A Novel Three-stage Process for Continuous Production of Penicillin G Acylase by a Temperature - sensitive Expression System of Bacillus subtilis Phagephi 105

This study is pertaining to the production of penicillin G acylase (PGA) by Bacillus subtilis 8105MU331 in which PGA gene, under the control of thermal-induced promoter, was integrated. The key process parameters including induced-temperature, induced- time, and culture temperature were optimized in flask culture. A three-stage cultivation process was developed for PGA production with the expression system of B. subtilis 8105MU331. Furthermore, a bioreactor with a thermal-induced apparatus was designed for continuous production of PGA, where cell growth, induction, and PGA expression could be conducted separately. At a dilution rate of 0.20 h–1, PGA production was taken under continuous cultivation in three-stage process. After continuous feeding, the cell density, pH, and residual glucose in the first- and third-reactor were maintained steady for up to 40 h. These results suggested that the new three-stage process might be feasible and very efficient for production of heterologous proteins

For One Child

The entirety of this project was completed on the foundation of the three focus areas, which were identified by our client as areas of high need. The client wanted to prioritize these three areas as they believed that these three areas were the most integral to the successful achievement of their mission, as well as to the overall health and longevity of the organization

HandDiffuse: Generative Controllers for Two-Hand Interactions via Diffusion Models

Existing hands datasets are largely short-range and the interaction is weak due to the self-occlusion and self-similarity of hands, which can not yet fit the need for interacting hands motion generation. To rescue the data scarcity, we propose HandDiffuse12.5M, a novel dataset that consists of temporal sequences with strong two-hand interactions. HandDiffuse12.5M has the largest scale and richest interactions among the existing two-hand datasets. We further present a strong baseline method HandDiffuse for the controllable motion generation of interacting hands using various controllers. Specifically, we apply the diffusion model as the backbone and design two motion representations for different controllers. To reduce artifacts, we also propose Interaction Loss which explicitly quantifies the dynamic interaction process. Our HandDiffuse enables various applications with vivid two-hand interactions, i.e., motion in-betweening and trajectory control. Experiments show that our method outperforms the state-of-the-art techniques in motion generation and can also contribute to data augmentation for other datasets. Our dataset, corresponding codes, and pre-trained models will be disseminated to the community for future research towards two-hand interaction modeling

Revisiting VAE for Unsupervised Time Series Anomaly Detection: A Frequency Perspective

Time series Anomaly Detection (AD) plays a crucial role for web systems. Various web systems rely on time series data to monitor and identify anomalies in real time, as well as to initiate diagnosis and remediation procedures. Variational Autoencoders (VAEs) have gained popularity in recent decades due to their superior de-noising capabilities, which are useful for anomaly detection. However, our study reveals that VAE-based methods face challenges in capturing long-periodic heterogeneous patterns and detailed short-periodic trends simultaneously. To address these challenges, we propose Frequency-enhanced Conditional Variational Autoencoder (FCVAE), a novel unsupervised AD method for univariate time series. To ensure an accurate AD, FCVAE exploits an innovative approach to concurrently integrate both the global and local frequency features into the condition of Conditional Variational Autoencoder (CVAE) to significantly increase the accuracy of reconstructing the normal data. Together with a carefully designed "target attention" mechanism, our approach allows the model to pick the most useful information from the frequency domain for better short-periodic trend construction. Our FCVAE has been evaluated on public datasets and a large-scale cloud system, and the results demonstrate that it outperforms state-of-the-art methods. This confirms the practical applicability of our approach in addressing the limitations of current VAE-based anomaly detection models.Comment: WWW 202

Development of vegetarian nugget using unripe jackfruit

Jackfruit (Artocarpus heterophyllus) is well known as a good source of carbohydrate and dietary fibre. Unripe jackfruit has fibrous texture that is very similar to meat, which makes it a suitable meat alternative. High consumption of less nutrient dense foods has increased the prevalence of non-communicable diseases. This research was carried out to investigate the effect of the addition of unripe jackfruit in vegetarian nugget on sensory properties and nutritional content. This was compared with the commercial vegetarian nugget as the control. A total of 4 formulations were produced followed by determination of the best formulation through sensory evaluation using the 9-point hedonic test. The F4 formulation with 25% unripe jackfruit and 75% of konjac-tofu was chosen as the best formulation as this formula achieved higher mean scores for all attributes (Appearance=7.28±1.578, Aroma=6.48±1.502, Taste=6.14±1.852, Texture=6.52±1.717, Overall acceptance=6.72±1.485) compared to other formulations. Proximate analysis showed that it contains carbohydrate (30.90%±0.32), crude protein (9.54%±0.22), crude fat (4.94%±0.23), crude fibre (2.60%±0.23), ash (2.21%±0.03), moisture content (49.81%±0.48), and energy content of 206.22 kcal. The unripe jackfruit nugget was developed as a new and healthy convenience food product which could be an alternative for the vegetarian consumer

SAPNet: a deep learning model for identification of single-molecule peptide post-translational modifications with surface enhanced Raman spectroscopy

Nanopore resistive pulse sensors are emerging technologies for single-molecule protein sequencing. But they can hardly detect small post-translational modifications (PTMs) such as hydroxylation in single-molecule level. While a combination of surface enhanced Raman spectroscopy (SERS) with plasmonic nanopores can detect the small PTMs, the blinking Raman peaks in the single-molecule SERS spectra leads to a big challenge in data analysis and PTM identification. Herein, we developed and validated a one-dimensional convolutional neural network (1D-CNN) for amino acids and peptides identification from their PTMs including hydroxylation and phosphorylation by their single-molecule SERS spectra, named Single Amino acid and Peptide Network (SAPNet). Our work combines cutting-edge plasmonic nanopore technology for SERS signal acquisition and deep learning for fully automated extraction of information from the SERS signals. The SAPNet model achieved an overall accuracy of 99.66% for the identification of amino acids from their modification, and 98.38% for the identification of peptides from their PTM translation. We also evaluated the model with out-of-sample examples with good performance. Our work can be beneficial for early detection of diseases such as cancers and Alzheimer's disease.Comment: 20 pages, 5 figures, 2 table

Prediction of Stroke Onset Time with Combined Fast High-Resolution Magnetic Resonance Spectroscopic and Quantitative T2 Mapping

OBJECTIVE: The purpose of this work is to develop a multispectral imaging approach that combines fast high-resolution 3D magnetic resonance spectroscopic imaging (MRSI) and fast quantitative T2 mapping to capture the multifactorial biochemical changes within stroke lesions and evaluate its potentials for stroke onset time prediction. METHODS: Special imaging sequences combining fast trajectories and sparse sampling were used to obtain whole-brain maps of both neurometabolites (2.0×3.0×3.0 mm3) and quantitative T2 values (1.9×1.9×3.0 mm3) within a 9-minute scan. Participants with ischemic stroke at hyperacute (0-24h, n = 23) or acute (24h-7d, n = 33) phase were recruited in this study. Lesion N-acetylaspartate (NAA), lactate, choline, creatine, and T2 signals were compared between groups and correlated with patient symptomatic duration. Bayesian regression analyses were employed to compare the predictive models of symptomatic duration using multispectral signals. RESULTS: In both groups, increased T2 and lactate levels, as well as decreased NAA and choline levels were detected within the lesion (all p<0.001). Changes in T2, NAA, choline, and creatine signals were correlated with symptomatic duration for all patients (all p<0.005). Predictive models of stroke onset time combining signals from MRSI and T2 mapping achieved the best performance (hyperacute: R2 = 0.438; all: R2 = 0.548). CONCLUSION: The proposed multispectral imaging approach provides a combination of biomarkers that index early pathological changes after stroke in a clinical-feasible time and improves the assessment of the duration of cerebral infarction. SIGNIFICANCE: Developing accurate and efficient neuroimaging techniques to provide sensitive biomarkers for prediction of stroke onset time is of great importance for maximizing the proportion of patients eligible for therapeutic intervention. The proposed method provides a clinically feasible tool for the assessment of symptom onset time post ischemic stroke, which will help guide time-sensitive clinical management

A visual method for direct selection of high-producing Pichia pastoris clones

<p>Abstract</p> <p>Background</p> <p>The methylotrophic yeast, <it>Pichia pastoris</it>, offers the possibility to generate a high amount of recombinant proteins in a fast and easy way to use expression system. Being a single-celled microorganism, <it>P. pastoris </it>is easy to manipulate and grows rapidly on inexpensive media at high cell densities. A simple and direct method for the selection of high-producing clones can dramatically enhance the whole production process along with significant decrease in production costs.</p> <p>Results</p> <p>A visual method for rapid selection of high-producing clones based on mannanase reporter system was developed. The study explained that it was possible to use mannanase activity as a measure of the expression level of the protein of interest. High-producing target protein clones were directly selected based on the size of hydrolysis holes in the selected plate. As an example, the target gene (9elp-hal18) was expressed and purified in <it>Pichia pastoris </it>using this technology.</p> <p>Conclusions</p> <p>A novel methodology is proposed for obtaining the high-producing clones of proteins of interest, based on the mannanase reporter system. This system may be adapted to other microorganisms, such as <it>Saccharomyces cerevisiae </it>for the selection of clones.</p