proportions were associated with (C)LBR (confounder-adjusted dbRDA, AdjP&amp;lt;0.05; Logistic regression, odds-ratio&amp;lt;1, AdjP&amp;lt;0.05, N = 275). Machine learning models determined that vaginal Peptoniphilus and Anaerococcusproportions alone did not have good accuracy/ROC for predicting (C)LBR (test-set;&amp;lt;70%). Notably, the number of HQ embryos showed good accuracy/ROC for CLBR (test-set; 80%/84.5%). Including vaginal Anaerococcus proportions only slightly improved the model’s accuracy/ROC (test-set; 82.5%/85.6%). Limitations, reasons for caution The data’s proportional nature restricted assessing whether the estrogen rise decreased Lactobacillus proportions or increased other bacteria. Luteal phase sampling was avoided given the intravaginal progesterone support. Larger datasets may further improve predictive models. Wider implications of the findings Our study highlights the confounding impact of baseline variables and the influence of the OS-associated estrogen rise on LRT microbiota. It questions the clinical relevance of the LRT microbiota’s impact on reproductive outcomes following ART, warning for caution in microbiota-based decisions in ART. Trial registration number NCT03105453

Diverse intrinsic properties shape transcript stability and stabilization in Mycolicibacterium smegmatis.

Mycobacteria regulate transcript degradation to facilitate adaptation to environmental stress. However, the mechanisms underlying this regulation are unknown. Here we sought to gain understanding of the mechanisms controlling mRNA stability by investigating the transcript properties associated with variance in transcript stability and stress-induced transcript stabilization. We measured mRNA half-lives transcriptome-wide in Mycolicibacterium smegmatis in log phase growth and hypoxia-induced growth arrest. The transcriptome was globally stabilized in response to hypoxia, but transcripts of essential genes were generally stabilized more than those of non-essential genes. We then developed machine learning models that enabled us to identify the non-linear collective effect of a compendium of transcript properties on transcript stability and stabilization. We identified properties that were more predictive of half-life in log phase as well as properties that were more predictive in hypoxia, and many of these varied between leadered and leaderless transcripts. In summary, we found that transcript properties are differentially associated with transcript stability depending on both the transcript type and the growth condition. Our results reveal the complex interplay between transcript features and microenvironment that shapes transcript stability in mycobacteria.

Tailoring Stress-Relieved Structure for SnSe Toward High Performance Potassium Ion Batteries.

Metal chalcogenides as an ideal family of anode materials demonstrate a high theoretical specific capacity for potassium ion batteries (PIBs), but the huge volume variance and poor cyclic stability hinder their practical applications. In this study, a design of a stress self-adaptive structure with ultrafine SnSe nanoparticles embedded in carbon nanofiber (SnSe@CNF) via the electrospinning technology is presented. Such an architecture delivers a record high specific capacity (272 mAh g-1 at 50mA g-1) and high-rate performance (125 mAh g-1 at 1 A g-1) as a PIB anode. It is decoded that the fundamental understanding for this great performance is that the ultrafine SnSe particles enhance the full utilization of the active material and achieve stress relief as the stored strain energy from cycling is insufficient to drive crack propagation and thus alleviates the intrinsic chemo-mechanical degradation of metal chalcogenides.

Adjunctive statistical standardization of quantitated adjuvant ER and PgR in CCTG MA.27.

567 Background: We proposed adjunctive statistical standardization of quantitated ER and PgR to improve inter-laboratory comparability of biomarker results and therapeutic management of breast cancer. Methods: We utilized CCTG MA.27 (NCT00066573), an adjuvant phase III trial of exemestane versus anastrozole in postmenopausal women with ER+ and/or PgR+ tumours. IHC ER and PgR HSCORE and % positivity (%+) were centrally assessed by machine image quantitation, and each statistically standardized to mean of 0, standard deviation of 1 following Box-Cox variance stabilization transformations of square for ER; for PgR, 1.) natural logarithm (0.1 added to 0 HSCOREs and 0 %+), 2.) square root. The primary endpoint was STEEP distant disease-free survival (DDFS) at the longest trial follow-up of median 4.1 years; a secondary endpoint was event-free survival (EFS). Survival was described with Kaplan-Meier plots and tested with the univariate Wilcoxon (Peto-Prentice) test statistic. We examined cut-points at standard deviations about mean of 0 (&lt;-1; (-1,0]; (0,1]; &gt;1) and explored single cut-points. Cox multivariate regressions were adjusted for age, T and N stage, grade, lymphovascular invasion, treatment, and baseline patient demographics; 2-sided Wald tests had nominal significance if p&lt;0.05. Results: Of the 7576 women accrued, 3048 women had machine-quantitated image analysis results: 2900 (95%) for ER; 2726 (89%) for PgR. Statistically standardized HSCORE and %+ units differentiated both univariate DDFS and EFS; DDFS was significantly different by ER levels (p&lt;0.001) and PgR levels (p&lt;0.001). In multivariable assessments, ER HSCORE and %+ were not significantly associated (p=0.52-0.88) with the DDFS primary endpoint in models with PgR, while higher PgR HSCORE and %+ had significantly better DDFS (p=.001, in all instances) in models with ER. Conclusions: DDFS was superior for patients with higher ER and PgR standardized units compared with those with HSCOREs and %+ &lt;-1. The adjunctive statistical standardization of ER and PgR performed here is similar to that mandated for clinical practice by the World Health Organization for BMD. [Table: see text]

ОЦЕНКА СТАБИЛЬНОСТИ УРОЖАЙНОСТИ ЯРОВОГО ЯЧМЕНЯ

The purpose of research is to determine the share of influence of factors in the overall variability of barley yield and assess stability using parametric and non-parametric statistical methods in the conditions of the south of the Moscow Region. According to the results of the study (2020–2022), significant contributions to yield variability were identified: environmental conditions – 81.9 %; genotypic characteristics – 7.4 and genotype × environment interaction – 8.2 %. 7 samples (280.7–345.2 g/m2) were classified as excee¬ding the standard. Evaluation by parametric (bi – regression coefficient; S2dᵢ – deviation from regression; θi – average variance component; θ(i) – GE variance component; Wi2 – environmental valence; σi2 – stability variance; CV – coefficient of variation) and nonparametric (S(1,2,3,6) – rank statistics; NP(i) – non-parametric stability statistics; KR – rank sum) stability indicators allowed us to identify differences in the response of the sample under study to environmental conditions. A high correlation was noted between S(3) and NP(4) (r = 0,90), S(1) and S(2) (r = 0,96), S(6) and NP(4) (r = 0,98), Wi2 and σi2, (r = 1,00), Wᵢ2 and θᵢ (r = 1,00), σ2ᵢ and θᵢ (r = 1,00), Wᵢ2 and θ(i) (r = –1,00), σ2ᵢ, and θ(i) (r = –1,00), θᵢ and θ(i) (r = –1,00). Relationship with productivity in such indicators as S(1), S(2), S(3), NP(1), Wᵢ2, σ2ᵢ, S2dᵢ, CV, θᵢ, θ(ᵢ) was weak, the greatest association was noted with bᵢ (r = 0,60), NP(3) (r = –0,67), NP(4) (r = –0,67), S(6) (r = –0,69), KR (r = –0,71), NP(2) (r = –0,78). Samples with a combination of stability and high yield were identified: Alei (k-31363, Russia), Maximus (k-31366, Russia), Austris (k-31368, Latvia), Povolzhsky Luch (k-31392, Russia).

Climate Risks and Stock Market Volatility over a Century in an Emerging Market Economy: The Case of South Africa

Because climate change broadcasts a large aggregate risk to the overall macroeconomy and the global financial system, we investigate how a temperature anomaly and/or its volatility affect the accuracy of forecasts of stock return volatility. To this end, we do not apply only the classical GARCH and GARCHX models, but rather we apply newly proposed model-free prediction methods, and use GARCH-NoVaS and GARCHX-NoVaS models to compute volatility predictions. These two models are based on a normalizing and variance-stabilizing transformation (NoVaS transformation) and are guided by a so-called model-free prediction principle. Applying the new models to data for South Africa, we find that climate-related information is helpful in forecasting stock return volatility. Moreover, the novel model-free prediction method can incorporate such exogenous information better than the classical GARCH approach, as revealed by the the squared prediction errors. More importantly, the forecast comparison test reveals that the advantage of applying exogenous information related to climate risks in prediction of the South African stock return volatility is significant over a century of monthly data (February 1910–February 2023). Our findings have important implications for academics, investors, and policymakers.

Abstract PO1-25-05: Adipose-enriched peri-tumoral stroma prognosticates poorer survival in breast cancers

Abstract Background: Adjuvant breast cancer therapy is informed by whether a tumour is positive or negative for the biomarkers ER, PgR, and HER2, often without regard to level of positivity. Quantitation has been proposed to improve therapeutic management. Adjunctive statistical standardization has been proposed to improve inter-laboratory comparability of biomarkers results. Methods: This primary report utilized adjunctive statistical standardization of machine-quantitated image analysis biomarker assessments. CCTG MA.27 (NCT00066573) is an adjuvant phase III trial of exemestane versus anastrozole in postmenopausal women with ER+ and/or PgR+ tumours. IHC ER, PgR, and HER2 were centrally assessed, with FISH (HER2;HER2/CEP17) determinations for equivocal IHC HER2. HSCOREs were statistically standardized to a mean of 0, standard deviation of 1 following Box-Cox variance stabilization transformations of square for ER and natural logarithm for PgR (0.1 was added to 0 HSCOREs). The primary endpoint was STEEP distant disease-free survival (DDFS) at the longest trial follow-up of median 4.1 years. Survival was described with Kaplan-Meier plots. The univariate Wilcoxon (Peto-Prentice) test statistic was used with usual designation of negative/positive (0; &amp;gt;0), and standardized cut-points at standard deviations about mean of 0(&amp;lt;-1; (-1,0]; (0,1]; &amp;gt;1). Cox multivariate regressions adjusted for age, T and N stage, grade, lymphovascular invasion, treatment, and baseline patient demographics, utilized likelihood ratio tests. Nominal significance was p=0.05. Results: Of the 7576 women accrued, 3048 had machine-quantitated image analysis results: 2900 (95%) for ER; 2726 (89%) for PgR. Only 8 women were ASCO/CAP ER- (HSCORE 0); PgR HSCORE was 0 for 533. Statistically standardized units differentiated DDFS ER levels (p&amp;lt; 0.001) and PgR levels (p&amp;lt; 0.001). In adjusted multivariate analyses, higher ER HSCORE was associated with better DDFS (p=0.05) with weak evidence of an association (p=0.11) for standardized HSCORE, and no significant association (respectively, p=0.28, p=0.54) in models with PgR. Higher PgR was associated with better DDFS (p=0.001) in all multivariate assessments, including those with ER. Conclusions: DDFS was superior for patients with higher ER and PgR standardized units compared with those with HSCOREs &amp;lt;-1. Adjunctive statistical standardization, similar to that mandated for clinical practice by the World Health Organization for BMD, should improve inter-laboratory comparability of biomarker results for similar patient populations. Biomarker N DDFS DDFS 5-year (%) 95% CI ER total 2900 ER &amp;lt;-1 506 86 (82, 91) ER (-1, 0] 934 94 (92, 96) ER ( 0, 1] 919 94 (92, 96) ER 1 541 96 (93, 98) PgR total 2726 PgR &amp;lt;-1 734 89 (86, 92) PgR (-1, 0] 439 92 (89, 95) PgR ( 0, 1] 967 95 (93, 96) PgR 1.0 586 98 (97,100) Citation Format: Hannah Lau, Veronique Kiak Mien Tan, Benita Kiat Tee Tan, Yirong Sim, Jelmar Quist, Aye Aye Thike, Puay Hoon Tan, Shazib Pervaiz, Anita Grigoriadis, Kanaga Sabapathy. Adipose-enriched peri-tumoral stroma prognosticates poorer survival in breast cancers [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-25-05.

Abstract PO1-27-10: Adjunctive statistical standardization of quantitated machine image analysis of Estrogen and Progesterone Receptors: CCTG MA.27 trial

Abstract Background: Adjuvant breast cancer therapy is informed by whether a tumour is positive or negative for the biomarkers ER, PgR, and HER2, often without regard to level of positivity. Quantitation has been proposed to improve therapeutic management. Adjunctive statistical standardization has been proposed to improve inter-laboratory comparability of biomarkers results. Methods: This primary report utilized adjunctive statistical standardization of machine-quantitated image analysis biomarker assessments. CCTG MA.27 (NCT00066573) is an adjuvant phase III trial of exemestane versus anastrozole in postmenopausal women with ER+ and/or PgR+ tumours. IHC ER, PgR, and HER2 were centrally assessed, with FISH (HER2;HER2/CEP17) determinations for equivocal IHC HER2. HSCOREs were statistically standardized to a mean of 0, standard deviation of 1 following Box-Cox variance stabilization transformations of square for ER and natural logarithm for PgR (0.1 was added to 0 HSCOREs). The primary endpoint was STEEP distant disease-free survival (DDFS) at the longest trial follow-up of median 4.1 years. Survival was described with Kaplan-Meier plots. The univariate Wilcoxon (Peto-Prentice) test statistic was used with usual designation of negative/positive (0; &amp;gt;0), and standardized cut-points at standard deviations about mean of 0(&amp;lt;-1; (-1,0]; (0,1]; &amp;gt;1). Cox multivariate regressions adjusted for age, T and N stage, grade, lymphovascular invasion, treatment, and baseline patient demographics, utilized likelihood ratio tests. Nominal significance was p=0.05. Results: Of the 7576 women accrued, 3048 had machine-quantitated image analysis results: 2900 (95%) for ER; 2726 (89%) for PgR. Only 8 women were ASCO/CAP ER- (HSCORE 0); PgR HSCORE was 0 for 533. Statistically standardized units differentiated DDFS ER levels (p&amp;lt; 0.001) and PgR levels (p&amp;lt; 0.001). In adjusted multivariate analyses, higher ER HSCORE was associated with better DDFS (p=0.05) with weak evidence of an association (p=0.11) for standardized HSCORE, and no significant association (respectively, p=0.28, p=0.54) in models with PgR. Higher PgR was associated with better DDFS (p=0.001) in all multivariate assessments, including those with ER. Conclusions: DDFS was superior for patients with higher ER and PgR standardized units compared with those with HSCOREs &amp;lt;-1. Adjunctive statistical standardization, similar to that mandated for clinical practice by the World Health Organization for BMD, should improve inter-laboratory comparability of biomarker results for similar patient populations. Biomarker N DDFS DDFS 5-year (%) 95% CI ER total 2900 ER &amp;lt;-1 506 86 (82, 91) ER (-1, 0] 934 94 (92, 96) ER ( 0, 1] 919 94 (92, 96) ER &amp;gt;1 541 96 (93, 98) PgR total 2726 PgR &amp;lt;-1 734 89 (86, 92) PgR (-1, 0] 439 92 (89, 95) PgR ( 0, 1] 967 95 (93, 96) PgR &amp;gt;1.0 586 98 (97,100) Citation Format: Judy-Anne Chapman, Jane Bayani, Sandip SenGupta, John MS Bartlett, Tammy Piper, Mary Anne Quintayo, Shakeel Virk, Paul Goss, James Ingle, Matthew Ellis, George Sledge Jr, George Budd, Manuela Rabaglio, Rafat Ansari, Richard Tozer, David D'Souza, Haji Chalchal, Silvana Spadafora, Vered Stearns, Edith A. Perez, Karen Gelmon, Timothy Whelan, Catherine Elliott, Lois Shepherd, Bingshu Chen, Karen Taylor. Adjunctive statistical standardization of quantitated machine image analysis of Estrogen and Progesterone Receptors: CCTG MA.27 trial [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-27-10.

The Distribution of Risk Across Healthcare Providers and Reducing Misclassification

Four linear multilevel mixed-risk models were compared using model assumption tests and predictions. Models varied by the number of random intercepts from 1 to 4, producing 2-level through 5-level models of the same measure, operative time. Normality of the dependent variable and residuals, variance homoscedasticity, level-1, and level-2 exogeneity were tested using the robust test of the level-1 residuals variance by surgeon, estimates of density, skew, and the Hausman test. Measure (operative time by hospital and surgeon) aberrancy and risk classification were evaluated using traditional methods and used to assess distribution measures. The dependent variable and the level-1 residuals required transformation for linearity and variance stabilization, respectively. Normality criteria were met for both level-1 and level-2 residuals and standardized residuals. The likelihood ratio comparing the four models was significantly larger for the 5-level (1016.1; P&amp;lt;0.00005) model than the likelihood ratio for the four-level and other models. Shrinkage was greatest for the 2-level model (0.039; P&amp;lt;0.00005) and least for the 5-level model (0.028; P&amp;lt;0.00005). Level-1 variance homoscedasticity was confirmed by the robust variance test across all models (P&amp;gt;F=1). Aberrant value detection did not require the exclusion of any observations, while prediction intervals revealed low or high risk for 54.2% of surgeons for the 2-level model and 8.6% for the 5-level model. The traditional (c2 = -11.01; P=1) and instrumental variable (c2 = 21.06; P=1) Hausman tests show that the null hypothesis cannot be rejected for level-1 or level-2 exogeneity. Once level-1 and level-2 exogeneity was confirmed, and since deconfounding was a model consideration, causal inferential capacity was assumed. The likelihood ratio, residual variance, shrinkage, and predictions show that the 5-level model is preferred to the other models.

Identification of Plant and Soil Characteristics Affecting Stability of Winter Wheat Cultivar in Temperate Climates

This study examines the significant variability in grain yield, thousand-grain weight, protein content, sedimentation value, and falling number among winter wheat cultivars across diverse trial locations, elucidating the profound influence of environmental factors on these traits. Employing Shukla’s stability variance and a multi-trait stability index (MTSI), cultivar stability is comprehensively assessed across multiple traits. Cultivars are ranked based on stability variance and cumulative ranking across all traits, with Bataja emerging as the most stable cultivar according to Shukla variance, while Apostel exhibits the lowest stability. Contrarily, MTSI rankings reveal distinct top performers, such as Medalistika and KWS Spencer. Canonical correspondence analysis (CCA) is utilized to discern relationships between stability and genotype characteristics, as well as trait values and soil properties/weather conditions. These findings contribute to the recommendation of stable cultivars for breeding programs and the optimization of crop management practices. Furthermore, this study underscores the need to explore causal relationships between accompanying variables, facilitating informed recommendations for plant breeders and advancing breeding progress amidst a changing climate. The use of multivariate statistical methods, including CCA, enhances our understanding of cultivar traits and stability, offering valuable insights for sustainable agriculture.

Asymptotic optimality of the square-root transformation on the gamma distribution using the Kullback–Leibler information number criterion

The Kullback–Leibler information number criterion is a useful criterion for determining an optimal power transformation to approximate the target distribution. When the normal distribution with a fixed variance is set as the target distribution, this criterion may be used to show the asymptotic optimality of the square-root transformation. In particular, when the Box–Cox transformation corresponding to the square-root transformation is applied, the Kullback–Leibler information number of the transformed gamma distribution with the scale parameter equal to the fixed variance converges to zero asymptotically as the shape parameter approaches infinity. That provides a stronger result on the mode of convergence of the variance-stabilizing power transformation on the gamma distribution. Moreover, an analogous result may be obtained by setting the Laplace distribution with a fixed scale parameter as the target distribution. This result utilizes a novel application of the asymptotic expansion for the normalized upper incomplete gamma function at the transition point.

Exploring the associations of demographics and scale measures with cognitive driving behavior among older drivers in China

Age-related changes and frailty are reasons for the high proportion of older drivers in certain types of crashes, such as giving right of way at intersections and turning left. The identified crash causes include the driver’s demographics, driving style, cognitive function, and mental workload. This study aimed to explore the associations of demographics and scale measures with cognitive driving behavior. Thirty-nine drivers, consisting of twenty younger drivers (18–60 years old) and nineteen older drivers (above 60 years old), participated in driving simulation experiments after completing scale tests. The selected scale measures included the demographic questionnaire, Multidimensional Driving Style Inventory (MDSI-C), Mini-Mental State Examination (MMSE), Trail Making Test Part A (TMT-A) and Part B (TMT-B), and the National Aeronautics and Space Administration Task Load Index (NASA-TLX) for obtaining subjective information from drivers. Driving scenarios were developed based on the driving characteristics of older adults to investigate age-related driving ability. The driving behavior parameters included reaction time, lateral stability, and driving speed, corresponding to reaction, perception, and execution. Three stepwise regression models showed that NASA-TLX, the interaction between age and driving experience, and the interaction between age and TMT-A significantly explained 53.3 % of reaction time variance; TMT-A, risk driving style, anxiety driving style, and gender significantly explained 53.5 % of lateral stability variance; TMT-A, NASA-TLX, and MMSE significantly explained 60.6 % of driving speed variance. Subsequently, the impact of four age-related predictor variables on driving behavior was further discussed. It is worth noting that a rich driving experience may compensate for driving performance. However cognitive impairment impairs this compensation. Driving behavior is influenced by a combination of various factors. Age, as a physiological indicator, is not sufficient to be a strong predictive factor for lateral stability and driving speed. The results provide a reference for traffic safety management departments to streamline driving suitability test procedures and propose targeted training methods for older drivers.

Handling sensory disabilities in a smart society

Billions of people live with visual and/or hearing impairments. Regrettably, their access to systems remains delayed, leaving them socially excluded. The need for universal access of next-generation systems and users’ inclusion is paramount. We pose that a smart society should respond to this crucial need. Following ability-based design principles, we introduce a simulated social robot that adapts to users’ sensory abilities. Its working was assessed via a Rock–Paper–Scissors game in an Intelligent Environment (IE), using three modes: where the user is able to see and hear, only see, or only hear. With this game, two user-studies were conducted using the UMUX-LITE usability score, an expectation rating, and the gap between experience and expectation, complemented with two open questions. A repeated measures Multivariate ANalysis Of VAriance (MANOVA) on the data from study 1 unveiled an overall difference between the three modes, F ( 6 , 6 ) = 6.823, η p 2 = .872, p = .017. Users expected applications to be harder to use with a disability, especially a visual impairment. All modes were considered accessible, with the experience exceeding expectations for the mode with a hearing impairment. In parallel, substantial variance was observed across participants and the results from the open questions suggested improvements. To reduce this variance and increase system stability, study 2 was run with an enhanced design. A repeated measures MANOVA on the data from study 2 confirmed study 1’s findings, F ( 6 , 6 ) = 12.801, η p 2 = .928, p = .003. Moreover, experiences exceeded expectations in all modes and the variance among participants was substantially decreased. We conclude that IE applications managed by a social robot can be adapted to user’s sensory abilities, improving smart society’s accessibility, and, hence, reducing social exclusion.