131 research outputs found
Brief Introduction to Contrastive Learning Pretext Tasks for Visual Representation
To improve performance in visual feature representation from photos or videos
for practical applications, we generally require large-scale human-annotated
labeled data while training deep neural networks. However, the cost of
gathering and annotating human-annotated labeled data is expensive. Given that
there is a lot of unlabeled data in the actual world, it is possible to
introduce self-defined pseudo labels as supervisions to prevent this issue.
Self-supervised learning, specifically contrastive learning, is a subset of
unsupervised learning methods that has grown popular in computer vision,
natural language processing, and other domains. The purpose of contrastive
learning is to embed augmented samples from the same sample near to each other
while pushing away those that are not. In the following sections, we will
introduce the regular formulation among different learnings. In the next
sections, we will discuss the regular formulation of various learnings.
Furthermore, we offer some strategies from contrastive learning that have
recently been published and are focused on pretext tasks for visual
representation
Transformer Encoder with Multiscale Deep Learning for Pain Classification Using Physiological Signals
Pain is a serious worldwide health problem that affects a vast proportion of
the population. For efficient pain management and treatment, accurate
classification and evaluation of pain severity are necessary. However, this can
be challenging as pain is a subjective sensation-driven experience. Traditional
techniques for measuring pain intensity, e.g. self-report scales, are
susceptible to bias and unreliable in some instances. Consequently, there is a
need for more objective and automatic pain intensity assessment strategies. In
this paper, we develop PainAttnNet (PAN), a novel transfomer-encoder
deep-learning framework for classifying pain intensities with physiological
signals as input. The proposed approach is comprised of three feature
extraction architectures: multiscale convolutional networks (MSCN), a
squeeze-and-excitation residual network (SEResNet), and a transformer encoder
block. On the basis of pain stimuli, MSCN extracts short- and long-window
information as well as sequential features. SEResNet highlights relevant
extracted features by mapping the interdependencies among features. The third
module employs a transformer encoder consisting of three temporal convolutional
networks (TCN) with three multi-head attention (MHA) layers to extract temporal
dependencies from the features. Using the publicly available BioVid pain
dataset, we test the proposed PainAttnNet model and demonstrate that our
outcomes outperform state-of-the-art models. These results confirm that our
approach can be utilized for automated classification of pain intensity using
physiological signals to improve pain management and treatment
Review and Analysis of Pain Research Literature through Keyword Co-occurrence Networks
Pain is a significant public health problem as the number of individuals with
a history of pain globally keeps growing. In response, many synergistic
research areas have been coming together to address pain-related issues. This
work conducts a review and analysis of a vast body of pain-related literature
using the keyword co-occurrence network (KCN) methodology. In this method, a
set of KCNs is constructed by treating keywords as nodes and the co-occurrence
of keywords as links between the nodes. Since keywords represent the knowledge
components of research articles, analysis of KCNs will reveal the knowledge
structure and research trends in the literature. This study extracted and
analyzed keywords from 264,560 pain-related research articles indexed in IEEE,
PubMed, Engineering Village, and Web of Science published between 2002 and
2021. We observed rapid growth in pain literature in the last two decades: the
number of articles has grown nearly threefold, and the number of keywords has
grown by a factor of 7. We identified emerging and declining research trends in
sensors/methods, biomedical, and treatment tracks. We also extracted the most
frequently co-occurring keyword pairs and clusters to help researchers
recognize the synergies among different pain-related topics
Mechanical Behavior of Hybrid Connectors for Rapid-Assembling Steel-Concrete Composite Beams
In order to achieve a kind of shear connector suitable for rapid-assembling steel-concrete composite beams, a new type of hybrid shear connectors is proposed, in which the concrete slab with prefabricated circular holes and the steel beam with welded studs are installed and positioned, and then epoxy mortar is filled in the prefabricated hole to fix the studs. To study the mechanical behavior of these hybrid connectors, test on 18 push-out specimens with different prefabricated circular holes are carried out. ABAQUS finite element software is adopted to verify the relationship between the numerical simulation and experiment, influences of the epoxy mortar strength and prefabricated circular holes diameter are studied. The results show that filling epoxy mortar in the prefabricated hole is beneficial to improve the stiffness and bearing capacity of the specimen; the change of epoxy mortar strength has a certain impact on the bearing capacity and stiffness of the hybrid connector; In the case of the same strength of the filling material, the size of the prefabricated circular holes diameter directly affects the stiffness and bearing capacity of the shear stud. The shear capacity equations proposed by considering the epoxy mortar strength and prefabricated holes diameter, and it has a wide applicability
Uncertainty Quantification in Neural-Network Based Pain Intensity Estimation
Improper pain management can lead to severe physical or mental consequences,
including suffering, and an increased risk of opioid dependency. Assessing the
presence and severity of pain is imperative to prevent such outcomes and
determine the appropriate intervention. However, the evaluation of pain
intensity is challenging because different individuals experience pain
differently. To overcome this, researchers have employed machine learning
models to evaluate pain intensity objectively. However, these efforts have
primarily focused on point estimation of pain, disregarding the inherent
uncertainty and variability present in the data and model. Consequently, the
point estimates provide only partial information for clinical decision-making.
This study presents a neural network-based method for objective pain interval
estimation, incorporating uncertainty quantification. This work explores three
algorithms: the bootstrap method, lower and upper bound estimation (LossL)
optimized by genetic algorithm, and modified lower and upper bound estimation
(LossS) optimized by gradient descent algorithm. Our empirical results reveal
that LossS outperforms the other two by providing a narrower prediction
interval. As LossS outperforms, we assessed its performance in three different
scenarios for pain assessment: (1) a generalized approach (single model for the
entire population), (2) a personalized approach (separate model for each
individual), and (3) a hybrid approach (separate model for each cluster of
individuals). Our findings demonstrate the hybrid approach's superior
performance, with notable practicality in clinical contexts. It has the
potential to be a valuable tool for clinicians, enabling objective pain
intensity assessment while taking uncertainty into account. This capability is
crucial in facilitating effective pain management and reducing the risks
associated with improper treatment.Comment: 26 pages, 5 figures, 9 table
Transcriptomic analysis reveals the functions of H2S as a gasotransmitter independently of Cys in Arabidopsis
Numerous studies have revealed the gasotransmitter functions of hydrogen sulfide (H2S) in various biological processes. However, the involvement of H2S in sulfur metabolism and/or Cys synthesis makes its role as a signaling molecule ambiguous. The generation of endogenous H2S in plants is closely related to the metabolism of Cys, which play roles in a variety of signaling pathway occurring in various cellular processes. Here, we found that exogenous H2S fumigation and Cys treatment modulated the production rate and content of endogenous H2S and Cys to various degrees. Furthermore, we provided comprehensive transcriptomic analysis to support the gasotransmitter role of H2S besides as a substrate for Cys synthesis. Comparison of the differentially expressed genes (DEGs) between H2S and Cys treated seedlings indicated that H2S fumigation and Cys treatment caused different influences on gene profiles during seedlings development. A total of 261 genes were identified to respond to H2S fumigation, among which 72 genes were co-regulated by Cys treatment. GO and KEGG enrichment analysis of the 189 genes, H2S but not Cys regulated DEGs, indicated that these genes mainly involved in plant hormone signal transduction, plant-pathogen interaction, phenylpropanoid biosynthesis, and MAPK signaling pathway. Most of these genes encoded proteins having DNA binding and transcription factor activities that play roles in a variety of plant developmental and environmental responses. Many stress-responsive genes and some Ca2+ signal associated genes were also included. Consequently, H2S regulated gene expression through its role as a gasotransmitter, rather than just as a substrate for Cys biogenesis, and these 189 genes were far more likely to function in H2S signal transduction independently of Cys. Our data will provide insights for revealing and enriching H2S signaling networks
Atomically dispersed antimony on carbon nitride for the artificial photosynthesis of hydrogen peroxide
Artificial photosynthesis offers a promising strategy to produce hydrogen peroxide (H2O2)—an environmentally friendly oxidant and a clean fuel. However, the low activity and selectivity of the two-electron oxygen reduction reaction (ORR) in the photocatalytic process greatly restricts the H2O2 production efficiency. Here we show a robust antimony single-atom photocatalyst (Sb-SAPC, single Sb atoms dispersed on carbon nitride) for the synthesis of H2O2 in a simple water and oxygen mixture under visible light irradiation. An apparent quantum yield of 17.6% at 420 nm together with a solar-to-chemical conversion efficiency of 0.61% for H2O2 synthesis was achieved. On the basis of time-dependent density function theory calculations, isotopic experiments and advanced spectroscopic characterizations, the photocatalytic performance is ascribed to the notably promoted two-electron ORR by forming μ-peroxide at the Sb sites and highly concentrated holes at the neighbouring N atoms. The in situ generated O2 via water oxidation is rapidly consumed by ORR, leading to boosted overall reaction kinetics
Ten new high-quality genome assemblies for diverse bioenergy sorghum genotypes
INTRODUCTION: Sorghum (Sorghum bicolor (L.) Moench) is an agriculturally and economically important staple crop that has immense potential as a bioenergy feedstock due to its relatively high productivity on marginal lands. To capitalize on and further improve sorghum as a potential source of sustainable biofuel, it is essential to understand the genomic mechanisms underlying complex traits related to yield, composition, and environmental adaptations. METHODS: Expanding on a recently developed mapping population, we generated de novo genome assemblies for 10 parental genotypes from this population and identified a comprehensive set of over 24 thousand large structural variants (SVs) and over 10.5 million single nucleotide polymorphisms (SNPs). RESULTS: We show that SVs and nonsynonymous SNPs are enriched in different gene categories, emphasizing the need for long read sequencing in crop species to identify novel variation. Furthermore, we highlight SVs and SNPs occurring in genes and pathways with known associations to critical bioenergy-related phenotypes and characterize the landscape of genetic differences between sweet and cellulosic genotypes. DISCUSSION: These resources can be integrated into both ongoing and future mapping and trait discovery for sorghum and its myriad uses including food, feed, bioenergy, and increasingly as a carbon dioxide removal mechanism
The taxonomic name resolution service : an online tool for automated standardization of plant names
© The Author(s), 2013. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in BMC Bioinformatics 14 (2013): 16, doi:10.1186/1471-2105-14-16.The digitization of biodiversity data is leading to the widespread application of taxon names that are superfluous, ambiguous or incorrect, resulting in mismatched records and inflated species numbers. The ultimate consequences of misspelled names and bad taxonomy are erroneous scientific conclusions and faulty policy decisions. The lack of tools for correcting this ‘names problem’ has become a fundamental obstacle to integrating disparate data sources and advancing the progress of biodiversity science. The TNRS, or Taxonomic Name Resolution Service, is an online application for automated and user-supervised standardization of plant scientific names. The TNRS builds upon and extends existing open-source applications for name parsing and fuzzy matching. Names are standardized against multiple reference taxonomies, including the Missouri Botanical Garden's Tropicos database. Capable of processing thousands of names in a single operation, the TNRS parses and corrects misspelled names and authorities, standardizes variant spellings, and converts nomenclatural synonyms to accepted names. Family names can be included to increase match accuracy and resolve many types of homonyms. Partial matching of higher taxa combined with extraction of annotations, accession numbers and morphospecies allows the TNRS to standardize taxonomy across a broad range of active and legacy datasets. We show how the TNRS can resolve many forms of taxonomic semantic heterogeneity, correct spelling errors and eliminate spurious names. As a result, the TNRS can aid the integration of disparate biological datasets. Although the TNRS was developed to aid in standardizing plant names, its underlying algorithms and design can be extended to all organisms and nomenclatural codes. The TNRS is accessible via a web interface at http://tnrs.iplantcollaborative.org/ webcite and as a RESTful web service and application programming interface. Source code is available at https://github.com/iPlantCollaborativeOpenSource/TNRS/ webcite.BJE was supported by NSF grant DBI 0850373 and TR by CSIRO Marine and Atmospheric Research, Australia,. BB and BJE acknowledge early financial support from Conservation International and TEAM who funded the development of early prototypes of taxonomic name resolution. The iPlant Collaborative (http://www.iplantcollaborative.org) is funded by a grant from the National Science Foundation (#DBI-0735191)
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