Soy consumption has been associated with many potential health benefits in reducing chronic diseases such as obesity, cardiovascular disease, insulin-resistance/type II diabetes, certain type of ...cancers, and immune disorders. These physiological functions have been attributed to soy proteins either as intact soy protein or more commonly as functional or bioactive peptides derived from soybean processing. These findings have led to the approval of a health claim in the USA regarding the ability of soy proteins in reducing the risk for coronary heart disease and the acceptance of a health claim in Canada that soy protein can help lower cholesterol levels. Using different approaches, many soy bioactive peptides that have a variety of physiological functions such as hypolipidemic, anti-hypertensive, and anti-cancer properties, and anti-inflammatory, antioxidant, and immunomodulatory effects have been identified. Some soy peptides like lunasin and soymorphins possess more than one of these properties and play a role in the prevention of multiple chronic diseases. Overall, progress has been made in understanding the functional and bioactive components of soy. However, more studies are required to further identify their target organs, and elucidate their biological mechanisms of action in order to be potentially used as functional foods or even therapeutics for the prevention or treatment of chronic diseases.
Sampling Matters in Deep Embedding Learning Chao-Yuan Wu; Manmatha, R.; Smola, Alexander J. ...
2017 IEEE International Conference on Computer Vision (ICCV),
2017-Oct.
Conference Proceeding
Odprti dostop
Deep embeddings answer one simple question: How similar are two images? Learning these embeddings is the bedrock of verification, zero-shot learning, and visual search. The most prominent approaches ...optimize a deep convolutional network with a suitable loss function, such as contrastive loss or triplet loss. While a rich line of work focuses solely on the loss functions, we show in this paper that selecting training examples plays an equally important role. We propose distance weighted sampling, which selects more informative and stable examples than traditional approaches. In addition, we show that a simple margin based loss is sufficient to outperform all other loss functions. We evaluate our approach on the Stanford Online Products, CAR196, and the CUB200-2011 datasets for image retrieval and clustering, and on the LFW dataset for face verification. Our method achieves state-of-the-art performance on all of them.
Objectives
Tanshinone I (Tan‐I) is one of the vital fatsoluble monomer components, which extracted from Chinese medicinal herb Salvia miltiorrhiza Bunge. It has been shown that Tan‐I exhibited ...anti‐tumour activities on different types of cancers. However, the underlying mechanisms by which Tan‐Ⅰ regulates apoptosis and autophagy in ovarian cancer remain unclear. Thus, this study aimed to access the therapy effect of Tan‐Ⅰ and the underlying mechanisms.
Methods
Ovarian cancer cells A2780 and ID‐8 were treated with different concentrations of Tan‐Ⅰ (0, 1.2, 2.4, 4.8 and 9.6 μg/mL) for 24 hours. The cell proliferation was analysed by CCK8 assay, EdU staining and clone formation assay. Apoptosis was assessed by the TUNEL assay and flow cytometry. The protein levels of apoptosis protein (Caspase‐3), autophagy protein (Beclin1, ATG7, p62 and LC3II/LC3I) and PI3K/AKT/mTOR pathway were determined by Western blot. Autophagic vacuoles in cells were observed with LC3 dyeing using confocal fluorescent microscopy. Anti‐tumour activity of Tan‐Ⅰ was accessed by subcutaneous xeno‐transplanted tumour model of human ovarian cancer in nude mice. The Ki67, Caspase‐3 level and apoptosis level were analysed by immunohistochemistry and TUNEL staining.
Results
Tan‐Ⅰ inhibited the proliferation of ovarian cancer cells A2780 and ID‐8 in a dose‐dependent manner, based on CCK8 assay, EdU staining and clone formation assay. In additional, Tan‐Ⅰ induced cancer cell apoptosis and autophagy in a dose‐dependent manner in ovarian cancer cells by TUNEL assay, flow cytometry and Western blot. Tan‐Ⅰ significantly inhibited tumour growth by inducing cell apoptosis and autophagy. Mechanistically, Tan‐Ⅰ activated apoptosis‐associated protein Caspase‐3 cleavage to promote cell apoptosis and inhibited PI3K/AKT/mTOR pathway to induce autophagy.
Conclusions
This is the first evidence that Tan‐Ⅰ induced apoptosis and promoted autophagy via the inactivation of PI3K/AKT/mTOR pathway on ovarian cancer and further inhibited tumour growth, which might be considered as effective strategy.
A ConvNet for the 2020s Liu, Zhuang; Mao, Hanzi; Wu, Chao-Yuan ...
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR),
2022-June
Conference Proceeding
The "Roaring 20s" of visual recognition began with the introduction of Vision Transformers (ViTs), which quickly superseded ConvNets as the state-of-the-art image classification model. A vanilla ViT, ...on the other hand, faces difficulties when applied to general computer vision tasks such as object detection and semantic segmentation. It is the hierarchical Transformers (e.g., Swin Transformers) that reintroduced several ConvNet priors, making Transformers practically viable as a generic vision backbone and demonstrating remarkable performance on a wide variety of vision tasks. However, the effectiveness of such hybrid approaches is still largely credited to the intrinsic superiority of Transformers, rather than the inherent inductive biases of convolutions. In this work, we reexamine the design spaces and test the limits of what a pure ConvNet can achieve. We gradually "modernize" a standard ResNet toward the design of a vision Transformer, and discover several key components that contribute to the performance difference along the way. The outcome of this exploration is a family of pure ConvNet models dubbed ConvNeXt. Constructed entirely from standard ConvNet modules, ConvNeXts compete favorably with Transformers in terms of accuracy and scalability, achieving 87.8% ImageNet top-1 accuracy and outperforming Swin Transformers on COCO detection and ADE20K segmentation, while maintaining the simplicity and efficiency of standard ConvNets.
This paper proposes a deep learning model, named DeepSleepNet, for automatic sleep stage scoring based on raw single-channelEEG. Most of the existing methods rely on hand-engineered features, which ...require prior knowledge of sleep analysis. Only a few of them encode the temporal information, such as transition rules, which is important for identifying the next sleep stages, into the extracted features. In the proposed model, we utilize convolutional neural networks to extract time-invariant features, and bidirectional-longshort-term memory to learn transition rules among sleep stages automatically from EEG epochs. We implement a two-step training algorithm to train our model efficiently. We evaluated our model using different single-channel EEGs (F4-EOG (left), Fpz-Cz, and Pz-Oz) from two public sleep data sets, that have different properties (e.g., sampling rate) and scoring standards (AASM and R&K). The results showed that our model achieved similar overall accuracy and macro F1-score (MASS: 86.2%-81.7, Sleep-EDF: 82.0%-76.9) compared with the state-of-the-art methods (MASS: 85.9%-80.5, Sleep-EDF: 78.9%-73.7) on both data sets. This demonstrated that, without changing the model architecture and the training algorithm, our model could automatically learn features for sleep stage scoring from different raw single-channel EEGs from different data sets without utilizing any hand-engineered features.
A
bstract
We derive all the dynamical second order transport coefficients for Dp-brane with
p
from 1 to 6 within the framework of fluid/gravity correspondence in this paper. The D5 and D6-brane do ...not have dual relativistic fluids; D3-brane corresponds to 4-dimensional conformal relativistic fluid; D1, D2 and D4-brane separately correspond to nonconformal relativistic fluids of dimensions 2, 3 and 5. The Haack-Yarom relation only exists for Dp-branes with
p
larger than 2 and is also satisfied by them. We also find that the Romatschke and Kleinert-Probst relations need to be generalized in order to be valid for relativistic fluids of dimensions other than 4.
The spatial organization of chromatin critically affects genome function. Recent chromosome-conformation-capture studies have revealed topologically associating domains (TADs) as a conserved feature ...of chromatin organization, but how TADs are spatially organized in individual chromosomes remains unknown. Here, we developed an imaging method for mapping the spatial positions of numerous genomic regions along individual chromosomes and traced the positions of TADs in human interphase autosomes and X chromosomes. We observed that chromosome folding deviates from the ideal fractal-globule model at large length scales and that TADs are largely organized into two compartments spatially arranged in a polarized manner in individual chromosomes. Active and inactive X chromosomes adopt different folding and compartmentalization configurations. These results suggest that the spatial organization of chromatin domains can change in response to regulation.
Thermoelectric technology enables the harvest of waste heat and its direct conversion into electricity. The conversion efficiency is determined by the materials figure of merit
Here we show a maximum
...of ~2.8 ± 0.5 at 773 kelvin in n-type tin selenide (SnSe) crystals out of plane. The thermal conductivity in layered SnSe crystals is the lowest in the out-of-plane direction two-dimensional (2D) phonon transport. We doped SnSe with bromine to make n-type SnSe crystals with the overlapping interlayer charge density (3D charge transport). A continuous phase transition increases the symmetry and diverges two converged conduction bands. These two factors improve carrier mobility, while preserving a large Seebeck coefficient. Our findings can be applied in 2D layered materials and provide a new strategy to enhance out-of-plane electrical transport properties without degrading thermal properties.
Epidemiological investigations suggest that soy consumption may be associated with a lower incidence of certain chronic diseases. Clinical studies also show that ingestion of soy proteins reduces the ...risk factors for cardiovascular disease. This led to the approval of the food-labeling health claim for soy proteins in the prevention of coronary heart disease by the U.S. FDA in 1999. Similar health petitions for soy proteins have also been approved thereafter in the United Kingdom, Brazil, South Africa, the Philippines, Indonesia, Korea, and Malaysia. However, the purported health benefits are quite variable in different studies. The Nutrition Committee of the American Heart Association has assessed 22 randomized trials conducted since 1999 and found that isolated soy protein with isoflavones (ISF) slightly decreased LDL cholesterol but had no effect on HDL cholesterol, triglycerides, lipoprotein(a), or blood pressure. The other effects of soy consumption were not evident. Although the contributing factors to these discrepancies are not fully understood, the source of soybeans and processing procedures of the protein or ISF are believed to be important because of their effects on the content and intactness of certain bioactive protein subunits. Some studies have documented potential safety concerns on increased consumption of soy products. Impacts of soy products on thyroid and reproductive functions as well as on certain types of carcinogenesis require further study in this context. Overall, existing data are inconsistent or inadequate in supporting most of the suggested health benefits of consuming soy protein or ISF.
In this paper, we study Multiscale Vision Transformers (MViTv2) as a unified architecture for image and video classification, as well as object detection. We present an improved version of MViT that ...incorporates decomposed relative positional embeddings and residual pooling connections. We instantiate this architecture in five sizes and evaluate it for ImageNet classification, COCO detection and Kinetics video recognition where it outperforms prior work. We further compare MViTv2s' pooling attention to window attention mechanisms where it outperforms the latter in accuracy/compute. Without bells-and-whistles, MViTv2 has state-of-the-art performance in 3 domains: 88.8% accuracy on ImageNet classification, 58.7 AP box on COCO object detection as well as 86.1% on Kinetics-400 video classification. Code and models are available at https://github.com/facebookresearch/mvit.
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