Role of endoplasmic reticulum stress in disuse osteoporosis

Osteoporosis is a major skeletal disease with low bone mineral density, which leads to an increased risk of bone fracture. Salubrinal is a synthetic chemical that inhibits dephosphorylation of eukaryotic translation initiation factor 2 alpha (eIF2α) in response to endoplasmic reticulum (ER) stress. To understand possible linkage of osteoporosis to ER stress, we employed an unloading mouse model and examined the effects of salubrinal in the pathogenesis of disuse osteoporosis. The results presented several lines of evidence that osteoclastogenesis in the development of osteoporosis was associated with ER stress, and salubrinal suppressed unloading-induced bone loss. Compared to the age-matched control, unloaded mice reduced the trabecular bone area/total area (B.Ar/T.Ar) as well as the number of osteoblasts, and they increased the osteoclasts number on the trabecular bone surface in a time-dependent way. Unloading-induced disuse osteoporosis significantly increased the expression of Bip, p-eIF2α and ATF4 in short-term within 6 h of tail suspension, but time-dependent decreased in HU2d to HU14d. Furthermore, a significant correlation of ER stress with the differentiation of osteoblasts and osteoclasts was observed. Administration of salubrinal suppressed the unloading-induced decrease in bone mineral density, B.Ar/T.Ar and mature osteoclast formation. Salubrinal also increased the colony-forming unit-fibroblasts and colony-forming unit-osteoblasts. It reduced the formation of mature osteoclasts, suppressed their migration and adhesion, and increased the expression of Bip, p-eIF2α and ATF4. Electron microscopy showed that rough endoplasmic reticulum expansion and a decreased number of ribosomes on ER membrane were observed in osteoblast of unloading mice, and the abnormal ER expansion was significantly improved by salubrinal treatment. A TUNEL assay together with CCAAT/enhancer binding protein homologous protein (CHOP) expression indicated that ER stress-induced osteoblast apoptosis was rescued by salubrinal. Collectively, the results support the notion that ER stress plays a key role in the pathogenesis of disuse osteoporosis, and salubrinal attenuates unloading-induced bone loss by altering proliferation and differentiation of osteoblasts and osteoclasts via eIF2α signaling

Utilizing Win Ratio Approaches and Two-Stage Enrichment Designs for Small-Sized Clinical Trials

Conventional methods for analyzing composite endpoints in clinical trials often only focus on the time to the first occurrence of all events in the composite. Therefore, they have inherent limitations because the individual patients' first event can be the outcome of lesser clinical importance. To overcome this limitation, the concept of the win ratio (WR), which accounts for the relative priorities of the components and gives appropriate priority to the more clinically important event, was examined. For example, because mortality has a higher priority than hospitalization, it is reasonable to give a higher priority when obtaining the WR. In this paper, we evaluate three innovative WR methods (stratified matched, stratified unmatched, and unstratified unmatched) for two and multiple components under binary and survival composite endpoints. We compare these methods to traditional ones, including the Cox regression, O'Brien's rank-sum-type test, and the contingency table for controlling study Type I error rate. We also incorporate these approaches into two-stage enrichment designs with the possibility of sample size adaptations to gain efficiency for rare disease studies

The Symphony of Alignment: Ensuring Fairness and Mitigating Bias in Foundation Models

Foundation models are poised to revolutionize decision-making across various domains, but their reliance on historical data can perpetuate and amplify existing biases. This risk of reinforcing societal stereotypes through biased outputs underscores the critical need to evaluate and mitigate biases in these models to ensure their responsible and ethical use. In this dissertation, we delve into three critical challenges in ensuring fairness and mitigating bias in foundation models and AI systems. It comprises three main contributions: (1) An exploration of fair learning under uncertainty, particularly when sensitive attributes are corrupted. The research proposes noise-resistant fair Empirical Risk Minimization approaches and a novel method for detecting groups with higher noise levels in labels. (2) An investigation into fairness and bias in multimodal applications of foundation models, including image search, multilingual text retrieval, and text-to-image generation. The study develops new intervention methods for mitigating gender bias in image search, reveals intrinsic trade-offs in multilingual fairness, and introduces association test in text-to-image generations. (3) The development of fairness influence functions to quantify the impact of individual data examples on model fairness. This approach offers insights into machine unlearning, with efficient approximation techniques for large-scale applications. Ultimately, the thesis strives to advance the understanding of fairness in foundation models through the development of both theoretical frameworks and practical evaluations for responsible AI

CFO: Calibration-Free Odds Bayesian Designs for Dose Finding in Clinical Trials

PURPOSECalibration-free odds type (CFO-type) designs have been demonstrated to be robust, model-free, and practically useful, which have become the state-of-the-art approach for dose finding. However, a key challenge for implementing such designs is a lack of accessible tools. We develop a user-friendly R package and Shiny web-based software to facilitate easy implementation of CFO-type designs. Moreover, we incorporate randomization into the CFO framework.METHODSWe created the R package CFO and leveraged R Shiny to build an interactive web application, CFO suite, for implementing CFO-type designs. We introduce the randomized CFO (rCFO) design by integrating the exploration-exploitation mechanism into the CFO framework.RESULTSThe CFO package and CFO suite encompass various variants tailored to different clinical settings. Beyond the fundamental CFO design, these include the two-dimensional CFO (2dCFO) for drug-combination trials, accumulative CFO (aCFO) for accommodating all dose information, rCFO for integrating exploration-exploitation via randomization, time-to-event CFO (TITE-CFO), and fractional CFO (fCFO) for addressing late-onset toxicity. Using all information and addressing delayed toxicity outcomes, TITE-aCFO and fractional-aCFO are also included. The package provides functions for determining the subsequent cohort dose, selecting the maximum tolerated dose, and conducting simulations to evaluate performance, with results presented through textual and graphical outputs.CONCLUSIONThe CFO package and CFO suite provide comprehensive and flexible tools for implementing CFO-type designs in phase I clinical trials. This work is highly significant as it integrates all existing CFO-type designs to facilitate novel trial designs with enhanced performance. In addition, this promotes the spread of statistical methods using a user-friendly R package and Shiny software. It strengthens collaborations between biostatisticians and clinicians, further enhancing trial performance in terms of efficiency and accuracy.published_or_final_versio

DPATD: Dual-Phase Audio Transformer for Denoising

Recent high-performance transformer-based speech enhancement models demonstrate that time domain methods could achieve similar performance as time-frequency domain methods. However, time-domain speech enhancement systems typically receive input audio sequences consisting of a large number of time steps, making it challenging to model extremely long sequences and train models to perform adequately. In this paper, we utilize smaller audio chunks as input to achieve efficient utilization of audio information to address the above challenges. We propose a dual-phase audio transformer for denoising (DPATD), a novel model to organize transformer layers in a deep structure to learn clean audio sequences for denoising. DPATD splits the audio input into smaller chunks, where the input length can be proportional to the square root of the original sequence length. Our memory-compressed explainable attention is efficient and converges faster compared to the frequently used self-attention module. Extensive experiments demonstrate that our model outperforms state-of-the-art methods.Comment: IEEE DD

DCHT: Deep Complex Hybrid Transformer for Speech Enhancement

Most of the current deep learning-based approaches for speech enhancement only operate in the spectrogram or waveform domain. Although a cross-domain transformer combining waveform- and spectrogram-domain inputs has been proposed, its performance can be further improved. In this paper, we present a novel deep complex hybrid transformer that integrates both spectrogram and waveform domains approaches to improve the performance of speech enhancement. The proposed model consists of two parts: a complex Swin-Unet in the spectrogram domain and a dual-path transformer network (DPTnet) in the waveform domain. We first construct a complex Swin-Unet network in the spectrogram domain and perform speech enhancement in the complex audio spectrum. We then introduce improved DPT by adding memory-compressed attention. Our model is capable of learning multi-domain features to reduce existing noise on different domains in a complementary way. The experimental results on the BirdSoundsDenoising dataset and the VCTK+DEMAND dataset indicate that our method can achieve better performance compared to state-of-the-art methods.Comment: IEEE DDP conferenc