bluuebunny/crossref_metadata_2025_split huggingface dataset:
TheivorylncRNA regulates seasonal color patterns in buckeye butterflies
Authors: Fandino Richard A. , Brady Noah K. , Chatterjee Martik , McDonald Jeanne M. C. , Livraghi Luca , van der Burg Karin R. L. , Mazo-Vargas Anyi , Markenscoff-Papadimitriou Eirene , Reed Robert D. 
Published: February 2024

AbstractLong non-coding RNAs (lncRNAs) are transcribed elements increasingly recognized for their roles in regulating gene expression. Thus far, however, we have little understanding of how lncRNAs contribute to evolution and adaptation. Here we show that a conserved lncRNA,ivory, is an important color patterning gene in the buckeye butterflyJunonia coenia.ivoryoverlaps withcortex, a locus linked to multiple cases of crypsis and mimicry in Lepidoptera. Along with a companion paper by Livraghi et. al., we argue thativory, notcortex, is the color pattern gene of interest at this locus. InJ. coeniaa cluster ofcis-regulatory elements (CREs) in the first intron ofivoryare genetically associated with natural variation in seasonal color pattern plasticity, and targeted deletions of these CREs phenocopy seasonal phenotypes. Deletions of differentivoryCREs produce other distinct phenotypes as well, including loss of melanic eyespot rings, and positive and negative changes in overall wing pigmentation. We show that the color pattern transcription factors Spineless, Bric-a-brac, and Ftz-f1 bind to theivorypromoter during wing pattern development, suggesting that they directly regulateivory. This case study demonstrates howcis-regulation of a single non-coding RNA can exert diverse and nuanced effects on the evolution and development of color patterns, including modulating seasonally plastic color patterns.SignificanceThe genomic locus hosting thecortexgene has been linked to numerous cases of color pattern adaptation in moths and butterflies, including crypsis, mimicry, and seasonal polyphenism. Here we show in buckeye butterflies that the actual color pattern gene at thecortexlocus is an evolutionarily conserved long non-coding RNA (lncRNA), dubbedivory, that overlaps withcortex. Compared with other wing pattern genes,ivorystands out because of the highly nuanced, quantitative changes in pigmentation that can be achieved by manipulating adjacentcis-regulatory sequences. This study highlights how lncRNAs can be important factors underlying morphological evolution, and emphasizes the importance of considering non-coding transcripts in comparative genomics.
Reovirus infection induces transcriptome-wide unique A-to-I editing changes in the murine fibroblasts
Authors: Tariq Ayesha , Piontkivska Helen 
Published: February 2024

AbstractThe conversion of Adenosine (A) to Inosine (I), by Adenosine Deaminases Acting on RNA or ADARs, is an essential post-transcriptional modification that contributes to proteome diversity and regulation in metazoans including humans. In addition to its transcriptome-regulating role, ADARs also play a major part in immune response to viral infection, where an interferon response activates interferon-stimulated genes, such as ADARp150, in turn dynamically regulating host-virus interactions. A previous report has shown that infection from reoviruses, despite strong activation of ADARp150, does not influence the editing of some of the major known editing targets, while likely editing others, suggesting a potentially nuanced editing pattern that may depend on different factors. However, the results were based on a handful of selected editing sites and did not cover the entire transcriptome. Thus, to determine whether and how reovirus infection specifically affects host ADAR editing patterns, we analyzed a publicly available deep-sequenced RNA-seq dataset, from murine fibroblasts infected with wild-type and mutant reovirus strains that allowed us to examine changes in editing patterns on a transcriptome-wide scale. To the best of our knowledge, this is the first transcriptome-wide report on host editing changes after reovirus infection. Our results demonstrate that reovirus infection induces unique nuanced editing changes in the host, including introducing sites uniquely edited in infected samples. Genes with edited sites are overrepresented in pathways related to immune regulation, cellular signaling, metabolism, and growth. Moreover, a shift in editing targets has also been observed, where the same genes are edited in infection and control conditions but at different sites, or where the editing rate is increased for some and decreased for other differential targets, supporting the hypothesis of dynamic and condition-specific editing by ADARs.
The Cas10 nuclease activity relieves host dormancy to facilitate spacer acquisition and retention during type III-A CRISPR immunity
Authors: Aviram Naama , Shilton Amanda K. , Lyn Nia G. , Reis Bernardo S. , Brivanlou Amir , Marraffini Luciano A. 
Published: February 2024

ABSTRACTA hallmark of CRISPR immunity is the acquisition of short viral DNA sequences, known as spacers, that are transcribed into guide RNAs to recognize complementary sequences. The staphylococcal type III-A CRISPR-Cas system uses guide RNAs to locate viral transcripts and start a response that displays two mechanisms of immunity. When immunity is triggered by an early-expressed phage RNA, degradation of viral ssDNA can cure the host from infection. In contrast, when the RNA guide targets a late-expressed transcript, defense requires the activity of Csm6, a non-specific RNase. Here we show that Csm6 triggers a growth arrest of the host that provides immunity at the population level which hinders viral propagation to allow the replication of non-infected cells. We demonstrate that this mechanism leads to defense against not only the target phage but also other viruses present in the population that fail to replicate in the arrested cells. On the other hand, dormancy limits the acquisition and retention of spacers that trigger it. We found that the ssDNase activity of type III-A systems is required for the re-growth of a subset of the arrested cells, presumably through the degradation of the phage DNA, ending target transcription and inactivating the immune response. Altogether, our work reveals a built-in mechanism within type III-A CRISPR-Cas systems that allows the exit from dormancy needed for the subsistence of spacers that provide broad-spectrum immunity.
Nanoscale interaction of endonuclease APE-1 with DNA characterized by atomic force microscopy
Authors: Vemulapalli Sridhar , Hashemi Mohtadin , Chen Yinglink , Pramanik Suravi , Bhakat Kishor K. , Lyubchenko Yuri L. 
Published: February 2024

AbstractApurinic/apyrimidinic endonuclease 1 (APE1) is involved in DNA replication, repair, and transcriptional regulation mechanisms. This multifunctional activity of APE1 should be supported by specific structural properties of APE1 that have not yet been elucidated. Here we applied atomic force microscopy (AFM) to characterize the interactions of APE1 with DNA. Complexes of APE1 with DNA containing G-rich segments were visualized, and analysis of the complexes revealed the affinity of APE1 to G-rich DNA sequences. Furthermore, loops in the DNA-APE1 complexes were visualized, and their yield was as high as 53 %. However, the loops were non-specific, with quantitative analysis revealing the yield of loops bridging two G-rich DNA segments to be 41%. Analysis of protein size in various complexes was performed, and these data showed that loops are formed by APE1 monomer, suggesting that APE1 has two DNA binding sites. The data lead us to a model for the interaction of APE1 with DNA that describes its molecular site search mechanism. The new properties of APE1 in organizing DNA, by bringing two distant sites together, may be important for facilitating the scanning for damage and coordinating repair and transcription.
Contrastive Fitness Learning: Reprogramming Protein Language Models for Low-NLearning of Protein Fitness Landscape
Authors: Zhao Junming , Zhang Chao , Luo Yunan 
Published: February 2024

AbstractMachine learning (ML) is revolutionizing our ability to model the fitness landscape of protein sequences, which is critical to answering fundamental life science questions and addressing important protein engineering applications, such as quantifying the pathogenicity of disease variants, forecasting viral evolution in a pandemic, and engineering new antibodies. Recently, the protein language model (pLM) has emerged as an effective ML tool in deciphering the intrinsic semantics of protein sequences and become the foundation of state-of-the-art ML solutions for many problems in protein biology. However, significant challenges remain in leveraging pLMs for protein fitness prediction, in part due to the disparity between the scarce number of sequences functionally characterized by high-throughput assays and the massive data samples required for training large pLMs. To bridge this gap, we introduce Contrastive Fitness Learning (ConFit), a pLM-based ML method for learning the protein fitness landscape with limited experimental fitness measurements as training data. We propose a novel contrastive learning strategy to fine-tune the pre-trained pLM, tailoring it to achieve protein-specific fitness prediction while avoiding overfitting, even when using a small number (low-N) of functionally assayed mutant sequences for supervised fine-tuning. Evaluated across over 30 benchmark datasets of protein fitness, ConFit consistently provided accurate fitness predictions and outperformed several competitive baseline methods. Further analysis revealed that ConFit’s capability of low-Nlearning enabled sample-efficient active learning for identifying high-fitness protein variants. Collectively, our work represents a novel strategy to harness the potential of pLMs to elucidate the protein sequence-function relationship. The source code of ConFit is available athttps://github.com/luo-group/ConFit.
The curious case of Hotel King’s Cross
Authors: Satpute Pratik , Bapat Gautam Surendra 
Published: February 2024

Learning outcomes
The learning outcomes of this study are to recall the fundamental concept of revenue management in the hotel industry (remembering); explain the various performance measures used to evaluate room revenue in hotels (understanding); use revenue management strategies to improve room revenue in hotel operations (applying); and examine and evaluate the optimal solution for revenue enhancement, considering factors such as capacity management, duration control and differential pricing (analyzing).

Case overview/synopsis
This case study delves into the challenges faced by Hotel King’s Cross, a business hotel located in Pune, Maharashtra, in the year 2022. A week before Christmas Eve, Soham Dande, the hotel’s revenue manager, sought a meeting with Rohan Chopra, the director of sales and marketing, to discuss “revenue optimization for the hotel.”
During their meeting, Dande mentioned that the hotel had fallen behind its budgeted room sales targets for 2022 across various metrics, such as room booking nights, occupancy percentage, average room rate and revenue per available room. Furthermore, the hotel was trailing behind its competitors. The situation was compounded by the management’s decision to raise the targets for 2023 by 5%–7%, factoring in upcoming events, competitive performance and pandemic-related losses over the past two years. Chopra faced the dilemma of formulating an action plan to achieve the ambitious 2023 targets and establish Hotel King’s Cross as a market leader.

Complexity academic level
Students undertaking executive development programs and graduate-level courses in non-profit hospitality and tourism management, as well as revenue management courses in the executive MBA, management development and graduate MBA programs, may all benefit from this case study.

Supplementary materials
Teaching notes are available for educators only.

Subject code
CSS12: Tourism and hospitality.
Exploratory search in information systems: a systematic review
Authors: Liu Yaxi , Qin Chunxiu , Wang Yulong , Ma XuBu 
Published: February 2024

Purpose
Exploratory search activities are ubiquitous in various information systems. Much potentially useful or even serendipitous information is discovered during the exploratory search process. Given its irreplaceable role in information systems, exploratory search has attracted growing attention from the information system community. Since few studies have methodically reviewed current publications, researchers and practitioners are unable to take full advantage of existing achievements, which, in turn, limits their progress in this field. Through a literature review, this study aims to recapitulate important research topics of exploratory search in information systems, providing a research landscape of exploratory search.

Design/methodology/approach
Automatic and manual searches were performed on seven reputable databases to collect relevant literature published between January 2005 and July 2023. The literature pool contains 146 primary studies on exploratory search in information system research.

Findings
This study recapitulated five important topics of exploratory search, namely, conceptual frameworks, theoretical frameworks, influencing factors, design features and evaluation metrics. Moreover, this review revealed research gaps in current studies and proposed a knowledge framework and a research agenda for future studies.

Originality/value
This study has important implications for beginners to quickly get a snapshot of exploratory search studies, for researchers to re-align current research or discover new interesting issues, and for practitioners to design information systems that support exploratory search.
Point and interval forecasting for wine prices: an approach based on artificial intelligence
Authors: Cui Huiyu , Guo Honggang , Wang Jianzhou , Wang Yong 
Published: February 2024

Purpose
With the rise in wine consumption, accurate wine price forecasts have significantly impacted restaurant and hotel purchasing decisions and inventory management. This study aims to develop a precise and effective wine price point and interval forecasting model.

Design/methodology/approach
The proposed forecast model uses an improved hybrid kernel extreme learning machine with an attention mechanism and a multi-objective swarm intelligent optimization algorithm to produce more accurate price estimates. To the best of the authors’ knowledge, this is the first attempt at applying artificial intelligence techniques to improve wine price prediction. Additionally, an effective method for predicting price intervals was constructed by leveraging the characteristics of the error distribution. This approach facilitates quantifying the uncertainty of wine price fluctuations, thus rendering decision-making by relevant practitioners more reliable and controllable.

Findings
The empirical findings indicated that the proposed forecast model provides accurate wine price predictions and reliable uncertainty analysis results. Compared with the benchmark models, the proposed model exhibited superiority in both one-step- and multi-step-ahead forecasts. Meanwhile, the model provides new evidence from artificial intelligence to explain wine prices and understand their driving factors.

Originality/value
This study is a pioneering attempt to evaluate the applicability and effectiveness of advanced artificial intelligence techniques in wine price forecasts. The proposed forecast model not only provides useful options for wine price forecasting but also introduces an innovative addition to existing forecasting research methods and literature.
A study on factors affecting Japanese consumer’s behavior toward 5G smartphone purchase
Authors: Fukui Mikihiro , Tan Caroline S.L. 
Published: February 2024

Purpose
The purpose of this study is to analyze the determinants influencing Japanese consumers’ behavior toward the purchase of 5G smartphones.

Design/methodology/approach
Empirical data was collected through an online survey of 320 Japanese respondents from ages 20 to 59. Data was analyzed by exploratory factor analysis, confirmatory factor analysis and multiple regression analysis.

Findings
The results indicate that hedonic value, user interface and price value (PV) positively influence attitude toward 5G smartphone purchase, while utilitarian value does not. In addition, the mediation effect of social influence (SI) is found to increase those positive effects. Male and female consumers displayed differences in the determinants, showcasing the effect of gender on attitude and behavior.

Originality/value
To the best of the authors’ knowledge, this study is the first research to analyze Japanese consumers’ behaviors’ toward 5G smartphone purchase. Past research for 3G/4G smartphones supported that usefulness positively influenced consumers’ behavior toward smartphones, so usefulness of 5G (e.g. high speed) was expected to be a factor to influence consumer behaviors’ toward 5G smartphones. However, this research reveals that a simple tagline emphasizing such usefulness may not be convincing enough to promote the 5G smartphones to Japanese consumers. Alternatively, the research indicates that introducing the concepts of joyfulness, improved HV and greater PV of the 5G smartphone could attract more consumers in Japan. In addition, SI could enhance the perception of the various benefits of 5G smartphones.
Strategic leadership and transactional leadership: the mediating effect of digital leadership in the world of Industry 4.0
Authors: Avwokeni Arthur Joseph 
Published: February 2024

PurposeThe dearth of leadership competencies to transform traditional industries to Industry 4.0 is a barrier to global production. This study explains the deficiencies in leadership competencies that hinder the transformation of traditional industries to Industry 4.0.Design/methodology/approachLeadership was explained into transactional leadership, digital leadership and Leadership 4.0. Then, the network of relationships between these leadership constructs was plotted in a path diagram to learn the mediating effect of digital leadership.FindingsThe results indicate that a lack of digital competencies to coordinate tasks, share information and solve problems in a digitalized environment is the barrier to the transformation.Practical implicationsThe findings can be used in human resources (HR) management. In addition, the findings provide evidence to present the contingency theory as a universal theory of leadership.Originality/valueThe study is the first to assess the mediating effect of digital leadership on transactional leadership to explain the changes to strategic leadership due to the emergence of Leadership 4.0.
Knowledge management and career readiness: a review and synthesis
Authors: Raut Sachin Kumar , Alon Ilan , Rana Sudhir , Kathuria Sakshi 
Published: February 2024

Purpose
This study aims to examine the relationship between knowledge management and career development in an era characterized by high levels of youth unemployment and a demand for specialized skills. Despite the increasing transition to a knowledge-based economy, there is a significant gap between young people’s skills and career readiness, necessitating an in-depth analysis of the role of knowledge management at the individual, organizational and national levels.

Design/methodology/approach
The authors conducted a qualitative study using the theory-context-characteristics-methodology approach based on a systematic literature review. The authors created an ecological framework for reflecting on knowledge management and career development, arguing for a multidisciplinary approach that invites collaboration across sectors to generate innovative and reliable solutions.

Findings
This study presents a comprehensive review of the existing literature and trends, noting the need for more focus on the interplay between knowledge management and career development. It emphasizes the need for businesses to promote the acquisition, storage, diffusion and application of knowledge and its circulation and exchange to create international business human capital.

Practical implications
The findings may help multinational corporations develop managerial training programs and recruitment strategies, given the demand for advanced knowledge-based skills in the modern workspace. The study also discusses the influences of education, experience and job skills on business managers’ performance, guiding the future recruitment of talents.

Originality/value
To the best of the authors’ knowledge, this review is among the first to assess the triadic relationship between knowledge management, career development and the global unemployment crisis. The proposed multidisciplinary approach seeks to break down existing silos, thus fostering a more comprehensive understanding of how to address these ongoing global concerns.
The impact of climate change news on the US stock market
Authors: Fedorova Elena , Iasakova Polina 
Published: February 2024

PurposeThis paper aims to investigate the impact of climate change news on the dynamics of US stock indices.Design/methodology/approachThe empirical basis of the study was 3,209 news articles. Sentiment analysis was performed by a pre-trained bidirectional FinBERT neural network. Thematic modeling is based on the neural network, BERTopic.FindingsThe results show that news sentiment can influence the dynamics of stock indices. In addition, five main news topics (finance and politics natural disasters and consequences industrial sector and Innovations activism and culture coronavirus pandemic) were identified, which showed a significant impact on the financial market.Originality/valueFirst, we extend the theoretical concepts. This study applies signaling theory and overreaction theory to the US stock market in the context of climate change. Second, in addition to the news sentiment, the impact of major news topics on US stock market returns is examined. Third, we examine the impact of sentimental and thematic news variables on US stock market indicators of economic sectors. Previous works reveal the impact of climate change news on specific sectors of the economy. This paper includes stock indices of the economic sectors most related to the topic of climate change. Fourth, the research methodology consists of modern algorithms. An advanced textual analysis method for sentiment classification is applied: a pre-trained bidirectional FinBERT neural network. Modern thematic modeling is carried out using a model based on the neural network, BERTopic. The most extensive topics are “finance and politics of climate change” and “natural disasters and consequences.”