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We propose the use of PolyPIC transfers [10] to construct a second order accurate discretization of the Navier-Stokes equations within a particle-in-cell framework on MAC grids. We investigate the accuracy of both APIC [16], [17], [8] and quadratic PolyPIC [10] transfers and demonstrate that they are suitable for constructing schemes converging with orders of approximately 1.5 and 2.5 respectively. We combine PolyPIC transfers with BDF-2 time integration and a splitting scheme for pressure and viscosity and demonstrate that the resulting scheme is second order accurate. Prior high order particle-in-cell schemes interpolate accelerations (not velocities) from the grid to particles and rely on moving least squares to transfer particle velocities to the computational grid. The proposed method instead transfers velocities to particles, which avoids the accumulation of noise on particle velocities but requires the polynomial reconstruction to be performed using polynomials that are one degree higher. Since this polynomial reconstruction occurs over the regular grid (rather than irregularly distributed particles), the resulting weighted least squares problem has a fixed sparse structure, can be solved efficiently in closed form, and is independent of particle coverage. 

Cells continuously experience and respond to different physical forces that are used to regulate their physiology and functions. Our ability to measure these mechanical cues is essential for understanding the bases of various mechanosensing and mechanotransduction processes. While multiple strategies have been developed to study mechanical forces within two-dimensional (2D) cell culture monolayers, the force measurement at cell-cell junctions in real three-dimensional (3D) cell models is still pretty rare. Considering that in real biological systems, cells are exposed to forces from 3D directions, measuring these molecular forces in their native environment is thus highly critical for the better understanding of different development and disease processes. We have recently developed a type of DNA-based molecular probe for measuring intercellular tensile forces in 2D cell models. Herein, we will report the further development and first-time usage of these molecular tension probes to visualize and detect mechanical forces within 3D spheroids and embryoid bodies (EBs). These probes can spontaneously anchor onto live cell membranes via the attached lipid moieties. By varying the concentrations of these DNA probes and their incubation time, we have first characterized the kinetics and efficiency of probe penetration and loading onto tumor spheroids and stem cell EBs of different sizes. After optimization, we have further imaged and measured E-cadherin-mediated forces in these 3D spheroids and EBs for the first time. Our results indicated that these DNA-based molecular tension probes can be used to study the spatiotemporal distributions of target mechanotransduction processes. These powerful imaging tools may be potentially applied to fill the gap between ongoing research of biomechanics in 2D systems and that in real 3D cell complexes. 

Many radar-gauge merging methods have been developed to produce improved rainfall data by leveraging the advantages of gauge and radar observations. Two popular merging methods, Regression Kriging and Bayesian Regression Kriging were utilized and compared in this study to produce hourly rainfall data from gauge networks and multi-source radar datasets. The authors collected, processed, and modeled the gauge and radar rainfall data (Stage IV, MRMS and RTMA radar data) of the two extreme storm events (i.e., Hurricane Harvey in 2017 and Tropical Storm Imelda in 2019) occurring in the coastal area in Southeast Texas with devastating flooding. The analysis of the modeled data on consideration of statistical metrics, physical rationality, and computational expenses, implies that while both methods can effectively improve the radar rainfall data, the Regression Kriging model demonstrates its superior performance over that of the Bayesian Regression Kriging model since the latter is found to be prone to overfitting issues due to the clustered gauge distributions. Moreover, the spatial resolution of rainfall data is found to affect the merging results significantly, where the Bayesian Regression Kriging model works unskillfully when radar rainfall data with a coarser resolution is used. The study recommends the use of high-quality radar data with properly spatial-interpolated gauge data to improve the radar-gauge merging methods. The authors believe that the findings of the study are critical for assisting hazard mitigation and future design improvement. 

Phycobilisomes (PBS) are antenna megacomplexes that transfer energy to photosystems II and I in thylakoids. PBS likely evolved from a basic, inefficient form into the predominant hemidiscoidal shape with radiating peripheral rods. However, it has been challenging to test this hypothesis because ancestral species are generally inaccessible. Here we use spectroscopy and cryo-electron microscopy to reveal a structure of a “paddle-shaped” PBS from a thylakoid-free cyanobacterium that likely retains ancestral traits. This PBS lacks rods and specialized ApcD and ApcF subunits, indicating relict characteristics. Other features include linkers connecting two chains of five phycocyanin hexamers (CpcN) and two core subdomains (ApcH), resulting in a paddle-shaped configuration. Energy transfer calculations demonstrate that chains are less efficient than rods. These features may nevertheless have increased light absorption by elongating PBS before multilayered thylakoids with hemidiscoidal PBS evolved. Our results provide insights into the evolution and diversification of light-harvesting strategies before the origin of thylakoids. 

Abstract Human induced pluripotent stem cell derived brain organoids have shown great potential for studies of human brain development and neurological disorders. However, quantifying the evolution of the electrical properties of brain organoids during development is currently limited by the measurement techniques, which cannot provide long‐term stable 3D bioelectrical interfaces with developing brain organoids. Here, a cyborg brain organoid platform is reported, in which “tissue‐like” stretchable mesh nanoelectronics are designed to match the mechanical properties of brain organoids and to be folded by the organogenetic process of progenitor or stem cells, distributing stretchable electrode arrays across the 3D organoids. The tissue‐wide integrated stretchable electrode arrays show no interruption to brain organoid development, adapt to the volume and morphological changes during brain organoid organogenesis, and provide long‐term stable electrical contacts with neurons within brain organoids during development. The seamless and noninvasive coupling of electrodes to neurons enables long‐term stable, continuous recording and captures the emergence of single‐cell action potentials from early‐stage brain organoid development.