Sexually Dimorphic Adaptation of Cardiac Function: Roles of Epoxyeicosatrienoic Acid and Peroxisome Proliferator-Activated Receptors

Epoxyeicosatrienoic acids (EETs) are cardioprotective mediators metabolized by soluble epoxide hydrolase (sEH) to form corresponding diols (DHETs). As a sex-susceptible target, sEH is involved in the sexually dimorphic regulation of cardiovascular function. Thus, we hypothesized that the female sex favors EET-mediated potentiation of cardiac function via downregulation of sEH expression, followed by upregulation of peroxisome proliferator-activated receptors (PPARs). Hearts were isolated from male (M) and female (F) wild-type (WT) and sEH-KO mice, and perfused with constant flow at different preloads. Basal coronary flow required to maintain the perfusion pressure at 100 mmHg was significantly greater in females than males, and sEH-KO than WT mice. All hearts displayed a dose-dependent decrease in coronary resistance and increase in cardiac contractility, represented as developed tension in response to increases in preload. These responses were also significantly greater in females than males, and sEH-KO than WT 14,15-EEZE abolished the sex-induced (F vs. M) and transgenic model-dependent (KO vs. WT) differences in the cardiac contractility, confirming an EET-driven response. Compared with M-WT controls, F-WT hearts expressed downregulation of sEH, associated with increased EETs and reduced DHETs, a pattern comparable to that observed in sEH-KO hearts. Coincidentally, F-WT and sEH-KO hearts exhibited increased PPARα expression, but comparable expression of eNOS, PPARβ, and EET synthases. In conclusion, female-specific downregulation of sEH initiates an EET-dependent adaptation of cardiac function, characterized by increased coronary flow via reduction in vascular resistance, and promotion of cardiac contractility, a response that could be further intensified by PPARα

Cathepsin B Regulates Collagen Expression by Fibroblasts via Prolonging TLR2/NF- κ

Fibroblasts are essential for tissue repair due to producing collagens, and lysosomal proteinase cathepsin B (CatB) is involved in promoting chronic inflammation. We herein report that CatB regulates the expression of collagens III and IV by fibroblasts in response to a TLR2 agonist, lipopolysaccharide from Porphyromonas gingivalis (P.g. LPS). In cultured human BJ fibroblasts, mRNA expression of CatB was significantly increased, while that of collagens III and IV was significantly decreased at 24 h after challenge with P.g. LPS (1 μg/mL). The P.g. LPS-decreased collagen expression was completely inhibited by CA-074Me, the specific inhibitor of CatB. Surprisingly, expression of collagens III and IV was significantly increased in the primary fibroblasts from CatB-deficient mice after challenge with P.g. LPS. The increase of CatB was accompanied with an increase of 8-hydroxy-2′-deoxyguanosine (8-OHdG) and a decrease of IκBα. Furthermore, the P.g. LPS-increased 8-OHdG and decreased IκBα were restored by CA-074Me. Moreover, 87% of CatB and 86% of 8-OHdG were colocalized with gingival fibroblasts of chronic periodontitis patients. The findings indicate the critical role of CatB in regulating the expression of collagens III and IV by fibroblasts via prolonging TLR2/NF-κB activation and oxidative stress. CatB-specific inhibitors may therefore improve chronic inflammation-delayed tissue repair

Machine learning and bioinformatics framework integration reveal potential characteristic genes related to immune cell infiltration in preeclampsia

Introduction: Preeclampsia is a disease that affects both the mother and child, with serious consequences. Screening the characteristic genes of preeclampsia and studying the placental immune microenvironment are expected to explore specific methods for the treatment of preeclampsia and gain an in-depth understanding of the pathological mechanism of preeclampsia.Methods: We screened for differential genes in preeclampsia by using limma package. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, disease ontology enrichment, and gene set enrichment analyses were performed. Analysis and identification of preeclampsia biomarkers were performed by using the least absolute shrinkage and selection operator regression model, support vector machine recursive feature elimination, and random forest algorithm. The CIBERSORT algorithm was used to analyze immune cell infiltration. The characteristic genes were verified by RT-qPCR.Results: We identified 73 differential genes, which mainly involved in reproductive structure and system development, hormone transport, etc. KEGG analysis revealed emphasis on cytokine–cytokine receptor interactions and interleukin-17 signaling pathways. Differentially expressed genes were dominantly concentrated in endocrine system diseases and reproductive system diseases. Our findings suggest that LEP, SASH1, RAB6C, and FLT1 can be used as placental markers for preeclampsia and they are associated with various immune cells.Conclusion: The differentially expressed genes in preeclampsia are related to inflammatory response and other pathways. Characteristic genes, LEP, SASH1, RAB6C, and FLT1 can be used as diagnostic and therapeutic targets for preeclampsia, and they are associated with immune cell infiltration. Our findings contribute to the pathophysiological mechanism exploration of preeclampsia. In the future, the sample size needs to be expanded for data analysis and validation, and the immune cells need to be further validated

Motion estimation and popout detection from energy neuron populations

Image motion analysis plays an important role in the everyday life of both humans and animals. Motion detectors based on gradients, correlations and energies have been proposed to account for the local motion processing in visual cortex of human. The motion energy model is one of the most widely accepted and biologically plausible models for modeling neurons in human brain. However, the computational costs limit the accuracy for image velocity extraction when utilizing a population of motion neurons to disambiguate the uncertainty of single neuronal responses. This thesis generalizes the motion energy model proposed by Adelson and Bergen and allows linear combinations of filters obtained from spatial and temporal impulse responses other than only sums and differences. The generalized motion filter is obtained by re-expressing the complex valued temporal filter as the real and imaginary parts and adding a phase shift on the imaginary path. This proposed phase shift motion energy model is approximately spatio-temporal orientation tuned where the orientation in space-time is adjusted by the newly introduced phase parameter. The phase shift model suggests an alternative way to construct the motion energy neuron population by varying phase values instead of varying temporal frequencies, which is referred to as the phase tuned population instead of frequency tuned population. The phase tuned population greatly reduces the computational costs by replacing the spatio-temporal filtering bank with a single spatio-temporal filter connected to different linear combinations of responses on the real and imaginary paths. This novel phase shift mechanism for population construction also sheds light on the functional role for interpreting the general believed rule of biological systems that similar mechanisms are utilized when processing information from different sources. The connection between the phase tuned motion energy neuron and the phase tuned disparity neuron is established by an analogy between the disparity model of combining spatially Gabor-filtered versions of left and right images and the motion model of combing spatially Gabor-filtered versions of two delayed input images with different phase shifts. A natural idea following the motion representation by phase shifts is to represent the spatial motion contrast with the phase difference between motion neuronal responses of different spatial regions. This idea facilitates the modeling of motion neurons directly sensitive to motion contrast instead of absolute motion. Based on the measurement of phase differences, the differential motion opponency is constructed to model the enhanced and suppressive interaction between center and surrounding regions and to detect motion popout. We validate our phase shift model with experiments by using both synthetic and real images in both 1-D and 2-D. With a small amount of spatial pooling, the performance of velocity estimates when using the phase tuned population exceeds that when using the frequency tuned population. Utilizing a confidence measure given by the normalized peak response with the average response across the population, the erroneous motion estimates can be reliably rejected. Finally, we demonstrate the efficiency and effectiveness of the motion estimation in the MultiMap vision system we developed. The system computes the population responses of phase tuned motion neuron population and the velocity map from inferred phase values

Experimental and Numerical Study of Jet Controlled Compression Ignition on Combustion Phasing Control in Diesel Premixed Compression Ignition Systems

In order to directly control the premixed combustion phasing, a Jet Controlled Compression Ignition (JCCI) for diesel premixed compression ignition systems is investigated. Experiments were conducted on a single cylinder natural aspirated diesel engine without EGR at 3000 rpm. Numerical models were validated by load sweep experiments at fixed spark timing. Detailed combustion characteristics were analyzed based on the BMEP of 2.18 bar. The simulation results showed that the high temperature jets of reacting active radical species issued from the ignition chamber played an important role on the onset of combustion in the JCCI system. The combustion of diesel pre-mixtures was initiated rapidly by the combustion products issued from the ignition chamber. Moreover, the flame propagation was not obvious, similar to that in Pre-mixed Charge Compression Ignition (PCCI). Consequently, spark timing sweep experiments were conducted. The results showed a good linear relationship between spark timing in the ignition chamber and CA10 and CA50, which indicated the ability for direct combustion phasing control in diesel PCCI. The NOx and soot emissions gradually changed with the decrease of spark advance angle. The maximum reduction of NOx and soot were both over 90%, and HC and CO emissions were increased

Newly developed gas-assisted sonodynamic therapy in cancer treatment

Sonodynamic therapy (SDT) is an emerging noninvasive treatment modality that utilizes low-frequency and low-intensity ultrasound (US) to trigger sensitizers to kill tumor cells with reactive oxygen species (ROS). Although SDT has attracted much attention for its properties including high tumor specificity and deep tissue penetration, its anticancer efficacy is still far from satisfactory. As a result, new strategies such as gas-assisted therapy have been proposed to further promote the effectiveness of SDT. In this review, the mechanisms of SDT and gas-assisted SDT are first summarized. Then, the applications of gas-assisted SDT for cancer therapy are introduced and categorized by gas types. Next, therapeutic systems for SDT that can realize real-time imaging are further presented. Finally, the challenges and perspectives of gas-assisted SDT for future clinical applications are discussed

Building-Level Urban Functional Area Identification Based on Multi-Attribute Aggregated Data from Cell Phones—A Method Combining Multidimensional Time Series with a SOM Neural Network

Portraying functional urban areas provides useful insights for understanding complex urban systems and formulating rational urban plans. Mobile phone user trajectory data are often used to infer the individual activity patterns of people and for functional area identification, but they are difficult to obtain because of personal privacy issues and have the drawback of a sparse spatial and temporal distribution. Deep learning models have been widely utilized in functional area recognition but are limited by the difficulty of acquiring training samples with large data volumes. This paper aims to achieve a fast and automatic identification of large-scale urban functional areas without prior knowledge. This paper uses Nanjing city as a test area, and a self-organizing map (SOM) neural network model based on an improved dynamic time warping (Ndim-DTW) distance is used to automatically identify the function of each building using mobile phone aggregated data containing work and residence attributes. The results show that the recognition accuracy reaches 88.7%, which is 12.4% higher than that of the K-medoids method based on the DTW distance using a single attribute and 7.8% higher than that of the K-medoids method based on the Ndim-DTW distance with multiple attributes, confirming the effectiveness of the multi-attribute mobile phone aggregated data and the SOM model based on the Ndim-DTW distance. Furthermore, at the traffic analysis zone (TAZ) level, this paper detects that Nanjing has seven functional area hotspots with a high degree of mixing. The results can provide a data basis for urban studies on, for example, the urban spatial structure, the separation of occupations and residences, and environmental suitability evaluation

Building-Level Urban Functional Area Identification Based on Multi-Attribute Aggregated Data from Cell Phones—A Method Combining Multidimensional Time Series with a SOM Neural Network

Portraying functional urban areas provides useful insights for understanding complex urban systems and formulating rational urban plans. Mobile phone user trajectory data are often used to infer the individual activity patterns of people and for functional area identification, but they are difficult to obtain because of personal privacy issues and have the drawback of a sparse spatial and temporal distribution. Deep learning models have been widely utilized in functional area recognition but are limited by the difficulty of acquiring training samples with large data volumes. This paper aims to achieve a fast and automatic identification of large-scale urban functional areas without prior knowledge. This paper uses Nanjing city as a test area, and a self-organizing map (SOM) neural network model based on an improved dynamic time warping (Ndim-DTW) distance is used to automatically identify the function of each building using mobile phone aggregated data containing work and residence attributes. The results show that the recognition accuracy reaches 88.7%, which is 12.4% higher than that of the K-medoids method based on the DTW distance using a single attribute and 7.8% higher than that of the K-medoids method based on the Ndim-DTW distance with multiple attributes, confirming the effectiveness of the multi-attribute mobile phone aggregated data and the SOM model based on the Ndim-DTW distance. Furthermore, at the traffic analysis zone (TAZ) level, this paper detects that Nanjing has seven functional area hotspots with a high degree of mixing. The results can provide a data basis for urban studies on, for example, the urban spatial structure, the separation of occupations and residences, and environmental suitability evaluation