Static stiffness modeling and sensitivity analysis for geared system used for rotary feeding

The positioning accuracy of rotary feed system under load greatly depends on the static stiffness of mechanical transmission system. This paper proposes a unified static stiffness model of rotary feed system with geared transmission system. Taking the torsional stiffness of transmission shaft and mesh stiffness of gear pairs into account, the motion equations of the whole transmission system are presented. Based on the static equilibrium, a unified expression for the relationship between torsional angles of two adjacent elements is derived. Then a unified static stiffness model is presented. Furthermore, analytical expressions for sensitivity analysis of the static stiffness on the individual element’s stiffness and design parameters are derived. The presented model is verified by a traditional model, and a good agreement is obtained. The influence of phase angle of meshing gear pairs on the resultant static stiffness is investigated. An example transmission system is employed to perform the sensitivity analysis and the results are analyzed. The proposed model provides an essential tool for the design of rotary feed system satisfying requirement of static stiffness

Comprehensive profiling of rhizome-associated alternative splicing and alternative polyadenylation in moso bamboo (Phyllostachys edulis).

Moso bamboo (Phyllostachys edulis) represents one of the fastest-spreading plants in the world, due in part to its well-developed rhizome system. However, the post-transcriptional mechanism for the development of the rhizome system in bamboo has not been comprehensively studied. We therefore used a combination of single-molecule long-read sequencing technology and polyadenylation site sequencing (PAS-seq) to re-annotate the bamboo genome, and identify genome-wide alternative splicing (AS) and alternative polyadenylation (APA) in the rhizome system. In total, 145 522 mapped full-length non-chimeric (FLNC) reads were analyzed, resulting in the correction of 2241 mis-annotated genes and the identification of 8091 previously unannotated loci. Notably, more than 42 280 distinct splicing isoforms were derived from 128 667 intron-containing full-length FLNC reads, including a large number of AS events associated with rhizome systems. In addition, we characterized 25 069 polyadenylation sites from 11 450 genes, 6311 of which have APA sites. Further analysis of intronic polyadenylation revealed that LTR/Gypsy and LTR/Copia were two major transposable elements within the intronic polyadenylation region. Furthermore, this study provided a quantitative atlas of poly(A) usage. Several hundred differential poly(A) sites in the rhizome-root system were identified. Taken together, these results suggest that post-transcriptional regulation may potentially have a vital role in the underground rhizome-root system

Identification of Hydrolyzable Tannins (Punicalagin, Punicalin and Geraniin) as Novel inhibitors of Hepatitis B Virus Covalently Closed Circular DNA

The development of new agents to target HBV cccDNA is urgently needed because of the limitations of current available drugs for treatment of hepatitis B. By using a cell-based assay in which the production of HBeAg is in a cccDNA-dependent manner, we screened a compound library derived from Chinese herbal remedies for inhibitors against HBV cccDNA. Three hydrolyzable tannins, specifically punicalagin, punicalin and geraniin, emerged as novel anti-HBV agents. These compounds significantly reduced the production of secreted HBeAg and cccDNA in a dose-dependent manner in our assay, without dramatic alteration of viral DNA replication. Furthermore, punicalagin did not affect precore/core promoter activity, pgRNA transcription, core protein expression, or HBsAg secretion. By employing the cell-based cccDNA accumulation and stability assay, we found that these tannins significantly inhibited the establishment of cccDNA and modestly facilitated the degradation of preexisting cccDNA. Collectively, our results suggest that hydrolyzable tannins inhibit HBV cccDNA production via a dual mechanism through preventing the formation of cccDNA and promoting cccDNA decay, although the latter effect is rather minor. These hydrolyzable tannins may serve as lead compounds for the development of new agents to cure HBV infection

Exploring EEG Features in Cross-Subject Emotion Recognition

Recognizing cross-subject emotions based on brain imaging data, e.g., EEG, has always been difficult due to the poor generalizability of features across subjects. Thus, systematically exploring the ability of different EEG features to identify emotional information across subjects is crucial. Prior related work has explored this question based only on one or two kinds of features, and different findings and conclusions have been presented. In this work, we aim at a more comprehensive investigation on this question with a wider range of feature types, including 18 kinds of linear and non-linear EEG features. The effectiveness of these features was examined on two publicly accessible datasets, namely, the dataset for emotion analysis using physiological signals (DEAP) and the SJTU emotion EEG dataset (SEED). We adopted the support vector machine (SVM) approach and the "leave-one-subject-out" verification strategy to evaluate recognition performance. Using automatic feature selection methods, the highest mean recognition accuracy of 59.06% (AUC = 0.605) on the DEAP dataset and of 83.33% (AUC = 0.904) on the SEED dataset were reached. Furthermore, using manually operated feature selection on the SEED dataset, we explored the importance of different EEG features in cross-subject emotion recognition from multiple perspectives, including different channels, brain regions, rhythms, and feature types. For example, we found that the Hjorth parameter of mobility in the beta rhythm achieved the best mean recognition accuracy compared to the other features. Through a pilot correlation analysis, we further examined the highly correlated features, for a better understanding of the implications hidden in those features that allow for differentiating cross-subject emotions. Various remarkable observations have been made. The results of this paper validate the possibility of exploring robust EEG features in cross-subject emotion recognition

Transient Super-strong Coronal Lines and Broad Bumps in the Galaxy SDSS J074820.67+471214.3

Variable super-strong coronal emission lines were observed in one galaxy, SDSS J095209.56+214313.3, and their origin remains controversy. In this paper, we report the detection of variable broad spectral bumps, reminiscent of supernova (SN) II-Plateau (II-P) spectra taken a few days after the shock breakout, in the second galaxy with variable super-strong coronal lines, SDSS J074820.67+471214.3. The coronal line spectrum shows unprecedented high ionization with strong [Fe X], [Fe XI], [Fe XIV], [S XII] and [Ar XIV], but without detectable optical [Fe VII] lines. The coronal line luminosities are similar to that observed in bright Seyfert galaxies, and 20 times more luminous than that reported in the hottest Type IIn SN 2005ip. The coronal lines ($\sigma ~120-240$ km s-1) are much broader than the narrow lines ($\sigma \sim 40$ km/s) from the star forming regions in the galaxy, but at nearly the same systematic redshift. We also detected a variable non-stellar continuum in optical and UV. In the follow-up spectra taken 4-5 years later, the coronal lines, SN-like feature, and non-stellar continuum disappeared, while the [O III] intensity increased by about a factor of ten. Our analysis suggests that the coronal line region should be at least ten light days in size, and be powered either by a quasi-steady ionizing source with a soft X-ray luminosity at least a few 10^{42} erg s-1 or by a very luminous soft X-ray outburst. These findings can be more naturally explained by a star tidally disrupted by the central black hole than by an SN explosion.Comment: 21 pages, 6 figures, accepted to Ap

Waterproofed Photomultiplier Tube Assemblies for the Daya Bay Reactor Neutrino Experiment

In the Daya Bay Reactor Neutrino Experiment 960 20-cm-diameter waterproof photomultiplier tubes are used to instrument three water pools as Cherenkov detectors for detecting cosmic-ray muons. Of these 960 photomultiplier tubes, 341 are recycled from the MACRO experiment. A systematic program was undertaken to refurbish them as waterproof assemblies. In the context of passing the water leakage check, a success rate better than 97% was achieved. Details of the design, fabrication, testing, operation, and performance of these waterproofed photomultiplier-tube assemblies are presented.Comment: 16 pages, 11 figures. Submitted to Nucl. Instr. Met

Efficient Adaptation of Large Vision Transformer via Adapter Re-Composing

The advent of high-capacity pre-trained models has revolutionized problem-solving in computer vision, shifting the focus from training task-specific models to adapting pre-trained models. Consequently, effectively adapting large pre-trained models to downstream tasks in an efficient manner has become a prominent research area. Existing solutions primarily concentrate on designing lightweight adapters and their interaction with pre-trained models, with the goal of minimizing the number of parameters requiring updates. In this study, we propose a novel Adapter Re-Composing (ARC) strategy that addresses efficient pre-trained model adaptation from a fresh perspective. Our approach considers the reusability of adaptation parameters and introduces a parameter-sharing scheme. Specifically, we leverage symmetric down-/up-projections to construct bottleneck operations, which are shared across layers. By learning low-dimensional re-scaling coefficients, we can effectively re-compose layer-adaptive adapters. This parameter-sharing strategy in adapter design allows us to significantly reduce the number of new parameters while maintaining satisfactory performance, thereby offering a promising approach to compress the adaptation cost. We conduct experiments on 24 downstream image classification tasks using various Vision Transformer variants to evaluate our method. The results demonstrate that our approach achieves compelling transfer learning performance with a reduced parameter count. Our code is available at \href{https://github.com/DavidYanAnDe/ARC}{https://github.com/DavidYanAnDe/ARC}.Comment: Paper is accepted to NeurIPS 202