59 research outputs found
Optimalization of Extraction Conditions for Increasing Microalgal Lipid Yield by Using Accelerated Solvent Extraction Method (ASE) Based on the Orthogonal Array Design
Revisiting Initializing Then Refining: An Incomplete and Missing Graph Imputation Network
With the development of various applications, such as social networks and
knowledge graphs, graph data has been ubiquitous in the real world.
Unfortunately, graphs usually suffer from being absent due to
privacy-protecting policies or copyright restrictions during data collection.
The absence of graph data can be roughly categorized into attribute-incomplete
and attribute-missing circumstances. Specifically, attribute-incomplete
indicates that a part of the attribute vectors of all nodes are incomplete,
while attribute-missing indicates that the whole attribute vectors of partial
nodes are missing. Although many efforts have been devoted, none of them is
custom-designed for a common situation where both types of graph data absence
exist simultaneously. To fill this gap, we develop a novel network termed
Revisiting Initializing Then Refining (RITR), where we complete both
attribute-incomplete and attribute-missing samples under the guidance of a
novel initializing-then-refining imputation criterion. Specifically, to
complete attribute-incomplete samples, we first initialize the incomplete
attributes using Gaussian noise before network learning, and then introduce a
structure-attribute consistency constraint to refine incomplete values by
approximating a structure-attribute correlation matrix to a high-order
structural matrix. To complete attribute-missing samples, we first adopt
structure embeddings of attribute-missing samples as the embedding
initialization, and then refine these initial values by adaptively aggregating
the reliable information of attribute-incomplete samples according to a dynamic
affinity structure. To the best of our knowledge, this newly designed method is
the first unsupervised framework dedicated to handling hybrid-absent graphs.
Extensive experiments on four datasets have verified that our methods
consistently outperform existing state-of-the-art competitors
FreMAE: Fourier Transform Meets Masked Autoencoders for Medical Image Segmentation
The research community has witnessed the powerful potential of
self-supervised Masked Image Modeling (MIM), which enables the models capable
of learning visual representation from unlabeled data. In this paper, to
incorporate both the crucial global structural information and local details
for dense prediction tasks, we alter the perspective to the frequency domain
and present a new MIM-based framework named FreMAE for self-supervised
pre-training for medical image segmentation. Based on the observations that the
detailed structural information mainly lies in the high-frequency components
and the high-level semantics are abundant in the low-frequency counterparts, we
further incorporate multi-stage supervision to guide the representation
learning during the pre-training phase. Extensive experiments on three
benchmark datasets show the superior advantage of our proposed FreMAE over
previous state-of-the-art MIM methods. Compared with various baselines trained
from scratch, our FreMAE could consistently bring considerable improvements to
the model performance. To the best our knowledge, this is the first attempt
towards MIM with Fourier Transform in medical image segmentation
How Fragile is Relation Extraction under Entity Replacements?
Relation extraction (RE) aims to extract the relations between entity names
from the textual context. In principle, textual context determines the
ground-truth relation and the RE models should be able to correctly identify
the relations reflected by the textual context. However, existing work has
found that the RE models memorize the entity name patterns to make RE
predictions while ignoring the textual context. This motivates us to raise the
question: ``are RE models robust to the entity replacements?'' In this work, we
operate the random and type-constrained entity replacements over the RE
instances in TACRED and evaluate the state-of-the-art RE models under the
entity replacements. We observe the 30\% - 50\% F1 score drops on the
state-of-the-art RE models under entity replacements. These results suggest
that we need more efforts to develop effective RE models robust to entity
replacements. We release the source code at
https://github.com/wangywUST/RobustRE
MASH Suite Pro: A Comprehensive Software Tool for Top-Down Proteomics
Top-down mass spectrometry (MS)-based proteomics is arguably a disruptive technology for the comprehensive analysis of all proteoforms arising from genetic variation, alternative splicing, and posttranslational modifications (PTMs). However, the complexity of top-down high-resolution mass spectra presents a significant challenge for data analysis. In contrast to the well-developed software packages available for data analysis in bottom-up proteomics, the data analysis tools in top-down proteomics remain underdeveloped. Moreover, despite recent efforts to develop algorithms and tools for the deconvolution of top-down high-resolution mass spectra and the identification of proteins from complex mixtures, a multifunctional software platform, which allows for the identification, quantitation, and characterization of proteoforms with visual validation, is still lacking. Herein, we have developed MASH Suite Pro, a comprehensive software tool for top-down proteomics with multifaceted functionality. MASH Suite Pro is capable of processing high-resolution MS and tandem MS (MS/MS) data using two deconvolution algorithms to optimize protein identification results. In addition, MASH Suite Pro allows for the characterization of PTMs and sequence variations, as well as the relative quantitation of multiple proteoforms in different experimental conditions. The program also provides visualization components for validation and correction of the computational outputs. Furthermore, MASH Suite Pro facilitates data reporting and presentation via direct output of the graphics. Thus, MASH Suite Pro significantly simplifies and speeds up the interpretation of high-resolution top-down proteomics data by integrating tools for protein identification, quantitation, characterization, and visual validation into a customizable and user-friendly interface. We envision that MASH Suite Pro will play an integral role in advancing the burgeoning field of top-down proteomics
RNA sequencing reveals CircRNA expression profiles in chicken embryo fibroblasts infected with velogenic Newcastle disease virus
IntroductionNewcastle disease virus (NDV) is an important avian pathogen prevalent worldwide; it has an extensive host range and seriously harms the poultry industry. Velogenic NDV strains exhibit high pathogenicity and mortality in chickens. Circular RNAs (circRNAs) are among the most abundant and conserved eukaryotic transcripts. They are part of the innate immunity and antiviral response. However, the relationship between circRNAs and NDV infection is unclear.MethodsIn this study, we used circRNA transcriptome sequencing to analyze the differences in circRNA expression profiles post velogenic NDV infection in chicken embryo fibroblasts (CEFs). Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used to reveal significant enrichment of differentially expressed (DE) circRNAs. The circRNA- miRNA-mRNA interaction networks were further predicted. Moreover, circ-EZH2 was selected to determine its effect on NDV infection in CEFs.ResultsNDV infection altered circRNA expression profiles in CEFs, and 86 significantly DE circRNAs were identified. GO and KEGG enrichment analyses revealed significant enrichment of DE circRNAs for metabolism-related pathways, such as lysine degradation, glutaminergic synapse, and alanine, aspartic-acid, and glutamic-acid metabolism. The circRNA- miRNA-mRNA interaction networks further demonstrated that CEFs might combat NDV infection by regulating metabolism through circRNA-targeted mRNAs and miRNAs. Furthermore, we verified that circ-EZH2 overexpression and knockdown inhibited and promoted NDV replication, respectively, indicating that circRNAs are involved in NDV replication.ConclusionsThese results demonstrate that CEFs exert antiviral responses by forming circRNAs, offering new insights into the mechanisms underlying NDV-host interactions
Monophosphorylation of cardiac troponin-I at Ser23/24 is sufficient to regulate cardiac myofibrillar Ca2+ sensitivity and calpain-induced proteolysis
The 2021 China report of the Lancet Countdown on health and climate change:Seizing the window of opportunity
Optimalization of Extraction Conditions for Increasing Microalgal Lipid Yield by Using Accelerated Solvent Extraction Method (ASE) Based on the Orthogonal Array Design
- …