Background: Cocaine users often report a loss of arousal for nondrug-related stimuli, which may contribute to their response to drug-related rewards. However, little is known about users' neural reactivity to emotional nondrug-related stimuli and the potential influence of gender.
Objectives: Test the hypotheses that cocaine-dependent individuals have an attenuated neural response to arousing stimuli relative to controls and that this difference is amplified in women.
Methods: The brain response to typically arousing positive and negative images as well as neutral images from the International Affective Picture System was measured in 40 individuals (20 non-treatment seeking cocaine-dependent and 20 age- and gender-matched control participants; 50% of whom were women). Images were displayed for 4 s each in blocks of five across two 270-second runs. General linear models assessed within and between group activation differences for the emotional images.
Results: Cocaine-dependent individuals had a significantly lower response to typically arousing positive and negative images than controls, with attenuated neural activity present in the medial prefrontal cortex (mPFC) and anterior cingulate cortex (ACC). Analyses by gender revealed less mPFC/ACC activation among female users, but not males, for both positive and negative images.
Conclusion: The dampened neural response to typically arousing stimuli among cocaine-dependent polydrug users suggests decreased salience processing for nondrug stimuli, particularly among female users. This decreased responding is consistent with data from other substance using populations and suggests that this may be a general feature of addiction. Amplifying the neural response to naturally arousing nondrug-related reinforcers may present an opportunity for unique behavioral and brain stimulation therapies.
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http://dx.doi.org/10.1080/00952990.2016.1192183 | DOI Listing |
Pharmaceutics
December 2024
Joint Department of Biomedical Engineering, The University of North Carolina at Chapel Hill and North Carolina State University, Chapel Hill, NC 27599, USA.
Background/objectives: Glioblastoma multiforme (GBM) is the most common high-grade primary brain cancer in adults. Despite efforts to advance treatment, GBM remains treatment resistant and inevitably progresses after first-line therapy. Induced neural stem cell (iNSC) therapy is a promising, personalized cell therapy approach that has been explored to circumvent challenges associated with the current GBM treatment.
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January 2025
Department of Mechanical and Aerospace Engineering, Politecnico di Torino, 10129 Turin, Italy.
This study investigates the potential of deploying a neural network model on an advanced programmable logic controller (PLC), specifically the Finder Opta™, for real-time inference within the predictive maintenance framework. In the context of Industry 4.0, edge computing aims to process data directly on local devices rather than relying on a cloud infrastructure.
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January 2025
Netcom Engineering S.p.A., Via Nuova Poggioreale, Centro Polifunzionale, Tower 7, 5th Floor, 80143 Naples, Italy.
This paper explores the development and testing of two Internet of Things (IoT) applications designed to leverage Vehicle-to-Infrastructure (V2I) communication for managing intelligent intersections. The first scenario focuses on enabling the rapid and safe passage of emergency vehicles through intersections by notifying approaching drivers via a mobile application. The second scenario enhances pedestrian safety by alerting drivers, through the same application, about the presence of pedestrians detected at crosswalks by a traffic sensor equipped with neural network capabilities.
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January 2025
Department of Electrical Engineering, American University of Sharjah, Sharjah 26666, United Arab Emirates.
Accurately identifying and discriminating between different brain states is a major emphasis of functional brain imaging research. Various machine learning techniques play an important role in this regard. However, when working with a small number of study participants, the lack of sufficient data and achieving meaningful classification results remain a challenge.
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January 2025
Key Laboratory of Concrete and Pre-Stressed Concrete Structures of the Ministry of Education, Southeast University, Nanjing 210096, China.
A method of bridge structure seismic response identification combining signal processing technology and deep learning technology is proposed. The short-time energy method is used to intelligently extract the non-smooth segments in the sensor acquired signals, and the short-time Fourier transform, continuous wavelet transform, and Meier frequency cestrum coefficients are used to analyze the spectrum of the non-smooth segments of the response of the bridge structure, and the response feature matrix is extracted and used to classify sequences or images in the LSTM network and the Resnet50 network. The results show that the signal processing techniques can effectively extract the structural response features and reduce the overfitting phenomenon of neural networks, and the combination of signal processing techniques and deep learning techniques can recognize the seismic response of bridge structures with high accuracy and efficiency.
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