bluuebunny/crossref_metadata_2025_split huggingface dataset:
Differential coding of fruit, leaf, and microbial odours in the brains ofDrosophila suzukiiandDrosophila melanogaster
Authors: Dumenil Claire , Yildirim Gülsüm , Haase Albrecht 
Published: September 2024

ABSTRACTThe flyDrosophila suzukii, a close relative ofD. melanogasterseverely damages the production of berry and stone fruits in large parts of the world. UnlikeD. melanogaster, which reproduces on overripe and fermenting fruits on the ground,D. suzukiiprefers to lay its eggs in ripening fruits still on the plants. Flies locate fruit hosts by their odorant volatiles, which are detected and encoded by a highly specialized olfactory system before being translated into behaviour. The exact information processing pathway is not yet fully understood, especially the evaluation of odour attractiveness. It is also unclear what differentiates the brains ofD. suzukiiandD. melanogasterto cause the crucial difference in host selection. We hypothesized that the basis for different behaviours is already formed at the level of the antennal lobe ofD. suzukiiandD. melanogaster,by different neuronal responses to volatiles associated with ripe and fermenting fruit. We thus investigated by 3Din vivotwo-photon calcium imaging how both species encoded odours from ripe fruits, leaves, fermented fruits, bacteria, and their mixtures in the antennal lobe. We then assessed their behavioural responses to mixtures of ripe and fermenting odours. The neural responses reflect species-dependent shifts in the odour code. In addition to this, morphological differences were also observed. Yet this was not directly reflected in different behavioural responses to the odours tested.
Chronic mitochondrial fragmentation elicits a neuroprotective Warburg-like effect inDrosophilaneurons
Authors: Richhariya Shlesha , Shin Daniel , Schlichting Matthias , Rosbash Michael 
Published: September 2024

AbstractMitochondrial fission and fusion are dynamic and important cellular processes, but the roles of these two very different mitochondrial forms – predominantly spherical and tubular - are not well-characterized in neurons of animals and especially in aging neurons. This is important because neurons are long-lived and mitochondrial dynamics is associated with neurodegenerative diseases. We used here an efficient cell type-specific CRISPR approach to knockout key fission-fusion genes and disrupt mitochondrial dynamics within the inessential clock neurons ofDrosophila. Surprisingly, fusion is much more important than fission for maintaining long-term neuronal function. Neurons survive chronic mitochondrial fragmentation due to loss of fusion by triggering a cancer-like transcriptomic response. This Warburg effect includes ATF4-mediated upregulation of the aerobic glycolysis geneLactate dehydrogenase(Ldh), and LDH is essential to prevent neurodegeneration of neurons deficient in the fusion geneOpa1. These results and others provide insights into the intersection of neuronal metabolism, aging and neurodegeneration.
Leveraging Multi-Modal Feature Learning for Predictions of Antibody Viscosity
Authors: Anapindi Krishna D. B. , Liu Kai , Wang Willie , Yu Yao , He Yan , Hsieh Edward J. , Huang Ying , Tomazela Daniela 
Published: September 2024

AbstractThe shift towards the subcutaneous administration route for biologics therapeutics has gained momentum due to its patient-friendly nature, convenience, reduced healthcare burden, and improved compliance compared to traditional intravenous infusions. However, one of the potentially significant challenges with this transition is managing the viscosity of the administered solutions. High viscosity can pose substantial development and manufacturability challenges, directly impacting the patient by increasing injection time and pain at the injection site. Moreover, high viscosity formulations can prolong residence time at the injection site, affecting absorption kinetics and potentially altering the intended pharmacological profile and therapeutic efficacy of the biologic candidate. This publication explores the application of a multimodal feature learning workflow for predicting the viscosity of antibodies in therapeutics discovery, integrating multiple data sources such as sequence, structural, physicochemical properties, and embeddings from a language model. This approach enables the model to learn from various underlying rules, including physicochemical rules from molecular simulations and molecular protein evolutionary rules by large, pre-trained deep learning foundation models. By comparing the effectiveness of this approach against other selected published viscosity prediction methods, this study offers insights on their intrinsic viscosity predictive potential and usability in therapeutics antibody early development pipelines.
Velamins: the first green-light emitting class of wild-type Ca2+-regulated photoproteins isolated from the ctenophoreVelamen parallelum
Authors: Soares Douglas M. M. , Galeazzo Gabriela A. , Sgro Germán G. , de Moraes Gabriela V. , Kronenberg Leora , Borukh Emmanuella , Migotto Alvaro E. , Gruber David F. , Sparks John S. , Pieribone Vincent A. , Stevani Cassius V. , Oliveira Anderson G. 
Published: September 2024

AbstractCa2+-regulated photoproteins (CaPhs) consist of single-chain globular proteins to which coelenterazine, a widely distributed marine luminogenic substrate (the luciferin), binds along with molecular oxygen, producing a stable peroxide. Upon Ca2+addition, CaPhs undergo conformational changes leading to the cyclization of the peroxide and the formation of a high-energy intermediate. Subsequently, its decomposition yields coelenteramide in an excited state and results in the emission of a flash of light. To date, all CaPhs reported produce blue light (λmax465-495 nm). Here, we report the cloning and functional characterization of a novel class of wild-type CaPhs capable of emitting green light: velamins, isolated from the bioluminescent ctenophoreVelamen parallelum. Ten unique photoprotein-like sequences were recovered and grouped in three main clusters. Representative sequences were cloned, expressed, purified, and regenerated into the active His-tagged α-, β-, and γ-velamins. Upon injection of a calcium-containing buffer into the velamin, a flash of green light (λmax500-508 nm) was observed across pH values ranging from 7 to 9. Whilst α-velamin isoforms exhibited the highest light emission activity, β- and γ-velamins were found to be more thermostable at higher temperatures. Velamins are the only known wild-type Ca2+-regulated photoproteins that exhibit the longest wavelength in light emission, making them a promising model for studying spectral modulation. As a result, velamins hold potential for enhancing the sensitivity of signal detection in analytical systems, particularly when dealing with complex biological matrices.
Directed evolution of a sequence-specific covalent protein tag for RNA labeling
Authors: Huang Rongbing , Ting Alice Y. 
Published: September 2024

AbstractEfficient methods for conjugating proteins to RNA are needed for RNA delivery, imaging, editing, interactome mapping, as well as for barcoding applications. Non-covalent coupling strategies using viral RNA binding proteins such as MCP have been applied extensively but are limited by tag size, sensitivity, and dissociation over time. We took inspiration from a sequence-specific, covalent protein-DNA conjugation method based on the Rep nickase of a porcine circovirus called “HUH tag”. Though wild-type HUH protein has no detectable activity towards an RNA probe, we engineered an RNA-reactive variant, called rHUH, through 7 generations of yeast display-based directed evolution. Our 13.4 kD rHUH has 12 mutations relative to HUH, and forms a covalent tyrosine-phosphate ester linkage with a 10-nucleotide RNA recognition sequence (“rRS”) within minutes. We engineered the sensitivity down to 1 nM of target RNA, shifted the metal ion requirement from Mn2+towards Mg2+, and demonstrated efficient labeling in mammalian cell lysate. This work paves the way toward a new methodology for sequence-specific covalent protein-RNA conjugation in biological systems.
Blanking on Blanks: Few Insect Microbiota Studies Control for Contaminants - A Systematic Review
Authors: Williamson Elisabeth , Hammer Tobin , Hogendoorn Katja , Eisenhofer Raphael 
Published: September 2024

ABSTRACTResearch on insect-microbe relationships is booming, with DNA sequencing being the most commonly used method to describe insect microbiota. However, sequencing is vulnerable to contamination, especially when the sample has low microbial biomass. Such low-biomass samples are common across insect taxa, developmental stages, and tissue types. Identifying putative contaminants is essential to distinguish between true microbiota and introduced contaminant DNA. It is therefore important that studies control for contamination, but how often this is done is unknown. To investigate the status quo of contamination control, we undertook a systematic literature review to quantify the prevalence of negative control usage and contamination control across the literature on insect microbiota (specifically bacterial communities) over the past 20 years. Two-thirds of the 243 insect microbiota studies evaluated did not include blanks (negative controls), and only 13.6% of the studies sequenced these blanks to control for contamination in their samples. Our findings highlight a major lack of contamination control in the field of insect microbiota research. This result suggests that a large number of microbes reported in the literature may be contaminants as opposed to insect-associated microbiota, and that more rigorous contamination control is needed to improve research reliability, validity and reproducibility. Based on our findings, we recommend a modified version of the guidelines outlined in the RIDES checklist: Report methodology, Include negative controls, Determine the level of contamination, Explore contamination downstream, and State the amount of off-target amplification.IMPORTANCEOur systematic review reveals a major lack of methodological rigour within the field of research on insect-associated microbiota. The small percentage of studies that control for contamination suggests that a considerable proportion of bacteria reported in the literature could be contaminants. The implication of this finding is that true microbiota may be masked or misrepresented, especially in insects with low microbial biomass.
TNF⍺-driven Aβ aggregation, synaptic dysfunction and hypermetabolism in human iPSC-derived cortical neurons
Authors: Díaz Alicia González , Belli Elisa , Mannini Benedetta , Urrutia Gustavo Antonio , Vendruscolo Michele 
Published: September 2024

SummaryAlzheimer’s disease (AD) patients exhibit an increased load of Aβ aggregates in the brain parenchyma. The neurotoxic nature of these aggregates has been underscored by recent advances in therapies aimed at reducing their load. To make further progress towards the development of increasingly effective treatments, there is a still largely unmet need for reliable cell models that comprehensively recapitulate aggregate-driven AD pathology. Here, we report a robust and scalable pipeline for generating human iPSC-derived cortical neurons that display Aβ aggregates in their axonal projections. This phenotype is caused by a repeated dosage of tumour necrosis factor-alpha (TNFα) to simulate the chronic inflammatory environment characteristic of AD and enhanced in neurons carrying the Swedish mutation. In association with the increased Aβ deposits in the cell bodies, this cell model exhibits other key hallmarks of AD, including structural alterations of synapses, electrophysiological asynchronous hyperactivity, and hypermetabolism. Overall, these results illustrate how repeated TNFα treatment models central aspects of AD pathology, and provides a platform that could be used for facilitating the translation of potential drugs to clinical applications.
Fast variance component analysis using large-scale ancestral recombination graphs
Authors: Zhu Jiazheng , Kalantzis Georgios , Pazokitoroudi Ali , Gunnarsson Árni Freyr , Loya Hrushikesh , Chen Han , Sankararaman Sriram , Palamara Pier Francesco 
Published: September 2024

AbstractRecent algorithmic advancements have enabled the inference of genome-wide ancestral recombination graphs (ARGs) from genomic data in large cohorts. These inferred ARGs provide a detailed representation of genealogical relatedness along the genome and have been shown to complement genotype imputation in complex trait analyses by capturing the effects of unobserved genomic variants. An inferred ARG can be used to construct a genetic relatedness matrix, which can be leveraged within a linear mixed model for the analysis of complex traits. However, these analyses are computationally infeasible for large datasets. We introduce a computationally efficient approach, called ARG-RHE, to estimate narrow-sense heritability and perform region-based association testing using an ARG. ARG-RHE relies on scalable randomized algorithms to estimate variance components and assess their statistical significance, and can be applied to multiple quantitative traits in parallel. We conduct extensive simulations to verify the computational efficiency, statistical power, and robustness of this approach. We then apply it to detect associations between 21,374 genes and 52 blood-related traits, using an ARG inferred from genotype data of 337,464 individuals from the UK Biobank. In these analyses, combining ARG-based and imputation-based testing yields 8% more gene-trait associations than using imputation alone, suggesting that inferred genome-wide genealogies may effectively complement genotype imputation in the analysis of complex traits.
Switching on Behavioral and Neural Rhythmicity to Retrieve Memories When the Number of Retained Items Exceeds Four
Authors: Ideriha Takuya , Ushiyama Junichi 
Published: September 2024

ABSTRACTEven when we experience difficulty in recalling memories, we nevertheless manage to retrieve the target items. However, the neural mechanisms that enable such difficult memory retrieval are unknown. Here, we report an intriguing phenomenon where our nervous system “switches on” behavioral/neural rhythmicity to retrieve memory when the number of candidate items exceeds four. In our experiments, participants learned and retrieved 2–5 color/letter pairs. Analyses of hundreds of reaction times revealed a significant tendency for memory recall to occur at discrete times corresponding to theta–alpha (4–13 Hz) cycles, but only when the number of memorized pairs exceeded four. Electrophysiological data localized theta–alpha rhythmicity around parietal electrodes, a region associated with the long-term memory system. Our findings suggest that neural rhythmicity facilitates memory retrieval when the number of candidate items exceeds four, which is known as the “magical number” corresponding to the limit of human cognitive capacity.
Rationalizing the Effects of RNA Modifications on Protein Interactions
Authors: Vandelli Andrea , Broglia Laura , Armaos Alexandros , Ponti Riccardo Delli , Tartaglia Gian Gaetano 
Published: September 2024

AbstractRNA modifications play a crucial role in regulating gene expression by altering RNA structure and modulating interactions with RNA-binding proteins (RBPs). In this study, we explore the impact of specific RNA chemical modifications—N6-methyladenosine (m⁶A), A-to-I editing, and pseudouridine (Ψ)—on RNA secondary structure and protein-RNA interactions. Utilizing genome-wide data, including RNA secondary structure predictions and protein-RNA interaction datasets, we classify proteins into distinct categories based on their binding behaviors: modification-specific and structure-independent, or modification-promiscuous and structure-dependent. For instance, m⁶A readers like YTHDF2 exhibit modification-specific and structure-independent binding, consistently attaching to m⁶A regardless of structural changes. Conversely, proteins such as U2AF2 display modification-promiscuous and structure-dependent behavior, altering their binding preferences in response to structural changes induced by different modifications. A-to-I editing, which causes significant structural changes, typically reduces protein interactions, while Ψ enhances RNA structural stability, albeit with variable effects on protein binding. To better predict these interactions, we developed the catRAPID 2.0 RNA modifications algorithm, which forecasts the effects of RNA modifications on protein-RNA binding propensities. This algorithm serves as a valuable tool for researchers, enabling the prediction and analysis of RNA modifications’ impact on protein interactions, thus offering new insights into RNA biology and engineering. The catRAPID 2.0 RNA modifications tool is available athttp://service.tartaglialab.com/new_submission/catrapid_omicsv2_rna_mod.Abstract Figure
Seeing Double: Molecular dynamics simulations reveal the stability of certain alternate protein conformations in crystal structures
Authors: Rosenberg Aviv A. , Vedula Sanketh , Bronstein Alex M. , Marx Ailie 
Published: September 2024

AbstractProteins jiggle around, adopting ensembles of interchanging conformations. Here we show through a large-scale analysis of the Protein Data Bank and using molecular dynamics simulations, that segments of protein chains can also commonly adopt dual, transiently stable conformations which is not explained by direct interactions. Our analysis highlights how alternate conformations can be maintained as non-interchanging, separated states intrinsic to the protein chain, namely through steric barriers or the adoption of transient secondary structure elements. We further demonstrate that despite the commonality of the phenomenon, current structural ensemble prediction methods fail to capture these bimodal distributions of conformations.
Housing customization: envisioning an interface to support co-design processes
Authors: Raposo Micaela , Eloy Sara , Dias Miguel Sales 
Published: September 2024

PurposeThere are several studies showing that end-users' participation in the architectural design process of houses is critical to their satisfaction (e.g. Önder et al., 2010; Ammar et al., 2013). Housing that is not adjusted to inhabitants' needs leads to modification works (Davidson et al., 2007) that could be avoided if their design was defined from the beginning with their participation. Digital technologies, such as 3D interactive visualization, benefit co-design processes by helping non-specialists better understand space and design possibilities (Salter et al., 2009; Schroth et al., 2006). However, the available literature shows that existing co-design digital tools were not developed based on potential users’ requirements. This paper aims to define the user requirements of a co-design tool for housing customization.Design/methodology/approachInterviews were conducted to gather information on how participatory processes occur in housing cooperatives and identify how potential users can collaborate in the design definition of their houses using a digital tool.FindingsThe interviews were analyzed, and requirements were defined.Originality/valueThis work contributes to the advancement of knowledge since the tool is defined based on requirements collected from potential end-users. By using a user-centered approach, the tool can contribute to more effective and informed collaboration.
Exploiting organizations' innovation performance via big data analytics: an absorptive knowledge perspective
Authors: Tseng Hsiao-Ting , Jia Shizhen (Jasper) , Nisar Tahir M. , Hajli Nick 
Published: September 2024

PurposeThe advantages of applying big data analytics for organizations to boost innovation performance are enormous. By collecting and analysing substantial amounts of data, firms can discern what works for their customer needs and update existing products while innovating new ones. Notwithstanding the evidence about the effects of big data analytics, the link between big data analytics and innovation performance is still underestimated. Especially in today's fast-changing and complicated environments, companies cannot simply take big data analytics as one innovative technical tool without fully understanding how to deploy it effectively.Design/methodology/approachThis study tries to investigate this relationship by building on the knowledge absorptive capacity perspective. The authors conceptualized effective use of big data analytics tools as one general absorptive capacity rather than a simple technical element or skill. Specifically, effectively utilize big data analytics tools can provide values and insights for new product innovation performance in a turbulent environment. Using online survey data from 108 managers, the authors assessed their hypotheses by applying the structural equation modelling method.FindingsThe authors found that big data analytics capacity, which can be conceptualized as one absorptive capacity, can positively influence product innovation performance. The authors also found that environmental turbulence has strong moderation effects on these two main relationships.Originality/valueThese results establish big data analytics can be regarded as one absorptive capacity, which can positively boost an organization's innovation performance.
Brand purpose: a literature review and BEING implementation framework
Authors: France Cassandra , Gonzalez-Arcos Claudia Fernanda , O’Rourke Anne-Maree , Spry Amanda , Bruce Bronwyn 
Published: September 2024

Purpose
While brand purpose has gained traction in academia and industry, it overlaps with other socially-oriented branding concepts, generating confusion and criticism around what brand purpose is and how it should be implemented. This study aims to clarify conceptualisations of brand purpose and related concepts, developing a managerial framework for effective implementation and contributing a future research agenda for scholars.

Design/methodology/approach
The paper presents the results of a systematic literature review on brand purpose, exploring the current knowledge. A total of 202 studies from 75 journals were drawn from a wide range of databases and met the identified criteria relevant to brand purpose, published up to and including 2023. Full papers were empirically analysed using qualitative iterative thematic analysis to identify common and emerging themes and synthesise this into a framework.

Findings
Beyond identifying the diverse applications of brand purpose, the BEING framework is proposed. This acronym captures five principles of brand purpose: beyond profit, enduring commitment, integrated values, nurturing stakeholders and genuine action.

Practical implications
The BEING framework provides clear managerial guidance for implementing brand purpose, urging brand leaders to enact meaningful brand purpose and champion purpose within organisations.

Social implications
As more brands pursue a higher purpose, this research elucidates the meaning of brand purpose and offers an actionable framework for brands to contribute to a better world.

Originality/value
This work examines the related branding concepts, clarifying the brand purpose concept and offering the BEING framework to articulate essential components of effective brand purpose.