The Effects of Cause-Related Marketing on Company and Brand Attitudes

Cause-related marketing, a practice of strategic philanthropy, has gained currency among firms seeking both social and economic benefits simultaneously. Unlike previous findings that have mainly shown the positive effects of cause-related marketing, this study focuses on when cause-related marketing efforts can backfire. Corporate credibility (high/low) and product-cause relatedness (risk related/non-risk related/unrelated) were manipulated so that participants were presented with six different cause-related marketing contexts. According to the results, attitude toward the company was mainly affected by the level of corporate credibility; participants in the low corporate credibility condition showed a less favorable company attitude. In addition to the main effect of corporate credibility, product-cause relatedness determined consumers attitude toward the brand; cause-related marketing adversely affected brand attitude when there was an association between the cause and the products risk

Factors Affecting Online Search Intention and Online Purchase Intention

This research focuses on various factors affecting online search intention which has been found to be a key predictor of online purchase intention. Data were collected from a sample consisting of mostly young adults with familiarity of computer use and online shopping experience. A structural equation model was employed to test hypotheses. According to the findings, utilitarian value of Internet information search, hedonic value of Internet information search, perceived benefits of Internet shopping, perceived risk of Internet shopping, and Internet purchase experience predicted online search intention well. The findings also showed that online search intention positively affects online purchase intention. Finally, theoretical and managerial implications are discussed

The limnological survey of a coastal lagoon in Korea: Lake Hwajinpo

Physicochemical parameters, plankton biomass, and sediment were surveyed from 1998 to 2000 on two months interval in a eutrophic costal lagoon (Lake Hwajinpo, Korea) segregated from the sea by a sand dune. Littoral zone is well developed and floating-leaved aquatic plants also thrive, A shallow sill divides the lake into two basins. It has permeation of seawater and chemoclines formed by salinity were observed at 1m demth all the year around. DO was often very low(＜1mgO₂/L) at hypolimnion. Temperature inversions were observed in November. Transparency was 0.2～1.7m. Nitrate and ammonium concentrations were very low (＜0.1mgN/L), even though TN was usually 2.0～3.5mgN/L. TN/TP was generally lower than the Redfield ratio. TSI was 63～74, COD, TP, and TN of sediment were 3.1～40.3mgO₂/g, 0.9～1.39mg/m³. Two basins showed different phytoplankton communities with Oscillatoria sp., Trachelomonas sp., Schiaochlarnys gekatinosa, and Anabaena spiroides dominant in South basin, and with Trachelomonas sp., Schroederia sp., Schizochlamys felatinosa, and Trachelomonas sp. dominant in the North basin. The seasonal succession of phytoplankton was very fast, possibly due to sudden changes in physical characteristics such as wind, turbidity, salinity and light, etc.Article信州大学山地水環境教育研究センター研究報告 2: 127-130(2004)departmental bulletin pape

Editorial: Advances in deep learning methods for medical image analysis

The rapid development of artificial intelligence (AI) technology is leading many innovations in the medical field and is playing a major role in establishing objective, consistent, and efficient medical environments with large-scale data. Deep learning represented by convolutional neural networks has achieved remarkable performance improvement in medical image processing fields such as image segmentation, registration, and enhancement. Furthermore, AI technology with deep learning is pioneering medical applications, such as lesion detection, differential diagnosis, disease prognosis, and surgical planning. More advanced AI technologies, such as transformers with self-attention mechanisms, allowing for learning global dependencies, have been widely applied, which further enhanced the capability of deep learning to analyze medical images. However, despite the remarkable advances in deep learning, many challenges remain. For example, when training data are biased or incomplete, deep learning models may fail to achieve the good generalization capability required to solve real-world problems. In addition, the limitations of deep learning models in interpreting results, and misunderstandings of their intended uses and hypotheses make it difficult for AI to gain trust in healthcare settings. In this regard, disease-specific neural networks, generalized learning methods, high-quality training data, and external evaluation based on testable hypotheses can ensure the reliability of medical AI technologies for humans

Differentiated function and localisation of SPO11-1 and PRD3 on the chromosome axis during meiotic DSB formation in Arabidopsis thaliana

During meiosis, DNA double-strand breaks (DSBs) occur throughout the genome, a subset of which are repaired to form reciprocal crossovers between chromosomes. Crossovers are essential to ensure balanced chromosome segregation and to create new combinations of genetic variation. Meiotic DSBs are formed by a topoisomerase-VI-like complex, containing catalytic (e.g. SPO11) proteins and auxiliary (e.g. PRD3) proteins. Meiotic DSBs are formed in chromatin loops tethered to a linear chromosome axis, but the interrelationship between DSB-promoting factors and the axis is not fully understood. Here, we study the localisation of SPO11-1 and PRD3 during meiosis, and investigate their respective functions in relation to the chromosome axis. Using immunocytogenetics, we observed that the localisation of SPO11-1 overlaps relatively weakly with the chromosome axis and RAD51, a marker of meiotic DSBs, and that SPO11-1 recruitment to chromatin is genetically independent of the axis. In contrast, PRD3 localisation correlates more strongly with RAD51 and the chromosome axis. This indicates that PRD3 likely forms a functional link between SPO11-1 and the chromosome axis to promote meiotic DSB formation. We also uncovered a new function of SPO11-1 in the nucleation of the synaptonemal complex protein ZYP1. We demonstrate that chromosome co-alignment associated with ZYP1 deposition can occur in the absence of DSBs, and is dependent on SPO11-1, but not PRD3. Lastly, we show that the progression of meiosis is influenced by the presence of aberrant chromosomal connections, but not by the absence of DSBs or synapsis. Altogether, our study provides mechanistic insights into the control of meiotic DSB formation and reveals diverse functional interactions between SPO11-1, PRD3 and the chromosome axis

Editorial: Advances in deep learning methods for medical image analysis

The rapid development of artificial intelligence (AI) technology is leading many innovations in the medical field and is playing a major role in establishing objective, consistent, and efficient medical environments with large-scale data. Deep learning represented by convolutional neural networks has achieved remarkable performance improvement in medical image processing fields such as image segmentation, registration, and enhancement. Furthermore, AI technology with deep learning is pioneering medical applications, such as lesion detection, differential diagnosis, disease prognosis, and surgical planning. More advanced AI technologies, such as transformers with self-attention mechanisms, allowing for learning global dependencies, have been widely applied, which further enhanced the capability of deep learning to analyze medical images. However, despite the remarkable advances in deep learning, many challenges remain. For example, when training data are biased or incomplete, deep learning models may fail to achieve the good generalization capability required to solve real-world problems. In addition, the limitations of deep learning models in interpreting results, and misunderstandings of their intended uses and hypotheses make it difficult for AI to gain trust in healthcare settings. In this regard, disease-specific neural networks, generalized learning methods, high-quality training data, and external evaluation based on testable hypotheses can ensure the reliability of medical AI technologies for humans

Nonlinear Color-Metallicity Relations of Globular Clusters. III. On the Discrepancy in Metallicity between Globular Cluster Systems and their Parent Elliptical Galaxies

One of the conundrums in extragalactic astronomy is the discrepancy in observed metallicity distribution functions (MDFs) between the two prime stellar components of early-type galaxies-globular clusters (GCs) and halo field stars. This is generally taken as evidence of highly decoupled evolutionary histories between GC systems and their parent galaxies. Here we show, however, that new developments in linking the observed GC colors to their intrinsic metallicities suggest nonlinear color-to-metallicity conversions, which translate observed color distributions into strongly-peaked, unimodal MDFs with broad metal-poor tails. Remarkably, the inferred GC MDFs are similar to the MDFs of resolved field stars in nearby elliptical galaxies and those produced by chemical evolution models of galaxies. The GC MDF shape, characterized by a sharp peak with a metal-poor tail, indicates a virtually continuous chemical enrichment with a relatively short timescale. The characteristic shape emerges across three orders of magnitude in the host galaxy mass, suggesting a universal process of chemical enrichment among various GC systems. Given that GCs are bluer than field stars within the same galaxy, it is plausible that the chemical enrichment processes of GCs ceased somewhat earlier than that of field stellar population, and if so, GCs preferentially trace the major, vigorous mode of star formation events in galactic formation. We further suggest a possible systematic age difference among GC systems, in that the GC systems in more luminous galaxies are older. This is consistent with the downsizing paradigm of galaxies and supports additionally the similar nature shared by GCs and field stars. Our findings suggest that GC systems and their parent galaxies have shared a more common origin than previously thought, and hence greatly simplify theories of galaxy formation.Comment: 55 pages, 7 figures, 5 tables; Accepted for publication in Ap