Heat shock protein 90 in neurodegenerative diseases

Hsp90 is a molecular chaperone with important roles in regulating pathogenic transformation. In addition to its well-characterized functions in malignancy, recent evidence from several laboratories suggests a role for Hsp90 in maintaining the functional stability of neuronal proteins of aberrant capacity, whether mutated or over-activated, allowing and sustaining the accumulation of toxic aggregates. In addition, Hsp90 regulates the activity of the transcription factor heat shock factor-1 (HSF-1), the master regulator of the heat shock response, mechanism that cells use for protection when exposed to conditions of stress. These biological functions therefore propose Hsp90 inhibition as a dual therapeutic modality in neurodegenerative diseases. First, by suppressing aberrant neuronal activity, Hsp90 inhibitors may ameliorate protein aggregation and its associated toxicity. Second, by activation of HSF-1 and the subsequent induction of heat shock proteins, such as Hsp70, Hsp90 inhibitors may redirect neuronal aggregate formation, and protect against protein toxicity. This mini-review will summarize our current knowledge on Hsp90 in neurodegeneration and will focus on the potential beneficial application of Hsp90 inhibitors in neurodegenerative diseases

Gait-based identification for elderly users in wearable healthcare systems

Abstract The increasing scope of sensitive personal information that is collected and stored in wearable healthcare devices includes physical, physiological, and daily activities, which makes the security of these devices very essential. Gait-based identity recognition is an emerging technology, which is increasingly used for the access control of wearable devices, due to its outstanding performance. However, gait-based identity recognition of elderly users is more challenging than that of young adults, due to significant intra-subject gait fluctuation, which becomes more pronounced with user age. This study introduces a gait-based identity recognition method used for the access control of elderly people-centred wearable healthcare devices, which alleviates the intra-subject gait fluctuation problem and provides a significant recognition rate improvement, as compared to available methods. Firstly, a gait template synthesis method is proposed to reduce the intra-subject gait fluctuation of elderly users. Then, an arbitration-based score level fusion method is defined to improve the recognition accuracy. Finally, the proposed method feasibility is verified using a public dataset containing acceleration signals from three IMUs worn by 64 elderly users with the age range from 50 to 79 years. The experimental results obtained prove that the average recognition rate of the proposed method reaches 96.7%. This makes the proposed method quite lucrative for the robust gait-based identification of elderly users of wearable healthcare devices

CPET: Effective Parameter-Efficient Tuning for Compressed Large Language Models

Parameter-efficient tuning (PET) has been widely explored in recent years because it tunes much fewer parameters (PET modules) than full-parameter fine-tuning (FT) while still stimulating sufficient knowledge from large language models (LLMs) for downstream tasks. Moreover, when PET is employed to serve multiple tasks, different task-specific PET modules can be built on a frozen LLM, avoiding redundant LLM deployments. Although PET significantly reduces the cost of tuning and deploying LLMs, its inference still suffers from the computational bottleneck of LLMs. To address the above issue, we propose an effective PET framework based on compressed LLMs, named "CPET". In CPET, we evaluate the impact of mainstream LLM compression techniques on PET performance and then introduce knowledge inheritance and recovery strategies to restore the knowledge loss caused by these compression techniques. Our experimental results demonstrate that, owing to the restoring strategies of CPET, collaborating task-specific PET modules with a compressed LLM can achieve comparable performance to collaborating PET modules with the original version of the compressed LLM and outperform directly applying vanilla PET methods to the compressed LLM

Towards Omni-supervised Referring Expression Segmentation

Referring Expression Segmentation (RES) is an emerging task in computer vision, which segments the target instances in images based on text descriptions. However, its development is plagued by the expensive segmentation labels. To address this issue, we propose a new learning task for RES called Omni-supervised Referring Expression Segmentation (Omni-RES), which aims to make full use of unlabeled, fully labeled and weakly labeled data, e.g., referring points or grounding boxes, for efficient RES training. To accomplish this task, we also propose a novel yet strong baseline method for Omni-RES based on the recently popular teacher-student learning, where the weak labels are not directly transformed into supervision signals but used as a yardstick to select and refine high-quality pseudo-masks for teacher-student learning. To validate the proposed Omni-RES method, we apply it to a set of state-of-the-art RES models and conduct extensive experiments on a bunch of RES datasets. The experimental results yield the obvious merits of Omni-RES than the fully-supervised and semi-supervised training schemes. For instance, with only 10% fully labeled data, Omni-RES can help the base model achieve 100% fully supervised performance, and it also outperform the semi-supervised alternative by a large margin, e.g., +14.93% on RefCOCO and +14.95% on RefCOCO+, respectively. More importantly, Omni-RES also enable the use of large-scale vision-langauges like Visual Genome to facilitate low-cost RES training, and achieve new SOTA performance of RES, e.g., 80.66 on RefCOCO

OpenDelta: A Plug-and-play Library for Parameter-efficient Adaptation of Pre-trained Models

The scale of large pre-trained models (PTMs) poses significant challenges in adapting to downstream tasks due to the high optimization overhead and storage costs associated with full-parameter fine-tuning. To address this, many studies explore parameter-efficient tuning methods, also framed as "delta tuning", which updates only a small subset of parameters, known as "delta modules", while keeping the backbone model's parameters fixed. However, the practicality and flexibility of delta tuning have been limited due to existing implementations that directly modify the code of the backbone PTMs and hard-code specific delta tuning methods for each PTM. In this paper, we present OpenDelta, an open-source library that overcomes these limitations by providing a plug-and-play implementation of various delta tuning methods. Our novel techniques eliminate the need to modify the backbone PTMs' code, making OpenDelta compatible with different, even novel PTMs. OpenDelta is designed to be simple, modular, and extensible, providing a comprehensive platform for researchers and practitioners to adapt large PTMs efficiently.Comment: Accepted to ACL 2023 Demo trac

X-Mesh: Towards Fast and Accurate Text-driven 3D Stylization via Dynamic Textual Guidance

Text-driven 3D stylization is a complex and crucial task in the fields of computer vision (CV) and computer graphics (CG), aimed at transforming a bare mesh to fit a target text. Prior methods adopt text-independent multilayer perceptrons (MLPs) to predict the attributes of the target mesh with the supervision of CLIP loss. However, such text-independent architecture lacks textual guidance during predicting attributes, thus leading to unsatisfactory stylization and slow convergence. To address these limitations, we present X-Mesh, an innovative text-driven 3D stylization framework that incorporates a novel Text-guided Dynamic Attention Module (TDAM). The TDAM dynamically integrates the guidance of the target text by utilizing text-relevant spatial and channel-wise attentions during vertex feature extraction, resulting in more accurate attribute prediction and faster convergence speed. Furthermore, existing works lack standard benchmarks and automated metrics for evaluation, often relying on subjective and non-reproducible user studies to assess the quality of stylized 3D assets. To overcome this limitation, we introduce a new standard text-mesh benchmark, namely MIT-30, and two automated metrics, which will enable future research to achieve fair and objective comparisons. Our extensive qualitative and quantitative experiments demonstrate that X-Mesh outperforms previous state-of-the-art methods.Comment: Technical repor

Serum Immunoglobulin A (IgA) Level Is a Potential Biomarker Indicating Cirrhosis during Chronic Hepatitis B Infection

Background. Serum immunoglobulins (Igs) are frequently elevated in patients with chronic liver disease, but currently there is a lack of sufficient data on serum Igs in patients with chronic hepatitis B virus (CHB) infection. This study aimed to evaluate serum IgA, IgG, and IgM levels in patients with HBV-related cirrhosis and to analyze, if altered, immunoglobulin levels that were associated with cirrhosis progress. Methods. A cohort of 174 CHB patients including 104 with cirrhosis (32 decompensated and 72 compensated) and 70 without cirrhosis and 55 healthy controls were enrolled. Serum immunoglobulin levels and biochemical and virological parameters were determined in the enrollment blood samples. Results. Serum IgA levels were significantly increased in cirrhosis group compared with noncirrhosis group and healthy controls (all P<0.001). Furthermore, serum IgA concentrations in decompensated cirrhosis patients were significantly higher than that of compensated patients (P=0.002). Multivariate analysis suggested that serum IgA, platelets, and albumin were independent predictors for cirrhosis (all P<0.001). Conclusions. Elevated IgA levels may function as an independent factor indicating cirrhosis, and there appears to be a strong association between increasing serum IgA level and disease progressing in patients with chronic HBV infection

The insulin signaling pathway in \u3cem\u3eDrosophila melanogaster\u3c/em\u3e: A nexus revealing an Achilles\u27 heel in DDT resistance

Insecticide resistance is an ongoing challenge in agriculture and disease vector control. Here, we demonstrate a novel strategy to attenuate resistance. We used genomics tools to target fundamental energy-associated pathways and identified a potential “Achilles\u27 heel” for resistance, a resistance-associated protein that, upon inhibition, results in a substantial loss in the resistance phenotype. Specifically, we compared the gene expression profiles and structural variations of the insulin/insulin-like growth factor signaling (IIS) pathway genes in DDT-susceptible (91-C) and -resistant (91-R) Drosophila melanogaster (Drosophila) strains. A total of eight and seven IIS transcripts were up- and down-regulated, respectively, in 91-R compared to 91-C. A total of 114 nonsynonymous mutations were observed between 91-C and 91-R, of which 51.8% were fixed. Among the differentially expressed transcripts, phosphoenolpyruvate carboxykinase (PEPCK), down-regulated in 91-R, encoded the greatest number of amino acid changes, prompting us to perform PEPCK inhibitor–pesticide exposure bioassays. The inhibitor of PEPCK, hydrazine sulfate, resulted in a 161- to 218-fold decrease in the DDT resistance phenotype (91-R) and more than a 4- to 5-fold increase in susceptibility in 91-C. A second target protein, Glycogen synthase kinase 3β (GSK3β-PO), had one amino acid difference between 91-C and 91-R, and the corresponding transcript was also down-regulated in 91-R. A GSK3β-PO inhibitor, lithium chloride, likewise reduced the resistance but to a lesser extent than did hydrazine sulfate for PEPCK. We demonstrate the potential role of IIS genes in DDT resistance and the potential discovery of an “Achilles\u27 heel” against pesticide resistance in this pathway

Neutrophil-to-Lymphocyte Ratio Predicts Early Mortality in Patients with HBV-Related Decompensated Cirrhosis

Background. The neutrophil-to-lymphocyte ratio (NLR) is an inflammation index that has been shown to independently predict poor clinical outcomes. We aimed to evaluate the clinical value of NLR in the prediction of 30-day mortality in patients with HBV-related decompensated cirrhosis (HBV-DeCi). Methods. This was a retrospective cohort study that included 148 patients with HBV-DeCi. Results. An elevated NLR was associated with increased severity of liver disease and mortality within 30 days. Multivariate analysis suggested that NLR, similar to the model for end-stage liver disease (MELD) score, is an additional independent predictor of 30-day mortality (P<0.01). Conclusion. Our results suggest that a high NLR can be considered a new independent biomarker for predicting 30-day mortality in patients with HBV-DeCi