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1. smICA: an open source repository for mapping the concentration of fluorescently labeled molecules in living cells on the basis of confocal imaging combined with time-correlated single-photon counting

Author

Kalwarczyk, Tomasz, Bubak, Grzegorz, Michalski, Jarosław, Kwapiszewska, Karina, Pilz, Marta, Mamot, Adam, Jemielity, Jacek, and Hołyst, Robert

Subjects
Quantitative Biology - Quantitative Methods
Abstract

Advanced microscopy techniques are essential for visualizing and tracking cellular components and molecules in biomedical research. However, conventional fluorescence microscopy methods often struggle with accurately measuring molecule concentrations in cells. To overcome these limitations, we introduce a novel approach that integrates laser scanning confocal microscopy with time-correlated single photon counting (TCSPC), supported by an open-source analysis tool called smICA (single-molecule Image to Concentration Analyzer). Our method, validated against traditional fluorescence correlation spectroscopy (FCS), offers enhanced accuracy in determining fluorescent molecule concentrations, particularly in cases where molecules are immobile or unevenly distributed. This is demonstrated using fluorescently labeled mRNA in living cells, highlighting the approach's effectiveness., Comment: 14 pages, 6 figures, 18, references

Published
2024

2. On algebraic sums, trees and ideals in the Baire space

Author

Mazurkiewicz, Łukasz, Michalski, Marcin, Rałowski, Robert, and Żeberski, Szymon

Subjects
Mathematics - General Topology, Primary: 03E75, 28A05, 54H05, Secondary: 03E17
Abstract

We work in the Baire space $\mathbb{Z}^\omega$ equipped with the coordinate-wise addition $+$. Consider a $\sigma-$ideal $\mathcal{I}$ and a family $\mathbb{T}$ of some kind of perfect trees. We are interested in results of the form: for every $A\in \mathcal{I}$ and a tree $T\in\mathbb{T}$ there exists $T'\in \mathbb{T}, T'\subseteq T$ such that $A+\underbrace{[T']+[T']+\dots +[T']}_{\text{n--times}}\in \mathcal{I}$ for each $n\in\omega$. Explored tree types include perfect trees, uniformly perfect trees, Miller trees, Laver trees and $\omega-$Silver trees. The latter kind of trees is an analogue of Silver trees from the Cantor space. Besides the standard $\sigma$-ideal $\mathcal{M}$ of meager sets, we also analyze $\mathcal{M}_-$ and fake null sets $\mathcal{N}$. The latter two are born out of the characterizations of their respective analogues in the Cantor space. The key ingredient in proofs were combinatorial characterizations of these ideals in the Baire space.

Published
2024

3. Stories in the two-state vector formalism

Author

Michalski, Patryk and Dragan, Andrzej

Subjects
Quantum Physics
Abstract

The two-state vector formalism of quantum mechanics is a time-symmetrized approach to standard quantum theory. In our work, we aim to establish rigorous foundations for the future investigation within this formalism. We introduce the concept of a story - a compatible pair consisting of a two-state vector and an ideal measurement. Using this concept, we examine the structure of the space comprising all two-state vectors. We conclude that some pairs of two-state vectors or their statistical mixtures cannot be physically distinguished. In particular, we demonstrate that not every non-separable two-state vector is distinguishable from a statistical mixture of separable two-state vectors. This leads us to formulate the definition of a strictly non-separable two-state vector as a genuine manifestation of entanglement between the past and the future., Comment: 6 pages

Published
2024

4. Empowering Clinicians with Medical Decision Transformers: A Framework for Sepsis Treatment

Author

Rahman, Aamer Abdul, Agarwal, Pranav, Noumeir, Rita, Jouvet, Philippe, Michalski, Vincent, and Kahou, Samira Ebrahimi

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

Offline reinforcement learning has shown promise for solving tasks in safety-critical settings, such as clinical decision support. Its application, however, has been limited by the lack of interpretability and interactivity for clinicians. To address these challenges, we propose the medical decision transformer (MeDT), a novel and versatile framework based on the goal-conditioned reinforcement learning paradigm for sepsis treatment recommendation. MeDT uses the decision transformer architecture to learn a policy for drug dosage recommendation. During offline training, MeDT utilizes collected treatment trajectories to predict administered treatments for each time step, incorporating known treatment outcomes, target acuity scores, past treatment decisions, and current and past medical states. This analysis enables MeDT to capture complex dependencies among a patient's medical history, treatment decisions, outcomes, and short-term effects on stability. Our proposed conditioning uses acuity scores to address sparse reward issues and to facilitate clinician-model interactions, enhancing decision-making. Following training, MeDT can generate tailored treatment recommendations by conditioning on the desired positive outcome (survival) and user-specified short-term stability improvements. We carry out rigorous experiments on data from the MIMIC-III dataset and use off-policy evaluation to demonstrate that MeDT recommends interventions that outperform or are competitive with existing offline reinforcement learning methods while enabling a more interpretable, personalized and clinician-directed approach.

Published
2024

5. Impact of Clinical Factors on 18F-Flotufolastat Detection Rates in Men With Recurrent Prostate Cancer: Exploratory Analysis of the Phase 3 SPOTLIGHT Study.

Author

Lowentritt, Benjamin, Jani, Ashesh, Helfand, Brian, Uchio, Edward, Morris, Michael, Michalski, Jeff, Chau, Albert, Davis, Phillip, Chapin, Brian, and Schuster, David

Abstract

PURPOSE: 18F-Flotufolastat (18F-rhPSMA-7.3) is a newly approved prostate-specific membrane antigen targeting radiopharmaceutical for diagnostic imaging of prostate cancer (PCa). SPOTLIGHT (National Clinical Trials 04186845) evaluated 18F-flotufolastat in men with suspected PCa recurrence. Here, we present results of predefined exploratory endpoints from SPOTLIGHT to evaluate the impact of clinical factors on 18F-flotufolastat detection rates (DR). METHODS AND MATERIALS: The impact of baseline prostate-specific antigen (PSA), PSA doubling time (PSAdt), and International Society of Urologic Pathology Grade Group (GG) on 18F-flotufolastat DR was evaluated among all SPOTLIGHT patients with an evaluable scan, with DR stratified according to the patients prior treatment (radical prostatectomy ± radiation therapy [RP] or radiation therapy only [RT]). The patients underwent positron emission tomography 50 to 70 minutes after receiving 18F-flotufolastat (296 MBq IV), and scans were read by 3 blinded central readers, with the majority read representing agreement between ≥2 readers. RESULTS: In total, 389 men (median PSA: 1.10 ng/mL) were evaluable. By majority read, 18F-flotufolastat identified distant lesions in 39% and 43% of patients treated with prior RP or RT, respectively. The overall DR broadly increased with increasing PSA (

Published
2024

6. CoDiNG -- Naming Game with Continuous Latent State of Agents

Author

Nurek, Mateusz, Kołaczek, Joanna, Michalski, Radosław, Szymański, Bolesław K., and Lizardo, Omar

Subjects
Computer Science - Social and Information Networks, Physics - Physics and Society
Abstract

Understanding the mechanisms behind opinion formation is crucial for gaining insight into the processes that shape political beliefs, cultural attitudes, consumer choices, and social movements. This work aims to explore a nuanced model that captures the intricacies of real-world opinion dynamics by synthesizing principles from cognitive science and employing social network analysis. The proposed model is a hybrid continuous-discrete extension of the well-known Naming Game opinion model. The added latent continuous layer of opinion strength follows cognitive processes in the human brain, akin to memory imprints. The discrete layer allows for the conversion of intrinsic continuous opinion into discrete form, which often occurs when we publicly verbalize our opinions. We evaluated our model using real data as ground truth and demonstrated that the proposed mechanism outperforms the classic Naming Game model in many cases, reflecting that our model is closer to the real process of opinion formation.

Published
2024

7. Temporal Link Prediction in Social Networks Based on Agent Behavior Synchrony and a Cognitive Mechanism

Author

Duan, Yueran, Nurek, Mateusz, Guan, Qing, Michalski, Radosław, and Holme, Petter

Subjects
Computer Science - Social and Information Networks
Abstract

Temporality, a crucial characteristic in the formation of social relationships, was used to quantify the long-term time effects of networks for link prediction models, ignoring the heterogeneity of time effects on different time scales. In this work, we propose a novel approach to link prediction in temporal networks, extending existing methods with a cognitive mechanism that captures the dynamics of the interactions. Our approach computes the weight of the edges and their change over time, similar to memory traces in the human brain, by simulating the process of forgetting and strengthening connections depending on the intensity of interactions. We utilized five ground-truth datasets, which were used to predict social ties, missing events, and potential links. We found: (a) the cognitive mechanism enables more accurate capture of the heterogeneity of the temporal effect, leading to an average precision improvement of 9\% compared to baselines with competitive AUC. (b) the local structure and synchronous agent behavior contribute differently to different types of datasets. (c) appropriately increasing the time intervals, which may reduce the negative impact from noise when dividing time windows to calculate the behavioral synchrony of agents, is effective for link prediction tasks., Comment: Equation 3 corrected, appendix added, acknowledgment extended

Published
2024

8. On the Limits of Multi-modal Meta-Learning with Auxiliary Task Modulation Using Conditional Batch Normalization

Author

Armengol-Estapé, Jordi, Michalski, Vincent, Kumar, Ramnath, St-Charles, Pierre-Luc, Precup, Doina, and Kahou, Samira Ebrahimi

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

Few-shot learning aims to learn representations that can tackle novel tasks given a small number of examples. Recent studies show that cross-modal learning can improve representations for few-shot classification. More specifically, language is a rich modality that can be used to guide visual learning. In this work, we experiment with a multi-modal architecture for few-shot learning that consists of three components: a classifier, an auxiliary network, and a bridge network. While the classifier performs the main classification task, the auxiliary network learns to predict language representations from the same input, and the bridge network transforms high-level features of the auxiliary network into modulation parameters for layers of the few-shot classifier using conditional batch normalization. The bridge should encourage a form of lightweight semantic alignment between language and vision which could be useful for the classifier. However, after evaluating the proposed approach on two popular few-shot classification benchmarks we find that a) the improvements do not reproduce across benchmarks, and b) when they do, the improvements are due to the additional compute and parameters introduced by the bridge network. We contribute insights and recommendations for future work in multi-modal meta-learning, especially when using language representations.

Published
2024

9. Network Diffusion -- Framework to Simulate Spreading Processes in Complex Networks

Author

Czuba, Michał, Nurek, Mateusz, Serwata, Damian, Qiu, Yu-Xuan, Jia, Mingshan, Musial, Katarzyna, Michalski, Radosław, and Bródka, Piotr

Subjects
Computer Science - Social and Information Networks, Computer Science - Multiagent Systems, Electrical Engineering and Systems Science - Systems and Control
Abstract

With the advancement of computational network science, its research scope has significantly expanded beyond static graphs to encompass more complex structures. The introduction of streaming, temporal, multilayer, and hypernetwork approaches has brought new possibilities and imposed additional requirements. For instance, by utilising these advancements, one can model structures such as social networks in a much more refined manner, which is particularly relevant in simulations of the spreading processes. Unfortunately, the pace of advancement is often too rapid for existing computational packages to keep up with the functionality updates. This results in a significant proliferation of tools used by researchers and, consequently, a lack of a universally accepted technological stack that would standardise experimental methods (as seen, e.g. in machine learning). This article addresses that issue by presenting an extended version of the Network Diffusion library. First, a survey of the existing approaches and toolkits for simulating spreading phenomena is shown and then, an overview of the framework functionalities. Finally, we report four case studies conducted with the package to demonstrate its usefulness: the impact of sanitary measures on the spread of COVID-19, the comparison of information diffusion on two temporal network models, and the effectiveness of seed selection methods in the task of influence maximisation in multilayer networks. We conclude the paper with a critical assessment of the library and the outline of still awaiting challenges to standardise research environments in computational network science., Comment: To be published in: Big Data Mining and Analytics (https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=8254253)

Published
2024
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10. On algebraic sums, trees and ideals in the Cantor space

Author

Michalski, Marcin, Rałowski, Robert, and Żeberski, Szymon

Subjects
Mathematics - Logic, Mathematics - General Topology, Primary: 03E75, 28A05, Secondary: 03E05, 54H05
Abstract

We work in the Cantor space $2^\omega$. The results of the paper adhere the following pattern. Let $\mathcal{I}\in \{\mathcal{M}, \mathcal{N}, \mathcal{M}\cap \mathcal{N}, \mathcal{E}\}$ and $T$ be a perfect, uniformly perfect or Silver tree. Then for every $A\in \mathcal{I}$ there exists $T'\subseteq T$ of the same kind as $T$ such that $A+\underbrace{[T']+[T']+\dots +[T']}_{\text{n--times}}\in \mathcal{I}$ for each $n\in\omega$. We also prove weaker statements for splitting trees. For the case $\mathcal{E}$ we also provide a simple characterization of basis of $\mathcal{E}$. We use these results to prove that the algebraic sum of a generalized Luzin set and a generalized Sierpi\'nski set belongs to $u_0$ and $v_0$, provided that $\mathfrak{c}$ is a regular cardinal.

Published
2024

16. The role of conformity in opinion dynamics modelling with multiple social circles

Author

Stępień, Stanisław, Jankowski, Jarosław, Bródka, Piotr, and Michalski, Radosław

Subjects
Computer Science - Social and Information Networks, Physics - Physics and Society
Abstract

Interaction with others influences our opinions and behaviours. Our activities within various social circles lead to different opinions expressed in various situations, groups, and ways of communication. Earlier studies on agent-based modelling of conformism within networks were based on a single-layer approach. Contrary to that, in this work, we propose a model incorporating conformism in which a person can share different continuous opinions on different layers depending on the social circle. Afterwards, we extend the model with more components that are known to influence opinions, e.g. authority or openness to new views. These two models are then compared to show that only sole conformism leads to opinion convergence., Comment: ICCS 2023 - 23rd International Conference on Computational Science

Published
2024
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17. BASE TTS: Lessons from building a billion-parameter Text-to-Speech model on 100K hours of data

Author

Łajszczak, Mateusz, Cámbara, Guillermo, Li, Yang, Beyhan, Fatih, van Korlaar, Arent, Yang, Fan, Joly, Arnaud, Martín-Cortinas, Álvaro, Abbas, Ammar, Michalski, Adam, Moinet, Alexis, Karlapati, Sri, Muszyńska, Ewa, Guo, Haohan, Putrycz, Bartosz, Gambino, Soledad López, Yoo, Kayeon, Sokolova, Elena, and Drugman, Thomas

Subjects
Computer Science - Machine Learning, Computer Science - Computation and Language, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

We introduce a text-to-speech (TTS) model called BASE TTS, which stands for $\textbf{B}$ig $\textbf{A}$daptive $\textbf{S}$treamable TTS with $\textbf{E}$mergent abilities. BASE TTS is the largest TTS model to-date, trained on 100K hours of public domain speech data, achieving a new state-of-the-art in speech naturalness. It deploys a 1-billion-parameter autoregressive Transformer that converts raw texts into discrete codes ("speechcodes") followed by a convolution-based decoder which converts these speechcodes into waveforms in an incremental, streamable manner. Further, our speechcodes are built using a novel speech tokenization technique that features speaker ID disentanglement and compression with byte-pair encoding. Echoing the widely-reported "emergent abilities" of large language models when trained on increasing volume of data, we show that BASE TTS variants built with 10K+ hours and 500M+ parameters begin to demonstrate natural prosody on textually complex sentences. We design and share a specialized dataset to measure these emergent abilities for text-to-speech. We showcase state-of-the-art naturalness of BASE TTS by evaluating against baselines that include publicly available large-scale text-to-speech systems: YourTTS, Bark and TortoiseTTS. Audio samples generated by the model can be heard at https://amazon-ltts-paper.com/., Comment: v1.1 (fixed typos)

Published
2024

18. Investigating Organic Carbon and Thermal History of CM Carbonaceous Chondrites Using Spectroscopy and Laboratory Techniques

Author

Tanbakouei, Safoura, Cheng, Rui-Lin, Ye, Binlong, Michalski, Josep Ryan, and King, Ashley J.

Subjects
Astrophysics - Earth and Planetary Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics, Physics - Geophysics
Abstract

The CM chondrites are characterized as primary accretionary rocks which originate from primitive water-rich asteroids formed during the early Solar System. Here, we study the mineralogy and organic characteristics of right CM and one ungrouped chondrite to better understand their alteration history; Queen Alexandra Range 93005 (QUE 93005), Murchison, LaPaz Icefield 02333 (LAP 02333), Miller Range (MIL 13005), Mackay Glacier 05231 (MCY 05231), Northwest Africa 8534 (NWA 8534), Northwest Africa 3340 (NWA 3340), Yamato 86695 (Y-86695), and the ungrouped carbonaceous chondrite Belgica 7904 (B-7904). Raman spectroscopy has been employed to detect the presence of organic carbon in the samples, specifically through the G band at approximately 1580 cm-1 and D band at around 1350 cm-1. The properties of organic matter in meteorites serve as valuable indicators for characterizing the structure and crystallinity of carbonaceous materials and estimating their thermal metamorphism degree. The R1 parameter, defined as the peak height ratio of the D and G bands, provides a quantifiable measure of this structural organization. Raman spectra are used to show the general mineralogy, thermal history and heating stage of CM and ungrouped chondrites. X-ray diffraction patterns further indicate the mineralogical compositions of the samples. Visible to near-infrared (VNIR) and attenuated total reflection (ATR) reflectance spectra illustrate the trends related to their mineralogy and furthermore infer aqueous alteration, thermal history of CM carbonaceous chondrites, formation and evolution of their parent bodies.

Published
2024

24. Kidney volume normative values in Central European children aged 0–19 years: a multicenter study

Author

Obrycki, Łukasz, Sarnecki, Jędrzej, Pac, Michał, Lichosik, Marianna, Sopińska, Małgorzata, Placzyńska, Małgorzata, Milart, Joanna, Stańczyk, Małgorzata, Mirecka, Julia, Wasilewska, Agnieszka, Michalski, Maciej, Lewandowska, Weronika, Dereziński, Tadeusz, Šuláková, Terezie, Šupík, David, Čekuolis, Andrius, Vitkevič, Renata, Wierzbicka, Aldona, Koziej, Jan, Skoczyński, Krzysztof, Horubała, Jan, Jankauskiene, Augustina, Kalicki, Bolesław, Jobs, Katarzyna, Tkaczyk, Marcin, Feber, Janusz, and Litwin, Mieczysław

Published
2024
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27. A digital, decentralized trial of exercise therapy in patients with cancer

Author

Underwood, Whitney P., Michalski, Meghan G., Lee, Catherine P., Fickera, Gina A., Chun, Su S., Eng, Stefan E., Liu, Lydia Y., Tsai, Brandon L., Moskowitz, Chaya S., Lavery, Jessica A., Van Zee, Kimberly J., Gardner, Ginger J., Mueller, Jennifer J., Dang, Chau T., Ehdaie, Behfar, Laudone, Vincent P., Eastham, James A., Scott, Jessica M., Boutros, Paul C., and Jones, Lee W.

Published
2024
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32. Polyphosphonate covalent organic frameworks

Author

Xu, Ke, Oestreich, Robert, Haj Hassani Sohi, Takin, Lounasvuori, Mailis, Ruthes, Jean G. A., Zorlu, Yunus, Michalski, Julia, Seiffert, Philipp, Strothmann, Till, Tholen, Patrik, Ozgur Yazaydin, A., Suta, Markus, Presser, Volker, Petit, Tristan, Janiak, Christoph, Beckmann, Jens, Schmedt auf der Günne, Jörn, and Yücesan, Gündoğ

Published
2024
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35. Multiscale and multimodal imaging for three-dimensional vascular and histomorphological organ structure analysis of the pancreas

Author

Salg, Gabriel Alexander, Steinle, Verena, Labode, Jonas, Wagner, Willi, Studier-Fischer, Alexander, Reiser, Johanna, Farjallah, Elyes, Guettlein, Michelle, Albers, Jonas, Hilgenfeld, Tim, Giese, Nathalia A., Stiller, Wolfram, Nickel, Felix, Loos, Martin, Michalski, Christoph W., Kauczor, Hans-Ulrich, Hackert, Thilo, Dullin, Christian, Mayer, Philipp, and Kenngott, Hannes Goetz

Published
2024
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40. SARS CoV-2 in tumor tissue in glioblastoma patients – preliminary study

Author

Brzozowski Michal, Kolesnik Marcin, Michalski Aleksander, Drop Katarzyna, and Polz-Dacewicz Malgorzata

Subjects
sars-cov-2, glioblastoma, sars-cov-2 antibody, covid-19, Medicine
Abstract

SARS-CoV-2 infection often causes neurological disorders. Experimental studies on an animal model have shown that SARS-CoV-2 is able to cross the blood-brain barrier. Researchers have also discovered that SARS-CoV-2 can infect glial cells. Gliomas are the most common type of brain tumor. Oncological patients are at high risk of infections, including SARS-CoV-2. Moreover, their weakened immunity causes the level of antibodies after infection or vaccination to be lower than in the healthy population. Therefore, the aim of our study was to evaluate the occurrence of SARS-CoV-2 RNA in tumor tissue collected during surgery. We also tested the level of anti-SARS-CoV-2 antibodies in these patients. The obtained results indicate the tropism of the virus to tumor tissue – glioblastoma. The level of anti-SARS antibodies was higher in patients with SARS-CoV-2 RNA detected in tumour tissue.

Published
2024
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41. The effect of caffeine dose on caffeine and paraxanthine changes in serum and saliva and CYP1A2 enzyme activity in athletes: a randomized placebo-controlled crossover trial

Author

Natalia Główka, Jakub Malik, Jacek Anioła, Emilia E. Zawieja, Agata Chmurzynska, and Krzysztof Durkalec-Michalski

Subjects
Enzyme activity, Ergogenic support, High-intensity functional training, Metabolism, Sports dietetics, Supplementation, Nutrition. Foods and food supply, TX341-641, Nutritional diseases. Deficiency diseases, RC620-627
Abstract

Abstract Background Although caffeine (CAF) supplementation has been shown to improve exercise performance, its dose-dependent effect on CAF metabolism has not been sufficiently investigated. The aim of this study was to evaluate the effects of 3, 6 and 9 mg of CAF/kgBM on changes of CAF and paraxanthine (PRX) in the serum and saliva at four time-points. Methods In a randomized, double-blind, placebo-controlled crossover design, acute pre-exercise supplementation in 26 moderately-trained athletes, participating in high-intensity functional training (HIFT), was examined. The study protocol involved CAF/PRX biochemical analyses of serum and saliva with respect to CYP1A2 polymorphism and CYP1A2 enzyme activity. Results Despite significant differences between the serum and saliva levels of CAF and PRX, there was no difference in the PRX/CAF ratio. The interaction effect of dose and time-points for PRX concentration was revealed. The main effects of dose were observed for CAF and the PRX/CAF ratio. The main effect of time-points was registered only for serum CAF. Conclusions Dose- and time-dependent effect of CAF supplementation on CAF and PRX in the serum and saliva of athletes was confirmed, but there was no effect of the CAF dose on CYP1A2 enzyme activity, nor was there an interaction of CYP1A2 with enzyme inducibility. The CAF/PRX correlation indicated the possibility of interchangeable use of serum and/or saliva analyses in exercise studies. Clinical trial registration This trial was registered prospectively at ClinicalTrials.gov (NCT03822663, registration date: 30/01/2019).

Published
2024
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42. Quantifying the chemical composition of weathering products of Hainan basalts with reflectance spectroscopy and its implications for Mars

Author

Xing Wu, JiaCheng Liu, WeiChao Sun, Yang Liu, Joseph Michalski, Wei Tan, XiaoRong Qin, and YongLiao Zou

Subjects
reflectance spectroscopy, weathered basalts, terrestrial analog, quantitative retrieval, mars, Science, Geophysics. Cosmic physics, QC801-809, Environmental sciences, GE1-350
Abstract

With the development of the hyperspectral remote sensing technique, extensive chemical weathering profiles have been identified on Mars. These weathering sequences, formed through precipitation-driven leaching processes, can reflect the paleoenvironments and paleoclimates during pedogenic processes. The specific composition and stratigraphic profiles mirror the mineralogical and chemical trends observed in weathered basalts on Hainan Island in south China. In this study, we investigated the laboratory reflectance spectra of a 53-m-long drilling core of a thick basaltic weathering profile collected from Hainan Island. We established a quantitative spectral model by combining the genetic algorithm and partial least squares regression (GA-PLSR) to predict the chemical properties (SiO2, Al2O3, Fe2O3) and index of laterization (IOL). The entire sample set was divided into a calibration set of 25 samples and a validation set of 12 samples. Specifically, the GA was used to select the spectral subsets for each composition, which were then input into the PLSR model to derive the chemical concentration. The coefficient of determination (R2) values on the validation set for SiO2, Al2O3, Fe2O3, and the IOL were greater than 0.9. In addition, the effects of various spectral preprocessing techniques on the model accuracy were evaluated. We found that the spectral derivative treatment boosted the prediction accuracy of the GA-PLSR model. The improvement achieved with the second derivative was more pronounced than when using the first derivative. The quantitative model developed in this work has the potential to estimate the contents of similar weathering basalt products, and thus infer the degree of alteration and provide insights into paleoclimatic conditions. Moreover, the informative bands selected by the GA can serve as a guideline for designing spectral channels for the next generation of spectrometers.

Published
2024
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43. The Effect of Two Somatic-Based Practices Dance and Martial Arts on Irisin, BDNF Levels and Cognitive and Physical Fitness in Older Adults: A Randomized Control Trial

Author

Hola V, Polanska H, Jandova T, Jaklová Dytrtová J, Weinerova J, Steffl M, Kramperova V, Dadova K, Durkalec-Michalski K, and Bartos A

Subjects
cognitive domains, ageing, cognitive performance, cognitive function, serum, biomarkers, Geriatrics, RC952-954.6
Abstract

Veronika Hola,1 Hana Polanska,1 Tereza Jandova,1 Jana Jaklová Dytrtová,1 Josefina Weinerova,2 Michal Steffl,1 Veronika Kramperova,1 Klara Dadova,1 Krzysztof Durkalec-Michalski,3 Ales Bartos2,4 1Faculty of Physical Education and Sport, Charles University, Prague, Czech Republic; 2University Hospital Kralovske Vinohrady, Department of Neurology, Prague, Czech Republic; 3Department of Sports Dietetics, Poznan University of Physical Education, Pozna&nacute;, 61-871, Poland; 4Third Faculty of Medicine, Charles University, Department of Neurology, Prague, Czech RepublicCorrespondence: Michal Steffl, Faculty of Physical Education and Sport Charles University, José Martího 31, Prague, 162 52, Czech Republic, Tel +420778701882, Email michal.steffl@ftvs.cuni.czBackground: Maintaining healthy brain function during ageing is of great importance, especially for the self-sufficiency of older adults. The main aim of this study was to determine the effects of dance and martial arts on exerkines Brain Derived Neurotrophic Factor (BDNF) and irisin blood serum levels.Methods: This randomized controlled trial examined the effects of dance and martial arts on serum Brain-Derived Neurotrophic Factor (BDNF) and irisin levels, as well as cognitive function, mood, and physical measures in older adults. Seventy-seven independently living older adults (mean age 70.3± 3.8 years) were randomized into three groups: dance (DG), martial arts (MaG), and control (CG), followed over 12 weeks. Generalized linear models were used to assess the interventions’ effects.Results: There was a significant increase in BDNF levels in both the DG (1.8 ± 4.9, p < 0.05) and MaG (3.5 ± 6.3, p < 0.05), while CG experienced a decrease (− 4.9 ± 8.2, p < 0.05). Between-group effects were significant for BDNF, with DG and MaG showing higher levels than CG (p < 0.05). No significant changes in irisin levels were found. Cognitive performance, particularly attention and mental flexibility (measured by the Trail Making Test A and B), significantly improved in the DG compared to CG (p < 0.05). Additionally, participants in DG showed improved mood based on the Geriatric Depression Scale (p < 0.05) compared to CG. Anthropometric T-scores were significantly associated with changes in irisin levels (p < 0.05) after intervention.Conclusion: The study found that dance and martial arts upregulated BDNF levels, with dance showing notable improvements in cognitive function and mood in older adults. Changes in anthropometric measures were linked to increased irisin levels. These findings suggest that both dance and martial arts may promote healthy brain function in aging populations.Trial Registration: NCT05363228.Keywords: cognitive domains, ageing, cognitive performance, cognitive function, serum, biomarkers

Published
2024

44. A digital, decentralized trial of exercise therapy in patients with cancer

Author

Whitney P. Underwood, Meghan G. Michalski, Catherine P. Lee, Gina A. Fickera, Su S. Chun, Stefan E. Eng, Lydia Y. Liu, Brandon L. Tsai, Chaya S. Moskowitz, Jessica A. Lavery, Kimberly J. Van Zee, Ginger J. Gardner, Jennifer J. Mueller, Chau T. Dang, Behfar Ehdaie, Vincent P. Laudone, James A. Eastham, Jessica M. Scott, Paul C. Boutros, and Lee W. Jones

Subjects
Computer applications to medicine. Medical informatics, R858-859.7
Abstract

Abstract We developed and evaluated the Digital Platform for Exercise (DPEx): a decentralized, patient-centric approach designed to enhance all aspects of clinical investigation of exercise therapy. DPEx integrated provision of a treadmill with telemedicine and remote biospecimen collection permitting all study procedures to be conducted in patient’s homes. Linked health biodevices enabled high-resolution monitoring of lifestyle and physiological response. Here we describe the rationale and development of DPEx as well as feasibility evaluation in three different cohorts of patients with cancer: a phase 0a development study among three women with post-treatment primary breast cancer; a phase 0b proof-of-concept trial of neoadjuvant exercise therapy in 13 patients with untreated solid tumors; and a phase 1a level-finding trial of neoadjuvant exercise therapy in 53 men with localized prostate cancer. Collectively, our study demonstrates the utility of a fully digital, decentralized approach to conduct clinical trials of exercise therapy in a clinical population.

Published
2024
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45. Reconsidering inequalities in COVID-19 vaccine uptake in Germany: a spatiotemporal analysis combining individual educational level and area-level socioeconomic deprivation

Author

Marvin Reis, Niels Michalski, Susanne Bartig, Elisa Wulkotte, Christina Poethko-Müller, Daniel Graeber, Angelika Schaffrath Rosario, Claudia Hövener, and Jens Hoebel

Subjects
COVID-19, Vaccine uptake, Socioeconomic factors, Social Deprivation, Diffusion of Innovation, Regional disparities, Medicine, Science
Abstract

Abstract Combining the frameworks of fundamental causes theory and diffusion of innovation, scholars had anticipated a delayed COVID-19 vaccination uptake for people in lower socioeconomic position depending on the socioeconomic context. We qualify these propositions and analyze educational differences in COVID-19 vaccination status over the first ten months of Germany’s vaccination campaign in 2021. Data from the study “Corona Monitoring Nationwide” (RKI-SOEP-2), collected between November 2021 and February 2022, is linked with district-level data of the German Index of Socioeconomic Deprivation (GISD). We estimated the proportion of people with at least one vaccination dose stratified by educational groups and within different settings of regional socioeconomic deprivation at three time points. Logistic multilevel regression models were applied to adjust for multiple covariates and to test cross-level-interactions between educational levels and levels of area-level socioeconomic deprivation. Vaccination rates were lower among respondents with lower education. With increasing area-level socioeconomic deprivation, educational differences were larger due to particularly low vaccination rates in groups with low education levels. The analysis of vaccination timing reveals that educational gaps and gaps by area-level socioeconomic deprivation had appeared early in the vaccination campaign and did not close completely before the 4th wave of COVID-19 infections

Published
2024
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46. Impact of virtual reality hypnosedation on perioperative pain and anxiety in port implantation under local anesthesia: a randomized controlled pilot trial (VIP Trial)

Author

Kira C. Steinkraus, Hannah Feldmann, Lisa S. Hunold, Sandra Graf, Colette Dörr-Harim, Nadir Nasir, Christoph W. Michalski, and Felix J. Hüttner

Subjects
Virtual reality, Hypnosedation, Port implantation, Local anesthesia, Perioperative anxiety, Outpatient surgery, Surgery, RD1-811
Abstract

Abstract Background This monocentric randomized controlled pilot trial investigates the impact of virtual reality (VR) hypnosedation on perioperative anxiety, pain, patient satisfaction, and medication usage during port implantation under local anesthesia. Methods A total of 120 patients undergoing elective port implantation between January 2022 and August 2023 were enrolled and randomized in a 1:1 ratio to either a VR hypnosedation group or a control group. The VR group used a commercially available VR headset with the HypnoVR application, providing various environments, musical backgrounds, and a guiding voice, while the control group underwent the procedure without VR. Patients with ASA > 3, chronic pain, cognitive issues, and contraindications against VR use were excluded. The main outcomes measured were perioperative pain and anxiety scores, with secondary outcomes including perioperative medication usage. Due to the nature of the interventions, blinding of patients and physicians was not feasible. Statistical analysis was primarily descriptive and exploratory, focusing on estimating effect sizes for future trials. Results The study found no significant differences in immediate postoperative pain with 1.43 ± 1.63 vs. 1.6 ± 2.05 (p = 0.62) or anxiety scores 30.65 ± 9.13 vs. 31.78 ± 13.34 (p = 0.60) between the no VR and VR group, respectively. Additionally, there was a trend to less usage of certain medications, particularly remifentanil (mean dose of 200 mg vs. 100 mg (p = 0.12)) and novaminsulfon (mean dose of 1250 mg vs. 900 mg (p = 0.26)) in the VR group vs. no VR group, respectively. However, these differences were not statistically significant and therefore no definitive conclusions can be drawn regarding medication usage based on this data. Conclusion While VR hypnosedation did not significantly reduce perioperative pain or anxiety in this pilot trial, the observed trends in reduced medication usage suggest potential benefits. These findings warrant further investigation in larger, confirmatory trials to better understand the role of VR in enhancing patient comfort and potentially reducing reliance on pharmacological interventions during surgical procedures. Trial registration German Clinical Trials Register: DRKS00028508; registration date 15 March 2022; Universal Trial Number: U1111-1275–4995.

Published
2024
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47. A performance characteristic curve for model evaluation: the application in information diffusion prediction

Author

Xie, Wenjin, Wang, Xiaomeng, Michalski, Radosław, and Jia, Tao

Subjects
Computer Science - Social and Information Networks, Computer Science - Artificial Intelligence
Abstract

The information diffusion prediction on social networks aims to predict future recipients of a message, with practical applications in marketing and social media. While different prediction models all claim to perform well, general frameworks for performance evaluation remain limited. Here, we aim to identify a performance characteristic curve for a model, which captures its performance on tasks of different complexity. We propose a metric based on information entropy to quantify the randomness in diffusion data, then identify a scaling pattern between the randomness and the prediction accuracy of the model. Data points in the patterns by different sequence lengths, system sizes, and randomness all collapse into a single curve, capturing a model's inherent capability of making correct predictions against increased uncertainty. Given that this curve has such important properties that it can be used to evaluate the model, we define it as the performance characteristic curve of the model. The validity of the curve is tested by three prediction models in the same family, reaching conclusions in line with existing studies. Also, the curve is successfully applied to evaluate two distinct models from the literature. Our work reveals a pattern underlying the data randomness and prediction accuracy. The performance characteristic curve provides a new way to systematically evaluate models' performance, and sheds light on future studies on other frameworks for model evaluation.

Published
2023

48. Around Eggleston Theorem

Author

Michalski, Marcin, Rałowski, Robert, and Żeberski, Szymon

Subjects
Mathematics - Logic, Primary: 03E75, 28A05, 54H05, Secondary: 03E17
Abstract

The motivation of this work are the two classical theorems on inscribing rectangles and squares into large subsets of the plane, namely Eggleston Theorem and Mycielski Theorem. Using Shoenfield Absoluteness Theorem we prove that for every Borel subset of the plane with uncountably many positive (with respect to measure or category) vertical section contains a rectangle $P\times B$ where $P$ is perfect and $B$ is Borel and positive. We also obtained a variant of Eggleston Theorem regarding the $\sigma$-ideal $\mathcal(E)$ generated by closed sets of measure zero. Furthermore we proved that every comeager (resp. conull) subset of the plane contains a rectangle $[T]\times H$, where $T$ is a Spinas tree containing a Silver tree and $H$ is comeager (resp. conull). Moreover we obtained a common generalization of Eggleston Theorem and Mycielski Theorem stating that every comeager (resp. conull) subset of the plane contains a rectangle $[T]\times H$ modulo diagonal, where $T$ is a uniformly perfect tree, $H$ is comeager (resp. conull) and $[T]\subseteq H$.

Published
2023

49. Biochemical Recurrence Surrogacy for Clinical Outcomes After Radiotherapy for Adenocarcinoma of the Prostate.

Author

Roy, Soumyajit, Romero, Tahmineh, Michalski, Jeff, Feng, Felix, Efstathiou, Jason, Lawton, Colleen, Bolla, Michel, Maingon, Philippe, de Reijke, Theo, Joseph, David, Ong, Wee, Sydes, Matthew, Dearnaley, David, Tree, Alison, Carrier, Nathalie, Nabid, Abdenour, Souhami, Luis, Incrocci, Luca, Heemsbergen, Wilma, Pos, Floris, Zapatero, Almudena, Guerrero, Araceli, Alvarez, Ana, San-Segundo, Carmen, Maldonado, Xavier, Reiter, Robert, Nickols, Nicholas, Ma, T, Farrell, Matthew, Neilsen, Beth, Juarez, Jesus, Deng, Jie, Vangala, Sitaram, Avril, Norbert, Jia, Angela, Zaorsky, Nicholas, Sun, Yilun, Spratt, Daniel, Kishan, Amar, Rettig, Matthew, Steinberg, Michael, and Valle, Luca

Subjects
Male, Humans, Prostate, Prostatic Neoplasms, Androgen Antagonists, Prostate-Specific Antigen, Adenocarcinoma
Abstract

PURPOSE: The surrogacy of biochemical recurrence (BCR) for overall survival (OS) in localized prostate cancer remains controversial. Herein, we evaluate the surrogacy of BCR using different surrogacy analytic methods. MATERIALS AND METHODS: Individual patient data from 11 trials evaluating radiotherapy dose escalation, androgen deprivation therapy (ADT) use, and ADT prolongation were obtained. Surrogate candidacy was assessed using the Prentice criteria (including landmark analyses) and the two-stage meta-analytic approach (estimating Kendalls tau and the R2). Biochemical recurrence-free survival (BCRFS, time from random assignment to BCR or any death) and time to BCR (TTBCR, time from random assignment to BCR or cancer-specific deaths censoring for noncancer-related deaths) were assessed. RESULTS: Overall, 10,741 patients were included. Dose escalation, addition of short-term ADT, and prolongation of ADT duration significantly improved BCR (hazard ratio [HR], 0.71 [95% CI, 0.63 to 0.79]; HR, 0.53 [95% CI, 0.48 to 0.59]; and HR, 0.54 [95% CI, 0.48 to 0.61], respectively). Adding short-term ADT (HR, 0.91 [95% CI, 0.84 to 0.99]) and prolonging ADT (HR, 0.86 [95% CI, 0.78 to 0.94]) significantly improved OS, whereas dose escalation did not (HR, 0.98 [95% CI, 0.87 to 1.11]). BCR at 48 months was associated with inferior OS in all three groups (HR, 2.46 [95% CI, 2.08 to 2.92]; HR, 1.51 [95% CI, 1.35 to 1.70]; and HR, 2.31 [95% CI, 2.04 to 2.61], respectively). However, after adjusting for BCR at 48 months, there was no significant treatment effect on OS (HR, 1.10 [95% CI, 0.96 to 1.27]; HR, 0.96 [95% CI, 0.87 to 1.06] and 1.00 [95% CI, 0.90 to 1.12], respectively). The patient-level correlation (Kendalls tau) for BCRFS and OS ranged between 0.59 and 0.69, and that for TTBCR and OS ranged between 0.23 and 0.41. The R2 values for trial-level correlation of the treatment effect on BCRFS and TTBCR with that on OS were 0.563 and 0.160, respectively. CONCLUSION: BCRFS and TTBCR are prognostic but failed to satisfy all surrogacy criteria. Strength of correlation was greater when noncancer-related deaths were considered events.

Published
2023

50. Genomic Classifier Performance in Intermediate-Risk Prostate Cancer: Results From NRG Oncology/RTOG 0126 Randomized Phase 3 Trial.

Author

Spratt, Daniel, Liu, Vinnie, Michalski, Jeff, Davicioni, Elai, Berlin, Alejandro, Simko, Jeffry, Efstathiou, Jason, Tran, Phuoc, Sandler, Howard, Hall, William, Thompson, Darby, Parliament, Matthew, Dayes, Ian, Correa, Rohann, Robertson, John, Gore, Elizabeth, Doncals, Desiree, Vigneault, Eric, Souhami, Luis, Karrison, Theodore, and Feng, Felix

Subjects
Male, Humans, Prostatic Neoplasms, Prostate-Specific Antigen, Androgen Antagonists, Retrospective Studies, Neoplasm Grading, Genomics, Disease Progression
Abstract

PURPOSE: Intermediate-risk prostate cancer is a heterogeneous disease state with diverse treatment options. The 22-gene Decipher genomic classifier (GC) retrospectively has shown to improve risk stratification in these patients. We assessed the performance of the GC in men with intermediate-risk disease enrolled in NRG Oncology/RTOG 01-26 with updated follow-up. METHODS AND MATERIALS: After National Cancer Institute approval, biopsy slides were collected from NRG Oncology/RTOG 01-26, a randomized phase 3 trial of men with intermediate-risk prostate cancer randomized to 70.2 Gy versus 79.2 Gy of radiation therapy without androgen deprivation therapy. RNA was extracted from the highest-grade tumor foci to generate the locked 22-gene GC model. The primary endpoint for this ancillary project was disease progression (composite of biochemical failure, local failure, distant metastasis, prostate cancer-specific mortality, and use of salvage therapy). Individual endpoints were also assessed. Fine-Gray or cause-specific Cox multivariable models were constructed adjusting for randomization arm and trial stratification factors. RESULTS: Two-hundred fifteen patient samples passed quality control for analysis. The median follow-up was 12.8 years (range, 2.4-17.7). On multivariable analysis, the 22-gene GC (per 0.1 unit) was independently prognostic for disease progression (subdistribution hazard ratio [sHR], 1.12; 95% confidence interval [CI], 1.00-1.26; P = .04), biochemical failure (sHR, 1.22; 95% CI, 1.10-1.37; P < .001), distant metastasis (sHR, 1.28; 95% CI, 1.06-1.55; P = .01), and prostate cancer-specific mortality (sHR, 1.45; 95% CI, 1.20-1.76; P < .001). Ten-year distant metastasis in GC low-risk patients was 4% compared with 16% for GC high-risk patients. In patients with lower GC scores, the 10-year difference in metastasis-free survival rate between arms was -7%, compared with 21% for higher GC patients (P-interaction = .04). CONCLUSIONS: This study represents the first validation of a biopsy-based gene expression classifier, assessing both its prognostic and predictive value, using data from a randomized phase 3 trial of intermediate-risk prostate cancer. Decipher improves risk stratification and can aid in treatment decision-making in men with intermediate-risk disease.

Published
2023

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