Variance Estimation and Construction of Confidence Intervals for GEE Estimator

The sandwich estimator, also known as the robust covariance matrix estimator, has achieved increasing use in the statistical literature as well as with the growing popularity of generalized estimating equations (GEE). A modified sandwich variance estimator is proposed, and its consistency and efficiency are studied. It is compared with other variance estimators, such as a model based estimator, the sandwich estimator and a corrected sandwich estimator. Confidence intervals for regression parameters based on these estimators are discussed. Simulation studies using clustered data to compare the performance of variance estimators are reported

Accuracy Enhancement with Processing Error Prediction and Compensation of a CNC Flame Cutting Machine Used in Spatial Surface Operating Conditions

This study deals with the precision performance of the CNC flame-cutting machine used in spatial surface operating conditions and presents an accuracy enhancement method based on processing error modeling prediction and real-time compensation. Machining coordinate systems and transformation matrix models were established for the CNC flame processing system considering both geometric errors and thermal deformation effects. Meanwhile, prediction and compensation models were constructed related to the actual cutting situation. Focusing on the thermal deformation elements, finite element analysis was used to measure the testing data of thermal errors, the grey system theory was applied to optimize the key thermal points, and related thermal dynamics models were carried out to achieve high-precision prediction values. Comparison experiments between the proposed method and the teaching method were conducted on the processing system after performing calibration. The results showed that the proposed method is valid and the cutting quality could be improved by more than 30% relative to the teaching method. Furthermore, the proposed method can be used under any working condition by making a few adjustments to the prediction and compensation models

5-HT1A Receptor Agonist Promotes Retinal Ganglion Cell Function by Inhibiting OFF-Type Presynaptic Glutamatergic Activity in a Chronic Glaucoma Model

Serotonin receptors are potential neuroprotective agents in degenerative diseases of the central nervous system. The protective effects of serotonin receptor (5-HT1A) agonists on the survival and function of retinal ganglion cells (RGCs) by regulating the release of the presynaptic neurotransmitter γ-aminobutyric acid (GABA) were confirmed in our previous study of a chronic glaucoma rat model. However, the roles of excitatory amino acids and their interactions with the 5-HT1A receptor in glaucoma remain unknown. Here, we found that ocular hypertension increased glutamine synthetase (GS) and excitatory amino acid transporter 2 (EAAT2) expression in rat retinas. In addition, the high expression of GS and EAAT2 induced by glaucoma was downregulated by the 5-HT1A receptor agonist 8-OH-DPAT and the 5-HT1A receptor antagonist WAY-100635, respectively. Patch-clamp techniques were used to record glutamate receptor-mediated spontaneous and miniature glutamatergic excitatory post-synaptic currents (sEPSCs and mEPSCs) as well as L-glutamate-induced current in OFF-type and ON-type RGCs in rat retinal slices. Although there were no significant differences in the frequency and amplitude of sEPSC and mEPSC release between normal and glaucoma OFF- and ON-type RGCs, exogenous 8-OH-DPAT administration specifically reduced the frequency, but not the amplitude, of sEPSC and mEPSC release in glaucoma OFF-type rather than ON-type RGCs; these effects were completely blocked by WAY-100635. In summary, 8-OH-DPAT decreases and increases GS and EAAT2 expression of glaucomatous retina, respectively, while decreasing sEPSC and mEPSC frequency. In contrast, WAY-100635 increases and decreases GS and EAAT2 expression of glaucomatous retina, respectively, while increasing sEPSC and mEPSC frequency. The reduction of glutamatergic presynaptic transmission by 8-OH-DPAT deactivates RGCs at the neural network level and reduces the excitotoxic damage in the pathological process of chronic glaucoma

Mechanical characteristics of graded origami bellows under axial tension

In this study, the mechanical characteristics of the graded origami bellows were numerically investigated and experimentally validated. Two graded geometric parameters were considered: pre-folding angle (θ) and layer height (Le). The sensitivities of the deployment process and energy absorption efficiency of origami bellows to variations in θ and Le under quasi-static loading and dynamic loading were numerically investigated. Results show that the origami bellows with positive gradients exhibited progressive deployment process. More than one deformation mechanism was triggered during deployment, indicating a mixed non-rigid deployment mode. A large gradient of θ had a notable effect on the energy absorption efficiency. Both specific energy absorption (SEA) and mean tensile force (Pm) decreased as the gradient of θ increased. Although the gradient of Le was insensitive to the force response, the SEA decreased as the gradient of Le increased. The dynamic behavior of the graded models indicated that both the initial peak force and SEA were affected by the graded parameters. In general, the proposed graded origami bellows show a controllable deployment process and a stable force response under axial tension

Outram: One-shot Global Localization via Triangulated Scene Graph and Global Outlier Pruning

One-shot LiDAR localization refers to the ability to estimate the robot pose from one single point cloud, which yields significant advantages in initialization and relocalization processes. In the point cloud domain, the topic has been extensively studied as a global descriptor retrieval (i.e., loop closure detection) and pose refinement (i.e., point cloud registration) problem both in isolation or combined. However, few have explicitly considered the relationship between candidate retrieval and correspondence generation in pose estimation, leaving them brittle to substructure ambiguities. To this end, we propose a hierarchical one-shot localization algorithm called Outram that leverages substructures of 3D scene graphs for locally consistent correspondence searching and global substructure-wise outlier pruning. Such a hierarchical process couples the feature retrieval and the correspondence extraction to resolve the substructure ambiguities by conducting a local-to-global consistency refinement. We demonstrate the capability of Outram in a variety of scenarios in multiple large-scale outdoor datasets. Our implementation is open-sourced: https://github.com/Pamphlett/Outram.Comment: 8 pages, 5 figure

Cryo-EM structures of lipopolysaccharide transporter LptB2FGC in lipopolysaccharide or AMP-PNP-bound states reveal its transport mechanism

Lipopolysaccharides (LPS) of Gram-negative bacteria are critical for the defence against cytotoxic substances and must be transported from the inner membrane (IM) to the outer membrane (OM) through a bridge formed by seven membrane proteins (LptBFGCADE). The IM component LptB2FG powers the process through a yet unclarified mechanism. Here we report three high-resolution cryo-EM structures of LptB2FG alone and complexed with LptC (LptB2FGC), trapped in either the LPS- or AMP-PNP-bound state. The structures reveal conformational changes between these states and substrate binding with or without LptC. We identify two functional transmembrane arginine-containing loops interacting with the bound AMP-PNP and elucidate allosteric communications between the domains. AMP-PNP binding induces an inward rotation and shift of the transmembrane helices of LptFG and LptC to tighten the cavity, with the closure of two lateral gates, to eventually expel LPS into the bridge. Functional assays reveal the functionality of the LptF and LptG periplasmic domains. Our findings shed light on the LPS transport mechanism

Optimized sand tube irrigation combined with nitrogen application improves jujube yield as well as water and nitrogen use efficiencies in an arid desert region of Northwest China

Efficient water-saving irrigation techniques and appropriate nitrogen (N) application are keys to solving the problems of water scarcity and irrational fertilization in jujube cultivation. In this study, first, the effects of sand tube irrigation (STI) on surface and subsurface wetted characteristics were investigated using in-situ infiltration tests in a jujube garden. Compared with surface drip irrigation (SD), STI reduced surface wetted area by 57.4% and wetted perimeter of the surface wetted circle by 37.1% and increased subsurface maximum infiltration distance of wetting front by 64.9%. At the optimal sand tube depth of 20 cm, surface wetted area of the surface wetted circle decreased by 65.4% and maximum infiltration distance of the wetting front increased by 70.9%, compared with SD. Two-year field experiments then investigated the effects of STI and SD on soil water storage, jujube leaf chlorophyll, net photosynthetic rate, actual water consumption, fruit yield, and water (WUE) and N (NUE) use efficiencies at four levels of N (pure nitrogen: N1, 0; N2, 286 kg ha–1; N3, 381 kg ha–1; N4, 476 kg ha–1) at the same irrigation amount (45 mm irrigation–1, total of 8). Compared with SD, STI increased soil water storage 18.0% (2021) and 15.6% (2022) during the entire growth period and also chlorophyll content, nitrogen balance index, and net photosynthetic rate, with both increasing and then decreasing with increasing N. Compared with SD, STI increased yields by 39.1% and 36.5% and WUE by 44.3% and 39.7% in 2021 and 2022, respectively. Nitrogen use efficiency was 2.5 (2021) and 1.6 (2022) times higher with STI than with SD. STI combined with N3 had the highest yield, WUE, NUE, and net income and is thus recommended as the optimal water–N combination. In conclusion, STI combined with appropriate N application can be an effective water-saving irrigation technology alternative to SD in jujube cultivation in arid areas

Radiomic Features From Multi-Parameter MRI Combined With Clinical Parameters Predict Molecular Subgroups in Patients With Medulloblastoma

The 2016 WHO classification of central nervous system tumors has included four molecular subgroups under medulloblastoma (MB) as sonic hedgehog (SHH), wingless (WNT), Grade 3, and Group 4. We aimed to develop machine learning models for predicting MB molecular subgroups based on multi-parameter magnetic resonance imaging (MRI) radiomics, tumor locations, and clinical factors. A total of 122 MB patients were enrolled retrospectively. After selecting robust, non-redundant, and relevant features from 5,529 extracted radiomics features, a random forest model was constructed based on a training cohort (n= 92) and evaluated on a testing cohort (n= 30). By combining radiographic features and clinical parameters, two combined prediction models were also built. The subgroup can be classified using an 11-feature radiomics model with a high area under the curve (AUC) of 0.8264 for WNT and modest AUCs of 0.6683, 0.6004, and 0.6979 for SHH, Group 3, and Group 4 in the testing cohort, respectively. Incorporating location and hydrocephalus into the radiomics model resulted in improved AUCs of 0.8403 and 0.8317 for WNT and SHH, respectively. After adding gender and age, the AUCs for WNT and SHH were further improved to 0.9097 and 0.8654, while the accuracies were 70 and 86.67% for Group 3 and Group 4, respectively. Prediction performance was excellent for WNT and SHH, while that for Group 3 and Group 4 needs further improvements. Machine learning algorithms offer potentials to non-invasively predict the molecular subgroups of MB.</p