Long noncoding RNAs (lncRNAs) have been implicated in hypoxia/HIF-1-associated cancer progression through largely unknown mechanisms. Here we identify MIR31HG as a hypoxia-inducible lncRNA and ...therefore we name it LncHIFCAR (long noncoding HIF-1α co-activating RNA); we describe its oncogenic role as a HIF-1α co-activator that regulates the HIF-1 transcriptional network, crucial for cancer development. Extensive analyses of clinical data indicate LncHIFCAR level is substantially upregulated in oral carcinoma, significantly associated with poor clinical outcomes and representing an independent prognostic predictor. Overexpression of LncHIFCAR induces pseudo-hypoxic gene signature, whereas knockdown of LncHIFCAR impairs the hypoxia-induced HIF-1α transactivation, sphere-forming ability, metabolic shift and metastatic potential in vitro and in vivo. Mechanistically, LncHIFCAR forms a complex with HIF-1α via direct binding and facilitates the recruitment of HIF-1α and p300 cofactor to the target promoters. Our results uncover an lncRNA-mediated mechanism for HIF-1 activation and establish the clinical values of LncHIFCAR in prognosis and potential therapeutic strategy for oral carcinoma.
Long noncoding RNAs (IncRNAs) have been implicated in a variety of physiological and pathological processes, including cancer. In prostate cancer, prostate cancer gene expression marker 1 (PCGEM1) is ...an androgen-induced prostate-specific IncRNA whose overexpression is highly associated with prostate tumors. PCGEM1's tumorigenie potential has been recently shown to be in part due to its ability to activate androgen receptor (AR). Here, we report a novel function of PCGEM1 that provides growth advantages for cancer cells by regulating tumor metabolism via c-Myc activation. PCGEM1 promotes glucose uptake for aerobic glycolysis, coupling with the pentose phosphate shunt to facilitate biosynthesis of nucleotide and lipid, and generates NADPH for redox homeostasis. We show that PCGEM1 regulates metabolism at a transcriptional level that affects multiple metabolic pathways, including glucose and g Iutamine metabolism, the pentose phosphate pathway, nucleotide and fatty acid biosynthesis, and the tricarboxylic acid cycle. The PCGEM1-mediated gene regulation takes place in part through AR activation, but predominantly through c-Myc activation, regardless of hormone or AR status. Significantly, PCGEM1 binds directly to target promoters, physically interacts with c-Myc, promotes chromatin recruitment of c-Myc, and enhances its transactivation activity. We also identified a c-Myc binding domain on PCGEM1 that contributes to the PCGEM1-dependent c-Myc activation and target induction. Together, our data uncover PCGEM1 as a key transcriptional regulator of central metabolic pathways in prostate cancer cells. By being a coactivator for both c-Myc and AR, PCGEM1 reprograms the androgen network and the central metabolism in a tumor-specific way, making it a promising target for therapeutic intervention.
Human action recognition in 3D skeleton sequences has attracted a lot of research attention. Recently, long short-term memory (LSTM) networks have shown promising performance in this task due to ...their strengths in modeling the dependencies and dynamics in sequential data. As not all skeletal joints are informative for action recognition, and the irrelevant joints often bring noise which can degrade the performance, we need to pay more attention to the informative ones. However, the original LSTM network does not have explicit attention ability. In this paper, we propose a new class of LSTM network, global context-aware attention LSTM, for skeleton-based action recognition, which is capable of selectively focusing on the informative joints in each frame by using a global context memory cell. To further improve the attention capability, we also introduce a recurrent attention mechanism, with which the attention performance of our network can be enhanced progressively. Besides, a two-stream framework, which leverages coarse-grained attention and fine-grained attention, is also introduced. The proposed method achieves state-of-the-art performance on five challenging datasets for skeleton-based action recognition.
In this study, using coconut fibers as raw material, activated carbon fibers were prepared
via
carbonization and KOH activation processes. The morphology, composition, specific surface area, pore ...structure and thermal stability of the resulting activated carbon fibers were systematically characterized. It was found that the activation process increases the specific surface area of carbon fibers to a greater extent
via
formation of a large number of micropores (0.7-1.8 nm) and a certain amount of slit-shaped mesopores (2-9 nm). The specific surface area and the pore volume of the activated carbon fibers reach 1556 m
2
g
−1
and 0.72 cm
3
g
−1
, respectively. The activation process can also decompose the tar deposits formed after the carbonization process by pyrolysis, making the surface of the activated carbon fibers smoother. To study the adsorption properties of the as-prepared activated carbon fibers, the adsorption capacities and adsorption kinetics of various organic dyes including methylene blue, Congo red and neutral red were investigated. The adsorption capacities of the dyes increased with the increasing initial dye concentrations, and varied greatly with the pH value of the system. In methylene blue and neutral red systems, the adsorption capacities reach the maximum at pH 9, and in the Congo red system, it reaches the maximum at pH 3. The adsorption capacities of the activated carbon fibers in methylene blue, Congo red and neutral red systems reached equilibrium at 150, 120, and 120 min, and the maximum adsorption capacities were 21.3, 22.1, and 20.7 mg g
−1
, respectively. The kinetics of the adsorption process was investigated using three models including pseudo-first-order, pseudo-second-order and intraparticle diffusion models. The results indicated that the dynamic adsorption processes of coconut-based activated carbon fibers to methylene blue, Congo red and neutral red were all in accordance with the second-order kinetic model, and the equations are as follows:
t
/
Q
t
= 0.1028 +
t
/21.3220,
t
/
Q
t
= 0.1128 +
t
/21.5982 and
t
/
Q
t
= 0.0210 +
t
/20.6612.
Activated carbon fibers with high micropore volume and large specific surface area were prepared from abundant and low-cost coconut fibers, which show excellent adsorption performances towards various dyes.
Chiral perovskites have emerged as a significant class of materials showing promising optoelectronic and spintronic applications. Reports of chiral perovskite ferroelectrics, however, have been ...scarce. In this work, we have successfully synthesized homochiral lead–iodide perovskite ferroelectrics (R)‐N‐(1‐phenylethyl)ethane‐1,2‐diaminiumPbI4 and (S)‐N‐(1‐phenylethyl)ethane‐1,2‐diaminiumPbI4 by introducing a methyl group into the organic cation of the parent (N‐benzylethane‐1,2‐diaminium)PbI4. Vibrational circular dichroism spectra identify the chiral mirroring relationship. They both undergo 222F2‐type paraelectric–ferroelectric behavior at around 378 K coupled with clear ferroelastic domain “ON/OFF” switching. Besides, they exhibit an evident thermochromism with color change from orange–yellow to orange–red. To our knowledge, the discovery of integrated ferroelectricity, ferroelasticity, and reversible thermochromism in chiral perovskites is unprecedented.
2D homochiral lead‐iodide perovskites were constructed by the introduction of a chiral center. The perovskites exhibit coexisting ferroelectricity, ferroelasticity, and reversible thermochromism, offering great application prospects for next‐generation smart devices.
Feature Boosting Network For 3D Pose Estimation Liu, Jun; Ding, Henghui; Shahroudy, Amir ...
IEEE transactions on pattern analysis and machine intelligence,
02/2020, Volume:
42, Issue:
2
Journal Article
Peer reviewed
Open access
In this paper, a feature boosting network is proposed for estimating 3D hand pose and 3D body pose from a single RGB image. In this method, the features learned by the convolutional layers are ...boosted with a new long short-term dependence-aware (LSTD) module, which enables the intermediate convolutional feature maps to perceive the graphical long short-term dependency among different hand (or body) parts using the designed Graphical ConvLSTM. Learning a set of features that are reliable and discriminatively representative of the pose of a hand (or body) part is difficult due to the ambiguities, texture and illumination variation, and self-occlusion in the real application of 3D pose estimation. To improve the reliability of the features for representing each body part and enhance the LSTD module, we further introduce a context consistency gate (CCG) in this paper, with which the convolutional feature maps are modulated according to their consistency with the context representations. We evaluate the proposed method on challenging benchmark datasets for 3D hand pose estimation and 3D full body pose estimation. Experimental results show the effectiveness of our method that achieves state-of-the-art performance on both of the tasks.
Emerging infectious diseases, such as severe acute respiratory syndrome (SARS) and Zika virus disease, present a major threat to public health
. Despite intense research efforts, how, when and where ...new diseases appear are still a source of considerable uncertainty. A severe respiratory disease was recently reported in Wuhan, Hubei province, China. As of 25 January 2020, at least 1,975 cases had been reported since the first patient was hospitalized on 12 December 2019. Epidemiological investigations have suggested that the outbreak was associated with a seafood market in Wuhan. Here we study a single patient who was a worker at the market and who was admitted to the Central Hospital of Wuhan on 26 December 2019 while experiencing a severe respiratory syndrome that included fever, dizziness and a cough. Metagenomic RNA sequencing
of a sample of bronchoalveolar lavage fluid from the patient identified a new RNA virus strain from the family Coronaviridae, which is designated here 'WH-Human 1' coronavirus (and has also been referred to as '2019-nCoV'). Phylogenetic analysis of the complete viral genome (29,903 nucleotides) revealed that the virus was most closely related (89.1% nucleotide similarity) to a group of SARS-like coronaviruses (genus Betacoronavirus, subgenus Sarbecovirus) that had previously been found in bats in China
. This outbreak highlights the ongoing ability of viral spill-over from animals to cause severe disease in humans.
The high similarities of different real-world vehicles and great diversities of the acquisition views pose grand challenges to vehicle re-identification (ReID), which traditionally maps the vehicle ...images into a high-dimensional embedding space for distance optimization, vehicle discrimination, and identification. To improve the discriminative capability and robustness of the ReID algorithm, we propose a novel end-to-end embedding adversarial learning network (EALN) that is capable of generating samples localized in the embedding space. Instead of selecting abundant hard negatives from the training set, which is extremely difficult if not impossible, with our embedding adversarial learning scheme, the automatically generated hard negative samples in the specified embedding space can greatly improve the capability of the network for discriminating similar vehicles. Moreover, the more challenging cross-view vehicle ReID problem, which requires the ReID algorithm to be robust with different query views, can also benefit from such a scheme based on the artificially generated cross-view samples. We demonstrate the promise of EALN through extensive experiments and show the effectiveness of hard negative and cross-view generation in facilitating vehicle ReID based on the comparisons with the state-of-the-art schemes.
Research on depth-based human activity analysis achieved outstanding performance and demonstrated the effectiveness of 3D representation for action recognition. The existing depth-based and ...RGB+D-based action recognition benchmarks have a number of limitations, including the lack of large-scale training samples, realistic number of distinct class categories, diversity in camera views, varied environmental conditions, and variety of human subjects. In this work, we introduce a large-scale dataset for RGB+D human action recognition, which is collected from 106 distinct subjects and contains more than 114 thousand video samples and 8 million frames. This dataset contains 120 different action classes including daily, mutual, and health-related activities. We evaluate the performance of a series of existing 3D activity analysis methods on this dataset, and show the advantage of applying deep learning methods for 3D-based human action recognition. Furthermore, we investigate a novel one-shot 3D activity recognition problem on our dataset, and a simple yet effective Action-Part Semantic Relevance-aware (APSR) framework is proposed for this task, which yields promising results for recognition of the novel action classes. We believe the introduction of this large-scale dataset will enable the community to apply, adapt, and develop various data-hungry learning techniques for depth-based and RGB+D-based human activity understanding.
Ferroptosis is a programmed cell death pathway discovered in recent years, and ferroptosis‐inducing agents have great potential as new antitumor candidates. Here, we report a IrIII complex (Ir1) ...containing a ferrocene‐modified diphosphine ligand that localizes in lysosomes. Under the acidic environments of lysosomes, Ir1 can effectively catalyze Fenton‐like reaction, produce hydroxyl radicals, induce lipid peroxidation, down‐regulate glutathione peroxidase 4, and result in ferroptosis. RNA sequencing analysis shows that Ir1 can significantly affect pathways related to ferroptosis and cancer immunity. Accordingly, Ir1 can induce immunogenic cells death and suppress tumor growth in vitro, regulate T cell activity and immune microenvironments in vivo. In conclusion, we show the potential of small molecules with ferroptosis‐inducing capabilities for effective cancer immunotherapy.
Ferroptosis‐inducing agents have potential as antitumor candidates. A ferrocene‐modified IrIII complex with Fenton‐like catalytic activity is used to disturb the cellular redox balance, which leads to lipid peroxidation and ferroptosis of cancer cells. Ferroptosis induced by the IrIII complex causes immunogenic cell death (ICD) of cancer cell in vitro, which enhances cancer immune response in vivo.
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