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Entangled multiphoton sources are essential for both fundamental tests of quantum foundations and building blocks of contemporary optical quantum technologies. While efforts over the past three decades have focused on creating multiphoton entanglement through multiplexing existing biphoton sources with linear optics and postselections, our work presents a groundbreaking approach. We observe genuine continuous-mode time-energy-entangled W-class triphotons with an unprecedented production rate directly generated through spontaneous six-wave mixing (SSWM) in a four-level triple-Λ atomic vapor cell. Using electromagnetically induced transparency and coherence control, our SSWM scheme allows versatile narrowband triphoton generation with advantageous properties, including long temporal coherence and controllable waveforms. This advancement is ideal for applications like long-distance quantum communications and information processing, bridging single photons and neutral atoms. Most importantly, our work establishes a reliable and efficient genuine triphoton source, facilitating accessible research on multiphoton entanglement. 

High-resolution albedo has the advantage of a higher spatial scale from tens to hundreds of meters, which can fill the gaps of albedo applications from the global scale to the regional scale and can solve problems related to land use change and ecosystems. The Sentinel-2 satellite provides high-resolution observations in the visible-to-NIR bands, giving possibilities to generate a high-resolution surface albedo at 10 m. This study attempted to evaluate the performance of the four data-driven machine learning algorithms (i.e., random forest (RF), artificial neural network (ANN), k-nearest neighbor (KNN), and XGBoost (XGBT)) for the generation of a Sentinel-2 albedo over flat and rugged terrain. First, we used the RossThick-LiSparseR model and the 3D discrete anisotropic radiative transfer (DART) model to build the narrowband surface reflectance and broadband surface albedo, which acted as the training and testing datasets over flat and rugged terrain. Second, we used the training and testing datasets to drive the four machine learning models, and evaluated the performance of these machine learning models for the generation of Sentinel-2 albedo. Finally, we used the four machine learning models to generate a Sentinel-2 albedo and compared them with in situ albedos to show the models’ application potentials. The results show that these machine learning models have great performance in estimating Sentinel-2 albedos at a 10 m spatial scale. The comparison with in situ albedos shows that the random forest model outperformed the others in estimating a high-resolution surface albedo based on Sentinel-2 datasets over the flat and rugged terrain, with an RMSE smaller than 0.0308 and R2 larger than 0.9472. 

ABSTRACT A wide range of biological systems, from microbial swarms to bird flocks, display emergent behaviors driven by coordinated movement of individuals. To this end, individual organisms interact by recognizing their kin and adjusting their motility based on others around them. However, even in the best-studied systems, the mechanistic basis of the interplay between kin recognition and motility coordination is not understood. Here, using a combination of experiments and mathematical modeling, we uncover the mechanism of an emergent social behavior in Myxococcus xanthus . By overexpressing the cell surface adhesins TraA and TraB, which are involved in kin recognition, large numbers of cells adhere to one another and form organized macroscopic circular aggregates that spin clockwise or counterclockwise. Mechanistically, TraAB adhesion results in sustained cell-cell contacts that trigger cells to suppress cell reversals, and circular aggregates form as the result of cells’ ability to follow their own cellular slime trails. Furthermore, our in silico simulations demonstrate a remarkable ability to predict self-organization patterns when phenotypically distinct strains are mixed. For example, defying naive expectations, both models and experiments found that strains engineered to overexpress different and incompatible TraAB adhesins nevertheless form mixed circular aggregates. Therefore, this work provides key mechanistic insights into M. xanthus social interactions and demonstrates how local cell contacts induce emergent collective behaviors by millions of cells. IMPORTANCE In many species, large populations exhibit emergent behaviors whereby all related individuals move in unison. For example, fish in schools can all dart in one direction simultaneously to avoid a predator. Currently, it is impossible to explain how such animals recognize kin through brain cognition and elicit such behaviors at a molecular level. However, microbes also recognize kin and exhibit emergent collective behaviors that are experimentally tractable. Here, using a model social bacterium, we engineer dispersed individuals to organize into synchronized collectives that create emergent patterns. With experimental and mathematical approaches, we explain how this occurs at both molecular and population levels. The results demonstrate how the combination of local physical interactions triggers intracellular signaling, which in turn leads to emergent behaviors on a population scale. 

ABSTRACT Single mutations frequently alter several aspects of cell behavior but rarely reveal whether a particular statistically significant change is biologically significant. To determine which behavioral changes are most important for multicellular self-organization, we devised a new methodology using Myxococcus xanthus as a model system. During development, myxobacteria coordinate their movement to aggregate into spore-filled fruiting bodies. We investigate how aggregation is restored in two mutants, csgA and pilC , that cannot aggregate unless mixed with wild-type (WT) cells. To this end, we use cell tracking to follow the movement of fluorescently labeled cells in combination with data-driven agent-based modeling. The results indicate that just like WT cells, both mutants bias their movement toward aggregates and reduce motility inside aggregates. However, several aspects of mutant behavior remain uncorrected by WT, demonstrating that perfect recreation of WT behavior is unnecessary. In fact, synergies between errant behaviors can make aggregation robust. IMPORTANCE Self-organization into spatial patterns is evident in many multicellular phenomena. Even for the best-studied systems, our ability to dissect the mechanisms driving coordinated cell movement is limited. While genetic approaches can identify mutations perturbing multicellular patterns, the diverse nature of the signaling cues coupled to significant heterogeneity of individual cell behavior impedes our ability to mechanistically connect genes with phenotype. Small differences in the behaviors of mutant strains could be irrelevant or could sometimes lead to large differences in the emergent patterns. Here, we investigate rescue of multicellular aggregation in two mutant strains of Myxococcus xanthus mixed with wild-type cells. The results demonstrate how careful quantification of cell behavior coupled to data-driven modeling can identify specific motility features responsible for cell aggregation and thereby reveal important synergies and compensatory mechanisms. Notably, mutant cells do not need to precisely recreate wild-type behaviors to achieve complete aggregation.