226 research outputs found
A Survey of PPG's Application in Authentication
Biometric authentication prospered because of its convenient use and
security. Early generations of biometric mechanisms suffer from spoofing
attacks. Recently, unobservable physiological signals (e.g.,
Electroencephalogram, Photoplethysmogram, Electrocardiogram) as biometrics
offer a potential remedy to this problem. In particular, Photoplethysmogram
(PPG) measures the change in blood flow of the human body by an optical method.
Clinically, researchers commonly use PPG signals to obtain patients' blood
oxygen saturation, heart rate, and other information to assist in diagnosing
heart-related diseases. Since PPG signals contain a wealth of individual
cardiac information, researchers have begun to explore their potential in cyber
security applications. The unique advantages (simple acquisition, difficult to
steal, and live detection) of the PPG signal allow it to improve the security
and usability of the authentication in various aspects. However, the research
on PPG-based authentication is still in its infancy. The lack of
systematization hinders new research in this field. We conduct a comprehensive
study of PPG-based authentication and discuss these applications' limitations
before pointing out future research directions.Comment: Accepted by Computer & Security (COSE
Comparative genomic analysis of pleurotus species reveals insights into the evolution and coniferous utilization of Pleurotus placentodes
Pleurotus placentodes (PPL) and Pleurotus cystidiosus (PCY) are economically valuable species. PPL grows on conifers, while PCY grows on broad-leaved trees. To reveal the genetic mechanism behind PPL’s adaptability to conifers, we performed de novo genome sequencing and comparative analysis of PPL and PCY. We determined the size of the genomes for PPL and PCY to be 36.12 and 42.74 Mb, respectively, and found that they contain 10,851 and 15,673 protein-coding genes, accounting for 59.34% and 53.70% of their respective genome sizes. Evolution analysis showed PPL was closely related to P. ostreatus with the divergence time of 62.7 MYA, while PCY was distantly related to other Pleurotus species with the divergence time of 111.7 MYA. Comparative analysis of carbohydrate-active enzymes (CAZYmes) in PPL and PCY showed that the increase number of CAZYmes related to pectin and cellulose degradation (e.g., AA9, PL1) in PPL may be important for the degradation and colonization of conifers. In addition, geraniol degradation and peroxisome pathways identified by comparative genomes should be another factors for PPL’s tolerance to conifer substrate. Our research provides valuable genomes for Pleurotus species and sheds light on the genetic mechanism of PPL’s conifer adaptability, which could aid in breeding new Pleurotus varieties for coniferous utilization
Deep Learning in Single-Cell Analysis
Single-cell technologies are revolutionizing the entire field of biology. The
large volumes of data generated by single-cell technologies are
high-dimensional, sparse, heterogeneous, and have complicated dependency
structures, making analyses using conventional machine learning approaches
challenging and impractical. In tackling these challenges, deep learning often
demonstrates superior performance compared to traditional machine learning
methods. In this work, we give a comprehensive survey on deep learning in
single-cell analysis. We first introduce background on single-cell technologies
and their development, as well as fundamental concepts of deep learning
including the most popular deep architectures. We present an overview of the
single-cell analytic pipeline pursued in research applications while noting
divergences due to data sources or specific applications. We then review seven
popular tasks spanning through different stages of the single-cell analysis
pipeline, including multimodal integration, imputation, clustering, spatial
domain identification, cell-type deconvolution, cell segmentation, and
cell-type annotation. Under each task, we describe the most recent developments
in classical and deep learning methods and discuss their advantages and
disadvantages. Deep learning tools and benchmark datasets are also summarized
for each task. Finally, we discuss the future directions and the most recent
challenges. This survey will serve as a reference for biologists and computer
scientists, encouraging collaborations.Comment: 77 pages, 11 figures, 15 tables, deep learning, single-cell analysi
Activated Met Signalling in the Developing Mouse Heart Leads to Cardiac Disease
BACKGROUND: The Hepatocyte Growth Factor (HGF) is a pleiotropic cytokine involved in many physiological processes, including skeletal muscle, placenta and liver development. Little is known about its role and that of Met tyrosine kinase receptor in cardiac development. METHODOLOGY/PRINCIPAL FINDINGS: In this study, we generated two transgenic mice with cardiac-specific, tetracycline-suppressible expression of either Hepatocyte Growth Factor (HGF) or the constitutively activated Tpr-Met kinase to explore: i) the effect of stimulation of the endogenous Met receptor by autocrine production of HGF and ii) the consequence of sustained activation of Met signalling in the heart. We first showed that Met is present in the neonatal cardiomyocytes and is responsive to exogenous HGF. Exogenous HGF starting from prenatal stage enhanced cardiac proliferation and reduced sarcomeric proteins and Connexin43 (Cx43) in newborn mice. As adults, these transgenics developed systolic contractile dysfunction. Conversely, prenatal Tpr-Met expression was lethal after birth. Inducing Tpr-Met expression during postnatal life caused early-onset heart failure, characterized by decreased Cx43, upregulation of fetal genes and hypertrophy. CONCLUSIONS/SIGNIFICANCE: Taken together, our data show that excessive activation of the HGF/Met system in development may result in cardiac damage and suggest that Met signalling may be implicated in the pathogenesis of cardiac disease
The state of the Martian climate
60°N was +2.0°C, relative to the 1981–2010 average value (Fig. 5.1). This marks a new high for the record. The average annual surface air temperature (SAT) anomaly for 2016 for land stations north of starting in 1900, and is a significant increase over the previous highest value of +1.2°C, which was observed in 2007, 2011, and 2015. Average global annual temperatures also showed record values in 2015 and 2016. Currently, the Arctic is warming at more than twice the rate of lower latitudes
Search for Neutral Higgs Bosons in Events with Multiple Bottom Quarks at the Tevatron
The combination of searches performed by the CDF and D0 collaborations at the
Fermilab Tevatron Collider for neutral Higgs bosons produced in association
with b quarks is reported. The data, corresponding to 2.6 fb-1 of integrated
luminosity at CDF and 5.2 fb-1 at D0, have been collected in final states
containing three or more b jets. Upper limits are set on the cross section
multiplied by the branching ratio varying between 44 pb and 0.7 pb in the Higgs
boson mass range 90 to 300 GeV, assuming production of a narrow scalar boson.
Significant enhancements to the production of Higgs bosons can be found in
theories beyond the standard model, for example in supersymmetry. The results
are interpreted as upper limits in the parameter space of the minimal
supersymmetric standard model in a benchmark scenario favoring this decay mode.Comment: 10 pages, 2 figure
Measurement of the Bottom-Strange Meson Mixing Phase in the Full CDF Data Set
We report a measurement of the bottom-strange meson mixing phase \beta_s
using the time evolution of B0_s -> J/\psi (->\mu+\mu-) \phi (-> K+ K-) decays
in which the quark-flavor content of the bottom-strange meson is identified at
production. This measurement uses the full data set of proton-antiproton
collisions at sqrt(s)= 1.96 TeV collected by the Collider Detector experiment
at the Fermilab Tevatron, corresponding to 9.6 fb-1 of integrated luminosity.
We report confidence regions in the two-dimensional space of \beta_s and the
B0_s decay-width difference \Delta\Gamma_s, and measure \beta_s in [-\pi/2,
-1.51] U [-0.06, 0.30] U [1.26, \pi/2] at the 68% confidence level, in
agreement with the standard model expectation. Assuming the standard model
value of \beta_s, we also determine \Delta\Gamma_s = 0.068 +- 0.026 (stat) +-
0.009 (syst) ps-1 and the mean B0_s lifetime, \tau_s = 1.528 +- 0.019 (stat) +-
0.009 (syst) ps, which are consistent and competitive with determinations by
other experiments.Comment: 8 pages, 2 figures, Phys. Rev. Lett 109, 171802 (2012
An epithelial-immune circuit amplifies inflammasome and IL-6 responses to SARS-CoV-2
Elevated levels of cytokines IL-1β and IL-6 are associated with severe COVID-19. Investigating the underlying mechanisms, we find that while primary human airway epithelia (HAE) have functional inflammasomes and support SARS-CoV-2 replication, they are not the source of IL-1β released upon infection. In leukocytes, the SARS-CoV-2 E protein upregulates inflammasome gene transcription via TLR2 to prime, but not activate, inflammasomes. SARS-CoV-2-infected HAE supply a second signal, which includes genomic and mitochondrial DNA, to stimulate leukocyte IL-1β release. Nuclease treatment, STING, and caspase-1 inhibition but not NLRP3 inhibition blocked leukocyte IL-1β release. After release, IL-1β stimulates IL-6 secretion from HAE. Therefore, infection alone does not increase IL-1β secretion by either cell type. Rather, bi-directional interactions between the SARS-CoV-2-infected epithelium and immune bystanders stimulates both IL-1β and IL-6, creating a pro-inflammatory cytokine circuit. Consistent with these observations, patient autopsy lungs show elevated myeloid inflammasome gene signatures in severe COVID-19., IL-1β and IL-6 are increased in severe COVID-19. Examining the underlying mechanisms, Barnett et al. show that SARS-CoV-2 E protein primes, and DNA from infected epithelial cells activates, inflammasome-dependent IL-1β secretion from leukocytes, which in turn stimulates IL-6 release from epithelial cells
The Liver Tumor Segmentation Benchmark (LiTS)
In this work, we report the set-up and results of the Liver Tumor
Segmentation Benchmark (LITS) organized in conjunction with the IEEE
International Symposium on Biomedical Imaging (ISBI) 2016 and International
Conference On Medical Image Computing Computer Assisted Intervention (MICCAI)
2017. Twenty four valid state-of-the-art liver and liver tumor segmentation
algorithms were applied to a set of 131 computed tomography (CT) volumes with
different types of tumor contrast levels (hyper-/hypo-intense), abnormalities
in tissues (metastasectomie) size and varying amount of lesions. The submitted
algorithms have been tested on 70 undisclosed volumes. The dataset is created
in collaboration with seven hospitals and research institutions and manually
reviewed by independent three radiologists. We found that not a single
algorithm performed best for liver and tumors. The best liver segmentation
algorithm achieved a Dice score of 0.96(MICCAI) whereas for tumor segmentation
the best algorithm evaluated at 0.67(ISBI) and 0.70(MICCAI). The LITS image
data and manual annotations continue to be publicly available through an online
evaluation system as an ongoing benchmarking resource.Comment: conferenc
Graphene-Based Nanocomposites for Energy Storage
Since the first report of using micromechanical cleavage method to produce graphene sheets in 2004, graphene/graphene-based nanocomposites have attracted wide attention both for fundamental aspects as well as applications in advanced energy storage and conversion systems. In comparison to other materials, graphene-based nanostructured materials have unique 2D structure, high electronic mobility, exceptional electronic and thermal conductivities, excellent optical transmittance, good mechanical strength, and ultrahigh surface area. Therefore, they are considered as attractive materials for hydrogen (H2) storage and high-performance electrochemical energy storage devices, such as supercapacitors, rechargeable lithium (Li)-ion batteries, Li–sulfur batteries, Li–air batteries, sodium (Na)-ion batteries, Na–air batteries, zinc (Zn)–air batteries, and vanadium redox flow batteries (VRFB), etc., as they can improve the efficiency, capacity, gravimetric energy/power densities, and cycle life of these energy storage devices. In this article, recent progress reported on the synthesis and fabrication of graphene nanocomposite materials for applications in these aforementioned various energy storage systems is reviewed. Importantly, the prospects and future challenges in both scalable manufacturing and more energy storage-related applications are discussed
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