Structure of the substrate-engaged SecA-SecY protein translocation machine.

The Sec61/SecY channel allows the translocation of many proteins across the eukaryotic endoplasmic reticulum membrane or the prokaryotic plasma membrane. In bacteria, most secretory proteins are transported post-translationally through the SecY channel by the SecA ATPase. How a polypeptide is moved through the SecA-SecY complex is poorly understood, as structural information is lacking. Here, we report an electron cryo-microscopy (cryo-EM) structure of a translocating SecA-SecY complex in a lipid environment. The translocating polypeptide chain can be traced through both SecA and SecY. In the captured transition state of ATP hydrolysis, SecAs two-helix finger is close to the polypeptide, while SecAs clamp interacts with the polypeptide in a sequence-independent manner by inducing a short β-strand. Taking into account previous biochemical and biophysical data, our structure is consistent with a model in which the two-helix finger and clamp cooperate during the ATPase cycle to move a polypeptide through the channel

Inverse spectral problem for the Schr\"odinger operator on the square lattice

We consider an inverse spectral problem on a quantum graph associated with the square lattice. Assuming that the potentials on the edges are compactly supported and symmetric, we show that the Dirichlet-to-Neumann map for a boundary value problem on a finite part of the graph uniquely determines the potentials. We obtain a reconstruction procedure, which is based on the reduction of the differential Schr\"odinger operator to a discrete one. As a corollary of the main results, it is proved that the S-matrix for all energies in any given open set in the continuous spectrum uniquely specifies the potentials on the square lattice

Association of lactate-to-albumin ratio with in-hospital and intensive care unit mortality in patients with intracerebral hemorrhage

BackgroundIntracerebral hemorrhage (ICH) is a severe stroke subtype with a high mortality rate; the lactate-to-albumin ratio (LAR) is a new biomarker for predicting clinical outcomes in patients with ICH. However, the relationship between LAR and mortality in patients with ICH treated in the intensive care unit (ICU) remains controversial. Therefore, in this study, we aimed to investigate the association between LAR and in-hospital and ICU mortality in patients with ICH.MethodsPatients with ICH were selected from the Medical Information Mart for Intensive Care III (MIMIC-III) database; their clinical information, including baseline characteristics, vital signs, comorbidities, laboratory test results, and scoring systems, was extracted. Univariate and multivariate Cox proportional hazards analyses were used to investigate the association of LAR with in-hospital and ICU mortality. The maximum selection statistical method and subgroup analysis were used to investigate these relationships further. Kaplan–Meier (KM) analysis was used to draw survival curves.ResultsThis study enrolled 237 patients with ICH whose lactate and albumin levels, with median values of 1.975 and 3.6 mg/dl, respectively, were measured within the first 24 h after ICU admission. LAR had an association with increased risk of in-hospital mortality [unadjusted hazards ratio (HR), 1.79; 95% confidence interval (CI), 1.32–2.42; p < 0.001] and ICU mortality (unadjusted HR, 1.88; 95% CI, 1.38–2.55; p < 0.001). A cut-off value of 0.963 mg/dl was used to classify patients into high LAR (≥0.963) and low LAR (<0.963) groups, and survival curves suggested that those two groups had significant survival differences (p = 0.0058 and 0.0048, respectively). Furthermore, the high LAR group with ICH had a significantly increased risk of in-hospital and ICU mortality compared to the low LAR group.ConclusionOur study suggests that a high LAR is associated with an increased risk of in-hospital and ICU mortality in patients with ICH. Thus, the LAR is a useful prognostic predictor of clinical outcomes in patients with ICH

Reinforcement-Enhanced Autoregressive Feature Transformation: Gradient-steered Search in Continuous Space for Postfix Expressions

Feature transformation aims to generate new pattern-discriminative feature space from original features to improve downstream machine learning (ML) task performances. However, the discrete search space for the optimal feature explosively grows on the basis of combinations of features and operations from low-order forms to high-order forms. Existing methods, such as exhaustive search, expansion reduction, evolutionary algorithms, reinforcement learning, and iterative greedy, suffer from large search space. Overly emphasizing efficiency in algorithm design usually sacrifices stability or robustness. To fundamentally fill this gap, we reformulate discrete feature transformation as a continuous space optimization task and develop an embedding-optimization-reconstruction framework. This framework includes four steps: 1) reinforcement-enhanced data preparation, aiming to prepare high-quality transformation-accuracy training data; 2) feature transformation operation sequence embedding, intending to encapsulate the knowledge of prepared training data within a continuous space; 3) gradient-steered optimal embedding search, dedicating to uncover potentially superior embeddings within the learned space; 4) transformation operation sequence reconstruction, striving to reproduce the feature transformation solution to pinpoint the optimal feature space.Comment: Accepted by NeurIPS 202

Traceable Group-Wise Self-Optimizing Feature Transformation Learning: A Dual Optimization Perspective

Feature transformation aims to reconstruct an effective representation space by mathematically refining the existing features. It serves as a pivotal approach to combat the curse of dimensionality, enhance model generalization, mitigate data sparsity, and extend the applicability of classical models. Existing research predominantly focuses on domain knowledge-based feature engineering or learning latent representations. However, these methods, while insightful, lack full automation and fail to yield a traceable and optimal representation space. An indispensable question arises: Can we concurrently address these limitations when reconstructing a feature space for a machine-learning task? Our initial work took a pioneering step towards this challenge by introducing a novel self-optimizing framework. This framework leverages the power of three cascading reinforced agents to automatically select candidate features and operations for generating improved feature transformation combinations. Despite the impressive strides made, there was room for enhancing its effectiveness and generalization capability. In this extended journal version, we advance our initial work from two distinct yet interconnected perspectives: 1) We propose a refinement of the original framework, which integrates a graph-based state representation method to capture the feature interactions more effectively and develop different Q-learning strategies to alleviate Q-value overestimation further. 2) We utilize a new optimization technique (actor-critic) to train the entire self-optimizing framework in order to accelerate the model convergence and improve the feature transformation performance. Finally, to validate the improved effectiveness and generalization capability of our framework, we perform extensive experiments and conduct comprehensive analyses.Comment: 21 pages, submitted to TKDD. arXiv admin note: text overlap with arXiv:2209.08044, arXiv:2205.1452

Self-Optimizing Feature Transformation

Feature transformation aims to extract a good representation (feature) space by mathematically transforming existing features. It is crucial to address the curse of dimensionality, enhance model generalization, overcome data sparsity, and expand the availability of classic models. Current research focuses on domain knowledge-based feature engineering or learning latent representations; nevertheless, these methods are not entirely automated and cannot produce a traceable and optimal representation space. When rebuilding a feature space for a machine learning task, can these limitations be addressed concurrently? In this extension study, we present a self-optimizing framework for feature transformation. To achieve a better performance, we improved the preliminary work by (1) obtaining an advanced state representation for enabling reinforced agents to comprehend the current feature set better; and (2) resolving Q-value overestimation in reinforced agents for learning unbiased and effective policies. Finally, to make experiments more convincing than the preliminary work, we conclude by adding the outlier detection task with five datasets, evaluating various state representation approaches, and comparing different training strategies. Extensive experiments and case studies show that our work is more effective and superior.Comment: Under review of TKDE. arXiv admin note: substantial text overlap with arXiv:2205.1452

Multimodal Information Fusion for High-Robustness and Low-Drift State Estimation of UGVs in Diverse Scenes

Currently, the autonomous positioning of unmanned ground vehicles (UGVs) still faces the problems of insufficient persistence and poor reliability, especially in the challenging scenarios where satellites are denied, or the sensing modalities such as vision or laser are degraded. Based on multimodal information fusion and failure detection (FD), this article proposes a high-robustness and low-drift state estimation system suitable for multiple scenes, which integrates light detection and ranging (LiDAR), inertial measurement units (IMUs), stereo camera, encoders, attitude and heading reference system (AHRS) in a loose coupling way. Firstly, a state estimator with variable fusion mode is designed based on the error-state extended Kalman filtering (ES-EKF), which can fuse encoder-AHRS subsystem (EAS), visual-inertial subsystem (VIS), and LiDAR subsystem (LS) and change its integration structure online by selecting a fusion mode. Secondly, in order to improve the robustness of the whole system in challenging environments, an information manager is created, which judges the health status of subsystems by degeneration metrics, and then online selects appropriate information sources and variables to enter the estimator according to their health status. Finally, the proposed system is extensively evaluated using the datasets collected from six typical scenes: street, field, forest, forest-at-night, street-at-night and tunnel-at-night. The experimental results show our framework is better or comparable accuracy and robustness than existing publicly available systems

The impact of the atmospheric turbulence-development tendency on new particle formation : a common finding on three continents

A new mechanism of new particle formation (NPF) is investigated using comprehensive measurements of aerosol physicochemical quantities and meteorological variables made in three continents, including Beijing, China; the Southern Great Plains site in the USA; and SMEAR II Station in Hyytiala, Finland. Despite the considerably different emissions of chemical species among the sites, a common relationship was found between the characteristics of NPF and the stability intensity. The stability parameter (zeta = Z/L, where Z is the height above ground and L is the Monin-Obukhov length) is found to play an important role; it drops significantly before NPF as the atmosphere becomes more unstable, which may serve as an indicator of nucleation bursts. As the atmosphere becomes unstable, the NPF duration is closely related to the tendency for turbulence development, which influences the evolution of the condensation sink. Presumably, the unstable atmosphere may dilute pre-existing particles, effectively reducing the condensation sink, especially at coarse mode to foster nucleation. This new mechanism is confirmed by model simulations using a molecular dynamic model that mimics the impact of turbulence development on nucleation by inducing and intensifying homogeneous nucleation events.Peer reviewe