Positive solutions for a Hadamard-type fractional p-Laplacian integral boundary value problem

In this paper we study the existence of positive solutions to a Hadamard-type fractional integral boundary value problem using fixed point index. We construct a new linear operator and obtain our main results under some conditions concerning the spectral radius of this linear operator. Our method improves and generalizes some results in the literature

G-VAE: A Continuously Variable Rate Deep Image Compression Framework

Rate adaption of deep image compression in a single model will become one of the decisive factors competing with the classical image compression codecs. However, until now, there is no perfect solution that neither increases the computation nor affects the compression performance. In this paper, we propose a novel image compression framework G-VAE (Gained Variational Autoencoder), which could achieve continuously variable rate in a single model. Unlike the previous solutions that encode progressively or change the internal unit of the network, G-VAE only adds a pair of gain units at the output of encoder and the input of decoder. It is so concise that G-VAE could be applied to almost all the image compression methods and achieve continuously variable rate with negligible additional parameters and computation. We also propose a new deep image compression framework, which outperforms all the published results on Kodak datasets in PSNR and MS-SSIM metrics. Experimental results show that adding a pair of gain units will not affect the performance of the basic models while endowing them with continuously variable rate

PKCδ is required for porcine reproductive and respiratory syndrome virus replication

AbstractProtein kinase C (PKC) that transduces signals to modulate a wide range of cellular functions has been shown to regulate a number of viral infections. Herein, we showed that inhibition of PKC with the PKC inhibitor GF109203X significantly impaired porcine reproductive and respiratory syndrome virus (PRRSV) replication. Inhibition of PKC led to virus yield reduction, which was associated with decreased viral RNA synthesis and lowered virus protein expression. And this inhibitory effect by PKC inhibitor was shown to occur at the early stage of PRRSV infection. Subsequently, we found that PRRSV infection activated PKCδ in PAMs and knockdown of PKCδ by small interfering RNA (siRNA) suppressed PRRSV replication, suggesting that novel PKCδ may play an important factor in PRRSV replication. Taken together, these data imply that PKC is involved in PRRSV infection and beneficial to PRRSV replication, extending our understanding of PRRSV replication

Multimodal Contrast Agents for Optoacoustic Brain Imaging in Small Animals

Optoacoustic (photoacoustic) imaging has demonstrated versatile applications in biomedical research, visualizing the disease pathophysiology and monitoring the treatment effect in an animal model, as well as toward applications in the clinical setting. Given the complex disease mechanism, multimodal imaging provides important etiological insights with different molecular, structural, and functional readouts in vivo. Various multimodal optoacoustic molecular imaging approaches have been applied in preclinical brain imaging studies, including optoacoustic/fluorescence imaging, optoacoustic imaging/magnetic resonance imaging (MRI), optoacoustic imaging/MRI/Raman, optoacoustic imaging/positron emission tomography, and optoacoustic/computed tomography. There is a rapid development in molecular imaging contrast agents employing a multimodal imaging strategy for pathological targets involved in brain diseases. Many chemical dyes for optoacoustic imaging have fluorescence properties and have been applied in hybrid optoacoustic/fluorescence imaging. Nanoparticles are widely used as hybrid contrast agents for their capability to incorporate different imaging components, tunable spectrum, and photostability. In this review, we summarize contrast agents including chemical dyes and nanoparticles applied in multimodal optoacoustic brain imaging integrated with other modalities in small animals, and provide outlook for further research

VE-KWS: Visual Modality Enhanced End-to-End Keyword Spotting

The performance of the keyword spotting (KWS) system based on audio modality, commonly measured in false alarms and false rejects, degrades significantly under the far field and noisy conditions. Therefore, audio-visual keyword spotting, which leverages complementary relationships over multiple modalities, has recently gained much attention. However, current studies mainly focus on combining the exclusively learned representations of different modalities, instead of exploring the modal relationships during each respective modeling. In this paper, we propose a novel visual modality enhanced end-to-end KWS framework (VE-KWS), which fuses audio and visual modalities from two aspects. The first one is utilizing the speaker location information obtained from the lip region in videos to assist the training of multi-channel audio beamformer. By involving the beamformer as an audio enhancement module, the acoustic distortions, caused by the far field or noisy environments, could be significantly suppressed. The other one is conducting cross-attention between different modalities to capture the inter-modal relationships and help the representation learning of each modality. Experiments on the MSIP challenge corpus show that our proposed model achieves 2.79% false rejection rate and 2.95% false alarm rate on the Eval set, resulting in a new SOTA performance compared with the top-ranking systems in the ICASSP2022 MISP challenge.Comment: 5 pages. Accepted at ICASSP202

hDOT1L Links Histone Methylation to Leukemogenesis

SummaryEpigenetic modifications play an important role in human cancer. One such modification, histone methylation, contributes to human cancer through deregulation of cancer-relevant genes. The yeast Dot1 and its human counterpart, hDOT1L, methylate lysine 79 located within the globular domain of histone H3. Here we report that hDOT1L interacts with AF10, an MLL (mixed lineage leukemia) fusion partner involved in acute myeloid leukemia, through the OM-LZ region of AF10 required for MLL-AF10-mediated leukemogenesis. We demonstrate that direct fusion of hDOT1L to MLL results in leukemic transformation in an hDOT1L methyltransferase activity-dependent manner. Transformation by MLL-hDOT1L and MLL-AF10 results in upregulation of a number of leukemia-relevant genes, such as Hoxa9, concomitant with hypermethylation of H3-K79. Our studies thus establish that mistargeting of hDOT1L to Hoxa9 plays an important role in MLL-AF10-mediated leukemogenesis and suggests that the enzymatic activity of hDOT1L may provide a potential target for therapeutic intervention

Fine-Grained Modeling and Optimization for Intelligent Resource Management in Big Data Processing

International audienceBig data processing at the production scale presents a highly complex environment for resource optimization (RO), a problem crucial for meeting performance goals and budgetary constraints of analytical users. The RO problem is challenging because it involves a set of decisions (the partition count, placement of parallel instances on machines, and resource allocation to each instance), requires multi-objective optimization (MOO), and is compounded by the scale and complexity of big data systems while having to meet stringent time constraints for scheduling. This paper presents a MaxCompute based integrated system to support multi-objective resource optimization via ne-grained instance-level modeling and optimization. We propose a new architecture that breaks RO into a series of simpler problems, new ne-grained predictive models, and novel optimization methods that exploit these models to make effective instance-level RO decisions well under a second. Evaluation using production workloads shows that our new RO system could reduce 37-72% latency and 43-78% cost at the same time, compared to the current optimizer and scheduler, while running in 0.02-0.23s