Initial research on vitamin E and cancer has focused on α-tocopherol (αT), but recent clinical studies on cancer-preventive effects of αT supplementation have shown disappointing results, which has ...led to doubts about the role of vitamin E, including different vitamin E forms, in cancer prevention. However, accumulating mechanistic and preclinical animal studies show that other forms of vitamin E, such as γ-tocopherol (γT), δ-tocopherol (δT), γ-tocotrienol (γTE), and δ-tocotrienol (δTE), have far superior cancer-preventive activities than does αT. These vitamin E forms are much stronger than αT in inhibiting multiple cancer-promoting pathways, including cyclo-oxygenase (COX)– and 5-lipoxygenase (5-LOX)–catalyzed eicosanoids, and transcription factors such as nuclear transcription factor κB (NF-κB) and signal transducer and activator of transcription factor 3 (STAT3). These vitamin E forms, but not αT, cause pro-death or antiproliferation effects in cancer cells via modulating various signaling pathways, including sphingolipid metabolism. Unlike αT, these vitamin E forms are quickly metabolized to various carboxychromanols including 13′-carboxychromanols, which have even stronger anti-inflammatory and anticancer effects than some vitamin precursors. Consistent with mechanistic findings, γT, δT, γTE, and δTE, but not αT, have been shown to be effective for preventing the progression of various types of cancer in preclinical animal models. This review focuses on cancer-preventive effects and mechanisms of γT, δT, γTE, and δTE in cells and preclinical models and discusses current progress in clinical trials. The existing evidence strongly indicates that these lesser-known vitamin E forms are effective agents for cancer prevention or as adjuvants for improving prevention, therapy, and control of cancer.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Two algorithms based on machine learning neural networks are proposed—the shallow learning (S‐L) and deep learning (D‐L) algorithms—that can potentially be used in atmosphere‐only typhoon forecast ...models to provide flow‐dependent typhoon‐induced sea surface temperature cooling (SSTC) for improving typhoon predictions. The major challenge of existing SSTC algorithms in forecast models is how to accurately predict SSTC induced by an upcoming typhoon, which requires information not only from historical data but more importantly also from the target typhoon itself. The S‐L algorithm composes of a single layer of neurons with mixed atmospheric and oceanic factors. Such a structure is found to be unable to represent correctly the physical typhoon‐ocean interaction. It tends to produce an unstable SSTC distribution, for which any perturbations may lead to changes in both SSTC pattern and strength. The D‐L algorithm extends the neural network to a 4 × 5 neuron matrix with atmospheric and oceanic factors being separated in different layers of neurons, so that the machine learning can determine the roles of atmospheric and oceanic factors in shaping the SSTC. Therefore, it produces a stable crescent‐shaped SSTC distribution, with its large‐scale pattern determined mainly by atmospheric factors (e.g., winds) and small‐scale features by oceanic factors (e.g., eddies). Sensitivity experiments reveal that the D‐L algorithms improve maximum wind intensity errors by 60–70% for four case study simulations, compared to their atmosphere‐only model runs.
Plain Language Summary
Forecasting accuracy with respect to storm track and intensity are two important factors for evaluating typhoon models. While 24‐h forecast errors of typhoon track have steadily improved to an order of 50 km, the prediction of typhoon intensity has remained one of the major challenges during the last decade. In this study, two algorithms based on machine‐learning neural networks are proposed‐the shallow learning (S‐L) and deep learning (D‐L) algorithms‐that can potentially be used in atmosphere‐only typhoon forecast models to provide flow‐dependent typhoon‐induced sea surface temperature cooling (SSTC) for improving typhoon predictions.
Key Points
A parameterization scheme based on deep learning neural network is proposed for atmosphere‐only typhoon forecast models
The deep learning algorithm is designed to combine information from historical data and the target typhoon
The scheme based on the deep learning algorithm achieves an equivalent representation as the fully coupled model
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Due to its storage and retrieval efficiency, cross-modal hashing (CMH) has been widely used for cross-modal similarity search in many multimedia applications. According to the training strategy, ...existing CMH methods can be mainly divided into two categories: relaxation-based continuous methods and discrete methods. In general, the training of relaxation-based continuous methods is faster than that of discrete methods, but the accuracy of relaxation-based continuous methods is not satisfactory. On the contrary, the accuracy of discrete methods is typically better than that of the relaxation-based continuous methods, but the training of discrete methods is very time-consuming. In this paper, we propose a novel CMH method, called Discrete Latent Factor model-based cross-modal Hashing (DLFH), for cross modal similarity search. DLFH is a discrete method which can directly learn the binary hash codes for CMH. At the same time, the training of DLFH is efficient. Experiments show that the DLFH can achieve significantly better accuracy than existing methods, and the training time of DLFH is comparable to that of the relaxation-based continuous methods which are much faster than the existing discrete methods.
The ocean's chemistry is changing due to the uptake of anthropogenic carbon dioxide (CO
). This phenomenon, commonly referred to as "Ocean Acidification", is endangering coral reefs and the broader ...marine ecosystems. In this study, we combine a recent observational seawater CO
data product, i.e., the 6
version of the Surface Ocean CO
Atlas (1991-2018, ~23 million observations), with temporal trends at individual locations of the global ocean from a robust Earth System Model to provide a high-resolution regionally varying view of global surface ocean pH and the Revelle Factor. The climatology extends from the pre-Industrial era (1750 C.E.) to the end of this century under historical atmospheric CO
concentrations (pre-2005) and the Representative Concentrations Pathways (post-2005) of the Intergovernmental Panel on Climate Change (IPCC)'s 5
Assessment Report. By linking the modeled pH trends to the observed modern pH distribution, the climatology benefits from recent improvements in both model design and observational data coverage, and is likely to provide improved regional OA trajectories than the model output could alone, therefore, will help guide the regional OA adaptation strategies. We show that air-sea CO
disequilibrium is the dominant mode of spatial variability for surface pH, and discuss why pH and calcium carbonate mineral saturation states, two important metrics for OA, show contrasting spatial variability.
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Hashing has been widely used for large-scale search due to its low storage cost and fast query speed. By using supervised information, supervised hashing can significantly outperform unsupervised ...hashing. Recently, discrete supervised hashing and feature learning based deep hashing are two representative progresses in supervised hashing. On one hand, hashing is essentially a discrete optimization problem. Hence, utilizing supervised information to directly guide discrete (binary) coding procedure can avoid sub-optimal solution and improve the accuracy. On the other hand, feature learning based deep hashing, which integrates deep feature learning and hash-code learning into an end-to-end architecture, can enhance the feedback between feature learning and hash-code learning. The key in discrete supervised hashing is to adopt supervised information to directly guide the discrete coding procedure in hashing. The key in deep hashing is to adopt the supervised information to directly guide the deep feature learning procedure. However, most deep supervised hashing methods cannot use the supervised information to directly guide both discrete (binary) coding procedure and deep feature learning procedure in the same framework. In this paper, we propose a novel deep hashing method, called deep discrete supervised hashing (DDSH). DDSH is the first deep hashing method which can utilize pairwise supervised information to directly guide both discrete coding procedure and deep feature learning procedure and thus enhance the feedback between these two important procedures. Experiments on four real datasets show that DDSH can outperform other state-of-the-art baselines, including both discrete hashing and deep hashing baselines, for image retrieval.
Accumulating evidence indicates that the gut microbiota can promote or inhibit colonic inflammation and carcinogenesis. Promotion of beneficial gut bacteria is considered a promising strategy to ...alleviate colonic diseases including colitis and colorectal cancer. Interestingly, dietary polyphenols, which have been shown to attenuate colitis and inhibit colorectal cancer in animal models and some human studies, appear to reach relatively high concentrations in the large intestine and to interact with the gut microbial community. This review summarizes the modulatory effects of polyphenols on the gut microbiota in humans and animals under healthy and diseased conditions including colitis and colitis-associated colorectal cancer (CAC). Existing human and animal studies indicate that polyphenols and polyphenol-rich whole foods are capable of elevating butyrate producers and probiotics that alleviate colitis and inhibit CAC, such as Lactobacillus and Bifidobacterium. Studies in colitis and CAC models indicate that polyphenols decrease opportunistic pathogenic or proinflammatory microbes and counteract disease-induced dysbiosis. Consistently, polyphenols also change microbial functions, including increasing butyrate formation. Moreover, polyphenol metabolites produced by the gut microbiota appear to have anticancer and anti-inflammatory activities, protect gut barrier integrity, and mitigate inflammatory conditions in cells and animal models. Based on these results, we conclude that polyphenol-mediated alteration of microbial composition and functions, together with polyphenol metabolites produced by the gut microbiota, likely contribute to the protective effects of polyphenols on colitis and CAC. Future research is needed to validate the causal role of the polyphenol–gut microbiota interaction in polyphenols' anti-colitis and anti-CAC effects, and to further elucidate mechanisms underlying such interaction.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Deep Cross-Modal Hashing Qing-Yuan Jiang; Wu-Jun Li
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR),
2017-July
Conference Proceeding
Due to its low storage cost and fast query speed, cross-modal hashing (CMH) has been widely used for similarity search in multimedia retrieval applications. However, most existing CMH methods are ...based on hand-crafted features which might not be optimally compatible with the hash-code learning procedure. As a result, existing CMH methods with hand-crafted features may not achieve satisfactory performance. In this paper, we propose a novel CMH method, called deep cross-modal hashing (DCMH), by integrating feature learning and hash-code learning intothe same framework. DCMH is an end-to-end learning framework with deep neural networks, one for each modality, to perform feature learning from scratch. Experiments on three real datasets with image-text modalities show that DCMH can outperform other baselines to achieve the state-of-the-art performance in cross-modal retrieval applications.
The application of density functional theory (DFT) has been accelerating the screening and design process of alloy catalysts for the carbon dioxide reduction reaction (CO
2
RR), but the catalyst ...design principle still cannot be universally used to date because of the time-consuming DFT calculations and the unclear structure-property relationship of alloy catalysts. To address these issues, we combine machine learning methods and descriptors based on the intrinsic properties of substrates and adsorbates to develop a model, which allows rapid screening through a large phase space of alloys with the usual DFT accuracy. Our ML scheme sheds light on the size of active centers on transition metals and alloys, the effect of alloying on engineering adsorption energy, and the coupling mechanism of different adsorbates with substrates. These findings not only help us understand the structure-property relationship of alloy catalysts and the reaction mechanism of the CO
2
RR, but also provide a basis for the design of catalysts. This universal design framework can be extended to other catalysts and other reactions towards efficient and cost-effective potential catalysts.
We develop a universal design scheme based on the machine learning method and the intrinsic properties of substrates and adsorbates, allowing accurate prediction and rapid screening through a large phase space of alloys and multiple adsorbates.
SUMMARY
Leaf senescence represents the final stage of leaf growth and development, and its onset and progression are strictly regulated; however, the underlying regulatory mechanisms remain largely ...unknown. In this study we found that WRKY42 was highly induced during leaf senescence. Loss‐of‐function wrky42 mutants showed delayed leaf senescence whereas the overexpression of WRKY42 accelerated senescence. Transcriptome analysis revealed 2721 differentially expressed genes between wild‐type and WRKY42‐overexpressing plants, including genes involved in salicylic acid (SA) and reactive oxygen species (ROS) synthesis as well as several senescence‐associated genes (SAGs). Moreover, WRKY42 activated the transcription of isochorismate synthase 1 (ICS1), respiratory burst oxidase homolog F (RbohF) and a few SAG genes. Consistently, the expression of these genes was reduced in wrky42 mutants but was markedly increased in transgenic Arabidopsis overexpressing WRKY42. Both in vitro electrophoretic mobility shift assays (EMSAs) and in vivo chromatin immunoprecipitation and dual luciferase assays demonstrated that WRKY42 directly bound to the promoters of ICS1 and RbohF, as well as a few SAGs, to activate their expression. Genetic analysis further showed that mutations of ICS1 and RbohF suppressed the early senescence phenotype evoked by WRKY42 overexpression. Thus, we have identified WRKY42 as a novel transcription factor positively regulating leaf senescence by directly activating the transcription of ICS1, RbohF and SAGs, without any seed yield penalty.
Significance Statement
The Arabidopsis transcription factor gene WRKY42 is induced during leaf senescence and is a senescence‐associated gene (SAG). WRKY42 positively regulates leaf senescence through activating the expression of genes implicated in salicylic acid (SA) and reactive oxygen species (ROS) synthesis, as well as the expression of several SAGs, without seed yield penalty.
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The Goos-Hänchen (GH) shift and the Imbert-Fedorov (IF) shift are optical phenomena which describe the longitudinal and transverse lateral shifts at the reflection interface, respectively. Here, we ...predict the GH and IF shifts in Weyl semimetals (WSMs)-a promising material harboring low energy Weyl fermions, a massless fermionic cousin of photons. Our results show that the GH shift in WSMs is valley independent, which is analogous to that discovered in a 2D relativistic material-graphene. However, the IF shift has never been explored in nonoptical systems, and here we show that it is valley dependent. Furthermore, we find that the IF shift actually originates from the topological effect of the system. Experimentally, the topological IF shift can be utilized to characterize the Weyl semimetals, design valleytronic devices of high efficiency, and measure the Berry curvature.
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