All in Tokens: Unifying Output Space of Visual Tasks via Soft Token

Unlike language tasks, where the output space is usually limited to a set of tokens, the output space of visual tasks is more complicated, making it difficult to build a unified visual model for various visual tasks. In this paper, we seek to unify the output space of visual tasks, so that we can also build a unified model for visual tasks. To this end, we demonstrate a single unified model that simultaneously handles two typical visual tasks of instance segmentation and depth estimation, which have discrete/fixed-length and continuous/varied-length outputs, respectively. We propose several new techniques that take into account the particularity of visual tasks: 1) Soft token. We employ soft token to represent the task output. Unlike hard tokens in the common VQ-VAE which are assigned one-hot to discrete codebooks/vocabularies, the soft token is assigned softly to the codebook embeddings. Soft token can improve the accuracy of both the next token inference and decoding of the task output; 2) Mask augmentation. Many visual tasks have corruption, undefined or invalid values in label annotations, i.e., occluded area of depth maps. We show that a mask augmentation technique can greatly benefit these tasks. With these new techniques and other designs, we show that the proposed general-purpose task-solver can perform both instance segmentation and depth estimation well. Particularly, we achieve 0.279 RMSE on the specific task of NYUv2 depth estimation, setting a new record on this benchmark. The general-purpose task-solver, dubbed AiT, is available at \url{https://github.com/SwinTransformer/AiT}

Involvement of the Akt/mTOR pathway on EGF-induced cell transformation

金沢大学医学部附属病院薬剤部Our previous study demonstrated that phosphatidylinositol 3-kinase (P13K) is necessary for epidermal growth factor (EGF)-induced cell transformation in mouse epidermal JB6 cells. Akt and the mammalian target of rapamycin (mTOR) are regarded as P13K downstream effectors. Therefore, in this study, we investigated the role of Akt and mTOR on EGF-induced cell transformation in JB6 cells using rapamycin, a specific mTOR inhibitor, and cells expressing dominant negative mutants of Akt1 (DNM-Akt1). We found that the treatment of cells with rapamycin inhibited EGF-induced cell transformation but only slightly inhibited JB6 cell proliferation at 72 h. Although LY294002, a P13K inhibitor, attenuated EGF-induced activator protein 1 (AP-1) activation, treatment with rapamycin did not affect AP-1 activity. Treatment with rapamycin inhibited EGF-induced phosphorylation and activation of ribosomal p70 S6 protein kinase (p70 S6K), an mTOR downstream target, but had no effect on phosphorylation and activation of Akt. Rapamycin also had no effect on EGF-induced phosphorylation of extracellular signal-regulated protein kinases (ERKs). We showed that introduction of DNM-Akt1 into JB6 mouse epidermal Cl 41 (JB6 Cl 41) cells inhibits EGF-induced cell transformation without blocking cell proliferation. The expression of DNM-Akt1 also suppressed EGF-induced p70 S6K activation as well as Akt activation. These results indicated an involvement of the Akt/mTOR pathway in EGF-induced cell transformation in JB6 cells. © 2005 Wiley-Liss, Inc

Genotypic and Environmental Effects on the Volatile Chemotype of Valeriana jatamansi Jones

Valeriana jatamansi Jones is an aromatic medicinal herb and important alternative to V. officinalis, which is utilized for medicinal purposes in China and India and also as spices in India. Bioactive ingredients of V. jatamansi vary in different regions. However, no information is currently available on influence of genotype and environmental factors in the volatile compounds, especially when germplasms and planting locations need to be selected. Based on the results of SNP and volatile constituents from GC-MS analysis, this study found various genotypes and chemotypes of V. jatamansi for wild plants from seven regions in China and common-garden samples; correlations between genotype and chemotype were revealed for the plants. Two distinct populations (PX, FY) were distinguishable from five others (GJ, YL, SY, DD, DY) according to their genotypes and volatile profiles, the consistency of which was observed showing that genotype could significantly influence chemotype. Wild populations and common-garden samples were also separated in their volatile profiles, demonstrating that environmental factors strongly affected their chemotypes. Compounds contributing to the discrimination were identified as discriminatory compounds. This investigation has explored and provided essential information concerning the correlation between genotype and chemotype as well as environmental factors and chemotype of V. jatamansi in some regions of China. Feasible plantation and conservation strategies of V. jatamansi could be further explored based on these results

Magnetic characteristics of permanent magnet guideways at low temperature and its effect on the levitation force of bulk YBaCuO superconductors

It is important to understand the influence of environmental temperature variation on high temperature superconducting (HTS) maglev performance in practice. As the on-board bulk high temperature superconductors (HTSCs) are kept in liquid nitrogen cryostats at all times, the actual temperature effects relevant to maglev performance are on the permanent magnet guideway (PMG), and these have been examined by exploring the PMG magnetic characteristics and the related interaction force with the on-board bulk HTSCs above it in this paper. The experimental temperature range was set from room temperature to 77 K. The experimental results show that the typical room temperature (RT) vertical magnetic flux density (defined as BPMG), 565 mT, of the PMG segment first increases as the temperature decreases to 138 K, before it reaches a peak of 634 mT within the temperature range from 138 K to 120 K, with a rapid growth rate to 12.2% over the RT value. And then BPMG decreases quickly to a stable value of 576 mT at 77 K. A final 1.9% enhancement ratio was obtained compared with the initial BPMG at room temperature. Furthermore, the levitation force exhibits a similar trend to the BPMG whatever the field cooling process and the zero field cooling process was conducted when the PMG temperature is reduced. Growth of 21.8% and 13.4% for the levitation force occurs for the field-cooling process and the zero-field-cooling process, respectively. The results provide basic data for environmental temperature effects on HTS maglev systems, which implies that the low temperature environment can have benefits towards enhancement of their levitation performance

A machine learning-based probabilistic computational framework for uncertainty quantification of actuation of clustered tensegrity structures.

Clustered tensegrity structures integrated with continuous cables are lightweight, foldable, and deployable. Thus, they can be used as flexible manipulators or soft robots. The actuation process of such soft structure has high probabilistic sensitivity. It is essential to quantify the uncertainty of actuated responses of the tensegrity structures and to modulate their deformation accurately. In this work, we propose a comprehensive data-driven computational approach to study the uncertainty quantification (UQ) and probability propagation in clustered tensegrity structures, and we have developed a surrogate optimization model to control the flexible structure deformation. An example of clustered tensegrity beam subjected to a clustered actuation is presented to demonstrate the validity of the approach and its potential application. The three main novelties of the data-driven framework are: (1) The proposed model can avoid the difficulty of convergence in nonlinear Finite Element Analysis (FEA), by two machine learning methods, the Gauss Process Regression (GPR) and Neutral Network (NN). (2) A fast real-time prediction on uncertainty propagation can be achieved by the surrogate model, and (3) Optimization of the actuated deformation comes true by using both Sequence Quadratic Programming (SQP) and Bayesian optimization methods. The results have shown that the proposed data-driven computational approach is powerful and can be extended to other UQ models or alternative optimization objectives

Cyclosporin A Inhibits Rotavirus Replication and Restores Interferon-Beta Signaling Pathway <i>In Vitro</i> and <i>In Vivo</i>

<div><p>Rotavirus (RV) is the most common cause of severe diarrhea among infants and young children. Currently, there is no specific drug available against rotavirus, largely due to the lack of an ideal target molecule which has hampered drug development. Our previous studies have revealed that cyclosporin A (CsA) might be potentially useful as an anti-RV drug. We therefore used both cellular and mouse models to study the immunological safety and effectiveness of CsA as an anti-RV drug. We found that CsA treatment of HT-29 cells before, during, and after viral infection efficiently inhibited Wa strain RV replication and restored IFN-β expression in a HT-29 cell line model. Exploring the underlying mechanisms showed that CsA promoted Interferon Regulatory Factor-5 (IRF-5) expression (a key positive regulator of the type I IFN signaling pathway), but not IRF-1, IRF-3, or IRF-7. Additionally, CsA inhibited SOCS-1 expression (the key negative regulator of IFN-α/β), but not SOCS-2 or SOCS-3. The antiviral effect of CsA was confirmed in an RV-infected neonatal mouse model by evaluation of antigen clearance and assessment of changes in intestinal tissue pathology. Also, no differences in T cell frequency or proliferation between the CsA- and vehicle-treated groups were observed. Thus, both our in vitro and in vivo findings suggest that CsA, through modulating the expression of key regulators in IFN signaling pathway, promote type I IFN-based intracellular innate immunity in RV host cells. These findings suggest that CsA may be a useful candidate to develop a new anti-RV strategy, although further evaluation and characterization of CsA on RV-induced diarrhea are warranted.</p></div

TPENAS: A Two-Phase Evolutionary Neural Architecture Search for Remote Sensing Image Classification

The application of deep learning in remote sensing image classification has been paid more and more attention by industry and academia. However, manually designed remote sensing image classification models based on convolutional neural networks usually require sophisticated expert knowledge. Moreover, it is notoriously difficult to design a model with both high classification accuracy and few parameters. Recently, neural architecture search (NAS) has emerged as an effective method that can greatly reduce the heavy burden of manually designing models. However, it remains a challenge to search for a classification model with high classification accuracy and few parameters in the huge search space. To tackle this challenge, we propose TPENAS, a two-phase evolutionary neural architecture search framework, which optimizes the model using computational intelligence techniques in two search phases. In the first search phase, TPENAS searches for the optimal depth of the model. In the second search phase, TPENAS searches for the structure of the model from the perspective of the whole model. Experiments on three open benchmark datasets demonstrate that our proposed TPENAS outperforms the state-of-the-art baselines in both classification accuracy and reducing parameters

Protein translation: biological processes and therapeutic strategies for human diseases

Abstract Protein translation is a tightly regulated cellular process that is essential for gene expression and protein synthesis. The deregulation of this process is increasingly recognized as a critical factor in the pathogenesis of various human diseases. In this review, we discuss how deregulated translation can lead to aberrant protein synthesis, altered cellular functions, and disease progression. We explore the key mechanisms contributing to the deregulation of protein translation, including functional alterations in translation factors, tRNA, mRNA, and ribosome function. Deregulated translation leads to abnormal protein expression, disrupted cellular signaling, and perturbed cellular functions- all of which contribute to disease pathogenesis. The development of ribosome profiling techniques along with mass spectrometry-based proteomics, mRNA sequencing and single-cell approaches have opened new avenues for detecting diseases related to translation errors. Importantly, we highlight recent advances in therapies targeting translation-related disorders and their potential applications in neurodegenerative diseases, cancer, infectious diseases, and cardiovascular diseases. Moreover, the growing interest lies in targeted therapies aimed at restoring precise control over translation in diseased cells is discussed. In conclusion, this comprehensive review underscores the critical role of protein translation in disease and its potential as a therapeutic target. Advancements in understanding the molecular mechanisms of protein translation deregulation, coupled with the development of targeted therapies, offer promising avenues for improving disease outcomes in various human diseases. Additionally, it will unlock doors to the possibility of precision medicine by offering personalized therapies and a deeper understanding of the molecular underpinnings of diseases in the future

Effect of cyclosporin A (CsA) on type I interferon expression.

<p>Wa rotavirus-infected HT-29 cells were treated with CsA at indicated doses at 12 h post-infection. Total cell lysates were collected at 24 h post-treatment. Total cellular RNA extracted from the total cell lysates was subjected to the real-time RT-PCR to quantify IFN-α (A) and IFN-β (B). IFN-α/IFN-β expression is expressed as mRNA levels relative (fold) to the control (uninfected HT-29 cells without CsA treatment, which is defined as 1). Data shown are expressed as mean ± standard deviation of triplicate cultures, and 3 independent experiments were carried out (*<i>P</i><0.05, **<i>P</i><0.01).</p