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When a constraint is removed, confluent cells migrate directionally into the available space. How the migration directionality and speed increase are initiated at the leading edge and propagate into neighboring cells are not well understood. Using a quantitative visualization technique-Particle Image Velocimetry (PIV)-we revealed that migration directionality and speed had strikingly different dynamics. Migration directionality increases as a wave propagating from the leading edge into the cell sheet, while the increase in cell migration speed is maintained only at the leading edge. The overall directionality steadily increases with time as cells migrate into the cell-free space, but migration speed remains largely the same. A particle-based compass (PBC) model suggests cellular interplay (which depends on cell-cell distance) and migration speed are sufficient to capture the dynamics of migration directionality revealed experimentally. Extracellular Ca2+ regulated both migration speed and directionality, but in a significantly different way, suggested by the correlation between directionality and speed only in some dynamic ranges. Our experimental and modeling results reveal distinct directionality and speed dynamics in collective migration, and these factors can be regulated by extracellular Ca2+ through cellular interplay. Quantitative visualization using PIV and our PBC model thus provide a powerful approach to dissect the mechanisms of collective cell migration.

Acrylic acid, N-acetyl-glucosamine and glucosamine were investigated for their role in the formation of acrylamide in California-style black ripe olives [CBROs]. Levels of acrylic acid and glucosamine are reported for the first time in fresh (333.50 ± 21.88 and 243.59 ± 10.06 nmol/g, respectively) and in brine-stored olives (184.50 ± 6.02 and 165.88 ± 11.51 nmol/g, respectively). Acrylamide levels significantly increased when acrylic acid (35.2%), N-acetyl-glucosamine (29.9%), and glucosamine (124.0%) were added to olives prior to sterilization. However, isotope studies indicate these compounds do not contribute carbon and/or nitrogen atoms to acrylamide. The base-catalyzed degradation of glucosamine is demonstrated in olive pulp and a strong correlation (r2 = 0.9513) between glucosamine in olives before sterilization and acrylamide formed in processed CBROs is observed. Treatment with sodium hydroxide (pH > 12) significantly reduces acrylamide levels over 1 to 5 d without impacting olive fruit texture.

The versatile reactivity of isothiocyanate intermediates enabled the diversity-oriented synthesis (DOS) of N-heterocycles in a DNA-compatible manner. We first reported a mild in situ conversion of DNA-conjugated amines to isothiocyanates. Subsequently, a set of diverse transformations was successfully developed to construct 2-thioxo-quinazolinones, 1,2,4-thiadiazoles, and 2-imino thiazolines. Finally, the feasibility of these approaches in constructing DELs was further demonstrated through enzymatic ligation and mock pool preparation. This study demonstrated the advantages of combining in situ conversion strategies with DOS, which effectively broadened the chemical and structural diversity of DELs. [ABSTRACT FROM AUTHOR]

Ground penetrating radar (GPR) is a non-destructive exploration technology that utilizes high-frequency ultra-wideband signals to detect the distribution of subsurface objects and media. Benefiting from the advantages of non-destructiveness, high efficiency, and high resolution, GPR has been widely applied in underground defect detection task of urban roads. Illustrating the radar echo wave of subsurface structure, GPR B-scan images are a main means to detect underground disaster; however, compared to natural images, automatic interpretation of GPR B-scan ones is a more challenging task because of same objects with different spectra, different objects with same spectrum as well as heavy noise pollution. Aiming to improve the accuracy of subsurface disasters detection methods, a disasters classification network, i.e., MA-ResNeXt, based on ResNetXt50 is proposed by combining multi-dimensional attention mechanism, atrous space pyramid pool and multi-scale feature extraction structure. The proposed network is trained and tested on real GPR B-scan images of three common subsurface disasters, e.g., void, cavity underneath pavement (CUP) and loosely infilled void (LIV). The comparison results show that classification accuracy of the proposed network approaches 98.2%, and illustrate that the network can effectively realize accurate classification of underground disasters. [ABSTRACT FROM AUTHOR]

The lower limit temperature in the crop water stress index (CWSI) model refers to the canopy temperature (Tc) or the canopy-air temperature differences (dT) under well-watered conditions, which has significant impacts on the accuracy of the model in quantifying plant water status. At present, the direct estimation of lower limit temperature based on data-driven method has been successfully used in crops, but its applicability has not been tested in forest ecosystems. We collected continuously and synchronously Tc and meteorological data in a Quercus variabilis plantation at the southern foot of Taihang Mountain to evaluate the feasibility of multiple linear regression model and BP neural network model for estimating the lower limit temperature and the accuracy of the CWSI indicating water status of the plantation. The results showed that, in the forest ecosystem without irrigation conditions, the lower limit temperature could be obtained by setting soil moisture as saturation in the multiple linear regression model and the BP neural network model with soil water content, wind speed, net radiation, vapor pressure deficit and air temperature as input parameters. Combining the lower limit temperature and the upper limit temperature determined by the theoretical equation to normalize the measured Tc and dT could realize the non-destructive, rapid, and automatic diagnosis of the water status of Q. variabilis plantation. Among them, the CWSI obtained by combining the lower limit temperature determined by the dT under well-watered condition calculated by the BP neural network model and the upper limit temperature was the most suitable for accurate monitoring water status of the plantation. The coefficient of determination, root mean square error, and index of agreement between the calculated CWSI and measured CWSI were 0.81, 0.08, and 0.90, respectively. This study could provide a reference method for efficient and accurate monitoring of forest ecosystem water status. [ABSTRACT FROM AUTHOR]

Radar coincidence imaging (RCI) is a high‐resolution radar imaging mode which constructs a temporal spatial stochastic radiation field (TSSRF) and uses the correlation between reference signal and echoes for imaging. The correlation between the reference matrix and the echoes is the main factor affecting the imaging performance. In fact, radar target characteristics cause fluctuations in the radar cross section and variations in the scattering intensity of each resolution element. The latter degrades the correlation between the reference matrix and the echoes, seriously affecting the image reconstruction. This paper designs the waveform based on image entropy under fixed transmitting and receiving arrays. The targets with fluctuating scattering intensity at each resolution element are statistically modelled. Simulation results show this approach can improve the imaging performance and reduce the energy dissipation degree of the target grid. [ABSTRACT FROM AUTHOR]