Only countable common cause systems exist

In this paper we give a positive answer to a problem posed by G. Hofer-Szabo and M. Redei (2004) regarding the existence of infinite common cause systems (CCSs). An example of a countably infinite CCS is presented, as well as the proof that no CCSs of greater cardinality exist

Completion of the Causal Completability Problem

We give a few results concerning the notions of causal completability and causal closedness of classical probability spaces (Hofer-Szabó, Rédei & Szabó [1999], Gyenis & Rédei [2004]). Answering a question from Hofer-Szabó et al. [1999], we prove that any classical probability space has a causally closed extension. We also employ the notion of causal up-to-n-closedness (Wroński & Marczyk [2010a]) to show that any finite classical probability space with rational probabilities on the atoms of the event algebra can be extended to a finite space which is causally up-to-3-closed. Lastly, we prove that any classical probability space can be extended to a space in which all correlations between events which are logically independent modulo measure zero event have a countably infinite common cause system (for the definition of the latter notion, see Hofer-Szabó & Rédei [2004]). Collectively, these results show that it is surprisingly easy to find Reichenbach-style explanations for correlations, underlining doubts as to whether this approach can yield a philosophically relevant account of causality

Establishing propositional truth-value in counterfactual and real-world contexts during sentence comprehension: Differential sensitivity of the left and right inferior frontal gyri

What makes a proposition true or false has traditionally played an essential role in philosophical and linguistic theories of meaning. A comprehensive neurobiological theory of language must ultimately be able to explain the combined contributions of real-world truth-value and discourse context to sentence meaning. This fMRI study investigated the neural circuits that are sensitive to the propositional truth-value of sentences about counterfactual worlds, aiming to reveal differential hemispheric sensitivity of the inferior prefrontal gyri to counterfactual truth-value and real-world truth-value. Participants read true or false counterfactual conditional sentences (“If N.A.S.A. had not developed its Apollo Project, the first country to land on the moon would be Russia/America”) and real-world sentences (“Because N.A.S.A. developed its Apollo Project, the first country to land on the moon has been America/Russia”) that were matched on contextual constraint and truth-value. ROI analyses showed that whereas the left BA 47 showed similar activity increases to counterfactual false sentences and to real-world false sentences (compared to true sentences), the right BA 47 showed a larger increase for counterfactual false sentences. Moreover, whole-brain analyses revealed a distributed neural circuit for dealing with propositional truth-value. These results constitute the first evidence for hemispheric differences in processing counterfactual truth-value and real-world truth-value, and point toward additional right hemisphere involvement in counterfactual comprehension

Combining low-dose CT-based radiomics and metabolomics for early lung cancer screening support

Due to its predominantly asymptomatic or mildly symptomatic progression, lung cancer is often diagnosed in advanced stages, resulting in poorer survival rates for patients. As with other cancers, early detection significantly improves the chances of successful treatment. Early diagnosis can be facilitated through screening programs designed to detect lung tissue tumors when they are still small, typically around 3mm in size. However, the analysis of extensive screening program data is hampered by limited access to medical experts. In this study, we developed a procedure for identifying potential malignant neoplastic lesions within lung parenchyma. The system leverages machine learning (ML) techniques applied to two types of measurements: low-dose Computed Tomography-based radiomics and metabolomics. Using data from two Polish screening programs, two ML algorithms were tested, along with various integration methods, to create a final model that combines both modalities to support lung cancer screening

BRONCO: Automated modelling of the bronchovascular bundle using the Computed Tomography Images

Segmentation of the bronchovascular bundle within the lung parenchyma is a key step for the proper analysis and planning of many pulmonary diseases. It might also be considered the preprocessing step when the goal is to segment the nodules from the lung parenchyma. We propose a segmentation pipeline for the bronchovascular bundle based on the Computed Tomography images, returning either binary or labelled masks of vessels and bronchi situated in the lung parenchyma. The method consists of two modules, modeling of the bronchial tree and vessels. The core revolves around a similar pipeline, the determination of the initial perimeter by the GMM method, skeletonization, and hierarchical analysis of the created graph. We tested our method on both low-dose CT and standard-dose CT, with various pathologies, reconstructed with various slice thicknesses, and acquired from various machines. We conclude that the method is invariant with respect to the origin and parameters of the CT series. Our pipeline is best suited for studies with healthy patients, patients with lung nodules, and patients with emphysema

Targeted RNAseq assay incorporating unique molecular identifiers for improved quantification of gene expression signatures and transcribed mutation fraction in fixed tumor samples.

BACKGROUND: Our objective was to assess whether modifications to a customized targeted RNA sequencing (RNAseq) assay to include unique molecular identifiers (UMIs) that collapse read counts to their source mRNA counts would improve quantification of transcripts from formalin-fixed paraffin-embedded (FFPE) tumor tissue samples. The assay (SET4) includes signatures that measure hormone receptor and PI3-kinase related transcriptional activity (SET METHODS: Modifications included steps to introduce eight nucleotides-long UMIs during reverse transcription (RT) in bulk solution, followed by polymerase chain reaction (PCR) of labeled cDNA in droplets, with optimization of the polymerase enzyme and reaction conditions. We used Lin\u27s concordance correlation coefficient (CCC) to measure concordance, including precision (Rho) and accuracy (Bias), and nonparametric tests (Wilcoxon, Levene\u27s) to compare the modified (NEW) SET4 assay to the original (OLD) SET4 assay and to whole transcriptome RNAseq using RNA from matched fresh frozen (FF) and FFPE samples from 12 primary breast cancers. RESULTS: The modified (NEW) SET4 assay measured single transcripts (p\u3c 0.001) and SET CONCLUSIONS: Modifications to the targeted RNAseq protocol for SET4 assay significantly increased the precision of UMI-based and reads-based measurements of individual transcripts, multi-gene signatures, and mutant transcript fraction, particularly with FFPE samples

Radiation-induced changes in serum lipidome of head and neck cancer patients

Cancer radiotherapy (RT) induces response of the whole patient’s body that could be detected at the blood level. We aimed to identify changes induced in serum lipidome during RT and characterize their association with doses and volumes of irradiated tissue. Sixty-six patients treated with conformal RT because of head and neck cancer were enrolled in the study. Blood samples were collected before, during and about one month after the end of RT. Lipid extracts were analyzed using MALDI-oa-ToF mass spectrometry in positive ionization mode. The major changes were observed when pre-treatment and within-treatment samples were compared. Levels of several identified phosphatidylcholines, including (PC34), (PC36) and (PC38) variants, and lysophosphatidylcholines, including (LPC16) and (LPC18) variants, were first significantly decreased and then increased in post-treatment samples. Intensities of changes were correlated with doses of radiation received by patients. Of note, such correlations were more frequent when low-to-medium doses of radiation delivered during conformal RT to large volumes of normal tissues were analyzed. Additionally, some radiation-induced changes in serum lipidome were associated with toxicity of the treatment. Obtained results indicated the involvement of choline-related signaling and potential biological importance of exposure to clinically low/medium doses of radiation in patient’s body response to radiation

Exhaustive Classication of Finite Classical Probability Spaces with Regard to the Notion of Causal Up-to-n-closedness

Extending the ideas from (Hofer-Szabó and Rédei [2006]), we introduce the notion of causal up-to-n-closedness of probability spaces. A probability space is said to be causally up-to-n-closed with respect to a relation of independence R_ind iff for any pair of correlated events belonging to R_ind the space provides a common cause or a common cause system of size at most n. We prove that a finite classical probability space is causally up-to-3-closed w.r.t. the relation of logical independence iff its probability measure is constant on the set of atoms of non-0 probability. (The latter condition is a weakening of the notion of measure uniformity.) Other independence relations are also considered

Ranking metrics in gene set enrichment analysis: do they matter?

Abstract Background There exist many methods for describing the complex relation between changes of gene expression in molecular pathways or gene ontologies under different experimental conditions. Among them, Gene Set Enrichment Analysis seems to be one of the most commonly used (over 10,000 citations). An important parameter, which could affect the final result, is the choice of a metric for the ranking of genes. Applying a default ranking metric may lead to poor results. Methods and results In this work 28 benchmark data sets were used to evaluate the sensitivity and false positive rate of gene set analysis for 16 different ranking metrics including new proposals. Furthermore, the robustness of the chosen methods to sample size was tested. Using k-means clustering algorithm a group of four metrics with the highest performance in terms of overall sensitivity, overall false positive rate and computational load was established i.e. absolute value of Moderated Welch Test statistic, Minimum Significant Difference, absolute value of Signal-To-Noise ratio and Baumgartner-Weiss-Schindler test statistic. In case of false positive rate estimation, all selected ranking metrics were robust with respect to sample size. In case of sensitivity, the absolute value of Moderated Welch Test statistic and absolute value of Signal-To-Noise ratio gave stable results, while Baumgartner-Weiss-Schindler and Minimum Significant Difference showed better results for larger sample size. Finally, the Gene Set Enrichment Analysis method with all tested ranking metrics was parallelised and implemented in MATLAB, and is available at https://github.com/ZAEDPolSl/MrGSEA . Conclusions Choosing a ranking metric in Gene Set Enrichment Analysis has critical impact on results of pathway enrichment analysis. The absolute value of Moderated Welch Test has the best overall sensitivity and Minimum Significant Difference has the best overall specificity of gene set analysis. When the number of non-normally distributed genes is high, using Baumgartner-Weiss-Schindler test statistic gives better outcomes. Also, it finds more enriched pathways than other tested metrics, which may induce new biological discoveries