The Allen Mouse Brain Common Coordinate Framework: A 3D Reference Atlas.

Recent large-scale collaborations are generating major surveys of cell types and connections in the mouse brain, collecting large amounts of data across modalities, spatial scales, and brain areas. Successful integration of these data requires a standard 3D reference atlas. Here, we present the Allen Mouse Brain Common Coordinate Framework (CCFv3) as such a resource. We constructed an average template brain at 10 µm voxel resolution by interpolating high resolution in-plane serial two-photon tomography images with 100 µm z-sampling from 1,675 young adult C57BL/6J mice. Then, using multimodal reference data, we parcellated the entire brain directly in 3D, labeling every voxel with a brain structure spanning 43 isocortical areas and their layers, 329 subcortical gray matter structures, 81 fiber tracts, and 8 ventricular structures. CCFv3 can be used to analyze, visualize, and integrate multimodal and multiscale datasets in 3D and is openly accessible (https://atlas.brain-map.org/).

An encoding generative modeling approach to dimension reduction and covariate adjustment in causal inference with observational studies.

In this article, we develop CausalEGM, a deep learning framework for nonlinear dimension reduction and generative modeling of the dependency among covariate features affecting treatment and response. CausalEGM can be used for estimating causal effects in both binary and continuous treatment settings. By learning a bidirectional transformation between the high-dimensional covariate space and a low-dimensional latent space and then modeling the dependencies of different subsets of the latent variables on the treatment and response, CausalEGM can extract the latent covariate features that affect both treatment and response. By conditioning on these features, one can mitigate the confounding effect of the high dimensional covariate on the estimation of the causal relation between treatment and response. In a series of experiments, the proposed method is shown to achieve superior performance over existing methods in both binary and continuous treatment settings. The improvement is substantial when the sample size is large and the covariate is of high dimension. Finally, we established excess risk bounds and consistency results for our method, and discuss how our approach is related to and improves upon other dimension reduction approaches in causal inference.

Nightly sleep duration predicts grade point average in the first year of college.

Academic achievement in the first year of college is critical for setting students on a pathway toward long-term academic and life success, yet little is known about the factors that shape early college academic achievement. Given the important role sleep plays in learning and memory, here we extend this work to evaluate whether nightly sleep duration predicts change in end-of-semester grade point average (GPA). First-year college students from three independent universities provided sleep actigraphy for a month early in their winter/spring academic term across five studies. Findings showed that greater early-term total nightly sleep duration predicted higher end-of-term GPA, an effect that persisted even after controlling for previous-term GPA and daytime sleep. Specifically, every additional hour of average nightly sleep duration early in the semester was associated with an 0.07 increase in end-of-term GPA. Sensitivity analyses using sleep thresholds also indicated that sleeping less than 6 h each night was a period where sleep shifted from helpful to harmful for end-of-term GPA, relative to previous-term GPA. Notably, predictive relationships with GPA were specific to total nightly sleep duration, and not other markers of sleep, such as the midpoint of a student's nightly sleep window or bedtime timing variability. These findings across five studies establish nightly sleep duration as an important factor in academic success and highlight the potential value of testing early academic term total sleep time interventions during the formative first year of college.

Locations and in situ structure of the polymerase complex inside the virion of vesicular stomatitis virus.

The polymerase complex of nonsegmented negative-strand RNA viruses primarily consists of a large (L) protein and a phosphoprotein (P). L is a multifunctional enzyme carrying out RNA-dependent RNA polymerization and all other steps associated with transcription and replication, while P is the nonenzymatic cofactor, regulating the function and conformation of L. The structure of a purified vesicular stomatitis virus (VSV) polymerase complex containing L and associated P segments has been determined; however, the location and manner of the attachments of L and P within each virion are unknown, limiting our mechanistic understanding of VSV RNA replication and transcription and hindering engineering efforts of this widely used anticancer and vaccine vector. Here, we have used cryo-electron tomography to visualize the VSV virion, revealing the attachment of the ring-shaped L molecules to VSV nucleocapsid proteins (N) throughout the cavity of the bullet-shaped nucleocapsid. Subtomogram averaging and three-dimensional classification of regions containing N and the matrix protein (M) have yielded the in situ structure of the polymerase complex. On average, ∼55 polymerase complexes are packaged in each virion. The capping domain of L interacts with two neighboring N molecules through flexible attachments. P, which exists as a dimer, bridges separate N molecules and the connector and C-terminal domains of L. Our data provide the structural basis for recruitment of L to N by P in virus assembly and for flexible attachments between L and N, which allow a quick response of L in primary transcription upon cell entry.

A compositional data model to predict the isotope distribution for average peptides using a compositional spline model.

We propose an updated approach for approximating the isotope distribution of average peptides given their monoisotopic mass. Our methodology involves in-silico cleavage of the entire UNIPROT database of human-reviewed proteins using Trypsin, generating a theoretical peptide dataset. The isotope distribution is computed using BRAIN. We apply a compositional data modelling strategy that utilizes an additive log-ratio transformation for the isotope probabilities followed by a penalized spline regression. Furthermore, due to the impact of the number of sulphur atoms on the course of the isotope distribution, we develop separate models for peptides containing zero up to five sulphur atoms. Additionally, we propose three methods to estimate the number of sulphur atoms based on an observed isotope distribution. The performance of the spline models and the sulphur prediction approaches is evaluated using a mean squared error and a modified Pearson's χ2 goodness-of-fit measure on an experimental UPS2 data set. Our analysis reveals that the variability in spectral accuracy, that is, the variability between MS1 scans, contributes more to the errors than the approximation of the theoretical isotope distribution by our proposed average peptide model. Moreover, we find that the accuracy of predicting the number of sulphur atoms based on the observed isotope distribution is limited by measurement accuracy.

Characterization of a novel multi-resistant Pseudomonas juntendi strain from China with chromosomal blaVIM-2 and a megaplasmid coharboring blaIMP-1-like and blaOXA-1.

BACKGROUND: Pseudomonas juntendi is a newly identified opportunistic pathogen, of which we have limited understanding. P. juntendi strains are often multidrug resistant, which complicates clinical management of infection. METHODS: A strain of Pseudomonas juntendi (strain L4326) isolated from feces was characterized by MALDI-TOF-MS and Average Nucleotide Identity BLAST. This strain was further subject to whole-genome sequencing and Maximum Likelihood phylogenetic analysis. The strain was phenotypically characterized by antimicrobial susceptibility testing and conjugation assays. RESULTS: We have isolated the novel P. juntendi strain L4236, which was multidrug resistant, but retained sensitivity to amikacin. L4236 harbored a megaplasmid that encoded blaOXA-1 and a novel blaIMP-1 resistance gene variant. P. juntendi strain L4236 was phylogenetically related to P. juntendi strain SAMN30525517. CONCLUSION: A rare P. juntendi strain was isolated from human feces in southern China with a megaplasmid coharboring blaIMP-1-like and blaOXA-1. Antimicrobial selection pressures may have driven acquisition of drug-resistance gene mutations and carriage of the megaplasmid.

Harnessing causal forests for epidemiologic research: key considerations.

Assessing heterogeneous treatment effects (HTEs) is an essential task in epidemiology. The recent integration of machine learning into causal inference has provided a new, flexible tool for evaluating complex HTEs: causal forest. In a recent paper, Jawadekar et al (Am J Epidemiol. 2023;192(7):1155-1165) introduced this innovative approach and offered practical guidelines for applied users. Building on their work, this commentary provides additional insights and guidance to promote the understanding and application of causal forest in epidemiologic research. We start with conceptual clarifications, differentiating between honesty and cross-fitting, and exploring the interpretation of estimated conditional average treatment effects. We then delve into practical considerations not addressed by Jawadekar et al, including motivations for estimating HTEs, calibration approaches, and ways to leverage causal forest output with examples from simulated data. We conclude by outlining challenges to consider for future advancements and applications of causal forest in epidemiologic research.

Detecting Nonadditive Biotic Interactions and Assessing Their Biological Relevance among Temperate Rainforest Trees.

AbstractInteractions between and within abiotic and biotic processes generate nonadditive density-dependent effects on species performance that can vary in strength or direction across environments. If ignored, nonadditivities can lead to inaccurate predictions of species responses to environmental and compositional changes. While there are increasing empirical efforts to test the constancy of pairwise biotic interactions along environmental and compositional gradients, few assess both simultaneously. Using a nationwide forest inventory that spans broad ambient temperature and moisture gradients throughout New Zealand, we address this gap by analyzing the diameter growth of six focal tree species as a function of neighbor densities and climate, as well as neighbor × climate and neighbor × neighbor statistical interactions. The most complex model featuring all interaction terms had the highest predictive accuracy. Compared with climate variables, biotic interactions typically had stronger effects on diameter growth, especially when subjected to nonadditivities from local climatic conditions and the density of intermediary species. Furthermore, statistically strong (or weak) nonadditivities could be biologically irrelevant (or significant) depending on whether a species pair typically interacted under average or more extreme conditions. Our study highlights the importance of considering both the statistical potential and the biological relevance of nonadditive biotic interactions when assessing species performance under global change.

Diffusion MRI head motion correction methods are highly accurate but impacted by denoising and sampling scheme.

Head motion correction is particularly challenging in diffusion-weighted MRI (dMRI) scans due to the dramatic changes in image contrast at different gradient strengths and directions. Head motion correction is typically performed using a Gaussian Process model implemented in FSL's Eddy. Recently, the 3dSHORE-based SHORELine method was introduced that does not require shell-based acquisitions, but it has not been previously benchmarked. Here we perform a comprehensive evaluation of both methods on realistic simulations of a software fiber phantom that provides known ground-truth head motion. We demonstrate that both methods perform remarkably well, but that performance can be impacted by sampling scheme and the extent of head motion and the denoising strategy applied before head motion correction. Furthermore, we find Eddy benefits from denoising the data first with MP-PCA. In sum, we provide the most extensive known benchmarking of dMRI head motion correction, together with extensive simulation data and a reproducible workflow. PRACTITIONER POINTS: Both Eddy and SHORELine head motion correction methods performed quite well on a large variety of simulated data. Denoising with MP-PCA can improve head motion correction performance when Eddy is used. SHORELine effectively corrects motion in non-shelled diffusion spectrum imaging data.

Doubly robust nonparametric instrumental variable estimators for survival outcomes.

Instrumental variable (IV) methods allow us the opportunity to address unmeasured confounding in causal inference. However, most IV methods are only applicable to discrete or continuous outcomes with very few IV methods for censored survival outcomes. In this article, we propose nonparametric estimators for the local average treatment effect on survival probabilities under both covariate-dependent and outcome-dependent censoring. We provide an efficient influence function-based estimator and a simple estimation procedure when the IV is either binary or continuous. The proposed estimators possess double-robustness properties and can easily incorporate nonparametric estimation using machine learning tools. In simulation studies, we demonstrate the flexibility and double robustness of our proposed estimators under various plausible scenarios. We apply our method to the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial for estimating the causal effect of screening on survival probabilities and investigate the causal contrasts between the two interventions under different censoring assumptions.

A generalizability score for aggregate causal effect.

Scientists frequently generalize population level causal quantities such as average treatment effect from a source population to a target population. When the causal effects are heterogeneous, differences in subject characteristics between the source and target populations may make such a generalization difficult and unreliable. Reweighting or regression can be used to adjust for such differences when generalizing. However, these methods typically suffer from large variance if there is limited covariate distribution overlap between the two populations. We propose a generalizability score to address this issue. The score can be used as a yardstick to select target subpopulations for generalization. A simplified version of the score avoids using any outcome information and thus can prevent deliberate biases associated with inadvertent access to such information. Both simulation studies and real data analysis demonstrate convincing results for such selection.

Doubly robust evaluation of high-dimensional surrogate markers.

When evaluating the effectiveness of a treatment, policy, or intervention, the desired measure of efficacy may be expensive to collect, not routinely available, or may take a long time to occur. In these cases, it is sometimes possible to identify a surrogate outcome that can more easily, quickly, or cheaply capture the effect of interest. Theory and methods for evaluating the strength of surrogate markers have been well studied in the context of a single surrogate marker measured in the course of a randomized clinical study. However, methods are lacking for quantifying the utility of surrogate markers when the dimension of the surrogate grows. We propose a robust and efficient method for evaluating a set of surrogate markers that may be high-dimensional. Our method does not require treatment to be randomized and may be used in observational studies. Our approach draws on a connection between quantifying the utility of a surrogate marker and the most fundamental tools of causal inference-namely, methods for robust estimation of the average treatment effect. This connection facilitates the use of modern methods for estimating treatment effects, using machine learning to estimate nuisance functions and relaxing the dependence on model specification. We demonstrate that our proposed approach performs well, demonstrate connections between our approach and certain mediation effects, and illustrate it by evaluating whether gene expression can be used as a surrogate for immune activation in an Ebola study.

Accurate prediction of dynamic protein-ligand binding using P-score ranking.

Protein-ligand binding prediction typically relies on docking methodologies and associated scoring functions to propose the binding mode of a ligand in a biological target. Significant challenges are associated with this approach, including the flexibility of the protein-ligand system, solvent-mediated interactions, and associated entropy changes. In addition, scoring functions are only weakly accurate due to the short time required for calculating enthalpic and entropic binding interactions. The workflow described here attempts to address these limitations by combining supervised molecular dynamics with dynamical averaging quantum mechanics fragment molecular orbital. This combination significantly increased the ability to predict the experimental binding structure of protein-ligand complexes independent from the starting position of the ligands or the binding site conformation. We found that the predictive power could be enhanced by combining the residence time and interaction energies as descriptors in a novel scoring function named the P-score. This is illustrated using six different protein-ligand targets as case studies.

Machine Learning-Guided Prediction of Desalination Capacity and Rate of Porous Carbons for Capacitive Deionization.

Nowadays, capacitive deionization (CDI) has emerged as a prominent technology in the desalination field, typically utilizing porous carbons as electrodes. However, the precise significance of electrode properties and operational conditions in shaping desalination performance remains blurry, necessitating numerous time-consuming and resource-intensive CDI experiments. Machine learning (ML) presents an emerging solution, offering the prospect of predicting CDI performance with minimal investment in electrode material synthesis and testing. Herein, four ML models are used for predicting the CDI performance of porous carbons. Among them, the gradient boosting model delivers the best performance on test set with low root mean square error values of 2.13 mg g-1 and 0.073 mg g-1 min-1 for predicting desalination capacity and rate, respectively. Furthermore, SHapley Additive exPlanations is introduced to analyze the significance of electrode properties and operational conditions. It highlights that electrolyte concentration and specific surface area exert a substantially more influential role in determining desalination performance compared to other features. Ultimately, experimental validation employing metal-organic frameworks-derived porous carbons and biomass-derived porous carbons as CDI electrodes is conducted to affirm the prediction accuracy of ML models. This study pioneers ML techniques for predicting CDI performance, offering a compelling strategy for advancing CDI technology.

High-quality pan-genome of Escherichia coli generated by excluding confounding and highly similar strains reveals an association between unique gene clusters and genomic islands.

The pan-genome analysis of bacteria provides detailed insight into the diversity and evolution of a bacterial population. However, the genomes involved in the pan-genome analysis should be checked carefully, as the inclusion of confounding strains would have unfavorable effects on the identification of core genes, and the highly similar strains could bias the results of the pan-genome state (open versus closed). In this study, we found that the inclusion of highly similar strains also affects the results of unique genes in pan-genome analysis, which leads to a significant underestimation of the number of unique genes in the pan-genome. Therefore, these strains should be excluded from pan-genome analysis at the early stage of data processing. Currently, tens of thousands of genomes have been sequenced for Escherichia coli, which provides an unprecedented opportunity as well as a challenge for pan-genome analysis of this classical model organism. Using the proposed strategies, a high-quality E. coli pan-genome was obtained, and the unique genes was extracted and analyzed, revealing an association between the unique gene clusters and genomic islands from a pan-genome perspective, which may facilitate the identification of genomic islands.

PEcnv: accurate and efficient detection of copy number variations of various lengths.

Copy number variation (CNV) is a class of key biomarkers in many complex traits and diseases. Detecting CNV from sequencing data is a substantial bioinformatics problem and a standard requirement in clinical practice. Although many proposed CNV detection approaches exist, the core statistical model at their foundation is weakened by two critical computational issues: (i) identifying the optimal setting on the sliding window and (ii) correcting for bias and noise. We designed a statistical process model to overcome these limitations by calculating regional read depths via an exponentially weighted moving average strategy. A one-run detection of CNVs of various lengths is then achieved by a dynamic sliding window, whose size is self-adopted according to the weighted averages. We also designed a novel bias/noise reduction model, accompanied by the moving average, which can handle complicated patterns and extend training data. This model, called PEcnv, accurately detects CNVs ranging from kb-scale to chromosome-arm level. The model performance was validated with simulation samples and real samples. Comparative analysis showed that PEcnv outperforms current popular approaches. Notably, PEcnv provided considerable advantages in detecting small CNVs (1 kb-1 Mb) in panel sequencing data. Thus, PEcnv fills the gap left by existing methods focusing on large CNVs. PEcnv may have broad applications in clinical testing where panel sequencing is the dominant strategy. Availability and implementation: Source code is freely available at https://github.com/Sherwin-xjtu/PEcnv.

Identification and characterization of a novel α-haemolytic streptococci, Streptococcus parapneumoniae sp. nov., which caused bacteremia with pyelonephritis.

OBJECTIVES: We report a case of bacteremia with pyelonephritis in an adult male with an underlying disease caused by α-hemolytic streptococci. α-Hemolytic streptococci were isolated from blood, but it was challenging to identify its species. This study aimed to characterize the causative bacterium SP4011 and to elucidate its species. METHODS: The whole-genome sequence and biochemical characteristics of SP4011 were determined. Based on the genome sequence, phylogenetic analysis was performed with standard strains of each species of α-hemolytic streptococci. Digital DNA-DNA hybridization (dDDH) and average nucleotide identity (ANI) values were calculated. RESULTS: SP4011 showed optochin susceptibility and bile solubility, but did not react with pneumococcal omni antiserum. Phylogenetic analysis of the whole-genome sequence showed that SP4011 clustered with S. pneumoniae and S. pseodopneumoniae and was most closely related to S. pseodopneumoniae. Genomic analysis revealed that ANI and dDDH values between SP4011 and S. pseodopneumoniae were 94.0 % and 56.0 %, respectively, and between SP4011 and S. pneumoniae were 93.3 % and 52.2 %, respectively. Biochemical characteristics also showed differences between SP4011 and S. pseodopneumoniae and between SP4011 and S. pneumoniae. These results indicate that SP4011 is a novel species. CONCLUSION: Our findings indicate that SP4011 is a novel species of the genus Streptococcus. SP4011 has biochemical characteristics similar to S. pneumoniae, making it challenging to differentiate and requiring careful clinical diagnosis. This isolate was proposed to be a novel species, Streptococcus parapneumoniae sp. nov. The strain type is SP4011T (= JCM 36068T = KCTC 21228T).

P-wave alternans rebound following pulmonary vein isolation predicts atrial arrhythmia recurrence.

INTRODUCTION: Numerous P-wave indices have been explored as biomarkers to assess atrial fibrillation (AF) risk and the impact of therapy with variable success. OBJECTIVE: We investigated the utility of P-wave alternans (PWA) to track the effects of pulmonary vein isolation (PVI) and to predict atrial arrhythmia recurrence. METHODS: This medical records study included patients who underwent PVI for AF ablation at our institution, along with 20 control subjects without AF or overt cardiovascular disease. PWA was assessed using novel artificial intelligence-enabled modified moving average (AI-MMA) algorithms. PWA was monitored from the 12-lead ECG at ~1 h before and ~16 h after PVI (n = 45) and at the 4- to 17-week clinically indicated follow-up visit (n = 30). The arrhythmia follow-up period was 955 ± 112 days. RESULTS: PVI acutely reduced PWA by 48%-63% (p < .05) to control ranges in leads II, III, aVF, the leads with the greatest sensitivity in monitoring PWA. Pre-ablation PWA was ~6 µV and decreased to ~3 µV following ablation. Patients who exhibited a rebound in PWA to pre-ablation levels at 4- to 17-week follow-up (p < .01) experienced recurrent atrial arrhythmias, whereas patients whose PWA remained reduced (p = .85) did not, resulting in a significant difference (p < .001) at follow-up. The AUC for PWA's prediction of first recurrence of atrial arrhythmia was 0.81 (p < .01) with 88% sensitivity and 82% specificity. Kaplan-Meier analysis estimated atrial arrhythmia-free survival (p < .01) with an adjusted hazard ratio of 3.4 (95% CI: 1.47-5.24, p < .02). CONCLUSION: A rebound in PWA to pre-ablation levels detected by AI-MMA in the 12-lead ECG at standard clinical follow-up predicts atrial arrhythmia recurrence.

Global, regional, and national burden of premenstrual syndrome, 1990-2019: an analysis based on the Global Burden of Disease Study 2019.

STUDY QUESTION: What is the burden of premenstrual syndrome (PMS) at the global, regional, and national levels across 21 regions and 204 countries and territories? SUMMARY ANSWER: Over the past few decades, the global prevalent cases of PMS have grown significantly from 652.5 million in 1990 to 956.0 million in 2019, representing a 46.5% increase. WHAT IS KNOWN ALREADY: PMS, which affects almost half of reproductive women worldwide, has substantial social, occupational, academic, and psychological effects on women's lives. However, no comprehensive and detailed epidemiological estimates of PMS by age and socio-demographic index (SDI) at global, regional, and national levels have been reported. STUDY DESIGN, SIZE, DURATION: An age- and SDI-stratified systematic analysis of the prevalence and years lived with disability (YLD) of PMS by age and SDI across 21 regions and 204 countries and territories has been performed. PARTICIPANTS/MATERIALS, SETTING, METHODS: The prevalence and YLD of PMS from 1990 to 2019 were retrieved directly from the Global Burden of Diseases (GBD) 2019 study. The number, rates per 100 000 persons, and average annual percentage changes (AAPCs) of prevalence and YLD were estimated at the global, regional, and national levels. MAIN RESULTS AND THE ROLE OF CHANCE: Globally, the prevalent cases of PMS increased by 46.5% from 652.5 million in 1990 to 956.0 million in 2019; in contrast, however, the age-standardized prevalence rate was approximately stable at 24 431.15/100 000 persons in 1990 and 24 406.51/100 000 persons in 2019 (AAPC, 0[95% CI: -0.01 to 0.01]). Globally, the YLD was 8.0 million in 2019 and 5.4 million in 1990, with a sizable increase over the past 30 years. The age-standardized YLD rate was stable (AAPC 0.01, P = 0.182), at 203.45/100 000 persons in 1990 and 203.76/100 000 persons in 2019. The age-standardized burden estimates were the highest in the low-middle SDI regions and the lowest in the high SDI regions. Peaks in burden rate estimates were all observed in the 40-44 years age group. Regional age-standardized burden estimates were the highest in South Asia and the lowest in Western Sub-Saharan Africa. The national age-standardized burden estimates were the highest in Pakistan and the lowest in Niger. LIMITATIONS, REASONS FOR CAUTION: The accuracy of the results depended on the quality and quantity of the GBD 2019 data. Fortunately, the GBD study endeavoured to retrieve data globally and applied multiple models to optimize the completeness, accuracy, and reliability of the data. In addition, the GBD study took the country as its basic unit and neglected the influence of race. Further study is warranted to compare differences in PMS burden associated with race. Finally, no data are available on the aetiology and risk information related to PMS, which might help us to better understand the trends and age distribution of PMS and help local governments formulate more detailed policies and comprehensive interventions. WIDER IMPLICATIONS OF THE FINDINGS: Although the age-standardized prevalence/YLD rate has been stable over the past 30 years, the absolute number of prevalent cases and YLD grew significantly worldwide from 1990 to 2019. Public health-related policies should be implemented to reduce the prevalence and alleviate the symptoms of PMS. Lifestyle changes and cognitive-behavioral therapy are critical in helping to reduce the burden of PMS. STUDY FUNDING/COMPETING INTEREST(S): This study was supported by the National Key Research and Development Program of China (grant number 2022YFC2704100) and the National Natural Science Foundation of China (No. 82001498, No. 82371648). The authors declare no conflict of interest. TRIAL REGISTRATION NUMBER: N/A.

Inverse Isotope Kinetic Effect of the Charge Transfer Reactions of Ar+ with H2O and D2O.

Hydrogen isotopic effect, as the key to revealing the origin of Earth's water, arises from the H/D mass difference and quantum dynamics at the transition state of reaction. The ion-molecule charge-exchange reaction between water (H2O/D2O) and argon ion (Ar+) proceeds spontaneously and promptly, where there is no transition-state or intermediate complex. In this energetically resonant process, we find an inverse kinetic isotope effect (KIE) leading to the higher charge transfer rate for D2O, by the velocity map imaging measurements of H2O+/D2O+ products. Using the average dipole orientation capture model, we estimate the orientation angles of C2v axis of H2O/D2O relative to the Ar+ approaching direction and attribute to the difference of stereodynamics. According to the long-distance Landau-Zener charge transfer model, this inverse KIE could be also attributed to the density-of-state difference of molecular bending motion between H2O+ and D2O+ around the resonant charge transfer.