Enhancing Laryngeal Spinocellular Carcinoma Image Security with DCT.

This work proposes a novel secret sharing scheme to enhance the security of Laryngeal Spinocellular Carcinoma or Laryngeal Squamous Cell Carcinoma (LSCC) images using the Discrete Cosine Transformation (DCT) as a cryptographic tool. The DCT-based secret sharing method divides LSCC images into shares, each containing DCT coefficients that represent the image's frequency components. The original image can only be reconstructed when a predefined number of shares are combined, ensuring confidentiality and preventing unauthorized access.

Employment and Social Security/Insurance among patients affected by mental disorders in Italy: A descriptive multi-center study.

Introduction: Many mental disorders especially chronic serious ones such as schizophrenia-spectrum disorders, are disabling syndromes and impact on patients' social and cognitive functioning, including work activity. Thus, affected patients may show a particular socio-economic vulnerability and need specific social security as well as rehabilitation interventions, including pensions or job-placements. In Italy, the Working Group named 'Employment and Social Security/Insurance in Mental Health (ESSIMH)' was founded in 2020 in order to collect research evidence on mental illness, employment, social security, and rehabilitation.

Reduced magnetic mismatch negativity: a shared deficit in psychosis and related risk.

Background: Abnormal auditory processing of deviant stimuli, as reflected by mismatch negativity (MMN), is often reported in schizophrenia (SCZ). At present, it is still under debate whether this dysfunctional response is specific to the full-blown SCZ diagnosis or rather a marker of psychosis in general. The present study tested MMN in patients with SCZ, bipolar disorder (BD), first episode of psychosis (FEP), and in people at clinical high risk for psychosis (CHR).

Closed-loop motor imagery EEG simulation for brain-computer interfaces.

In a brain-computer interface (BCI) system, the testing of decoding algorithms, tasks, and their parameters is critical for optimizing performance. However, conducting human experiments can be costly and time-consuming, especially when investigating broad sets of parameters. Attempts to utilize previously collected data in offline analysis lack a co-adaptive feedback loop between the system and the user present online, limiting the applicability of the conclusions obtained to real-world uses of BCI.

Benefits of deep learning classification of continuous noninvasive brain-computer interface control.

. Noninvasive brain-computer interfaces (BCIs) assist paralyzed patients by providing access to the world without requiring surgical intervention. Prior work has suggested that EEG motor imagery based BCI can benefit from increased decoding accuracy through the application of deep learning methods, such as convolutional neural networks (CNNs).