A Hierarchical Context-aware Modeling Approach for Multi-aspect and Multi-granular Pronunciation Assessment

Automatic Pronunciation Assessment (APA) plays a vital role in Computer-assisted Pronunciation Training (CAPT) when evaluating a second language (L2) learner's speaking proficiency. However, an apparent downside of most de facto methods is that they parallelize the modeling process throughout different speech granularities without accounting for the hierarchical and local contextual relationships among them. In light of this, a novel hierarchical approach is proposed in this paper for multi-aspect and multi-granular APA. Specifically, we first introduce the notion of sup-phonemes to explore more subtle semantic traits of L2 speakers. Second, a depth-wise separable convolution layer is exploited to better encapsulate the local context cues at the sub-word level. Finally, we use a score-restraint attention pooling mechanism to predict the sentence-level scores and optimize the component models with a multitask learning (MTL) framework. Extensive experiments carried out on a publicly-available benchmark dataset, viz. speechocean762, demonstrate the efficacy of our approach in relation to some cutting-edge baselines.Comment: Accepted to Interspeech 202

Insights on Distinct Left Atrial Remodeling Between Atrial Fibrillation and Heart Failure With Preserved Ejection Fraction

BACKGROUND: Heart failure with preserved ejection fraction (HFpEF) and atrial fibrillation (AF) commonly coexist with overlapping pathophysiology like left atrial (LA) remodeling, which might differ given different underlying mechanisms. OBJECTIVES: We sought to investigate the different patterns of LA wall remodeling in AF vs. HFpEF. METHODS: We compared LA wall characteristics including wall volume (LAWV), wall thickness (LAWT), and wall thickness heterogeneity (LAWT[SD]) and LA structure, function among the controls (without AF or HFpEF, n = 115), HFpEF alone (n = 59), AF alone (n = 37), and HFpEF+AF (n = 38) groups using multi-detector computed tomography and echocardiography. RESULTS: LA wall remodeling was most predominant and peak atrial longitudinal strain (PALS) was worst in HFpEF+AF patients as compared to the rest. Despite lower E/e' (9.8 ± 3.8 vs. 13.4 ± 6.4) yet comparable LA volume, LAWT and PALS in AF alone vs. HFpEF alone, LAWV [12.6 (11.6–15.3) vs. 12.0 (10.2–13.7); p = 0.01] and LAWT(SD) [0.68 (0.61–0.71) vs. 0.60 (0.56–0.65); p < 0.001] were significantly greater in AF alone vs. HFpEF alone even after multi-variate adjustment and propensity matching. After excluding the HFpEF+AF group, both LAWV and LAWT [SD] provided incremental values when added to PALS or LAVi (all p for net reclassification improvement <0.05) in discriminating AF alone, with LAWT[SD] yielding the largest C-statistic (0.78, 95% CI: 0.70–0.86) among all LA wall indices. CONCLUSIONS: Despite a similar extent of LA enlargement and dysfunction in HFpEF vs. AF alone, larger LAWV and LAWT [SD] can distinguish AF from HFpEF alone, suggesting the distinct underlying pathophysiological mechanism of LA remodeling in AF vs. HFpEF

Insights on Distinct Left Atrial Remodeling Between Atrial Fibrillation and Heart Failure With Preserved Ejection Fraction

Background: Heart failure with preserved ejection fraction (HFpEF) and atrial fibrillation (AF) commonly coexist with overlapping pathophysiology like left atrial (LA) remodeling, which might differ given different underlying mechanisms. Objectives: We sought to investigate the different patterns of LA wall remodeling in AF vs. HFpEF. Methods: We compared LA wall characteristics including wall volume (LAWV), wall thickness (LAWT), and wall thickness heterogeneity (LAWT[SD]) and LA structure, function among the controls (without AF or HFpEF, n = 115), HFpEF alone (n = 59), AF alone (n = 37), and HFpEF+AF (n = 38) groups using multi-detector computed tomography and echocardiography. Results: LA wall remodeling was most predominant and peak atrial longitudinal strain (PALS) was worst in HFpEF+AF patients as compared to the rest. Despite lower E/e' (9.8 ± 3.8 vs. 13.4 ± 6.4) yet comparable LA volume, LAWT and PALS in AF alone vs. HFpEF alone, LAWV [12.6 (11.6–15.3) vs. 12.0 (10.2–13.7); p = 0.01] and LAWT(SD) [0.68 (0.61–0.71) vs. 0.60 (0.56–0.65); p &lt; 0.001] were significantly greater in AF alone vs. HFpEF alone even after multi-variate adjustment and propensity matching. After excluding the HFpEF+AF group, both LAWV and LAWT [SD] provided incremental values when added to PALS or LAVi (all p for net reclassification improvement &lt;0.05) in discriminating AF alone, with LAWT[SD] yielding the largest C-statistic (0.78, 95% CI: 0.70–0.86) among all LA wall indices. Conclusions: Despite a similar extent of LA enlargement and dysfunction in HFpEF vs. AF alone, larger LAWV and LAWT [SD] can distinguish AF from HFpEF alone, suggesting the distinct underlying pathophysiological mechanism of LA remodeling in AF vs. HFpEF.</p

Association of Female Menopause With Atrioventricular Mechanics and Outcomes

BACKGROUND: Despite known sex differences in cardiac structure and function, little is known about how menopause and estrogen associate with atrioventricular mechanics and outcomes. OBJECTIVE: To study how, sex differences, loss of estrogen in menopause and duration of menopause, relate to atrioventricular mechanics and outcomes. METHODS: Among 4051 asymptomatic adults (49.8 ± 10.8 years, 35%women), left ventricular (LV) and left atrial (LA) mechanics were assessed using speckle-tracking. RESULTS: Post-menopausal (vs. pre-menopausal) women had similar LV ejection fraction but reduced GLS, reduced PALS, increased LA stiffness, higher LV sphericity and LV torsion (all p < 0.001). Multivariable analysis showed menopause to be associated with greater LV sphericity (0.02, 95%CI 0.01, 0.03), higher indexed LV mass (LVMi), lower mitral e’, lower LV GLS (0.37, 95%CI 0.04–0.70), higher LV torsion, larger LA volume, worse PALS (∼2.4-fold) and greater LA stiffness (0.028, 95%CI 0.01–0.05). Increasing years of menopause was associated with further reduction in GLS, markedly worse LA mechanics despite greater LV sphericity and higher torsion. Lower estradiol levels correlated with more impaired LV diastolic function, impaired LV GLS, greater LA stiffness, and increased LV sphericity and LV torsion (all p < 0.05). Approximately 5.5% (37/669) of post-menopausal women incident HF over 2.9 years of follow-up. Greater LV sphericity [adjusted hazard ratio (aHR) 1.04, 95%CI 1.00–1.07], impaired GLS (aHR 0.87, 95%CI 0.78–0.97), reduced peak left atrial longitudinal strain (PALS, aHR 0.94, 95%CI 0.90–0.99) and higher LA stiffness (aHR 10.5, 95%CI 1.69–64.6) were independently associated with the primary outcome of HF hospitalizations in post-menopause. Both PALS < 23% (aHR:1.32, 95%CI 1.01–3.49) and GLS < 16% (aHR:5.80, 95%CI 1.79–18.8) remained prognostic for the incidence of HF in post-menopausal women in dichotomous analyses, even after adjusting for confounders. Results were consistent with composite outcomes of HF hospitalizations and 1-year all-cause mortality as well. CONCLUSION: Menopause was associated with greater LV/LA remodeling and reduced LV longitudinal and LA function in women. The cardiac functional deficit with menopause and lower estradiol levels, along with their independent prognostic value post-menopause, may elucidate sex differences in heart failure further

Cyclin T1-Dependent Genes in Activated CD4+ T and Macrophage Cell Lines Appear Enriched in HIV-1 Co-Factors

HIV-1 is dependent upon cellular co-factors to mediate its replication cycle in CD4+ T cells and macrophages, the two major cell types infected by the virus in vivo. One critical co-factor is Cyclin T1, a subunit of a general RNA polymerase II elongation factor known as P-TEFb. Cyclin T1 is targeted directly by the viral Tat protein to activate proviral transcription. Cyclin T1 is up-regulated when resting CD4+ T cells are activated and during macrophage differentiation or activation, conditions that are also necessary for high levels of HIV-1 replication. Because Cyclin T1 is a subunit of a transcription factor, the up-regulation of Cyclin T1 in these cells results in the induction of cellular genes, some of which might be HIV-1 co-factors. Using shRNA depletions of Cyclin T1 and transcriptional profiling, we identified 54 cellular mRNAs that appear to be Cyclin T1-dependent for their induction in activated CD4+ T Jurkat T cells and during differentiation and activation of MM6 cells, a human monocytic cell line. The promoters for these Cyclin T1-dependent genes (CTDGs) are over-represented in two transcription factor binding sites, SREBP1 and ARP1. Notably, 10 of these CTDGs have been reported to be involved in HIV-1 replication, a significant over-representation of such genes when compared to randomly generated lists of 54 genes (p value<0.00021). The results of siRNA depletion and dominant-negative protein experiments with two CTDGs identified here, CDK11 and Casein kinase 1 gamma 1, suggest that these genes are involved either directly or indirectly in HIV-1 replication. It is likely that the 54 CTDGs identified here include novel HIV-1 co-factors. The presence of CTDGs in the protein space that was available for HIV-1 to sample during its evolution and acquisition of Tat function may provide an explanation for why CTDGs are enriched in viral co-factors

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14