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Article ; Online: Liquid Modernity and the Holocaust

Dariusz Brzeziński

Studia Litteraria et Historica, Iss

2022 Volume 11

Abstract: In Modernity and the Holocaust, Zygmunt Bauman argued that the Holocaust had been by no means a negation of the civilising process, but was, on the contrary, its consequence. He claimed that the constitutive features of solid modernity, such as ... ...

Abstract	In Modernity and the Holocaust, Zygmunt Bauman argued that the Holocaust had been by no means a negation of the civilising process, but was, on the contrary, its consequence. He claimed that the constitutive features of solid modernity, such as bureaucratic culture, the rise of instrumental rationality and the domination of blueprint utopia, were reflected in the genesis and course of the Holocaust. Bauman’s main aim in writing the book was to make fundamental changes in the problematization of modernity in the social sciences, and to highlight the need to develop norms, values and attitudes that were in opposition to modernity. An overview of these facts provides a starting point for my analysis of Bauman’s later analyses of the Holocaust. By focusing on two issues that he emphasised in the context of the genesis of the Holocaust, namely the mechanism of adiaphorization and the process of the social construction of “otherness”, I show how these phenomena materialize in liquid modernity. I identify their connections with a range of features of the liquid modern condition, including the development of individualism and consumer culture, the sense of insecurity and the “nostalgic turn”. I emphasise that, according to Bauman, both moral indifference and xenophobia are increasingly significant phenomena in contemporary society, which may entail pernicious – and currently barely foreseeable – consequences. I argue that the way Bauman wrote about both solid and liquid modernity was characterised by a dystopian poetics, which was a foundation for his utopian thought.
Keywords	modernity ; Holocaust ; Zygmunt Bauman ; adiaphorization ; otherness ; Anthropology ; GN1-890 ; Ethnology. Social and cultural anthropology ; GN301-674
Subject code	390
Language	German
Publishing date	2022-12-01T00:00:00Z
Publisher	Institute of Slavic Studies, Polish Academy of Sciences
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Fibonacci and k-Subsecting Recursive Feature Elimination

Brzezinski, Dariusz

2020

Abstract: Feature selection is a data mining task with the potential of speeding up classification algorithms, enhancing model comprehensibility, and improving learning accuracy. However, finding a subset of features that is optimal in terms of predictive accuracy ...

Abstract	Feature selection is a data mining task with the potential of speeding up classification algorithms, enhancing model comprehensibility, and improving learning accuracy. However, finding a subset of features that is optimal in terms of predictive accuracy is usually computationally intractable. Out of several heuristic approaches to dealing with this problem, the Recursive Feature Elimination (RFE) algorithm has received considerable interest from data mining practitioners. In this paper, we propose two novel algorithms inspired by RFE, called Fibonacci- and k-Subsecting Recursive Feature Elimination, which remove features in logarithmic steps, probing the wrapped classifier more densely for the more promising feature subsets. The proposed algorithms are experimentally compared against RFE on 28 highly multidimensional datasets and evaluated in a practical case study involving 3D electron density maps from the Protein Data Bank. The results show that Fibonacci and k-Subsecting Recursive Feature Elimination are capable of selecting a smaller subset of features much faster than standard RFE, while achieving comparable predictive performance.
Keywords	Computer Science - Machine Learning ; Statistics - Machine Learning
Subject code	006
Publishing date	2020-07-29
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: confidence-planner

Klorek, Antoni / Roszak, Karol / Szczech, Izabela / Brzezinski, Dariusz

Easy-to-Use Prediction Confidence Estimation and Sample Size Planning

2023

Abstract: Machine learning applications, especially in the fields of me\-di\-cine and social sciences, are slowly being subjected to increasing scrutiny. Similarly to sample size planning performed in clinical and social studies, lawmakers and funding agencies may ...

Abstract	Machine learning applications, especially in the fields of me\-di\-cine and social sciences, are slowly being subjected to increasing scrutiny. Similarly to sample size planning performed in clinical and social studies, lawmakers and funding agencies may expect statistical uncertainty estimations in machine learning applications that impact society. In this paper, we present an easy-to-use python package and web application for estimating prediction confidence intervals. The package offers eight different procedures to determine and justify the sample size and confidence of predictions from holdout, bootstrap, cross-validation, and progressive validation experiments. Since the package builds directly on established data analysis libraries, it seamlessly integrates into preprocessing and exploratory data analysis steps. Code related to this paper is available at: https://github.com/dabrze/confidence-planner.
Keywords	Statistics - Methodology ; Computer Science - Machine Learning
Subject code	310
Publishing date	2023-01-12
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Towards Explainable TOPSIS

Susmaga, Robert / Szczech, Izabela / Brzezinski, Dariusz

Visual Insights into the Effects of Weights and Aggregations on Rankings

2023

Abstract: Multi-Criteria Decision Analysis (MCDA) is extensively used across diverse industries to assess and rank alternatives. Among numerous MCDA methods developed to solve real-world ranking problems, TOPSIS remains one of the most popular choices in many ... ...

Abstract	Multi-Criteria Decision Analysis (MCDA) is extensively used across diverse industries to assess and rank alternatives. Among numerous MCDA methods developed to solve real-world ranking problems, TOPSIS remains one of the most popular choices in many application areas. TOPSIS calculates distances between the considered alternatives and two predefined ones, namely the ideal and the anti-ideal, and creates a ranking of the alternatives according to a chosen aggregation of these distances. However, the interpretation of the inner workings of TOPSIS is difficult, especially when the number of criteria is large. To this end, recent research has shown that TOPSIS aggregations can be expressed using the means (M) and standard deviations (SD) of alternatives, creating MSD-space, a tool for visualizing and explaining aggregations. Even though MSD-space is highly useful, it assumes equally important criteria, making it less applicable to real-world ranking problems. In this paper, we generalize the concept of MSD-space to weighted criteria by introducing the concept of WMSD-space defined by what is referred to as weight-scaled means and standard deviations. We demonstrate that TOPSIS and similar distance-based aggregation methods can be successfully illustrated in a plane and interpreted even when the criteria are weighted, regardless of their number. The proposed WMSD-space offers a practical method for explaining TOPSIS rankings in real-world decision problems.
Keywords	Computer Science - Artificial Intelligence
Subject code	006
Publishing date	2023-06-13
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Incorporation of Tumor-Free Distance and Other Alternative Ultrasound Biomarkers into a Myometrial Invasion-Based Model Better Predicts Lymph Node Metastasis in Endometrial Cancer: Evidence and Future Prospects.

Liro, Marcin / Śniadecki, Marcin / Wycinka, Ewa / Wojtylak, Szymon / Brzeziński, Michał / Jastrzębska, Joanna / Wydra, Dariusz

Diagnostics (Basel, Switzerland)

2022 Volume 12, Issue 11

Abstract: Myometrial invasion (MI) is a parameter currently used in transvaginal ultrasound (TVS) in endometrial cancer (EC) to determine local staging; however, without molecular diagnostics, it is insufficient for the selection of high-risk cases, i.e., those ... ...

Abstract	Myometrial invasion (MI) is a parameter currently used in transvaginal ultrasound (TVS) in endometrial cancer (EC) to determine local staging; however, without molecular diagnostics, it is insufficient for the selection of high-risk cases, i.e., those with a high risk of lymph node metastases (LNM). The study’s objective was to answer the question of which TVS markers, or their combination, reflecting the molecular changes in EC, can improve the prediction of LNM. Methods: The TVS examination was performed on 116 consecutive EC patients included in this prospective study. The results from the final histopathology were a reference standard. Univariate and multivariate logistic models of analyzed TVS biomarkers (tumor [T] size, T area [AREA], T volume [SPE-VOL], MI, T-free distance to serosa [TFD], endo-myometrial irregularity, [EMIR], cervical stromal involvement, CSI) were evaluated to assess the relative accuracy of the possible LNM predictors., Spline functions were applied to avoid a potential bias in assuming linear relations between LNM and continuous predictors. Calculations were made in R using libraries splines, glmulti, and pROC. Results: LNM was found in 20 out of the 116 (17%) patients. In univariate analysis, only uMI, EMIR, uCSI and uTFD were significant predictors of LNM. The accuracy was 0.707 (AUC 0.684, 95% CI 0.568−0.801) for uMI (p < 0.01), 0.672 (AUC 0.664, 95% CI 0.547−0.781) for EMIR (p < 0.01), 0.776 (AUC 0.647, 95% CI 0.529−0.765) for uCSI (p < 0.01), and 0.638 (AUC 0.683, 95% CI 0.563−0.803) for uTFD (p < 0.05). The cut-off value for uTFD was 5.2 mm. However, AREA and VOL revealed a significant relationship by nonlinear analysis as well. Among all possible multivariate models, the one comprising interactions of splines of uTFD with uMI and splines of SPE-VOL with uCSI showed the most usefulness. Accuracy was 0.802 (AUC 0.791, 95% CI 0.673−0.91) Conclusions: A combination of uTFD for patients with uMI > 50%, and SPE-VOL for patients with uCSI, allows for the most accurate prediction of LNM in EC, rather than uMI alone.
Language	English
Publishing date	2022-10-27
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2662336-5
ISSN	2075-4418
ISSN	2075-4418
DOI	10.3390/diagnostics12112604
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Incorporation of Tumor-Free Distance and Other Alternative Ultrasound Biomarkers into a Myometrial Invasion-Based Model Better Predicts Lymph Node Metastasis in Endometrial Cancer

Marcin Liro / Marcin Śniadecki / Ewa Wycinka / Szymon Wojtylak / Michał Brzeziński / Joanna Jastrzębska / Dariusz Wydra

Diagnostics, Vol 12, Iss 2604, p

Evidence and Future Prospects

2022 Volume 2604

Abstract	Myometrial invasion (MI) is a parameter currently used in transvaginal ultrasound (TVS) in endometrial cancer (EC) to determine local staging; however, without molecular diagnostics, it is insufficient for the selection of high-risk cases, i.e., those with a high risk of lymph node metastases (LNM). The study’s objective was to answer the question of which TVS markers, or their combination, reflecting the molecular changes in EC, can improve the prediction of LNM. Methods: The TVS examination was performed on 116 consecutive EC patients included in this prospective study. The results from the final histopathology were a reference standard. Univariate and multivariate logistic models of analyzed TVS biomarkers (tumor [T] size, T area [AREA], T volume [SPE-VOL], MI, T-free distance to serosa [TFD], endo-myometrial irregularity, [EMIR], cervical stromal involvement, CSI) were evaluated to assess the relative accuracy of the possible LNM predictors., Spline functions were applied to avoid a potential bias in assuming linear relations between LNM and continuous predictors. Calculations were made in R using libraries splines , glmulti , and pROC . Results: LNM was found in 20 out of the 116 (17%) patients. In univariate analysis, only uMI, EMIR, uCSI and uTFD were significant predictors of LNM. The accuracy was 0.707 (AUC 0.684, 95% CI 0.568–0.801) for uMI ( p < 0.01), 0.672 (AUC 0.664, 95% CI 0.547–0.781) for EMIR ( p < 0.01), 0.776 (AUC 0.647, 95% CI 0.529–0.765) for uCSI ( p < 0.01), and 0.638 (AUC 0.683, 95% CI 0.563–0.803) for uTFD ( p < 0.05). The cut-off value for uTFD was 5.2 mm. However, AREA and VOL revealed a significant relationship by nonlinear analysis as well. Among all possible multivariate models, the one comprising interactions of splines of uTFD with uMI and splines of SPE-VOL with uCSI showed the most usefulness. Accuracy was 0.802 (AUC 0.791, 95% CI 0.673–0.91) Conclusions: A combination of uTFD for patients with uMI > 50%, and SPE-VOL for patients with uCSI, allows for the most ...
Keywords	endometrial cancer ; ultrasound ; lymph nodes ; staging ; metastases ; biomarkers ; Medicine (General) ; R5-920
Subject code	610
Language	English
Publishing date	2022-10-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: On the evolution of the quality of macromolecular models in the PDB.

Brzezinski, Dariusz / Dauter, Zbigniew / Minor, Wladek / Jaskolski, Mariusz

The FEBS journal

2020 Volume 287, Issue 13, Page(s) 2685–2698

Abstract: Crystallographic models of biological macromolecules have been ranked using the quality criteria associated with them in the Protein Data Bank (PDB). The outcomes of this quality analysis have been correlated with time and with the journals that ... ...

Abstract	Crystallographic models of biological macromolecules have been ranked using the quality criteria associated with them in the Protein Data Bank (PDB). The outcomes of this quality analysis have been correlated with time and with the journals that published papers based on those models. The results show that the overall quality of PDB structures has substantially improved over the last ten years, but this period of progress was preceded by several years of stagnation or even depression. Moreover, the study shows that the historically observed negative correlation between journal impact and the quality of structural models presented therein seems to disappear as time progresses.
MeSH term(s)	Algorithms ; Computational Biology/methods ; Databases, Protein/standards ; Macromolecular Substances/chemistry ; Models, Molecular ; Protein Conformation ; Protein Domains ; Proteins/chemistry ; Quality Control
Chemical Substances	Macromolecular Substances ; Proteins
Language	English
Publishing date	2020-04-20
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2173655-8
ISSN	1742-4658 ; 1742-464X
ISSN (online)	1742-4658
ISSN	1742-464X
DOI	10.1111/febs.15314
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: Random Similarity Forests

Piernik, Maciej / Brzezinski, Dariusz / Zawadzki, Pawel

2022

Abstract: The wealth of data being gathered about humans and their surroundings drives new machine learning applications in various fields. Consequently, more and more often, classifiers are trained using not only numerical data but also complex data objects. For ... ...

Abstract	The wealth of data being gathered about humans and their surroundings drives new machine learning applications in various fields. Consequently, more and more often, classifiers are trained using not only numerical data but also complex data objects. For example, multi-omics analyses attempt to combine numerical descriptions with distributions, time series data, discrete sequences, and graphs. Such integration of data from different domains requires either omitting some of the data, creating separate models for different formats, or simplifying some of the data to adhere to a shared scale and format, all of which can hinder predictive performance. In this paper, we propose a classification method capable of handling datasets with features of arbitrary data types while retaining each feature's characteristic. The proposed algorithm, called Random Similarity Forest, uses multiple domain-specific distance measures to combine the predictive performance of Random Forests with the flexibility of Similarity Forests. We show that Random Similarity Forests are on par with Random Forests on numerical data and outperform them on datasets from complex or mixed data domains. Our results highlight the applicability of Random Similarity Forests to noisy, multi-source datasets that are becoming ubiquitous in high-impact life science projects.
Keywords	Computer Science - Machine Learning ; Quantitative Biology - Quantitative Methods
Subject code	006
Publishing date	2022-04-11
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Ultrasound Measurement of Tumor-Free Distance from the Serosal Surface as the Alternative to Measuring the Depth of Myometrial Invasion in Predicting Lymph Node Metastases in Endometrial Cancer.

Liro, Marcin / Śniadecki, Marcin / Wycinka, Ewa / Wojtylak, Szymon / Brzeziński, Michał / Stańczak, Agata / Wydra, Dariusz

Diagnostics (Basel, Switzerland)

2021 Volume 11, Issue 8

Abstract: Background: Ultrasonography's usefulness in endometrial cancer (EC) diagnosis consists in its roles in staging and prediction of metastasis. Ultrasound-measured tumor-free distance from the tumor to the uterine serosa (uTFD) is a promising marker for ... ...

Abstract	Background: Ultrasonography's usefulness in endometrial cancer (EC) diagnosis consists in its roles in staging and prediction of metastasis. Ultrasound-measured tumor-free distance from the tumor to the uterine serosa (uTFD) is a promising marker for these diagnostic and prognostic variables. The aim of the study was to determine the usefulness of this biomarker in locoregional staging, and thus in the prediction of lymph node metastasis (LNM). Methods: We conducted a single-institutional, prospective study on 116 consecutive patients with EC who underwent 2D transvaginal ultrasound examination. The uTFD marker was compared with the depth of ultrasound-measured myometrial invasion (uMI). Univariable and multivariable logit models were evaluated to assess the predictive power of the uTFD and uMI in regard to LNM. The reference standard was a final histopathology result. Survival was assessed by the Kaplan-Meier method. Results: LNM was found in 17% of the patients (20/116). In the univariable analysis, uMI and uTFD were significant predictors of LNM. The accuracy was 70.7%, and the NPV was 92.68% (OR 4.746, 95% CI 1.710-13.174) for uMI ( Conclusions: Both uMI and uTFD, either alone or in combination, were valuable tools for gaining additional preoperative information on expected lymph node status. Negative lymph nodes status was better described by ultrasound biomarkers than a positive status. It was easier to use the uTFD rather than the uMI measurement as a biomarker of EC invasion, and the former still maintained a similar predictive value for lymph node metastases to the latter at diagnosis.
Language	English
Publishing date	2021-08-14
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2662336-5
ISSN	2075-4418
ISSN	2075-4418
DOI	10.3390/diagnostics11081472
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: On the evolution of the quality of macromolecular models in the PDB

Brzezinski, Dariusz / Dauter, Zbigniew / Minor, Wladek / Jaskolski, Mariusz

FEBS journal. 2020 July, v. 287, no. 13

2020

Abstract	Crystallographic models of biological macromolecules have been ranked using the quality criteria associated with them in the Protein Data Bank (PDB). The outcomes of this quality analysis have been correlated with time and with the journals that published papers based on those models. The results show that the overall quality of PDB structures has substantially improved over the last ten years, but this period of progress was preceded by several years of stagnation or even depression. Moreover, the study shows that the historically observed negative correlation between journal impact and the quality of structural models presented therein seems to disappear as time progresses.
Keywords	biological models ; databases ; evolution ; exhibitions ; journals ; proteins
Language	English
Dates of publication	2020-07
Size	p. 2685-2698.
Publishing place	John Wiley & Sons, Ltd
Document type	Article
Note	NALT-AP-4-rerunAP2-fuzzy ; JOURNAL ARTICLE
ZDB-ID	2173655-8
ISSN	1742-4658 ; 1742-464X
ISSN (online)	1742-4658
ISSN	1742-464X
DOI	10.1111/febs.15314
Database	NAL-Catalogue (AGRICOLA)

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