bluuebunny/crossref_metadata_2025_split huggingface dataset:
Tribus: Semi-automated discovery of cell identities and phenotypes from multiplexed imaging and proteomic data
Authors: Kang Ziqi , Szabo Angela , Farago Teodora , Perez-Villatoro Fernando , Junquera Ada , Shah Saundarya , Launonen Inga-Maria , Anttila Ella , Casado Julia , Elias Kevin , Virtanen Anni , Haltia Ulla-Maija , Färkkilä Anniina 
Published: March 2024

AbstractMotivationMultiplexed imaging and single-cell analysis are increasingly applied to investigate the tissue spatial ecosystems in cancer and other complex diseases. Accurate single-cell phenotyping based on marker combinations is a critical but challenging task due to (i) low reproducibility across experiments with manual thresholding, and, (ii) labor-intensive ground-truth expert annotation required for learning-based methods.ResultsWe developed Tribus, an interactive knowledge-based classifier for multiplexed images and proteomic datasets that avoids hard-set thresholds and manual labeling. We demonstrated that Tribus recovers fine-grained cell types, matching the gold standard annotations by human experts. Additionally, Tribus can target ambiguous populations and discover phenotypically distinct cell subtypes. Through benchmarking against three similar methods in four public datasets with ground truth labels, we show that Tribus outperforms other methods in accuracy and computational efficiency, reducing runtime by an order of magnitude. Finally, we demonstrate the performance of Tribus in rapid and precise cell phenotyping with two large in-house whole-slide imaging datasets.AvailabilityTribus is available athttps://github.com/farkkilab/tribusas an open-source Python package.
Algebraic structures emerge from the self-supervised learning of natural sounds
Authors: Orhan Pierre , Boubenec Yves , King Jean-Remi 
Published: March 2024

Humans can spontaneously detect complex algebraic structures. Historically, two opposing views explain this ability, at the root of language and music acquisition. Some argue for the existence of an innate and specific mechanism, like “merge” (Chomsky) or “neural recursion” (Dehaene). Others argue that this ability emerges from experience (e.g. Bates): i.e. when generic learning principles continuously process sensory inputs. These two views, however, remain difficult to test experimentally. Here, we use deep learning models to evaluate the factors that lead to the spontaneous detection of algebraic structures in the auditory modality. Specifically, we train multiple deep learning models with a variable amount of natural sounds and a self-supervised learning objective. We then expose these models to the experimental paradigms classically used to evaluate the processing of algebraic structures. Like humans, these models spontaneously detect repeated sequences, probabilistic chunks and complex algebraic structures. Also like humans, this ability diminishes with structure complexity. Importantly, this ability can emerge from experience alone: the more the models are exposed to natural sounds, the more they spontaneously detect increasingly complex structures. Finally, this ability does not emerge in models pretrained only on speech, and emerges more rapidly in models pretrained with music than environmental sounds. Overall, our study provides an operational framework to clarify sufficient built-in and acquired principles that model human’s advanced capacity to detect algebraic structures in sounds.Significance StatementExperimentalists have repeatedly observed a human advantage in the detection of algebraic structures, notably through auditory paradigms. This ability to detect structure is thought to be key to the emergence of complex cognitive operations. Yet, it remains debated if this ability is discovered or innate in the form of a specific mechanism. In this article, the authors show how a model progressively and spontaneously learns to detect auditory structure. The model replicate several experimental findings but only under certain developmental conditions. Notably, exposition to music or environmental sounds, but not speech, is sufficient for the emergence of algebraic structure detection. As a result, this work proposes self-supervised learning as a developmental model of abstract cognitive abilities.
tstrait: a quantitative trait simulator for ancestral recombination graphs
Authors: Tagami Daiki , Bisschop Gertjan , Kelleher Jerome 
Published: March 2024

AbstractSummaryAncestral recombination graphs (ARGs) encode the ensemble of correlated genealogical trees arising from recombination in a compact and efficient structure, and are of fundamental importance in population and statistical genetics. Recent breakthroughs have made it possible to simulate and infer ARGs at biobank scale, and there is now intense interest in using ARG-based methods across a broad range of applications, particularly in genome-wide association studies (GWAS). Sophisticated methods exist to simulate ARGs using population genetics models, but there is currently no software to simulate quantitative traits directly from these ARGs. To apply existing quantitative trait simulators users must export genotype data, losing important information about ancestral processes and producing prohibitively large files when applied to the biobank-scale datasets currently of interest in GWAS. We presenttstrait, an open-source Python library to simulate quantitative traits on ARGs, and show how this user-friendly software can quickly simulate phenotypes for biobank-scale datasets on a laptop computer.Availability and Implementationtstraitis available for download on the Python Package Index. Full documentation with examples and workflow templates is available onhttps://tskit.dev/tstrait/docs/, and the development version is maintained on GitHub (https://github.com/tskit-dev/tstrait).Contactdaiki.tagami@hertford.ox.ac.uk
Oligonucleotide insecticides (contact unmodified antisense DNA biotechnology) and RNA biocontrols (double-stranded RNA technology): newly born fraternal twins in plant protection
Authors: Oberemok V.V. , Gal’chinsky N.V. 
Published: March 2024

AbstractThe rapid development of molecular genetics, methods of synthesis of nucleic acids, and bioinformatics have created fundamentally new opportunities in plant protection. DNA and RNA have become active ingredients of insecticides, going beyond their usual role as carriers of hereditary information. Now nucleic acids are increasingly acting as active tools for insect pest control using the mechanisms of DNA containment (oligonucleotide insecticides, contact unmodified antisense DNA (CUAD) biotechnology) and RNA interference (RNA biocontrols, double-stranded RNA biotechnology). Partial understanding of how antisense DNA oligonucleotides might work began in the late 1970s of XX century and for a long time (until the beginning of the XXI century) these biomolecules had not been implemented in plant protection in the form of any biotechnology based on unmodified or modified DNA fragments. RNA interference was discovered in the late 90s of XX century and also went through quite a long path of development in plant protection before the first encouraging results of its application as insecticides appeared. Both, CUAD biotechnology and double-stranded RNA technology aim to inactivate the expression of genes of insect pests using antisense DNA and double-stranded RNA that help trigger degradation of the target RNA. As two new classes of the next-generation insecticides, these products are supposed to have long operational life, low carbon footprint, high selectivity in action and rapid biodegradation in ecosystems. To date, antisense CUAD biotechnology shows the best results on sap-sucking insects while double-stranded RNA technology shows the best insecticidal potential on beetles from Tenebrionidae and Chrysomelidae families. With a high probability, oligonucleotide insecticides and RNA biocontrols, will be effective against certain groups of insects and can complement each other in complex formulations for control of wide range of pests.
Chronic IL-6 overproduction induces a tolerogenic response in aged mice after peripheral nerve injury
Authors: Manich Gemma , Barbanti Ruggero , Peris Marta , Villacampa Nàdia , Almolda Beatriz , González Berta , Castellano Bernardo 
Published: March 2024

ABSTRACTHighlights/Main pointsAstrocyte-targeted IL-6 overproduction during aging increases basal microglial acivation in the facial nucleus.During aging, chronic IL-6 overproduction does not modify microglial response after peripheral nerve injury but increases T-lymphocyte infiltration.Chronic IL-6 overproduction in aged mice does not modify facial motor neuron survival after facial nerve axotomy.Interleukin-6 (IL-6) is the main cytokine controlling neuroinflammation and microglial activation during aging, and the increase of IL-6 levels correlate well with chronic neuroinflammation and age-related neurodegenerative diseases. Despite the relevance of IL-6 in these conditions, the effect of this cytokine in microglia activation and neuroinflammation upon CNS injuries during aging has been scarcely explored. Previous results from our group showed that adult and aged transgenic mice with astrocyte-targeted overproduction of IL-6 (GFAP-IL6Tg) presented features of a primed microglial phenotype in basal conditions compared to wild-type (WT) mice, and that after CNS lesions, microglia showed and exacerbated response associated with increased neuronal death in adult mice. In this work, we aimed to study whether chronic IL-6 overproduction influenced microglia response to CNS injury during aging. With this aim, we performed facial nerve axotomy (FNA) in aged 21-23-month-old WT and GFAP-IL6Tg animals, and analysed facial motor neuron (FMN) survival, glial reactivity, antigen presentation, and lymphocyte infiltration both at basal conditions (non-lesioned) and after FNA. Our results showed that non-lesioned aged transgenic mice presented higher Iba1, CD11b, and CD68 levels than aged WT mice, indicative of a priming effect in the aged facial nucleus. After FNA, we detected similar levels of microglial and astroglia activation, but a remarkable increase in T-lymphocyte infiltration in GFAP-IL6Tg axotomized group. Despite slight differences in the neuroinflammatory response, aged GFAP-IL6Tg animals showed a similar rate of FMN death compared to aged WT mice. Overall, our work shows that transgenic IL-6 overproduction induces a primed microglial phenotype under basal conditions in aged animals, with a reduced fold-increase in the microglial response after FNA compared to aged WT mice and similar lesion outcomes, suggestive of a tolerant microglial phenotype. This study suggests a tolerant effect of chronic IL-6 overproduction in microglia during aging in basal conditions and after CNS lesions.
Differences in Protein Capture by SP3 and SP4 Demonstrate Mechanistic Insights of Proteomics Clean-up Techniques
Authors: Conforti Jessica M. , Ziegler Amanda M. , Worth Charli S. , Nambiar Adhwaitha M. , Bailey Jacob T. , Taube Joseph H. , Gallagher Elyssia S. 
Published: March 2024

ABSTRACTThe goal of proteomics experiments is to identify proteins to observe changes in cellular processes and diseases. One challenge in proteomics is the removal of contaminants following protein extraction, which can limit protein identification. Single-pot, solid-phase-enhanced sample preparation (SP3) is a clean-up technique in which proteins are captured on carboxylate-modified particles through a proposed hydrophilic-interaction-liquid-chromatography (HILIC)-like mechanism. However, recent results have suggested that proteins are captured in SP3 due to a protein-aggregation mechanism. Thus, solvent precipitation, single-pot, solid-phase-enhanced sample preparation (SP4) is a newer clean-up technique that employs protein-aggregation to capture proteins without modified particles. SP4 has previously enriched low-solubility proteins, though differences in protein capture could affect which proteins are detected and identified. We hypothesize that the mechanisms of capture for SP3 and SP4 are distinct. Herein, we assess the proteins identified and enriched using SP3 versus SP4 for MCF7 subcellular fractions and correlate protein capture in each method to protein hydrophobicity. Our results indicate that SP3 captures more hydrophilic proteins through a combination of HILIC-like and protein-aggregation mechanisms, while SP4 captures more hydrophobic proteins through a protein-aggregation mechanism. From these results, we recommend clean-up techniques based on protein-sample hydrophobicity to yield high proteome coverage in biological samples.
Enabling Greenhouse Gas Emission Reduction while Improving Rice Yield with a Methane-Derived Microbial Biostimulant
Authors: Kumar Sarma Rajeev , David Einstein Mariya , Pavithra Gangigere Jagadish , Sajith Gopalakrishnan Kumar , Lesharadevi Kuppan , Akshaya Selvaraj , Bassavaraddi Chavaddi , Navyashree Gopal , Arpitha Panakanahalli Shivaramu , Sreedevi Padmanabhan , Zainuddin Khan , Saiyyeda Firdous , Babu Bondalakunta Ravindra , Prashanth Muralidhar Udagatti , Ravikumar Ganesan , Basavaraj Palabhanvi , Kumar Chavana Sandeep , Kumar Vinod Munisanjeeviah Lakshmi Devi , Parthasarathi Theivasigamani , Subbian Ezhilkani 
Published: March 2024

ABSTRACTRice is a vital crop for food security and human nutrition, yet its cultivation produces ∼11% of total global anthropogenic methane (CH4) emissions - the second most important greenhouse gas (GHG). Modifications to rice management practice are necessary, both to increase yield and mitigate GHG emissions. We investigated the effect of a methane-derived microbial biostimulant on grain yield and GHG emissions from rice fields. Applications of microbial biostimulant resulted in significant enhancement of grain yield, even under different nitrogen management, with consistent reduction in GHG emissions. The study further outlines a potential mechanism for broad and diverse positive effects of microbial biostimulant on the paddy crop including in photosynthesis, tillering and panicle development. Observations from the study will help stakeholders and policy makers, leverage biological solutions like methane-derived microbial biostimulant to improve crop yield and address food security, while reducing anthropogenic CH4 emissions to meet targets agreed at COP26.
Deep learning insights into cosmological structure formation
Authors: Lucie-Smith Luisa , Peiris Hiranya V. , Pontzen Andrew , Nord Brian , Thiyagalingam Jeyan 
Published: March 2024

The evolution of linear initial conditions present in the early Universe into extended halos of dark matter at late times can be computed using cosmological simulations. However, a theoretical understanding of this complex process remains elusive; in particular, the role of anisotropic information in the initial conditions in establishing the final mass of dark matter halos remains a long-standing puzzle. Here, we build a deep learning framework to investigate this question. We train a three-dimensional convolutional neural network to predict the mass of dark matter halos from the initial conditions, and quantify in full generality the amounts of information in the isotropic and anisotropic aspects of the initial density field about final halo masses. We find that anisotropies add a small, albeit statistically significant amount of information over that contained within spherical averages of the density field about final halo mass. However, the overall scatter in the final mass predictions does not change qualitatively with this additional information, only decreasing from 0.9 dex to 0.7 dex. Given such a small improvement, our results demonstrate that isotropic aspects of the initial density field essentially saturate the relevant information about final halo mass. Therefore, instead of searching for information directly encoded in initial conditions anisotropies, a more promising route to accurate, fast halo mass predictions is to add approximate dynamical information based e.g. on perturbation theory. More broadly, our results indicate that deep learning frameworks can provide a powerful tool for extracting physical insight into cosmological structure formation.

Published by the American Physical Society
2024
Communication skills training: a quantitative systematic review
Authors: Hamlin Emily L.B. , McGloin Rory , Bridgemohan Alex 
Published: March 2024

Purpose
Organizational leaders value effective communication in employees, but ineffective communication persists (Peart, 2019). Communication Skills Training (CST) programs often face time and engagement constraints (Wright et al., 2006), necessitating increased resources for improvement. This study investigates the fields in which communication skills are examined and the research methods used. Given past training-communication associations, it explores CST programs' positive impact through experimental research (Bakker & van Wingerden, 2021). The study aims to enhance CST research by providing insights into effective training methods for facilitators and practitioners.

Design/methodology/approach
This systematic literature review examines CST's effectiveness in improving employee communication outcomes and identifying key factors. It highlights the importance of communication training, provides insights into existing research, and identifies areas for future investigation. As communication training is not context-specific, a comprehensive understanding of its effectiveness is necessary. This review establishes a foundational framework to support the creation of impactful training programs.

Findings
This research reaffirms the importance of Communication Skills Training (CST) in enhancing key competencies such as communication competence, self-efficacy, and empathy for workplace success, benefiting collaboration, conflict resolution, and problem-solving. It provides a foundational understanding of CST's impact, serving as a resource for researchers, trainers, and leaders, while also emphasizing the need for further research, including larger experiments, diverse skill sets, and long-term assessment. In our digital age, exploring contemporary skills, including digital communication, is essential for comprehensive training. The systematic categorization of skills into intrapersonal and interactional dimensions ensures consistency and supports in-depth analysis.

Originality/value
To address the broad range, outcome skills were categorized as intrapersonal or interactional, excluding the behavioral aspect concerning societal improvement. Leveraging Zimmerman's (1995) empowerment theory to enhance focus, this framework provides value for diverse CST research outcomes. Intrapersonal encompassed self-views, like self-efficacy and knowledge, while interactional involved understanding and aligning exchanges with personal goals. This categorization enhances research clarity and effectiveness by systematically understanding how these skills are related.
I don’t want to switch! So, am I trapped? An in-depth qualitative inquiry of resistance to change leading to competency trap
Authors: Rath Alisha , Jena Lalatendu Kesari 
Published: March 2024

Purpose
This paper explores the evident issue of organizational resistance to change. The study delves deep into the underlying resistance factors in organizations using a qualitative approach.

Design/methodology/approach
The qualitative study employs a grounded theory coding pattern and network analysis to explore the underlying themes. This approach gives a panoramic view of resistance to change by comprehending the themes of existing literature and verbatims collected from the respondents.

Findings
The results of the comprehensive interviews and analyses suggest a significant change in the definition of resistance to change. The network analysis led to the identification of subthemes, antecedents, and consequences of resistance to change. The study highlights the importance of innovation, personnel training, mistrust, and the role of motivation.

Practical implications
The insights gained from this practitioner-oriented study help navigate organizations to identify the issues related to resistance to change. The study also helps trace the issue of over-specialization, which promotes competency traps in organizations. The role of leaders in managing resistance to change is evident, which practitioners can consider for the more significant benefit of organizations.

Originality/value
The study proposes a nuanced understanding of resistance to change by using a qualitative approach to comprehend the phenomena from the perspective of leaders and employees. Using a coding approach for theme generation and network analysis for visualization ascertains the methodical rigor and comprehensive understanding alongside generating practitioner-oriented recommendations.
Enhancing FM digitalization strategies for shopping complexes amidst post-COVID-19: a case study analysis and improvement insights
Authors: Chua Shirley Jin Lin , Beh Shiuan Ping , Myeda Nik Elyna , Ali Azlan Shah 
Published: March 2024

PurposeThis study aims to improve the use of digitalization in facilities management (FM) for shopping complex facilities in the post-COVID-19 era. The resumption of economic activities, especially in shopping complexes, poses challenges for FM with throngs of shoppers. To tackle these challenges, enhanced and innovative FM practices are necessary.Design/methodology/approachThe study used a qualitative research approach, incorporating case studies, interviews, observations and documentation. It focused on super-regional shopping complexes in the Klang Valley, Malaysia, selecting two complexes for qualitative data collection. Supplementary data were gathered from various sources, including government policy publications, websites, books, journal papers and archival records.FindingsThe research provides valuable insights into FM innovations and the application of FM digitalization in shopping complexes after the COVID-19 pandemic. It also addresses challenges faced by FM teams during this period. Recommendations for implementing FM digitalization in super-regional shopping complexes post-COVID-19 include developing skilled personnel, defining appropriate work scopes, strategies and policies, using cost-effective software, and increasing occupant awareness. The involvement of outsourced service providers is advised, emphasizing their understanding of the organization’s business model and innovative approaches.Originality/valueThe findings offer new perspectives on the characteristics of FM digitalization in the commercial sector during business disruptions caused by the pandemic. The proposed strategies are grounded in real industry implementations, aiming to enhance the FM digitalization approach for improved business performance.