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Folk dances have been classified in various ways in all cultures around the world. War-themed dances occupy a special place in these classifications. The factor that makes war dances special is that they are a powerful tool that holds the spirit of the society they belong to and strengthens the sense of unity. For example, it has been seen that the stance of a hero or the memory of war, which is ingrained in the memory of a society, shapes the stance of all members of that society. Sword dances, one of the armed dances included in war dances, are seen as a symbol of heroism in many cultures around the world. Since that the effect of the sword is directly based on the power of the wrist, the dances performed with the sword have deeply affected the audience as a sign of bravery and valor since ancient times. Sword dances, which are the most common type of war dances, are sometimes performed solo by one person, sometimes by two people, and sometimes by a large group of dancers in opposing lines or in a circle. Sword dances, which are also seen in ancient Anatolian civilizations, are important in terms of forming the origin of European sword dances. Because such dances appear in many cultures ranging from the Hittites to the Phrygians, the Lydians to the Hellenistic period, and the Byzantine civilization. The presence of sword dances is also seen in today’s Anatolian Turkish culture, which was formed by the combination of the “Steppe” culture, which the Turks brought with them after the conquest of Anatolia, with the Mediterranean and Mesopotamian cultures. The main purpose of the study is to reveal what the sword dances seen in today’s Anatolia are and how they are distributed throughout Anatolia. In line with this purpose, in this study, sharp weapons such as wedges, machetes, yams, daggers and knives are explained under the heading of swords in a way that limits them to Anatolian geography. In the study, the document analysis technique, which includes the analysis and analysis of written and visual materials related to the subject, was used. In addition, the images of the related dances were included in the study with the QR code system and the similar features and differences of these dances were discussed separately.

The cyclic GMP-AMP synthase (cGAS)-stimulator of interferon genes (STING) signaling pathway serves as an immune sentinel for cytosolic DNA, recognizing double-stranded DNA (dsDNA) derived from abnormally localized nuclear DNA or mitochondrial DNA (mtDNA), and plays a pivotal role in innate immune responses and tumor immune surveillance. Conventional antitumor therapies induce genomic instability and mitochondrial stress, leading to the release of nuclear DNA and mtDNA into the cytosol, thereby activating the cGAS-STING pathway. This activation triggers the production of type I interferons (IFN-I) and pro-inflammatory cytokines, which reshape the tumor immune microenvironment (TIME). However, the complexity of TIME reveals a “double-edged sword” effect of cGAS-STING signaling: while it activates antitumor immune responses, it also promotes immune escape and metastasis through the regulation of immunosuppressive cells and stromal components. This review comprehensively delineates the differential regulatory mechanisms of the pathway within TIME constituents, highlighting its multifaceted roles in tumor immunity. Furthermore, it reviews recent advances and challenges in targeting the cGAS-STING pathway for cancer immunotherapy, with the aim of advancing cGAS-STING signaling modulation as a key therapeutic strategy to reprogram TIME and overcome immunosuppression in antitumor treatment.

Different regions of the human cerebral cortex are specialized for different emotions, but the principles underlying this specialization have remained unknown. According to the sword and shield hypothesis, hemispheric specialization for affective motivation, a basic dimension of human emotion, varies across individuals according to the way they use their hands to perform approach- and avoidance-related actions. In a test of this hypothesis, here we measured approach motivation before and after five sessions of transcranial direct current stimulation to increase excitation in the left or right dorsolateral prefrontal cortex, in healthy adults whose handedness ranged from strongly left-handed to strongly right-handed. The strength and direction of participants' handedness predicted whether electrical stimulation to frontal cortex caused an increase or decrease in their experience of approach-related emotions. The organization of approach motivation in the human cerebral cortex varies across individuals as predicted by the organization of the individuals’ motor systems. These results show that the large-scale cortical organization of abstract concepts corresponds with the way people use their hands to interact with the world. Affective motivation may re-use neural circuits that evolved for performing approach- and avoidance-related motor actions. This article is part of the theme issue ‘Varieties of abstract concepts: development, use and representation in the brain’.

To improve our understanding of the bright side and the dark side of political ties and determine the processes linking political ties to firm performance in emerging markets, we investigate the underlying mechanism of political ties’ effects from the perspective of dynamic capability theory and institutional theory. We posit that reduced market-focused innovation capability and strengthened legitimacy mediate the effect of political ties on firm performance. In addition, to capture the nature of the relationship between political ties and performance, we adopt a contingency perspective in our examination of the moderating roles of legal enforceability and competitive intensity. Specifically, we suggest that legal enforceability buffers the negative impact of political ties on market-focused innovation capability but mitigates the positive impact of political ties on firm legitimacy. Moreover, competitive intensity enhances the positive impact of market-focused innovation capability and firm legitimacy on firm performance. We test our hypotheses using a survey with 362 respondents in China. In conclusion, our findings provide important insights into how Chinese firms effectively utilize political ties to improve their performance.

Allergy is an immunoglobulin E (IgE)-mediated process, and its incidence and prevalence have increased worldwide in recent years. Therapeutic agents for allergic diseases are continuously being developed, but side effects follow when used for a long-term use. Therefore, treatments based on natural products that are safe for the body are urgently required. Sword bean (Canavalia gladiata) pod (SBP) has been traditionally used to treat inflammatory diseases, but there is still no scientific basis for its anti-allergic effect. Accordingly, this study investigates the anti-allergic effect and its mechanism of SBP in vitro and in vivo. SBP reduced the nitric oxide production and decreased mRNA and protein expression of inflammatory mediates (inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2)), and inhibited the phosphorylation of nuclear factor kappa B (NF-κB), a major signaling molecule in the inflammatory response. Additionally, SBP extract treatment inhibited phosphatidylinositol-3-kinase/mammalian target of rapamycin (PI3K/mTOR) signaling activity to further inhibit degranulation and allergy mediator generation and control the balance of Th1/Th2 cells, which can induce an allergic reaction when disrupted. Furthermore, the SBP extract exhibited anti-allergic effects in anti-dinitrophenyl IgE-induced RBL-2H3 cells and ovalbumin-treated mice. These findings have potential clinical implications for the treatment as well as prevention of allergic diseases.

Accurate streamflow prediction in human‐regulated catchments remains a formidable challenge due to the complex disturbance of hydrological processes. To consider human disturbance in hydrological modeling, this study introduces a novel static attribute collection that combines river‐reach attributes with catchment attributes, referred to as multiscale attributes. The attribute collection is assembled into two deep learning (DL) methods, that is, the Long Short‐Term Memory (named as Multiscale LSTM) and the Differentiable Parameter Learning (DPL) model, and the performance is evaluated across 95 human‐regulated catchments in the United States (USA) and 24 catchments in the Yellow River Basin in China. In the USA, the Multiscale LSTM and the DPL models achieve similar performance with median Kling‐Gupta Efficiency (KGE) of 0.78 and 0.71, respectively. However, in the Yellow River Basin, the KGE values are 0.58 for Multiscale LSTM and 0.24 for DPL. These results highlight the DL models' ability to leverage multiscale attributes for improved performance compared to traditional catchment attributes. The performance of Multiscale LSTM and DPL models is predominantly influenced by river‐scale attributes, encompassing factors such as connectivity status index (CSI), degree of regulation (DOR), sediment trapping (SED), and number of dams. Additionally, satellite‐derived attributes such as mean and maximum river width (Width), slope and mean water surface elevation (WSE) from the Surface Water and Ocean Topography River Database (SWORD) contribute valuable insights into anthropogenic influences. Moreover, our study highlights the significance of selecting the appropriate training data period, which emerges as the most dominant factor affecting model performance across human‐regulated catchments. The diversity of data during the training period enables the model to capture a broad spectrum of hydrological signatures within these catchments. Consequently, this study emphasizes the advantages of Multiscale LSTM and underscores the significance of considering both natural and anthropogenic signatures to enhance hydrological predictions within human‐regulated environments. Plain Language Summary Understanding how water flows in rivers is crucial for managing reservoirs, preventing floods, and making smart decisions about water use in human‐regulated catchments. Previous research using data‐driven models has predominantly focused on long short‐term memory (LSTM) and differentiable parameter learning (DPL) models in natural catchments with minimal human influences. Identifying the effectiveness of these models in human‐regulated catchments has posed a significant challenge. To address this gap, we intentionally compared the performance of LSTM and DPL models, incorporating multiscale attributes as inputs. The results underscore the significant superiority of the LSTM model over the DPL model in human‐regulated catchments, emphasizing the crucial role of river attributes in enhancing model performance. Additionally, we observed that the DPL model exhibited higher sensitivity to the dynamics of human activities, and struggles to provide accurate simulations during periods of elevated human impacts. These findings elucidate the divergent capabilities of these models in representing hydrological processes in human‐regulated catchments. Key Points Long Short‐Term Memory is able to capture irregular streamflow patterns by utilizing a novel collection of multiscale attributes as input The performance of the model is extremely sensitive to the training data period across human‐regulated catchments Differentiable Parameter Learning may be susceptible to the dynamics of human activities, and struggles to provide accurate simulations

Based on panel data from 30 provinces in China, this paper uses a two-way fixed effect model to empirically test the influence of regional digital economy development on the level of low-carbon, inclusive growth. The empirical study shows that: (1) The digital economy has a significant inverted U-shaped impact on China’s regional low-carbon, inclusive growth. It shows that regional digital economic development has a significant inverted U-shaped impact on low-carbon, inclusive growth (the inflection point is 0.3081), and it was found that most of the observations fall on the left side of the inverted U shape. (2) The inverted U-shaped influence has significant heterogeneity in the regional location, information degree, and factor productivity level. (3) The digital economy promotes low-carbon, inclusive growth mainly by improving the overall efficiency of source allocation, but low-carbon, inclusive growth may be curbed by distorting the allocation of capital elements. (4) Via dimensionality reduction analysis, we found that the inverted U-shaped impact of digital applications and digital finance on regional low-carbon, inclusive growth is more obvious. In addition, we also found that the inverted U-shaped impact of regional digital economic development on low-carbon ecology and social inclusiveness is more obvious. This study provides an important reference value for relevant departments to formulate low-carbon, inclusive development policies from the perspective of regional digital economic development.

The recently launched Surface Water and Ocean Topography (SWOT) Mission is expected to provide transformative observations of water surface elevation, width, and slope and produce derived estimates of discharge for global rivers along rivers in the SWOT River Database (SWORD). However, the hydrographic representation of rivers in SWORD differs from hydrography data sets commonly used for modeling purposes, such as Multi-Error-Removed Improved Terrain (MERIT)-Basins. Here, we develop links between the river networks of SWORD and MERIT-Basins (MB) to enable interoperability between SWOT data products and hydrologic modeling frameworks. This data set, termed MERIT-SWORD, identifies a subset of ∼277,000 global MB river reaches that most closely represent the location and extent of the SWORD river network and establishes bidirectional, one-to-many translations between reaches in the two hydrographic data sets. The MERIT-SWORD data set serves to unite SWOT observations with river routing models, allowing for the seamless and standardized assimilation of SWOT vector products into global river simulations and the provision of improved a priori discharge estimates for SWOT discharge computation. Plain Language SummaryThe Surface Water and Ocean Topography (SWOT) Mission observes the water surface elevation, width, and slope of river reaches described in the SWOT River Database (SWORD). The location and extent of rivers in SWORD differ considerably from other river network data sets that are commonly used for hydrological modeling such as Multi-Error-Removed Improved Terrain (MERIT)-Basins. Here, we present and publicly share the MERIT-SWORD data set which links rivers in SWORD to rivers in MERIT-Basins (MB), and vice versa. These links between river reaches in the two data sets can allow for model simulations to be used as a first guess for SWOT measurements, and for observations from SWOT to be easily transferred to hydrologic modeling frameworks based on the MB river network for data assimilation.