We present a variational renormalization group (RG) approach based on a reversible generative model with hierarchical architecture. The model performs hierarchical change-of-variables transformations ...from the physical space to a latent space with reduced mutual information. Conversely, the neural network directly maps independent Gaussian noises to physical configurations following the inverse RG flow. The model has an exact and tractable likelihood, which allows unbiased training and direct access to the renormalized energy function of the latent variables. To train the model, we employ probability density distillation for the bare energy function of the physical problem, in which the training loss provides a variational upper bound of the physical free energy. We demonstrate practical usage of the approach by identifying mutually independent collective variables of the Ising model and performing accelerated hybrid Monte Carlo sampling in the latent space. Lastly, we comment on the connection of the present approach to the wavelet formulation of RG and the modern pursuit of information preserving RG.
Ribosomal proteins (RPs), in conjunction with rRNA, are major components of ribosomes involved in the cellular process of protein biosynthesis, known as "translation". The viruses, as the small ...infectious pathogens with limited genomes, must recruit a variety of host factors to survive and propagate, including RPs. At present, more and more information is available on the functional relationship between RPs and virus infection. This review focuses on advancements in my own understanding of critical roles of RPs in the life cycle of viruses. Various RPs interact with viral mRNA and proteins to participate in viral protein biosynthesis and regulate the replication and infection of virus in host cells. Most interactions are essential for viral translation and replication, which promote viral infection and accumulation, whereas the minority represents the defense signaling of host cells by activating immune pathway against virus. RPs provide a new platform for antiviral therapy development, however, at present, antiviral therapeutics with RPs involving in virus infection as targets is limited, and exploring antiviral strategy based on RPs will be the guides for further study.
Structure determination of membrane proteins has been a long-standing challenge to understand the molecular basis of life processes. Detergents are widely used to study the structure and function of ...membrane proteins by various experimental methods, and the application of membrane mimetics is also a prevalent trend in the field of cryo-EM analysis. This review focuses on the widely-used detergents and corresponding properties and structures, and also discusses the growing interests in membrane mimetic systems used in cryo-EM studies, providing insights into the role of detergent alternatives in structure determination.
Automated breast cancer multi-classification from histopathological images plays a key role in computer-aided breast cancer diagnosis or prognosis. Breast cancer multi-classification is to identify ...subordinate classes of breast cancer (Ductal carcinoma, Fibroadenoma, Lobular carcinoma, etc.). However, breast cancer multi-classification from histopathological images faces two main challenges from: (1) the great difficulties in breast cancer multi-classification methods contrasting with the classification of binary classes (benign and malignant), and (2) the subtle differences in multiple classes due to the broad variability of high-resolution image appearances, high coherency of cancerous cells, and extensive inhomogeneity of color distribution. Therefore, automated breast cancer multi-classification from histopathological images is of great clinical significance yet has never been explored. Existing works in literature only focus on the binary classification but do not support further breast cancer quantitative assessment. In this study, we propose a breast cancer multi-classification method using a newly proposed deep learning model. The structured deep learning model has achieved remarkable performance (average 93.2% accuracy) on a large-scale dataset, which demonstrates the strength of our method in providing an efficient tool for breast cancer multi-classification in clinical settings.
A 2rotaxane‐based molecular shuttle with an acid‐responsive asymmetric macrocycle on a symmetric dumbbell axle is reported. Upon adding TFA, the macrocycle, namely the amine naphthotube, is ...protonated and translocates from the di(quaternary ammonium) station to the triazole stations because of electrostatic repulsion and weakened binding. The shuttling kinetics are slow due to the steric hindrance caused by the ethyl group on the quaternary ammonium center and can be followed by 1H NMR spectroscopy. Interestingly, it was found that the shuttling kinetics depends on the concentration of TFA. A kinetic intermediate was detected and can even be captured in the presence of a high concentration of TFA. Extensive control experiments revealed that the shuttling kinetics and the capture of the kinetic intermediate are related to the different protonation states of the rotaxanes.
A 2rotaxane‐based molecular shuttle with an acid‐responsive asymmetric macrocycle on a symmetric dumbbell axle has been synthesized. The shuttling kinetics can be controlled by the acid concentration and the kinetic intermediate can even be captured at a high concentration of acid. This was explained by invoking different protonation states of the rotaxane at different concentrations of acid.
Noncovalent interactions between all the neighboring components in biomolecular machines are responsible for their synchronized motion and thus complex functions. This strategy has rarely been used ...in multicomponent molecular machines. Here, we report four 3rotaxane‐based molecular shuttles. Noncovalent interactions among the three components (two interacting macrocycles and one axle) not only cause a “systems‐level” effect on the relative positions of the two macrocycles along the axle, but also result in a synchronized motion of the two macrocycles when adding partial amount of stimuli. Moreover, the intermediate state with one shuttled macrocycle even exist predominantly in the solution during the titration of stimuli, which is theoretically unexpected for the 3rotaxane with two non‐interacting rings. This biomimetic strategy may provide a method for constructing highly complex molecular machines.
Molecular machines: Four 3rotaxane‐based molecular shuttles with two interacting macrocycles have been synthesized. The interactions among the three components (two macrocycles and one axle) were shown to not only affect the relative positions of the macrocycles along the axles with different spacer lengths, but also cause a synchronized motion of the two macrocycles when adding stimuli.
Collagen is the major component of the tumor microenvironment and participates in cancer fibrosis. Collagen biosynthesis can be regulated by cancer cells through mutated genes, transcription factors, ...signaling pathways and receptors; furthermore, collagen can influence tumor cell behavior through integrins, discoidin domain receptors, tyrosine kinase receptors, and some signaling pathways. Exosomes and microRNAs are closely associated with collagen in cancer. Hypoxia, which is common in collagen-rich conditions, intensifies cancer progression, and other substances in the extracellular matrix, such as fibronectin, hyaluronic acid, laminin, and matrix metalloproteinases, interact with collagen to influence cancer cell activity. Macrophages, lymphocytes, and fibroblasts play a role with collagen in cancer immunity and progression. Microscopic changes in collagen content within cancer cells and matrix cells and in other molecules ultimately contribute to the mutual feedback loop that influences prognosis, recurrence, and resistance in cancer. Nanoparticles, nanoplatforms, and nanoenzymes exhibit the expected gratifying properties. The pathophysiological functions of collagen in diverse cancers illustrate the dual roles of collagen and provide promising therapeutic options that can be readily translated from bench to bedside. The emerging understanding of the structural properties and functions of collagen in cancer will guide the development of new strategies for anticancer therapy.
Land use change and climate variability are two key factors impacting watershed hydrology, which is strongly related to the availability of water resources and the sustainability of local ecosystems. ...This study assessed separate and combined hydrological impacts of land use change and climate variability in the headwater region of a typical arid inland river basin, known as the Heihe River Basin, northwest China, in the recent past (1995-2014) and near future (2015-2024), by combining two land use models (i.e., Markov chain model and Dyna-CLUE) with a hydrological model (i.e., SWAT). The potential impacts in the near future were explored using projected land use patterns and hypothetical climate scenarios established on the basis of analyzing long-term climatic observations. Land use changes in the recent past are dominated by the expansion of grassland and a decrease in farmland; meanwhile the climate develops with a wetting and warming trend. Land use changes in this period induce slight reductions in surface runoff, groundwater discharge and streamflow whereas climate changes produce pronounced increases in them. The joint hydrological impacts are similar to those solely induced by climate changes. Spatially, both the effects of land use change and climate variability vary with the sub-basin. The influences of land use changes are more identifiable in some sub-basins, compared with the basin-wide impacts. In the near future, climate changes tend to affect the hydrological regimes much more prominently than land use changes, leading to significant increases in all hydrological components. Nevertheless, the role of land use change should not be overlooked, especially if the climate becomes drier in the future, as in this case it may magnify the hydrological responses.
As globalization is facing increasing challenges, regionalization demonstrates the potential to effectively address many transboundary issues. Current international fisheries management has attracted ...criticisms, among which the poor incentives for countries to attend and comply with the rules are notable. This paper aims to explore whether the incorporation of fisheries policies into regional economic blocs can be a solution to improve cross-border fisheries management. The development, problems, and future of the Common Fisheries Policy (CFP) of the European Union are explored in detail. This paper concludes that the evolution and implementation of the CFP provide some precious lessons for the world. An appropriately designed regional fisheries scheme would help to create incentives for countries to participate in regional regimes and improve their fisheries management. Economic incentives, a good institutional design, and financial and scientific support are critical factors in favor of adopting common fisheries policies under regional economic frameworks.
The automatic detection of atrial fibrillation (AF) is crucial for its association with the risk of embolic stroke. Most of the existing AF detection methods usually convert 1D time-series ...electrocardiogram (ECG) signal into 2D spectrogram to train a complex AF detection system, which results in heavy training computation and high implementation cost. This paper proposes an AF detection method based on an end-to-end 1D convolutional neural network (CNN) architecture to raise the detection accuracy and reduce network complexity. By investigating the impact of major components of a convolutional block on detection accuracy and using grid search to obtain optimal hyperparameters of the CNN, we develop a simple, yet effective 1D CNN. Since the dataset provided by PhysioNet Challenge 2017 contains ECG recordings with different lengths, we also propose a length normalization algorithm to generate equal-length records to meet the requirement of CNN. Experimental results and analysis indicate that our method of 1D CNN achieves an average
score of 78.2%, which has better detection accuracy with lower network complexity, as compared with the existing deep learning-based methods.
PDF
