The First Polarimetric View on Quasi-Periodic Oscillations in a Black Hole X-ray Binary

We present the first polarimetric analysis of Quasi-Periodic Oscillations (QPO) in a black hole binary utilizing \textit{IXPE} data. Our study focuses on Swift J1727.8--1613, which experienced a massive outburst that was observed by various telescopes across different wavelengths. The \textit{IXPE} observation we studied was conducted during the Hard-Intermediate state. The polarization degree (PD) and polarization angle (PA) were measured at 4.28$\pm$0.20\% and $1.9^{\circ}\pm1.4^{\circ}$, respectively. Remarkably, significant QPO signals were detected during this observation, with a QPO frequency of approximately 1.34 Hz and a fractional root-mean-square (RMS) amplitude of about 12.3\%. Furthermore, we conducted a phase-resolved analysis of the QPO using the Hilbert-Huang transform technique. The photon index showed a strong modulation with respect to the QPO phase. In contrast, the PD and PA exhibit no modulations in relation to the QPO phase, which is inconsistent with the expectation of the Lense-Thirring precession of the inner flow. Further theoretical studies are needed to conform with the observational results.Comment: Accepted for publication in APJ

The mHz quasi-regular modulations of 4U 1630--47 during its 1998 outburst

We present the results of a detailed timing and spectral analysis of the quasi-regular modulation (QRM) phenomenon in the black hole X-ray binary 4U 1630--47 during its 1998 outburst observed by Rossi X-ray Timing Explore (RXTE). We find that the $\sim$ 50-110 mHz QRM is flux dependent, and the QRM is detected with simultaneous low frequency quasi-periodic oscillations (LFQPOs). According to the behavior of the power density spectrum, we divide the observations into four groups. In the first group, namely behavior A, LFQPOs are detected, but no mHz QRM. The second group, namely behavior B, a QRM with frequency above $\sim$ 88 mHz is detected and the $\sim$ 5 Hz and $\sim$ 7 Hz LFQPOs are almost overlapping. In the third group, namely behavior C, the QRM frequency below $\sim$ 88 mHz is detected and the LFQPOs are significantly separated. In the forth group, namely behavior D, neither QRM nor LFQPOs are detected. We study the energy-dependence of the fractional rms, centroid frequency, and phase-lag of QRM and LFQPOs for behavior B and C. We then study the evolution of QRM and find that the frequency of QRM increases with hardness, while its rms decreases with hardness. We also analyze the spectra of each observation, and find that the QRM rms of behavior B has a positive correlation with $\rm F_{\rm powerlaw}$ / $\rm F_{\rm total}$. Finally, we give our understanding for this mHz QRM phenomena.Comment: 14pages, 15 figure

Cross-oncopanel study reveals high sensitivity and accuracy with overall analytical performance depending on genomic regions.

Targeted sequencing using oncopanels requires comprehensive assessments of accuracy and detection sensitivity to ensure analytical validity. By employing reference materials characterized by the U.S. Food and Drug Administration-led SEquence Quality Control project phase2 (SEQC2) effort, we perform a cross-platform multi-lab evaluation of eight Pan-Cancer panels to assess best practices for oncopanel sequencing. All panels demonstrate high sensitivity across targeted high-confidence coding regions and variant types for the variants previously verified to have variant allele frequency (VAF) in the 5-20% range. Sensitivity is reduced by utilizing VAF thresholds due to inherent variability in VAF measurements. Enforcing a VAF threshold for reporting has a positive impact on reducing false positive calls. Importantly, the false positive rate is found to be significantly higher outside the high-confidence coding regions, resulting in lower reproducibility. Thus, region restriction and VAF thresholds lead to low relative technical variability in estimating promising biomarkers and tumor mutational burden. This comprehensive study provides actionable guidelines for oncopanel sequencing and clear evidence that supports a simplified approach to assess the analytical performance of oncopanels. It will facilitate the rapid implementation, validation, and quality control of oncopanels in clinical use.All SEQC2 participants freely donated their time, reagents, and computing resources for the completion and analysis of this project. Part of this work was carried out with the support of the Intramural Research Program of the National Institutes of Health (to Mehdi Pirooznia), National Institute of Environmental Health Sciences (to Pierre Bushel), and National Library of Medicine (to Danielle Thierry-Mieg, Jean Thierry-Mieg, and Chunlin Xiao). Leming Shi and Yuanting Zheng were supported by the National Key R&D Project of China (2018YFE0201600), the National Natural Science Foundation of China (31720103909), and Shanghai Municipal Science and Technology Major Project (2017SHZDZX01). Donald J. Johann, Jr. acknowledges the support by FDA BAA grant HHSF223201510172C. Timothy Mercer and Ira Deveson were supported by the National Health and Medical Research Council (NHMRC) of Australia grants APP1108254, APP1114016, and APP1173594 and Cancer Institute NSW Early Career Fellowship 2018/ECF013. This research has also been, in part, financially supported by the MEYS of the CR under the project CEITEC 2020 (LQ1601), by MH CR, grant No. (NV19-03-00091). Part of this work was carried out with the support of research infrastructure EATRIS-CZ, ID number LM2015064, funded by MEYS CR. Boris Tichy and Nikola Tom were supported by research infrastructure EATRIS-CZ, ID number LM2018133 funded by MEYS CR and MEYS CR project CEITEC 2020 (LQ1601).S