Regulating effect of β-ketoacyl synthase domain of fatty acid synthase on fatty acyl chain length in de novo fatty acid synthesis

Fatty acid synthase (FAS) is a multifunctional homodimeric protein, and is the key enzyme required for the anabolic conversion of dietary carbohydrates to fatty acids. FAS synthesizes long-chain fatty acids from three substrates: acetyl-CoA as a primer, malonyl-CoA as a 2 carbon donor, and NADPH for reduction. The entire reaction is composed of numerous sequential steps, each catalyzed by a specific functional domain of the enzyme. FAS comprises seven different functional domains, among which the β-ketoacyl synthase (KS) domain carries out the key condensation reaction to elongate the length of fatty acid chain. Acyl tail length controlled fatty acid synthesis in eukaryotes is a classic example of how a chain building multienzyme works. Different hypotheses have been put forward to explain how those sub-units of FAS are orchestrated to produce fatty acids with proper molecular weight. In the present study, molecular dynamics simulation based binding free energy calculation and access tunnels analysis showed that the C16 acyl tail fatty acid, the major product of FAS, fits to the active site on KS domain better than any other substrates. These simulations supported a new hypothesis about the mechanism of fatty acid production ratio: the geometric shape of active site on KS domain might play a determinate role

Denoising Magnetic Resonance Spectroscopy (MRS) Data Using Stacked Autoencoder for Improving Signal-to-Noise Ratio and Speed of MRS

Background: Magnetic resonance spectroscopy (MRS) enables non-invasive detection and measurement of biochemicals and metabolites. However, MRS has low signal-to-noise ratio (SNR) when concentrations of metabolites are in the range of the million molars. Standard approach of using a high number of signal averaging (NSA) to achieve sufficient NSR comes at the cost of a long acquisition time. Purpose: We propose to use deep-learning approaches to denoise MRS data without increasing the NSA. Methods: The study was conducted using data collected from the brain spectroscopy phantom and human subjects. We utilized a stack auto-encoder (SAE) network to train deep learning models for denoising low NSA data (NSA = 1, 2, 4, 8, and 16) randomly truncated from high SNR data collected with high NSA (NSA=192) which were also used to obtain the ground truth. We applied both self-supervised and fully-supervised training approaches and compared their performance of denoising low NSA data based on improved SNRs. Results: With the SAE model, the SNR of low NSA data (NSA = 1) obtained from the phantom increased by 22.8% and the MSE decreased by 47.3%. For low NSA images of the human parietal and temporal lobes, the SNR increased by 43.8% and the MSE decreased by 68.8%. In all cases, the chemical shift of NAA in the denoised spectra closely matched with the high SNR spectra, suggesting no distortion to the spectra from denoising. Furthermore, the denoising performance of the SAE model was more effective in denoising spectra with higher noise levels. Conclusions: The reported SAE denoising method is a model-free approach to enhance the SNR of low NSA MRS data. With the denoising capability, it is possible to acquire MRS data with a few NSA, resulting in shorter scan times while maintaining adequate spectroscopic information for detecting and quantifying the metabolites of interest

Preparation and Properties of 1, 3, 5, 7-Tetranitro-1, 3, 5, 7-Tetrazocane-based Nanocomposites

A new insensitive explosive based on octahydro-1, 3, 5, 7-tetranitro-1, 3, 5, 7-tetrazocine (HMX) was prepared by spray drying using Viton A as a binder. The HMX sample without binder (HMX-1) was obtained by the same spray drying process also. The samples were characterised by Scanning Electron Microscope, and X-ray diffraction. The Differential Scanning Calorimetry and the impact sensitivity of HMX-1 and nanocomposites were also being tested. The nanocomposite morphology was found to be microspherical (1 μm to 7 μm diameter) and composed of many tiny particles, 100 nm to 200 nm in size. The crystal type of HMX-1 and HMX/Viton A agrees with raw HMX. The activation energy of raw HMX, HMX-1 and HMX/Viton A is 523.16 kJ mol-1, 435.74 kJ mol-1 and 482.72 kJ mol-1, respectively. The self-ignition temperatures of raw HMX, HMX-1 and HMX/Viton A is 279.01 °C, 277.63 °C, and 279.34 °C, respectively. The impact sensitivity order of samples is HMX/Viton A < HMX-1 < raw HMX from low to high.Defence Science Journal, Vol. 65, No. 2, March 2015, pp.131-134, DOI:http://dx.doi.org/10.14429/dsj.65.784

Bootstrap Generalization Ability from Loss Landscape Perspective

Domain generalization aims to learn a model that can generalize well on the unseen test dataset, i.e., out-of-distribution data, which has different distribution from the training dataset. To address domain generalization in computer vision, we introduce the loss landscape theory into this field. Specifically, we bootstrap the generalization ability of the deep learning model from the loss landscape perspective in four aspects, including backbone, regularization, training paradigm, and learning rate. We verify the proposed theory on the NICO++, PACS, and VLCS datasets by doing extensive ablation studies as well as visualizations. In addition, we apply this theory in the ECCV 2022 NICO Challenge1 and achieve the 3rd place without using any domain invariant methods.Comment: 18 pages, 4 figure