With the increase in life expectancy in the global population, aging societies have emerged in many countries, including China. As a common sensory defect in the elderly population, the prevalence of age-related hearing loss and its influence on society are increasing yearly. Metabolic syndrome is currently one of the main health problems in the world. Many studies have demonstrated that metabolic syndrome and its components are correlated with a variety of age-related diseases of the peripheral sensory system, including age-related hearing loss. Both age-related hearing loss and metabolic syndrome are high-prevalence chronic diseases, and many people suffer from both at the same time. In recent years, more and more studies have found that mitochondrial dysfunction occurs in both metabolic syndrome and age-related hearing loss. Therefore, to better understand the impact of metabolic syndrome on age-related hearing loss from the perspective of mitochondrial dysfunction, we reviewed the literature related to the relationship between age-related hearing loss and metabolic syndrome and their components to discern the possible role of mitochondria in both conditions.
We revisit the likelihood ratio between a pretrained large language model (LLM) and its finetuned variant as a criterion for out-of-distribution (OOD) detection. The intuition behind such a criterion is that, the pretrained LLM has the prior knowledge about OOD data due to its large amount of training data, and once finetuned with the in-distribution data, the LLM has sufficient knowledge to distinguish their difference. Leveraging the power of LLMs, we show that, for the first time, the likelihood ratio can serve as an effective OOD detector. Moreover, we apply the proposed LLM-based likelihood ratio to detect OOD questions in question-answering (QA) systems, which can be used to improve the performance of specialized LLMs for general questions. Given that likelihood can be easily obtained by the loss functions within contemporary neural network frameworks, it is straightforward to implement this approach in practice. Since both the pretrained LLMs and its various finetuned models are available, our proposed criterion can be effortlessly incorporated for OOD detection without the need for further training. We conduct comprehensive evaluation across on multiple settings, including far OOD, near OOD, spam detection, and QA scenarios, to demonstrate the effectiveness of the method.
While the internet provides accessible medical information, often times it does not cater to the average patient's ability to understand medical text at a 6th and 8th grade reading level, per American Medical Association (AMA)/National Institute of Health (NIH) recommendations. This study looks to analyze current online materials relating to posterior cruciate ligament (PCL) surgery and their readability, understandability, and actionability.
Scrubs have become widespread office attire for plastic surgeons. The purpose of this study is to evaluate the public perception of scrub color and style for plastic surgeons.
Diffusion models have achieved impressive success in generating photorealistic images, but challenges remain in ensuring precise semantic alignment with input prompts. Optimizing the initial noisy latent offers a more efficient alternative to modifying model architectures or prompt engineering for improving semantic alignment. A latest approach, InitNo, refines the initial noisy latent by leveraging attention maps; however, these maps capture only limited information, and the effectiveness of InitNo is highly dependent on the initial starting point, as it tends to converge on a local optimum near this point. To this end, this paper proposes leveraging the language comprehension capabilities of large vision-language models (LVLMs) to guide the optimization of the initial noisy latent, and introduces the Noise Diffusion process, which updates the noisy latent to generate semantically faithful images while preserving distribution consistency. Furthermore, we provide a theoretical analysis of the condition under which the update improves semantic faithfulness. Experimental results demonstrate the effectiveness and adaptability of our framework, consistently enhancing semantic alignment across various diffusion models. The code is available at https://github.com/Bomingmiao/NoiseDiffusion.
Chronic heart failure (CHF) is associated with increased levels of oxidant stress in many tissues. High levels of reactive oxygen species (ROS) impairs cardiac, neural and renal function. Increased ROS in CHF is, in part, due to low levels of a variety of antioxidant enzymes. Many of these enzymes are regulated by the activity of the transcription factor Nuclear factor E2‐related factor 2 (Nrf2), which binds to antioxidant response elements (ARE) on multiple genes. Activation of Nrf2 has been shown to modulate a variety of disease states as well as several negative effects of the aging process. We hypothesized that pharmacological activation of systemic Nrf2 would provide beneficial hemodynamic effects in a rodent model of CHF. Male Sprague Dawley rats were subjected to left coronary artery ligation or sham surgery, and were divided into four groups (Sham+Vehicle, Sham+Nrf2 activator, CHF+Vehicle and CHF+Nrf2 activator) based on echocardiographic analysis 4 weeks post myocardial infarction. The Nrf2 activator, bardoxolone was administered daily for another 2 weeks by intraperitoneal injection (i.p) injection. Control animals were treated with the saline vehicle. Hemodynamics were evaluated by ventricular catheterization (Millar) and echocardiography (Vevo 2100). Animals were sacrificed, tissues taken and kept at −80 °C until analyzed. Our results demonstrated that Bardoxolone did not cause any changes in LV ejection fraction in either group (72.1±1.3 sham‐vehicle vs 73.0±1.2 sham‐bardoxolone; 32.9± 0.8 CHF‐vehicle vs 33.5±0.8 CHF‐bardoxolone). However, Bardoxolone significantly reduced LV systolic pressure and mean arterial pressure in CHF rats (LVSP; Vehicle 138.0±8.0 vs bardoxolone 117.0±3.9 mm Hg, p=0.04; MAP; Vehicle 97.8±2.9 vs bardoxolone 87.5±4.5 mm Hg, p=0.07). In addition, Bardoxolone had no effect on LVEDP or LV dp/dt max or min in rats with CHF. Molecular studies further revealed that In CHF rats Nrf2 mRNA was increased in the non‐infarcted areas of the heart by 2.5‐fold. Importantly, both mRNA and protein levels of NAD(P)H dehydrogenase [quinone] 1 (NQO1), a known target for Nrf2, was significantly increased by bardoxolone. Co‐IP experiments in skeletal muscle also showed that Bardoxolone reduced NF‐kB and increased Nrf2 binding to the CREB‐Binding Protein, respectively. These data suggest that activation of Nrf2 may provide beneficial antioxidant effects on cardiac muscle in CHF and reduces afterload. The mechanisms for the peripheral effects have not been determined. Support or Funding Information Supported by PO1 HL 62222 and an APS summer undergraduate fellowship This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
Introduction: This systematic review examines the use of machine learning (ML) algorithms to detect hypernasal speech in patients with cleft palate (CP), which can persist after primary repair surgery, and require revision. Due to a shortage of speech language pathologists (SLPs), automated detection tools could help improve access to care in underserved areas. The study compares the characteristics and accuracy of different types of machine learning algorithms. Methods: On August 18, 2021, searches were conducted across 8 databases: PubMed, SCOPUS, Cochrane, IEEE, ACM, L&LB, PsychInfo, and CINAHL. Search terms used were: (Artificial Intelligence OR Machine Learning OR Neural networks AND Cleft lip OR Cleft palate OR Hypernasality OR Velopharyngeal Insufficiency). To be included, papers needed to describe ML algorithms for CP speech detection and report concordance to human professional speech clinicians. Results: Database searches yielded 135 unique articles. Five articles met full inclusion criteria and 3 additional articles were identified by hand searching references of articles that passed initial screening. These algorithms were categorized as either Feature Dependent non-Deep learning (n = 5) or Feature Dependent deep learning (n = 2) algorithms or Feature Independent deep learning (n = 3) algorithms. Their pooled average concordance were 0.85, 0.93, and 0.91 respectively. Their average training database sizes were 3587, 3921, and 6306 speech samples respectively. Conclusion: Machine learning algorithms have been shown to be an effective tool for the evaluation of hypernasal speech. This systematic review has shown that ML algorithms are able to detect hypernasality with high concordance, consistent with professional speech language clinicians in a rapid, and autonomous manner. ML algorithms can extend the reach of speech language pathologists and complement their gold standard, this long-term outcome monitoring has great potential to improve treatment outcomes.
In indoor positioning, signal fluctuation is highly location-dependent. However, signal uncertainty is one critical yet commonly overlooked dimension of the radio signal to be fingerprinted. This paper reviews the commonly used Gaussian Processes (GP) for probabilistic positioning and points out the pitfall of using GP to model signal fingerprint uncertainty. This paper also proposes Deep Gaussian Processes (DGP) as a more informative alternative to address the issue. How DGP better measures uncertainty in signal fingerprinting is evaluated via simulated and realistically collected datasets.
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