bluuebunny/crossref_metadata_2025_split huggingface dataset:
Super-resolution STED imaging in the inner and outer whole-mount mouse retina
Authors: Kremers Leon , Sarieva Kseniia , Hoffmann Felix , Ueffing Marius , Euler Thomas , Nikić-Spiegel Ivana , Schubert Timm 
Published: December 2022

AbstractSince its invention in 1994, super-resolution microscopy has become a popular tool for advanced imaging of biological structures, allowing visualisation of subcellular structures at a spatial scale below the diffraction limit. Thus, it is not surprising that recently, different super-resolution techniques are being applied in neuroscience, e.g. to resolve the clustering of neurotransmitter receptors and protein complex composition in presynaptic terminals. Still, the vast majority of these experiments were carried out either in cell cultures or very thin tissue sections, while there are only a few examples of super-resolution imaging in thick (> ~50 μm) biological samples. In that context, the mammalian whole-mount retina has rarely been studied with super-resolution microscopy. Here, we aimed at establishing a stimulated-emission-depletion (STED) microscopy protocol for imaging whole-mount retina. To this end, we developed sample preparation including horizontal slicing of retinal tissue, an immunolabeling protocol with STED-compatible fluorophores and optimised the STED microscope’s settings. We labelled subcellular structures in somata, dendrites, and axons of retinal ganglion cells in the inner mouse retina. Under optimal conditions, we achieved a mean lateral spatial resolution of ~120 nm (using the full width of half-maximum as a proxy) for the thinnest filamentous structures in our preparation and a resolution enhancement of two or higher compared to conventional confocal images. When combined with horizontal slicing of the retina, these settings allowed us visualisation of putative GABAergic horizontal cell synapses in the outer retina with a similar resolution. Taken together, we successfully established a STED protocol for reliable super-resolution imaging in the whole-mount mouse retina, which enables investigating, for instance, protein complex composition and cytoskeletal ultrastructure at retinal synapses in health and disease.
Startle: a star homoplasy approach for CRISPR-Cas9 lineage tracing
Authors: Sashittal Palash , Schmidt Henri , Chan Michelle , Raphael Benjamin J. 
Published: December 2022

AbstractCRISPR-Cas9 based genome editing combined with single-cell sequencing enables the tracing of the history of cell divisions, or cellular lineage, in tissues and whole organisms. While standard phylogenetic approaches may be applied to reconstruct cellular lineage trees from this data, the unique features of the CRISPR-Cas9 editing process motivate the development of specialized models that describe the evolution of CRISPR-Cas9 induced mutations. Here, we introduce thestar homoplasymodel, a novel evolutionary model that constrains a phylogenetic character to mutate at most once along a lineage, capturing thenon-modifiabilityproperty of CRISPR-Cas9 mutations. We derive a combinatorial characterization of star homoplasy phylogenies by identifying a relationship between the star homoplasy model and the binary perfect phylogeny model. We use this characterization to develop an algorithm, Startle (Star tree lineage estimator), that computes a maximum parsimony star homoplasy phylogeny. We demonstrate that Startle infers more accurate phylogenies on simulated CRISPR-based lineage tracing data compared to existing methods; particularly on data with high amounts of dropout and homoplasy. Startle also infers more parsimonious phylogenies with fewer metastatic migrations on a lineage tracing dataset from mouse metastatic lung adenocarcinoma.Code availabilitySoftware is available athttps://github.com/raphael-group/startle
Brain-based predictions of psychiatric illness-linked behaviors across the sexes
Authors: Dhamala Elvisha , Ooi Leon Qi Rong , Chen Jianzhong , Ricard Jocelyn A. , Berkeley Emily , Chopra Sidhant , Qu Yueyue , Lawhead Connor , Yeo B.T. Thomas , Holmes Avram J. 
Published: December 2022

AbstractBackgroundIndividual differences in functional brain connectivity can be used to predict both the presence of psychiatric illness and variability in associated behaviors. However, despite evidence for sex differences in functional network connectivity and in the prevalence, presentation, and trajectory of psychiatric illnesses, the extent to which disorder-relevant aspects of network connectivity are shared or unique across the sexes remains to be determined.MethodsIn this work, we used predictive modelling approaches to evaluate whether shared or unique functional connectivity correlates underlie the expression of psychiatric illness-linked behaviors in males and females in data from the Adolescent Brain Cognitive Development study (n=5260; 2571 females).ResultsWe demonstrate that functional connectivity profiles predict individual differences in externalizing behaviors in males and females, but only predict internalizing behaviors in females. Furthermore, models trained to predict externalizing behaviors in males generalize to predict internalizing behaviors in females, and models trained to predict internalizing behaviors in females generalize to predict externalizing behaviors in males. Finally, the neurobiological correlates of many behaviors are largely shared within and across sexes: functional connections within and between heteromodal association networks including default, limbic, control, and dorsal attention networks are associated with internalizing and externalizing behaviors as well as attentional deficits.ConclusionsTaken together, these findings suggest that shared neurobiological patterns may manifest as distinct behaviors across the sexes. These results highlight the need to consider factors beyond just neurobiology in the diagnosis and treatment of psychiatric illnesses.
A retrospective analysis of COVID-19 non-pharmaceutical interventions for Mexico and Peru: a modeling study
Authors: Acuña-Zegarra M. Adrian , Santana-Cibrian Mario , Rodriguez Hernandez-Vela Carlos E. , Mena Ramsés H. , Velasco-Hernández Jorge X. 
Published: December 2022

AbstractWe model the observed dynamics of COVID-19 in Mexico and Peru and explore the impact of hypothetical non-pharmaceutical interventions applied on key days of civic, religious, or political nature that increased contacts and transmission events. Using as a baseline the observed epidemic curve, we apply hypothetical reductions in the contact rates during the first year of the pandemic: i) near the beginning, ii) at the beginning of the second outbreak, and iii) end of the year. The effects of the interventions are different for Mexico and Peru but underlie the fact that strong early interventions do reduce the prevalence and, in general, allow for an epidemic evolution of relatively lower prevalence than interventions applied once the epidemic is underway. We provide evidence that key calendar days are good approximations of times when contact rates change and, therefore, are efficient periods for effective interventions particularly in places with low testing and lack of contact tracing. This has helped us to recreate different outbreaks of the COVID-19 disease dynamics in Mexico and Peru and explore the impact of hypothetical interventions that reduce the contact rate.
Gene complementation analysis suggests that dodder plants (Cuscutaspp.) do not depend on the host FT protein for flowering
Authors: Mäckelmann Sina , Känel Andrea , Kösters Lara M. , Lyko Peter , Prüfer Dirk , Noll Gundula A. , Wicke Susann 
Published: December 2022

AbstractDodder (Cuscutaspp.) is a genus of parasitic plants that form physiological bridges (haustoria) with their hosts to facilitate the transfer of water and nutrients. The parasites also repurpose nucleic acids and proteins translocating through the haustoria, potentially including the host florigen protein (FT), which is postulated to trigger floral transition in the parasite. Here, we identified the endogenous FT-FD flowering module inCuscuta campestris. We detected the expression of two parasite-encodedC. campestris(Cc)FT genes in haustoria, whereas a newly found CcFD-like gene was expressed ubiquitously.C. campestrisflowered while growing on mutant tobacco plants lacking the floral activators NtFT4 and NtFT5, indicating that host FT proteins are not required to initiate the parasite’s floral transition. We also showed that CcFT1 (identical to CaFT fromCuscuta australis) and CcFT2 can rescue a non-floweringNtft4−Ntft5−double knockout tobacco phenotype. Together, our results show thatCuscutaspp. produce a potent endogenous florigen as well as other proteins likely to be involved in floral transition. FT gene expression profiles in the haustoria suggest thatCuscutaspp. transition to flowering at least partly in response to host signals (e.g., sugars) that can activate the parasite’s FT-FD module. AlthoughC. campestrisandC. australisappear not to depend on the host FT protein for floral transition, the nature of the mobile host signals that influence floral development in these parasites remain unclear.Significance StatementParasitic higher plants are known for their sophisticated adaptations that facilitate the transfer of water and nutrients from their hosts. They can also synchronize their transition from vegetative to reproductive development to match the host plant. Despite this high degree of synchronization, dodder plants maintain a potent endogenous floral activator module, which enables the parasite to switch to reproductive development autonomously. Synchronization must therefore involve other stimuli from the host plant, which are currently unknown. Understanding the environmental cues that trigger flowering, and the corresponding network of genetic and physiological regulators and integrators, may lead to new strategies that reduce the reproductive fitness of parasitic plants to protect crops and ensure food security.Data ServersThis article is available as preprint (ID: BIORXIV/2022/520981) athttps://www.biorxiv.orgunder the CC BY-NC 4.0 license. Reusable data files have been deposited athttps://datadryad.org, accessible during peer-review under:https://datadryad.org/stash/share/DK8Olh2VqFwbGNL0GtkGt24dD0GhWhJn82oLBC1XK70
Using UAV-Imagery for Leaf Segmentation in Diseased Plants via Mask-Based Data Augmentation and Extension of Leaf-based Phenotyping Parameters
Authors: Barreto Abel , Reifenrath Lasse , Vogg Richard , Sinz Fabian , Mahlein Anne-Katrin 
Published: December 2022

AbstractIn crop production plant diseases cause significant yield losses. Therefore, the detection and scoring of disease occurrence is of high importance. The quantification of plant diseases requires the identification of leaves as individual scoring units. Diseased leaves are very dynamic and complex biological object which constantly change in form and color after interaction with plant pathogens. To address the task of identifying and segmenting individual leaves in agricultural fields, this work uses unmanned aerial vehicle (UAV), multispectral imagery of sugar beet fields and deep instance segmentation networks (Mask R-CNN). Based on standard and copy-paste image augmentation techniques, we tested and compare five strategies for achieving robustness of the network while keeping the number of labeled images within reasonable bounds. Additionally, we quantified the influence of environmental conditions on the network performance. Metrics of performance show that multispectral UAV images recorded under sunny conditions lead to a drop of up to 7% of average precision (AP) in comparison with images under cloudy, diffuse illumination conditions. The lowest performance in leaf detection was found on images with severe disease damage and sunny weather conditions. Subsequently, we used Mask R-CNN models in an image-processing pipeline for the calculation of leaf-based parameters such as leaf area, leaf slope, disease incidence, disease severity, number of clusters, and mean cluster area. To describe epidemiological development, we applied this pipeline in time-series in an experimental trial with five varieties and two fungicide strategies. Disease severity of the model with the highest AP results shows the highest correlation with the same parameter assessed by experts. Time-series development of disease severity and disease incidence demonstrates the advantages of multispectral UAV-imagery for contrasting varieties for resistance, and the limits for disease control measurements. With this work we highlight key components to consider for automatic leaf segmentation of diseased plants using UAV imagery, such as illumination and disease condition. Moreover, we offer a tool for delivering leaf-based parameters relevant to optimize crop production thought automated disease quantification imaging tools.
A deep learning-based approach to facilitate the as-built state recognition of indoor construction works
Authors: Ekanayake Biyanka , Ahmadian Fard Fini Alireza , Wong Johnny Kwok Wai , Smith Peter 
Published: December 2022

Purpose
Recognising the as-built state of construction elements is crucial for construction progress monitoring. Construction scholars have used computer vision-based algorithms to automate this process. Robust object recognition from indoor site images has been inhibited by technical challenges related to indoor objects, lighting conditions and camera positioning. Compared with traditional machine learning algorithms, one-stage detector deep learning (DL) algorithms can prioritise the inference speed, enable real-time accurate object detection and classification. This study aims to present a DL-based approach to facilitate the as-built state recognition of indoor construction works.

Design/methodology/approach
The one-stage DL-based approach was built upon YOLO version 4 (YOLOv4) algorithm using transfer learning with few hyperparameters customised and trained in the Google Colab virtual machine. The process of framing, insulation and drywall installation of indoor partitions was selected as the as-built scenario. For training, images were captured from two indoor sites with publicly available online images.

Findings
The DL model reported a best-trained weight with a mean average precision of 92% and an average loss of 0.83. Compared to previous studies, the automation level of this study is high due to the use of fixed time-lapse cameras for data collection and zero manual intervention from the pre-processing algorithms to enhance visual quality of indoor images.

Originality/value
This study extends the application of DL models for recognising as-built state of indoor construction works upon providing training images. Presenting a workflow on training DL models in a virtual machine platform by reducing the computational complexities associated with DL models is also materialised.
Digital transformation using industry 4.0 technology by food and beverage companies in post COVID-19 period: from DCV and IDT perspective
Authors: Chatterjee Sheshadri , Chaudhuri Ranjan , Vrontis Demetris , Galati Antonino 
Published: December 2022

PurposeThis study aims to understand the influence of digital transformation, using Industry 4.0 technology, on food and beverage companies in the post-COVID-19 period.Design/methodology/approachTo achieve the aim of this study, the authors have used innovation diffusion theory (IDT), dynamic capability view (DCV) theory and relevant literature, to develop a conceptual model. For the data, we surveyed 14, different sized food and beverage companies in India and the authors took inputs from 312 respondents at those companies to validate the conceptual model using the PLS-SEM approach.FindingsThe results from this study suggest that there is a considerable appetite for food and beverage companies to use Industry 4.0 technologies, as a part of their digital transformation journey in the post-COVID-19 scenario.Practical implicationsFood and beverage companies are going through a digital transformation process, which has been accelerated after the COVID-19 pandemic. Managers and leaders of the food and beverage companies need to support activities to adopt Industry 4.0 technologies. Moreover, the leaders should sponsor various readiness and training programs so that their employees successfully adopt these modern technologies.Originality/valueThis is a primary study on food and beverage companies that are using Industry 4.0 technologies or are in the process of digital transformation. The usage of both IDT and DCV to develop the unique conceptual model is an important contribution to the body of knowledge. Moreover, the proposed model has a high explanatory power, which makes the model effective.
Post-pandemic intention to participate in the tourism and hospitality (T&H) events: an integrated investigation through the lens of the theory of planned behavior and perception of Covid-19
Authors: Al Rousan Ramzi , Khasawneh Nermin , Sujood Sujood , Bano Naseem 
Published: December 2022

PurposeThis paper aims to predict the post-pandemic intention of Indian consumers to participate in tourism and hospitality T&H events by incorporating the perception of Covid-19 (PCOV) into the theory of planned behavior (TPB). It also examines the relationship among attitude, subjective norm, perceived behavioral control, behavioral intention and PCOV.Design/methodology/approachThis research employed a quantitative research technique, with data collected using an online questionnaire-based survey from August 23 to October 29, 2021. The sampling approach is convenient, and the sample of this study is comprised of individuals who follow events pages on social networking sites.FindingsThe findings of SEM revealed that the proposed model has better explanatory and predictive power. The path analysis showed that all the variables of the TPB are significantly and positively associated with the post-pandemic intention to participate in the T&H events. However, the PCOV is negatively associated with the post-pandemic intention.Research limitations/implicationsThe results of this study give an idea about the post-pandemic intention of Indian consumers to participate in T&H events. The results of this study revealed important practical implications for event management organizations, travel agencies that deal in events, the T&H industry, tourism planners and other stakeholders, as these findings may be used to develop effective marketing strategies.Originality/valueThere has been a lot of research on TPB in the context of coronavirus, but to the best of the authors' knowledge, there is a gap in the literature regarding the intention to participate in T&H events. Hence, this research fills that gap.
Integrating and transitioning the project front-end and project initiation phases in South African electrical engineering industrial projects
Authors: Siriram Rajenlall 
Published: December 2022

PurposeThe objective of this research is to explore integration and transition activities in large industrial projects. The purpose is to (a) obtain a better understanding of the integration and transition activities between the project front-end (FE) and project initiation phases (PIPs), (b) explore what, how and when these integrations and transitions occur, and (c) explore what the integration and transition activities mean to project practitioners.Design/methodology/approachA qualitative research design methodology is followed, based on interviews using open-ended questions. An expert panel is used to provide responses to questions pertaining to the integration and transition between the project FE and PIP. The research is focused on managing large projects in the South African electrical engineering industrial projects industry. A literature review combined with empirical analysis reflects the importance of integrating and transitioning in project business.FindingsThe findings provide guidance to researchers and practitioners on integration and transition mechanisms, how and when these occur. It highlights the benefits of integration and transition activities. Important lessons for researchers and practitioners are provided together with areas for future research.Originality/valueThis is an interpretative analysis of expert opinion. Expert panel members are experienced at senior decision-making level, and their expertise was accessed based on experience, education and knowledge. This extensive experience is shared in this paper providing insights into their opinions, experiences, success and failures. These inputs together with the literature review provide interesting implications for both a theoretical foundation as well as practical implications for practitioners.
“The perfect moment is this one. The effect of mindfulness on employees: a perspective from self-determination theory”
Authors: Zeshan Muhammad , de La Villarmois Olivier , Rasool Shahid , Niazi Abdur Rafeh Khan 
Published: December 2022

Purpose
This paper aims to show the direct and indirect effects of mindfulness on the employees’ commitment in the employees who perform monotonous work. Moreover, it also shows the role of basic psychological needs proposed by self-determination theory (SDT), on the relationship between mindfulness and commitment.

Design/methodology/approach
This paper has used a time-lagged approach. Data has been collected from the nurses in public sector hospitals through a survey strategy. Structural equation modeling has been used to validate the measure and to test the hypotheses.

Findings
The results of thi study reveal that there is a positive relationship between mindfulness and employee affective organizational commitment. This study also shows that in the existence of a high level of autonomy, mindfulness does have more effect on commitment. Moreover, this study also shows that this relationship is mediated by employee boredom. However, this mediation is not moderated by the satisfaction of the need for autonomy.

Practical implications
This study serves as a guide for frontline managers in situations where they want their subordinates who perform monotonous and boring work to remain committed to the organization. This study also emphasizes the recruitment of employees who may show more trait mindfulness.

Originality/value
This study enriches the literature in the field of organizational behavior by showing how basic psychological needs proposed by SDT collaborate with mindfulness in producing employees’ positive attitudes.
Does customer’s perception of ethicality influence brand affect, image and equity in the banking sector
Authors: Manansala Giovanni , Arasanmi Chris Niyi , Ojo Adedapo Oluwaseyi 
Published: December 2022

Purpose
This study aims to examine ethical practices in the banking sector by testing the relationships between customer perceptions of ethicality and brand attributes like affect, image and equity.

Design/methodology/approach
Drawing on the social exchange theory, the authors advance the consumer’s perspective in explaining brand equity in the banking sector. Following the survey technique, the authors used the Hayes’ Macro Process in analysing the data collected from 148 bank customers in New Zealand.

Findings
The findings suggest that customers’ perception of ethicality, brand image and affect are significantly associated with brand equity. Also, brand image and affect significantly mediate the relationship between customer’s perception of ethicality and brand equity.

Research limitations/implications
The main limitation of this study is the use of survey and cross-sectional methods. Future research may adopt mixed-method techniques to provide insightful information on how these variables influence brand equity.

Originality/value
The study demonstrates the mechanisms that facilitate brand equity and contributes to theory by analysing the factors of brand equity in the banking sector, which has been less investigated.