The abundance of cellulose found in natural resources such as wood, and the wide spectrum of structural diversity of cellulose nanomaterials in the form of micro‐nano‐sized particles and fibers, have ...sparked a tremendous interest to utilize cellulose's intriguing mechanical properties in designing high‐performance functional materials, where cellulose's structure–mechanics relationships are pivotal. In this progress report, multiscale mechanics understanding of cellulose, including the key role of hydrogen bonding, the dependence of structural interfaces on the spatial hydrogen bond density, the effect of nanofiber size and orientation on the fracture toughness, are discussed along with recent development on enabling experimental design techniques such as structural alteration, manipulation of anisotropy, interface and topology engineering. Progress in these fronts renders cellulose a prospect of being effectuated in an array of emerging sustainable applications and being fabricated into high‐performance structural materials that are both strong and tough.
The structure–mechanics relationships of cellulose molecular chains give rise to unconventional cellulose‐based functional materials that possess multiple beneficial mechanical properties such as high strength and toughness, in addition to other fundamentally attractive properties such as low‐cost, lightweight, and sustainability.
Music genre categorization is a fundamental use of sound processing methods in the realm of music retrieval. Typically, people are responsible for categorizing music genres. Machine learning ...approaches can automate this procedure. Therefore, in recent years, several approaches have been suggested to achieve this objective. Nevertheless, the given findings indicate that there is still a discrepancy between the observed results and an optimal categorization method. Hence, this paper introduces a novel approach for accurately forecasting music genres by using deep learning methodologies. The proposed approach involves preprocessing the input signals and then representing the characteristics of each signal using a combination of Mel Frequency Cepstral Coefficients (MFCC) and Short-Time Fourier Transform (STFT) features. Subsequently, a convolutional neural network (CNN) is applied to process each group of these characteristics. The proposed technique utilizes two CNN models to analyze MFCC and STFT data. Although the structure of these models is identical, the hyper-parameters of each model are individually adjusted using the black hole optimization (BHO) algorithm. Here, the optimization method fine-tunes the hyperparameters of each CNN model to minimize their training error. Ultimately, the results of two Convolutional Neural Network (CNN) models are combined to determine the music genre using a classifier based on SoftMax. The efficacy of the suggested methodology in categorizing music genres has been assessed using the GTZAN and Extended-Ballroom datasets. The experimental findings demonstrated that the suggested approach achieved classification accuracies of 95.2 % and 95.7 % in the two datasets, respectively, indicating its superiority over earlier efforts.
Abstract As the most rigid cytoskeletal filaments, microtubules bear compressive forces in living cells, balancing the tensile forces within the cytoskeleton to maintain the cell shape. It is often ...observed that, in living cells, microtubules under compression severely buckle into short wavelengths. By contrast, when compressed, isolated microtubules in vitro buckle into single long-wavelength arcs. The critical buckling force of the microtubules in vitro is two orders of magnitude lower than that of the microtubules in living cells. To explain this discrepancy, we describe a mechanics model of microtubule buckling in living cells. The model investigates the effect of the surrounding filament network and the cytosol on the microtubule buckling. The results show that, while the buckling wavelength is set by the interplay between the microtubules and the elastic surrounding filament network, the buckling growth rate is set by the viscous cytosol. By considering the nonlinear deformation of the buckled microtubule, the buckling amplitude can be determined at the kinetically constrained equilibrium. The model quantitatively correlates the microtubule bending rigidity, the surrounding filament network elasticity, and the cytosol viscosity with the buckling wavelength, the buckling growth rate, and the buckling amplitude of the microtubules. Such results shed light on designing a unified experimental protocol to measure various critical mechanical properties of subcellular structures in living cells.
The asymmetric synthesis of alkynyl and monofluoroalkenyl isoindolinones from N‐methoxy benzamides and α,α‐difluoromethylene alkynes is enabled by C−H activation with a chiral CpRhIII catalyst. ...Remarkably, product formation is solvent‐dependent; alkynyl isoindolinones are afforded in MeOH (up to 86 % yield, 99.6 % ee) whereas monofluoroalkenyl isoindolinones are generated in iPrCN (up to 98:2 Z/E, 93 % yield, 86 % ee). Mechanistic studies revealed chiral allene and E‐configured alkenyl rhodium species as reaction intermediates. The latter is transformed into the corresponding Z‐configured monofluoroalkene upon protonation in the iPrCN system and into an alkyne by an unusual anti β‐F elimination in the MeOH system. Notably, kinetic resolution processes occur in this reaction. Despite the moderate enantiocontrol for the formation of the chiral allene, the Z‐monofluoroalkenyl isoindolinones and alkynyl isoindolinones were obtained in good enantiopurities by one or two sequential kinetic resolution processes.
A matter of solvent: Alkynyl and monofluoroalkenyl isoindolinones were generated with good enantioselectivities from N‐methoxy benzamides and α,α‐difluoromethylene alkynes by C−H activation with a chiral CpRhIII catalyst. Remarkably, the product formation is solvent‐dependent; whereas alkynyl isoindolinones are formed in methanol, monofluoroalkenyl isoindolinones are generated in isobutyronitrile.
Early diagnosis and metastasis monitoring for pancreatic cancer are extremely difficult due to a lack of sensitive liquid biopsy methods and reliable biomarkers. Herein, we developed easy-to-prepare ...and effective polydopamine-modified immunocapture substrates and an ultrathin polydopamine-encapsulated antibody-reporter-Ag(shell)-Au(core) multilayer (PEARL) Surface-Enhanced Raman Scattering (SERS) nano-tag with a quantitative signal of the Raman reporter at 1072 cm
, which achieved ultrasensitive and specific detection of pancreatic cancer-derived exosomes with a detection limit of only one exosome in 2 μL of sample solution (approximately 9 × 10
mol L
). Furthermore, by analyzing a 2 μL clinical serum sample, the migration inhibitory factor (MIF) antibody-based SERS immunoassay could not only discriminate pancreatic cancer patients (
= 71) from healthy individuals (
= 32), but also distinguish metastasized tumors from metastasis-free tumors, and Tumor Node Metastasis (TNM) P1-2 stages from the P3 stage (the discriminatory sensitivity was 95.7%). Thus, this novel immunoassay provides a powerful tool for the early diagnosis, classification and metastasis monitoring of pancreatic cancer patients.
There is clear evidence that micro- and nanoplastics are accumulating in the environment, and their increasing concern of potential harm to wildlife has been identified as a major global issue. ...However, identification of nanoplastics in environmental samples remains a great challenge, and thus highlighting the great need for new approach. Herein, for the first time, we show that surface enhanced Raman spectroscopy (SERS) offered a feasible approach to identify trace polystyrene (PS) nanoplastics, which is the most produced nanoplastics and also widely presented in the natural environment. We found that when PS nanoplastics were surrounded by SERS-active silver nanoparticles (AgNPs), a set of Raman spectra with chemical information could be obtained via SERS mapping. This map showed the potential PS distribution of the nanoplastics on a silicon wafer, allowing a quick and detailed analysis of the nanoplastics. Moreover, the proposed method was able to identify previously undetectable plastic particles as small as ~50 nm spiked in real water, demonstrating the power of SERS to probe nanoplastics. Our work is thus an important step in nanoplastic research, and we believe that this approach can be further developed to study the occurrence, formation, and transports of nanoplastics in the natural environment.
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•SERS was for the first time demonstrated to be a facile and efficient approach for the identification of nanoplastics.•Previously undetectable plastic particles as small as ~50 nm was identified.•Applicable for real environmental waters.
This paper focuses on the Jidu 濟瀆 (i.e., the Ji River 濟水), one of the four waterways (sidu 四瀆) in imperial China. Even though it vanished a long time ago, the Jidu had always been a part of the ...traditional Chinese ritual system of mountain- and water-directed state sacrifices. From the Western Han dynasty to the Qing dynasty, it continuously received regular state sacrifices. However, Western scholars have failed to notice it. Some modern Chinese and Japanese scholars have studied the development of the Jidu sacrifice, but its embodied political and religious significances for the state and local society were largely ignored. To remedy this neglect, I provide here, with new discoveries and conclusions, the first comprehensive study of the Jidu sacrifice in imperial China. Surrounding this coherent theme, this paper draws several original arguments from its four sections. The first section is a brief history of the state sacrifice to the Jidu. In the second section, I analyze the ideas of state authority, political legitimacy, religious belief, and cosmology, as these underlie the ritual performance concerning the Jidu. I argue that the Jidu was not only tightly associated with controlling water but was also a symbol and mechanism of political legitimacy. Relying on concrete official and local records, in the third section I further investigate the role that the Jidu God played in local society. I argue that after the Song dynasty, the Jidu God was transformed into a regional protector of local society and savior of local people in addition to an official water god. In the fourth section, I, for the first time, examine the interaction between the Jidu cult and other religious traditions including Daoism, Buddhism, and folk religion.
The pore characteristics of the low-rank coal are different from medium- and high-rank coals. The low-temperature N2 adsorption (LP-N2A) measurements with a single low-rank coal were launched, and ...the dynamic change of pore structures under various pretreatment temperatures from 120°C to 300°C was studied. The isothermal curves of the DFS coal sample feature IV type, the hysteresis loops convert from H4 type to H2 type, and the hysteresis loops tend to be closed with the increased pretreatment temperatures. The mesopores are dominant in the DFS coal. The dynamic of pore volume (PV) and pore specific surface area (SSA) features the three-step-style change with the cut-off temperature points at 150°C and 240°C, and this has a relationship with the loss of the moisture and volatiles in the DFS coal sample. The pores with an aperture below 10 nm are the dominant mesopores in the DFS coal, and the mesopore volume features bimodal pattern distribution with a higher left peak of approximately 1.7 nm and a lower right peak of approximately 3-5 nm, and the right peak continuously right shift with the increase pretreatment temperatures. The total mesopore volume decreases with the upgrading temperatures, while the ratio of pores greater than 5 nm increases. Finally, the mesopore evolution model with the increased pretreatment temperatures was summarized.
Fine-grained car recognition aims to recognize the category information of a car, such as car make, car model, or even the year of manufacture. A number of recent studies have shown that a deep ...convolutional neural network (DCNN) trained on a large-scale data set can achieve impressive results at a range of generic object classification tasks. In this paper, we propose a spatially weighted pooling (SWP) strategy, which considerably improves the robustness and effectiveness of the feature representation of most dominant DCNNs. More specifically, the SWP is a novel pooling layer, which contains a predefined number of spatially weighted masks or pooling channels. The SWP pools the extracted features of DCNNs with the guidance of its learnt masks, which measures the importance of the spatial units in terms of discriminative power. As the existing methods that apply uniform grid pooling on the convolutional feature maps of DCNNs, the proposed method can extract the convolutional features and generate the pooling channels from a single DCNN. Thus minimal modification is needed in terms of implementation. Moreover, the parameters of the SWP layer can be learned in the end-to-end training process of the DCNN. By applying our method to several fine-grained car recognition data sets, we demonstrate that the proposed method can achieve better performance than recent approaches in the literature. We advance the state-of-the-art results by improving the accuracy from 92.6% to 93.1% on the Stanford Cars-196 data set and 91.2% to 97.6% on the recent CompCars data set. We have also tested the proposed method on two additional large-scale data sets with impressive results observed.
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