33 research outputs found
Identification and validation of critical genes with prognostic value in gastric cancer
Background: Gastric cancer (GC) is a digestive system tumor with high morbidity and mortality rates. Molecular targeted therapies, including those targeting human epidermal factor receptor 2 (HER2), have proven to be effective in clinical treatment. However, better identification and description of tumor-promoting genes in GC is still necessary for antitumor therapy.Methods: Gene expression and clinical data of GC patients were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Last absolute shrinkage and selection operator (LASSO) Cox regression were applied to build a prognostic model, the Prognosis Score. Functional enrichment and single-sample gene set enrichment analysis (ssGSEA) were used to explore potential mechanisms. Western blotting, RNA interference, cell migration, and wound healing assays were used to detect the expression and function of myosin light chain 9 (MYL9) in GC.Results: A four-gene prognostic model was constructed and GC patients from TCGA and meta-GEO cohorts were stratified into high-prognosis score groups or low-prognosis score groups. GC patients in the high-prognosis score group had significantly poorer overall survival (OS) than those in the low-prognosis score groups. The GC prognostic model was formulated as PrognosisScore = (0.06 × expression of BGN) - (0.008 × expression of ATP4A) + (0.12 × expression of MYL9) - (0.01 × expression of ALDH3A1). The prognosis score was identified as an independent predictor of OS. High expression of MYL9, the highest weighted gene in the prognosis score, was correlated with worse clinical outcomes. Functional analysis revealed that MYL9 is mainly associated with the biological function of epithelial-mesenchymal transition (EMT). Knockdown of MYL9 expression inhibits migration of GC cells in vitro.Conclusion: We found that PrognosisScore is potential reliable prognostic marker and verified that MYL9 promotes the migration and metastasis of GC cells
Online Video Super-Resolution with Convolutional Kernel Bypass Graft
Deep learning-based models have achieved remarkable performance in video
super-resolution (VSR) in recent years, but most of these models are less
applicable to online video applications. These methods solely consider the
distortion quality and ignore crucial requirements for online applications,
e.g., low latency and low model complexity. In this paper, we focus on online
video transmission, in which VSR algorithms are required to generate
high-resolution video sequences frame by frame in real time. To address such
challenges, we propose an extremely low-latency VSR algorithm based on a novel
kernel knowledge transfer method, named convolutional kernel bypass graft
(CKBG). First, we design a lightweight network structure that does not require
future frames as inputs and saves extra time costs for caching these frames.
Then, our proposed CKBG method enhances this lightweight base model by
bypassing the original network with ``kernel grafts'', which are extra
convolutional kernels containing the prior knowledge of external pretrained
image SR models. In the testing phase, we further accelerate the grafted
multi-branch network by converting it into a simple single-path structure.
Experiment results show that our proposed method can process online video
sequences up to 110 FPS, with very low model complexity and competitive SR
performance
Online Streaming Video Super-Resolution with Convolutional Look-Up Table
Online video streaming has fundamental limitations on the transmission
bandwidth and computational capacity and super-resolution is a promising
potential solution. However, applying existing video super-resolution methods
to online streaming is non-trivial. Existing video codecs and streaming
protocols (\eg, WebRTC) dynamically change the video quality both spatially and
temporally, which leads to diverse and dynamic degradations. Furthermore,
online streaming has a strict requirement for latency that most existing
methods are less applicable. As a result, this paper focuses on the rarely
exploited problem setting of online streaming video super resolution. To
facilitate the research on this problem, a new benchmark dataset named
LDV-WebRTC is constructed based on a real-world online streaming system.
Leveraging the new benchmark dataset, we proposed a novel method specifically
for online video streaming, which contains a convolution and Look-Up Table
(LUT) hybrid model to achieve better performance-latency trade-off. To tackle
the changing degradations, we propose a mixture-of-expert-LUT module, where a
set of LUT specialized in different degradations are built and adaptively
combined to handle different degradations. Experiments show our method achieves
720P video SR around 100 FPS, while significantly outperforms existing
LUT-based methods and offers competitive performance compared to efficient
CNN-based methods
PIT: Optimization of Dynamic Sparse Deep Learning Models via Permutation Invariant Transformation
Dynamic sparsity, where the sparsity patterns are unknown until runtime,
poses a significant challenge to deep learning. The state-of-the-art
sparsity-aware deep learning solutions are restricted to pre-defined, static
sparsity patterns due to significant overheads associated with preprocessing.
Efficient execution of dynamic sparse computation often faces the misalignment
between the GPU-friendly tile configuration for efficient execution and the
sparsity-aware tile shape that minimizes coverage wastes (non-zero values in
tensor).
In this paper, we propose PIT, a deep-learning compiler for dynamic sparsity.
PIT proposes a novel tiling mechanism that leverages Permutation Invariant
Transformation (PIT), a mathematically proven property, to transform multiple
sparsely located micro-tiles into a GPU-efficient dense tile without changing
the computation results, thus achieving both high GPU utilization and low
coverage waste. Given a model, PIT first finds feasible PIT rules for all its
operators and generates efficient GPU kernels accordingly. At runtime, with the
novel SRead and SWrite primitives, PIT rules can be executed extremely fast to
support dynamic sparsity in an online manner. Extensive evaluation on diverse
models shows that PIT can accelerate dynamic sparsity computation by up to 5.9x
(average 2.43x) over state-of-the-art compilers
Diversity and Distribution of nirK-Harboring Denitrifying Bacteria in the Water Column in the Yellow River Estuary
Intestinal Bacterial Diversity and Functional Analysis of Three Lepidopteran Corn Ear Worm Larvae
Insects, as the most abundant animal group on earth, and their symbionts help their hosts to adapt to various environments. Conogethes punctiferalis, Ostrinia furnacalis and Helicoverpa armigera are three main pests co-occurring in the ear stage of corn, which significantly affect the yield and quality of corn. The purpose of this study was to compare the diversity and function of the intestinal bacteria of the three co-occurring lepidopteran pests, C. punctiferalis, O. furnacalis and H. armigera, and to explore the reason of their prevalence from the microbiota’s view. Our results showed the difference of diversity and abundance of the gut bacteria of three co-occurring lepidopteran pests at the ear stage. Proteobacteria and Firmicutes were the dominant phyla, and the Enterobacteriaceae and Enterococcaceae were the dominant families in the three pests. Compared with the other two pests, Bacteroidetes was found much more in C. punctiferalis. In addition, C. punctiferalis showed more correlation and similarity in bacteria composition with corn endophytic bacteria, as well as had obvious advantages in metabolic, environmental information processing, cellular processes and organic systems function pathways. Our findings may provide insight into the prevalence of corn earworm larvae from the perspective of gut microbiota and function prediction
Defect-regulated charge carrier dynamics in two-dimensional ZnO/MoS2 heterostructure
Van der Waals ZnO/MoS2 heterostructure has been experimentally demonstrated as one of the potential candidates for photocatalyst, however, the charge carrier dynamics upon photoexcitation still remains unclear. By using nonadiabatic molecular dynamics simulations, we mainly focus on the influences of interfacial point defects on photogenerated charge separation in the ZnO/MoS2. The results reveal that oxygen vacancy in ZnO layer can induce a higher hole transfer efficiency compared to the pristine ZnO/MoS2, which attributes to the enhanced nonadiabatic coupling, originating from an out-of-plane vibration mode of S atoms, a decreased energy gap for intralayer hole transfer and stronger energy state oscillation. Alternatively, S vacancy in MoS2 introducing additional energy states in the band gap of ZnO/MoS2, serves as charge carrier recombination channels, and significantly reduces charge carrier lifetime, while doping O atom in S vacancy can compensate this effect. This study provides helpful guidance to design functional devices for solar energy photovoltaic conversion, based on two-dimensional ZnO/MoS2 heterostructures
Individual Differences in the Accuracy of Judgments of Learning Are Related to the Gray Matter Volume and Functional Connectivity of the Left Mid-Insula
The judgment of learning (JOL) is an important form of prospective metamemory judgment, and the biological basis of the JOL process is an important topic in metamemory research. Although previous task-related functional magnetic resonance imaging (MRI) studies have examined the brain regions underlying the JOL process, the neural correlates of individual differences in JOL accuracy require further investigation. This study used structural and resting-state functional MRI to investigate whether individual differences in JOL accuracy are related to the gray matter (GM) volume and functional connectivity of the bilateral insula and medial Brodmann area (BA) 11, which are assumed to be related to JOL accuracy. We found that individual differences in JOL accuracy were related to the GM volume of the left mid-insula and to the functional connectivity between the left mid-insula and various other regions, including the left superior parietal lobule/precuneus, bilateral inferior parietal lobule/intraparietal sulcus, right frontal pole and left parahippocampal gyrus/fusiform gyrus/cerebellum. Further analyses indicated that the functional connectivity related to individual differences in JOL accuracy could be divided into two factors and might support information integration and selective attention processes underlying accurate JOLs. In addition, individual differences in JOL accuracy were not related to the GM volume or functional connectivity of the medial BA 11. Our findings provide novel evidence for the role of the left mid-insula and its functional connectivity in the JOL process