arXiv subjects

Jiahao Ma

Publications and source records attributed to Jiahao Ma.

25 records · Page 2Linked to original sources

arXiv2023

Comparative Analysis of Machine Learning Algorithms for Predicting On-Target and Off-Target Effects of CRISPR-Cas13d for gene editing

CRISPR-Cas13 is a system that utilizes single stranded RNAs for RNA editing. Prediction of on-target and off-target effects for the CRISPR-Cas13d dependency enables us to design specific single guide RNAs (sgRNAs) that help locate the desired RNA target positions. In this study, we compared the performance of multiple machine learning algorithms in predicting these effects using a reported dataset. Our results show that Catboost is the most accurate model with high sensitivity. This finding represents a significant advancement in our understanding of how to chose modeling methods to deal with RNA sequence feaatures effictivelys. Furthermore, our approach can potentially be applied to other CRISPR systems and genetic engineering techniques. Overall, this work has important implications for developing safer and more effective gene therapies and biotechnological applications.

q-bio.QM↗

arXiv2023

Multiview Detection with Cardboard Human Modeling

Multiview detection uses multiple calibrated cameras with overlapping fields of views to locate occluded pedestrians. In this field, existing methods typically adopt a ``human modeling - aggregation'' strategy. To find robust pedestrian representations, some intuitively incorporate 2D perception results from each frame, while others use entire frame features projected to the ground plane. However, the former does not consider the human appearance and leads to many ambiguities, and the latter suffers from projection errors due to the lack of accurate height of the human torso and head. In this paper, we propose a new pedestrian representation scheme based on human point clouds modeling. Specifically, using ray tracing for holistic human depth estimation, we model pedestrians as upright, thin cardboard point clouds on the ground. Then, we aggregate the point clouds of the pedestrian cardboard across multiple views for a final decision. Compared with existing representations, the proposed method explicitly leverages human appearance and reduces projection errors significantly by relatively accurate height estimation. On four standard evaluation benchmarks, the proposed method achieves very competitive results. Our code and data will be released at https://github.com/ZichengDuan/MvCHM.

cs.CV↗

arXiv2023

Voxelized 3D Feature Aggregation for Multiview Detection

Multi-view detection incorporates multiple camera views to alleviate occlusion in crowded scenes, where the state-of-the-art approaches adopt homography transformations to project multi-view features to the ground plane. However, we find that these 2D transformations do not take into account the object's height, and with this neglection features along the vertical direction of same object are likely not projected onto the same ground plane point, leading to impure ground-plane features. To solve this problem, we propose VFA, voxelized 3D feature aggregation, for feature transformation and aggregation in multi-view detection. Specifically, we voxelize the 3D space, project the voxels onto each camera view, and associate 2D features with these projected voxels. This allows us to identify and then aggregate 2D features along the same vertical line, alleviating projection distortions to a large extent. Additionally, because different kinds of objects (human vs. cattle) have different shapes on the ground plane, we introduce the oriented Gaussian encoding to match such shapes, leading to increased accuracy and efficiency. We perform experiments on multiview 2D detection and multiview 3D detection problems. Results on four datasets (including a newly introduced MultiviewC dataset) show that our system is very competitive compared with the state-of-the-art approaches. %Our code and data will be open-sourced.Code and MultiviewC are released at https://github.com/Robert-Mar/VFA.

cs.CV↗

arXiv2022

Simulation and application of COVID-19 compartment model using physics-informed neural network

COVID-19 pandemic has had a disruptive and irreversible impact globally, yet traditional epidemiological modeling approaches such as the susceptible-infected-recovered (SIR) model have exhibited limited effectiveness in forecasting of the up-to-date pandemic situation. In this work, susceptible-vaccinated-exposed-infected-dead-recovered (SVEIDR) model and its variants -- aged and vaccination-structured SVEIDR models -- are introduced to encode the effect of social contact for different age groups and vaccination status. Then, we implement the physics-informed neural network (PiNN) on both simulated and real-world data. The PiNN model enables robust analysis of the dynamic spread, prediction, and parameter optimization of the COVID-19 compartmental models. The models exhibit relative root mean square error (RRMSE) of $<4\%$ for all components and provide incubation, death, and recovery rates of $γ= 0.0130$, $λ=0.0001$, and $ρ=0.0037$, respectively, for the first 310 days of the epidemic in the US with RRMSE of $<0.35\%$ for all components. To further improve the model performance, temporally varying parameters can be included, such as vaccination, transmission, and incubation rates. Our implementation highlights PiNN as a reliable candidate approach for forecasting real-world data and can be applied to other compartmental model variants of interest.

q-bio.QM↗

arXiv2022

Scanning Electron Microscopy and Metabolite Measurement Revealed the Stress Mechanism of PS-COOH Microplastics on Rhodotorula mucilaginosa AN5

Microplastics in the marine environment have been paid more and more attention by researchers, and the impact of these substances on marine microorganisms can not be ignored. Studies have shown that PS-COOH Microplastics are harmful to marine molluscs, algae and monads. This study explore the effect and mechanism of microplastics (80 nm PS-COOH) on Antarctic marine yeast, Rhodotorula mucilaginosa AN5 by bacterial count, Scanning Electron Microscopy (SEM) and metabolite analysis. The results illustrates that a 50 mg/L concentration of PS-COOH could inhibit 36.15% growth of yeast cells and 10 mg/L inhibit 80.20%. Microplastics stress causes changes in the content of some oxidative stress substances, including reactive oxygen species (ROS) 42.86% , malondialdehyde (MDA) 54.06% content and the activities of antioxidant enzymes such as catalase (CAT) 36.00% , peroxidase (POD) 66.67% and superoxide dismutase (SOD) 25.40%. These results revealed the possible stress effect of microplastic pollution on marine yeast and may affect bottom layer of marine ecosystem.

q-bio.CB↗

arXiv2022

Simulation, Modeling and Prediction of a Pharmacodynamic Animal Tissue Culture Compartment Model by Physical Informed Neural Network

Compartment models of cell culture are widely used in cytology, pharmacology, toxicology and other fields. Numerical simulation, data modeling and prediction of compartment models can be realized by traditional differential equation modeling methods. At the same time, with the development of software and hardware, Physical Informed Neural Network (PINN) is widely used to solve differential equation models. This work models, simulates and predicts the cell culture compartment model based on the machine learning framework PyTorch with an 16 hidden layers neural network, including 8 linear layers and 8 feedback active layers. The results showed a loss value of 0.0004853 for three-component four-parameter quantitative pharmacodynamic model predictions in this way, which is evaluated by Mean Square Error (MSE). In summary, Physical Informed Neural Network can serve as an effective tool to deal with cell culture compartment models and may perform better in dealing with big datasets.

q-bio.QM↗

arXiv2022

Bioinformatic analysis for structure and function of Glutamine synthetase(GS)

Objective: To predict structure and function of Glutamine synthetase (GS) from Pseudoalteromonas sp. by bioinformatics technology, and to provide a theoretical basis for further study. Methods: Open reading frame (ORF) of GS sequence from Pseudoalteromonas sp. was obtained by ORF finder and was translated into amino acid residue. The structure domain was analyzed by Blast. By the method of analysis tools: Protparam, ProtScale, SignalP-4.0, TMHMM, SOPMA, SWISS-MODEL, NCBI SMART-BLAST and MAGA 7.0, the structure and function of the protein were predicted and analyzed. Results: The results showed that the sequence was GS with 468 amino acid residues, theoretical molecular weight was 51986.64 Da. The protein has the closest evolutionary status with Shewanella oneidensis. Then it had no signal peptide site and transmembrane domain. Secondary structure of GS contained 35.04% alpha-helix, 16.67% Extended chain, 5.34% beta-turn, 42.95% RandomCoil. Conclusions: This GU was a variety of biological functions of protein that may be used as a molecular samples of microbial nitrogen metabolism in extreme environments.

q-bio.BM↗