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Impaired wound healing is a significant financial and medical burden. The synthesis and deposition of extracellular matrix (ECM) in a new wound is a dynamic process that is constantly changing and adapting to the biochemical and biomechanical signaling from the extracellular microenvironments of the wound. This drives either a regenerative or fibrotic and scar-forming healing outcome. Disruptions in ECM deposition, structure, and composition lead to impaired healing in diseased states, such as in diabetes. Valid measures of the principal determinants of successful ECM deposition and wound healing include lack of bacterial contamination, good tissue perfusion, and reduced mechanical injury and strain. These measures are used by wound-care providers to intervene upon the healing wound to steer healing toward a more functional phenotype with improved structural integrity and healing outcomes and to prevent adverse wound developments. In this review, we discuss bioengineering advances in 1) non-invasive detection of biologic and physiologic factors of the healing wound, 2) visualizing and modeling the ECM, and 3) computational tools that efficiently evaluate the complex data acquired from the wounds based on basic science, preclinical, translational and clinical studies, that would allow us to prognosticate healing outcomes and intervene effectively. We focus on bioelectronics and biologic interfaces of the sensors and actuators for real time biosensing and actuation of the tissues. We also discuss high-resolution, advanced imaging techniques, which go beyond traditional confocal and fluorescence microscopy to visualize microscopic details of the composition of the wound matrix, linearity of collagen, and live tracking of components within the wound microenvironment. Computational modeling of the wound matrix, including partial differential equation datasets as well as machine learning models that can serve as powerful tools for physicians to guide their decision-making process are discussed. 

We present the first data release of the ALMA-IMF Large Program, which covers the 12m-array continuum calibration and imaging. The ALMA-IMF Large Program is a survey of fifteen dense molecular cloud regions spanning a range of evolutionary stages that aims to measure the core mass function. We describe the data acquisition and calibration done by the Atacama Large Millimeter/submillimeter Array (ALMA) observatory and the subsequent calibration and imaging we performed. The image products are combinations of multiple 12 m array configurations created from a selection of the observed bandwidth using multi-term, multi-frequency synthesis imaging and deconvolution. The data products are self-calibrated and exhibit substantial noise improvements over the images produced from the delivered data. We compare different choices of continuum selection, calibration parameters, and image weighting parameters, demonstrating the utility and necessity of our additional processing work. Two variants of continuum selection are used and will be distributed: the “best-sensitivity” ( bsens ) data, which include the full bandwidth, including bright emission lines that contaminate the continuum, and “cleanest” ( cleanest ), which select portions of the spectrum that are unaffected by line emission. We present a preliminary analysis of the spectral indices of the continuum data, showing that the ALMA products are able to clearly distinguish free-free emission from dust emission, and that in some cases we are able to identify optically thick emission sources. The data products are made public with this release. 

Memristive devices are promising candidates to emulate biological computing. However, the typical switching voltages (0.2-2 V) in previously described devices are much higher than the amplitude in biological counterparts. Here we demonstrate a type of diffusive memristor, fabricated from the protein nanowires harvested from the bacterium Geobacter sulfurreducens, that functions at the biological voltages of 40-100 mV. Memristive function at biological voltages is possible because the protein nanowires catalyze metallization. Artificial neurons built from these memristors not only function at biological action potentials (e.g., 100 mV, 1 ms) but also exhibit temporal integration close to that in biological neurons. The potential of using the memristor to directly process biosensing signals is also demonstrated. 

Aims. Thanks to the high angular resolution, sensitivity, image fidelity, and frequency coverage of ALMA, we aim to improve our understanding of star formation. One of the breakthroughs expected from ALMA, which is the basis of our Cycle 5 ALMA-IMF Large Program, is the question of the origin of the initial mass function (IMF) of stars. Here we present the ALMA-IMF protocluster selection, first results, and scientific prospects. Methods. ALMA-IMF imaged a total noncontiguous area of ~53 pc 2 , covering extreme, nearby protoclusters of the Milky Way. We observed 15 massive (2.5 −33 × 10 3 M ⊙ ), nearby (2−5.5 kpc) protoclusters that were selected to span relevant early protocluster evolutionary stages. Our 1.3 and 3 mm observations provide continuum images that are homogeneously sensitive to point-like cores with masses of ~0.2 M ⊙ and ~0.6 M ⊙ , respectively, with a matched spatial resolution of ~2000 au across the sample at both wavelengths. Moreover, with the broad spectral coverage provided by ALMA, we detect lines that probe the ionized and molecular gas, as well as complex molecules. Taken together, these data probe the protocluster structure, kinematics, chemistry, and feedback over scales from clouds to filaments to cores. Results. We classify ALMA-IMF protoclusters as Young (six protoclusters), Intermediate (five protoclusters), or Evolved (four proto-clusters) based on the amount of dense gas in the cloud that has potentially been impacted by H  II region(s). The ALMA-IMF catalog contains ~700 cores that span a mass range of ~0.15 M ⊙ to ~250 M ⊙ at a typical size of ~2100 au. We show that this core sample has no significant distance bias and can be used to build core mass functions (CMFs) at similar physical scales. Significant gas motions, which we highlight here in the G353.41 region, are traced down to core scales and can be used to look for inflowing gas streamers and to quantify the impact of the possible associated core mass growth on the shape of the CMF with time. Our first analysis does not reveal any significant evolution of the matter concentration from clouds to cores (i.e., from 1 pc to 0.01 pc scales) or from the youngest to more evolved protoclusters, indicating that cloud dynamical evolution and stellar feedback have for the moment only had a slight effect on the structure of high-density gas in our sample. Furthermore, the first-look analysis of the line richness toward bright cores indicates that the survey encompasses several tens of hot cores, of which we highlight the most massive in the G351.77 cloud. Their homogeneous characterization can be used to constrain the emerging molecular complexity in protostars of high to intermediate masses. Conclusions. The ALMA-IMF Large Program is uniquely designed to transform our understanding of the IMF origin, taking the effects of cloud characteristics and evolution into account. It will provide the community with an unprecedented database with a high legacy value for protocluster clouds, filaments, cores, hot cores, outflows, inflows, and stellar clusters studies.