Using multiomics to explore the weight differences between genders in Muscovy ducks.

Sexual dimorphism in poultry, especially in Muscovy ducks, is a proven phenomenon characterized by significant differences in body weight, growth patterns, and gene expression between male and female individuals. However, there is a dearth of research on the candidate genes and mechanisms underlying these weight differences. We selected 301 Muscovy ducks and recorded their weekly body weights from birth. We utilized 3 non-linear growth models (Logistic, Bertalanffy, and Gompertz) to fit the growth curve of Muscovy ducks, it was found that the logistic model was the most suitable model for describing the growth curve of Muscovy ducks. The results from the logistic model showed that the inflection point of male Muscovy ducks occurred at a later age, and they had a heavier mature body weight than female Muscovy ducks. At 10 wk of age, we collected Muscovy duck breast muscle tissues for transcriptome sequencing (RNA-seq). To exclude the impact of weight difference, 185 differentially expressed genes (DEGs), such as PPAR, FABP3, PLIN1, and FOXO1, were screened. These DEGs were predominantly enriched in terms related to mitochondria, lipids, and nucleic acids. In addition, the gut microbiota has the ability to influence host physiology through the regulation of multiple processes, including playing a crucial role in host muscle growth and development. We randomly selected male and female Muscovy ducks for 16S rRNA sequencing analysis of their cecal microbiota. The results showed that there were significant differences in the composition of cecal microbiota between male and female Muscovy ducks. At the genus level, the relative abundance of Enterenecus and CAG_269 were lower in males compared to females, while Lawsonibacter, Parabacteroides_B, Streptococcus, UBA2658, Caccousia, and Butyricimonas were higher in males than in females. In summary, this study provides a scientific theoretical basis for revealing the different growth patterns of male and female Muscovy ducks, and offers explanations from both the molecular level and microbiological perspectives.

Driving Fatigue Detection with Three Non-Hair-Bearing EEG Channels and Modified Transformer Model.

Driving fatigue is the main cause of traffic accidents, which seriously affects people's life and property safety. Many researchers have applied electroencephalogram (EEG) signals for driving fatigue detection to reduce negative effects. The main challenges are the practicality and accuracy of the EEG-based driving fatigue detection method when it is applied on the real road. In our previous study, we attempted to improve the practicality of fatigue detection based on the proposed non-hair-bearing (NHB) montage with fewer EEG channels, but the recognition accuracy was only 76.47% with the random forest (RF) model. In order to improve the accuracy with NHB montage, this study proposed an improved transformer architecture for one-dimensional feature vector classification based on introducing the Gated Linear Unit (GLU) in the Attention sub-block and Feed-Forward Networks (FFN) sub-block of a transformer, called GLU-Oneformer. Moreover, we constructed an NHB-EEG-based feature set, including the same EEG features (power ratio, approximate entropy, and mutual information (MI)) in our previous study, and the lateralization features of the power ratio and approximate entropy based on the strategy of brain lateralization. The results indicated that our GLU-Oneformer method significantly improved the recognition performance and achieved an accuracy of 86.97%. Our framework demonstrated that the combination of the NHB montage and the proposed GLU-Oneformer model could well support driving fatigue detection.

Reorganization of Brain Functional Network during Task Switching before and after Mental Fatigue.

Mental fatigue is a widely studied topic on account of its serious negative effects. But how the neural mechanism of task switching before and after mental fatigue remains a question. To this end, this study aims to use brain functional network features to explore the answer to this question. Specifically, task-state EEG signals were recorded from 20 participants. The tasks include a 400-s 2-back-task (2-BT), followed by a 6480-s of mental arithmetic task (MAT), and then a 400-s 2-BT. Network features and functional connections were extracted and analyzed based on the selected task switching states, referred to from Pre_2-BT to Pre_MAT before mental fatigue and from Post_MAT to Post_2-BT after mental fatigue. The results showed that mental fatigue has been successfully induced by long-term MAT based on the significant changes in network characteristics and the high classification accuracy of 98% obtained with Support Vector Machines (SVM) between Pre_2-BT and Post_2-BT. when the task switched from Pre_2-BT to Pre_MAT, delta and beta rhythms exhibited significant changes among all network features and the selected functional connections showed an enhanced trend. As for the task switched from Post_MAT to Post_2-BT, the network features and selected functional connectivity of beta rhythm were opposite to the trend of task switching before mental fatigue. Our findings provide new insights to understand the neural mechanism of the brain in the process of task switching and indicate that the network features and functional connections of beta rhythm can be used as neural markers for task switching before and after mental fatigue.

The Impacts of Obstructive Sleep Apnea Severity on Brain White Matter Integrity and Cognitive Functions in Children: A Diffusion Tensor Imaging Study.

OBJECTIVE: To investigate the impacts of obstructive sleep apnea (OSA) on white matter (WM) integrity and cognitive functions of pediatric patients with different levels of OSA severity. METHODS: Fifty-eight children with OSA and thirty-four healthy controls (HC) were recruited. All participants underwent diffusion tensor imaging (DTI) examination, polysomnography (PSG), and neurocognitive assessments. Patients were divided into mild OSA (MG) and moderate-severe OSA (SG) groups. WM integrity, PSG data, and neurocognitive assessment scores were compared among those groups. RESULTS: For apnea hypopnea index (AHI), obstructive apnea hypopnea index (OAHI), arousal index, SpO2 nadir, and attention, SG was worse than both MG and HC with MG worse than HC. For baseline SpO2 and intelligence, SG was worse than both MG and HC with no significant difference between MG and HC. Impaired WM integrity was observed in bilateral anterior thalamic radiation, bilateral inferior fronto-occipital fasciculus, bilateral inferior longitudinal fasciculus, right superior longitudinal fasciculus, right hippocampus, left cingulate gyrus, right uncinate fasciculus, callosum forceps major, and callosum forceps minor only for SG than for HC. WM integrity was significantly correlated with OSA severity and neurocognitive assessment scores only for SG, but not for MG. CONCLUSION: Decreased baseline SpO2, WM impairment, and intelligence decline were all observed only for SG, but not for MG, implying an associated relationship among decreased SpO2, WM impairment and WM impairment. Thus, for SG, additional assessments of brain damage and cognitive function decline are needed for prognostic evaluation of OSA.

Gastrointestinal Development and Microbiota Responses of Geese to Honeycomb Flavonoids Supplementation.

Background: Geese are conventionally considered to be herbivorous, which could also be raised with concentrate feeding diets without green grass because of the similar gastrointestinal tract with other poultry. However, the geese gut microbiota profiles and their interactions with epithelial cells are still of limited study. Flavonoids were well-documented to shape gut microbiota and promote epithelial barrier functions individually or cooperatively with other metabolites. Therefore, in the present study, honeycomb flavonoids (HF) were supplemented to investigate the effects on growth performances, intestinal development, and gut microbiome of geese. Material and Methods: A total of 400 1-day-old male lion-head geese with similar birth weight (82.6 ± 1.4 g) were randomly divided into five treatments: the control treatment (CON) and the HF supplementation treatments, HF was supplemented arithmetically to increase from 0.25 to 1%. Growth performance, carcass performances, and intestines' development parameters were measured to determine the optimum supplement. Junction proteins including ZO-1 and ZO-2 and cecal microbiota were investigated to demonstrate the regulatory effects of HF on both microbiota and intestinal epithelium. Results: Results showed that 0.5% of HF supplement had superior growth performance, carcass performance, and the total parameters of gastrointestinal development to other treatments. Further research showed that tight junction proteins including ZO-1 and ZO-2 significantly up-regulated, while Firmicutes and some probiotics including Clostridiales, Streptococcus, Lachnoclostridium, and Bifidobacterium, remarkably proliferated after HF supplement. In conclusion, HF supplement in concentrate-diet feeding geese effectively increased the growth performances by regulating the gut microbiota to increase the probiotic abundance to promote the nutrient digestibility and fortify the epithelial development and barrier functions to facilitate the nutrient absorption and utilization.

A Practical Application for Quantitative Brain Fatigue Evaluation Based on Machine Learning and Ballistocardiogram.

Brain fatigue is often associated with inattention, mental retardation, prolonged reaction time, decreased work efficiency, increased error rate, and other problems. In addition to the accumulation of fatigue, brain fatigue has become one of the important factors that harm our mental health. Therefore, it is of great significance to explore the practical and accurate brain fatigue detection method, especially for quantitative brain fatigue evaluation. In this study, a biomedical signal of ballistocardiogram (BCG), which does not require direct contact with human body, was collected by optical fiber sensor cushion during the whole process of cognitive tasks for 20 subjects. The heart rate variability (HRV) was calculated based on BCG signal. Machine learning classification model was built based on random forest to quantify and recognize brain fatigue. The results showed that: Firstly, the heart rate obtained from BCG signal was consistent with the result displayed by the medical equipment, and the absolute difference was less than 3 beats/min, and the mean error is 1.30 ± 0.81 beats/min; secondly, the random forest classifier for brain fatigue evaluation based on HRV can effectively identify the state of brain fatigue, with an accuracy rate of 96.54%; finally, the correlation between HRV and the accuracy was analyzed, and the correlation coefficient was as high as 0.98, which indicates that the accuracy can be used as an indicator for quantitative brain fatigue evaluation during the whole task. The results suggested that the brain fatigue quantification evaluation method based on the optical fiber sensor cushion and machine learning can carry out real-time brain fatigue detection on the human brain without disturbance, reduce the risk of human accidents in human-machine interaction systems, and improve mental health among the office and driving personnel.

Effects of obstructive sleep apnoea severity on neurocognitive and brain white matter alterations in children according to sex: a tract-based spatial statistics study.

OBJECTIVES: To investigate alterations in neurocognitive, attention, paediatric sleep questionnaire (PSQ) scores and whole brain white matter (WM) integrity between children with mild and severe obstructive sleep apnoea (OSA) according to sex and whether these changes are associated with OSA severity. METHODS: Fifty-seven children (36 males and 21 females) diagnosed with OSA were recruited for this study. Children of both sexes were divided into mild (male-MG, female-MG) and severe (male-SG, female-SG) groups according to OSA severity. Polysomnography (PSG), neurocognitive, attention and PSQ tests were compared between groups by one-way samples analysis of variance (ANOVA) F test. Diffusion tensor imaging (DTI) was scanned using a 3T GE MRI scanner and analysed by Tract-based Spatial Statistics (TBSS). Spearman correlation was calculated between DTI Eigenvalues and clinical characteristics. RESULTS: Compared to mild OSA patients, severe OSA patients presented greater severity of obstructive apnoea hypopnea index (OAHI), neurocognition, PSQ and attention tests in both male and female patients. Brain WM integrity in the male-SG, compared to the male-MG, demonstrated significantly reduced fractional anisotropy (FA) values in the right middle frontal gyrus and the right frontal sub-gyral regions and increased axial diffusivity (AD) values in the right inferior frontal gyrus, left parietal angular gyrus and sub-gyral regions, while no differences were found between the female-MG and female-SG. Alterations in male-SG brain regions were observably correlated with severity in male OSA patients. CONCLUSIONS: The integrity of WM, which regulates autonomic, cognitive, and attention functions, is impaired in male, but not female, children with severe OSA.

Association between mild or moderate obstructive sleep apnea-hypopnea syndrome and cognitive dysfunction in children.

BACKGROUND: Childhood obstructive sleep apnea-hypopnea syndrome (OSAHS), the most common sleep-related breathing disorder, may lead to cognitive impairment. This study aims to investigate the association between mild or moderate childhood OSAHS and cognitive dysfunction. METHODS: A total of 59 children (4-12 years of age) diagnosed with mild or moderate OSAHS by polysomnography and 60 age-  and sex-matched healthy children were included in the study. The China-Wechsler Younger Children Scale of Intelligence and China-Wechsler Intelligence Scale for Children were used to evaluate the cognition of the participating children aged <6 years and ≥6 years, respectively. RESULTS: In the <6-years-old subgroup, children with OSAHS had significantly lower scores of full-scale IQ (FIQ), verbal IQ (VIQ), comprehension test, and visual analysis than the healthy children (all p < 0.05). In the ≥6-years-old subgroup, VIQ and classification test scores were significantly lower in children with OSAHS than in the healthy controls (all p < 0.05). FIQ, VIQ, and performance IQ (PIQ) scores did not correlate with AHI, OAHI, and the lowest nocturnal SO2. Notably, in the <6-years-old subgroup of OSAHS, the accumulated time of SO2<90% (p = 0.046) and the percentage of the accumulated time of SO2<90% in the total sleep time (p = 0.034) correlated with PIQ negatively and significantly. CONCLUSIONS: Mild to moderate childhood OSAHS may adversely affect cognitive function, particularly in young children (<6 years of age). This study may increase the awareness of childhood OSAHS-associated cognitive dysfunction and advocate early interventions in childhood OSAHS.