141 research outputs found
Multi-objective Non-intrusive Hearing-aid Speech Assessment Model
Without the need for a clean reference, non-intrusive speech assessment
methods have caught great attention for objective evaluations. While deep
learning models have been used to develop non-intrusive speech assessment
methods with promising results, there is limited research on hearing-impaired
subjects. This study proposes a multi-objective non-intrusive hearing-aid
speech assessment model, called HASA-Net Large, which predicts speech quality
and intelligibility scores based on input speech signals and specified
hearing-loss patterns. Our experiments showed the utilization of pre-trained
SSL models leads to a significant boost in speech quality and intelligibility
predictions compared to using spectrograms as input. Additionally, we examined
three distinct fine-tuning approaches that resulted in further performance
improvements. Furthermore, we demonstrated that incorporating SSL models
resulted in greater transferability to OOD dataset. Finally, this study
introduces HASA-Net Large, which is a non-invasive approach for evaluating
speech quality and intelligibility. HASA-Net Large utilizes raw waveforms and
hearing-loss patterns to accurately predict speech quality and intelligibility
levels for individuals with normal and impaired hearing and demonstrates
superior prediction performance and transferability
Study on the Correlation between Objective Evaluations and Subjective Speech Quality and Intelligibility
Subjective tests are the gold standard for evaluating speech quality and
intelligibility, but they are time-consuming and expensive. Thus, objective
measures that align with human perceptions are crucial. This study evaluates
the correlation between commonly used objective measures and subjective speech
quality and intelligibility using a Chinese speech dataset. Moreover, new
objective measures are proposed combining current objective measures using deep
learning techniques to predict subjective quality and intelligibility. The
proposed deep learning model reduces the amount of training data without
significantly impacting prediction performance. We interpret the deep learning
model to understand how objective measures reflect subjective quality and
intelligibility. We also explore the impact of including subjective speech
quality ratings on speech intelligibility prediction. Our findings offer
valuable insights into the relationship between objective measures and human
perceptions
KCNN2 polymorphisms and cardiac tachyarrhythmias
Potassium calcium-activated channel subfamily N member 2 (KCNN2) encodes an integral membrane protein that forms small-conductance calcium-activated potassium (SK) channels. Recent studies in animal models show that SK channels are important in atrial and ventricular repolarization and arrhythmogenesis. However, the importance of SK channels in human arrhythmia remains unclear. The purpose of the present study was to test the association between genetic polymorphism of the SK2 channel and the occurrence of cardiac tachyarrhythmias in humans. We enrolled 327 Han Chinese, including 72 with clinically significant ventricular tachyarrhythmias (VTa) who had a history of aborted sudden cardiac death (SCD) or unexplained syncope, 98 with a history of atrial fibrillation (AF), and 144 normal controls. We genotyped 12 representative tag single nucleotide polymorphisms (SNPs) across a 141-kb genetic region containing the KCNN2 gene; these captured the full haplotype information. The rs13184658 and rs10076582 variants of KCNN2 were associated with VTa in both the additive and dominant models (odds ratio [OR] 2.89, 95% confidence interval [CI] = 1.505-5.545, P = 0.001; and OR 2.55, 95% CI = 1.428-4.566, P = 0.002, respectively). After adjustment for potential risk factors, the association remained significant. The population attributable risks of these 2 variants of VTa were 17.3% and 10.6%, respectively. One variant (rs13184658) showed weak but significant association with AF in a dominant model (OR 1.91, CI = 1.025-3.570], P = 0.042). There was a significant association between the KCNN2 variants and clinically significant VTa. These findings suggest an association between KCNN2 and VTa; it also appears that KCNN2 variants may be adjunctive markers for risk stratification in patients susceptible to SCD
Serum Vascular Adhesion Protein-1 Predicts 10-Year Cardiovascular and Cancer Mortality in Individuals With Type 2 Diabetes
Growth Differentiation Factor 15 May Predict Mortality of Peripheral and Coronary Artery Diseases and Correlate with Their Risk Factors
Use of guideline-recommended management in established coronary heart disease in the observational DYSIS II study
Abstract Background Guidelines recommend lifestyle modification and medications to control risk factors in coronary heart disease (CHD). Using data from the observational DYSIS II study, we sought to evaluate the use of guideline-recommended treatments at discharge for acute coronary syndromes or in the chronic phase for CHD, and participation in rehabilitation/secondary prevention programs. Methods and results Between 2013 and 2014, 10,661 patients (3867 with ACS, 6794 with stable CHD) were enrolled in 332 primary and secondary care centers in 18 countries (Asia-Pacific, Europe, Middle East/Africa). Patients with incident ACS were younger and more likely to be smokers than patients with recurrent ACS or stable CHD (both p Conclusions The high prevalence of risk factors in all CHD patients and reduced rates of secondary prevention medications in stable CHD offer areas for improvement. Translational aspects The findings of DYSIS II may reinforce the importance of adopting a healthy lifestyle and prescribing (by clinicians) and adhering (by patients) to evidence-based medications in the management of coronary heart disease, not only during the short-term but also over the longer term after a cardiac ischemic event. The results may help to increase the proportion of ACS patients who are referred to cardiac rehabilitation centres
Language to Rewards for Robotic Skill Synthesis
Large language models (LLMs) have demonstrated exciting progress in acquiring
diverse new capabilities through in-context learning, ranging from logical
reasoning to code-writing. Robotics researchers have also explored using LLMs
to advance the capabilities of robotic control. However, since low-level robot
actions are hardware-dependent and underrepresented in LLM training corpora,
existing efforts in applying LLMs to robotics have largely treated LLMs as
semantic planners or relied on human-engineered control primitives to interface
with the robot. On the other hand, reward functions are shown to be flexible
representations that can be optimized for control policies to achieve diverse
tasks, while their semantic richness makes them suitable to be specified by
LLMs. In this work, we introduce a new paradigm that harnesses this realization
by utilizing LLMs to define reward parameters that can be optimized and
accomplish variety of robotic tasks. Using reward as the intermediate interface
generated by LLMs, we can effectively bridge the gap between high-level
language instructions or corrections to low-level robot actions. Meanwhile,
combining this with a real-time optimizer, MuJoCo MPC, empowers an interactive
behavior creation experience where users can immediately observe the results
and provide feedback to the system. To systematically evaluate the performance
of our proposed method, we designed a total of 17 tasks for a simulated
quadruped robot and a dexterous manipulator robot. We demonstrate that our
proposed method reliably tackles 90% of the designed tasks, while a baseline
using primitive skills as the interface with Code-as-policies achieves 50% of
the tasks. We further validated our method on a real robot arm where complex
manipulation skills such as non-prehensile pushing emerge through our
interactive system.Comment: https://language-to-reward.github.io
Cardiac Myosin Binding Protein C and MAP-Kinase Activating Death Domain-Containing Gene Polymorphisms and Diastolic Heart Failure
OBJECTIVE: Myosin binding protein C (MYBPC3) plays a role in ventricular relaxation. The aim of the study was to investigate the association between cardiac myosin binding protein C (MYBPC3) gene polymorphisms and diastolic heart failure (DHF) in a human case-control study. METHODS: A total of 352 participants of 1752 consecutive patients from the National Taiwan University Hospital and its affiliated hospital were enrolled. 176 patients diagnosed with DHF confirmed by echocardiography were recruited. Controls were matched 1-to-1 by age, sex, hypertension, diabetes, renal function and medication use. We genotyped 12 single nucleotide polymorphisms (SNPs) according to HapMap Han Chinese Beijing databank across a 40 kb genetic region containing the MYBPC3 gene and the neighboring DNA sequences to capture 100% of haplotype variance in all SNPs with minor allele frequencies ≥ 5%. We also analyzed associations of these tagging SNPs and haplotypes with DHF and linkage disequilibrium (LD) structure of the MYBPC3 gene. RESULTS: In a single locus analysis, SNP rs2290149 was associated with DHF (allele-specific p = 0.004; permuted p = 0.031). The SNP with a minor allele frequency of 9.4%, had an odds ratio 2.14 (95% CI 1.25-3.66; p = 0.004) for the additive model and 2.06 for the autosomal dominant model (GG+GA : AA, 95% CI 1.17-3.63; p = 0.013), corresponding to a population attributable risk fraction of 12.02%. The haplotypes in a LD block of rs2290149 (C-C-G-C) was also significantly associated with DHF (odds ratio 2.10 (1.53-2.89); permuted p = 0.029). CONCLUSIONS: We identified a SNP (rs2290149) among the tagging SNP set that was significantly associated with early DHF in a Chinese population
Use of ecstasy and other psychoactive substances among school-attending adolescents in Taiwan: national surveys 2004–2006
<p>Abstract</p> <p>Background</p> <p>With the backdrop of a global ecstasy epidemic, this study sought to examine the trend, correlates, and onset sequence of ecstasy use among adolescents in Taiwan, where a well-established gateway drug such as marijuana is much less popular.</p> <p>Methods</p> <p>A multistage probability survey of school-attending adolescents in grades 7, 9, 10, and 12, aged 11–19 years, was conducted in 2004, 2005, and 2006. A self-administered anonymous questionnaire elicited response rates ranging from 94.3% to 96.6%. The sample sizes were 18232 respondents in 2004, 17986 in 2005, and 17864 in 2006.</p> <p>Results</p> <p>In terms of lifetime prevalence and incidence, ecstasy and ketamine by and large appeared as the first and second commonly used illegal drugs, respectively, among middle (grades 7 and 9) and high school students (grades 10 and 12) during the 3-year survey period; however, this order was reversed in the middle school-aged students starting in 2006. Having sexual experience, tobacco use, and betel nut use were factors consistently associated with the onset of ecstasy use across years. The majority of ecstasy users had been involved in polydrug use, such as the use of ketamine (41.4%–53.5%), marijuana (12.7%–18.7%), and methamphetamine (4.2%–9.5%).</p> <p>Conclusion</p> <p>From 2004 to 2006, a decline was noted in the prevalence and incidence rate of ecstasy, a leading illegal drug used by school-attending adolescents in Taiwan since the early 2000s. The emerging ketamine use trend may warrant more attention in the future.</p
Women with endometriosis have higher comorbidities: Analysis of domestic data in Taiwan
AbstractEndometriosis, defined by the presence of viable extrauterine endometrial glands and stroma, can grow or bleed cyclically, and possesses characteristics including a destructive, invasive, and metastatic nature. Since endometriosis may result in pelvic inflammation, adhesion, chronic pain, and infertility, and can progress to biologically malignant tumors, it is a long-term major health issue in women of reproductive age. In this review, we analyze the Taiwan domestic research addressing associations between endometriosis and other diseases. Concerning malignant tumors, we identified four studies on the links between endometriosis and ovarian cancer, one on breast cancer, two on endometrial cancer, one on colorectal cancer, and one on other malignancies, as well as one on associations between endometriosis and irritable bowel syndrome, one on links with migraine headache, three on links with pelvic inflammatory diseases, four on links with infertility, four on links with obesity, four on links with chronic liver disease, four on links with rheumatoid arthritis, four on links with chronic renal disease, five on links with diabetes mellitus, and five on links with cardiovascular diseases (hypertension, hyperlipidemia, etc.). The data available to date support that women with endometriosis might be at risk of some chronic illnesses and certain malignancies, although we consider the evidence for some comorbidities to be of low quality, for example, the association between colon cancer and adenomyosis/endometriosis. We still believe that the risk of comorbidity might be higher in women with endometriosis than that we supposed before. More research is needed to determine whether women with endometriosis are really at risk of these comorbidities
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