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A Robust Gene Expression Prognostic Signature for Overall Survival in High-Grade Serous Ovarian Cancer.
The objective of this research was to develop a robust gene expression-based prognostic signature and scoring system for predicting overall survival (OS) of patients with high-grade serous ovarian cancer (HGSOC). Transcriptomic data of HGSOC patients were obtained from six independent studies in the NCBI GEO database. Genes significantly deregulated and associated with OS in HGSOCs were selected using GEO2R and Kaplan-Meier analysis with log-rank testing, respectively. Enrichment analysis for biological processes and pathways was performed using Gene Ontology analysis. A resampling/cross-validation method with Cox regression analysis was used to identify a novel gene expression-based signature associated with OS, and a prognostic scoring system was developed and further validated in nine independent HGSOC datasets. We first identified 488 significantly deregulated genes in HGSOC patients, of which 232 were found to be significantly associated with their OS. These genes were significantly enriched for cell cycle division, epithelial cell differentiation, p53 signaling pathway, vasculature development, and other processes. A novel 11-gene prognostic signature was identified and a prognostic scoring system was developed, which robustly predicted OS in HGSOC patients in 100 sampling test sets. The scoring system was further validated successfully in nine additional HGSOC public datasets. In conclusion, our integrative bioinformatics study combining transcriptomic and clinical data established an 11-gene prognostic signature for robust and reproducible prediction of OS in HGSOC patients. This signature could be of clinical value for guiding therapeutic selection and individualized treatment
Tetraaquabis[4-(1H-imidazol-1-yl-κN 3)benzoato]cobalt(II)
In the title compound, [Co(C10H7N2O2)2(H2O)4], the CoII atom lies on an inversion centre and displays a slightly distorted octahedral geometry. The coordination sphere is defined by two mutually trans N atoms from two 4-(imidazol-1-yl)benzoate ligands and the O atoms from four water molecules. The crystal structure is stabilized by O—H⋯O hydrogen bonds
Boundary Proposal Network for Two-Stage Natural Language Video Localization
We aim to address the problem of Natural Language Video Localization
(NLVL)-localizing the video segment corresponding to a natural language
description in a long and untrimmed video. State-of-the-art NLVL methods are
almost in one-stage fashion, which can be typically grouped into two
categories: 1) anchor-based approach: it first pre-defines a series of video
segment candidates (e.g., by sliding window), and then does classification for
each candidate; 2) anchor-free approach: it directly predicts the probabilities
for each video frame as a boundary or intermediate frame inside the positive
segment. However, both kinds of one-stage approaches have inherent drawbacks:
the anchor-based approach is susceptible to the heuristic rules, further
limiting the capability of handling videos with variant length. While the
anchor-free approach fails to exploit the segment-level interaction thus
achieving inferior results. In this paper, we propose a novel Boundary Proposal
Network (BPNet), a universal two-stage framework that gets rid of the issues
mentioned above. Specifically, in the first stage, BPNet utilizes an
anchor-free model to generate a group of high-quality candidate video segments
with their boundaries. In the second stage, a visual-language fusion layer is
proposed to jointly model the multi-modal interaction between the candidate and
the language query, followed by a matching score rating layer that outputs the
alignment score for each candidate. We evaluate our BPNet on three challenging
NLVL benchmarks (i.e., Charades-STA, TACoS and ActivityNet-Captions). Extensive
experiments and ablative studies on these datasets demonstrate that the BPNet
outperforms the state-of-the-art methods.Comment: AAAI 202
Deep sequencing-based transcriptome profiling analysis of bacteria-challenged Lateolabrax japonicus reveals insight into the immune-relevant genes in marine fish
Group Equivariant BEV for 3D Object Detection
Recently, 3D object detection has attracted significant attention and
achieved continuous improvement in real road scenarios. The environmental
information is collected from a single sensor or multi-sensor fusion to detect
interested objects. However, most of the current 3D object detection approaches
focus on developing advanced network architectures to improve the detection
precision of the object rather than considering the dynamic driving scenes,
where data collected from sensors equipped in the vehicle contain various
perturbation features. As a result, existing work cannot still tackle the
perturbation issue. In order to solve this problem, we propose a group
equivariant bird's eye view network (GeqBevNet) based on the group equivariant
theory, which introduces the concept of group equivariant into the BEV fusion
object detection network. The group equivariant network is embedded into the
fused BEV feature map to facilitate the BEV-level rotational equivariant
feature extraction, thus leading to lower average orientation error. In order
to demonstrate the effectiveness of the GeqBevNet, the network is verified on
the nuScenes validation dataset in which mAOE can be decreased to 0.325.
Experimental results demonstrate that GeqBevNet can extract more rotational
equivariant features in the 3D object detection of the actual road scene and
improve the performance of object orientation prediction.Comment: 8 pages,3 figures,accepted by International Joint Conference on
Neural Networks (IJCNN)202
Plasmoid ejection and secondary current sheet generation from magnetic reconnection in laser-plasma interaction
Reconnection of the self-generated magnetic fields in laser-plasma
interaction was first investigated experimentally by Nilson {\it et al.} [Phys.
Rev. Lett. 97, 255001 (2006)] by shining two laser pulses a distance apart on a
solid target layer. An elongated current sheet (CS) was observed in the plasma
between the two laser spots. In order to more closely model magnetotail
reconnection, here two side-by-side thin target layers, instead of a single
one, are used. It is found that at one end of the elongated CS a fan-like
electron outflow region including three well-collimated electron jets appears.
The ( MeV) tail of the jet energy distribution exhibits a power-law
scaling. The enhanced electron acceleration is attributed to the intense
inductive electric field in the narrow electron dominated reconnection region,
as well as additional acceleration as they are trapped inside the rapidly
moving plasmoid formed in and ejected from the CS. The ejection also induces a
secondary CS
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