Machine Learning on Microstructure-Property Relationship of Lithium-Ion Conducting Oxide Solid Electrolytes.

Understanding the structure-property relationship of lithium-ion conducting solid oxide electrolytes is essential to accelerate their development and commercialization. However, the structural complexity of nonideal materials increases the difficulty of study. Here, we develop an algorithmic framework to understand the effect of microstructure on the properties by linking the microscopic morphology images to their ionic conductivities. We adopt garnet and perovskite polycrystalline oxides as examples and quantify the microscopic morphologies via extracting determined physical parameters from the images. It directly visualizes the effect of physical parameters on their corresponding ionic conductivities. As a result, we can determine the microstructural features of a Li-ion conductor with high ionic conductivity, which can guide the synthesis of highly conductive solid electrolytes. Our work provides a novel approach to understanding the microstructure-property relationship for solid-state ionic materials, showing the potential to extend to other structural/functional ceramics with various physical properties in other fields.

Optical ReLU-like activation function based on a semiconductor laser with optical injection.

Artificial neural networks usually consist of successive linear multiply-accumulate operations and nonlinear activation functions. However, most optical neural networks only achieve the linear operation in the optical domain, while the optical implementation of activation function remains challenging. Here we present an optical ReLU-like activation function (with 180° rotation) based on a semiconductor laser subject to the optical injection in an experiment. The ReLU-like function is achieved in a broad regime above the Hopf bifurcation of the injection-locking diagram and is operated in the continuous-wave mode. In particular, the slope of the activation function is reconfigurable by tuning the frequency difference between the master laser and the slave laser.

Asynchronous photonic time-delay reservoir computing.

Time-delay reservoir computing uses a nonlinear node associated with a feedback loop to construct a large number of virtual neurons in the neural network. The clock cycle of the computing network is usually synchronous with the delay time of the feedback loop, which substantially constrains the flexibility of hardware implementations. This work shows an asynchronous reservoir computing network based on a semiconductor laser with an optical feedback loop, where the clock cycle (20 ns) is considerably different to the delay time (77 ns). The performance of this asynchronous network is experimentally investigated under various operation conditions. It is proved that the asynchronous reservoir computing shows highly competitive performance on the prediction task of Santa Fe chaotic time series, in comparison with the synchronous counterparts.

Smoothed Quantile Regression with Large-Scale Inference.

Quantile regression is a powerful tool for learning the relationship between a response variable and a multivariate predictor while exploring heterogeneous effects. This paper focuses on statistical inference for quantile regression in the "increasing dimension" regime. We provide a comprehensive analysis of a convolution smoothed approach that achieves adequate approximation to computation and inference for quantile regression. This method, which we refer to as conquer, turns the non-differentiable check function into a twice-differentiable, convex and locally strongly convex surrogate, which admits fast and scalable gradient-based algorithms to perform optimization, and multiplier bootstrap for statistical inference. Theoretically, we establish explicit non-asymptotic bounds on estimation and Bahadur-Kiefer linearization errors, from which we show that the asymptotic normality of the conquer estimator holds under a weaker requirement on dimensionality than needed for conventional quantile regression. The validity of multiplier bootstrap is also provided. Numerical studies confirm conquer as a practical and reliable approach to large-scale inference for quantile regression. Software implementing the methodology is available in the R package conquer.

Semiparametric regression modeling of the global percentile outcome.

When no single outcome is sufficient to capture the multidimensional impairments of a disease, investigators often rely on multiple outcomes for comprehensive assessment of global disease status. Methods for assessing covariate effects on global disease status include the composite outcome and global test procedures. One global test procedure is the O'Brien's rank-sum test, which combines information from multiple outcomes using a global rank-sum score. However, existing methods for the global rank-sum do not lend themselves to regression modeling. We consider sensible regression strategies for the global percentile outcome (GPO), under the transformed linear model and the monotonic index model. Posing minimal assumptions, we develop estimation and inference procedures that account for the special features of the GPO. Asymptotics are established using U-statistic and U-process techniques. We illustrate the practical utilities of the proposed methods via extensive simulations and application to a Parkinson's disease study.

Factor Structure for the Sport Concussion Assessment Tool Symptom Scale in Adolescents After Concussion.

OBJECTIVE: To examine the factor structure of the Sport Concussion Assessment Tool-5 (SCAT5) symptom scale in adolescents on their initial presentation to a concussion clinic within the typical recovery period after concussion (ie, <30 days). We hypothesize that the SCAT5 symptoms represent various clinically meaningful groups. A secondary purpose was to examine the effects of sex on the factor structure of the SCAT5 symptom scale. STUDY DESIGN: Retrospective cross-sectional analysis. SETTING: Tertiary, institutional. PATIENTS: Nine hundred eighty-one adolescents (45% women) aged between 13 and 18 years. INDEPENDENT VARIABLES: Adolescents completed the SCAT5 symptom scale. MAIN OUTCOME MEASURES: The factor structure of SCAT5 examined using a principal axis factor analysis. RESULTS: A 5-factor structure model explained 61% of the variance in symptoms. These 5 factors are identified as Energy (17%), Mental Health (13%), Migrainous (13%), Cognitive (9%), and Vestibulo-Ocular (9%). A similar 5-factor model emerged for each sex, and the proportion of variance in symptoms explained by the 5-factor model was comparable between the sexes. CONCLUSIONS: The findings of this report indicate that the SCAT5 symptoms aggregated into 5 delineated factors, and these factors were largely consistent across the sexes. The delineation of symptoms into 5 factors provides preliminary validation for the presence of different concussion phenotypes. Confirmatory factor analysis is warranted to examine the applicability and clinical utility of the use of the 5-factor structure in a clinical setting.

Caricaturing can improve facial expression recognition in low-resolution images and age-related macular degeneration.

Previous studies of age-related macular degeneration (AMD) report impaired facial expression recognition even with enlarged face images. Here, we test potential benefits of caricaturing (exaggerating how the expression's shape differs from neutral) as an image enhancement procedure targeted at mid- to high-level cortical vision. Experiment 1 provides proof-of-concept using normal vision observers shown blurred images as a partial simulation of AMD. Caricaturing significantly improved expression recognition (happy, sad, anger, disgust, fear, surprise) by ∼4%-5% across young adults and older adults (mean age 73 years); two different severities of blur; high, medium, and low intensity of the original expression; and all intermediate accuracy levels (impaired but still above chance). Experiment 2 tested AMD patients, running 19 eyes monocularly (from 12 patients, 67-94 years) covering a wide range of vision loss (acuities 6/7.5 to poorer than 6/360). With faces pre-enlarged, recognition approached ceiling and was only slightly worse than matched controls for high- and medium-intensity expressions. For low-intensity expressions, recognition of veridical expressions remained impaired and was significantly improved with caricaturing across all levels of vision loss by 5.8%. Overall, caricaturing benefits emerged when improvement was most needed, that is, when initial recognition of uncaricatured expressions was impaired.

HIGH DIMENSIONAL CENSORED QUANTILE REGRESSION.

Censored quantile regression (CQR) has emerged as a useful regression tool for survival analysis. Some commonly used CQR methods can be characterized by stochastic integral-based estimating equations in a sequential manner across quantile levels. In this paper, we analyze CQR in a high dimensional setting where the regression functions over a continuum of quantile levels are of interest. We propose a two-step penalization procedure, which accommodates stochastic integral based estimating equations and address the challenges due to the recursive nature of the procedure. We establish the uniform convergence rates for the proposed estimators, and investigate the properties on weak convergence and variable selection. We conduct numerical studies to confirm our theoretical findings and illustrate the practical utility of our proposals.

Assessment of impaired coordination between respiration and deglutition in children and young adults with ataxia telangiectasia.

AIM: This cross-sectional investigation aimed to assess the value of non-invasive measures of temporal respiratory-swallow coupling in individuals with ataxic swallowing. METHOD: Twenty participants (11 males, 9 females; range 9-21y) with ataxia telangiectasia were presented with water and pudding boluses. Their 193 swallows were compared with 2200 swallows from 82 age-matched healthy controls. The two components of airway protection during swallowing that were analyzed were: direction of peri-deglutitive airflow and duration of deglutitive inhibition of respiratory airflow (DIORA). RESULTS: Safe expiratory patterns of peri-deglutitive airflow occurred significantly less often in participants with ataxia telangiectasia than in age-matched control participants (younger p<0.015 and older p<0.001). The frequency of an expiratory pattern of peri-deglutitive airflow increased with age in participants in the comparison group (p=0.006), but not in those with ataxia telangiectasia (p=0.234). With age, mean duration of DIORA decreased in controls (p<0.001) but was unchanged in participants with ataxia telangiectasia (p=0.164). INTERPRETATION: Non-invasive quantitative measures of respiratory-swallow coupling capture temporal relationships that plausibly contribute to airway compromise from dysphagia. Changes in respiratory-swallow coupling observed with advancing age in control participants were not seen in participants with ataxia telangiectasia. Measures of perturbations may herald swallowing problems prior to development of pulmonary and nutritional sequelae.

Stability of an ERP-based measure of brain network activation (BNA) in athletes: A new electrophysiological assessment tool for concussion.

PRIMARY OBJECTIVE: To determine test-re-test reliabilities of novel Evoked Response Potential (ERP)-based Brain Network Activation (BNA) scores in healthy athletes. RESEARCH DESIGN: Observational, repeated-measures study. METHODS AND DESIGN: Forty-two healthy male and female high school and collegiate athletes completed auditory oddball and go/no-go ERP assessments at baseline, 1 week, 6 weeks and 1 year. The BNA algorithm was applied to the ERP data, considering electrode location, frequency band, peak latency and normalized amplitude to generate seven unique BNA scores for each testing session. MAIN OUTCOMES AND RESULTS: Mean BNA scores, intra-class correlation coefficient (ICC) values and reliable change (RC) values were calculated for each of the seven BNA networks. BNA scores ranged from 46.3 ± 34.9 to 69.9 ± 22.8, ICC values ranged from 0.46-0.65 and 95% RC values ranged from 38.3-68.1 across the seven networks. CONCLUSIONS: The wide range of BNA scores observed in this population of healthy athletes suggests that a single BNA score or set of BNA scores from a single after-injury test session may be difficult to interpret in isolation without knowledge of the athlete's own baseline BNA score(s) and/or the results of serial tests performed at additional time points. The stability of each BNA network should be considered when interpreting test-re-test BNA score changes.

GLOBALLY ADAPTIVE QUANTILE REGRESSION WITH ULTRA-HIGH DIMENSIONAL DATA.

Quantile regression has become a valuable tool to analyze heterogeneous covaraite-response associations that are often encountered in practice. The development of quantile regression methodology for high dimensional covariates primarily focuses on examination of model sparsity at a single or multiple quantile levels, which are typically prespecified ad hoc by the users. The resulting models may be sensitive to the specific choices of the quantile levels, leading to difficulties in interpretation and erosion of confidence in the results. In this article, we propose a new penalization framework for quantile regression in the high dimensional setting. We employ adaptive L1 penalties, and more importantly, propose a uniform selector of the tuning parameter for a set of quantile levels to avoid some of the potential problems with model selection at individual quantile levels. Our proposed approach achieves consistent shrinkage of regression quantile estimates across a continuous range of quantiles levels, enhancing the flexibility and robustness of the existing penalized quantile regression methods. Our theoretical results include the oracle rate of uniform convergence and weak convergence of the parameter estimators. We also use numerical studies to confirm our theoretical findings and illustrate the practical utility of our proposal.

Generalized F test and generalized deviance test in two-way ANOVA models for randomized trials.

We consider the problem of detecting treatment effects in a randomized trial in the presence of an additional covariate. By reexpressing a two-way analysis of variance (ANOVA) model in a logistic regression framework, we derive generalized F tests and generalized deviance tests, which provide better power in detecting common location-scale changes of treatment outcomes than the classical F test. The null distributions of the test statistics are independent of the nuisance parameters in the models, so the critical values can be easily determined by Monte Carlo methods. We use simulation studies to demonstrate how the proposed tests perform compared with the classical F test. We also use data from a clinical study to illustrate possible savings in sample sizes.

A new theoretical approach to improving face recognition in disorders of central vision: face caricaturing.

Damage to central vision, of which age-related macular degeneration (AMD) is the most common cause, leaves patients with only blurred peripheral vision. Previous approaches to improving face recognition in AMD have employed image manipulations designed to enhance early-stage visual processing (e.g., magnification, increased HSF contrast). Here, we argue that further improvement may be possible by targeting known properties of mid- and/or high-level face processing. We enhance identity-related shape information in the face by caricaturing each individual away from an average face. We simulate early- through late-stage AMD-blur by filtering spatial frequencies to mimic the amount of blurring perceived at approximately 10° through 30° into the periphery (assuming a face seen premagnified on a tablet computer). We report caricature advantages for all blur levels, for face viewpoints from front view to semiprofile, and in tasks involving perceiving differences in facial identity between pairs of people, remembering previously learned faces, and rejecting new faces as unknown. Results provide a proof of concept that caricaturing may assist in improving face recognition in AMD and other disorders of central vision.

SGTR+: End-to-End Scene Graph Generation With Transformer.

Scene Graph Generation (SGG) remains a challenging visual understanding task due to its compositional property. Most previous works adopt a bottom-up, two-stage or point-based, one-stage approach, which often suffers from high time complexity or suboptimal designs. In this paper, we propose a novel SGG method to address the aforementioned issues, formulating the task as a bipartite graph construction problem. To address the issues above, we create a transformer-based end-to-end framework to generate the entity and entity-aware predicate proposal set, and infer directed edges to form relation triplets. Moreover, we design a graph assembling module to infer the connectivity of the bipartite scene graph based on our entity-aware structure, enabling us to generate the scene graph in an end-to-end manner. Based on bipartite graph assembling paradigm, we further propose a new technical design to address the efficacy of entity-aware modeling and optimization stability of graph assembling. Equipped with the enhanced entity-aware design, our method achieves optimal performance and time-complexity. Extensive experimental results show that our design is able to achieve the state-of-the-art or comparable performance on three challenging benchmarks, surpassing most of the existing approaches and enjoying higher efficiency in inference.

Multi-Modal Modality-Masked Diffusion Network for Brain MRI Synthesis With Random Modality Missing.

Synthesis of unavailable imaging modalities from available ones can generate modality-specific complementary information and enable multi-modality based medical images diagnosis or treatment. Existing generative methods for medical image synthesis are usually based on cross-modal translation between acquired and missing modalities. These methods are usually dedicated to specific missing modality and perform synthesis in one shot, which cannot deal with varying number of missing modalities flexibly and construct the mapping across modalities effectively. To address the above issues, in this paper, we propose a unified Multi-modal Modality-masked Diffusion Network (M2DN), tackling multi-modal synthesis from the perspective of "progressive whole-modality inpainting", instead of "cross-modal translation". Specifically, our M2DN considers the missing modalities as random noise and takes all the modalities as a unity in each reverse diffusion step. The proposed joint synthesis scheme performs synthesis for the missing modalities and self-reconstruction for the available ones, which not only enables synthesis for arbitrary missing scenarios, but also facilitates the construction of common latent space and enhances the model representation ability. Besides, we introduce a modality-mask scheme to encode availability status of each incoming modality explicitly in a binary mask, which is adopted as condition for the diffusion model to further enhance the synthesis performance of our M2DN for arbitrary missing scenarios. We carry out experiments on two public brain MRI datasets for synthesis and downstream segmentation tasks. Experimental results demonstrate that our M2DN outperforms the state-of-the-art models significantly and shows great generalizability for arbitrary missing modalities.

AN OMNIBUS TEST FOR DETECTION OF SUBGROUP TREATMENT EFFECTS VIA DATA PARTITIONING.

Late-stage clinical trials have been conducted primarily to establish the efficacy of a new treatment in an intended population. A corollary of population heterogeneity in clinical trials is that a treatment might be effective for one or more subgroups, rather than for the whole population of interest. As an example, the phase III clinical trial of panitumumab in metastatic colorectal cancer patients failed to demonstrate its efficacy in the overall population, but a subgroup associated with tumor KRAS status was found to be promising (Peeters et al. (Am. J. Clin. Oncol. 28 (2010) 4706-4713)). As we search for such subgroups via data partitioning based on a large number of biomarkers, we need to guard against inflated type I error rates due to multiple testing. Commonly-used multiplicity adjustments tend to lose power for the detection of subgroup treatment effects. We develop an effective omnibus test to detect the existence of, at least, one subgroup treatment effect, allowing a large number of possible subgroups to be considered and possibly censored outcomes. Applied to the panitumumab trial data, the proposed test would confirm a significant subgroup treatment effect. Empirical studies also show that the proposed test is applicable to a variety of outcome variables and maintains robust statistical power.

Auto-segmentation and time-dependent systematic analysis of mesoscale cellular structure in ß-cells during insulin secretion.

The mesoscale description of the subcellular organization informs about cellular mechanisms in disease state. However, applications of soft X-ray tomography (SXT), an important approach for characterizing organelle organization, are limited by labor-intensive manual segmentation. Here we report a pipeline for automated segmentation and systematic analysis of SXT tomograms. Our approach combines semantic and first-applied instance segmentation to produce separate organelle masks with high Dice and Recall indexes, followed by analysis of organelle localization based on the radial distribution function. We demonstrated this technique by investigating the organization of INS-1E pancreatic ß-cell organization under different treatments at multiple time points. Consistent with a previous analysis of a similar dataset, our results revealed the impact of glucose stimulation on the localization and molecular density of insulin vesicles and mitochondria. This pipeline can be extended to SXT tomograms of any cell type to shed light on the subcellular rearrangements under different drug treatments.

A multistep protein lysate array quantification method and its statistical properties.

The protein lysate array is an emerging technology for quantifying the protein concentration ratios in multiple biological samples. Statistical inference for a parametric quantification procedure has been inadequately addressed in the literature, mainly because the appropriate asymptotic theory involves a problem with the number of parameters increasing with the number of observations. In this article, we develop a multistep procedure for the Sigmoidal models, ensuring consistent estimation of the concentration levels with full asymptotic efficiency. The results obtained in the article justify inferential procedures based on large sample approximations. Simulation studies and real data analysis are used in the article to illustrate the performance of the proposed method in finite samples. The multistep procedure is convenient to work with asymptotically, and is recommended for its statistical efficiency in protein concentration estimation and improved numerical stability by focusing on optimization of lower-dimensional objective functions.

Bent line quantile regression with application to an allometric study of land mammals' speed and mass.

Quantile regression, which models the conditional quantiles of the response variable given covariates, usually assumes a linear model. However, this kind of linearity is often unrealistic in real life. One situation where linear quantile regression is not appropriate is when the response variable is piecewise linear but still continuous in covariates. To analyze such data, we propose a bent line quantile regression model. We derive its parameter estimates, prove that they are asymptotically valid given the existence of a change-point, and discuss several methods for testing the existence of a change-point in bent line quantile regression together with a power comparison by simulation. An example of land mammal maximal running speeds is given to illustrate an application of bent line quantile regression in which this model is theoretically justified and its parameters are of direct biological interests.

A tail-based test to detect differential expression in RNA-sequencing data.

RNA sequencing data have been abundantly generated in biomedical research for biomarker discovery and other studies. Such data at the exon level are usually heavily tailed and correlated. Conventional statistical tests based on the mean or median difference for differential expression likely suffer from low power when the between-group difference occurs mostly in the upper or lower tail of the distribution of gene expression. We propose a tail-based test to make comparisons between groups in terms of a specific distribution area rather than a single location. The proposed test, which is derived from quantile regression, adjusts for covariates and accounts for within-sample dependence among the exons through a specified correlation structure. Through Monte Carlo simulation studies, we show that the proposed test is generally more powerful and robust in detecting differential expression than commonly used tests based on the mean or a single quantile. An application to TCGA lung adenocarcinoma data demonstrates the promise of the proposed method in terms of biomarker discovery.