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Light sheet microscopy is a powerful technique for high-speed three-dimensional imaging of subcellular dynamics and large biological specimens. However, it often generates datasets ranging from hundreds of gigabytes to petabytes in size for a single experiment. Conventional computational tools process such images far slower than the time to acquire them and often fail outright due to memory limitations. To address these challenges, we present PetaKit5D, a scalable software solution for efficient petabyte-scale light sheet image processing. This software incorporates a suite of commonly used processing tools that are optimized for memory and performance. Notable advancements include rapid image readers and writers, fast and memory-efficient geometric transformations, high-performance Richardson-Lucy deconvolution and scalable Zarr-based stitching. These features outperform state-of-the-art methods by over one order of magnitude, enabling the processing of petabyte-scale image data at the full teravoxel rates of modern imaging cameras. The software opens new avenues for biological discoveries through large-scale imaging experiments.

At each cell division, nanometer-scale motors and microtubules give rise to the micron-scale spindle. Many mitotic motors step helically around microtubules in vitro, and most are predicted to twist the spindle in a left-handed direction. However, the human spindle exhibits only slight global twist, raising the question of how these molecular torques are balanced. Here, we find that anaphase spindles in the epithelial cell line MCF10A have a high baseline twist, and we identify factors that both increase and decrease this twist. The midzone motors KIF4A and MKLP1 are together required for left-handed twist at anaphase, and we show that KIF4A generates left-handed torque in vitro. The actin cytoskeleton also contributes to left-handed twist, but dynein and its cortical recruitment factor LGN counteract it. Together, our work demonstrates that force generators regulate twist in opposite directions from both within and outside the spindle, preventing strong spindle twist during chromosome segregation.

As an important urban lake in Fuling District, Chongqing City, there is water blooms in Tonggu Lake, which affect the ecological environment. This study analyzed and studied the external and internal status of water pollution through site survey, water quality testing and analysis, and laboratory data analysis, and diagnosed the water environment of Tonggu Lake, which is helpful for providing strong data support for the follow-up solution to the problem of water bloom in Tonggu Lake.

Lattice light sheet microscopy excels at the noninvasive imaging of three-dimensional (3D) dynamic processes at high spatiotemporal resolution within cells and developing embryos. Recently, several papers have called into question the performance of lattice light sheets relative to the Gaussian sheets most common in light sheet microscopy. Here, we undertake a theoretical and experimental analysis of various forms of light sheet microscopy, which demonstrates and explains why lattice light sheets provide substantial improvements in resolution and photobleaching reduction. The analysis provides a procedure to select the correct light sheet for a desired experiment and specifies the processing that maximizes the use of all fluorescence generated within the light sheet excitation envelope for optimal resolution while minimizing image artifacts and photodamage. We also introduce a new type of "harmonic balanced" lattice light sheet that improves performance at all spatial frequencies within its 3D resolution limits and maintains this performance over lengthened propagation distances allowing for expanded fields of view.

Mass spectrometric analysis of the anionic products of interaction between bimetallic palladium-copper tetrahydride anions, PdCuH4-, and carbon dioxide, CO2, in a reaction cell shows an efficient generation of the PdCuCO2H4- intermediate and formate/formic acid complexes. Multiple structures of PdCuH4- and PdCuCO2H4- are identified by a synergy between anion photoelectron spectroscopy and quantum chemical calculations. The higher energy PdCuH4- isomer is shown to drive the catalytic hydrogenation of CO2, emphasizing the importance of accounting for higher energy isomers for cluster catalytic activity. This study represents the first example of CO2 hydrogenation by bimetallic hydride clusters.

Mass spectrometric analysis of anionic products that result from interacting Ir− with H2O shows the efficient generation of [Ir(H2O)]− complexes and IrO− molecular anions. Anion photoelectron spectra of [Ir(H2O)]−, formed under various source conditions, exhibit spectral features that are due to three different forms of the complex: the solvated anion–molecule complex, Ir−(H2O), as well as the intermediates, [H–Ir–OH]− and [H2–Ir–O]−, where one and two O–H bonds have been broken, respectively. The measured and calculated vertical detachment energy values are in good agreement and, thus, support identification of all three types of isomers. The calculated reaction pathway shows that the overall reaction Ir− + H2O → IrO− + H2 is exothermic. Two minimum energy crossing points were found, which shuttle intermediates and products between singlet and triplet potential surfaces. This study presents the first example of water activation and splitting by single Ir− anions. [ABSTRACT FROM AUTHOR]

We report a joint photoelectron spectroscopic and theoretical study of the PtZnH5(-) cluster anion. This cluster exhibited an unprecedented planar pentagonal coordination for Pt and an unusual stability and high intensity in the mass spectrum. Both are due to the σ-aromaticity found in the H5-cycle supported by the 5d orbitals on the Pt atom. σ-Aromaticity in all-H systems has been predicted in the past but never found in experimentally observed species. Besides fundamental importance, mixed transition-metal hydrides can be found as intermediates in catalytic processes, and thus, the unexpected stability facilitated by σ-aromaticity can be appreciated also in practical applications.

Lane occupancy is a crucial indicator of traffic flow and is significant for traffic management and planning. However, predicting lane occupancy is challenging due to numerous influencing factors, such as weather, holidays, and events, which render the data nonsmooth. To enhance lane occupancy prediction accuracy, this study introduces a fusion model that combines the CT-Transformer (CSPNet-Attention and Two-stage Transformer framework) with the Temporal Convolutional Neural Network-Long Short-Term Memory (TCN-LSTM) models alongside the Variational Mode. This includes a long-term lane occupancy prediction model utilizing the Variational Mode Decomposition (VMD) technique. Initially, the Variational Mode Decomposition decomposes the original traffic flow data into multiple smooth subsequences. Subsequently, each subsequence's autocorrelation and partial correlation coefficients ascertain the presence of seasonal characteristics. Based on these characteristics, the CT-Transformer and TCN-LSTM models process each subsequence for long-term lane occupancy rate prediction, respectively. Finally, predictions from both models are integrated using variable modes to derive the ultimate lane occupancy predictions. The core CT-Transformer model, an enhancement of the GBT (Two-stage Transformer) model, comprises two phases: autoregressive and prediction. The autoregressive phase leverages historical data for initial predictions inputted into the prediction phase. Here, the novel CSPNet-Attention mechanism replaces the conventional attention mechanism in the Encoder, reducing memory usage and computational resource loss, thereby enhancing the model's accuracy and robustness. Experiments on the PeMS public dataset demonstrate that the proposed model surpasses existing methods in predicting long-term lane occupancy, offering decent reliability and generalizability. [ABSTRACT FROM AUTHOR]

The results of a combined experimental and computational study of the uranium atom are presented with the aim of determining its electron affinity. Experimentally, the electron affinity of uranium was measured via negative ion photoelectron spectroscopy of the uranium atomic anion, U−. Computationally, the electron affinities of both thorium and uranium were calculated by conducting relativistic coupled-cluster and multi-reference configuration interaction calculations. The experimentally determined value of the electron affinity of the uranium atom was determined to be 0.309 ± 0.025 eV. The computationally predicted electron affinity of uranium based on composite coupled cluster calculations and full four-component spin–orbit coupling was found to be 0.232 eV. Predominately due to a better convergence of the coupled cluster sequence for Th and Th−, the final calculated electron affinity of Th, 0.565 eV, was in much better agreement with the accurate experimental value of 0.608 eV. In both cases, the ground state of the anion corresponds to electron attachment to the 6d orbital. [ABSTRACT FROM AUTHOR]

Close-lying dipole-bound and valence-bound states in the nitromethane anion make this molecule an ideal system for studying the coupling between these two electronically different states. In this work, dipole-bound and valence-bound nitromethane anions were generated by Rydberg electron transfer and characterized by anion photoelectron spectroscopy. The presence of the dipole-bound state was demonstrated through its photoelectron spectral signature, i.e., a single narrow peak at very low electron binding energy, its strong Rydberg quantum number, n*, dependence, and its relatively large anisotropy parameter, β. This work goes the furthest yet in supporting the doorway model of electron attachment to polar molecules. [ABSTRACT FROM AUTHOR]

Traffic flow forecasting, as an integral part of intelligent transportation systems, plays a critical part in traffic planning. Previous studies have primarily focused on short-term traffic flow prediction, paying insufficient attention to long-term prediction. In this study, we propose a hybrid model that utilizes variational mode decomposition (VMD) and the auto-correlation mechanism for long-term prediction. In view of the periodic and stochastic characteristics of traffic flow, VMD is able to decompose the data into intrinsic mode functions with different frequencies, which in turn helps the model extract the internal features of the data and better capture the changes of traffic flow data in the cycle. Additionally, we improve the residual structure by adding a convolutional layer to propose a correction module and use it together with the auto-correlation mechanism to jointly build an encoder and decoder to extract features from different data components (intrinsic mode functions) and fuse the extracted features for output. To meet the requirements of long-term forecasting, we set the traffic flow forecast length to 4 levels: 96, 192, 336, and 720. We validated our model using the departure statistics dataset of a taxi parking lot at Beijing Capital International Airport and achieved the best prediction performance in terms of mean squared error and mean absolute error, compared to the baseline model. [ABSTRACT FROM AUTHOR]

Using a combination of Rydberg electron transfer and negative ion photoelectron spectroscopy, we revisited an earlier study which, based on several separate pieces of evidence, had concluded that trans- and gauche-succinonitrile can form quadrupole bound anions (QBAs) and dipole bound anions (DBAs), respectively. In the present work, succinonitrile anions were formed by Rydberg electron transfer and interrogated by negative ion photoelectron spectroscopy. The resulting anion photoelectron spectra exhibited distinctive spectral features for both QBA and DBA species in the same spectrum, thereby providing direct spectroscopic confirmation of previous indirect conclusions. Just as importantly, this work also introduces the integrated combination of Rydberg electron transfer and anion photoelectron spectroscopy as a powerful, tandem technique for studying diffuse excess electron states. [ABSTRACT FROM AUTHOR]

The p-chloroaniline anion was generated by Rydberg electron transfer and studied via velocity-map imaging anion photoelectron spectroscopy. The vertical detachment energy (VDE) of the p-chloroaniline anion was measured to be 6.6 meV. This value is in accord with the VDE of 10 meV calculated by Skurski and co-workers. They found the binding of the excess electron in the p-chloroaniline anion to be due almost entirely to electron correlation effects, with only a small contribution from the long-range dipole potential. As such, the p-chloroaniline anion is the first essentially correlation-bound anion to be observed experimentally. [ABSTRACT FROM AUTHOR]