Table1_PCGIMA: developing the web server for human position-defined CpG islands methylation analysis.DOCX

Introduction: CpG island (CGI) methylation is one of the key epigenomic mechanisms for gene expression regulation and chromosomal integrity. However, classical CGI prediction methods are neither easy to locate those short and position-sensitive CGIs (CpG islets), nor investigate genetic and expression pattern for CGIs under different CpG position- and interval- sensitive parameters in a genome-wide perspective. Therefore, it is urgent for us to develop such a bioinformatic algorithm that not only can locate CpG islets, but also provide CGI methylation site annotation and functional analysis to investigate the regulatory mechanisms for CGI methylation.Methods: This study develops Human position-defined CGI prediction method to locate CpG islets using high performance computing, and then builds up a novel human genome annotation and analysis method to investigate the connections among CGI, gene expression and methylation. Finally, we integrate these functions into PCGIMA to provide relevant online computing and visualization service.Results: The main results include: (1) Human position-defined CGI prediction method is more efficient to predict position-defined CGIs with multiple consecutive (d) values and locate more potential short CGIs than previous CGI prediction methods. (2) Our annotation and analysis method not only can investigate the connections between position-defined CGI methylation and gene expression specificity from a genome-wide perspective, but also can analysis the potential association of position-defined CGIs with gene functions. (3) PCGIMA (http://www.combio-lezhang.online/pcgima/home.html) provides an easy-to-use analysis and visualization platform for human CGI prediction and methylation.Discussion: This study not only develops Human position-defined CGI prediction method to locate short and position-sensitive CGIs (CpG islets) using high performance computing to construct MR-CpGCluster algorithm, but also a novel human genome annotation and analysis method to investigate the connections among CGI, gene expression and methylation. Finally, we integrate them into PCGIMA for online computing and visualization.</p

Table2_PCGIMA: developing the web server for human position-defined CpG islands methylation analysis.DOCX

Introduction: CpG island (CGI) methylation is one of the key epigenomic mechanisms for gene expression regulation and chromosomal integrity. However, classical CGI prediction methods are neither easy to locate those short and position-sensitive CGIs (CpG islets), nor investigate genetic and expression pattern for CGIs under different CpG position- and interval- sensitive parameters in a genome-wide perspective. Therefore, it is urgent for us to develop such a bioinformatic algorithm that not only can locate CpG islets, but also provide CGI methylation site annotation and functional analysis to investigate the regulatory mechanisms for CGI methylation.Methods: This study develops Human position-defined CGI prediction method to locate CpG islets using high performance computing, and then builds up a novel human genome annotation and analysis method to investigate the connections among CGI, gene expression and methylation. Finally, we integrate these functions into PCGIMA to provide relevant online computing and visualization service.Results: The main results include: (1) Human position-defined CGI prediction method is more efficient to predict position-defined CGIs with multiple consecutive (d) values and locate more potential short CGIs than previous CGI prediction methods. (2) Our annotation and analysis method not only can investigate the connections between position-defined CGI methylation and gene expression specificity from a genome-wide perspective, but also can analysis the potential association of position-defined CGIs with gene functions. (3) PCGIMA (http://www.combio-lezhang.online/pcgima/home.html) provides an easy-to-use analysis and visualization platform for human CGI prediction and methylation.Discussion: This study not only develops Human position-defined CGI prediction method to locate short and position-sensitive CGIs (CpG islets) using high performance computing to construct MR-CpGCluster algorithm, but also a novel human genome annotation and analysis method to investigate the connections among CGI, gene expression and methylation. Finally, we integrate them into PCGIMA for online computing and visualization.</p

Table_1_A latent profile analysis of subjective exercise experiences among physically vulnerable college students and psychiatric symptoms correlates during three phases of the COVID-19 pandemic in Wuhan, China.docx

IntroductionPhysical activity among college students since the COVID-19 pandemic was well studied; however, subjective exercise experience and the emotional response toward physical exercise received less attention.MethodsThe present study used latent profile analysis (LPA) to explore the latent class of subjective exercise experience among physically vulnerable college students who scored 59 points or less in tests under the National Student Physical Health Standard. Three non-duplicated samples at different stages of the COVID-19 pandemic were collected in March 2020 (N = 127), March 2021 (N = 118), and November 2021 (N = 206) respectively. Psychometrically validated scales, namely, Subjective Exercise Experiences Scale (SEES), Generalized Anxiety Disorder (GAD-7), and Patient Health Questionnaire (PHQ-9) were used to measure subjective exercise experience, anxiety symptoms, and depressive symptoms.Results and discussionLPA revealed a 3-class solution for the subjective exercise experience of physically unfit students, namely, the “negative experience group” (30.82%), the “fatigue group” (41.91%), and the “positive experience group” (27.27%). Multinomial regression showed that probable anxiety [odds ratio (OR) = 0.12] was associated with the overall negative exercise experience while probable depression (OR = 0.19) was associated with psychological fatigue. Women (OR = 0.496) were more likely to experience overall negative exercise experience, and the outbreak of the COVID- 19 (OR = 2.14) pandemic influenced the psychological distress of the subjective exercise experience compared with the other two phases in the post-COVID- 19 era. Our findings provided significant implications for physical education targeting university students that interventions should be tailored differently for three profiles of the subjective exercise experience.</p

Reliable and Low-Power Multilevel Resistive Switching in TiO₂ Nanorod Arrays Structured with a TiO_<i>x</i> Seed Layer

The electrical performance of TiO₂ nanorod array (NRA)-based resistive switching memory devices is examined in this paper. The formation of a seed layer on the fluorine-doped tin oxide (FTO) glass substrate after treatment in TiCl₄ solution, before the growth of TiO₂ NRAs on the FTO substrate via a hydrothermal process, is shown to significantly improve the resistive switching performance of the resulting TiO₂ NRA-based device. As fabricated, the Al/TiO₂ NRA/TiO_<i>x</i> layer/FTO device displayed electroforming-free bipolar resistive switching behavior while maintaining a stable ON/OFF ratio for more than 500 direct sweeping cycles over a retention period of 3 × 10⁴ s. Meanwhile, the programming current as low as ∼10^–8 A and 10^–10 A for low resistance state and high resistance state respectively makes the fabricated devices suitable for low-power memristor applications. The TiO_<i>x</i> precursor seed layer not only promotes the uniform and preferred growth of TiO₂ nanorods on the FTO substrate but also functions as an additional source layer of trap centers due to its oxygen-deficient composition. Our data suggest that the primary conduction mechanism in these devices arises from trap-mediated space-charge-limited current (SCLC). Multilevel memory performance in this new device is achieved by varying the SET voltage. The origin of this effect is also discussed

Cellular Localization of Aquaporin-1 in the Human and Mouse Trigeminal Systems

<div><p>Previous studies reported that a subpopulation of mouse and rat trigeminal neurons express water channel aquaporin-1 (AQP1). In this study we make a comparative investigation of AQP1 localization in the human and mouse trigeminal systems. Immunohistochemistry and immunofluorescence results showed that AQP1 was localized to the cytoplasm and cell membrane of some medium and small-sized trigeminal neurons. Additionally, AQP1 was found in numerous peripheral trigeminal axons of humans and mice. In the central trigeminal root and brain stem, AQP1 was specifically expressed in astrocytes of humans, but was restricted to nerve fibers within the central trigeminal root and spinal trigeminal tract and nucleus in mice. Furthermore, AQP1 positive nerve fibers were present in the mucosal and submucosal layers of human and mouse oral tissues, but not in the muscular and subcutaneous layers. Fluorogold retrograde tracing demonstrated that AQP1 positive trigeminal neurons innervate the mucosa but not skin of cheek. These results reveal there are similarities and differences in the cellular localization of AQP1 between the human and mouse trigeminal systems. Selective expression of AQP1 in the trigeminal neurons innervating the oral mucosa indicates an involvement of AQP1 in oral sensory transduction.</p> </div

Colocalization of AQP1 and β-tubulin III in the oral submucosa, muscle and skin of humans (A–C, G–I, M–O) and mice (D–F, J–L, P–R).

<p>(<b>A–F</b>) AQP1 (red) and β-tubulin III (green) are coexpressed within a number of the nerve bundles (arrowheads) of the cheek submucosa from the two species. AQP1 positive microvessels (arrows) are also scatted among the nerve bundles. (<b>G–R</b>) There are a large number of β-tubulin III (green) positive nerve fibers, and a few AQP1 positive microvessels (arrowheads), within the cheek intermusclar (G–L) and subcutaneous regions (M–R). No overlap between β-tubulin III and AQP1 immunoreactivity is observed. Scale bars = 100 µm.</p

Localization of AQP1 in the peripheral and central branches of human (A–C,G–L) and mouse (D–F,M–S) trigeminal neurons.

<p>(<b>A–F</b>) Representative images of immunohistochemistry for AQP1. (<b>G–R</b>) Representative images of double immunofluorescence with AQP1 (red) and GFAP (green in H and N) or β-tubulin III (green in K and Q). A considerable proportion of axons are positive for AQP1 within the human (transverse section, A) and mouse (longitude section, D) mandibular nerve. The human trigeminal central root exhibits dense immunoreactivity for AQP1 (B, C, G, J), which is colocalized with GFAP (I), but not β-tubulin III (L). In contrast, in the mouse trigeminal central root, there are a population of AQP1 positive axons (E, F, M, P) that coexpress β-tubulin III (R) but not GFAP (O). Scale bars = 100 µm in A, D, F; 300 µm in B; 200 µm in E; 50 µm in C, G–R.</p

The percentage of AQP1-immunoreactive neurons in the trigeminal ganglia of humans and mice.

<p>The Data were expressed as a percentage of positive neurons respect to the total neuronal population in each class of neurons (small- medium- and large-sized neurons) (n = 3 in humans and n = 5 in mice).</p

Localization of AQP1 in the brain stem of humans (A, B, G–L) and mice (C–F).

<p>(<b>A–F</b>) Representative images of immunohistochemistry for AQP1. (<b>G–L</b>) Representative images of double immunofluorescence of AQP1 (red) with GFAP (green in H) or β-tubulin III (green in K) in the sp5. There is extensive expression of AQP1 throughout the human medulla oblongata including the spinal trigeminal tract (sp5) and spinal trigeminal nucleus (Sp5, A and B). AQP1 and GFAP are highly colocalized at the glial lamellae along the medulla surface as well as astrocyte processes within the brain parenchyma (G–I). Interestingly, astrocyte cell bodies (arrowheads in I) do not express AQP1. No β-tubulin III positive axons coexpress AQP1 in the sp5 (L). In the mouse brain stem, dense dot-like AQP1 labeled nerve fibers are present in the caudal part of sensory root of the trigeminal nerve (s5, E) and sp5 (C and D), but not at the rostral part of s5 or at the motor root of the trigeminal nerve (m5, F). Moreover, AQP1 positive axonal terminals are observed in the caudal and interpolar parts of the trigeminal nucleus (Sp5C and Sp5I, C and D), but not in the principal sensory trigeminal nucleus (Pr5, E). Scale bars = 1 mm in A; 200 µm in B, G–L; 400 µm in C–F.</p