The Roles of Histamine Receptor 1 (hrh1) in Neurotransmitter System Regulation, Behavior, and Neurogenesis in Zebrafish

Histamine receptors mediate important physiological processes and take part in the pathophysiology of different brain disorders. Histamine receptor 1 (HRH1) is involved in the development of neurotransmitter systems, and its role in neurogenesis has been proposed. Altered HRH1 binding and expression have been detected in the brains of patients with schizophrenia, depression, and autism. Our goal was to assess the role of hrh1 in zebrafish development and neurotransmitter system regulation through the characterization of hrh1(-/-) fish generated by the CRISPR/Cas9 system. Quantitative PCR, in situ hybridization, and immunocytochemistry were used to study neurotransmitter systems and genes essential for brain development. Additionally, we wanted to reveal the role of this histamine receptor in larval and adult fish behavior using several quantitative behavioral methods including locomotion, thigmotaxis, dark flash and startle response, novel tank diving, and shoaling behavior. Hrh1(-/-) larvae displayed normal behavior in comparison with hrh1(+/+) siblings. Interestingly, a transient abnormal expression of important neurodevelopmental markers was evident in these larvae, as well as a reduction in the number of tyrosine hydroxylase 1 (Th1)-positive cells, th1 mRNA, and hypocretin (hcrt)-positive cells. These abnormalities were not detected in adulthood. In summary, we verified that zebrafish lacking hrh1 present deficits in the dopaminergic and hypocretin systems during early development, but those are compensated by the time fish reach adulthood. However, impaired sociability and anxious-like behavior, along with downregulation of choline O-acetyltransferase a and LIM homeodomain transcription factor Islet1, were displayed by adult fish.Peer reviewe

Comparative Analysis of <i>Gracilaria chouae</i> Polysaccharides Derived from Different Geographical Regions: Focusing on Their Chemical Composition, Rheological Properties, and Gel Characteristics

Polysaccharides derived from diverse sources exhibit distinct rheological and gel properties, exerting a profound impact on their applicability in the food industry. In this study, we collected five Gracilaria chouae samples from distinct geographical regions, namely Rizhao (RZ), Lianyungang (LYG), Ningde (ND), Beihai (BH), and a wild source from Beihai (BHW). We conducted analyses on the chemical composition, viscosity, and rheological properties, as well as gel properties, to investigate the influence of chemical composition on variations in gel properties. The results revealed that the total sugar, sulfate content, and monosaccharide composition of G. chouae polysaccharides exhibit similarity; however, their anhydrogalactose content varies within a range of 15.31% to 18.98%. The molecular weight distribution of G. chouae polysaccharides ranged from 1.85 to 2.09 × 103 kDa. The apparent viscosity of the LYG and BHW polysaccharides was relatively high, whereas that of RZ and ND was comparatively low. The gel strength displayed a similar trend. BHW and LYG exhibited solid-like behavior, while ND, RZ, and BH demonstrated liquid-like characteristics at low frequencies. The redundancy analysis (RDA) analysis revealed a positive correlation between the texture profile analysis (TPA) characteristics and anhydrogalactose. The study could provide recommendations for the diverse applications of G. chouae polysaccharides derived from different geographical regions

Cloning, Expression and Characterization of a Novel Cold-adapted β-galactosidase from the Deep-sea Bacterium <i>Alteromonas</i> sp. ML52

The bacterium Alteromonas sp. ML52, isolated from deep-sea water, was found to synthesize an intracellular cold-adapted &#946;-galactosidase. A novel &#946;-galactosidase gene from strain ML52, encoding 1058 amino acids residues, was cloned and expressed in Escherichia coli. The enzyme belongs to glycoside hydrolase family 2 and is active as a homotetrameric protein. The recombinant enzyme had maximum activity at 35 &#176;C and pH 8 with a low thermal stability over 30 &#176;C. The enzyme also exhibited a Km of 0.14 mM, a Vmax of 464.7 U/mg and a kcat of 3688.1 S&#8722;1 at 35 &#176;C with 2-nitrophenyl-&#946;-d-galactopyranoside as a substrate. Hydrolysis of lactose assay, performed using milk, indicated that over 90% lactose in milk was hydrolyzed after incubation for 5 h at 25 &#176;C or 24 h at 4 &#176;C and 10 &#176;C, respectively. These properties suggest that recombinant Alteromonas sp. ML52 &#946;-galactosidase is a potential biocatalyst for the lactose-reduced dairy industry

Exploring resting-state EEG oscillations in patients with Neuromyelitis Optica Spectrum Disorder

Background and objective: Quantitative resting-state electroencephalography (rs-EEG) is a convenient method for characterizing the functional impairments and adaptations of the brain that has been shown to be valuable for assessing many neurological and psychiatric disorders, especially in monitoring disease status and assisting neuromodulation treatment. However, it has not yet been explored in patients with neuromyelitis optica spectrum disorder (NMOSD). This study aimed to investigate the rs-EEG features of NMOSD patients and explore the rs-EEG features related to disease characteristics and complications (such as anxiety, depression, and fatigue). Methods: A total of 32 NMOSD patients and 20 healthy controls (HCs) were recruited; their demographic and disease information were collected, and their anxiety, depression, and fatigue symptoms were evaluated. The rs-EEG power spectra of all the participants were obtained. After excluding the participants with low-quality rs-EEG data during processing, statistical analysis was conducted based on the clinical information and rs-EEG data of 29 patients and 19 HCs. The rs-EEG power (the mean spectral energy (MSE) of absolute power and relative power in all frequency bands, as well as the specific power for all electrode sites) of NMOSD patients and HCs was compared. Furthermore, correlation analyses were performed between rs-EEG power and other variables for NMOSD patients (including the disease characteristics and complications). Results: The distribution of the rs-EEG power spectra in NMOSD patients was similar to that in HCs. The dominant alpha-peaks shifted significantly towards a lower frequency for patients when compared to HCs. The delta and theta power was significantly increased in the NMOSD group compared to that in the HC group. The alpha oscillation power was found to be significantly negatively associated with the degree of anxiety (reflected by the anxiety subscore of hospital anxiety and depression scale (HADS)) and the degree of depression (reflected by the depression subscore of HADS). The gamma oscillation power was revealed to be significantly positively correlated with the fatigue severity scale (FSS) score, while further analysis indicated that the electrode sites of almost the whole brain region showing correlations with fatigue. Regarding the disease variables, no statistically significant rs-EEG features were related to the main disease features in NMOSD patients. Conclusion: The results of this study suggest that the rs-EEG power spectra of NMOSD patients show increased slow oscillations and are potential biomarkers of widespread white matter microstructural damage in NMOSD. Moreover, this study revealed the rs-EEG features associated with anxiety, depression, and fatigue in NMOSD patients, which might help in the evaluation of these complications and the development of neuromodulation treatment. Quantitative rs-EEG analysis may play an important role in the management of NMOSD patients, and future studies are warranted to more comprehensively understand its application value

Artificial intelligence in paleontology

The accumulation of large datasets and increasing data availability have led to the emergence of data-driven paleontological studies, which reveal an unprecedented picture of evolutionary history. However, the fast-growing quantity and complication of data modalities make data processing laborious and inconsistent, while also lacking clear benchmarks to evaluate data collection and generation, and the performances of different methods on similar tasks. Recently, artificial intelligence (AI) has become widely practiced across scientific disciplines, but not so much to date in paleontology where traditionally manual workflows have been more usual. In this study, we review &gt;70 paleontological AI studies since the 1980s, covering major tasks including micro- and macrofossil classification, image segmentation, and prediction. These studies feature a wide range of techniques such as Knowledge-Based Systems (KBS), neural networks, transfer learning, and many other machine learning methods to automate a variety of paleontological research workflows. Here, we discuss their methods, datasets, and performance and compare them with more conventional AI studies. We attribute the recent increase in paleontological AI studies most to the lowering of the entry bar in training and deployment of AI models rather than innovations in fossil data compilation and methods. We also present recently developed AI implementations such as diffusion model content generation and Large Language Models (LLMs) that may interface with paleontological research in the future. Even though AI has not yet been a significant part of the paleontologist's toolkit, successful implementation of AI is growing and shows promise for paradigm-transformative effects on paleontological research in the years to come