Thermodynamic Profiles Research Articles

The impact of cloud microphysics and ice nucleation on Southern Ocean clouds assessed with single-column modeling and instrument simulators

Abstract. Supercooled liquid clouds are common at higher latitudes (especially over the Southern Ocean) and are critical for constraining climate projections. We take advantage of the Macquarie Island Cloud and Radiation Experiment (MICRE) to perform an analysis of observed and simulated cloud processes over the Southern Ocean in a region and season dominated by supercooled liquid clouds. Using a single-column version of the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS), we compare two different cloud microphysical schemes to ground-based observations of cloud, precipitation, and radiation over a 2.5-month period (1 January–17 March 2017). Both schemes are able to reproduce aspects of the cloud and radiation observations during MICRE to within the uncertainty of the data when the thermodynamic profile is prescribed with relaxation. There are differences in water mass and representation of reflectivity between the schemes. A sensitivity study of the cloud microphysics schemes, one a bulk one-moment scheme and the other a two-moment scheme with prediction of mass and number, indicates that several key processes create differences between the schemes. Surface radiative fluxes and total water path are highly sensitive to the formation and fall speed of precipitation. The prediction of hydrometeor number with the two-moment scheme yields a better comparison with observed reflectivity and radiative fluxes, despite predicting higher liquid water contents than observed. With the two-moment scheme, we are also able to test the sensitivity of the results to the input of liquid cloud condensation nuclei (CCN) and ice nuclei (IN). The cloud properties and resulting radiative effects are found to be sensitive to the CCN and IN concentrations. More CCN and IN increase liquid and ice water paths, respectively. Thus, both the dynamic environment and aerosols, integrated through the cloud microphysics, are important for properly representing Southern Ocean cloud radiative effects.

Investigating the Impact of Multiphysics on Modeled Planetary Boundary Layer Height Estimates during the PECAN Field Campaign

Abstract Accurate representation of the planetary boundary layer (PBL) in numerical weather prediction is crucial to air quality, weather forecasting, and climate change research and operations. Here, we evaluate the estimates of PBL height (PBLH) from a set of convective-permitting simulations conducted using the Weather Research and Forecasting (WRF) Model during the Plains Elevated Convection at Night (PECAN) campaign (from June to mid-July 2015). Our analysis aims to quantify the variability in PBLH, along with its associated surface variables and atmospheric profiles, to gauge the influence of model physics on the accuracy of simulated PBL properties. Specifically, we conducted twenty-four 45-day retrospective simulations encompassing different combinations of three PBL and two microphysics schemes, two initial and boundary condition (ICBC) datasets, and two model vertical grid spacings. We compare these simulations with measurements from surface sites and radiosonde launches across four sites in the southern Great Plains during PECAN. Our findings indicate that deriving PBLH from modeled atmospheric profiles yields a narrower spread (200–300 m) compared to PBLH calculated from the model’s PBL scheme (400–500 m) relative to radiosonde-derived PBLH under fair weather conditions. The choice of PBL scheme and ICBCs exerts the most significant impact (by up to 400 m) on the spread of modeled PBLH and associated atmospheric profiles, primarily influencing the representation of surface heating, transport, and mixing processes in the model. The model has difficulty reproducing the correct thermodynamic profile under cloud-intense conditions. These results underscore the benefit of using the physically consistent approach in retrieving, evaluating, and assimilating PBLH estimates, as well as the utility of multiphysics to better address model uncertainties. Significance Statement Studies on weather forecast models typically involve running the model multiple times and adjusting model inputs slightly each time to produce a range of predicted variables. This range is crucial for evaluating forecast probabilities and integrating observations into the model. We found that using different model physics schemes can result in an equal or greater spread for predicted variables (here, we focus on boundary layer height and related variables), compared to input tweaks alone. Additionally, a particular scheme can introduce biases. These findings underscore the importance of employing multiple physics schemes in the model, as they widen the coverage (potentially doubling) of outputs, and thus better address forecast uncertainties.

Effects of SARS-CoV-2 Main Protease Mutations at Positions L50, E166, and L167 Rendering Resistance to Covalent and Noncovalent Inhibitors.

SARS-CoV-2 propagation under nirmatrelvir and ensitrelvir pressure selects for main protease (MPro) drug-resistant mutations E166V (DRM2), L50F/E166V (DRM3), E166A/L167F (DRM4), and L50F/E166A/L167F (DRM5). DRM2-DRM5 undergoes N-terminal autoprocessing to produce mature MPro with dimer dissociation constants (Kdimer) 2-3 times larger than that of the wildtype. Co-selection of L50F restores catalytic activity of DRM2 and DRM4 from ∼10 to 30%, relative to that of the wild-type enzyme, without altering Kdimer. Binding affinities and thermodynamic profiles that parallel the drug selection pressure, exhibiting significant decreases in affinity through entropy/enthalpy compensation, were compared with GC373. Reorganization of the active sites due to mutations observed in the inhibitor-free DRM3 and DRM4 structures as compared to MProWT may account for the reduced binding affinities, although DRM2 and DRM3 complexes with ensitrelvir are almost identical to MProWT-ensitrelvir. Chemical reactivity changes of the mutant active sites due to differences in electrostatic and protein dynamics effects likely contribute to losses in binding affinities.

Regime-Dependent Characteristics and Predictability of Cold-Season Precipitation Events in the St. Lawrence River Valley

Abstract The St. Lawrence River Valley experiences a variety of precipitation types (p-types) during the cold season, such as rain, freezing rain, ice pellets, and snow. These varied precipitation types exert considerable impacts on aviation, road transportation, power generation and distribution, and winter recreation and are shaped by diverse multiscale processes that interact with the region’s complex topography. This study utilizes ERA5 reanalysis data, surface cyclone climatology, and hourly station observations from Montréal, Québec, and Burlington, Vermont, during October–April 2000–18 to investigate the spectrum of synoptic-scale weather regimes that induce cold-season precipitation across the St. Lawrence River Valley. In particular, k-means clustering and self-organizing maps (SOMs) are used to classify cyclone tracks passing near the St. Lawrence River Valley, and their accompanying thermodynamic profiles, into a set of event types that include a U.S. East Coast track, a central U.S. track, and two Canadian clipper tracks. Composite analyses are subsequently performed to reveal the synoptic-scale environments and the characteristic p-types that most frequently accompany each event type. Global Ensemble Forecast System version 12 (GEFSv12) reforecasts are then used to examine the relative predictability of cyclone characteristics and the local thermodynamic profile associated with each event type at 0–5-day forecast lead times. The analysis suggests that forecasted cyclones near the St. Lawrence River Valley develop too quickly and are located left-of-track relative to the reanalysis on average, which has implications for forecasts of the local thermodynamic profile and p-type across the region when the temperature is near 0°C. Significance Statement Diverse precipitation types are observed when near-surface temperatures approach 0°C during the cold season, especially across the St. Lawrence River Valley in southern Québec. This study classifies cold-season precipitation events impacting the St. Lawrence River Valley based on the track of storm systems across the region and quantifies the average meteorological characteristics and predictability of each track. Our analysis reveals that forecasted low pressure systems develop too quickly and are left of their observed track 0–5 days prior to an event on average, which has implications for forecasted temperatures and the type of precipitation observed across the region. Our results can inform future operational forecasts of cold-season precipitation events by providing a storm-focused perspective on forecast errors during these impactful events.

Element Formation in Radiation-hydrodynamics Simulations of Kilonovae

Understanding the details of r-process nucleosynthesis in binary neutron star merger (BNSM) ejecta is key to interpreting kilonova observations and identifying the role of BNSMs in the origin of heavy elements. We present a self-consistent, two-dimensional, ray-by-ray radiation-hydrodynamic evolution of BNSM ejecta with an online nuclear network (NN) up to a timescale of days. For the first time, an initial numerical relativity ejecta profile composed of the dynamical component and spiral-wave and disk winds is evolved including detailed r-process reactions and nuclear heating effects. A simple model for the jet energy deposition is also included. Our simulation highlights that the common approach of relating in postprocessing the final nucleosynthesis yields to the initial thermodynamic profile of the ejecta can lead to inaccurate predictions. Moreover, we find that neglecting the details of the radiation-hydrodynamic evolution of the ejecta in nuclear calculations can introduce deviations of up to 1 order of magnitude in the final abundances of several elements, including very light and second r-process peak elements. The presence of a jet affects element production only in the innermost part of the polar ejecta, and it does not alter the global nucleosynthesis results. Overall, our analysis shows that employing an online NN improves the reliability of nucleosynthesis and kilonova light-curve predictions.

Three-dimensional Thermodynamic Structures of the Intracluster Medium across Edges in the X-Ray Surface Brightness of Massive, Bright, Dynamically Active Galaxy Clusters

We present a detailed study of three-dimensional (3D) thermodynamic structures of the intracluster medium (ICM) across edges in the X-ray surface brightness of four massive, bright, dynamically active galaxy clusters (A3667, A2319, A520, and A2146), with the Chandra X-ray Observatory. Based on a forward modeling approach developed in previous work, we extend this approach with more generalized ICM density and temperature profiles, allowing us to apply uniformly to the observed X-ray surface brightness profiles to detect edges and measure the 3D thermodynamic profiles of the ICM simultaneously and self-consistently. With the forward modeling analysis, we find, in agreement with previous works, that the obtained 3D thermodynamic structures of the ICM across the edges in A3667 and A2319 are consistent with the characteristics of cold fronts, whereas those in A520 and A2146 are consistent with the nature of shock fronts. We find that the azimuthal distribution of the pressure ratio at the cold front in A3667 shows a different trend from that in A2319. For the shock fronts in A520 and A2146, the observed 3D temperature profiles of the ICM indicate that the temperature is highest at the position of the shock front. In the case of the sector exhibiting M=2.4 in A520, the ICM temperature appears isothermal with a temperature of ∼10 keV until ∼300 kpc away from the shock front in the post-shock region, being consistent with the hypothesis of the instant-equilibration model for shock heating.

Underestimates of Orographic Precipitation in Idealized Simulations. Part II: Underlying Causes

Abstract Warm-sector orographic precipitation in a midlatitude cyclone encountering a ridge is simulated in a “Cyc+Mtn” experiment. A second “Shear” simulation is conducted with horizontally uniform unidirectional flow over the same mountain having thermodynamic and cross-mountain wind profiles identical to those on the centerline in the “Cyc+Mtn” simulation. The relationship between integrated vapor transport (IVT) and orographic precipitation in the Mtn+Cyc case is consistent with observations, yet the same IVT in the Shear simulation produces far less precipitation. The difference between the precipitation rates in the Cyc+Mtn and Shear cases is traced to differences in the cross-mountain moisture-flux convergence and is further isolated to differences in the cross-mountain-velocity convergence over the windward slope. The winds at the ridge crest are stronger in the Shear case, leading to more velocity divergence and decreased moisture-flux convergence. The stronger ridge-crest winds in the Shear case are produced by a stronger mountain wave, which persists after being generated during the artificial startup of the Shear simulation. Initializing with a gradually ramped-up unidirectional flow and integrating to a quasi-steady state fails to adequately capture the processes regulating the leeside circulations. Even worse results are obtained if the shear flow is instantaneously accelerated from rest. An alternative microphysical explanation for the precipitation difference between the Cyc+Mtn and Shear simulations is examined using additional numerical experiments that enhance the seeder–feeder process. Although such enhancements increase precipitation, the increase is too small to account for the differences between the Cyc+Mtn and Shear simulations. Significance Statement Our results highlight the difficulty in conducting studies of mesoscale atmospheric circulations in isolation from their surrounding environment. This approach often allows for more computationally efficient and detailed analysis of the target phenomena by reducing the size of the computational domain. But, at least in the case of orographic precipitation, such isolation can significantly limit the realism of the simulated process.

Synthesis and Thermodynamic Evaluation of Sialyl-Tn MUC1 Glycopeptides Binding to Macrophage Galactose-Type Lectin.

Interactions between the tumor-associated carbohydrate antigens of Mucin 1 (MUC1) and the carbohydrate-binding proteins, lectins, often lead to the creation of a pro-tumor microenvironment favoring tumor initiation, progression, metastasis, and immune evasion. Macrophage galactose binding lectin (MGL) is a C-type lectin receptor found on antigen-presenting cells that facilitates the uptake of carbohydrate antigens for antigen presentation, modulating the immune response homeostasis, autoimmunity, and cancer. Considering the crucial role of tumor-associated forms of MUC1 and MGL in tumor immunology, a thorough understanding of their binding interaction is essential for it to be exploited for cancer vaccine strategies. The synthesis of MUC1 glycopeptide models carrying a single or multiple Tn and/or sialyl-Tn antigen(s) is described. A novel approach for the sialyl-Tn threonine building block suitable for the solid phase peptide synthesis was developed. The thermodynamic profile of the binding interaction between the human MGL and MUC1 glycopeptide models was analyzed using isothermal titration calorimetry. The measured dissociation constants for the sialyl-Tn-bearing peptide epitopes were consistently lower compared to the Tn antigen and ranged from 10 μM for mono- to 1 μM for triglycosylated MUC1 peptide, respectively. All studied interactions, regardless of the glycan's site of attachment or density, exhibited enthalpy-driven thermodynamics.

The Evolution of Meteorological Satellite Cloud-Detection Methodologies for Atmospheric Parameter Retrievals

The accurate detection of clouds is an important first step in the processing of remotely sensed satellite data analyses and subsequent cloud model predictions. While initial cloud retrieval technology began with the exploitation of one or two bands of satellite imagery, it has accelerated rapidly in recent years as sensor and retrieval technology, creating a new era in space observation exploration. Additionally, the initial emphasis in satellite retrieval technology focused on cloud detection for cloud forecast models, but more recently, cloud screening in satellite-acquired data is playing an increasingly critical role in the investigation of cloud-free data for the retrieval of soil moisture, vegetation cover, ocean color concentration and sea surface temperatures, as well as the environmental monitoring of a host of products, e.g., atmospheric aerosol data, to study the Earth’s atmospheric and climatic systems. With about 60% of the Earth covered by clouds, on average, it is necessary to accurately detect clouds in remote sensing data to screen cloud contaminate data in remote sensing analyses. In this review, the evolution of cloud-detection methodologies is highlighted with advancement in sensor hardware technology and machine learning algorithmic advances. The review takes into consideration the meteorological sensors usually used for atmospheric parameters estimation (thermodynamic profiles, aerosols, cloud microphysical parameters). Moreover, a discussion is presented on methods for obtaining the cloud-truth data needed to determine the accuracy of these cloud-detection approaches.

Comparison of models for the warm-hot circumgalactic medium around Milky Way-like galaxies

ABSTRACT A systematic comparison of the models of the circumgalactic medium (CGM) and their observables is crucial to understanding the predictive power of the models and constraining physical processes that affect the thermodynamics of CGM. This paper compares four analytic CGM models: precipitation, isentropic, cooling flow, and baryon pasting models for the hot, volume-filling CGM phase, all assuming hydrostatic or quasi-hydrostatic equilibrium. We show that for fiducial parameters of the CGM of a Milky Way (MW)-like galaxy ($M_{\rm vir} \sim 10^{12}~{\rm M}_{\odot }$ at $z\sim 0$), the thermodynamic profiles – entropy, density, temperature, and pressure – show most significant differences between different models at small ($r\lesssim 30$ kpc) and large scales ($r\gtrsim 100$ kpc) while converging at intermediate scales. The slope of the entropy profile, which is one of the most important differentiators between models, is $\approx 0.8$ for the precipitation and cooling flow models, while it is $\approx 0.6$ and 0 for the baryon pasting and isentropic models, respectively. We make predictions for various observational quantities for an MW mass halo for the different models, including the projected Sunyaev–Zeldovich effect, soft X-ray emission (0.5–2 keV), dispersion measure, and column densities of oxygen ions (O vi, O vii, and O viii) observable in absorption. We provide Python packages to compute the thermodynamic and observable quantities for the different CGM models.

Multi-step nucleation of C-S-H: DFT simulation on silicate oligomerization and Si(Qn) evolution dynamics

Understanding the nucleation processes of calcium-silicate-hydrate (C-S-H) is fundamental to advancing the performance of cement. This study employs Density Functional Theory (DFT) to reveal the initial stages of C-S-H formation by focusing on silicate oligomerization. It examines the progression from monomer silicate species to the precursor formation, containing a range of silicate tetrahedra. Through DFT simulations, free energy changes and barriers of oligomerization reactions up to pentamers are obtained, unveiling an anionic condensation mechanism that establishes the kinetic and thermodynamic profile of this transformation. Observations reveal a dynamic evolution of Si species, with Si(Q3) indicating a transient presence, suggesting an intricate pathway towards the well-established silicate chain structure of C-S-H. This study provides detailed computational insight into the energy barriers, reaction pathways, and the thermodynamics of C-S-H formation, contributing substantial knowledge to the field of cement chemistry and its multi-step nucleation theory.

Sensitivity of thermodynamic profiles retrieved from ground-based microwave and infrared observations to additional input data from active remote sensing instruments and numerical weather prediction models

Abstract. Accurate and continuous estimates of the thermodynamic structure of the lower atmosphere are highly beneficial to meteorological process understanding and its applications, such as weather forecasting. In this study, the Tropospheric Remotely Observed Profiling via Optimal Estimation (TROPoe) physical retrieval is used to retrieve temperature and humidity profiles from various combinations of input data collected by passive and active remote sensing instruments, in situ surface platforms, and numerical weather prediction models. Among the employed instruments are microwave radiometers (MWRs), infrared spectrometers (IRSs), radio acoustic sounding systems (RASSs), ceilometers, and surface sensors. TROPoe uses brightness temperatures and/or radiances from MWRs and IRSs, as well as other observational inputs (virtual temperature from the RASS, cloud-base height from the ceilometer, pressure, temperature, and humidity from the surface sensors) in a physical iterative retrieval approach. This starts from a climatologically reasonable profile of temperature and water vapor, with the radiative transfer model iteratively adjusting the assumed temperature and humidity profiles until the derived brightness temperatures and radiances match those observed by the MWR and/or IRS instruments within a specified uncertainty, as well as within the uncertainties of the other observations, if used as input. In this study, due to the uniqueness of the dataset that includes all the abovementioned sensors, TROPoe is tested with different observational input combinations, some of which also include information higher than 4 km above ground level (a.g.l.) from the operational Rapid Refresh numerical weather prediction model. These temperature and humidity retrievals are assessed against independent collocated radiosonde profiles under non-cloudy conditions to assess the sensitivity of the TROPoe retrievals to different input combinations.

Mechanical and Thermal Forcing for Upslope Flows and Cumulus Convection over the Sierras de Córdoba

Abstract The upslope flow processes affecting the vertical extent of orographic cumulus convection are examined using observations from the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) Field Campaign. Specifically, clear-air returns from the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) C-band radar (CSAPR2) are used to characterize the structure and variability of the ridge-normal (i.e., up/downslope) flow components, which transport mass to the crest of Argentina’s Sierras de Córdoba and contribute to convective initiation. Data are compiled for the entire CACTI period (Oct-Apr), including days with clear skies, shallow cumuli, cumulus congestus, and deep convection. To examine shared variability amongst >70,000 radar scans we use (a) a principal component analysis (PCA) to isolate modes of variability in the upslope flow, and (b) composite analysis based on convective outcomes, determined from GOES16 satellite observations. These data are contextualized with observed surface sensible heat fluxes, thermodynamic profiles, and synoptic-scale analysis. Results indicate distinct thermally and mechanically forced upslope flow modes, modulated by diurnal heating and synoptic-scale variations, respectively. In some instances, there is a superposition of thermal and mechanical forcing, yielding either deeper or shallower upslope flow. The composite analyses based on satellite data show that successively deeper convective outcomes are associated with successively deeper upslope flow layers that more readily transport mass to the ridge crest in conjunction with lower lifting condensation levels, facilitating convective initiation. These results help to isolate the forcing mechanisms for orographic convection, and thus provide a foundation for parameterizing orographic convective processes in coarse resolution models.

Radiative effects of precipitation on the global energy budget and Arctic amplification

Radiative forcing is an essential metric for accurate climate prediction. Clouds are a well-known source of uncertainty, but the radiative effects of precipitation (REP) are poorly understood and excluded from most general circulation models (GCMs). This is because conventional GCMs treat precipitation diagnostically, and thus, are transparent to shortwave and longwave radiation. In this study, we investigated the REP at global and regional scales by employing three sub-models incorporating (1) diagnostic precipitation, (2) prognostic precipitation without REP, and (3) prognostic precipitation with REP. We found that REP alters not only the local thermodynamic profile but also the remote precipitation rate and distribution through changes in atmospheric circulation. The polar surface temperature increases by more than 1 K in the winter when considering REP. The 34 CMIP6 models show systematic differences in Arctic amplification depending on REP, emphasising that GCMs should include REP to improve confidence in simulating atmosphere-ocean-cryosphere interactions.

The FLAMINGO project: Galaxy clusters in comparison to X-ray observations

Abstract Galaxy clusters are important probes for both cosmology and galaxy formation physics. We test the cosmological, hydrodynamical FLAMINGO simulations by comparing to observations of the gaseous properties of clusters measured from X-ray observations. FLAMINGO contains unprecedented numbers of massive galaxy groups (>106) and clusters (>105) and includes variations in both cosmology and galaxy formation physics. We predict the evolution of cluster scaling relations as well as radial profiles of the temperature, density, pressure, entropy, and metallicity for different masses and redshifts. We show that the differences between volume-, and X-ray-weighting of particles in the simulations, and between cool-core non cool-core samples, are similar in size as the differences between simulations for which the stellar and AGN feedback has been calibrated to produce significantly different gas fractions. Compared to thermally-driven AGN feedback, kinetic jet feedback calibrated to produce the same gas fraction at R500c yields a hotter core with higher entropies and lower densities, which translates into a smaller fraction of cool-core clusters. Stronger feedback, calibrated to produce lower gas fractions and hence lower gas densities, results in higher temperatures, entropies, and metallicities, but lower pressures. The scaling relations and thermodynamic profiles show almost no evolution with respect to self-similar expectations, except for the metallicity decreasing with redshift. We find that the temperature, density, pressure, and entropy profiles of clusters in the fiducial FLAMINGO simulation are in excellent agreement with observations, while the metallicities in the core are too high.

Role of Metal-Organic Framework Topology on Thermodynamics of Polyoxometalate Encapsulation.

Polyoxometalates (POMs) are discrete anionic clusters whose rich redox properties, strong Bro̷nsted acidity, and high availability of active sites make them potent catalysts for oxidation reactions. Metal-organic frameworks (MOFs) have emerged as tunable, porous platforms to immobilize POMs, thus increasing their solution stability and catalytic activity. While POM@MOF composite materials have been widely used for a variety of applications, little is known about the thermodynamics of the encapsulation process. Here, we utilize an up-and-coming technique in the field of heterogeneous materials, isothermal titration calorimetry (ITC), to obtain full thermodynamic profiles (ΔH, ΔS, ΔG, and Ka) of POM binding. Six different 8-connected hexanuclear Zr-MOFs were investigated to determine the impact of MOF topology (csq, scu, and the) on POM encapsulation thermodynamics.

Extracting Atmospheric Stability Information from Dual-Doppler Radar Scans in the AWAKEN Campaign

Dual-Doppler (DD) derived wind direction (β) fields are used in a novel approach to assess atmospheric stability by way of power spectral density (PSD) and bulk statistical analyses. Data on atmospheric stability, thermodynamic profiles, and wind behavior, as acquired during the AWAKEN campaign, are examined for a measurement time period featuring two stability transitions to analyze differences in flow structure as a function of stability regime. Analysis of the β fields yielded promising results when compared to stability regime and can be applied to other cases.

Zooming by in the CARPoolGP Lane: New CAMELS-TNG Simulations of Zoomed-in Massive Halos

Galaxy formation models within cosmological hydrodynamical simulations contain numerous parameters with nontrivial influences over the resulting properties of simulated cosmic structures and galaxy populations. It is computationally challenging to sample these high dimensional parameter spaces with simulations, in particular for halos in the high-mass end of the mass function. In this work, we develop a novel sampling and reduced variance regression method, CARPoolGP, which leverages built-in correlations between samples in different locations of high dimensional parameter spaces to provide an efficient way to explore parameter space and generate low-variance emulations of summary statistics. We use this method to extend the Cosmology and Astrophysics with machinE Learning Simulations to include a set of 768 zoom-in simulations of halos in the mass range of 1013–1014.5 M ⊙ h −1 that span a 28-dimensional parameter space in the IllustrisTNG model. With these simulations and the CARPoolGP emulation method, we explore parameter trends in the Compton Y–M, black hole mass–halo mass, and metallicity–mass relations, as well as thermodynamic profiles and quenched fractions of satellite galaxies. We use these emulations to provide a physical picture of the complex interplay between supernova and active galactic nuclei feedback. We then use emulations of the Y–M relation of massive halos to perform Fisher forecasts on astrophysical parameters for future Sunyaev–Zeldovich observations and find a significant improvement in forecasted constraints. We publicly release both the simulation suite and CARPoolGP software package.

X-ray-inferred kinematics of the core intracluster medium in Perseus-like clusters: Insights from the TNG-Cluster simulation

The intracluster medium (ICM) of galaxy clusters encodes the impact of the physical processes that shape these massive halos, including feedback from central supermassive black holes (SMBHs). In this study, we examine the gas thermodynamics, kinematics, and the effects of SMBH feedback on the core of Perseus-like galaxy clusters with a new simulation suite: TNG-Cluster. We first make a selection of simulated clusters similar to Perseus based on the total mass and inner ICM properties, such as their cool-core nature. We identify 30 Perseus-like systems among the 352 TNG-Cluster halos at z = 0. Many exhibit thermodynamical profiles and X-ray morphologies with disturbed features such as ripples, bubbles, and shock fronts that are qualitatively similar to X-ray observations of Perseus. To study observable gas motions, we generate XRISM mock X-ray observations and conduct a spectral analysis of the synthetic data. In agreement with existing Hitomi measurements, TNG-Cluster predicts subsonic gas turbulence in the central regions of Perseus-like clusters, with a typical line-of-sight velocity dispersion of 200 km s−1. This implies that turbulent pressure contributes < 10% to the dominant thermal pressure. In TNG-Cluster, such low (inferred) values of ICM velocity dispersion coexist with high-velocity outflows and bulk motions of relatively small amounts of super-virial hot gas, moving up to thousands of km s−1. However, detecting these outflows in observations may prove challenging due to their anisotropic nature and projection effects. Driven by SMBH feedback, such outflows are responsible for many morphological disturbances in the X-ray maps of cluster cores. They also increase both the inferred and intrinsic ICM velocity dispersion. This effect is somewhat stronger when velocity dispersion is measured from higher-energy lines.

Unveiling therapeutic frontiers: DON/DRP-104 as innovative Plasma kallikrein inhibitors against carcinoma-associated hereditary angioedema shocks - a comprehensive molecular dynamics exploration

Human plasma kallikrein (PKa) is a member of the serine protease family and serves as a key mediator of the kallikrein-kinin system (KKS), which is known for its regulatory roles in inflammation, vasodilation, blood pressure, and coagulation. Genetic dysregulation of KKS leads to Hereditary Angioedema (HAE), which is characterized by spontaneous, painful swelling in various body regions. Importantly, HAE frequently coexists with various cancers. Despite substantial efforts towards the development of PKa inhibitors for HAE, there remains a need for bifunctional agents addressing both anti-cancer and anti-HAE aspects, especially against carcinoma-associated comorbid HAE conditions. Consequently, we investigated the therapeutic potential of the anti-glutamine prodrug, isopropyl(S)-2-((S)-2-acetamido-3-(1H-indol-3-yl)-propanamido)-6-diazo-5-oxo-hexanoate (DRP-104), and its active form, 6-Diazo-5-oxo-l-norleucine (DON), recognized for their anti-cancer properties, as novel PKa inhibitors. Utilizing structure-based in silico methods, we conducted a comparative analysis with berotralstat, a clinically approved HAE prophylactic, and sebetralstat, an investigational HAE therapeutic agent, in Phase 3 clinical trials. Inhibiting PKa with DON resulted in relatively heightened structural stability, rigidity, restricted protein folding, and solvent-accessible loop exposure, contributing to increased intra-atomic hydrogen bond formation. Conversely, PKa inhibition with DRP-104 induced restricted residue flexibility and significantly disrupted the critical SER195-HIS57 arrangement in the catalytic triad. Both DON and DRP-104, along with the reference drugs, induced strong cooperative intra-residue motion and bidirectional displacement in the PKa architecture. The results revealed favorable binding kinetics of DON/DRP-104, showing thermodynamic profiles that were either superior or comparable to those of the reference drugs. These findings support their consideration for clinical investigations into the management of carcinoma-associated HAE.