400 research outputs found
Brain informed transfer learning for categorizing construction hazards
A transfer learning paradigm is proposed for "knowledge" transfer between the
human brain and convolutional neural network (CNN) for a construction hazard
categorization task. Participants' brain activities are recorded using
electroencephalogram (EEG) measurements when viewing the same images (target
dataset) as the CNN. The CNN is pretrained on the EEG data and then fine-tuned
on the construction scene images. The results reveal that the EEG-pretrained
CNN achieves a 9 % higher accuracy compared with a network with same
architecture but randomly initialized parameters on a three-class
classification task. Brain activity from the left frontal cortex exhibits the
highest performance gains, thus indicating high-level cognitive processing
during hazard recognition. This work is a step toward improving machine
learning algorithms by learning from human-brain signals recorded via a
commercially available brain-computer interface. More generalized visual
recognition systems can be effectively developed based on this approach of
"keep human in the loop"
EEG-based performance-driven adaptive automated hazard alerting system in security surveillance support
Computer-vision technologies have emerged to assist security surveillance.
However, automation alert/alarm systems often apply a low-beta threshold to
avoid misses and generates excessive false alarms. This study proposed an
adaptive hazard diagnosis and alarm system with adjustable alert threshold
levels based on environmental scenarios and operator's hazard recognition
performance. We recorded electroencephalogram (EEG) data during hazard
recognition tasks. The linear ballistic accumulator model was used to decompose
the response time into several psychological subcomponents, which were further
estimated by a Markov chain Monte Carlo algorithm and compared among different
types of hazardous scenarios. Participants were most cautious about falling
hazards, followed by electricity hazards, and had the least conservative
attitude toward structural hazards. Participants were classified into three
performance-level subgroups using a latent profile analysis based on task
accuracy. We applied the transfer learning paradigm to classify subgroups based
on their time-frequency representations of EEG data. Additionally, two
continual learning strategies were investigated to ensure a robust adaptation
of the model to predict participants' performance levels in different hazardous
scenarios. These findings can be leveraged in real-world brain-computer
interface applications, which will provide human trust in automation and
promote the successful implementation of alarm technologies
A privacy-preserving data storage and service framework based on deep learning and blockchain for construction workers' wearable IoT sensors
Classifying brain signals collected by wearable Internet of Things (IoT)
sensors, especially brain-computer interfaces (BCIs), is one of the
fastest-growing areas of research. However, research has mostly ignored the
secure storage and privacy protection issues of collected personal
neurophysiological data. Therefore, in this article, we try to bridge this gap
and propose a secure privacy-preserving protocol for implementing BCI
applications. We first transformed brain signals into images and used
generative adversarial network to generate synthetic signals to protect data
privacy. Subsequently, we applied the paradigm of transfer learning for signal
classification. The proposed method was evaluated by a case study and results
indicate that real electroencephalogram data augmented with artificially
generated samples provide superior classification performance. In addition, we
proposed a blockchain-based scheme and developed a prototype on Ethereum, which
aims to make storing, querying and sharing personal neurophysiological data and
analysis reports secure and privacy-aware. The rights of three main transaction
bodies - construction workers, BCI service providers and project managers - are
described and the advantages of the proposed system are discussed. We believe
this paper provides a well-rounded solution to safeguard private data against
cyber-attacks, level the playing field for BCI application developers, and to
the end improve professional well-being in the industry
Weighing votes in human-machine collaboration for hazard recognition: Inferring hazard perceptual threshold and decision confidence from electroencephalogram wavelets
Purpose: Human-machine collaboration is a promising strategy to improve
hazard inspection. However, research on the effective integration of opinions
from humans with machines for optimal group decision making is lacking. Hence,
considering the benefits of a brain-computer interface (BCI) to enable
intuitive commutation, this study proposes a novel method to predict human
hazard response choices and decision confidence from brain activities for a
superior confidence-weighted voting strategy. Methodology: First, we developed
a Bayesian inference-based algorithm to ascertain the decision threshold above
which a hazard is reported from human brain signals. This method was tested
empirically with electroencephalogram (EEG) data collected in a laboratory
setting and cross-validated using behavioral indices of the signal detection
theory. Subsequently, based on numerical simulations, the decision criteria for
low-, medium-, and high-confidence level differentiations characterized by
parietal alpha-band EEG power were determined. Findings : The investigated
hazard recognition task was described as a process of probabilistic inference
involving a decision uncertainty evaluation. The results demonstrated the
feasibility of EEG measurements in observing human internal representations of
hazard discrimination. Moreover, the optimal criteria to differentiate between
low-, medium-, and high-confidence levels were obtained by benchmarking against
an optimal Bayesian observer. Originality: This research demonstrates the
potential of a BCI as an effective channel for telecommunication, laying the
foundation for the design of future hazard detection techniques in the
collaborative human-machine systems research field
The Effect of Safflower Yellow on Spinal Cord Ischemia Reperfusion Injury in Rabbits
Safflower yellow (SY) is the safflower extract and is the one of traditional Chinese medicine. The aim of the present work was to investigate the effect of SY on spinal cord ischemia reperfusion injury (SCIRI) in rabbits. The models of spinal cord ischemia reperfusion (SI/R) were constructed, and the degree of the post-ischemic injury was assessed by means of the neurological deficit scores and plasma levels of lipid peroxidation reactioin and neuronal morphologic changes. SCIRI remarkably affected the functional activities of the hind limbs and activated lipid peroxidation reaction. SY could attenuate apoptosis and SCIRI by enhancing Bcl-2 expression and inhibiting Bax and caspase-3 activation
Tuning the Magnetic Ordering Temperature of Hexagonal Ferrites by Structural Distortion Control
To tune the magnetic properties of hexagonal ferrites, a family of
magnetoelectric multiferroic materials, by atomic-scale structural engineering,
we studied the effect of structural distortion on the magnetic ordering
temperature (TN). Using the symmetry analysis, we show that unlike most
antiferromagnetic rare-earth transition-metal perovskites, a larger structural
distortion leads to a higher TN in hexagonal ferrites and manganites, because
the K3 structural distortion induces the three-dimensional magnetic ordering,
which is forbidden in the undistorted structure by symmetry. We also revealed a
near-linear relation between TN and the tolerance factor and a power-law
relation between TN and the K3 distortion amplitude. Following the analysis, a
record-high TN (185 K) among hexagonal ferrites was predicted in hexagonal
ScFeO3 and experimentally verified in epitaxially stabilized films. These
results add to the paradigm of spin-lattice coupling in antiferromagnetic
oxides and suggests further tunability of hexagonal ferrites if more lattice
distortion can be achieved
Pharmacokinetics, tissue distribution, excretion, and metabolism of a novel antitumor agent, gambogenic acid, in rats
The plasma pharmacokinetics, tissue distribution, excretion, and metabolism of gambogenic acid (GNA), potential antitumor candidate, were investigated in rats. GNA showed linear pharmacokinetic characteristics in rats within the test dose (1, 2, and 4 mg/kg). The t1/2β was 40.38-41.16 min. GNA showed an extensive distribution into multiple tissues, and the bile excretion is the major pathway of excretion, accounting for 52.12 %. About 40 % of GNA might undergo metabolism in vivo and the main phase I metabolites of GNA may be 10-hydroxygambogenic acid and 9,10-epoxygambogenic acid.Colegio de Farmacéuticos de la Provincia de Buenos Aire
Pharmacokinetics, tissue distribution, excretion, and metabolism of a novel antitumor agent, gambogenic acid, in rats
The plasma pharmacokinetics, tissue distribution, excretion, and metabolism of gambogenic acid (GNA), potential antitumor candidate, were investigated in rats. GNA showed linear pharmacokinetic characteristics in rats within the test dose (1, 2, and 4 mg/kg). The t1/2β was 40.38-41.16 min. GNA showed an extensive distribution into multiple tissues, and the bile excretion is the major pathway of excretion, accounting for 52.12 %. About 40 % of GNA might undergo metabolism in vivo and the main phase I metabolites of GNA may be 10-hydroxygambogenic acid and 9,10-epoxygambogenic acid.Colegio de Farmacéuticos de la Provincia de Buenos Aire
Thymidine Kinase 2 Deficiency-Induced mtDNA Depletion in Mouse Liver Leads to Defect beta-Oxidation
Thymidine kinase 2 (TK2) deficiency in humans causes mitochondrial DNA (mtDNA) depletion syndrome. To study the molecular mechanisms underlying the disease and search for treatment options, we previously generated and described a TK2 deficient mouse strain (TK2(-/-)) that progressively loses its mtDNA. The TK2(-/-) mouse model displays symptoms similar to humans harboring TK2 deficient infantile fatal encephalomyopathy. Here, we have studied the TK2(-/-) mouse model to clarify the pathological role of progressive mtDNA depletion in liver for the severe outcome of TK2 deficiency. We observed that a gradual depletion of mtDNA in the liver of the TK2(-/-) mice was accompanied by increasingly hypertrophic mitochondria and accumulation of fat vesicles in the liver cells. The levels of cholesterol and nonesterified fatty acids were elevated and there was accumulation of long chain acylcarnitines in plasma of the TK2(-/-) mice. In mice with hepatic mtDNA levels below 20%, the blood sugar and the ketone levels dropped. These mice also exhibited reduced mitochondrial beta-oxidation due to decreased transport of long chain acylcarnitines into the mitochondria. The gradual loss of mtDNA in the liver of the TK2(-/-) mice causes impaired mitochondrial function that leads to defect beta-oxidation and, as a result, insufficient production of ketone bodies and glucose. This study provides insight into the mechanism of encephalomyopathy caused by TK2 deficiency-induced mtDNA depletion that may be used to explore novel therapeutic strategies
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