A black hole on a three-brane in five-dimensional spacetime was predicted by Dadhich, Maartens, Papadopoulos and Rezania (DMPR). In order to reveal some signatures for observations, we investigate a ...timelike particle’s motion around the DMPR brane-world black holes. We find that, both in the innermost stable circular orbits (ISCO) and the marginally bound orbits (MBO), the particle’s angular momentum and its radius decrease with the increase of
Q
, where
Q
is a tidal charge parameter and may be negative and positive in the brane-world black holes. From these results, the corresponding periodic orbits with different energy levels are analyzed numerically by employing a taxonomy, which is related to the adiabatic inspiral regime in the gravitational wave radiation. It clearly shows that a rational number defined by the taxonomy increases with the particle’s energy. In addition, periodic orbits with
Q
<
0
in the DMPR brane-world black holes have higher energy in comparison to the ones with
Q
>
0
and in the Schwarzschild black holes. Our results might provide hints for distinguishing the DMPR brane-world black holes from other black holes by the timelike particle’s periodic orbits in the future.
We investigate neutral and charged test particles’ motions around quantum-corrected Schwarzschild black holes immersed in an external magnetic field. Taking the innermost stable circular orbits of ...neutral timelike particles into account, we find that the black holes can mimic different ranges of the Kerr black hole’s spin |
a
/
M
| from 0.15 to 0.99. Our analysis of charged test particles’ motions suggests that the values of the angular momentum
l
and the energy
E
2
are slightly higher than Schwarzschild black holes. The allowed regions of the
(
l
,
E
2
)
demonstrate that the critical energy
E
c
2
divides the charged test particle’s bounded trajectory into three types. With the help of a Monte Carlo method, we study the charged particles’ probabilities of falling into the black holes and find that the probability density function against
l
depends on the signs of the particles’ charges. Finally, the epicyclic frequencies of the charged particles are considered with respect to the observed twin peak quasi-periodic oscillations frequencies. Our results might provide hints for distinguishing quantum-corrected Schwarzschild black holes from Schwarzschild ones by using the dynamics of charged test particles around the strong gravitational field.
In this paper, we propose a robust visual tracking method by casting tracking as a sparse approximation problem in a particle filter framework. In this framework, occlusion, noise, and other ...challenging issues are addressed seamlessly through a set of trivial templates. Specifically, to find the tracking target in a new frame, each target candidate is sparsely represented in the space spanned by target templates and trivial templates. The sparsity is achieved by solving an ℓ 1 -regularized least-squares problem. Then, the candidate with the smallest projection error is taken as the tracking target. After that, tracking is continued using a Bayesian state inference framework. Two strategies are used to further improve the tracking performance. First, target templates are dynamically updated to capture appearance changes. Second, nonnegativity constraints are enforced to filter out clutter which negatively resembles tracking targets. We test the proposed approach on numerous sequences involving different types of challenges, including occlusion and variations in illumination, scale, and pose. The proposed approach demonstrates excellent performance in comparison with previously proposed trackers. We also extend the method for simultaneous tracking and recognition by introducing a static template set which stores target images from different classes. The recognition result at each frame is propagated to produce the final result for the whole video. The approach is validated on a vehicle tracking and classification task using outdoor infrared video sequences.
Pyruvate carboxylase (PC) is a key enzyme for gluconeogenesis. PC deficiency (PCD) is an extremely rare autosomal recessive metabolic disease and is divided into three types. Type B PCD is clinically ...featured by lactic acidosis, hyperammonemia, hypercitrullinemia, hypotonia, abnormal movement, and seizures.
Here, we report the first case of type B PCD in China, presenting with intractable lactic acidosis shortly after birth. A compound heterozygous mutation in the PC gene was identified by whole-exome sequencing, NM_001040716.2: c.1154_1155del and c.152G>A, which were inherited from her asymptomatic parents, respectively. Furthermore, prenatal neuroradiological presentations including widened posterior horns of lateral ventricles, huge subependymal cysts, and increased biparietal diameter and head circumference were concerned. Symptomatic treatment was taken and the infant died at 26 days.
To our knowledge, this is the minimum gestational age (22w5d) that's when the prenatal onset of the neuroradiologic phenotype of PCD was observed. PCD has a poor prognosis and lacks an effective treatment, so this paper is shared to highlight the importance of PCD prenatal diagnosis in the absence of family history.
Deep learning methods have shown considerable potential for hyperspectral image (HSI) classification, which can achieve high accuracy compared with traditional methods. However, they often need a ...large number of training samples and have a lot of parameters and high computational overhead. To solve these problems, this article proposes new network architecture, LiteDepthwiseNet, for HSI classification. Based on 3-D depthwise convolution, LiteDepthwiseNet can decompose standard convolution into depthwise convolution and pointwise convolution, which can achieve high classification performance with minimal parameters. Moreover, we remove the ReLU layer and batch normalization layer in the original 3-D depthwise convolution, which is likely to improve the overfitting phenomenon of the model on small-sized data sets. In addition, focal loss is used as the loss function to improve the model's attention on difficult samples and unbalanced data, and its training performance is significantly better than that of cross-entropy loss or balanced cross-entropy loss. Experiment results on five benchmark hyperspectral data sets show that LiteDepthwiseNet achieves state-of-the-art performance with a very small number of parameters and low computational cost.
Severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) infection causing coronavirus disease 2019 (COVID‐19) has spread worldwide. Whether antibodies are important for the adaptive immune ...responses against SARS‐CoV‐2 infection needs to be determined. Here, 26 cases of COVID‐19 in Jinan, China, were examined and shown to be mild or with common clinical symptoms, and no case of severe symptoms was found among these patients. Strikingly, a subset of these patients had SARS‐CoV‐2 and virus‐specific IgG coexist for an unexpectedly long time, with two cases for up to 50 days. One COVID‐19 patient who did not produce any SARS‐CoV‐2–bound IgG successfully cleared SARS‐CoV‐2 after 46 days of illness, revealing that without antibody‐mediated adaptive immunity, innate immunity alone may still be powerful enough to eliminate SARS‐CoV‐2. This report may provide a basis for further analysis of both innate and adaptive immunity in SARS‐CoV‐2 clearance, especially in nonsevere cases.
Highlights
1.SARS‐CoV‐2 could exist in patients who have virus‐specific IgG for an unexpectedly long time (36‐50 days).
2.One COVID‐19 patient who did not produce any SARS‐CoV‐2–specific IgG successfully cleared SARS‐CoV‐2 after 46 days of illness.
3.Innate immunity might be powerful enough to eliminate SARS‐CoV‐2.
Hierarchical neural networks have been shown to be effective in learning representative image features and recognizing object classes. However, most existing networks combine the low/middle level ...cues for classification without accounting for any spatial structures. For applications such as understanding a scene, how the visual cues are spatially distributed in an image becomes essential for successful analysis. This paper extends the framework of deep neural networks by accounting for the structural cues in the visual signals. In particular, two kinds of neural networks have been proposed. First, we develop a multitask deep convolutional network, which simultaneously detects the presence of the target and the geometric attributes (location and orientation) of the target with respect to the region of interest. Second, a recurrent neuron layer is adopted for structured visual detection. The recurrent neurons can deal with the spatial distribution of visible cues belonging to an object whose shape or structure is difficult to explicitly define. Both the networks are demonstrated by the practical task of detecting lane boundaries in traffic scenes. The multitask convolutional neural network provides auxiliary geometric information to help the subsequent modeling of the given lane structures. The recurrent neural network automatically detects lane boundaries, including those areas containing no marks, without any explicit prior knowledge or secondary modeling.
Dysregulation of long noncoding RNAs (lncRNAs) plays important roles in carcinogenesis and tumor progression, including hepatocellular carcinoma (HCC). Small nucleolar RNA host gene 3 (SNHG3) has ...been considered as an lncRNA to be associated with a poor prognosis in patients with HCC. Here, we reported that SNHG3 expression was significantly higher in the highly metastatic HCC (HCCLM3) cells compared with the lowly metastatic HCC cells (Hep3B and PLC/PRF/5). Furthermore, forced expression of SNHG3 promoted cell invasion, epithelial‐mesenchymal transition (EMT), and sorafenib resistance in HCC. Moreover, SNHG3 overexpression induced HCC cells EMT via miR‐128/CD151 cascade activation. Clinically, our data revealed that increased SNHG3 expression is correlated with poor HCC survival outcomes and sorafenib response. These data suggest that SNHG3 may be a novel therapeutic target and a biomarker for predicting response to sorafenib treatment of HCC.
Here, our data revealed that increase in small nucleolar RNA host gene 3 (SNHG3) expression is correlated with poor hepatocellular carcinoma survival outcomes and sorafenib response. These data suggest that SNHG3 may be a novel therapeutic target and a biomarker for predicting response to sorafenib treatment of HCC.
The ongoing outbreak of a new coronavirus (2019‐nCoV, or severe acute respiratory syndrome coronavirus 2 SARS‐CoV‐2) has caused an epidemic of the acute respiratory syndrome known as coronavirus ...disease (COVID‐19) in humans. SARS‐CoV‐2 rapidly spread to multiple regions of China and multiple other countries, posing a serious threat to public health. The spike (S) proteins of SARS‐CoV‐1 and SARS‐CoV‐2 may use the same host cellular receptor, angiotensin‐converting enzyme 2 (ACE2), for entering host cells. The affinity between ACE2 and the SARS‐CoV‐2 S protein is much higher than that of ACE2 binding to the SARS‐CoV S protein, explaining why SARS‐CoV‐2 seems to be more readily transmitted from human to human. Here, we report that ACE2 can be significantly upregulated after infection of various viruses, including SARS‐CoV‐1 and SARS‐CoV‐2, or by the stimulation with inflammatory cytokines such as interferons. We propose that SARS‐CoV‐2 may positively induce its cellular entry receptor, ACE2, to accelerate its replication and spread; high inflammatory cytokine levels increase ACE2 expression and act as high‐risk factors for developing COVID‐19, and the infection of other viruses may increase the risk of SARS‐CoV‐2 infection. Therefore, drugs targeting ACE2 may be developed for the future emerging infectious diseases caused by this cluster of coronaviruses.
Highlights
Virus infection and inflammatory cytokines can stimulate angiotensin‐converting enzyme 2 (ACE2) expression. ACE2 is upregulated by the activation of RNA‐sensing pathways. ACE2 is a novel interferon‐stimulated gene (ISG). The increase in ACE2 induced by various viruses and inflammatory cytokines may facilitate severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) infection and spreading.
We consider slow/fast systems where the slow system is driven by fractional Brownian motion with Hurst parameter
H
>
1
2
. We show that unlike in the case
H
=
1
2
, convergence to the averaged ...solution takes place in probability and the limiting process solves the ‘naïvely’ averaged equation. Our proof strongly relies on the recently obtained stochastic sewing lemma.
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