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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

DeepPhospho accelerates DIA phosphoproteome profiling through in silico library generation.

Phosphoproteomics integrating data-independent acquisition (DIA) enables deep phosphoproteome profiling with improved quantification reproducibility and accuracy compared to data-dependent acquisition (DDA)-based phosphoproteomics. DIA data mining heavily relies on a spectral library that in most cases is built on DDA analysis of the same sample. Construction of this project-specific DDA library impairs the analytical throughput, limits the proteome coverage, and increases the sample size for DIA phosphoproteomics. Herein we introduce a deep neural network, DeepPhospho, which conceptually differs from previous deep learning models to achieve accurate predictions of LC-MS/MS data for phosphopeptides. By leveraging in silico libraries generated by DeepPhospho, we establish a DIA workflow for phosphoproteome profiling which involves DIA data acquisition and data mining with DeepPhospho predicted libraries, thus circumventing the need of DDA library construction. Our DeepPhospho-empowered workflow substantially expands the phosphoproteome coverage while maintaining high quantification performance, which leads to the discovery of more signaling pathways and regulated kinases in an EGF signaling study than the DDA library-based approach. DeepPhospho is provided as a web server as well as an offline app to facilitate user access to model training, predictions and library generation.

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.

Concussion-Recovery Trajectories Among Tactical Athletes: Results From the CARE Consortium.

CONTEXT: Assessments of the duration of concussion recovery have primarily been limited to sport-related concussions and male contact sports. Furthermore, whereas durations of symptoms and return-to-activity (RTA) protocols encompass total recovery, the trajectory of each duration has not been examined separately. OBJECTIVE: To identify individual (eg, demographics, medical history), initial concussion injury (eg, symptoms), and external (eg, site) factors associated with symptom duration and RTA-protocol duration after concussion. DESIGN: Cohort study. SETTING: Three US military service academies. PATIENTS OR OTHER PARTICIPANTS: A total of 10 604 cadets at participating US military service academies enrolled in the study and completed a baseline evaluation and up to 5 postinjury evaluations. A total of 726 cadets (451 men, 275 women) sustained concussions during the study period. MAIN OUTCOME MEASURE(S): Number of days from injury (1) until the participant became asymptomatic and (2) to complete the RTA protocol. RESULTS: Varsity athlete cadets took less time than nonvarsity cadets to become asymptomatic (hazard ratio [HR] = 1.75, 95% confidence interval = 1.38, 2.23). Cadets who reported less symptom severity on the Sport Concussion Assessment Tool, third edition (SCAT3), within 48 hours of concussion had 1.45 to 3.77 times shorter symptom-recovery durations than those with more symptom severity. Similar to symptom duration, varsity status was associated with a shorter RTA-protocol duration (HR = 1.74, 95% confidence interval = 1.34, 2.25), and less symptom severity on the SCAT3 was associated with a shorter RTA-protocol duration (HR range = 1.31 to 1.47). The academy that the cadet attended was associated with the RTA-protocol duration (P < .05). CONCLUSIONS: The initial total number of symptoms reported and varsity athlete status were strongly associated with symptom and RTA-protocol durations. These findings suggested that external (varsity status and academy) and injury (symptom burden) factors influenced the time until RTA.

Effect of Routine Sport Participation on Short-Term Clinical Neurological Outcomes: A Comparison of Non-Contact, Contact, and Collision Sport Athletes.

OBJECTIVES: To compare pre-season to post-season changes on a battery of clinical neurological outcome measures between non-contact, contact, and collision sport athletes over multiple seasons of play. METHODS: 244 high school and collegiate athletes participating in multiple non-contact, contact, and collision sports completed standardized annual pre-season and post-season assessments over 1-4 years. Pre/post-season changes in 10 outcome measures assessing concussion symptoms, neurocognitive performance, and balance were compared between the groups using linear mixed models. RESULTS: Small, but statistically significant overall pre/post-season change differences were present between the groups for Axon computerized neurocognitive test processing speed, attention, and working memory speed scores (Axon-PS, Axon-Att, Axon-WMS), as well as Balance Error Scoring System (BESS) total score. Small seasonal declines not exceeding reliable-change thresholds were observed in the collision sport group relative to the contact and non-contact groups for Axon-PS and Axon-Att scores. The collision and contact sport groups demonstrated less pre-/post-season improvement than the non-contact sport group for Axon-WMA and BESS, with less BESS improvement also observed in the collision sport group relative to the contact sport group. Overall, longitudinal performance on all 10 outcome measures remained stable in all 3 groups over 4 years. CONCLUSION: Our findings do not necessarily support the notion that participation in sports associated with exposure to repetitive head impacts has clinically meaningful cumulative effects over the course of a season, nor over four consecutive seasons in high school and collegiate athletes.

Challenges and Opportunities in Statistics and Data Science: Ten Research Areas.

As a discipline that deals with many aspects of data, statistics is a critical pillar in the rapidly evolving landscape of data science. The increasingly vital role of data, especially big data, in many applications, presents the field of statistics with unparalleled challenges and exciting opportunities. Statistics plays a pivotal role in data science by assisting with the use of data and decision making in the face of uncertainty. In this article, we present ten research areas that could make statistics and data science more impactful on science and society. Focusing on these areas will help better transform data into knowledge, actionable insights and deliverables, and promote more collaboration with computer and other quantitative scientists and domain scientists.

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.

A cohort study to identify and evaluate concussion risk factors across multiple injury settings: findings from the CARE Consortium.

BACKGROUND: Concussion, or mild traumatic brain injury, is a major public health concern affecting 42 million individuals globally each year. However, little is known regarding concussion risk factors across all concussion settings as most concussion research has focused on only sport-related or military-related concussive injuries. METHODS: The current study is part of the Concussion, Assessment, Research, and Education (CARE) Consortium, a multi-site investigation on the natural history of concussion. Cadets at three participating service academies completed annual baseline assessments, which included demographics, medical history, and concussion history, along with the Sport Concussion Assessment Tool (SCAT) symptom checklist and Brief Symptom Inventory (BSI-18). Clinical and research staff recorded the date and injury setting at time of concussion. Generalized mixed models estimated concussion risk with service academy as a random effect. Since concussion was a rare event, the odds ratios were assumed to approximate relative risk. RESULTS: Beginning in 2014, 10,604 (n = 2421, 22.83% female) cadets enrolled over 3 years. A total of 738 (6.96%) cadets experienced a concussion, 301 (2.84%) concussed cadets were female. Female sex and previous concussion were the most consistent estimators of concussion risk across all concussion settings. Compared to males, females had 2.02 (95% CI: 1.70-2.40) times the risk of a concussion regardless of injury setting, and greater relative risk when the concussion occurred during sport (Odds Ratio (OR): 1.38 95% CI: 1.07-1.78). Previous concussion was associated with 1.98 (95% CI: 1.65-2.37) times increased risk for any incident concussion, and the magnitude was relatively stable across all concussion settings (OR: 1.73 to 2.01). Freshman status was also associated with increased overall concussion risk, but was driven by increased risk for academy training-related concussions (OR: 8.17 95% CI: 5.87-11.37). Medical history of headaches in the past 3 months, diagnosed ADD/ADHD, and BSI-18 Somatization symptoms increased overall concussion risk. CONCLUSIONS: Various demographic and medical history factors are associated with increased concussion risk. While certain factors (e.g. sex and previous concussion) are consistently associated with increased concussion risk, regardless of concussion injury setting, other factors significantly influence concussion risk within specific injury settings. Further research is required to determine whether these risk factors may aid in concussion risk reduction or prevention.

Caricaturing as a general method to improve poor face recognition: Evidence from low-resolution images, other-race faces, and older adults.

There are multiple well-established situations in which humans' face recognition performance is poor, including for low-resolution images, other-race faces, and in older adult observers. Here we show that caricaturing faces-that is, exaggerating their appearance away from an average face-can provide a useful applied method for improving face recognition across all these circumstances. We employ a face-name learning task offering a number of methodological advantages (e.g., valid comparison of the size of the caricature improvement across conditions differing in overall accuracy). Across six experiments, we (a) extend previous evidence that caricaturing can improve recognition of low-resolution (blurred) faces; (b) show for the first time that caricaturing improves recognition and perception of other-race faces; and (c) show for the first time that caricaturing improves recognition in observers across the whole adult life span (testing older adults, M age = 71 years). In size, caricature benefits were at least as large where natural face recognition is poor (other-race, low resolution, older adults) as for the naturally best situation (own-race high-resolution faces in young adults). We discuss potential for practical applicability to improving face recognition in low-vision patients (age-related macular degeneration, bionic eye), security settings (police, passport control), eyewitness testimony, and prosopagnosia. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Caricaturing faces to improve identity recognition in low vision simulations: How effective is current-generation automatic assignment of landmark points?

PURPOSE: Previous behavioural studies demonstrate that face caricaturing can provide an effective image enhancement method for improving poor face identity perception in low vision simulations (e.g., age-related macular degeneration, bionic eye). To translate caricaturing usefully to patients, assignment of the multiple face landmark points needed to produce the caricatures needs to be fully automatised. Recent development in computer science allows automatic face landmark detection of 68 points in real time and in multiple viewpoints. However, previous demonstrations of the behavioural effectiveness of caricaturing have used higher-precision caricatures with 147 landmark points per face, assigned by hand. Here, we test the effectiveness of the auto-assigned 68-point caricatures. We also compare this to the hand-assigned 147-point caricatures. METHOD: We assessed human perception of how different in identity pairs of faces appear, when veridical (uncaricatured), caricatured with 68-points, and caricatured with 147-points. Across two experiments, we tested two types of low-vision images: a simulation of blur, as experienced in macular degeneration (testing two blur levels); and a simulation of the phosphenised images seen in prosthetic vision (at three resolutions). RESULTS: The 68-point caricatures produced significant improvements in identity discrimination relative to veridical. They were approximately 50% as effective as the 147-point caricatures. CONCLUSION: Realistic translation to patients (e.g., via real time caricaturing with the enhanced signal sent to smart glasses or visual prosthetic) is approaching feasibility. For maximum effectiveness software needs to be able to assign landmark points tracing out all details of feature and face shape, to produce high-precision caricatures.

Improving face identity perception in age-related macular degeneration via caricaturing.

Patients with age-related macular degeneration (AMD) have difficulty recognising people's faces. We tested whether this could be improved using caricaturing: an image enhancement procedure derived from cortical coding in a perceptual 'face-space'. Caricaturing exaggerates the distinctive ways in which an individual's face shape differs from the average. We tested 19 AMD-affected eyes (from 12 patients; ages 66-93 years) monocularly, selected to cover the full range of vision loss. Patients rated how different in identity people's faces appeared when compared in pairs (e.g., two young men, both Caucasian), at four caricature strengths (0, 20, 40, 60% exaggeration). This task gives data reliable enough to analyse statistically at the individual-eye level. All 9 eyes with mild vision loss (acuity ≥ 6/18) showed significant improvement in identity discrimination (higher dissimilarity ratings) with caricaturing. The size of improvement matched that in normal-vision young adults. The caricature benefit became less stable as visual acuity further decreased, but caricaturing was still effective in half the eyes with moderate and severe vision loss (significant improvement in 5 of 10 eyes; at acuities from 6/24 to poorer than <6/360). We conclude caricaturing has the potential to help many AMD patients recognise faces.