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The amount of vapor in the impact-generated protolunar disk carries implications for the dynamics, devolatilization, and moderately volatile element isotope fractionation during lunar formation. The equation of state (EoS) used in simulations of the giant impact is required to calculate the vapor mass fraction (VMF) of the modeled protolunar disk. Recently, a new version of M-ANEOS (Stewart M-ANEOS) was released with an improved treatment of heat capacity and expanded experimental Hugoniot. Here, we compare this new M-ANEOS version with a previous version (N-SPH M-ANEOS) and assess the resulting differences in smoothed particle hydrodynamics (SPH) simulations. We find that Stewart M-ANEOS results in cooler disks with smaller values of VMF and in differences in disk mass that are dependent on the initial impact angle. We also assess the implications of the minimum “cutoff” density (ρ_c), similar to a maximum smoothing length, that is set as a fast-computing alternative to an iteratively calculated smoothing length. We find that the low particle resolution of the disk typically results in >40% of disk particles falling toρ_c, influencing the dynamical evolution and VMF of the disk. Our results show that the choice of EoS,ρ_c, and particle resolution can cause the VMF and disk mass to vary by tens of percent. Moreover, small values ofρ_cproduce disks that are prone to numerical instability and artificial shocks. We recommend that future giant impact SPH studies review smoothing methods and ensure the thermodynamic stability of the disk over simulated time.

 

The World Health Organization recently published the first list of priority fungal pathogens highlighting multipleCandidaspecies, includingCandida glabrata,Candida albicans, andCandida auris. However, prior studies in these pathogens have been mainly limited to the use of two drug resistance cassettes,NatMXandHphMX, limiting genetic manipulation capabilities in prototrophic laboratory strains and clinical isolates. In this study, we expanded the toolkit forC. glabrata,C. auris, andC. albicansto includeKanMXandBleMXwhen coupled with anin vitroassembled CRISPR-Cas9 ribonucleoprotein (RNP)-based system. Repurposing these drug resistance cassettes forCandida, we were able to make single gene deletions, sequential and simultaneous double gene deletions, epitope tags, and rescue constructs. We applied these drug resistance cassettes to interrogate the ergosterol pathway, a critical pathway for both the azole and polyene antifungal drug classes. Using our approach, we determined for the first time that the deletion ofERG3inC. glabrata,C. auris,andC. albicansprototrophic strains results in azole drug resistance, which further supports the conservation of the Erg3-dependent toxic sterol model. Furthermore, we show that anERG5deletion inC. glabratais azole susceptible at subinhibitory concentrations, suggesting that Erg5 could act as an azole buffer for Erg11. Finally, we identified a synthetic growth defect when bothERG3andERG5are deleted inC. glabrata,which suggests the possibility of another toxic sterol impacting growth. Overall, we have expanded the genetic tools available to interrogate complex pathways in prototrophic strains and clinical isolates.

The increasing problem of drug resistance and emerging pathogens is an urgent global health problem that necessitates the development and expansion of tools for studying fungal drug resistance and pathogenesis. Prior studies inCandida glabrata,Candida auris, andCandida albicanshave been mainly limited to the use ofNatMX/SAT1andHphMX/CaHygfor genetic manipulation in prototrophic strains and clinical isolates. In this study, we demonstrated thatNatMX/SAT1, HphMX, KanMX,and/orBleMXdrug resistance cassettes when coupled with a CRISPR-ribonucleoprotein (RNP)-based system can be efficiently utilized for deleting or modifying genes in the ergosterol pathway ofC. glabrata,C. auris, andC. albicans. Moreover, the utility of these tools has provided new insights intoERGgenes and their relationship to azole resistance inCandida. Overall, we have expanded the toolkit forCandidapathogens to increase the versatility of genetically modifying complex pathways involved in drug resistance and pathogenesis.

 

Long-standing interpretations of the Last Glacial Maximum (21,000 ± 2000 years ago) in Australia suggest that the period was extremely cold and arid, during which the Indo-Australian summer monsoon system collapsed, and human populations declined and retreated to ecological refuges to survive. Here, we use transient iTRACE simulations, combined with palaeoclimate proxy records and archaeological data to re-interpret the late Last Glacial Maximum and terminal Pleistocene (21,000 – 11,000 years) in Australia. The model suggests climates during the peak Last Glacial Maximum were cooler than present (−4 to −11 °C), but there is no evidence of monsoon collapse or substantial decreases in moisture balance across Australia. Kernel Density Estimates of archaeological ages show relatively stable and persistent human activity across most regions throughout the late Last Glacial Maximum and terminal Pleistocene, consistent with genetic evidence. Spatial coverage of archaeological sites steadily increased across the terminal Pleistocene; however, substantial population change is not evident.

 

Abstract The Southern Ocean contributes substantially to the global biological carbon pump (BCP). Salps in the Southern Ocean, in particular Salpa thompsoni , are important grazers that produce large, fast-sinking fecal pellets. Here, we quantify the salp bloom impacts on microbial dynamics and the BCP, by contrasting locations differing in salp bloom presence/absence. Salp blooms coincide with phytoplankton dominated by diatoms or prymnesiophytes, depending on water mass characteristics. Their grazing is comparable to microzooplankton during their early bloom, resulting in a decrease of ~1/3 of primary production, and negative phytoplankton rates of change are associated with all salp locations. Particle export in salp waters is always higher, ranging 2- to 8- fold (average 5-fold), compared to non-salp locations, exporting up to 46% of primary production out of the euphotic zone. BCP efficiency increases from 5 to 28% in salp areas, which is among the highest recorded in the global ocean. 

The lipid molecule phosphatidylinositol (4,5)-bisphosphate [PI(4,5)P2] controls all aspects of plasma membrane (PM) function in animal cells, from its selective permeability to the attachment of the cytoskeleton. Although disruption of PI(4,5)P2 is associated with a wide range of diseases, it remains unclear how cells sense and maintain PI(4,5)P2 levels to support various cell functions. Here, we show that the PIP4K family of enzymes, which synthesize PI(4,5)P2 via a minor pathway, also function as sensors of tonic PI(4,5)P2 levels. PIP4Ks are recruited to the PM by elevated PI(4,5)P2 levels, where they inhibit the major PI(4,5)P2-synthesizing PIP5Ks. Perturbation of this simple homeostatic mechanism reveals differential sensitivity of PI(4,5)P2-dependent signaling to elevated PI(4,5)P2 levels. These findings reveal that a subset of PI(4,5)P2-driven functions might drive disease associated with disrupted PI(4,5)P2 homeostasis.

 

Recent works have shown that strongly magnetized plasmas characterized by having a gyrofrequency greater than the plasma frequency exhibit novel transport properties. One example is that the friction force on a test charge shifts, obtaining components perpendicular to its velocity in addition to the typical stopping power component antiparallel to its velocity. Here, we apply a recent generalization of the Boltzmann equation for strongly magnetized plasmas to calculate the ion–electron temperature relaxation rate. Strong magnetization is generally found to increase the temperature relaxation rate perpendicular to the magnetic field and to cause the temperatures parallel and perpendicular to the magnetic field to not relax at equal rates. This, in turn, causes a temperature anisotropy to develop during the equilibration. Strong magnetization also breaks the symmetry of independence of the sign of the charges of the interacting particles on the collision rate, commonly known as the “Barkas effect.” It is found that the combination of oppositely charged interaction and strong magnetization causes the ion–electron parallel temperature relaxation rate to be significantly suppressed, scaling inversely proportional to the magnetic field strength. 

Fast radio bursts (FRBs) are transient radio signals of extragalactic origins that are subjected to propagation effects such as dispersion and scattering. It follows then that these signals hold information regarding the medium they have traversed and are hence useful as cosmological probes of the Universe. Recently, FRBs were used to make an independent measure of the Hubble constant H0, promising to resolve the Hubble tension given a sufficient number of detected FRBs. Such cosmological studies are dependent on FRB population statistics, cosmological parameters, and detection biases, and thus it is important to accurately characterize each of these. In this work, we empirically characterize the sensitivity of the Fast Real-time Engine for Dedispersing Amplitudes (FREDDA) which is the current detection system for the Australian Square Kilometre Array Pathfinder (ASKAP). We coherently redisperse high-time resolution data of 13 ASKAP-detected FRBs and inject them into FREDDA to determine the recovered signal-to-noise ratios as a function of dispersion measure. We find that for 11 of the 13 FRBs, these results are consistent with injecting idealized pulses. Approximating this sensitivity function with theoretical predictions results in a systematic error of 0.3 km s−1 Mpc−1 on H0 when it is the only free parameter. Allowing additional parameters to vary could increase this systematic by up to $\sim 1\,$ km s−1 Mpc−1. We estimate that this systematic will not be relevant until ∼400 localized FRBs have been detected, but will likely be significant in resolving the Hubble tension.

 

We introduce Spatial Predictive Control (SPC), a technique for solving the following problem: given a collection of robotic agents with black-box positional low-level controllers (PLLCs) and a mission-specific distributed cost function, how can a distributed controller achieve and maintain cost-function minimization without a plant model and only positional observations of the environment? Our fully distributed SPC controller is based strictly on the position of the agent itself and on those of its neighboring agents. This information is used in every time step to compute the gradient of the cost function and to perform a spatial look-ahead to predict the best next target position for the PLLC. Using a simulation environment, we show that SPC outperforms Potential Field Controllers, a related class of controllers, on the drone flocking problem. We also show that SPC works on real hardware, and is therefore able to cope with the potential sim-to-real transfer gap. We demonstrate its performance using as many as 16 Crazyflie 2.1 drones in a number of scenarios, including obstacle avoidance.