Seasonal patterns and bloom dynamics of phytoplankton based on satellite-derived chlorophyll-a in the eastern yellow sea.

Satellite-derived chlorophyll-a concentration (Chl-a) is essential for assessing environmental conditions, yet its application in the optically complex waters of the eastern Yellow Sea (EYS) is challenged. This study refines the Chl-a algorithm for the EYS employing a switching approach based on normalized water-leaving radiance at 555 nm wavelength according to turbidity conditions to investigate phytoplankton bloom patterns in the EYS. The refined Chl-a algorithm (EYS algorithm) outperforms prior algorithms, exhibiting a strong alignment with in situ Chl-a. Employing the EYS algorithm, seasonal and bloom patterns of Chl-a are detailed for the offshore and nearshore EYS areas. Distinct seasonal Chl-a patterns and factors influencing bloom initiation differed between the areas, and the peak Chl-a during the bloom period from 2018 to 2020 was significantly lower than the average year in both areas. Specifically, bimodal and unimodal peak patterns in Chl-a were observed in the offshore and nearshore areas, respectively. By investigating the relationships between environmental factors and bloom parameters, we identified that major controlling factors governing bloom initiation were mixed layer depth (MLD) and suspended particulate matter (SPM) in the offshore and nearshore areas, respectively. Additionally, this study proposed that the recent decrease in the peak Chl-a might be caused by rapid environmental changes such as the warming trend of sea surface temperature (SST) and the limitation of nutrients. For example, external forcing, phytoplankton growth, and nutrient dynamics can change due to increased SST and limitation of nutrients, which can lead to a decrease in Chl-a. This study contributes to understanding phytoplankton dynamics in the EYS, highlighting the importance of region-specific considerations in comprehending Chl-a patterns and bloom dynamics.

Deep learning model for differentiating nasal cavity masses based on nasal endoscopy images.

BACKGROUND: Nasal polyps and inverted papillomas often look similar. Clinically, it is difficult to distinguish the masses by endoscopic examination. Therefore, in this study, we aimed to develop a deep learning algorithm for computer-aided diagnosis of nasal endoscopic images, which may provide a more accurate clinical diagnosis before pathologic confirmation of the nasal masses. METHODS: By performing deep learning of nasal endoscope images, we evaluated our computer-aided diagnosis system's assessment ability for nasal polyps and inverted papilloma and the feasibility of their clinical application. We used curriculum learning pre-trained with patches of nasal endoscopic images and full-sized images. The proposed model's performance for classifying nasal polyps, inverted papilloma, and normal tissue was analyzed using five-fold cross-validation. RESULTS: The normal scores for our best-performing network were 0.9520 for recall, 0.7900 for precision, 0.8648 for F1-score, 0.97 for the area under the curve, and 0.8273 for accuracy. For nasal polyps, the best performance was 0.8162, 0.8496, 0.8409, 0.89, and 0.8273, respectively, for recall, precision, F1-score, area under the curve, and accuracy. Finally, for inverted papilloma, the best performance was obtained for recall, precision, F1-score, area under the curve, and accuracy values of 0.5172, 0.8125, 0.6122, 0.83, and 0.8273, respectively. CONCLUSION: Although there were some misclassifications, the results of gradient-weighted class activation mapping were generally consistent with the areas under the curve determined by otolaryngologists. These results suggest that the convolutional neural network is highly reliable in resolving lesion locations in nasal endoscopic images.

Exploring early life social and executive function development in infants and risk for autism: a prospective cohort study protocol of NICU graduates and infants at risk for cerebral palsy.

BACKGROUND: Delays in early social and executive function are predictive of later developmental delays and eventual neurodevelopmental diagnoses. There is limited research examining such markers in the first year of life. High-risk infant groups commonly present with a range of neurodevelopmental challenges, including social and executive function delays, and show higher rates of autism diagnoses later in life. For example, it has been estimated that up to 30% of infants diagnosed with cerebral palsy (CP) will go on to be diagnosed with autism later in life. METHODS: This article presents a protocol of a prospective longitudinal study. The primary aim of this study is to identify early life markers of delay in social and executive function in high-risk infants at the earliest point in time, and to explore how these markers may relate to the increased risk for social and executive delay, and risk of autism, later in life. High-risk infants will include Neonatal Intensive Care Unit (NICU) graduates, who are most commonly admitted for premature birth and/or cardiovascular problems. In addition, we will include infants with, or at risk for, CP. This prospective study will recruit 100 high-risk infants at the age of 3-12 months old and will track social and executive function across the first 2 years of their life, when infants are 3-7, 8-12, 18 and 24 months old. A multi-modal approach will be adopted by tracking the early development of social and executive function using behavioural, neurobiological, and caregiver-reported everyday functioning markers. Data will be analysed to assess the relationship between the early markers, measured from as early as 3-7 months of age, and the social and executive function as well as the autism outcomes measured at 24 months. DISCUSSION: This study has the potential to promote the earliest detection and intervention opportunities for social and executive function difficulties as well as risk for autism in NICU graduates and/or infants with, or at risk for, CP. The findings of this study will also expand our understanding of the early emergence of autism across a wider range of at-risk groups.

Pectin Nanoporous Structures Prepared via Salt-Induced Phase Separation and Ambient Azeotropic Evaporation Processes.

Polysaccharide nanoporous structures are suitable for various applications, ranging from biomedical scaffolds to adsorption materials, owing to their biocompatibility and large surface areas. Pectin, in particular, can create 3D nanoporous structures in aqueous solutions by binding with calcium cations and creating nanopores by phase separation; this process involves forming hydrogen bonds between alcohols and pectin chains in water and alcohol mixtures and the resulting penetration of alcohols into calcium-bound pectin gels. However, owing to the dehydration and condensation of polysaccharide chains during drying, it has proven to be challenging to maintain the 3D nanoporous structure without using a freeze-drying process or supercritical fluid. Herein, we report a facile method for creating polysaccharide-based xerogels, involving the co-evaporation of water with a nonsolvent (e.g., a low-molecular-weight hydrophobic alcohol such as isopropyl or n-propyl alcohol) at ambient conditions. Experiments and coarse-grained molecular dynamics simulations confirmed that salt-induced phase separation and hydrogen bonding between hydrophobic alcohols and pectin chains were the dominant processes in mixtures of pectin, water, and hydrophobic alcohols. Furthermore, the azeotropic evaporation of water and alcohol mixed in approximately 1:1 molar ratios was maintained during the natural drying process under ambient conditions, preventing the hydration and aggregation of the hydrophilic pectin chains. These results introduce a simple and convenient process to produce 3D polysaccharide xerogels under ambient conditions.

Ubiquitin ligase RNF20 coordinates sequential adipose thermogenesis with brown and beige fat-specific substrates.

In mammals, brown adipose tissue (BAT) and inguinal white adipose tissue (iWAT) execute sequential thermogenesis to maintain body temperature during cold stimuli. BAT rapidly generates heat through brown adipocyte activation, and further iWAT gradually stimulates beige fat cell differentiation upon prolonged cold challenges. However, fat depot-specific regulatory mechanisms for thermogenic activation of two fat depots are poorly understood. Here, we demonstrate that E3 ubiquitin ligase RNF20 orchestrates adipose thermogenesis with BAT- and iWAT-specific substrates. Upon cold stimuli, BAT RNF20 is rapidly downregulated, resulting in GABPα protein elevation by controlling protein stability, which stimulates thermogenic gene expression. Accordingly, BAT-specific Rnf20 suppression potentiates BAT thermogenic activity via GABPα upregulation. Moreover, upon prolonged cold stimuli, iWAT RNF20 is gradually upregulated to promote de novo beige adipogenesis. Mechanistically, iWAT RNF20 mediates NCoR1 protein degradation, rather than GABPα, to activate PPARγ. Together, current findings propose fat depot-specific regulatory mechanisms for temporal activation of adipose thermogenesis.