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Although the seasonal prediction skill of climate models has improved significantly in recent decades, the prediction skill of the Arctic Oscillation (AO), the dominant climate mode over the Northern Hemisphere, remains poor. Additionally, the local representation of AO impacts has diverged from observations, which limits seasonal prediction skill of climate models. In this study, we attempted to improve prediction skill of surface air temperature (SAT) with two post-processing on dynamical model’s seasonal forecast: (1) correction of the AO impact on SAT pattern, and (2) correction of AO index (AOI). The first correction involved replacing the inaccurately simulated impact of AO on SAT with that observed. For the second correction, we employed a empirical prediction model of AOI based on multiple linear regression model based on three precursors: summer sea surface temperature, autumn sea-ice concentration, and autumn snow cover extent. The application of the first correction led to a decrease in prediction skills. However, a significant improvement in SAT prediction skills is achieved when both corrections are applied. The average correlation coefficients for the North America and Eurasian regions increased from 0.23 and 0.06 to 0.28 and 0.30, respectively.

This study investigates the sensitivity of the boreal winter prediction skill of Community Atmosphere Model 5 to the choice of the dynamical core. Both finite volume (FV) and spectral element (SE) dynamical cores are tested. An additional FV with the SE topography (FV SE ) is also conducted to isolate the possible influence of the topography. The three dynamical core experiments, which ran from 2001/2002–2017/2018, are validated using Japanese 55 year reanalysis data. It turns out that the SE (−4.27 °C) has a smaller cold bias in boreal-winter surface air temperature (SAT) than the FV (−5.17 °C) and FV SE (−5.29 °C), particularly in North America, East Asia, and Southern Europe/Northern Africa. Significant North Atlantic Oscillation-like biases are also identified in the mid-troposphere. These biases affect seasonal prediction skills. Although the overall prediction skills of boreal-winter SAT, quantified by the anomaly correlation coefficient (ACC), and root-mean-square error (RMSE), are reasonably good (ACC = 0.40 and RMSE = 0.47 in the mean values of SE, FV, and FV SE ), they significantly differ from one region to another, depending on the choice of dynamical cores. For North America and Southern Europe/Northern Africa, SE shows better skills than FV SE and FV. Conversely, in East Asia, FV and FV SE outperform SE. These results suggest that the appropriate choice of the dynamical cores and the bottom boundary conditions could improve the boreal-winter seasonal prediction on a regional scale.

Rosa rugosa is a medicinal plant known for its potential anti-inflammatory, antioxidant, anti-cancer, and antimicrobial benefits. The pharmacological effects of Rosa rugosa extract on hair loss have not yet been documented. This research sought to assess the inhibitory effects and mechanisms of action of Rosa rugosa water extract (RWE) in a mouse model of dihydrotestosterone (DHT)-induced alopecia. The study was conducted using C57BL/6 mice, which were assigned to five groups: control, DHT-treated, Rosa rugosa water extract (RWE) at doses of 25 mg/kg and 100 mg/kg body weight, and bicalutamide-treated. To induce hair loss, dihydrotestosterone (1 mg/day per body weight) was administered via intraperitoneal injections, and dorsal hair removal was timed to align with the telogen phase. Each group received oral treatments for a period of 23 days. In this study, we assessed hair growth activity, examined histological changes, and performed immunoblot analysis. We noted improvements in hair length and thickness. Additionally, the protein expression of growth factors associated with hair growth, including vascular endothelial growth factor (VEGF), epidermal growth factor (EGF), and insulin-like growth factor-1 (IGF-1), showed significant increases in the group treated with RWE. Additionally, treatment with RWE suppressed the protein expression of hair growth inhibitory factors, including dickkopf WNT signaling pathway inhibitor 1 (DKK1) and interleukin (IL)-6. Moreover, hair growth regulatory pathway related factors, including ERK, AKT, and GSK-3β, were activated. These findings indicate that RWE could serve as a promising natural therapy for preventing hair loss by enhancing the production of factors that promote hair growth while inhibiting those that suppress it.

Chronic and extensive exposure of ultraviolet (UV)-irradiation causes human skin sunburn, inflammation, or photoaging, which is associated with downregulated collagen synthesis. This study investigated the effects of fermented blackberry (Rubus fruticosus B., FBB) by Lactobacillus plantarum JBMI F5 (LP) on UVB-induced photoaging in human foreskin fibroblast (Hs68) as well as in SKH-1 hairless mice. FBB pretreatment inhibited UVB-mediated type-1 procollagen degradation, matrix metalloproteinase (MMP)-1 and MMP-2 protein expression, and suppressed nuclear factor-κB (NF-κB) activation as well as mitogen-activated protein kinase (MAPK) phosphorylation in Hs68. In addition, FBB administration diminished the wrinkle formation in dorsal skin and epidermal thickening in UVB-irradiated hairless mice. Moreover, UVB-induced Type-1 procollagen reduction and antioxidant enzyme inactivation were reversed by FBB administration. These results suggest that FBB may have antiphotoaging effects on UVB-induced wrinkle formation by maintaining the extracellular matrix density in the dermis, which occurs via regulation of reactive oxygen species and related MAPK and NF-κB signaling. Therefore, FBB can be a potential candidate for protecting skin aging against UV irradiation.

Exosomes are nano-sized particles with a structure similar to cells, and they are attracting attention as a premium cosmetic raw material because they can be effectively absorbed through skin pores and delivered without decomposing the active ingredients. In this study, the effects of exosome extracts derived from Zanthoxylum piperitum, Lagerstroemia indica, and Pinus densiflora on skin barrier enhancement and moisturizing were evaluated using HaCaT cells. Cell viability was confirmed through MTS assay, and the skin barrier improvement effect was evaluated by analyzing interleukin (IL)-6 expression in an inflammatory response induced by TNFα/IFN-γ. In addition, procollagen, matrix metalloproteinase (MMP)-1, hyaluronic acid, collagenase inhibitory activity, and elastase inhibitory activity were measured to verify the moisturizing effect. The results of the study show that exosome treatment did not affect the viability of HaCaT cells, and the skin barrier improvement effect was confirmed by decreasing IL-6 expression, which increased due to TNF-α/IFN-γ treatment. In addition, after exosome treatment, the expression of procollagen and hyaluronic acid increased, the expression of MMP-1 decreased, and significant improvements in collagenase and elastase inhibitory activities were observed, suggesting a skin moisturizing effect. The results of this study indicate that exosome extracts derived from Pinus densiflora, Zanthoxylum piperitum, and Lagerstroemia indica can contribute to enhancing the skin barrier and moisturizing, providing basic data for the development of exosome-based cosmetic raw materials.

Perilla frutescens (L.) Britt. var. japonica (Hassk.) Hara (PF), is a medical herb of the Lamiaceae family. We have previously reported that the PF sprout extract (PFSE) is effective in treating hyperglycemia. However, the role of PFSE on glomerular mesangial cells (MCs) proliferation and the extracellular matrix (ECM) accumulation in a diabetic condition are still unclear. Therefore, in this study, we have investigated the role of PFSE on cell proliferation and ECM accumulation in murine glomerular MCs (MMCs), cultured under a high glucose (HG) condition. PFSE treatment attenuated HG-induced MMCs proliferation and hypertrophy. Moreover, the HG-induced ECM protein, collagen IV and fibronectin, overexpression was abolished by the PFFSE treatment. In addition, PFSE inhibited reactive oxygen species (ROS) overproduction and NOX2 and NOX4 expression in MMCs under a HG condition. Our data further revealed the involvement of mesangial cell damage in AMP-activated kinase (AMPK) activation. PFSE strongly activated AMPK in MMCs under hyperglycemic conditions. These results suggest that PFSE inhibits HG-medicated MC fibrosis through suppressing the activation of NOX2/4 and the AMPK activation mechanism. PFSE may be useful for the prevention or treatment of diabetic nephropathy.

Blackberries (Rubus fruticosus), which are known to include a variety of bioactive substances, have been extensively studied for their antioxidant properties. Blackberries possess multiple health beneficial effects, including anti-inflammation, anti-atherosclerosis, anti-tumor and immunomodulatory activity. However, the potential biological effects and precise molecular mechanisms of the fermented extracts remain largely unexplored. In this research, we demonstrate the effect of blackberries fermented with Lactobacillus for addressing obesity. We investigated the effect of blackberries fermented by Lactobacillus on mice fed a high-fat (60% kcal) diet for 12 weeks. Fermented blackberry administration reduced the body weight and epididymal fat caused by a high-fat diet compared to the obese group. The triglyceride and total cholesterol, which are blood lipid indicators, and the levels of leptin, which is an insulin resistance indicator, were significantly increased in the obese group but were significantly decreased in the fermented blackberries-treated group. Additionally, the expression of adipogenesis marker proteins, such as CEBPα, PPAR-γ and SREBP-1, was significantly increased in the obese group, whereas it was decreased in the fermented blackberries-treated group. These results suggest that fermented blackberries have a protective effect against high-fat-diet-induced obesity by inhibiting adipogenesis and are a potential candidate for the treatment of obesity.

Cervi cornu extracts have been used in traditional medicine for the treatment of various disorders, including osteoporosis. However, since it is not easy to separate the active ingredients, limited research has been conducted on their functional properties. In this study, we extracted the low-molecular-weight (843 Da) collagen NP-2007 from cervi cornu by enzyme hydrolyzation to enhance absorption and evaluated the therapeutic effect in monosodium iodoacetate-induced rat osteoarthritis (OA) model. NP-2007 was orally administered at 50, 100, and 200 mg/kg for 21 days. We showed that the production of matrix metalloproteinase-2, -3, and -9, decreased after NP-2007 treatment. The levels of tumor necrosis factor (TNF)-α, interleukin (IL)-1β, IL-6, and prostaglandin E2 were also reduced after treatment of NP-2007. Furthermore, the administration of NP-2007 resulted in effective preservation of both the synovial membrane and knee cartilage and significantly decreased the transformation of fibrous tissue. We verified that the treatment of NP-2007 significantly reduced the production of nitric oxide and pro-inflammatory cytokines including TNF-α, IL-1β, and IL-6 in lipopolysaccharides-stimulated RAW 264.7 cells by regulation of the NF-kB and MAPK signaling pathways. This study indicates that NP-2007 can alleviate symptoms of osteoarthritis and can be applied as a novel treatment for OA treatment.

GloSea5, a seasonal forecast system of the UK Met Office, shows reasonable skill among state-of-the-art operational seasonal forecast systems. However, the average surface temperature (T2m) in winter (December–February) of GloSea5 is particularly low in East Asia. To improve the seasonal forecast skill over East Asia, we focused on the high skill score of global teleconnection patterns simulated by GloSea5. Among the well-predicted teleconnection patterns, we selected those highly correlated with the East Asian T2m: East Atlantic (EA), Polar/Eurasia (PE), East Atlantic/Western Russia (EAWR), and West Pacific (WP) patterns. A multiple linear regression model was constructed using the selected teleconnection indices as predictors. These results are promising. The statistical skill-score evaluation of the constructed linear regression model using the anomaly correlation coefficient (ACC), root mean squared error (RMSE), and mean-squared skill score (MSSS) showed an improvement in the predicted T2m of East Asia, where the values of ACC and MSSS increased by 0.25 and 0.37, respectively, and the RMSE decreased by 0.63 compared to the dynamic forecast model results. These results suggest that a well-designed combined statistical and dynamical approach for seasonal prediction can be beneficial for some regions where the predictability of the dynamic model exhibits a low value.

Systems metabolic engineering is facilitating the development of high-performing microbial cell factories for producing chemicals and materials. However, constructing an efficient microbial cell factory still requires exploring and selecting various host strains, as well as identifying the best-suited metabolic engineering strategies, which demand significant time, effort, and costs. Here, we comprehensively evaluate the capacities of various microbial cell factories and propose strategies for systems metabolic engineering steps, including host strain selection, metabolic pathway reconstruction, and metabolic flux optimization. We analyze the metabolic capacities of five representative industrial microorganisms as cell factories for the production of 235 different bio-based chemicals and suggest the most suitable host strain for the corresponding chemical production. To improve the innate metabolic capacity by constructing more efficient metabolic pathways, heterologous metabolic reactions, and cofactor exchanges are systematically analyzed. Additionally, we present metabolic engineering strategies, which include up- and down-regulation target reactions, for the improved production of chemicals. Altogether, this study will serve as a comprehensive resource for the systems metabolic engineering of microorganisms in the bio-based production of chemicals. Constructing an efficient microbial cell factory still requires exploring and selecting various host strains, as well as identifying the best-suited metabolic engineering strategies, which demand significant time, effort, and costs. Here the authors calculate the maximum yields of 235 bio-based chemicals in 5 different microbes, evaluated heterologous reactions and cofactor swaps, and predicted engineering strategies, providing a comprehensive resource for systems metabolic engineering.