Continual learning in the presence of repetition.

Continual learning (CL) provides a framework for training models in ever-evolving environments. Although re-occurrence of previously seen objects or tasks is common in real-world problems, the concept of repetition in the data stream is not often considered in standard benchmarks for CL. Unlike with the rehearsal mechanism in buffer-based strategies, where sample repetition is controlled by the strategy, repetition in the data stream naturally stems from the environment.

Mitigating Accuracy-Robustness Trade-Off via Balanced Multi-Teacher Adversarial Distillation.

Adversarial Training is a practical approach for improving the robustness of deep neural networks against adversarial attacks. Although bringing reliable robustness, the performance towards clean examples is negatively affected after Adversarial Training, which means a trade-off exists between accuracy and robustness. Recently, some studies have tried to use knowledge distillation methods in Adversarial Training, achieving competitive performance in improving the robustness but the accuracy for clean samples is still limited.

Revisiting the Trade-Off Between Accuracy and Robustness via Weight Distribution of Filters.

Adversarial attacks have been proven to be potential threats to Deep Neural Networks (DNNs), and many methods are proposed to defend against adversarial attacks. However, while enhancing the robustness, the accuracy for clean examples will decline to a certain extent, implying a trade-off existed between the accuracy and adversarial robustness. In this paper, to meet the trade-off problem, we theoretically explore the underlying reason for the difference of the filters' weight distribution between standard-trained and robust-trained models and then argue that this is an intrinsic property for static neural networks, thus they are difficult to fundamentally improve the accuracy and adversarial robustness at the same time.

Assessment of Amino Acid Electrostatic Parametrizations of the Polarizable Gaussian Multipole Model.

Accurate parametrization of amino acids is pivotal for the development of reliable force fields for molecular modeling of biomolecules such as proteins. This study aims to assess amino acid electrostatic parametrizations with the polarizable Gaussian Multipole (pGM) model by evaluating the performance of the pGM-perm (with atomic permanent dipoles) and pGM-ind (without atomic permanent dipoles) variants compared to the traditional RESP model. The 100-conf-combterm fitting strategy on tetrapeptides was adopted, in which (1) all peptide bond atoms (-CO-NH-) share identical set of parameters and (2) the total charges of the two terminal -acetyl (ACE) and -methylamide (NME) groups were set to neutral.

Pectin-Coated Iron-Based Metal-Organic Framework Nanoparticles for Enhanced Foliar Adhesion and Targeted Delivery of Fungicides.

Conventional agrochemicals are underutilized due to their large particle sizes, poor foliar retention rates, and difficult translocation in plants, and the development of functional nanodelivery carriers with high adhesion to the plant body surface and efficient uptake and translocation in plants remains challenging. In this study, a nanodelivery system based on a pectin-encapsulated iron-based MOF (TF@Fe-MOF-PT NPs) was constructed to enhance the utilization of thifluzamide (TF) in rice plants by taking advantage of the pectin affinity for plant cell walls. The prepared TF@Fe-MOF-PT NPs exhibited an average particle size of 126.

Long-term toxic effects of iron-based metal-organic framework nanopesticides on earthworm-soil microorganism interactions in the soil environment.

With the widespread use of controlled-release nanopesticides in field conditions, the interactions between these nanopesticides and biological systems are complex and highly uncertain. The toxicity of iron-based metal organic frameworks (CF@MIL-101-SL) loaded with chlorfenapyr (CF) to terrestrial invertebrate earthworms in filter paper and soil environments and the potential mechanisms of interactions in the nanopesticide-earthworm-cornfield soil microorganism system were investigated for the first time. The results showed that CF@MIL-101-SL was more poisonous to earthworms in the contact filter paper test than suspension concentrate of CF (CF-SC), and conversely, CF@MIL-101-SL was less poisonous to earthworms in the soil test.

AmberTools.

AmberTools is a free and open-source collection of programs used to set up, run, and analyze molecular simulations. The newer features contained within AmberTools23 are briefly described in this Application note.

Streamlining and Optimizing Strategies of Electrostatic Parameterization.

Accurate characterization of electrostatic interactions is crucial in molecular simulation. Various methods and programs have been developed to obtain electrostatic parameters for additive or polarizable models to replicate electrostatic properties obtained from experimental measurements or theoretical calculations. Electrostatic potentials (ESPs), a set of physically well-defined observables from quantum mechanical (QM) calculations, are well suited for optimization efforts due to the ease of collecting a large amount of conformation-dependent data.

Research on Driving Obstacle Detection Technology in Foggy Weather Based on GCANet and Feature Fusion Training.

The issues of the degradation of the visual sensor's image quality in foggy weather and the loss of information after defogging have brought great challenges to obstacle detection during autonomous driving. Therefore, this paper proposes a method for detecting driving obstacles in foggy weather. The driving obstacle detection in foggy weather was realized by combining the GCANet defogging algorithm with the detection algorithm-based edge and convolution feature fusion training, with a full consideration of the reasonable matching between the defogging algorithm and the detection algorithm on the basis of the characteristics of obvious target edge features after GCANet defogging.

Transferability of the Electrostatic Parameters of the Polarizable Gaussian Multipole Model.

Accuracy and transferability are the two highly desirable properties of molecular mechanical force fields. Compared with the extensively used point-charge additive force fields that apply fixed atom-centered point partial charges to model electrostatic interactions, polarizable force fields are thought to have the advantage of modeling the atomic polarization effects. Previous works have demonstrated the accuracy of the recently developed polarizable Gaussian multipole (pGM) models.

Accurate Reproduction of Quantum Mechanical Many-Body Interactions in Peptide Main-Chain Hydrogen-Bonding Oligomers by the Polarizable Gaussian Multipole Model.

A key advantage of polarizable force fields is their ability to model the atomic polarization effects that play key roles in the atomic many-body interactions. In this work, we assessed the accuracy of the recently developed polarizable Gaussian Multipole (pGM) models in reproducing quantum mechanical (QM) interaction energies, many-body interaction energies, as well as the nonadditive and additive contributions to the many-body interactions for peptide main-chain hydrogen-bonding conformers, using glycine dipeptide oligomers as the model systems. Two types of pGM models were considered, including that with (pGM-perm) and without (pGM-ind) permanent atomic dipoles.

: A Program for Electrostatic Parameterizations of Additive and Induced Dipole Polarizable Force Fields.

Molecular modeling at the atomic level has been applied in a wide range of biological systems. The widely adopted additive force fields typically use fixed atom-centered partial charges to model electrostatic interactions. However, the additive force fields cannot accurately model polarization effects, leading to unrealistic simulations in polarization-sensitive processes.

Development of a Pantetheine Force Field Library for Molecular Modeling.

Pantetheine is ubiquitous in nature in various forms of pantetheine-containing ligands (PCLs), including coenzyme A and phosphopantetheine. Lack of scalable force field libraries for PCLs has hampered the computational studies of biological macromolecules containing PCLs. We describe here the development of the first generation Pantetheine Force Field (PFF) library that is compatible with Amber force fields; parameterized using Gasteiger, AM1-BCC, or RESP charging methods combined with and parameter sets.

Molecular Basis for Polyketide Ketoreductase-Substrate Interactions.

Polyketides are a large class of structurally and functionally diverse natural products with important bioactivities. Many polyketides are synthesized by reducing type II polyketide synthases (PKSs), containing transiently interacting standalone enzymes. During synthesis, ketoreductase (KR) catalyzes regiospecific carbonyl to hydroxyl reduction, determining the product outcome, yet little is known about what drives specific KR-substrate interactions.

Elucidation of WW domain ligand binding specificities in the Hippo pathway reveals STXBP4 as YAP inhibitor.

The Hippo pathway, which plays a critical role in organ size control and cancer, features numerous WW domain-based protein-protein interactions. However, ~100 WW domains and 2,000 PY motif-containing peptide ligands are found in the human proteome, raising a "WW-PY" binding specificity issue in the Hippo pathway. In this study, we have established the WW domain binding specificity for Hippo pathway components and uncovered a unique amino acid sequence required for it.

Computational structural enzymology methodologies for the study and engineering of fatty acid synthases, polyketide synthases and nonribosomal peptide synthetases.

Various computational methodologies can be applied to enzymological studies on enzymes in the fatty acid, polyketide, and non-ribosomal peptide biosynthetic pathways. These multi-domain complexes are called fatty acid synthases, polyketide synthases, and non-ribosomal peptide synthetases. These mega-synthases biosynthesize chemically diverse and complex bioactive molecules, with the intermediates being chauffeured between catalytic partners via a carrier protein.

Improved expression of recombinant fusion defensin gene plasmids packed with chitosan-derived nanoparticles and effect on antibacteria and mouse immunity.

In order to develop a secure and competent technique to express the human immune gene for fighting infections, we cloned and expressed the BD2/3 using VR1020 (a eukaryotic expression plasmid). BD2/3 contains human β-defensin 2 (BD2) and human BD3. To explore safe and effective DNA delivery molecules and , the fusion genes of BD2/3 were used as an immune-labelled gene to verify transfection effectivness of modified chitosan (CS).

Co-expression of mFat-1 and pig IGF-1 genes by recombinant plasmids in modified chitosan nanoparticles and its synergistic effect on mouse immunity.

To develop a cost-effective molecular regulator to improve growth metabolism and immunity of animals, a recombinant plasmid co-expressing fatty acid desaturase (mFat-1) and pig insulin growth like factor 1 (IGF-1) genes was constructed by the 2 A self-cleavage technique. After entrapment within modified chitosan nanoparticles (chitosan modified with polyethyleneglycol-polyethylenimine, CPP), the recombinant plasmid was injected intramuscularly into mice. Compared with controls, co-expression of mFat-1 and IGF-1 significantly raised the level of serum IGF-1, and increased the liver and muscle docosa hexaenoic acid (DHA) content.