cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.1568332.v1
title: An Adaptive, Automatic Multiple-Case Deletion Technique for Detecting Influence in Regression

description: Critical to any regression analysis is the identification of observations that exert a strong influence on the fitted regression model. Traditional regression influence statistics such as Cook's distance and DFFITS, each based on deleting single observations, can fail in the presence of multiple influential observations if these influential observations “mask” one another, or if other effects such as “swamping” occur. Masking refers to the situation where an observation reveals itself as influential only after one or more other observations are deleted. Swamping occurs when points that are not actually outliers/influential are declared to be so because of the effects on the model of other unusual observations. One computationally expensive solution to these problems is the use of influence statistics that delete multiple rather than single observations. In this article, we build on previous work to produce a computationally feasible algorithm for detecting an unknown number of influential observations in the presence of masking. An important difference between our proposed algorithm and existing methods is that we focus on the data that remain after observations are deleted, rather than on the deleted observations themselves. Further, our approach uses a novel confirmatory step designed to provide a secondary assessment of identified observations. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1568337.v4
title: Combined evaluation of LC3B puncta and HMGB1 expression predicts residual risk of relapse after adjuvant chemotherapy in breast cancer

description: In spite of adjuvant chemotherapy, a significant fraction of patients with localized breast cancer (BC) relapse after optimal treatment. We determined the occurrence of cytoplasmic MAP1LC3B/LC3B (microtubule-associated protein 1 light chain 3B)-positive puncta, as well as the presence of nuclear HMGB1 (high mobility group box 1) in cancer cells within surgical BC specimens by immunohistochemistry, first in a test cohort (152 patients) and then in a validation cohort of localized BC patients who all received adjuvant anthracycline-based chemotherapy (1646 patients). Cytoplasmic LC3B<sup>+</sup> puncta inversely correlated with the intensity of SQSTM1 staining, suggesting that a high percentage cells of LC3B<sup>+</sup> puncta reflects increased autophagic flux. After setting optimal thresholds in the test cohort, cytoplasmic LC3B<sup>+</sup> puncta and nuclear HMGB1 were scored as positive in 27.2% and 28.6% of the tumors, respectively, in the validation cohort, while 8.7% were considered as double positive. LC3B<sup>+</sup> puncta or HMGB1 expression alone did not constitute independent prognostic factors for metastasis-free survival (MFS) in multivariate analyses. However, the combined positivity for LC3B<sup>+</sup> puncta and nuclear HMGB1 constituted an independent prognostic factor significantly associated with prolonged MFS (hazard ratio: 0.49 95% confidence interval [0.26–0.89]; <i>P</i> = 0.02), and improved breast cancer specific survival (hazard ratio: 0.21 95% confidence interval [0.05–0.85]; <i>P</i> = 0.029). Subgroup analyses revealed that within patients with poor-prognosis BC, HMGB1<sup>+</sup> LC3B<sup>+</sup> double-positive tumors had a better prognosis than BC that lacked one or both of these markers. Altogether, these results suggest that the combined positivity for LC3B<sup>+</sup> puncta and nuclear HMGB1 is a positive predictor for longer BC survival.
DOI: 10.6084/m9.figshare.1568332.v3
title: An Adaptive, Automatic Multiple-Case Deletion Technique for Detecting Influence in Regression

description: Critical to any regression analysis is the identification of observations that exert a strong influence on the fitted regression model. Traditional regression influence statistics such as Cook's distance and DFFITS, each based on deleting single observations, can fail in the presence of multiple influential observations if these influential observations “mask” one another, or if other effects such as “swamping” occur. Masking refers to the situation where an observation reveals itself as influential only after one or more other observations are deleted. Swamping occurs when points that are not actually outliers/influential are declared to be so because of the effects on the model of other unusual observations. One computationally expensive solution to these problems is the use of influence statistics that delete multiple rather than single observations. In this article, we build on previous work to produce a computationally feasible algorithm for detecting an unknown number of influential observations in the presence of masking. An important difference between our proposed algorithm and existing methods is that we focus on the data that remain after observations are deleted, rather than on the deleted observations themselves. Further, our approach uses a novel confirmatory step designed to provide a secondary assessment of identified observations. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1568337
title: Combined evaluation of LC3B puncta and HMGB1 expression predicts residual risk of relapse after adjuvant chemotherapy in breast cancer

description: In spite of adjuvant chemotherapy, a significant fraction of patients with localized breast cancer (BC) relapse after optimal treatment. We determined the occurrence of cytoplasmic MAP1LC3B/LC3B (microtubule-associated protein 1 light chain 3B)-positive puncta, as well as the presence of nuclear HMGB1 (high mobility group box 1) in cancer cells within surgical BC specimens by immunohistochemistry, first in a test cohort (152 patients) and then in a validation cohort of localized BC patients who all received adjuvant anthracycline-based chemotherapy (1646 patients). Cytoplasmic LC3B<sup>+</sup> puncta inversely correlated with the intensity of SQSTM1 staining, suggesting that a high percentage cells of LC3B<sup>+</sup> puncta reflects increased autophagic flux. After setting optimal thresholds in the test cohort, cytoplasmic LC3B<sup>+</sup> puncta and nuclear HMGB1 were scored as positive in 27.2% and 28.6% of the tumors, respectively, in the validation cohort, while 8.7% were considered as double positive. LC3B<sup>+</sup> puncta or HMGB1 expression alone did not constitute independent prognostic factors for metastasis-free survival (MFS) in multivariate analyses. However, the combined positivity for LC3B<sup>+</sup> puncta and nuclear HMGB1 constituted an independent prognostic factor significantly associated with prolonged MFS (hazard ratio: 0.49 95% confidence interval [0.26–0.89]; <i>P</i> = 0.02), and improved breast cancer specific survival (hazard ratio: 0.21 95% confidence interval [0.05–0.85]; <i>P</i> = 0.029). Subgroup analyses revealed that within patients with poor-prognosis BC, HMGB1<sup>+</sup> LC3B<sup>+</sup> double-positive tumors had a better prognosis than BC that lacked one or both of these markers. Altogether, these results suggest that the combined positivity for LC3B<sup>+</sup> puncta and nuclear HMGB1 is a positive predictor for longer BC survival.
DOI: 10.6084/m9.figshare.1568329
title: Blocking Orthogonal Designs With Mixed Integer Linear Programming

description: We present a mixed integer linear programming approach to orthogonally block two-level, multilevel, and mixed-level orthogonal designs. The approach involves an exact optimization technique which guarantees an optimal solution. It can be applied to many problems where combinatorial methods for blocking orthogonal designs cannot be used. By means of 54-run and 64-run examples, we demonstrate that our approach outperforms two benchmark techniques in terms of the number of estimable two-factor interaction contrasts and in terms of the D-efficiency for models with main effects and some two-factor interaction contrasts. We demonstrate the generic nature of our approach by applying it to the most challenging instances in a catalog of all orthogonal designs of strength 3 with up to 81 runs as well as a small catalog of strength-4 designs. The approach can also be applied to strength-2 designs, but, for these cases, alternative methods described in the literature may perform equally well. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1568337.v6
title: Combined evaluation of LC3B puncta and HMGB1 expression predicts residual risk of relapse after adjuvant chemotherapy in breast cancer

description: In spite of adjuvant chemotherapy, a significant fraction of patients with localized breast cancer (BC) relapse after optimal treatment. We determined the occurrence of cytoplasmic MAP1LC3B/LC3B (microtubule-associated protein 1 light chain 3B)-positive puncta, as well as the presence of nuclear HMGB1 (high mobility group box 1) in cancer cells within surgical BC specimens by immunohistochemistry, first in a test cohort (152 patients) and then in a validation cohort of localized BC patients who all received adjuvant anthracycline-based chemotherapy (1646 patients). Cytoplasmic LC3B<sup>+</sup> puncta inversely correlated with the intensity of SQSTM1 staining, suggesting that a high percentage cells of LC3B<sup>+</sup> puncta reflects increased autophagic flux. After setting optimal thresholds in the test cohort, cytoplasmic LC3B<sup>+</sup> puncta and nuclear HMGB1 were scored as positive in 27.2% and 28.6% of the tumors, respectively, in the validation cohort, while 8.7% were considered as double positive. LC3B<sup>+</sup> puncta or HMGB1 expression alone did not constitute independent prognostic factors for metastasis-free survival (MFS) in multivariate analyses. However, the combined positivity for LC3B<sup>+</sup> puncta and nuclear HMGB1 constituted an independent prognostic factor significantly associated with prolonged MFS (hazard ratio: 0.49 95% confidence interval [0.26–0.89]; <i>P</i> = 0.02), and improved breast cancer specific survival (hazard ratio: 0.21 95% confidence interval [0.05–0.85]; <i>P</i> = 0.029). Subgroup analyses revealed that within patients with poor-prognosis BC, HMGB1<sup>+</sup> LC3B<sup>+</sup> double-positive tumors had a better prognosis than BC that lacked one or both of these markers. Altogether, these results suggest that the combined positivity for LC3B<sup>+</sup> puncta and nuclear HMGB1 is a positive predictor for longer BC survival.
DOI: 10.6084/m9.figshare.1568332.v2
title: An Adaptive, Automatic Multiple-Case Deletion Technique for Detecting Influence in Regression

description: Critical to any regression analysis is the identification of observations that exert a strong influence on the fitted regression model. Traditional regression influence statistics such as Cook's distance and DFFITS, each based on deleting single observations, can fail in the presence of multiple influential observations if these influential observations “mask” one another, or if other effects such as “swamping” occur. Masking refers to the situation where an observation reveals itself as influential only after one or more other observations are deleted. Swamping occurs when points that are not actually outliers/influential are declared to be so because of the effects on the model of other unusual observations. One computationally expensive solution to these problems is the use of influence statistics that delete multiple rather than single observations. In this article, we build on previous work to produce a computationally feasible algorithm for detecting an unknown number of influential observations in the presence of masking. An important difference between our proposed algorithm and existing methods is that we focus on the data that remain after observations are deleted, rather than on the deleted observations themselves. Further, our approach uses a novel confirmatory step designed to provide a secondary assessment of identified observations. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1568337.v5
title: Combined evaluation of LC3B puncta and HMGB1 expression predicts residual risk of relapse after adjuvant chemotherapy in breast cancer

description: In spite of adjuvant chemotherapy, a significant fraction of patients with localized breast cancer (BC) relapse after optimal treatment. We determined the occurrence of cytoplasmic MAP1LC3B/LC3B (microtubule-associated protein 1 light chain 3B)-positive puncta, as well as the presence of nuclear HMGB1 (high mobility group box 1) in cancer cells within surgical BC specimens by immunohistochemistry, first in a test cohort (152 patients) and then in a validation cohort of localized BC patients who all received adjuvant anthracycline-based chemotherapy (1646 patients). Cytoplasmic LC3B<sup>+</sup> puncta inversely correlated with the intensity of SQSTM1 staining, suggesting that a high percentage cells of LC3B<sup>+</sup> puncta reflects increased autophagic flux. After setting optimal thresholds in the test cohort, cytoplasmic LC3B<sup>+</sup> puncta and nuclear HMGB1 were scored as positive in 27.2% and 28.6% of the tumors, respectively, in the validation cohort, while 8.7% were considered as double positive. LC3B<sup>+</sup> puncta or HMGB1 expression alone did not constitute independent prognostic factors for metastasis-free survival (MFS) in multivariate analyses. However, the combined positivity for LC3B<sup>+</sup> puncta and nuclear HMGB1 constituted an independent prognostic factor significantly associated with prolonged MFS (hazard ratio: 0.49 95% confidence interval [0.26–0.89]; <i>P</i> = 0.02), and improved breast cancer specific survival (hazard ratio: 0.21 95% confidence interval [0.05–0.85]; <i>P</i> = 0.029). Subgroup analyses revealed that within patients with poor-prognosis BC, HMGB1<sup>+</sup> LC3B<sup>+</sup> double-positive tumors had a better prognosis than BC that lacked one or both of these markers. Altogether, these results suggest that the combined positivity for LC3B<sup>+</sup> puncta and nuclear HMGB1 is a positive predictor for longer BC survival.
DOI: 10.6084/m9.figshare.1568328
title: Bayesian Model Fusion for Forecasting Civil Unrest

description: With the rapid rise in social media, alternative news sources, and blogs, ordinary citizens have become information producers as much as information consumers. Highly charged prose, images, and videos spread virally, and stoke the embers of social unrest by alerting fellow citizens to relevant happenings and spurring them into action. We are interested in using Big Data approaches to generate forecasts of civil unrest from open source indicators. The heterogenous nature of data coupled with the rich and diverse origins of civil unrest call for a multi-model approach to such forecasting. We present a modular approach wherein a collection of models use overlapping sources of data to independently forecast protests. Fusion of alerts into one single alert stream becomes a key system informatics problem and we present a statistical framework to accomplish such fusion. Given an alert from one of the numerous models, the decision space for fusion has two possibilities: (i) release the alert or (ii) suppress the alert. Using a Bayesian decision theoretical framework, we present a fusion approach for releasing or suppressing alerts. The resulting system enables real-time decisions and more importantly tuning of precision and recall. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1568327
title: Cokriging-Based Sequential Design Strategies Using Fast Cross-Validation Techniques for Multi-Fidelity Computer Codes

description: Cokriging-based surrogate models have become popular in recent decades to approximate a computer code output from a few simulations using both coarse and more complex versions of the code. In practical applications, it is common to sequentially add new simulations to obtain more accurate approximations. We propose a method of cokriging-based sequential design, which combines both the error evaluation provided by the cokriging model and the observed errors of a leave-one-out cross-validation procedure. This method is proposed in two versions, the first one selects points one at a time. The second one allows us to parallelize the simulations and to add several design points at a time. The main advantage of the suggested strategies is that at a new design point they choose which code versions should be simulated (i.e., the complex code or one of its fast approximations). A multifidelity application is used to illustrate the efficiency of the proposed approaches. In this example, the accurate code is a two-dimensional finite element model and the less accurate one is a one-dimensional approximation of the system. This article has supplementary material online.
DOI: 10.6084/m9.figshare.1568329.v1
title: Blocking Orthogonal Designs With Mixed Integer Linear Programming

description: We present a mixed integer linear programming approach to orthogonally block two-level, multilevel, and mixed-level orthogonal designs. The approach involves an exact optimization technique which guarantees an optimal solution. It can be applied to many problems where combinatorial methods for blocking orthogonal designs cannot be used. By means of 54-run and 64-run examples, we demonstrate that our approach outperforms two benchmark techniques in terms of the number of estimable two-factor interaction contrasts and in terms of the D-efficiency for models with main effects and some two-factor interaction contrasts. We demonstrate the generic nature of our approach by applying it to the most challenging instances in a catalog of all orthogonal designs of strength 3 with up to 81 runs as well as a small catalog of strength-4 designs. The approach can also be applied to strength-2 designs, but, for these cases, alternative methods described in the literature may perform equally well. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1568328.v1
title: Bayesian Model Fusion for Forecasting Civil Unrest

description: With the rapid rise in social media, alternative news sources, and blogs, ordinary citizens have become information producers as much as information consumers. Highly charged prose, images, and videos spread virally, and stoke the embers of social unrest by alerting fellow citizens to relevant happenings and spurring them into action. We are interested in using Big Data approaches to generate forecasts of civil unrest from open source indicators. The heterogenous nature of data coupled with the rich and diverse origins of civil unrest call for a multi-model approach to such forecasting. We present a modular approach wherein a collection of models use overlapping sources of data to independently forecast protests. Fusion of alerts into one single alert stream becomes a key system informatics problem and we present a statistical framework to accomplish such fusion. Given an alert from one of the numerous models, the decision space for fusion has two possibilities: (i) release the alert or (ii) suppress the alert. Using a Bayesian decision theoretical framework, we present a fusion approach for releasing or suppressing alerts. The resulting system enables real-time decisions and more importantly tuning of precision and recall. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1568328.v3
title: Bayesian Model Fusion for Forecasting Civil Unrest

description: With the rapid rise in social media, alternative news sources, and blogs, ordinary citizens have become information producers as much as information consumers. Highly charged prose, images, and videos spread virally, and stoke the embers of social unrest by alerting fellow citizens to relevant happenings and spurring them into action. We are interested in using Big Data approaches to generate forecasts of civil unrest from open source indicators. The heterogenous nature of data coupled with the rich and diverse origins of civil unrest call for a multi-model approach to such forecasting. We present a modular approach wherein a collection of models use overlapping sources of data to independently forecast protests. Fusion of alerts into one single alert stream becomes a key system informatics problem and we present a statistical framework to accomplish such fusion. Given an alert from one of the numerous models, the decision space for fusion has two possibilities: (i) release the alert or (ii) suppress the alert. Using a Bayesian decision theoretical framework, we present a fusion approach for releasing or suppressing alerts. The resulting system enables real-time decisions and more importantly tuning of precision and recall. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1568329.v3
title: Blocking Orthogonal Designs With Mixed Integer Linear Programming

description: We present a mixed integer linear programming approach to orthogonally block two-level, multilevel, and mixed-level orthogonal designs. The approach involves an exact optimization technique which guarantees an optimal solution. It can be applied to many problems where combinatorial methods for blocking orthogonal designs cannot be used. By means of 54-run and 64-run examples, we demonstrate that our approach outperforms two benchmark techniques in terms of the number of estimable two-factor interaction contrasts and in terms of the D-efficiency for models with main effects and some two-factor interaction contrasts. We demonstrate the generic nature of our approach by applying it to the most challenging instances in a catalog of all orthogonal designs of strength 3 with up to 81 runs as well as a small catalog of strength-4 designs. The approach can also be applied to strength-2 designs, but, for these cases, alternative methods described in the literature may perform equally well. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1568327.v1
title: Cokriging-Based Sequential Design Strategies Using Fast Cross-Validation Techniques for Multi-Fidelity Computer Codes

description: Cokriging-based surrogate models have become popular in recent decades to approximate a computer code output from a few simulations using both coarse and more complex versions of the code. In practical applications, it is common to sequentially add new simulations to obtain more accurate approximations. We propose a method of cokriging-based sequential design, which combines both the error evaluation provided by the cokriging model and the observed errors of a leave-one-out cross-validation procedure. This method is proposed in two versions, the first one selects points one at a time. The second one allows us to parallelize the simulations and to add several design points at a time. The main advantage of the suggested strategies is that at a new design point they choose which code versions should be simulated (i.e., the complex code or one of its fast approximations). A multifidelity application is used to illustrate the efficiency of the proposed approaches. In this example, the accurate code is a two-dimensional finite element model and the less accurate one is a one-dimensional approximation of the system. This article has supplementary material online.