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Turbulent rotations of the magnetic field vector are observed in the Alfvénic streams of the solar wind where the magnetic field strength remains close to a constant. They can lead to reversals of the radial magnetic field component or switchbacks. It is not ruled out from the data that the rotations are divisible into the sum of small random angular deflections. In this work, we develop tools aimed at the analysis of the one-point statistical properties of the directional fluctuations of the magnetic field vector in the solar wind. The angular fluctuations are modeled by a drift-diffusion process which admits the exponential distribution as steady-state solution. Realizations of the stochastic process are obtained by solving the corresponding Langevin equation. It is shown that the cumulative effects of consecutive small-angle deflections can yield frequent reversals of the magnetic field vector even when the concentration parameter of the directional data is large. The majority of the rotations are associated with nearly transverse magnetic field fluctuations in this case.

 

We investigate the role of perpendicular diffusion in shaping the energetic ion spectrum in corotating interaction regions (CIRs), focusing on its mass-to-charge (A/Q) ratio dependence. We simulate a synthetic CIR using the EUropean Heliospheric FORecasting Information Asset and model the subsequent ion acceleration and transport by solving the focused transport equation incorporating both parallel and perpendicular diffusion. Our results reveal distinct differences in ion spectra between scenarios with and without perpendicular diffusion. In the absence of perpendicular diffusion, ion spectra near CIRs show a strong (A/Q)^ϵdependence withϵdepending on the turbulence spectral index, agreeing with theoretical predictions. In contrast, the incorporation of perpendicular diffusion, characterized by a weakA/Qdependence, leads to similar spectra for different ion species. This qualitatively agrees with observations of energetic particles in CIRs.

 

Habitat degradation and loss of genetic diversity are common threats faced by almost all of today’s wild cats. Big cats, such as tigers and lions, are of great concern and have received considerable conservation attention through policies and international actions. However, knowledge of and conservation actions for small wild cats are lagging considerably behind. The black-footed cat,Felis nigripes, one of the smallest felid species, is experiencing increasing threats with a rapid reduction in population size. However, there is a lack of genetic information to assist in developing effective conservation actions. A de novo assembly of a high-quality chromosome-level reference genome of the black-footed cat was made, and comparative genomics and population genomics analyses were carried out. These analyses revealed that the most significant genetic changes in the evolution of the black-footed cat are the rapid evolution of sensory and metabolic-related genes, reflecting genetic adaptations to its characteristic nocturnal hunting and a high metabolic rate. Genomes of the black-footed cat exhibit a high level of inbreeding, especially for signals of recent inbreeding events, which suggest that they may have experienced severe genetic isolation caused by habitat fragmentation. More importantly, inbreeding associated with two deleterious mutated genes may exacerbate the risk of amyloidosis, the dominant disease that causes mortality of about 70% of captive individuals. Our research provides comprehensive documentation of the evolutionary history of the black-footed cat and suggests that there is an urgent need to investigate genomic variations of small felids worldwide to support effective conservation actions.

 

Clouded leopards (Neofelisspp.), a morphologically and ecologically distinct lineage of big cats, are severely threatened by habitat loss and fragmentation, targeted hunting, and other human activities. The long-held poor understanding of their genetics and evolution has undermined the effectiveness of conservation actions. Here, we report a comprehensive investigation of the whole genomes, population genetics, and adaptive evolution ofNeofelis. Our results indicate the genusNeofelisarose during the Pleistocene, coinciding with glacial-induced climate changes to the distributions of savannas and rainforests, and signatures of natural selection associated with genes functioning in tooth, pigmentation, and tail development, associated with clouded leopards’ unique adaptations. Our study highlights high-altitude adaptation as the main factor driving nontaxonomic population differentiation inNeofelis nebulosa. Population declines and inbreeding have led to reduced genetic diversity and the accumulation of deleterious variation that likely affect reproduction of clouded leopards, highlighting the urgent need for effective conservation efforts.

 

Additive manufacturing has been recognized as an industrial technological revolution for manufacturing, which allows fabrication of materials with complex three-dimensional (3D) structures directly from computer-aided design models. Using two or more constituent materials with different physical and mechanical properties, it becomes possible to construct interpenetrating phase composites (IPCs) with 3D interconnected structures to provide superior mechanical properties as compared to the conventional reinforced composites with discrete particles or fibers. The mechanical properties of IPCs, especially response to dynamic loading, highly depend on their 3D structures. In general, for each specified structural design, it could take hours or days to perform either finite element analysis (FEA) or experiments to test the mechanical response of IPCs to a given dynamic load. To accelerate the physics-based prediction of mechanical properties of IPCs for various structural designs, we employ a deep neural operator (DNO) to learn the transient response of IPCs under dynamic loading as surrogate of physics-based FEA models. We consider a 3D IPC beam formed by two metals with a ratio of Young’s modulus of 2.7, wherein random blocks of constituent materials are used to demonstrate the generality and robustness of the DNO model. To obtain FEA results of IPC properties, 5000 random time-dependent strain loads generated by a Gaussian process kennel are applied to the 3D IPC beam, and the reaction forces and stress fields inside the IPC beam under various loading are collected. Subsequently, the DNO model is trained using an incremental learning method with sequence-to-sequence training implemented in JAX, leading to a 100X speedup compared to widely used vanilla deep operator network models. After an offline training, the DNO model can act as surrogate of physics-based FEA to predict the transient mechanical response in terms of reaction force and stress distribution of the IPCs to various strain loads in one second at an accuracy of 98%. Also, the learned operator is able to provide extended prediction of the IPC beam subject to longer random strain loads at a reasonably well accuracy. Such superfast and accurate prediction of mechanical properties of IPCs could significantly accelerate the IPC structural design and related composite designs for desired mechanical properties. 

In this work, we extend Leighton’s diffusion model describing the turbulent mixing of magnetic footpoints on the solar wind source surface. The present Lagrangian stochastic model is based on the spherical Ornstein–Uhlenbeck process with drift that is controlled by the rotation frequency Ω of the Sun, the Lagrangian integral timescaleτ_L, and the root-mean-square footpoint velocityV_rms. The Lagrangian velocity and the positions of magnetic footpoints on the solar wind source surface are obtained from the solutions of a set of stochastic differential equations, which are solved numerically. The spherical diffusion model of Leighton is recovered in the singular Markov limit when the Lagrangian integral timescale tends to zero while keeping the footpoint diffusivity finite. In contrast to the magnetic field lines driven by standard Brownian processes on the solar wind source surface, the interplanetary magnetic field lines are smooth differentiable functions with finite path lengths in our model. The path lengths of the boundary-driven interplanetary magnetic field lines and their probability distributions at 1 au are computed numerically, and their dependency with respect to the controlling parameters is investigated. The path-length distributions are shown to develop a significant skewness as the width of the distributions increases.

 

The blast fungusMagnaporthe oryzaeproduces invasive hyphae in living rice cells during early infection, separated from the host cytoplasm by plant-derived interfacial membranes. However, the mechanisms underpinning this intracellular biotrophic growth phase are poorly understood. Here, we show that theM. oryzaeserine/threonine protein kinase Rim15 promotes biotrophic growth by coordinating cycles of autophagy and glutaminolysis in invasive hyphae. Alongside inducing autophagy, Rim15 phosphorylates NAD-dependent glutamate dehydrogenase, resulting in increased levels of α-ketoglutarate that reactivate target-of-rapamycin (TOR) kinase signaling, which inhibits autophagy. DeletingRIM15attenuates invasive hyphal growth and triggers plant immunity; exogenous addition of α-ketoglutarate prevents these effects, while glucose addition only suppresses host defenses. Our results indicate that Rim15-dependent cycles of autophagic flux liberate α-ketoglutarate – via glutaminolysis – to reactivate TOR signaling and fuel biotrophic growth while conserving glucose for antioxidation-mediated host innate immunity suppression.

 

The human brain development experiences a complex evolving cortical folding from a smooth surface to a convoluted ensemble of folds. Computational modeling of brain development has played an essential role in better understanding the process of cortical folding, but still leaves many questions to be answered. A major challenge faced by computational models is how to create massive brain developmental simulations with affordable computational sources to complement neuroimaging data and provide reliable predictions for brain folding. In this study, we leveraged the power of machine learning in data augmentation and prediction to develop a machine-learning-based finite element surrogate model to expedite brain computational simulations, predict brain folding morphology, and explore the underlying folding mechanism. To do so, massive finite element method (FEM) mechanical models were run to simulate brain development using the predefined brain patch growth models with adjustable surface curvature. Then, a GAN-based machine learning model was trained and validated with these produced computational data to predict brain folding morphology given a predefined initial configuration. The results indicate that the machine learning models can predict the complex morphology of folding patterns, including 3-hinge gyral folds. The close agreement between the folding patterns observed in FEM results and those predicted by machine learning models validate the feasibility of the proposed approach, offering a promising avenue to predict the brain development with given fetal brain configurations.

 

Cyclopropanes are important structural motifs found in many natural products and are essential to the pharmaceutical and agrochemical industries. Here, we report a bioinspired cobalt catalyst that catalyzes the intermolecular cyclopropanation of various terminal olefins using ethyl diazoacetate (EDA) in high efficiency. This cobalt catalytic system is operationally simple under very mild conditions, enabling the synthesis of cyclopropane products with remarkable yields in short reaction time. Preliminary mechanistic studies suggest the presence of cobalt carbene radical species during the reaction.