bluuebunny/crossref_metadata_2025_split huggingface dataset:
Phase-separated TRB-PRC2 aggregates contribute to Polycomb silencing in plants
Authors: Xuan Hua , Liu Yue , zhao Jingze , Shi Nan , Li Yanzhuo , Zhou Yulu , Pi Limin , Li Shaoqing , Xu Guoyong , Yang Hongchun 
Published: March 2022

AbstractLocal accumulation of Polycomb Repressive Complex 2 (PRC2) is essential to gene silencing. Liquid-liquid phase separation (LLPS) mechanism is emerging as a paradigm to concentrate transcriptional machinery for effective gene regulation. Here, we elucidate that a rice single Myb transcription factor TRBF2 forms phase-separated droplets, which aggregate with PRC2 through direct protein interaction. Furthermore, TRB1, the closest homolog of TRBF2 in Arabidopsis, also forms phase-separated aggregates with PRC2. Mutants of TRBF2 and PRC2 component CLF display similar developmental defects, share common differentially expressed genes, and reduced H3K27me3 chromatin regions. Chromatin immunoprecipitation analysis supports that TRBF2 concentrates PRC2 at target loci to promote H3K27me3 deposition. Therefore, we propose that the aggregation of the plant-specific TRBs with PRC2 by the LLPS mechanism contributes to Polycomb silencing.One-Sentence SummaryThe phase-separated plant-specific single Myb transcription factor aggregates with PRC2 to facilitate Polycomb silencing.
Hyperbolic geometry-based deep learning methods to produce population trees from genotype data
Authors: Patel Aman , Montserrat Daniel Mas , Bustamante Carlos , Ioannidis Alexander 
Published: March 2022

AbstractThe production of population-level trees using the genomic data of individuals is a fundamental task in the field of population genetics. Typically, these trees are produced using methods like hierarchical clustering, neighbor joining, or maximum likelihood. However, such methods are non-parametric: they require all data to be present at the time of tree formation, and the addition of new data points necessitates the regeneration of the entire tree, a potentially expensive process. They also do not easily integrate with larger workflows. In this study, we aim to address these problems by introducing parametric deep learning methods for tree formation from genotype data. Our models specifically create continuous representations of population trees in hyperbolic space, which has previously proven highly effective in embedding hierarchically structured data. We present two different architectures - a multi-layer perceptron (MLP) and a variational autoencoder (VAE) - and we analyze their performance using a variety of metrics along with comparisons to established tree-building methods. Both models tested produce embedding spaces that reflect human evolutionary history. In addition, we demonstrate the generalizability of these models by verifying that addition of new samples to an existing tree occurs in a semantically meaningful manner. Finally, we use Dasgupta’s Cost to compare the quality of trees generated by our models to those produced by established methods. Despite the fact that the benchmark methods are directly fit on the evaluation data, our models are able to outperform some of these and achieve highly comparable performance overall.Author summaryTree production is a vital task in population genetics, but current approaches fall prey to several common shortfalls. Most notably, they lack the ability to add new data points after tree generation, and they are often difficult to use in larger pipelines. By leveraging cutting-edge advances pairing deep learning with hyperbolic geometry, we develop multiple models designed to rectify these issues. Through experiments on a dataset of humans from globally widespread ancestries, we demonstrate the generalizability of our models to new data, and we also show strong empirical performance with respect to currently used methods. In addition, we show that the data representations produced by our models are semantically meaningful and reflect known facts about human evolutionary history. Finally, we discuss the additional benefits our models could provide, including improved visualization, greater privacy preservation, and improved integration with downstream machine learning tasks. In conclusion, we present models that are accurate, flexible, and generalizable, with the potential to facilitate a variety of further applications.
Exploring a novel genomic safe-haven site in the human pathogenic mould Aspergillus fumigatus
Authors: Furukawa Takanori , van Rhijn Norman , Chown Harry , Rhodes Johanna , Alfuraji Narjes , Fortune-Grant Rachael , Bignell Elaine , Fisher Matthew C. , Bromley Michael 
Published: March 2022

ABSTRACTAspergillus fumigatus is the most important airborne fungal pathogen and allergen of humans causing high morbidity and mortality worldwide. The factors that govern pathogenicity of this organism are multi-factorial and are poorly understood. Molecular tools to dissect the mechanisms of pathogenicity in A. fumigatus have improved significantly over the last 20 years however many procedures have not been standardised for A. fumigatus. Here, we present a new genomic safe-haven locus at the site of an inactivated transposon, named SH-aft4, which can be used to insert DNA sequences in the genome of this fungus without impacting its phenotype. We show that we are able to effectively express a transgene construct from the SH-aft4 and that natural regulation of promoter function is conserved at this site. Furthermore, the SH-aft4 locus is highly conserved in the genome of a wide range of clinical and environmental isolates including the isolates commonly used by many laboratories CEA10, Af293 and ATCC46645, allowing a wide range of isolates to be manipulated. Our results show that the aft4 locus can serve as a site for integration of a wide range of genetic constructs to aid functional genomics studies of this important human fungal pathogen.
Rhythmic modulation of prediction errors: a possible role for the beta-range in speech processing
Authors: Hovsepyan Sevada , Olasagasti Itsaso , Giraud Anne-Lise 
Published: March 2022

AbstractNatural speech perception requires processing the current acoustic input while keeping in mind the preceding one and predicting the next. This complex computational problem could be handled by a multi timescale hierarchical inferential process that coordinates information flow up and down the language hierarchy. While theta and low-gamma neural frequency scales are convincingly involved in bottom-up syllable-tracking and phoneme-level speech encoding, the beta rhythm is more loosely associated with top-down processes without being assigned yet a specific computational function. Here we tested the hypothesis that the beta rhythm drives the precision of states during the speech recognition hierarchical inference process. We used a predictive coding model that recognizes syllables on-line in natural sentences, in which the precision of prediction errors is rhythmically modulated, resulting in alternating bottom-up vs. top-down processing regimes. We show that recognition performance increases with the rate of precision updates, with an optimal efficacy in the beta range (around 20 Hz). The model further performs when prediction errors pertaining respectively to syllable timing and syllable identity oscillate in antiphase. These results suggest that online syllable recognition globally benefits from the alternation of bottom-up and top-down dominant regime at beta rate, and that the gain is stronger when different features are also analyzed in alternation. These results speak to a discontinuous account of inferential operations in speech processing.
Local translation provides the asymmetric distribution of CaMKII required for associative memory formation
Authors: Chen Nannan , Zhang Yunpeng , Adel Mohamed , Kuklin Elena A. , Reed Martha L. , Mardovin Jacob D. , Bakthavachalu Baskar , VijayRaghavan K. , Ramaswami Mani , Griffith Leslie C. 
Published: March 2022

SUMMARYHow compartment-specific local proteomes are generated and maintained is inadequately understood, particularly in neurons, which display extreme asymmetries. Here we show that local enrichment of Ca2+/calmodulin-dependent protein kinase II (CaMKII) in axons of Drosophila mushroom body neurons is necessary for cellular plasticity and associative memory formation. Enrichment is achieved via enhanced axoplasmic translation of CaMKII mRNA, through a mechanism requiring the RNA-binding protein Mub and a 23-base Mub-recognition element in the CaMKII 3’UTR. Perturbation of either dramatically reduces axonal, but not somatic, CaMKII protein without altering the distribution or amount of mRNA in vivo and both are necessary and sufficient to enhance axonal translation of reporter mRNA. Together, these data identify elevated levels of translation of an evenly distributed mRNA as a novel strategy for generating subcellular biochemical asymmetries. They further demonstrate the importance of distributional asymmetry in the computational and biological functions of neurons.
The force required to remove tubulin from the microtubule lattice
Authors: Kuo Yin-Wei , Mahamdeh Mohammed , Tuna Yazgan , Howard Jonathon 
Published: March 2022

AbstractSevering enzymes and molecular motors extract tubulin from the walls of microtubules by exerting mechanical force on subunits buried in the lattice. However, how much force is needed to remove tubulin from microtubules is unknown, as is the pathway by which subunits are removed. Using a site-specific functionalization method, we applied forces to the C-terminus of α-tubulin with an optical tweezer and found that a force of ∼30 pN is required to extract tubulin from the microtubule wall. Consistent with this force, we show that several kinesins can also extract tubulin. Additionally, we discovered that partial unfolding is an intermediate step in tubulin removal. The unfolding and extraction forces are similar to those generated by AAA-unfoldases, suggesting that severing proteins such as spastin and katanin use an unfoldase mechanism. Our results reveal the response of tubulin to mechanical force and advance our understanding of severing enzymes and microtubule stability.
The role of social presence and trust on customer loyalty
Authors: Attar Razaz Waheeb , Amidi Asra , Hajli Nick 
Published: March 2022

PurposeE-commerce and social media technologies can significantly benefit the food and beverage industry by reducing costs, streamlining supply activities, and, most importantly, engaging users in active interaction and enhancing social presence. This research aims to propose a model to examine the role of trust and social presence on loyalty in the food and beverage industry. Moreover, the mediating role of trust is the link between social presence and loyalty examined in this study.Design/methodology/approachA survey has been conducted to examine the structural model. The research model is tested using structural equation modelling (PLS-SEM).FindingsThe result indicated the effect of Social presence and Trust in social media on Customer loyalty in the context of online shopping. Our finding contributes remarkable insights into the food and beverage industry, particularly in the COVID-19 era, as more consumers buy through e-commerce platforms.Originality/valueThis study expands the understanding of the role of the managers of social commerce websites in maintaining customer loyalty. Hence, the social commerce site managers can use this finding to develop strategies for building customer trust and, ultimately, customer loyalty.
A new multi-fidelity surrogate modelling method for engineering design based on neural network and transfer learning
Authors: Li Mushi , Liu Zhao , Huang Li , Zhu Ping 
Published: March 2022

PurposeCompared with the low-fidelity model, the high-fidelity model has both the advantage of high accuracy, and the disadvantage of low efficiency and high cost. A series of multi-fidelity surrogate modelling method were developed to give full play to the respective advantages of both low-fidelity and high-fidelity models. However, most multi-fidelity surrogate modelling methods are sensitive to the amount of high-fidelity data. The purpose of this paper is to propose a multi fidelity surrogate modelling method whose accuracy is less dependent on the amount of high-fidelity data.Design/methodology/approachA multi-fidelity surrogate modelling method based on neural networks was proposed in this paper, which utilizes transfer learning ideas to explore the correlation between different fidelity datasets. A low-fidelity neural network was built by using a sufficient amount of low-fidelity data, which was then finetuned by a very small amount of HF data to obtain a multi-fidelity neural network based on this correlation.FindingsNumerical examples were used in this paper, which proved the validity of the proposed method, and the influence of neural network hyper-parameters on the prediction accuracy of the multi-fidelity model was discussed.Originality/valueThrough the comparison with existing methods, case study shows that when the number of high-fidelity sample points is very small, the R-square of the proposed model exceeds the existing model by more than 0.3, which shows that the proposed method can be applied to reducing the cost of complex engineering design problems.
Employability of graduates and postgraduates in respect to retail industry: a study of selected states of North India
Authors: Malhotra Neha , Iyer Vidya M. , Dave Kartik 
Published: March 2022

Purpose
Taking a multidimensional perspective, the study aims to identify and examine the factors that are essential for employability. The study pursues to understand the underlying causes that facilitate the development of appropriate skills and attitudes that influence the development of employability in an individual.

Design/methodology/approach
The study pursued an exploratory research design followed by a descriptive. It explored the employability factors from the perspectives of the recruiter, student (candidate) and trainer. Additionally, it followed a causal research approach to examine the relationship between factors related to the personal environment, academic system, recruiter’s bias, industry and job attributes and overall employability. The antecedents of employability and their impact were examined with all the constructs taking the recruiter, student and trainer data sets.

Findings
The study identifies and analyses the antecedent factors that influence the employability development among college graduates and undergraduates. With its findings, the study identifies the changes or improvements required in teaching and learning mechanisms, grooming students and societal practices, social and economic biases in accessibility to facilities leading to employability. It also calls for a reflection on individual’s own attitude, motivation and abilities. The study calls for an assessment of the education systems and academic quality to develop employability among students. It conjures that the alignment of teacher and learner with industry requirements is an imperative requirement for the expansion of the industry and, in particular, the retail sector.

Practical implications
The study will be of significance to the policymakers and academicians while designing the retail-specific courses and aims toward addressing the mismatch amidst the demand and supply of manpower in the retail industry. The study has been designed to make it highly relevant for the students who are potential manpower to the retail sector as well as the employees who have already entered the industry. It addresses the major factors required for India to develop a cohesive and inclusive ecosystem that provides for the benefit of millions of youths in India.

Originality/value
The literature lacks a coherent meaning and measurement of employability and identifies the need to empirically explore and examine employability skills. Furthermore, the scant empirical research focuses largely on employee or student perspective, even when the role of recruitment in the assessment of employability is most critical. This study empirically evaluates the market from the perspective of students, employees, employers, trainers and academicians and develops a model that gives a holistic picture of the causes that influence the development of employability in an individual.
Perceived corporate social responsibility and innovative work behaviour: the role of passion at work
Authors: Alhumoudi Ranya Saeed , Singh Sanjay Kumar , Ahmad Syed Zamberi 
Published: March 2022

Purpose
Perceived corporate social responsibility (CSR) reflects personal views towards CSR activities that impact emotion, attitude and behaviour. This paper aims to investigate how perceived CSR predicts innovative work behaviour (IWB).

Design/methodology/approach
This study used a survey questionnaire to collect data from Gen Y 508 nurses working in the United Arab Emirates health-care sector. This study used a structural equation model to examine the study’s hypotheses.

Findings
Perceived CSR was found positively and significantly related to employees’ passion at work (PAW). This study also found that PAW positively and significantly mediates the relationship between perceived CSR and IWB in the organisation.

Research limitations/implications
This study’s main limitation is that it used only quantitative methodology to examine the hypotheses. Thus, this study suggests mixed-method investigations in future research.

Practical implications
This study suggests that organisations should significantly engage in CSR-related activities, as it increases employees’ PAW and cause IWB to be in full swing. Passionate Gen Y nurses will improve the quality of patient care services with productive, IWB.

Originality/value
The study’s findings give an insight into how Gen Y nurses’ perceptions of CSR would affect their attitudinal and emotional variables. This study contributes to the knowledge domain of IWB in the organisation. Passionate Gen Y nurses will add value to the health-care system by implementing IWB.
Do consumer boycotts really matter with global companies? The moderating effect of gender differences
Authors: Kim Changju , Yan Xiuyan , Park Soohyun 
Published: March 2022

PurposeDrawing on the theory of planned behavior, this study aims to conduct an empirical investigation on whether and how psychological and motivational factors (i.e. attitudes, subjective norms and perceived behavioral control) affect actual purchase behavior. It does so through the lens of boycott intention and gender differences in the context of boycott campaigns.Design/methodology/approachFocusing on the South Korean boycott campaign against Japanese companies, this study employs a structural equation model using survey data from 571 South Korean consumers to test the hypotheses.FindingsWhile the three psychological and motivational factors inhibit all three dimensions of actual purchase behavior (i.e. purchase frequency, number of items purchased and purchase amount) through increased boycott intention, perceived behavioral control of boycotts directly curb South Korean consumers from purchasing Japanese products. Additionally, the effect of boycott intention on overall actual purchase behavior is stronger for men than for women, suggesting a moderating role of gender.Practical implicationsTo mitigate the devastating impact of unexpected consumers' boycott campaigns, this study advises that global brand management and attractive online channels are essential while considering the differential impact of gender.Originality/valueBy conceptualizing three dimensions of actual purchase behavior capturing behavioral changes before and after a boycott, this study highlights the linkages between psychological and motivational factors, intentions and behaviors. Additionally, this study attempts to clarify the previously conflicting evidence on gender's role in boycott campaigns while taking a culture-inclusive psychologies approach to gender.
How consumers relate to wine during COVID-19 – a comparative, two nation study
Authors: Duarte Alonso Abel , Bressan Alessandro , Vu Oanh Thi Kim , Ha Do Lan Thi , Garibaldi Roberta , Pozzi Andrea 
Published: March 2022

Purpose
The purpose of this study is to examine consumers’ relationship with wine during the COVID-19 crisis and associated implications for hospitality and tourism operations. Despite the severe impacts of COVID-19’s aftermath, including recent reports of a global decrease in wine consumption, academic research has neglected this domain. Consequently, there is an empirical and conceptual vacuum that the present research will address.

Design/methodology/approach
The study proposes a comparative investigation, with data gathered from 241 Italian and Spanish wine consumers through an online questionnaire.

Findings
Overall, the analysis reveals marginal changes in wine consumption during the crisis. At the same time, however, an increased interest for more wine events and an organised wine route, for learning more about wines and new wine harvests, and the influence of comments from others in enhancing one’s wine knowledge during the crisis is also identified.

Originality/value
The study is one of the few studies conducted to date that seek to ascertain the extent to which wine consumers’ involvement with wine has changed during a major crisis. The findings and subsequent analysis contribute to the development of five distinctive dimensions associated with consumers: the more involved/interested, the inquisitive/explorer, the non-traditional/avid, the frugal/less involved and the conservative/avid. The first two dimensions, which are supported by mean scores close to the level of agreement (mean = 4.0) have a number of practical and conceptual implications. In addition, a conceptual framework illustrating various conceptual/empirical relationships is proposed.
Soft actuator using sponge units with constrained film and layer jamming
Authors: Liu Xiaoyi , Liang Zehao 
Published: March 2022

Purpose
This paper aims to propose a soft actuator that combines a sponge-based actuating structure and a layer-jamming-based stiffness-improving structure in a cavity.

Design/methodology/approach
The proposed soft actuator consists of film-constrained sponge units (FCSUs) and jamming layers. The FCSUs in the proposed soft actuator bend under vacuum pressure, causing bending deformation of the entire actuator. The jamming layers are strongly coupled through friction under vacuum pressure, increasing the stiffness of the entire actuator. The performance of the proposed soft actuator was examined by measuring its stiffness, bending deformation and response performance. A four-finger soft robotic gripper was proposed based on the proposed soft actuator.

Findings
Through experiments, it was shown that the proposed soft actuator exhibited acceptable bending deformation, stiffness and response. Moreover, the proposed four-finger soft gripper could effectively grasp objects in daily life.

Originality/value
In this study, the authors proposed a novel bending actuator (with a volume of approximately 43.2 cm3) based on FCSUs and jamming layers. To the best of the authors’ knowledge, this is the first study to combine a sponge-based actuating structure and a layer-jamming structure in a cavity to achieve simultaneous change in actuation and stiffness. The soft actuator exhibited good bending deformation and high stiffness simultaneously under vacuum pressure. Consequently, it could be used effectively to fabricate soft grippers.