This study introduces a novel method for predicting drilling pressure in bolt support systems by optimizing Gaussian process time series regression (GPR) using hybrid optimization algorithms. The ...research initially identified significant variations in prediction outcomes based on different kernel functions and historical points combinations in the GPR algorithm. To address this, we explored 160 distinct schemes combining 10 kernel functions and 16 historical points for numerical analysis. Applying three hybrid optimization algorithms-Genetic Algorithm-GPR (GA-GPR), Particle Swarm Optimization-GPR (PSO-GPR), and Ant Colony Algorithm-GPR (ACA-GPR)-we iteratively optimized these key parameters. The PSO-GPR algorithm emerged as the most effective, achieving an 80% prediction accuracy with a deviation range of 1-2 MPa, acceptable in practical drilling operations. This optimization led to the RQ kernel function with 18 historical points as the optimal combination, yielding an RMSE value of 0.0047246, in contrast to the least effective combination (E kernel function with 6 historical points) producing an RMSE of 0.035704. The final outcome of this study is a robust and efficient prediction system for underground bolt support drilling pressure, verified through practical application. This approach significantly enhances the accuracy and efficiency of support systems in geotechnical engineering, demonstrating the practical applicability of the PSO-GPR model in real-world scenarios.
•We report a familial cluster of COVID-19 to assess potential transmission of the disease during the incubation period.•A familial cluster of four patients with COVID-19 in Zhoushan, China had ...contact with an asymptomatic family member, who developed symptoms later.•The infectivity during the incubation period for SARS-CoV-2 is a big challenge for controlling the disease.
We report a familial cluster of 2019 novel coronavirus disease (COVID-19) to assess its potential transmission during the incubation period. The first patient in this familial cluster was identified during the presymptomatic period, as a close contact of a confirmed patient. Five family members had close contact with this first patient during his incubation period, with four of them confirmed positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in the subsequent sampling tests.
Even though it is known that urbanization affects rainfall, studies vary regarding the magnitude and location of rainfall change. To develop a comprehensive understanding of rainfall modification due ...to urbanization, a systematic meta-analysis is undertaken. The initial search identified over 2000 papers of which 489 were carefully analyzed. From these papers, 85 studies from 48 papers could be used in a quantitative meta-analysis assessment. Results were analyzed for case studies versus climatological assessments, observational versus modeling studies and for day versus night. Results highlight that urbanization modifies rainfall, such that mean precipitation is enhanced by 18% downwind of the city, 16% over the city, 2% on the left and 4% on the right with respect to the storm direction. The rainfall enhancement occurred approximately 20-50 km from the city center. Study results help develop a more complete picture of the role of urban processes in rainfall modification and highlight that rainfall increases not only downwind of the city but also over the city. These findings have implications for urban flooding as well as hydroclimatological studies. This meta-analysis highlights the need for standardizing how the results are presented in future studies to aid the generalization of findings.
For many regenerative electrochemical energy‐conversion systems, hybrid electrocatalysts comprising transition metal (TM) oxides and heteroatom‐doped (e.g., nitrogen‐doped) carbonaceous materials are ...promising bifunctional oxygen reduction reaction/oxygen evolution reaction electrocatalysts, whose enhanced electrocatalytic activities are attributed to the synergistic effect originated from the TM–N–C active sites. However, it is still ambiguous which configuration of nitrogen dopants, either pyridinic or pyrrolic N, when bonded to the TM in oxides, predominately contributes to the synergistic effect. Herein, an innovative strategy based on laser irradiation is described to controllably tune the relative concentrations of pyridinic and pyrrolic nitrogen dopants in the hybrid catalyst, i.e., NiCo2O4 NPs/N‐doped mesoporous graphene. Comparative studies reveal the dominant role of pyridinic‐NCo bonding, instead of pyrrolic‐N bonding, in synergistically promoting reversible oxygen electrocatalysis. Moreover, density functional theory calculations provide deep insights into the corresponding synergistic mechanism. The optimized hybrid, NiCo/NLG‐270, manifests outstanding reversible oxygen electrocatalytic activities, leading to an overpotential different ΔE among the lowest value for highly efficient bifunctional catalysts. In a practical reversible Zn–air battery, NiCo/NLG‐270 exhibits superior charge/discharge performance and long‐term durability compared to the noble metal electrocatalysts.
An innovative strategy based on laser irradiation is developed to selectively regulate relative contents of pyridinic and pyrrolic nitrogen in NiCo2O4/N‐graphene hybrids. Strong chemical bonding forms between nitrogen and cobalt, and pyridinic‐NCo bonds, instead of pyrrolic‐NCo bonds, are identified to predominantly contribute to synergistic catalysis, leading to substantially enhanced oxygen electrocatalytic activities, outperforming a combination of benchmark noble metal catalysts.
Type III-A CRISPR-Cas systems are prokaryotic RNA-guided adaptive immune systems that use a protein-RNA complex, Csm, for transcription-dependent immunity against foreign DNA. Csm can cleave RNA and ...single-stranded DNA (ssDNA), but whether it targets one or both nucleic acids during transcription elongation is unknown. Here, we show that binding of a Thermus thermophilus (T. thermophilus) Csm (TthCsm) to a nascent transcript in a transcription elongation complex (TEC) promotes tethering but not direct contact of TthCsm with RNA polymerase (RNAP). Biochemical experiments show that both TthCsm and Staphylococcus epidermidis (S. epidermidis) Csm (SepCsm) cleave RNA transcripts, but not ssDNA, at the transcription bubble. Taken together, these results suggest that Type III systems primarily target transcripts, instead of unwound ssDNA in TECs, for immunity against double-stranded DNA (dsDNA) phages and plasmids. This reveals similarities between Csm and eukaryotic RNA interference, which also uses RNA-guided RNA targeting to silence actively transcribed genes.
Oxygen evolution reaction (OER) is a pivotal reaction in many technologies for renewable energy, such as water splitting, metal–air batteries, and regenerative fuel cells. However, this reaction is ...known to be kinetically sluggish and proceeds at rather high overpotential due to the universal scaling relationship, namely, the adsorption energies of intermediates are linearly correlated and cannot be optimized simultaneously. Several approaches have been proposed to break the scaling relationship by introducing additional active sites; however, positive experimental results are still absent. Herein, a different solution is suggested on the basis of dynamic tridimensional adsorption of the OER intermediates at NiO/NiFe layered double hydroxide intersection, by which the adsorption energy of each intermediate can be adjusted independently, so as to bypass the scaling relationship and achieve high catalytic performance. Experimentally, the OER overpotential is reduced to ≈205 mV at current density of 30 mA cm−2, which represents the best performance achieved by state‐of‐the‐art OER catalysts.
The oxygen evolution reaction (OER), a key reaction for energy conversion and storage, is kinetically sluggish due to the limits of the scaling relationship. A strategy to bypass the scaling relationship through dynamic tridimensional adsorption of OER intermediates is reported, and OER overpotential is reduced to 205 mV at current density of 30 mA cm−2.
Scoring functions are a class of computational methods widely applied in structure-based drug design for evaluating protein–ligand interactions. Dozens of scoring functions have been published since ...the early 1990s. In literature, scoring functions are typically classified as force-field-based, empirical, and knowledge-based. This classification scheme has been quoted for more than a decade and is still repeatedly quoted by some recent publications. Unfortunately, it does not reflect the recent progress in this field. Besides, the naming convention used for describing different types of scoring functions has been somewhat jumbled in literature, which could be confusing for newcomers to this field. Here, we express our viewpoint on an up-to-date classification scheme and appropriate naming convention for current scoring functions. We propose that they can be classified into physics-based methods, empirical scoring functions, knowledge-based potentials, and descriptor-based scoring functions. We also outline the major difference and connections between different categories of scoring functions.
SUMMARY
Carotenoids are important natural pigments that give bright colors to plants. The difference in the accumulation of carotenoids is one of the key factors in the formation of various colors in ...carrot taproots. Carotenoid cleavage dioxygenases (CCDs), including CCD and 9‐cis epoxycarotenoid dioxygenase, are the main enzymes involved in the cleavage of carotenoids in plants. Seven CCD genes have been annotated from the carrot genome. In this study, through expression analysis, we found that the expression level of DcCCD4 was significantly higher in the taproot of white carrot (low carotenoid content) than orange carrot (high carotenoid content). The overexpression of DcCCD4 in orange carrots caused the taproot color to be pale yellow, and the contents of α‐ and β‐carotene decreased sharply. Mutant carrot with loss of DcCCD4 function exhibited yellow color (the taproot of the control carrot was white). The accumulation of β‐carotene was also detected in taproot. Functional analysis of the DcCCD4 enzyme in vitro showed that it was able to cleave α‐ and β‐carotene at the 9, 10 (9′, 10′) double bonds. In addition, the number of colored chromoplasts in the taproot cells of transgenic carrots overexpressing DcCCD4 was significantly reduced compared with that in normal orange carrots. Results showed that DcCCD4 affects the accumulation of carotenoids through cleavage of α‐ and β‐carotene in carrot taproot.
Significance Statement
We analyzed seven carotenoid cleavage dioxygenase genes annotated from the carrot genome. Functional analysis revealed that DcCCD4 catalyzes the degradation of α‐ and β‐carotene to affect carotenoid accumulation. This work may provide a reference for generating new plant varieties with ideal color.
Abstract The non-stationary property of electromyography (EMG) signals in real life settings usually hinders the clinical application of the myoelectric pattern recognition for prosthesis control. ...The classical EMG pattern recognition approach consists of two separate steps: training and testing, without considering the changes between training and testing data induced by electrode shift, fatigue, impedance changes and psychological factors, and often results in performance degradation. The aim of this study was to develop an adaptive myoelectric pattern recognition system, aiming to retrain the classifier online with the testing data without supervision, providing a self-correction mechanism for suppressing misclassifications. This paper presents an adaptive unsupervised classifier based on support vector machine (SVM) to improve the classification performance. Experimental data from 15 healthy subjects were used to evaluate performance. Preliminary study on intra-session and inter-session EMG data was conducted to verify the performance of the unsupervised adaptive SVM classifier. The unsupervised adaptive SVM classifier outperformed the conventional SVM by 3.3% and 8.0% for the combination of time-domain and autoregressive features in the intra-session and inter-session tests, respectively. The proposed approach is capable of incorporating the useful information in testing data to the classification model by taking into account the overtime changes in the testing data with respect to the training data to retrain the original classifier, therefore providing a self-correction mechanism for suppressing misclassifications.
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