In this paper, we present the Lipschitz regularization theory and algorithms for a novel Loss-Sensitive Generative Adversarial Network (LS-GAN). Specifically, it trains a loss function to distinguish ...between real and fake samples by designated margins, while learning a generator alternately to produce realistic samples by minimizing their losses. The LS-GAN further regularizes its loss function with a Lipschitz regularity condition on the density of real data, yielding a regularized model that can better generalize to produce new data from a reasonable number of training examples than the classic GAN. We will further present a Generalized LS-GAN (GLS-GAN) and show it contains a large family of regularized GAN models, including both LS-GAN and Wasserstein GAN, as its special cases. Compared with the other GAN models, we will conduct experiments to show both LS-GAN and GLS-GAN exhibit competitive ability in generating new images in terms of the Minimum Reconstruction Error (MRE) assessed on a separate test set. We further extend the LS-GAN to a conditional form for supervised and semi-supervised learning problems, and demonstrate its outstanding performance on image classification tasks.
Representation learning with small labeled data have emerged in many problems, since the success of deep neural networks often relies on the availability of a huge amount of labeled data that is ...expensive to collect. To address it, many efforts have been made on training sophisticated models with few labeled data in an unsupervised and semi-supervised fashion. In this paper, we will review the recent progresses on these two major categories of methods. A wide spectrum of models will be categorized in a big picture, where we will show how they interplay with each other to motivate explorations of new ideas. We will review the principles of learning the transformation equivariant, disentangled, self-supervised and semi-supervised representations, all of which underpin the foundation of recent progresses. Many implementations of unsupervised and semi-supervised generative models have been developed on the basis of these criteria, greatly expanding the territory of existing autoencoders, generative adversarial nets (GANs) and other deep networks by exploring the distribution of unlabeled data for more powerful representations. We will discuss emerging topics by revealing the intrinsic connections between unsupervised and semi-supervised learning, and propose in future directions to bridge the algorithmic and theoretical gap between transformation equivariance for unsupervised learning and supervised invariance for supervised learning, and unify unsupervised pretraining and supervised finetuning. We will also provide a broader outlook of future directions to unify transformation and instance equivariances for representation learning, connect unsupervised and semi-supervised augmentations, and explore the role of the self-supervised regularization for many learning problems.
Representation learning has significantly been developed with the advance of contrastive learning methods. Most of those methods are benefited from various data augmentations that are carefully ...designated to maintain their identities so that the images transformed from the same instance can still be retrieved. However, those carefully designed transformations limited us to further explore the novel patterns exposed by other transformations. Meanwhile, as shown in our experiments, direct contrastive learning for stronger augmented images can not learn representations effectively. Thus, we propose a general framework called Contrastive Learning with Stronger Augmentations (CLSA) to complement current contrastive learning approaches. Here, the distribution divergence between the weakly and strongly augmented images over the representation bank is adopted to supervise the retrieval of strongly augmented queries from a pool of instances. Experiments on the ImageNet dataset and downstream datasets showed the information from the strongly augmented images can significantly boost the performance. For example, CLSA achieves top-1 accuracy of 76.2% on ImageNet with a standard ResNet-50 architecture with a single-layer classifier fine-tuned, which is almost the same level as 76.5% of supervised results.
Abscisic acid (ABA) is an important phytohormone regulating plant growth, development, and stress responses. It has an essential role in multiple physiological processes of plants, such as stomatal ...closure, cuticular wax accumulation, leaf senescence, bud dormancy, seed germination, osmotic regulation, and growth inhibition among many others. Abscisic acid controls downstream responses to abiotic and biotic environmental changes through both transcriptional and posttranscriptional mechanisms. During the past 20 years, ABA biosynthesis and many of its signaling pathways have been well characterized. Here we review the dynamics of ABA metabolic pools and signaling that affects many of its physiological functions.
Abscisic acid (ABA) is the major stress hormone that coordinates plant growth, development and abiotic stress responses. In this review, we summarized the recent progresses on its metabolism, transport and signaling, and discussed the open questions about ABA dynamics and functions.
Microbially mediated anaerobic oxidation of methane (AOM) is a key process in the regulation of methane emissions to the atmosphere. Iron can serve as an electron acceptor for AOM, and it has been ...suggested that Fe(III)-dependent AOM potentially comprises a major global methane sink. Although it has been proposed that anaerobic methanotrophic (ANME) archaea can facilitate this process, their active metabolic pathways have not been confirmed. Here we report the enrichment and characterisation of a novel archaeon in a laboratory-scale bioreactor fed with Fe(III) oxide (ferrihydrite) and methane. Long-term performance data, in conjunction with the
C- and
Fe-labelling batch experiments, demonstrated that AOM was coupled to Fe(III) reduction to Fe(II) in this bioreactor. Metagenomic analysis showed that this archaeon belongs to a novel genus within family Candidatus Methanoperedenaceae, and possesses genes encoding the "reverse methanogenesis" pathway, as well as multi-heme c-type cytochromes which are hypothesised to facilitate dissimilatory Fe(III) reduction. Metatranscriptomic analysis revealed upregulation of these genes, supporting that this archaeon can independently mediate AOM using Fe(III) as the terminal electron acceptor. We propose the name Candidatus "Methanoperedens ferrireducens" for this microorganism. The potential role of "M. ferrireducens" in linking the carbon and iron cycles in environments rich in methane and iron should be investigated in future research.
This technical note presents necessary and sufficient conditions for the stability and stabilization of fractional-order interval systems. The results are obtained in terms of linear matrix ...inequalities. Two illustrative examples are given to show that our results are effective and less conservative for checking the robust stability and designing the stabilizing controller for fractional-order interval systems.
Deep neural networks have been successfully applied to many real-world applications. However, such successes rely heavily on large amounts of labeled data that is expensive to obtain. Recently, many ...methods for semi-supervised learning have been proposed and achieved excellent performance. In this study, we propose a new EnAET framework to further improve existing semi-supervised methods with self-supervised information. To our best knowledge, all current semi-supervised methods improve performance with prediction consistency and confidence ideas. We are the first to explore the role of self-supervised representations in semi-supervised learning under a rich family of transformations. Consequently, our framework can integrate the self-supervised information as a regularization term to further improve all current semi-supervised methods. In the experiments, we use MixMatch, which is the current state-of-the-art method on semi-supervised learning, as a baseline to test the proposed EnAET framework. Across different datasets, we adopt the same hyper-parameters, which greatly improves the generalization ability of the EnAET framework. Experiment results on different datasets demonstrate that the proposed EnAET framework greatly improves the performance of current semi-supervised algorithms. Moreover, this framework can also improve supervised learning by a large margin, including the extremely challenging scenarios with only 10 images per class. The code and experiment records are available in https://github.com/maple-research-lab/EnAET .
The synthetic methodology for direct indole functionalizations is of great significance in indole chemistry and has been intensively investigated in the last few decades. From the perspective of ...green chemistry, oxygen is the best choice as the terminal oxidant in molecular synthesis. Hence, aerobic oxidative functionalization of indoles became a hot research topic in the last decade. Numerous efficient protocols in this field have been discovered that enable facile and efficient transformations of indoles to related valuable compounds, which are summarized and discussed in detail in this review.
Natural killer (NK) cells play a critical role in the innate antitumor immune response. Recently, NK cell dysfunction has been verified in various malignant tumors, including hepatocellular carcinoma ...(HCC). However, the molecular biological mechanisms of NK cell dysfunction in human HCC are still obscure.
The expression of circular ubiquitin-like with PHD and ring finger domain 1 RNA (circUHRF1) in HCC tissues, exosomes, and cell lines was detected by qRT-PCR. Exosomes were isolated from the culture medium of HCC cells and plasma of HCC patients using an ultracentrifugation method and the ExoQuick Exosome Precipitation Solution kit and then characterized by transmission electronic microscopy, NanoSight and western blotting. The role of circUHRF1 in NK cell dysfunction was assessed by ELISA. In vivo circRNA precipitation, RNA immunoprecipitation, and luciferase reporter assays were performed to explore the molecular mechanisms of circUHRF1 in NK cells. In a retrospective study, the clinical characteristics and prognostic significance of circUHRF1 were determined in HCC tissues.
Here, we report that the expression of circUHRF1 is higher in human HCC tissues than in matched adjacent nontumor tissues. Increased levels of circUHRF1 indicate poor clinical prognosis and NK cell dysfunction in patients with HCC. In HCC patient plasma, circUHRF1 is predominantly secreted by HCC cells in an exosomal manner, and circUHRF1 inhibits NK cell-derived IFN-γ and TNF-α secretion. A high level of plasma exosomal circUHRF1 is associated with a decreased NK cell proportion and decreased NK cell tumor infiltration. Moreover, circUHRF1 inhibits NK cell function by upregulating the expression of TIM-3 via degradation of miR-449c-5p. Finally, we show that circUHRF1 may drive resistance to anti-PD1 immunotherapy in HCC patients.
Exosomal circUHRF1 is predominantly secreted by HCC cells and contributes to immunosuppression by inducing NK cell dysfunction in HCC. CircUHRF1 may drive resistance to anti-PD1 immunotherapy, providing a potential therapeutic strategy for patients with HCC.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
A visible‐light‐mediated photoredox Minisci‐type alkylation with ethers as the alkylating reagent is reported. User‐friendly LiBr has been found to be the key promoter for this radical coupling. The ...reaction exhibits broad functional group tolerance for both C2 and C4 couplings/alkylations of quinolines. Mechanistic studies suggest that the bromide additive could not only dramatically enhance the reaction but also alter the reaction mechanism probably over a reductive catalytic cycle.
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