Inactivity of human β,β-carotene-9′, 10-′dioxygenase (BCO2) underlies retinal accumulation of the human macular carotenoid pigment

pre-printThe macula of the primate retina uniquely concentrates high amounts of the xanthophyll carotenoids lutein, zeaxanthin, and meso-zeaxanthin, but the underlying biochemical mechanisms for this spatial- and species-specific localization have not been fully elucidated. For example, despite abundant retinal levels in mice and primates of a binding protein for zeaxanthin and mesozeaxanthin, the pi-isoform of glutathione S-transferase (GSTP1), only human and monkey retinas naturally contain detectable levels of these carotenoids. We therefore investigated whether or not differences in expression, localization, and activity between mouse and primate carotenoid metabolic enzymes could account for this species-specific difference in retinal accumulation. We focused on β,β-carotene-9',10'-dioxygenase (BCO2; also known as BCDO2), the only known mammalian xanthophyll cleavage enzyme. RT-PCR, western blot analysis, and immunohistochemistry confirmed that BCO2 is expressed in both mouse and primate retinas. Cotransfection of expression plasmids of human or mouse BCO2 into E. coli strains engineered to produce zeaxanthin demonstrated that only mouse BCO2 is an active zeaxanthin cleavage enzyme. Surface plasmon resonance (SPR) binding studies showed that the binding affinities between human BCO2 and lutein, zeaxanthin, and meso-zeaxanthin are 10- to 40-fold weaker than those for mouse BCO2, implying that ineffective capture of carotenoids by human BCO2 prevents cleavage of xanthophyll carotenoids. Moreover, BCO2 knockout mice, unlike wild-type mice, accumulate zeaxanthin in their retinas. Our results provide a novel explanation for how primates uniquely concentrate xanthophyll carotenoids at high levels in retinal tissue

VATLM: Visual-Audio-Text Pre-Training with Unified Masked Prediction for Speech Representation Learning

Although speech is a simple and effective way for humans to communicate with the outside world, a more realistic speech interaction contains multimodal information, e.g., vision, text. How to design a unified framework to integrate different modal information and leverage different resources (e.g., visual-audio pairs, audio-text pairs, unlabeled speech, and unlabeled text) to facilitate speech representation learning was not well explored. In this paper, we propose a unified cross-modal representation learning framework VATLM (Visual-Audio-Text Language Model). The proposed VATLM employs a unified backbone network to model the modality-independent information and utilizes three simple modality-dependent modules to preprocess visual, speech, and text inputs. In order to integrate these three modalities into one shared semantic space, VATLM is optimized with a masked prediction task of unified tokens, given by our proposed unified tokenizer. We evaluate the pre-trained VATLM on audio-visual related downstream tasks, including audio-visual speech recognition (AVSR), visual speech recognition (VSR) tasks. Results show that the proposed VATLM outperforms previous the state-of-the-art models, such as audio-visual pre-trained AV-HuBERT model, and analysis also demonstrates that VATLM is capable of aligning different modalities into the same space. To facilitate future research, we release the code and pre-trained models at https://aka.ms/vatlm.Comment: 10 page

An improved model using convolutional sliding window-attention network for motor imagery EEG classification

IntroductionThe classification model of motor imagery-based electroencephalogram (MI-EEG) is a new human-computer interface pattern and a new neural rehabilitation assessment method for diseases such as Parkinson's and stroke. However, existing MI-EEG models often suffer from insufficient richness of spatiotemporal feature extraction, learning ability, and dynamic selection ability.MethodsTo solve these problems, this work proposed a convolutional sliding window-attention network (CSANet) model composed of novel spatiotemporal convolution, sliding window, and two-stage attention blocks.ResultsThe model outperformed existing state-of-the-art (SOTA) models in within- and between-individual classification tasks on commonly used MI-EEG datasets BCI-2a and Physionet MI-EEG, with classification accuracies improved by 4.22 and 2.02%, respectively.DiscussionThe experimental results also demonstrated that the proposed type token, sliding window, and local and global multi-head self-attention mechanisms can significantly improve the model's ability to construct, learn, and adaptively select multi-scale spatiotemporal features in MI-EEG signals, and accurately identify electroencephalogram signals in the unilateral motor area. This work provided a novel and accurate classification model for MI-EEG brain-computer interface tasks and proposed a feasible neural rehabilitation assessment scheme based on the model, which could promote the further development and application of MI-EEG methods in neural rehabilitation

Biochemistry and Molecular Biology Developmentally Regulated Production of meso- Zeaxanthin in Chicken Retinal Pigment Epithelium/ Choroid and Retina

PURPOSE. meso-Zeaxanthin is a carotenoid that is rarely encountered in nature outside of the vertebrate eye. It is not a constituent of a normal human diet, yet this carotenoid comprises onethird of the primate macular pigment. In the current study, we undertook a systematic approach to biochemically characterize the production of meso-zeaxanthin in the vertebrate eye. METHODS. Fertilized White Leghorn chicken eggs were analyzed for the presence of carotenoids during development. Yolk, liver, brain, serum, retina, and RPE/choroid were isolated, and carotenoids were extracted. The samples were analyzed on C-30 or chiral HPLC columns to determine the carotenoid composition. RESULTS. Lutein and zeaxanthin were found in all studied nonocular tissues, but no mesozeaxanthin was ever detected. Among the ocular tissues, the presence of meso-zeaxanthin was consistently observed starting at embryonic day 17 (E17) in the RPE/choroid, several days before its consistent detection in the retina. If RPE/choroid of an embryo was devoid of mesozeaxanthin, the corresponding retina was always negative as well. CONCLUSIONS. This is the first report of developmentally regulated synthesis of mesozeaxanthin in a vertebrate system. Our observations suggest that the RPE/choroid is the primary site of meso-zeaxanthin synthesis. Identification of meso-zeaxanthin isomerase enzyme in the developing chicken embryo will facilitate our ability to determine the biochemical mechanisms responsible for production of this unique carotenoid in other higher vertebrates, such as humans

Artificial Intelligence Enabled Cyberspace Security Defense

Cyberspace is regarded as the fifth largest activity space following land, sea, air, and space. Protecting cyberspace security is a major issue related to national security, national sovereignty, and the legitimate rights and interests of the people. With the rapid development of artificial intelligence (AI) technology and its application in various fields, cyberspace security has been facing new challenges. This study analyzes the new risks of cyberspace security in the era of AI, such as more intelligent network attacks, more frequent large-scale network attacks, higher concealment of network attacks, stronger confrontation game of network attacks, and easier exposure to stealing of important data. AI technology has significant advantages in dealing with massive data, multi-source heterogeneous data, and real-time dynamic data, which can significantly improve the defense capability of cyberspace. This study introduces some key problems and technologies of AI-enabled cyberspace security defense, particularly the construction of a cyberspace security knowledge brain and the detection of network attacks. Furthermore, we propose the corresponding countermeasures and suggestions from three aspects: the construction of a dynamic and scalable network security knowledge brain, the promotion of intelligent detection against network attacks, and the evaluation of AI technologies’ security

Relationship between Concentrations of Lutein and StARD3 among Pediatric and Geriatric Human Brain Tissue

Lutein, a dietary carotenoid, selectively accumulates in human retina and brain. While many epidemiological studies show evidence of a relationship between lutein status and cognitive health, lutein’s selective uptake in human brain tissue and its potential function in early neural development and cognitive health have been poorly evaluated at a molecular level. The objective of this study was to evaluate the cross-sectional relationship between concentrations of brain lutein and StARD3 (identified as its binding protein in retinal tissue) among three age groups: infants (1–4 months, n = 10), older adults (55–86 years, n = 8), and centenarians (98–105 years, n = 10). Brain lutein concentrations were analyzed by high-performance liquid chromatography and StARD3 levels were analyzed by Western Blot analysis. The strong relationship in infant brains (r = 0.75, P \u3c 0.001) suggests that lutein has a role in neural development. The relationship remained significant but weaker in older adults (r = 0.51, P \u3c 0.05) and insignificant in centenarians (r = 0.08, P \u3e 0.05), seven of whom had mild cognitive impairment (MCI) or dementia. These exploratory findings suggest an age-related decrease or abnormality of StARD3 activity in human brain. Given that StARD3 is also involved in cholesterol transportation, a process that is aberrant in neurodegenerative diseases, the potential protective function of lutein against these diseases remains to be explored