Application of Machine Learning in the Detection of Antimicrobial Resistance

Antimicrobial resistance (AMR) has become one of the significant global threats to both human and animal health, intensifying the need for rapid and precise AMR diagnostic methods. Traditional antimicrobial susceptibility testing (AST) is time-consuming, low throughput, and limited to cultivable bacteria. Machine learning offers a promising avenue for automated AMR prediction. However, most existing models emphasize features related only to known resistance genes and variants, relying heavily on AMR reference databases, and thus may overlook new AMR-related features. To address the above challenges, my first study introduces genome-wide machine learning models to detect AMR without dependence on prior AMR knowledge efficiently. Specifically, I assessed various models, including logistic regression (LR), support vector machine (SVM), random forest (RF), and convolutional neural network (CNN), for predicting resistance against four antibiotics. The findings illustrated that these models can effectively predict AMR with label encoding, one-hot encoding, and frequency matrix chaos game representation (FCGR) encoding on whole-genome sequencing data. Generally, RF and CNN outperformed LR and SVM. Importantly, I identified specific mutations associated with AMR for each antibiotic. Moreover, current AMR studies focus on single-drug resistance prediction, ignoring the cumulative nature of antimicrobial resistance over time, which makes rapid identification of multi-drug resistance (MDR) a challenge. Therefore, in my second study, in order to overcome these limitations, I constructed five multi-label classification (MLC) models for MDR problems. The findings revealed that the ECC (Ensemble Classifier Chains) model surpassed the other MLC methods, demonstrating marked effectiveness in predicting MDR. Furthermore, the constraints of limited training samples and data imbalances present significant barriers to the generalization and accuracy of AMR models. To overcome these challenges, in my third study, I have proposed a deep transfer learning model based on a CNN architecture. First, I pre-train the model on four datasets, then the best-performing model is used as the source model for transfer learning, and the model is retrained on small datasets by transferring the architecture and weights from the source model. The results showed that the deep transfer learning model improves model performance for AMR prediction on small and imbalanced datasets. In an era where data security and privacy are crucial, federated learning (FL) and swarm learning (SL) present solutions by maintaining data locally during training, which reduces the necessity to transfer sensitive information to a centralized server and improves efficiency by distributing computational load. Moreover, swarm learning achieves decentralization by not requiring a central server to manage the parameters compared to federated learning, which further improves the security of the data. Thus, in my fourth study, I delve into the application of swarm learning specifically within the context of AMR

A Novel Regret Theory-Based Decision-Making Method Combined with the Intuitionistic Fuzzy Canberra Distance

In practical decision-making, the behavior factors of decision makers often affect the final decision-making results. Regret theory is an important behavioral decision theory. Based on the regret theory, a novel decision-making method is proposed for the multiattribute decision-making problem with incomplete attribute weight information, and the attribute values are expressed by Atanassov intuitionistic fuzzy numbers. At first, a new distance of intuitionistic fuzzy sets is put forward based on the traditional Canberra distance. Then, we utilize it for the definition of the regret value (rejoice) for the attribute value of each alternative with the corresponding values of the positive point (negative point). The objective of this method is to maximize the comprehensive perceived utility of the alternative set by the decision maker. The optimal attribute weight vector is solved, and the optimal comprehensive perceived utility value of each alternative is obtained. Finally, according to the optimal comprehensive perceived utility value, the rank order of all alternatives is concluded

New monatomic layer clusters for advanced catalysis materials

Noble metals have been widely applied as catalysts in chemical production, energy conversion, and emission control [1-3], but their high cost and scarcity are major obstacles for any large-scale practical applications. It is therefore of great interest to explore new active material systems that require less mass loading of noble metal catalysts but with even better performance. Recently, intense research has been devoted towards downsizing the noble metals into single-atom catalysts (SACs) [4,5]. SACs, with single-atom active centers, were first reported by Qiao et al. [4]. They synthesized a single Pt atom catalyst supported on FeOx (Pt1/FeOx), which offered extremely high efficiency on an atomic percent basis and showed excellent performance towards CO oxidation

Fabrication and Optical Manipulation of Microrobots for Biomedical Applications

International audienceOptical manipulation is a technology that enables accurate manipulation of micro-robots in fluidic environment. Optical micro-robots, which can be used as micro-tools to perform indirect micro-objects manipulation via optical tweezers (OT), have been employed for various biomedical applications. Supported by the latest advances in three-dimensional (3D) micro-fabrication, micro-robots with sophisticated structures can be created with good reproducibility and yield, further expanding the use of OT for manipulating micro-robots. In this review, emerging concepts and recent progress of allied techniques (e.g., micro-fabrication and materials), as well as the general trend of optical micro-robots, are introduced. Technically, the paper also covers visual perception and manipulation of micro-robots with OT. We further highlight open research challenges and future research directions

Ordered platinum-bismuth intermetallic clusters with Pt-skin for a highly efficient electrochemical ethanol oxidation reaction

The ethanol oxidation reaction is extensively explored, but electrocatalysts that could achieve a complete oxidation pathway to CO 2 /CO 32- are much less reported. Here, we synthesize a monatomic Pt layer (Pt-skin) on ordered intermetallic PtBi clusters (PtBi@Pt) supported on graphene via a single atom self-assembling (SAS) method to form a superior catalyst. The PtBi@Pt with an ultrafine size (∼2 nm) delivers an extremely high mass activity of 9.01 mA μg Pt-1 , which is 8-fold more active than the commercial Pt/C; significantly, in situ Fourier transform infrared spectroscopy indicates that ethanol is completely oxidized to CO 32- on the PtBi@Pt, accompanied by 12 electron transfer, as is further demonstrated by the density functional theory results

Deep Transfer Learning Enables Robust Prediction of Antimicrobial Resistance for Novel Antibiotics

Antimicrobial resistance (AMR) has become one of the serious global health problems, threatening the effective treatment of a growing number of infections. Machine learning and deep learning show great potential in rapid and accurate AMR predictions. However, a large number of samples for the training of these models is essential. In particular, for novel antibiotics, limited training samples and data imbalance hinder the models’ generalization performance and overall accuracy. We propose a deep transfer learning model that can improve model performance for AMR prediction on small, imbalanced datasets. As our approach relies on transfer learning and secondary mutations, it is also applicable to novel antibiotics and emerging resistances in the future and enables quick diagnostics and personalized treatments

Force‐Induced Synergetic Pigmentary and Structural Color Change of Liquid Crystalline Elastomer with Nanoparticle‐Enhanced Mechanosensitivity

Abstract The ability of some animals to rapidly change their colors can greatly improve their chances of escaping predators or hunting prey. A classic example is cephalopods, which can rapidly shift through a wide range of colors. This ability is based on the synergetic effect of the change of pigmentary and structural colors exhibited by their own two categories of color‐changing cells: supernatant chromatophores offer various pigmentary colors and lower iridophores or leucophores reflect the different structural colors by adjusting their periodicities. Here, a mechanochromic liquid crystalline elastomer with force‐induced synergetic pigmentary and structural color change, whose mechanosensitivity is enhanced by the stress‐concentration induced by the doped nanoparticle, is presented. The materials have a large color‐changing gamut and high mechanochromic sensitivity, which exhibit great potential in the field of mechanical detectors, sensors, and anti‐counterfeiting materials

DataSheet1_Efficacy of traditional Chinese medicine on diabetic cardiomyopathy in animal models: a systematic review and meta-analysis.DOCX

Background: Diabetic cardiomyopathy (DCM) is a severe complication of diabetes that can diminish the quality of life in patients and is a leading cause of death. Research has demonstrated the effectiveness of Traditional Chinese Medicine (TCM) in reducing blood sugar levels and protecting cardiovascular function in both animal models and clinical research studies. Nevertheless, the efficacy of TCM in animal models of DCM has not been analyzed systematically.Method: We searched the following electronic bibliographic databases: Web of Science, PubMed, Cochrane Library, and CNKI(China National Knowledge Infrastructure). Studies that reported the efficacy of TCM in animals with DCM were included. The literature search was conducted using the terms. The data will be restricted from the year 2013 to 24 April 2023, 24 studies were included in the meta-analysis.Result: A total of 24 Traditional Chinese Medicine interventions and 2157 animals met the inclusion criteria. The pooled data revealed that TCM interventions resulted in significant improvements in body weight (BW), heart weight (HW) to body weight ratio (HW/BW), triglyceride (TG) and cholesterol (TC) levels, ejection fraction (EF), fractional shortening (FS) and E/A ratio. Subgroup analysis and meta-regression revealed that the type of TCM, duration of intervention, method of modeling, and animal species were potential sources of heterogeneity.Conclusion: TCM interventions were associated with significant improvements in body weight, heart weight to body weight ratio, triglyceride and cholesterol levels, left ventricular internal dimension in systole, ejection fraction, fractional shortening and E/A ratio. The heterogeneity in the results was found to be potentially due to the type of TCM, duration of intervention, method of modeling, and animal species, as shown in subgroup analysis and meta-regression.Systematic Review Registration: identifier CRD42023402908</p