Long Non-coding RNAs Contribute to the Inhibition of Proliferation and EMT by Pterostilbene in Human Breast Cancer

Background: There is increasing evidence that long non-coding RNAs (lncRNAs) are involved in the process of carcinogenesis and treatment using chemotherapy. Pterostilbene, a phytochemical agent with natural antioxidant and anti-inflammatory properties, has been shown to modulate oncogenic processes in many cancers. However, there has been limited research on the association between pterostilbene and the expression of lncRNAs.Methods: MCF7 breast cancer cells were treated with various concentrations of pterostilbene and their gene expression profile was analyzed by quantitative real-time PCR, Western blotting and immunofluorescence.Results: Treatment with pterostilbene inhibited cell proliferation and epithelial-to-mesenchymal transition (EMT), and increased cell apoptosis, autophagy and ER stress. The Akt/mTOR pathway was downregulated, but p38 MAPK/Erk signaling was activated in cells following treatment with pterostilbene. Pterostilbene increased the expression of the lncRNAs MEG3, TUG1, H19, and DICER1-AS1 whereas the expression of LINC01121, PTTG3P, and HOTAIR declined. Knockdown of lncRNA H19 resulted in a reduction of the cell invasion, with the cells becoming more sensitive to pterostilbene therapy.Conclusions: These results suggest that efficient optimum disruption of lncRNA expression might possibly improve the anti-tumor effects of phytochemical agents, thus serving as a potential therapy for breast cancer

Linocarpon bambusina X. Zhang & Tibpromma 2023, sp. nov.

<i>Linocarpon bambusina</i> X. Zhang & Tibpromma, <i>sp. nov.</i> (Fig. 2) <p>Index Fungorum number: IF900063; Facesoffungi number: FoF 12953</p> <p>Holotype: GMB1360</p> <p> Etymology: Species epithet refers to the host genus “ <i>bambusa</i> ” from which the holotype was collected.</p> <p> <i>Saprobic</i> on dead culms of bamboo. <b>Sexual morph:</b> <i>Ascomata</i> 150–330 × 360–560 μm (<i>x</i> = 223.5 × 484 μm, n = 10), solitary or aggregated, mostly aggregated, semi-immersed, black, shiny, dome-shaped, raised, subglobose, flattened at the base, central ostiole with papillate. <i>Ostiole</i> periphysate, carbonaceous. <i>Peridium</i> 15–50 μm wide (<i>x</i> = 32 μm, n = 10), outer cells merging with the host epidermal cells, composed of brown to dark brown cells of <i>textura angularis</i>. <i>Hamathecium</i> 2.5–6 µm wide (<i>x</i> = 4 μm, n = 20), hyaline, hypha-like, septate paraphyses. <i>Asci</i> 75–140 × 9–20 μm (<i>x</i> = 105 × 12 μm, n = 50), 8-spored, unitunicate, long fusiform, shortly pedicellate, furcate pedicel, with a J- subapical ring. <i>Ascospores</i> 65–100 × 2–6 μm (<i>x</i> = 87 × 4.5 μm, n = 50), fasciculate, filiform, straight or curved, hyaline, 28–30 septa, parallel when immature and becoming spiral when mature in asci, with guttules when immature, ends slightly rounded, without appendage or mucilaginous sheath, smooth-walled. <b>Asexual morph:</b> Undetermined.</p> <p> <b>Culture characteristics:</b> Ascospores germinated on PDA within 24 h, and cultured at 25–28 ˚C after one month, pure mycelia flossy, curled, colonies, circular, umbonate, white to pale brown in above, the reverse side is brown in the middle and yellow-white at the margin.</p> <p> <b>Material examined:</b> China, Yunnan Province, Lijiang City, on dead bamboo culms, 13 July 2021, D.Q. Dai, DDQ02097 (holotype, GMB 1360; isotype, KUN-HKAS 125776; ex-type living culture, GMBCC 1155).</p> <p> <b>Notes:</b> In the phylogenetic tree, our new species <i>Linocarpon bambusina</i> formed a well-separated clade sister to <i>L. pandanicola</i> (HKUCC3783, HKUCC 4385, HKUM16280) with moderate bootstrap support (55% ML) (Fig. 1). The comparison of LSU nucleotides between our taxon and <i>L. pandanicola</i> (HKUCC 4385) resulted in 8.1% difference (65/800 bp, without gaps), but <i>L. pandanicola</i> did not have SSU and <i>tef</i> 1-α genes to compare with <i>L. bambusina</i>. <i>Linocaropn bambusina</i> can be distinguished from <i>L. pandanicola</i> by having shiny ascomata, ostiolar papillate, fusiform asci with club shape, ascospores with 28–30 septa, and no appendages or mucilaginous sheath, while these characteristics are not present in <i>L. pandanicola</i>. In addition, our taxon and <i>L. arengae</i> are similar in asci pedicellate with a J- subapical ring, and filiform ascospores with straight or curved but can be distinguished by having semi-immersed ascomata, fusiform asci, ascospores with 28–30 septa, not constricted at septa, guttules, and no appendages or mucilaginous sheath, while <i>L. arengae</i> has immersed ascomata, cylindrical asci, aseptate ascospores, containing numerous refringent septum-like bands with polar mucilaginous appendage at the apex (Konta <i>et al.</i> 2017). Finally, <i>L. bambusina</i> can be distinguished by having solitary or aggregated, mostly aggregated, shiny, semi-immersed ascomata, with a J- subapical ring asci, ascospores with 28–30 septa, without appendage or mucilaginous sheath, while <i>L. bambusicola</i> has gregarious, superficial, ascomata, asci non-amyloid ring, aseptate ascospores, and the basal end sometimes with 1–3 minute mucilaginous drops (Cai <i>et al.</i> 2004). In addition, our new species is distinguished morphologically by its relatively semi-immersed ascomata, 28–30 septate ascospores, and no appendages or mucilaginous sheath, which is different from other species in <i>Linocarpon</i>.</p>Published as part of <i>Zhang, Xian, Dai, Dongqin, Du, Tianye, Karunarathna, Samantha C. & Tibpromma, Saowaluck, 2023, Morpho-phylogeny characterization of Linocarpon bambusina sp. nov. (Linocarpaceae, Chaetosphaeriales) associated with bamboo in Yunnan Province, China, pp. 89-103 in Phytotaxa 584 (2)</i> on pages 95-97, DOI: 10.11646/phytotaxa.584.2.2, <a href="http://zenodo.org/record/7639251">http://zenodo.org/record/7639251</a&gt

Hepatobiliary surgery based on intelligent image segmentation technology

Liver disease is an important disease that seriously threatens human health. It accounts for the highest proportion in various malignant tumors, and its incidence rate and mortality are on the rise, seriously affecting human health. Modern imaging has developed rapidly, but the application of image segmentation in liver tumor surgery is still rare. The application of image processing technology represented by artificial intelligence (AI) in surgery can greatly improve the efficiency of surgery, reduce surgical complications, and reduce the cost of surgery. Hepatocellular carcinoma is the most common malignant tumor in the world, and its mortality is second only to lung cancer. The resection rate of liver cancer surgery is high, and it is a multidisciplinary surgery, so it is necessary to explore the possibility of effective switching between different disciplines. Resection of hepatobiliary and pancreatic tumors is one of the most challenging and lethal surgical procedures. The operation requires a high level of doctors’ experience and understanding of anatomical structures. The surgical segmentation is slow and there may be obvious complications. Therefore, the surgical system needs to make full use of the relevant functions of AI technology and computer vision analysis software, and combine the processing strategy based on image processing algorithm and computer vision analysis model. Intelligent optimization algorithm, also known as modern heuristic algorithm, is an algorithm with global optimization performance, strong universality, and suitable for parallel processing. This algorithm generally has a strict theoretical basis, rather than relying solely on expert experience. In theory, the optimal solution or approximate optimal solution can be found in a certain time. This work studies the hepatobiliary surgery through intelligent image segmentation technology, and analyzes them through intelligent optimization algorithm. The research results showed that when other conditions were the same, there were three patients who had adverse reactions in hepatobiliary surgery through intelligent image segmentation technology, accounting for 10%. The number of patients with adverse reactions in hepatobiliary surgery by conventional methods was nine, accounting for 30%, which was significantly higher than the former, indicating a positive relationship between intelligent image segmentation technology and hepatobiliary surgery

Multi‐state unified calculation model of rail potential and stray current in DC railway systems

Abstract In order to calculate the dynamic distribution of rail potential and stray current accurately, a multi‐state unified calculation model of rail potential and stray current is proposed, and the characteristics of different protection devices and reflux system in multi‐state DC railway systems are analysed. In the proposed model, the multi‐state characteristics of the traction substation, train, protection devices and reflux system are uniformly simulated by unified node models. The mixed simulation parameters of the catenary and reflux system are unified to lumped parameter networks, and the multi‐node voltage equations are established based on the simulation model. Then, the power flow of DC railway systems is calculated using an iterative solution, and the dynamic distribution of rail potential and stray current are solved by differential equations. The multi‐state unified calculation model is built and the distribution of rail potential and stray current in different conditions are simulated based on the parameters of Wuxi Subway Line 2. In order to verify the multi‐state unified calculation model proposed, field tests and simulations are carried out. Results show that the multi‐state unified calculation model can unify the complex states of DC railway systems and calculate the distribution of rail potential and stray current accurately

Morphology and phylogenetic analyses reveal Montagnula puerensis sp. nov. (Didymosphaeriaceae, Pleosporales) from southwest China

Du, Tianye, Hyde, Kevin D., Mapook, Ausana, Mortimer, Peter E., Xu, Jianchu, Karunarathna, Samantha C., Tibpromma, Saowaluck (2021): Morphology and phylogenetic analyses reveal Montagnula puerensis sp. nov. (Didymosphaeriaceae, Pleosporales) from southwest China. Phytotaxa 514 (1): 1-25, DOI: 10.11646/phytotaxa.514.1.1, URL: http://dx.doi.org/10.11646/phytotaxa.514.1.

PNMC: Four-dimensional conebeam CT reconstruction combining prior network and motion compensation

International audienceFour-dimensional conebeam computed tomography (4D CBCT) is an efficient technique to overcome motion artifacts caused by organ motion during breathing. 4D CBCT reconstruction in a single scan usually divides projections into different groups of sparsely sampled data based on the respiratory phases. The reconstructed images within each group present poor image quality due to the limited number of projections. To improve the image quality of 4D CBCT in a single scan, we propose a novel reconstruction scheme that combines prior knowledge with motion compensation. We apply the reconstructed images of the full projections within a single routine as prior knowledge, providing structural information for the network to enhance the restoration structure. The prior network (PN-Net) is proposed to extract features of prior knowledge and fuse them with the sparsely sampled data using an attention mechanism. The prior knowledge guides the reconstruction process to restore the approximate organ structure and alleviates severe streaking artifacts. The deformation vector field (DVF) extracted using deformable image registration among different phases is then applied in the motion-compensated ordered-subset simultaneous algebraic reconstruction algorithm to generate 4D CBCT images. Proposed method has been evaluated using simulated and clinical datasets and has shown promising results by comparative experiment. Compared with previous methods, our approach exhibits significant improvements across various evaluation metrics

Numerical simulation of groundwater in hyporheic zone with coupled parameter stochastic scheme

Groundwater numerical modeling is a crucial scientific tool for understanding groundwater circulation and supporting regional water resource planning and management. The effectiveness of these models depends largely on the accuracy of hydrogeological parameters within aquifers, which are often spatially heterogeneous and randomly distributed due to complex geological and tectonic factors. Traditional modeling approaches frequently overlook this randomness, compromising the precision and resolution of groundwater simulations. This study focuses on a section of the Qingshui River in the Huaihe River Basin. Using field and laboratory data, probability distribution functions for key parameters like hydraulic conductivity, specific yield, and specific storage were developed. These functions were integrated into the groundwater model to reflect the inherent stochastic nature of aquifer properties. This integration significantly enhanced model accuracy, reducing the root mean square error of simulated water levels from 0.47–1.43 m to 0.13–0.16 m and improving the Nash-Sutcliffe efficiency coefficients (NSE) from −2.96–0.73 to 0.94–0.98. Additionally, the model facilitated analysis of the interactions between river and groundwater, particularly in the hyporheic zone, under various scenarios. It identified spatial and temporal variations in groundwater recharge dynamics and delay effects at different distances from the river channel. For instance, recharge rates at 50 m and 150 m from the river were 0.295 m/day and 0.015 m/day, respectively, indicating stronger recharge closer to the river. The study also assessed the impact of varying river flows, riverbed permeability, and irrigation practices on water exchanges between the river and groundwater. These factors were found to significantly influence the intensity of water exchange, seepage, and groundwater reserves. This research provides valuable insights for managing river-groundwater interactions and analyzing the ecological environment of surrounding groundwater systems, underscoring the importance of incorporating stochastic characteristics into groundwater modeling