STRUCTURAL STUDIES OF THE PEANUT ALLERGEN PROTEIN ARA H 2

Presented in this dissertation are comprehensive studies of the structures of the peanut allergen protein Ara h 2 and the effect of food processing (roasting) on it. A detailed elucidation of the primary structure and PTM of Ara h 2 from the raw peanuts has been described. Ara h 2 isoforms were purified and cleaved via microwave accelerated trypsin digestion. The peptide mixtures were analyzed by LC-MS/MS and targeted CID. De novo sequencing of the MS/MS spectra revealed the protein sequence of each Ara h 2 isoform. Several hydroxyproline sites have been discovered while disulfide bond structures have been partially determined. Using anti-Ara h 2 antibodies, Western blotting of 1-D gels of the raw and dark roasted peanuts was carried out in order to characterize the changes of Ara h 2 between these two samples. The result indicates that Ara h 2 may present in a much heavier form in the roasted peanuts, possibly due to crosslinking and aggregation with other proteins. Subsequent LC-MS/MS studies of trypsin digestion of five gel pieces (>100, 100-50, 50-25, 25-16 kDa) from 1-D gels of the raw and dark roasted peanuts suggests that roasting process causes the crosslinking of Ara h 2 with other proteins. This supports our results from the immunological studies

Almost disjointness preserving functionals on Banach lattices of differentiable functions (Research on preserver problems on Banach algebras and related topics)

Let C¹[0, 1] be the space of all continuously differentiable function on [0, 1]. When define the order f ≥ g by f(0) ≥ g(0) and f′ ≥ g′ pointwise on [0, 1], and the norm is defined by ∥f∥σ = |f(0)| + ∥f′∥∞, the space C¹[0, 1] is a Banach lattice. We will give the representation of bounded ε-disjointness preserving linear functionals of C¹[0, 1]

RayDF: Neural Ray-surface Distance Fields with Multi-view Consistency

In this paper, we study the problem of continuous 3D shape representations. The majority of existing successful methods are coordinate-based implicit neural representations. However, they are inefficient to render novel views or recover explicit surface points. A few works start to formulate 3D shapes as ray-based neural functions, but the learned structures are inferior due to the lack of multi-view geometry consistency. To tackle these challenges, we propose a new framework called RayDF. It consists of three major components: 1) the simple ray-surface distance field, 2) the novel dual-ray visibility classifier, and 3) a multi-view consistency optimization module to drive the learned ray-surface distances to be multi-view geometry consistent. We extensively evaluate our method on three public datasets, demonstrating remarkable performance in 3D surface point reconstruction on both synthetic and challenging real-world 3D scenes, clearly surpassing existing coordinate-based and ray-based baselines. Most notably, our method achieves a 1000x faster speed than coordinate-based methods to render an 800x800 depth image, showing the superiority of our method for 3D shape representation. Our code and data are available at https://github.com/vLAR-group/RayDFComment: Added the last 3 authors in the camera-ready version. NeurIPS 2023. Code and data are available at: https://github.com/vLAR-group/RayD

Public perceptions and discussions of synthetic nicotine on Twitter

BackgroundAs alternative replacement products for tobacco-derived nicotine, synthetic nicotine products have recently emerged and gained increasing popularity. This study analyzes public perception and discussion of synthetic nicotine products on Twitter (now “X”).MethodsThrough Twitter streaming API (Application Programming Interface), we have collected 2,764 Twitter posts related to synthetic nicotine from December 12, 2021, to October 17, 2022, using keywords related to synthetic nicotine. By applying an inductive approach, two research assistants manually determined the relevance of tweets to synthetic nicotine products and assessed the attitude of tweets as positive, negative, and neutral of tweets toward synthetic nicotine, and the main topics.ResultsAmong 1,007 tweets related to synthetic nicotine products, the proportion of negative tweets (383/1007, 38.03%) toward synthetic nicotine products was significantly higher than that of positive tweets (218/1007, 21.65%) with a p-value <0.05. Among negative tweets, major topics include the concern about addiction and health risks of synthetic nicotine products (44.91%) and synthetic nicotine as a policy loophole (31.85%). Among positive tweets, top topics include alternative replacement for nicotine (39.91%) and reduced health risks (31.19%).ConclusionThere are mixed attitudes toward synthetic nicotine products on Twitter, resulting from different perspectives. Future research could incorporate demographic information to understand the attitudes of various population groups

Assessment of landslide susceptibility in the Huangshui River Basin based on catastrophe theory

The Huangshui River Basin, an important upstream tributary of the upper reaches of the Yellow River, serves as the political, economic, and cultural center of Qinghai Province, and is also a region with a high incidence of geological disasters. Within this basin, various types of disasters occur frequently, resulting in significant economic losses and casualties. These disasters also exhibit distinct regional characteristics. Based on the analysis of landform, geological rock group, geological structure, hydrometeorology and human engineering activities in the Huangshui River Basin, the comprehensive classification standard of geological disaster susceptibility was established, and the susceptibility of landslide disaster in the Huangshui River Basin was categorized into five levels: extremely high, high, medium, low and very low. Using a matlab-based platform for catastrophe theory, the inherent relationships among various assessment factors are fully considered, and the single-point disaster risk evaluation is extended to regional disaster susceptibility evaluation. Validation of the assessment results through ROC analysis demonstrates the high accuracy of this method, providing theoretical support for geological disaster prevention and control

Dynamic ASR Pathways: An Adaptive Masking Approach Towards Efficient Pruning of A Multilingual ASR Model

Neural network pruning offers an effective method for compressing a multilingual automatic speech recognition (ASR) model with minimal performance loss. However, it entails several rounds of pruning and re-training needed to be run for each language. In this work, we propose the use of an adaptive masking approach in two scenarios for pruning a multilingual ASR model efficiently, each resulting in sparse monolingual models or a sparse multilingual model (named as Dynamic ASR Pathways). Our approach dynamically adapts the sub-network, avoiding premature decisions about a fixed sub-network structure. We show that our approach outperforms existing pruning methods when targeting sparse monolingual models. Further, we illustrate that Dynamic ASR Pathways jointly discovers and trains better sub-networks (pathways) of a single multilingual model by adapting from different sub-network initializations, thereby reducing the need for language-specific pruning

Research progress on the application of shoulder orthosis in rehabilitation of abnormal gait post-stroke hemiplegia

Post-stroke hemiplegia usually has an adverse impact on motor ability and stability. Patients often develop shoulder subluxation and abnormal gait due to muscle weakness， bilateral limb muscle tension imbalance， sensory abnormalities and poor joint and posture control， etc. Shoulder orthosis is often used to prevent or treat shoulder subluxation in the early stage of stroke hemiplegia， but it is still controversial. To explore the role of shoulder orthosis beyond the prevention and treatment of shoulder subluxation， and to provide theoretical basis for the selection and wearing of shoulder orthosis，the mechanism underlying the role of shoulder orthosis in improving abnormal gait post-stroke hemiplegia was elaborated， and the effects of different types of shoulder orthosis on the rehabilitation of abnormal gait post-stroke were compared

Prompting Large Language Models with Speech Recognition Abilities

Large language models have proven themselves highly flexible, able to solve a wide range of generative tasks, such as abstractive summarization and open-ended question answering. In this paper we extend the capabilities of LLMs by directly attaching a small audio encoder allowing it to perform speech recognition. By directly prepending a sequence of audial embeddings to the text token embeddings, the LLM can be converted to an automatic speech recognition (ASR) system, and be used in the exact same manner as its textual counterpart. Experiments on Multilingual LibriSpeech (MLS) show that incorporating a conformer encoder into the open sourced LLaMA-7B allows it to outperform monolingual baselines by 18% and perform multilingual speech recognition despite LLaMA being trained overwhelmingly on English text. Furthermore, we perform ablation studies to investigate whether the LLM can be completely frozen during training to maintain its original capabilities, scaling up the audio encoder, and increasing the audio encoder striding to generate fewer embeddings. The results from these studies show that multilingual ASR is possible even when the LLM is frozen or when strides of almost 1 second are used in the audio encoder opening up the possibility for LLMs to operate on long-form audio