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Metallosphaera sedulais a thermoacidophilic archaeon that obtains all of its energy for growth from aerobic respiration and oxidative phosphorylation at the expense of selected organic and inorganic sources of electrons. Initial velocities for the oxidation of soluble ferrous ions by intact cells at 60 °C and pH 1.5 were determined using an integrating cavity absorption meter that permitted accurate absorbance measurements to quantify the increase in soluble ferric iron in the presence of turbid suspensions of the live organisms.M. sedulathat was cultured on yeast extract either in the absence or the presence of 20 mM soluble ferrous iron exhibited turnover numbers for soluble iron oxidation of 304 ± 26 and 333 ± 31 attamoles/cell/min, respectively. These functional data were consistent with the transcriptomic evidence presented by others, that the proteins presumably responsible for aerobic respiration on soluble iron are expressed constitutively inM. sedula. Intact cells ofM. sedulawere characterized by electrical impedance, laser light diffraction, and transmission electron microscopic measurements. All three types of measurements were consistent with the surprising observation that cells cultured on yeast extract in the presence of soluble iron bifurcated into approximately equal numbers of coccoidal cells of two sizes, smaller cells with an average diameter of 0.6 μm and larger cells with an average diameter of 1.35 μm. Cells cultured on the same concentration of yeast extract but in the absence of soluble iron comprised a single cell size with an intermediate average diameter of 1.06 μm. This unexpected bifurcation of a clonal cell population into two demonstrably different sizes when the extracellular nutrient environment changes has not previously been reported forM. sedula, or any other single-celled archaeon or eubacterium. 

DeepTensor is a computationally efficient framework for low-rank decomposition of matrices and tensors using deep generative networks. We decompose a tensor as the product of low-rank tensor factors where each low-rank tensor is generated by a deep network (DN) that is trained in a self-supervised manner to minimize the mean-square approximation error. Our key observation is that the implicit regularization inherent in DNs enables them to capture nonlinear signal structures that are out of the reach of classical linear methods like the singular value decomposition (SVD) and principal components analysis (PCA). We demonstrate that the performance of DeepTensor is robust to a wide range of distributions and a computationally efficient drop-in replacement for the SVD, PCA, nonnegative matrix factorization (NMF), and similar decompositions by exploring a range of real-world applications, including hyperspectral image denoising, 3D MRI tomography, and image classification. 

We take a random matrix theory approach to random sketching and show an asymptotic first-order equivalence of the regularized sketched pseudoinverse of a positive semidefinite matrix to a certain evaluation of the resolvent of the same matrix. We focus on real-valued regularization and extend previous results on an asymptotic equivalence of random matrices to the real setting, providing a precise characterization of the equivalence even under negative regularization, including a precise characterization of the smallest nonzero eigenvalue of the sketched matrix. We then further characterize the second-order equivalence of the sketched pseudoinverse. We also apply our results to the analysis of the sketch-and-project method and to sketched ridge regression. Last, we prove that these results generalize to asymptotically free sketching matrices, obtaining the resulting equivalence for orthogonal sketching matrices and comparing our results to several common sketches used in practice. 

During the 2022 New Mexico monsoon season, we deployed two X‐ray scintillation detectors, coupled with a 180 MHz data acquisition system to detect X‐rays from natural lightning at the Langmuir Lab mountain‐top facility, located at 3.3 km above mean sea level. Data acquisition was triggered by an electric field antenna calibrated to pick up lightning within a few km of the X‐ray detectors. We report the energies of over 240 individual photons, ranging between 13 keV and 3.8 MeV, as registered by the LaBr3(Ce) scintillation detector. These detections were associated with four lightning flashes. Particularly, four‐stepped leaders and seven dart leaders produced energetic radiation. The reported photon energies allowed us to confirm that the X‐ray energy distribution of natural stepped and dart leaders follows a power‐law distribution with an exponent ranging between 1.09 and 1.96, with stepped leaders having a harder spectrum. Characterization of the associated leaders and return strokes was done with four different electric field sensing antennas, which can measure a wide range of time scales, from the static storm field to the fast change associated with dart leaders. 

Nanoparticle syntheses are designed to produce thedesired product in high yield but traditionally neglect atom economy. Here we report that the simple, but significant, change of the solvent from 1-octadecene (1-ODE) to the operationally inert octadecane (ODA) permits an atom-economical synthesis of copper selenophosphate (Cu3PSe4) nanoparticles. This change eliminates the competing selenium (Se) delivery pathways from our first report that required an excess of Se. Instead Se0 powder is dispersed in ODA, which promotes a formal eight-electron transfer between Cu3−xP and Se0. Powder X-ray diffraction and transmission electron microscopy confirm the purity of the Cu3PSe4, while 1H and 13C NMR indicate the absence of oxidized ODA or Se species. We utilize the direct pathway to gain insights into stoichiometry and ligand identity using thermogravimetric analysis and X-ray photoelectron spectroscopy. Given the prevalence of 1- ODE in nanoparticle synthesis, this approach could be applied to other chalcogenide reaction pathways to improve stoichiometry and atom-economy.