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Single-molecule localization microscopy (SMLM) breaks the optical diffraction limit by numerically localizing sparse fluorescence emitters to achieve super-resolution imaging. Spectroscopic SMLM or sSMLM further allows simultaneous spectroscopy and super-resolution imaging of fluorescence molecules. Hence, sSMLM can extract spectral features with single-molecule sensitivity, higher precision, and higher multiplexity than traditional multicolor microscopy modalities. These new capabilities enabled advanced multiplexed and functional cellular imaging applications. While sSMLM suffers from reduced spatial precision compared to conventional SMLM due to splitting photons to form spatial and spectral images, several methods have been reported to mitigate these weaknesses through innovative optical design and image processing techniques. This review summarizes the recent progress in sSMLM, its applications, and our perspective on future work.

Graphical Abstract

Understanding the mechanisms that generate genetic variation, and thus contribute to the process of adaptation, is a major goal of evolutionary biology. Mutation and genetic exchange have been well studied as mechanisms to generate genetic variation. However, there are additional factors, such as genome architecture, that may also impact the amount of genetic variation in some populations, and the extent to which these variation generating mechanisms are themselves shaped by natural selection is still an open question. To test the effect of genome architecture on the generation of genetic variation, and hence evolvability, we studied Tetrahymena thermophila, a ciliate with an unusual genome structure and mechanism of nuclear division, called amitosis, whereby homologous chromosomes are randomly distributed to daughter cells. Amitosis leads to genetic variation among the asexual descendants of a newly produced sexual progeny because different progeny cells will contain different combinations of parental alleles. We hypothesize that amitosis thus increases the evolvability of newly produced sexual progeny relative to their unmated parents and species that undergo mitosis. To test this hypothesis, we used experimental evolution and simulations to compare the rate of adaptation in T. thermophila populations founded by a single sexual progeny to parental populations thatmore »had not had sex in many generations. The populations founded by a sexual progeny adapted more quickly than parental populations in both laboratory populations and simulated populations. This suggests that the additional genetic variation generated by amitosis of a heterozygote can increase the rate of adaptation following sex and may help explain the evolutionary success of the unusual genetic architecture of Tetrahymena and ciliates more generally.

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When robots operate in real-world off-road environments with unstructured terrains, the ability to adapt their navigational policy is critical for effective and safe navigation. However, off-road terrains introduce several challenges to robot navigation, including dynamic obstacles and terrain uncertainty, leading to inefficient traversal or navigation failures. To address these challenges, we introduce a novel approach for adaptation by negotiation that enables a ground robot to adjust its navigational behaviors through a negotiation process. Our approach first learns prediction models for various navigational policies to function as a terrain-aware joint local controller and planner. Then, through a new negotiation process, our approach learns from various policies' interactions with the environment to agree on the optimal combination of policies in an online fashion to adapt robot navigation to unstructured off-road terrains on the fly. Additionally, we implement a new optimization algorithm that offers the optimal solution for robot negotiation in real-time during execution. Experimental results have validated that our method for adaptation by negotiation outperforms previous methods for robot navigation, especially over unseen and uncertain dynamic terrains.

Recently, many regression based conditional independence (CI) test methods have been proposed to solve the problem of causal discovery. These methods provide alternatives to test CI by first removing the information of the controlling set from the two target variables, and then testing the independence between the corresponding residuals Res1 and Res2. When the residuals are linearly uncorrelated, the independence test between them is nontrivial. With the ability to calculate inner product in high-dimensional space, kernel-based methods are usually used to achieve this goal, but still consume considerable time. In this paper, we investigate the independence between two linear combinations under linear non-Gaussian structural equation model. We show that the dependence between the two residuals can be captured by the difference between the similarity of (Res1, Res2) and that of (Res1, Res3) (Res3 is generated by random permutation) in high-dimensional space. With this result, we design a new method called SCIT for CI test, where permutation test is performed to control Type I error rate. The proposed method is simpler yet more efficient and effective than the existing ones. When applied to causal discovery, the proposed method outperforms the counterparts in terms of both speed and Type II error rate,more »especially in the case of small sample size, which is validated by our extensive experiments on various datasets.« less

We present a precise measurement of the asymptotic normalization coefficient (ANC) for the¹⁶O ground state (GS) through the¹²C(¹¹B,⁷Li)¹⁶O transfer reaction using the Quadrupole‐3‐Dipole (Q3D) magnetic spectrograph. The present work sheds light on the existing discrepancy of more than 2 orders of magnitude between the previously reported GS ANC values. This ANC is believed to have a strong effect on the¹²C(α,γ)¹⁶O reaction rate by constraining the external capture to the¹⁶O ground state, which can interfere with the high-energy tail of the 2⁺subthreshold state. Based on the new ANC, we determine the astrophysicalS-factor and the stellar rate of the¹²C(α,γ)¹⁶O reaction. An increase of up to 21% in the total reaction rate is found within the temperature range of astrophysical relevance compared with the previous recommendation of a recent review. Finally, we evaluate the impact of our new rate on the pair-instability mass gap for black holes (BH) by evolving massive helium core stars using the MESA stellar evolution code. The updated¹²C(α,γ)¹⁶O reaction rate decreases the lower and upper edges of the BH gap about 12% and 5%, respectively.

Collaborative localization is an essential capability for a team of robots such as connected vehicles to collaboratively estimate object locations from multiple perspectives with reliant cooperation. To enable collaborative localization, four key challenges must be addressed, including modeling complex relationships between observed objects, fusing observations from an arbitrary number of collaborating robots, quantifying localization uncertainty, and addressing latency of robot communications. In this paper, we introduce a novel approach that integrates uncertainty-aware spatiotemporal graph learning and model-based state estimation for a team of robots to collaboratively localize objects. Specifically, we introduce a new uncertainty-aware graph learning model that learns spatiotemporal graphs to represent historical motions of the objects observed by each robot over time and provides uncertainties in object localization. Moreover, we propose a novel method for integrated learning and model-based state estimation, which fuses asynchronous observations obtained from an arbitrary number of robots for collaborative localization. We evaluate our approach in two collaborative object localization scenarios in simulations and on real robots. Experimental results show that our approach outperforms previous methods and achieves state-of-the-art performance on asynchronous collaborative localization.

Abstract Objective. Transcranial magnetic stimulation (TMS) is a non-invasive brain stimulation method that is used to study brain function and conduct neuropsychiatric therapy. Computational methods that are commonly used for electric field (E-field) dosimetry of TMS are limited in accuracy and precision because of possible geometric errors introduced in the generation of head models by segmenting medical images into tissue types. This paper studies E-field prediction fidelity as a function of segmentation accuracy. Approach. The errors in the segmentation of medical images into tissue types are modeled as geometric uncertainty in the shape of the boundary between tissue types. For each tissue boundary realization, we then use an in-house boundary element method to perform a forward propagation analysis and quantify the impact of tissue boundary uncertainties on the induced cortical E-field. Main results. Our results indicate that predictions of E-field induced in the brain are negligibly sensitive to segmentation errors in scalp, skull and white matter (WM), compartments. In contrast, E-field predictions are highly sensitive to possible cerebrospinal fluid (CSF) segmentation errors. Specifically, the segmentation errors on the CSF and gray matter interface lead to higher E-field uncertainties in the gyral crowns, and the segmentation errors on CSF and WMmore »interface lead to higher uncertainties in the sulci. Furthermore, the uncertainty of the average cortical E-fields over a region exhibits lower uncertainty relative to point-wise estimates. Significance. The accuracy of current cortical E-field simulations is limited by the accuracy of CSF segmentation accuracy. Other quantities of interest like the average of the E-field over a cortical region could provide a dose quantity that is robust to possible segmentation errors.« less