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Her şey bir göstergedir. Kullandığımız sözcükler, yazdığımız metinler, izlediğimiz film ya da diziler, fotoğraflar veya sosyal medyada kullanılan her bir içeriği ‘gösterge’ olarak değerlendirebiliriz. Bu göstergeler bir temsil olarak bize sürekli bir şeyler söylemektedirler. Bu açıdan düşünüldüğünde çocuklar için hazırlanan çizgi filmlerin de bir gösterge olması kaçınılmazdır. Bundan yola çıkarak, otuz yılı aşkın süredir tüm dünyada gerek televizyonda gerekse sinemalarda güncelliğini kaybetmeden yayınlanan ‘Mutant Ninja Kaplumbağalar’ (Teenage Mutant Ninja Turtles) adlı çizgi filmin de göstergeleri olabileceği düşünülmüştür. Bu nedenle çalışmanın amacı, ‘Mutant Ninja Kaplumbağalar’ adlı çizgi film ve animasyonların nasıl bir anlatısı olduğunu, bu anlatıdaki temsillerin, çizgi film ve animasyonlarda yer alan karakterler ve adları, karakterlerin kullandığı silahlar, yedikleri yiyecekler, giysileri, kullandıkları renkler ve mekanlar aracılığıyla çözümlemektir. Ayrıca, genel olarak Mutant Ninja Kaplumbağalar’da ele alınan konunun neyi temsil ettiği ilgili karakterler aracılığıyla analiz edilmiştir. Çalışmada, göstergebilimsel olarak incelenen her bir göstergenin de belirli kültürel anlamla üretildiği görülmüştür. Rönesans’tan günümüze kadar gelen kültürel miras belirli bir topluma ait kültürü yansıtmaktadır. Dolayısıyla kitle iletişim araçları ile yayılan kültür, kendisinden farklı kültürleri etkisine alabilmektedir. Everything is a sign. We can consider the words we use, the texts we write, the movies or TV series we watch, photos or any content used in social media as a 'sign'. These signs are constantly telling us something as a representation. When considered from this point of view, it is inevitable that cartoons prepared for children will also be an sign. Based on this, it has been thought that the cartoon called 'Teenage Mutant Ninja Turtles', which has been broadcast all over the world for more than thirty years, both on television and in cinemas without losing its currency, may also be signs. For this reason, the aim of the study is to analyze the narrative of the cartoons and animations called 'Teenage Mutant Ninja Turtles' through the representations in this narrative, the characters and their names in the cartoons and animations, the weapons used by the characters, the food they eat, their clothes, the colors and places they use. In addition, what the subject discussed in Mutant Ninja Turtles in general represents was analyzed through the relevant characters. In the study, it has been seen that each sign examined semiotically is produced with a certain cultural meaning. The cultural heritage from the Renaissance to the present reflects the culture of a particular society. Therefore, the culture spread by mass media can influence different cultures from itself.

This paper provides a novel approach to estimating CO₂ emissions with high precision using machine learning based on DPRNNs with NiOA. The data preparation used in the present methodology involves sophisticated stages such as Principal Component Analysis (PCA) as well as Blind Source Separation (BSS) to reduce noise as well as to improve feature selection. This purified input dataset is used in the DPRNNs model, where both short and long-term temporal dependencies in the data are captured well. NiOA is utilized to tune those parameters; as a result, the prediction accuracy is quite spectacular. Experimental results also demonstrate that the proposed NiOA-DPRNNs framework gets the highest value of R 2 (0.9736), lowest error rates and fitness values than other existing models and optimization methods. From the Wilcoxon and ANOVA analyses, one can approve the specificity and consistency of the findings. Liebert and Ruple firmly rethink this rather simple output as a robust theoretic and empirical framework for evaluating and projecting CO 2 emissions; they also view it as a helpful guide for policymakers fighting global warming. Further study can build up this theory to include other greenhouse gases and create methods enabling instantaneous tracking for sophisticated and responsive approaches.

• Plant roots are specialized belowground organs that spatiotemporally shape their development in function of varying soil conditions. This root plasticity relies on intricate molecular networks driven by phytohormones, such as auxin and jasmonate (JA). Loss-of-function of the NOVEL INTERACTOR OF JAZ (NINJA), a core component of the JA signaling pathway, leads to enhanced triterpene biosynthesis, in particular of the thalianol gene cluster, in Arabidopsis thaliana roots. • We have investigated the biological role of thalianol and its derivatives by focusing on Thalianol Synthase (THAS) and Thalianol Acyltransferase 2 (THAA2), two thalianol cluster genes that are upregulated in the roots of ninja mutant plants. THAS and THAA2 activity was investigated in yeast, and metabolite and phenotype profiling of thas and thaa2 loss-of-function plants was carried out. • THAA2 was shown to be responsible for the acetylation of thalianol and its derivatives, both in yeast and in planta. In addition, THAS and THAA2 activity was shown to modulate root development. • Our results indicate that the thalianol pathway is not only controlled by phytohormonal cues, but also may modulate phytohormonal action itself, thereby affecting root development and interaction with the environment.

In light of increased international efforts to combat climate change, sustainable infrastructure is shifting toward green hydrogen produced through renewable-powered electrolysis. Still, it is challenging to forecast the production of green hydrogen because environmental and system factors are variable both in time and space. We introduce a new system that utilizes a Spatio-Temporal Graph Convolutional Network (STGCN) and a novel algorithm, the Ninja Optimization Algorithm (NiOA), to address this issue. Using the framework, binary NiOA performs feature selection, while continuous NiOA optimizes both the model architecture and the number of variables in the data simultaneously. It is clear from the research that forecasting results have shown significant improvement. The STGCN model achieved an R 2 of 0.8769 and an MSE of 0.00375, whereas the STGCN with NiOA reached an R 2 of 0.9815 and an MSE of only . Due to these improvements, adaptive metaheuristics show even greater promise in delivering more accurate forecasting and reduced computational requirements for addressing critical environmental issues. The suggested strategy can be followed repeatedly, providing a solid framework for the effective modeling of renewable energy systems and making green hydrogen projects more dependable.

Soil organic carbon (SOC) plays a critical role in global carbon cycling, influencing climate regulation, soil fertility, and sustainable land management. However, accurate SOC prediction remains a challenging task due to the complex, high-dimensional, and nonlinear nature of soil data. Recent advances in machine learning (ML) have improved SOC estimation, yet these models often suffer from overfitting and computational inefficiency when effective feature selection and hyperparameter tuning are not applied. To address these challenges, we propose a novel integration of the Ninja Optimization Algorithm (NiOA) for simultaneous feature selection and hyperparameter optimization, aimed at enhancing both predictive accuracy and computational efficiency. In our experimental setup, 80% of the dataset was allocated for training and 20% for testing. The baseline Support Vector Machine (SVR) model achieved a mean squared error (MSE) of 0.00513, which was reduced to 0.00011 after applying binary NiOA (bNiOA) for feature selection. After full NiOA-based hyperparameter tuning, the MSE improved further to 7.52 × 1 0 − 7 , corresponding to a 99.98% reduction in prediction error. Thus, the proposed NiOA-enhanced framework demonstrates considerable potential in advancing SOC modeling. It offers a scalable, interpretable, and high-precision solution that can be effectively applied in data-scarce environments, particularly in support of sustainable land management and climate change adaptation strategies.

Key messageNCP1, a NINJA family protein lacking EAR motif, acts as a negative regulator of ABA signaling by interacting with and inhibiting the activity of transcriptional activator ABP9.The phytohormone abscisic acid plays a pivotal role in regulating plant responses to a variety of abiotic stresses including drought and salinity. Maize ABP9 is an ABRE-binding bZIP transcription activator that enhances plant tolerance to multiple stresses by positively regulating ABA signaling, but the molecular mechanism by which ABP9 is regulated in mediating ABA responses remains unknown. Here, we report the identification of an ABP9-interacting protein, named ABP Nine Complex Protein 1 (NCP1) and its functional characterization. NCP1 belongs to the recently identified NINJA family proteins, but lacks the conserved EAR motif, which is a hallmark of this class of transcriptional repressors. In vitro and in vivo assays confirmed that NCP1 physically interacts with ABP9 and that they are co-localized in the nucleus. In addition, NCP1 and ABP9 are similarly induced with similar patterns by ABA treatment and osmotic stress. Interestingly, NCP1 over-expressing Arabidopsis plants exhibited a reduced sensitivity to ABA and decreased drought tolerance. Transient assay in maize protoplasts showed that NCP1 inhibits the activity of ABP9 in activating ABRE-mediated reporter gene expression, a notion further supported by genetic analysis of drought and ABA responses in the transgenic plants over-expressing both ABP9 and NCP1. These data together suggest that NCP1 is a novel negative regulator of ABA signaling via interacting with and inhibiting the activity of ABP9.

The mandate by the Energy Independence and Security Act of 2007 to increase renewable fuel production in the USA has resulted in extensive research into the sustainability of perennial bioenergy crops such as switchgrass (Panicum virgatum) and miscanthus (Miscanthus× giganteus). Perennial grassland crops have been shown to support greater aboveground biodiversity and ecosystem function than annual crops. However, management considerations, such as what crop to plant or whether to use fertilizer, may alter belowground diversity and ecosystem functioning associated with these grasslands as well. In this study, we compared crop type (switchgrass or miscanthus) and nitrogen fertilization effects on arbuscular mycorrhizal fungal (AMF) and soil nematode abundance, activity, and diversity in a long‐term experiment. We quantified AMF root colonization, AMF extra‐radical hyphal length, soil glomalin concentrations, AMF richness and diversity, plant‐parasitic nematode abundance, and nematode family richness and diversity in each treatment. Mycorrhizal activity and diversity were higher with switchgrass than with miscanthus, leading to higher potential soil carbon contributions via increased hyphal growth and glomalin production. Plant‐parasitic nematode (PPN) abundance was 2.3 ×  higher in miscanthus plots compared to switchgrass, mostly due to increases in dagger nematodes (Xiphinema). The higher PPN abundance in miscanthus may be a consequence of lower AMF in this species, as AMF can provide protection against PPN through a variety of mechanisms. Nitrogen fertilization had minor negative effects on AMF and nematode diversity associated with these crops. Overall, we found that crop type and fertilizer application associated with perennial bioenergy cropping systems can have detectable effects on the diversity and composition of soil communities, which may have important consequences for the ecosystem services provided by these systems.

Background The universal qPCR data exchange file format RDML is today well accepted by the scientific community, part of the MIQE guidelines and implemented in many qPCR instruments. With the increased use of RDML new challenges emerge. The flexibility of the RDML format resulted in some implementations that did not meet the expectations of the consortium in the level of support or the use of elements. Results In the current RDML version 1.2 the description of the elements was sharpened. The open source editor RDML-Ninja was released ( http://sourceforge.net/projects/qpcr-ninja/ ). RDML-Ninja allows to visualize, edit and validate RDML files and thus clarifies the use of RDML elements. Furthermore RDML-Ninja serves as reference implementation for RDML and enables migration between RDML versions independent of the instrument software. The database RDMLdb will serve as an online repository for RDML files and facilitate the exchange of RDML data ( http://www.rdmldb.org ). Authors can upload their RDML files and reference them in publications by the unique identifier provided by RDMLdb. The MIQE guidelines propose a rich set of information required to document each qPCR run. RDML provides the vehicle to store and maintain this information and current development aims at further integration of MIQE requirements into the RDML format. Conclusions The editor RDML-Ninja and the database RDMLdb enable scientists to evaluate and exchange qPCR data in the instrument-independent RDML format. We are confident that this infrastructure will build the foundation for standardized qPCR data exchange among scientists, research groups, and during publication.

Purpose There are over 500 medically related applications (apps) for mobile devices. Very few of these applications undergo testing and peer-review for accuracy. The purpose of this study is to assess the accuracy of limb deformity measurements on the Bone Ninja app compared to PACS and to determine the intra- and inter-observer variability among different orthopaedic practitioners. Methods Four participants (attending, senior and junior resident, and physician assistant) measured the leg length (LL), the lateral distal femoral angle (LDFA), and the medial proximal tibial angle (MPTA) of 48 limbs (24 patients), twice with both Bone Ninja and PACS. The difference between the measurements obtained with the Bone Ninja app and PACS were measured. We determined the consistency of the intra-observer intra-class correlation coefficient (ICC) for both systems. Results There were no statistical differences in leg length discrepancy (LLD), MPTA, or LDFA measurements between Bone Ninja and PACS (p = 0.96, 0.87, and 0.97, respectively). The intra-observer ICC for the LL, LDFA, and MPTA was similar between Bone Ninja and PACS (0.83, 0.89, and 0.96 vs. 0.96, 0.93, and 0.95, respectively). The inter-observer ICC was similar between Bone Ninja and PACS (0.95, 0.96, and 0.99 vs. 0.99, 0.98, and 0.98, respectively). Conclusions This study demonstrates that Bone Ninja is an accurate educational tool for measuring LLD, LDFA, and MPTA. Both systems are reliable instruments for evaluating limb length differences and angles on standing radiographs for pre-operative deformity planning and education. This is the first study to evaluate the accuracy of Bone Ninja compared to the gold standard of PACS.