Cellular landscaping of COVID-19 and gynaecological cancers: An infrequent correlation

COVID-19 resulted in a mortality rate of 3-6% caused by SARS-CoV-2 and its variant leading to unprecedented consequences of acute respiratory distress septic shock and multiorgan failure. In such a situation, evaluation, diagnosis, treatment, and care for cancer patients are difficult tasks faced by medical staff. Moreover, patients with gynaecological cancer appear to be more prone to severe infection and mortality from COVID-19 due to immunosuppression by chemotherapy and coexisting medical disorders. To deal with such a circumtances oncologists have been obliged to reconsider the entire diagnostic, treatment, and management approach. This review will provide and discuss the molecular link with gynaecological cancer under COVID-19 infection, providing a novel bilateral relationship between the two infections. Moreover, the authors have provided insights to discuss the pathobiology of COVID-19 in gynaecological cancer and their risks associated with such comorbidity. Furthermore, we have depicted the overall impact of host immunity along with guidelines for the treatment of patients with gynaecological cancer under COVID-19 infection. We have also discussed the feasible scope for the management of COVID-19 and gynaecological cancer

Potentially underestimated gas flaring activities—a new approach to detect combustion using machine learning and NASA’s Black Marble product suite

Monitoring changes in greenhouse gas (GHG) emission is critical for assessing climate mitigation efforts towards the Paris Agreement goal. A crucial aspect of science-based GHG monitoring is to provide objective information for quality assurance and uncertainty assessment of the reported emissions. Emission estimates from combustion events (gas flaring and biomass burning) are often calculated based on activity data (AD) from satellite observations, such as those detected from the visible infrared imaging radiometer suite (VIIRS) onboard the Suomi-NPP and NOAA-20 satellites. These estimates are often incorporated into carbon models for calculating emissions and removals. Consequently, errors and uncertainties associated with AD propagate into these models and impact emission estimates. Deriving uncertainty of AD is therefore crucial for transparency of emission estimates but remains a challenge due to the lack of evaluation data or alternate estimates. This work proposes a new approach using machine learning (ML) for combustion detection from NASA’s Black Marble product suite and explores the assessment of potential uncertainties through comparison with existing detections. We jointly characterize combustion using thermal and light emission signals, with the latter improving detection of probable weaker combustion with less distinct thermal signatures. Being methodologically independent, the differences in ML-derived estimates with existing approaches can indicate the potential uncertainties in detection. The approach was applied to detect gas flares over the Eagle Ford Shale, Texas. We analyzed the spatio-temporal variations in detections and found that approximately 79.04% and 72.14% of the light emission-based detections are missed by ML-derived detections from VIIRS thermal bands and existing datasets, respectively. This improvement in combustion detection and scope for uncertainty assessment is essential for comprehensive monitoring of resulting emissions and we discuss the steps for extending this globally

Comprehensive profiling of rRNA-derived small RNAs in Arabidopsis thaliana using rsRNAfinder pipeline

Ribosomal RNA (rRNA) gives rise to non-random small RNA fragments known as ribosomal-derived small RNAs (rsRNAs), which despite their biological importance, have been relatively understudied in comparison to other short non-coding RNAs. There exists a compelling necessity to develop a methodology for the identification, categorization, and quantification of rsRNAs from small RNA sequencing (sRNA-seq) data sets, considering the unique characteristics of ribosomal RNA (rRNA). To bridge this gap, we introduce ‘rsRNAfinder’ a specialized pipeline designed within the Snakemake framework. This analytical approach enables robust identification of rsRNAs using sRNA-seq datasets from Arabidopsis thaliana. Our methodology constitutes an integrated bioinformatic pipeline designed for different kinds of analysis. 1. sRNA-seq data analysis: It performs in-depth analysis of reference-aligned sRNA-seq data, facilitating rsRNA annotation and quantification. 2. Parametric reporting: Our pipeline provides comprehensive reports encompassing key parameters such as rsRNA size distributions, strandedness, genomic origin, and source rRNA origin. 3. Illustrative validation: We have demonstrated the utility of our approach by conducting comprehensive rsRNA annotation in Arabidopsis thaliana. This validation reveals unique rsRNAs originating from all rRNA types, each of them distinguished by distinct identity, abundance, and length

Probing z≳6z \gtrsim 6 massive black holes with gravitational waves

We investigate the coalescence of massive black hole ($M_{\rm BH}\gtrsim 10^{6}~\rm M_{\odot}$) binaries (MBHBs) at $6<z<10$ by adopting a suite of cosmological hydrodynamical simulations of galaxy formation, zoomed-in on biased ($>3 \sigma$) overdense regions ($M_h\sim 10^{12}~\rm M_{\odot}$ dark matter halos at $z = 6$) of the Universe. We first analyse the impact of different resolutions and AGN feedback prescriptions on the merger rate, assuming instantaneous mergers. Then, we compute the halo bias correction factor due to the overdense simulated region. Our simulations predict merger rates that range between 3 - 15 $\rm yr^{-1}$ at $z\sim 6$, depending on the run considered, and after correcting for a bias factor of $\sim 20-30$. For our fiducial model, we further consider the effect of delay in the MBHB coalescence due to dynamical friction. We find that 83 per cent of MBHBs will merge within the Hubble time, and 21 per cent within 1 Gyr, namely the age of the Universe at $z > 6$. We finally compute the expected properties of the gravitational wave (GW) signals and find the fraction of LISA detectable events with high signal-to-noise ratio (SNR $>$ 5) to range between 66-69 per cent. However, identifying the electro-magnetic counterpart of these events remains challenging due to the poor LISA sky localization that, for the loudest signals ($\mathcal M_c\sim 10^6~\rm M_{\odot}$ at $z=6$), is around 10 $\rm deg^2$.Comment: 17 pages, 11 figures, 3 tables. Accepted for publication in MNRA

Probing z > 6 massive black holes with gravitational waves

We investigate the coalescence of massive black hole (MBH≳10^6 M⊙) binaries (MBHBs) at 6 &lt; z &lt; 10 by adopting a suite of cosmological hydrodynamical simulations of galaxy formation, zoomed-in on biased (&gt;3σ) overdense regions (Mh ~ 1012 M⊙ dark matter haloes at z = 6) of the Universe. We first analyse the impact of different resolutions and AGN feedback prescriptions on the merger rate, assuming instantaneous mergers. Then, we compute the halo bias correction factor due to the overdense simulated region. Our simulations predict merger rates that range between 3 and 15 yr−1 at z ~6, depending on the run considered, and after correcting for a bias factor of ~20-30. For our fiducial model, we further consider the effect of delay in the MBHB coalescence due to dynamical friction. We find that 83 per cent of MBHBs will merge within the Hubble time, and 21 per cent within 1 Gyr, namely the age of the Universe at z &gt; 6. We finally compute the expected properties of the gravitational wave (GW) signals and find the fraction of LISA detectable events with high signal-to-noise ratio (SNR &gt; 5) to range between 66 per cent and 69 per cent. However, identifying the electro-magnetic counterpart of these events remains challenging due to the poor LISA sky localization that, for the loudest signals (Mc∼106 M⊙ at z = 6), is around 10 deg^2

Tomatidine targets ATF4-dependent signaling and induces ferroptosis to limit pancreatic cancer progression

Summary: Pancreatic ductal adenocarcinoma (PDAC) is an aggressive cancer with high metastasis and therapeutic resistance. Activating transcription factor 4 (ATF4), a master regulator of cellular stress, is exploited by cancer cells to survive. Prior research and data reported provide evidence that high ATF4 expression correlates with worse overall survival in PDAC. Tomatidine, a natural steroidal alkaloid, is associated with inhibition of ATF4 signaling in multiple diseases. Here, we discovered that in vitro and in vivo tomatidine treatment of PDAC cells inhibits tumor growth. Tomatidine inhibited nuclear translocation of ATF4 and reduced the transcriptional binding of ATF4 with downstream promoters. Tomatidine enhanced gemcitabine chemosensitivity in 3D ECM-hydrogels and in vivo. Tomatidine treatment was associated with induction of ferroptosis signaling validated by increased lipid peroxidation, mitochondrial biogenesis, and decreased GPX4 expression in PDAC cells. This study highlights a possible therapeutic approach utilizing a plant-derived metabolite, tomatidine, to target ATF4 activity in PDAC

Astrophysics with the Laser Interferometer Space Antenna

submitted to Living Reviews In RelativityLaser Interferometer Space Antenna (LISA) will be a transformative experiment for gravitational wave astronomy as it will offer unique opportunities to address many key astrophysical questions in a completely novel way. The synergy with ground-based and other space-based instruments in the electromagnetic domain, by enabling multi-messenger observations, will add further to the discovery potential of LISA. The next decade is crucial to prepare the astrophysical community for LISA's first observations. This review outlines the extensive landscape of astrophysical theory, numerical simulations, and astronomical observations that are instrumental for modeling and interpreting the upcoming LISA datastream. To this aim, the current knowledge in three main source classes for LISA is reviewed: ultra-compact stellar-mass binaries, massive black hole binaries, and extreme or intermediate mass ratio inspirals. The relevant astrophysical processes and the established modeling techniques are summarized. Likewise, open issues and gaps in our understanding of these sources are highlighted, along with an indication of how LISA could help make progress in the different areas. New research avenues that LISA itself, or its joint exploitation with studies in the electromagnetic domain, will enable, are also illustrated. Improvements in modeling and analysis approaches, such as the combination of numerical simulations and modern data science techniques, are discussed. This review is intended to be a starting point for using LISA as a new discovery tool for understanding our Universe

Astrophysics with the Laser Interferometer Space Antenna

submitted to Living Reviews In RelativityLaser Interferometer Space Antenna (LISA) will be a transformative experiment for gravitational wave astronomy as it will offer unique opportunities to address many key astrophysical questions in a completely novel way. The synergy with ground-based and other space-based instruments in the electromagnetic domain, by enabling multi-messenger observations, will add further to the discovery potential of LISA. The next decade is crucial to prepare the astrophysical community for LISA's first observations. This review outlines the extensive landscape of astrophysical theory, numerical simulations, and astronomical observations that are instrumental for modeling and interpreting the upcoming LISA datastream. To this aim, the current knowledge in three main source classes for LISA is reviewed: ultra-compact stellar-mass binaries, massive black hole binaries, and extreme or intermediate mass ratio inspirals. The relevant astrophysical processes and the established modeling techniques are summarized. Likewise, open issues and gaps in our understanding of these sources are highlighted, along with an indication of how LISA could help make progress in the different areas. New research avenues that LISA itself, or its joint exploitation with studies in the electromagnetic domain, will enable, are also illustrated. Improvements in modeling and analysis approaches, such as the combination of numerical simulations and modern data science techniques, are discussed. This review is intended to be a starting point for using LISA as a new discovery tool for understanding our Universe

Astrophysics with the Laser Interferometer Space Antenna

submitted to Living Reviews In RelativityLaser Interferometer Space Antenna (LISA) will be a transformative experiment for gravitational wave astronomy as it will offer unique opportunities to address many key astrophysical questions in a completely novel way. The synergy with ground-based and other space-based instruments in the electromagnetic domain, by enabling multi-messenger observations, will add further to the discovery potential of LISA. The next decade is crucial to prepare the astrophysical community for LISA's first observations. This review outlines the extensive landscape of astrophysical theory, numerical simulations, and astronomical observations that are instrumental for modeling and interpreting the upcoming LISA datastream. To this aim, the current knowledge in three main source classes for LISA is reviewed: ultra-compact stellar-mass binaries, massive black hole binaries, and extreme or intermediate mass ratio inspirals. The relevant astrophysical processes and the established modeling techniques are summarized. Likewise, open issues and gaps in our understanding of these sources are highlighted, along with an indication of how LISA could help make progress in the different areas. New research avenues that LISA itself, or its joint exploitation with studies in the electromagnetic domain, will enable, are also illustrated. Improvements in modeling and analysis approaches, such as the combination of numerical simulations and modern data science techniques, are discussed. This review is intended to be a starting point for using LISA as a new discovery tool for understanding our Universe

Astrophysics with the Laser Interferometer Space Antenna

submitted to Living Reviews In RelativityLaser Interferometer Space Antenna (LISA) will be a transformative experiment for gravitational wave astronomy as it will offer unique opportunities to address many key astrophysical questions in a completely novel way. The synergy with ground-based and other space-based instruments in the electromagnetic domain, by enabling multi-messenger observations, will add further to the discovery potential of LISA. The next decade is crucial to prepare the astrophysical community for LISA's first observations. This review outlines the extensive landscape of astrophysical theory, numerical simulations, and astronomical observations that are instrumental for modeling and interpreting the upcoming LISA datastream. To this aim, the current knowledge in three main source classes for LISA is reviewed: ultra-compact stellar-mass binaries, massive black hole binaries, and extreme or intermediate mass ratio inspirals. The relevant astrophysical processes and the established modeling techniques are summarized. Likewise, open issues and gaps in our understanding of these sources are highlighted, along with an indication of how LISA could help make progress in the different areas. New research avenues that LISA itself, or its joint exploitation with studies in the electromagnetic domain, will enable, are also illustrated. Improvements in modeling and analysis approaches, such as the combination of numerical simulations and modern data science techniques, are discussed. This review is intended to be a starting point for using LISA as a new discovery tool for understanding our Universe