Effectiveness of digital modified behavioral activation treatment program for rural older adults with depressive symptoms in Western Hunan: study protocol for a multi-center randomized controlled trial.

Background: Rural older adults experience a high burden of depressive symptoms and significant barriers to accessing mental health services. The Modified Behavioral Activation Treatment (MBAT) has been verified to be effective among rural older adults in China. Due to its structured format and skill-based learning, it is well suited for digital-based delivery.

Achieving more human brain-like vision via human EEG representational alignment.

Despite advancements in artificial intelligence, object recognition models still lag behind in emulating visual information processing in human brains. Recent studies have highlighted the potential of using neural data to mimic brain processing; however, these often rely on invasive neural recordings from non-human subjects, leaving a critical gap in understanding human visual perception. Addressing this gap, we present, for the first time, 'Re(presentational)Al(ignment)net', a vision model aligned with human brain activity based on non-invasive EEG, demonstrating a significantly higher similarity to human brain representations.

Dynamic saccade context triggers more stable object-location binding.

Our visual systems rapidly perceive and integrate information about object identities and locations. There is long-standing debate about if and how we achieve world-centered (spatiotopic) object representations across eye movements, with many studies reporting persistent retinotopic (eye-centered) effects even for higher level object-location binding. But these studies are generally conducted in fairly static experimental contexts.

Bridging the gap between EEG and DCNNs reveals a fatigue mechanism of facial repetition suppression.

Facial repetition suppression, a well-studied phenomenon characterized by decreased neural responses to repeated faces in visual cortices, remains a subject of ongoing debate regarding its underlying neural mechanisms. Our research harnesses advanced multivariate analysis techniques and the prowess of deep convolutional neural networks (DCNNs) in face recognition to bridge the gap between human electroencephalogram (EEG) data and DCNNs, especially in the context of facial repetition suppression. Our innovative reverse engineering approach, manipulating the neuronal activity in DCNNs and conducted representational comparisons between brain activations derived from human EEG and manipulated DCNN activations, provided insights into the underlying facial repetition suppression.

Human EEG and artificial neural networks reveal disentangled representations of object real-world size in natural images.

Remarkably, human brains have the ability to accurately perceive and process the real-world size of objects, despite vast differences in distance and perspective. While previous studies have delved into this phenomenon, distinguishing this ability from other visual perceptions, like depth, has been challenging. Using the THINGS EEG2 dataset with high time-resolution human brain recordings and more ecologically valid naturalistic stimuli, our study uses an innovative approach to disentangle neural representations of object real-world size from retinal size and perceived real-world depth in a way that was not previously possible.

Dynamic saccade context triggers more stable object-location binding.

Our visual systems rapidly perceive and integrate information about object identities and locations. There is long-standing debate about how we achieve world-centered (spatiotopic) object representations across eye movements, with many studies reporting persistent retinotopic (eye-centered) effects even for higher-level object-location binding. But these studies are generally conducted in fairly static experimental contexts.

Generate your neural signals from mine: individual-to-individual EEG converters.

Most models in cognitive and computational neuroscience trained on one subject do not generalize to other subjects due to individual differences. An ideal individual-to-individual neural converter is expected to generate real neural signals of one subject from those of another one, which can overcome the problem of individual differences for cognitive and computational models. In this study, we propose a novel individual-to-individual EEG converter, called EEG2EEG, inspired by generative models in computer vision.

Parasitoid vectors a plant pathogen, potentially diminishing the benefits it confers as a biological control agent.

Huanglongbing (HLB) is a destructive disease of citrus primarily transmitted by the Asian citrus psyllid (ACP). Biocontrol of ACP is an environmentally sustainable alternative to chemicals. However, the risk of parasitoid rational application in ACP biocontrol has never been evaluated.

NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data.

In studies of cognitive neuroscience, multivariate pattern analysis (MVPA) is widely used as it offers richer information than traditional univariate analysis. Representational similarity analysis (RSA), as one method of MVPA, has become an effective decoding method based on neural data by calculating the similarity between different representations in the brain under different conditions. Moreover, RSA is suitable for researchers to compare data from different modalities and even bridge data from different species.

Comprehensive Assessment of Candidate Reference Genes for Gene Expression Studies Using RT-qPCR in , a Predominant Parasitoid of .

(Waterston) is a predominant parasitoid of the Asian citrus psyllid (ACP), a destructive citrus pest and vector of huanglongbing (HLB) disease in the fields of southern China. To explore the functioning of target genes in , the screening of specific reference genes is critical for carrying out the reverse transcriptase-quantitative polymerase chain reaction (RT-qPCR) under different experimental conditions. However, no reference gene(s) for has yet been reported.

Communication between mitochondria and other organelles: a brand-new perspective on mitochondria in cancer.

Mitochondria are energy factories of cells and are important pivots for intracellular interactions with other organelles. They interact with the endoplasmic reticulum, peroxisomes, and nucleus through signal transduction, vesicle transport, and membrane contact sites to regulate energy metabolism, biosynthesis, immune response, and cell turnover. However, when the communication between organelles fails and the mitochondria are dysfunctional, it may induce tumorigenesis.