bluuebunny/crossref_metadata_2025_split huggingface dataset:
Unraveling the Role of P450 Reductase in Herbicide Metabolic Resistance Mechanism
Authors: Rigon Carlos Alberto Gonsiorkiewicz , Iwakami Satoshi , Gaines Todd A. , Dayan Franck E. 
Published: December 2023

ABSTRACTPlants require cytochrome P450 reductase (CPR) to supply two electrons for cytochrome P450 monooxygenase enzymes (P450) to react with an organic substrate. The transfer of electrons to the P450 active site in the P450 catalytic site relies on a robust and intricate CPR:P450 complex in the endoplasmic reticulum membrane. Transgenic Arabidopsis plants carryingCYP81A12fromEchinochloa phyllopogon, which metabolizes a broad spectrum of herbicides, were crossed with CPR knockoutatr1oratr2mutant lines. Homozygous gene knockout was confirmed using PCR, and gene copy number ofCYP81A12was determined using ddPCR. Arabidopsis lines expressingCYP81A12in combination withatr1oratr2knockout were used for herbicide dose-response and metabolism studies. Knocking outATR1in transgenic ArabidopsisCYP81A12significantly reduced herbicide resistance. Transgenic mutant plants (CYP81A12 atr1-b) had a 3.6-, 5.6-, 6.8- and at least 26-fold reduction in resistance to mesotrione, 2,4-D, penoxsulam and chlorsulfuron, respectively, in the dose-response assay. Knockouts of theATR2also decreased herbicide resistance, but to a lower magnitude thanATR1. These results corroborate ½ MS medium assay, and herbicide resistance reduction was observed for additional tested herbicides, bensulfuron, propoxycarbazone and bentazon. Our findings highlight the importance of CPRs in metabolic herbicide resistance in plants, by identifying that a single CPR knockout can reverse herbicide sensitivity . The different CPRs found in weeds have potential as target genes to manage metabolic herbicide resistance evolution. We further provide an in-depth exploration of the evolutionary implications in weed management arising from the results.HIGHLIGHTSKnocking out cytochrome P450 reductase 1 in herbicide tolerant Arabidopsis reduces herbicide resistance, highlighting CPRs as targets for managing herbicide resistance evolution in weeds.
A new method for the reproducible development of aptamers (Neomers)
Authors: Meehan Cathal , Hamilton Erika L. , Mansour Chloé G. , Lecocq Soizic , Drake Cole J. , An Yisi , Rodrigues Emily , Penner Gregory 
Published: December 2023

AbstractThe development of aptamers has been almost exclusively performed based on the SELEX method since their inception. While this method represents a powerful means of harnessing the in vitro evolution of sequences that bind to a given target, there are significant constraints in the design. The most significant constraint has been the reliance on counter selection on off-targets to drive specificity. Counter selection has not been as effective at driving aptamer specificity as the presence of immune tolerance, the capacity of the immune system to remove antibodies that bind to host targets, is for antibody development. This deficiency has constrained the commercial efficacy of aptamers to date. These limitations have been addressed with our design of a novel platform for aptamer identification. This new design is based on what we refer to as a Neomer library with sixteen random nucleotides interspersed with fixed sequences. The fixed sequences are designed to minimize the potential for hybridization, such that secondary structure is driven by the random nucleotides. The use of sixteen random nucleotides reduces the possible library sequences to 4.29 × 109. This enables the application of the same sequences to either the same target or different targets while maintaining a high level of structural diversity. In effect, this introduces the capacity for reproducibility in aptamer selection and in silico approach to replicating immune tolerance We provide here an overview of the new method and a description of the performance of aptamers selected for interleukin 6 developed using this approach.
Live-imaging reveals Coordinated Cell Migration and Cardiac Fate Determination during Mammalian Gastrulation
Authors: Abukar Shayma , Embacher Peter A. , Ciccarelli Alessandro , Varsani-Brown Sunita , North Isabel G.W. , Dean Jamie A. , Briscoe James , Ivanovitch Kenzo 
Published: December 2023

AbstractHeart development involves the specification of cardiac progenitors at distinct stages and locations. Using live-imaging of mouse embryos between gastrulation and heart tube formation, we tracked individual mesodermal cells and reconstructed their lineage trees for up to five cell divisions. We found independent unipotent progenitors emerging at specific times, contributing exclusively to either left ventricle/atrioventricular canal (LV/AVC) or atrial myocytes. LV/AVC progenitors differentiated early to form the cardiac crescent, while atrial progenitors later generated the heart tube’s inflow tract during morphogenesis. We also identified short-lived bipotent progenitors with broad potential, illustrating early developmental plasticity. Sister cells from bipotent progenitors displayed greater dispersion and more diverse migratory trajectories within the anterior mesoderm than those from unipotent progenitors. Bipotent progenitors contributing to extraembryonic mesoderm (ExEm) exhibited the fastest and most dispersed migrations, whereas those giving rise to endocardial, LV/AVC, and pericardial cells showed a more gradual divergence, with late-stage behavioural shifts: endocardial cells increased in speed, while pericardial cells slowed relative to LV/AVC cells. Together the data reveal the regulation of individual cell directionality and cardiac fate allocation within the seemingly unorganised migratory pattern of mesoderm cells.
Matrix Linear Models for connecting metabolite composition to individual characteristics
Authors: Farage Gregory , Zhao Chenhao , Choi Hyo Young , Garrett Timothy J. , Kechris Katerina , Elam Marshall B. , Sen Śaunak 
Published: December 2023

AbstractHigh-throughput metabolomics data provide a detailed molecular window into biological processes. We consider the problem of assessing how the association of metabolite levels with individual (sample) characteristics such as sex or treatment may depend on metabolite characteristics such as pathway. Typically this is one in a two-step process: In the first step we assess the association of each metabolite with individual characteristics. In the second step an enrichment analysis is performed by metabolite characteristics among significant associations. We combine the two steps using a bilinear model based on the matrix linear model (MLM) framework we have previously developed for high-throughput genetic screens. Our framework can estimate relationships in metabolites sharing known characteristics, whether categorical (such as type of lipid or pathway) or numerical (such as number of double bonds in triglycerides). We demonstrate how MLM offers flexibility and interpretability by applying our method to three metabolomic studies. We show that our approach can separate the contribution of the overlapping triglycerides characteristics, such as the number of double bonds and the number of carbon atoms. The proposed method have been implemented in the open-source Julia package,MatrixLM. Data analysis scripts with example data analyses are also available.
Using a decision tree to predict the number of COVID cases: a tutorial for beginners
Authors: Moctezuma Lucy , Rivera Lorena Benitez , van Nouhuijs Florentine , Orcales Faye , Kim Allen , Campbell Ross , Fuse Megumi , Pennings Pleuni S. 
Published: December 2023

ABSTRACTThis manuscript describes the development of a module that is part of a learning platform named “NIGMS Sandbox for Cloud-based Learning”https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorialNIGMS Sandboxat the beginning of this Supplement. This module delivers learning materials on machine learning and decision tree concepts in an interactive format that uses appropriate cloud resources for data access and analyses.Machine learning (ML) is an important tool in biomedical research and can lead to improvements in diagnosis, treatment, and prevention of diseases. During the COVID pandemic ML was used for predictions at the patient and community levels. Given its ubiquity, it is important that future doctors, researchers and teachers get acquainted with ML and its contributions to research. Our goal is to make it easier for everyone to learn about machine learning. The learning module we present here is based on a small COVID dataset, videos, annotated code and the use of Google Colab or the Google Cloud Platform (GCP). The benefit of these platforms is that students do not have to set up a programming environment on their computer which saves time and is also an important democratization factor. The module focuses on learning the basics of decision trees by applying them to COVID data. It introduces basic terminology used in supervised machine learning and its relevance to research. Our experience with biology students at San Francisco State University suggests that the material increases interest in ML.
A systematic review of the methods used to ensure rigour, transparency and validity in rapid evaluations, rapid appraisals and rapid assessments
Authors: Clark Sigrún Eyrúnardóttir , San Juan Norha Vera , Moniz Thomas , Appleton Rebecca , Barnett Phoebe , Vindrola-Padros Cecilia 
Published: December 2023

AbstractRapid approaches are essential when resources are limited in research settings and in the real-time evaluation of changing services. Limitations exist in their design and implementation which can lead to a reduced level of trust in findings. This study identified the methods used across rapid evaluations, appraisals and assessments to improve rigour, transparency and validity. Four scientific databases and one search engine were searched between 11-16th August 2022. Screening led to the inclusion of 169 articles that provided a much-needed repository of methods that can be used during the design and implementation of rapid studies to improve rigour. We suggest that this repository of methods inform the development of transparent reporting standards for future rapid evaluations, appraisals and assessments.
Higher-order thalamocortical projections selectively control excitability via NMDAR and mGluRI-mediated mechanisms
Authors: Brandalise Federico , Chéreau Ronan , Raig Claudia Morin , Bawa Tanika , Mule Nandkishor , Pagès Stéphane , Markopoulos Foivos , Holtmaat Anthony 
Published: December 2023

SUMMARYThe apical dendrites of layer (L) 2/3 pyramidal neurons in the mouse somatosensory cortex integrate synaptic input from long-range projections. Among those, inputs from the higher-order thalamic posteromedial nucleus may facilitate sensory-evoked cortical activity, but it remains elusive how this role emerges. Here we show usingex vivodendritic recordings that these projections provide dense synaptic input to broad tufted neurons residing predominantly in L2 and cooperate with other inputs to produce NMDA spikes. They have the unique capacity to block two-pore domain potassium leak channels via group 1 metabotropic glutamate receptor (mGluRI) signaling, which increases excitability. Slender tufted L2/3 neurons and other long-range projections fail to invoke these mechanisms.In vivoimaging of calcium signals confirms the presence of mGluRI-dependent modulation of feedback-mediated spiking in L2. Our results imply that higher-order thalamocortical projections regulate neuronal excitability in a cell type and input-selective manner through fast NMDAR and mGluRI-dependent mechanisms.
Event Detection and Classification from Multimodal Time Series with Application to Neural Data
Authors: Sadras Nitin , Pesaran Bijan , Shanechi Maryam M. 
Published: December 2023

AbstractThe detection of events in time-series data is a common signal-processing problem. When the data can be modeled as a known template signal with an unknown delay in Gaussian noise, detection of the template signal can be done with a traditional matched filter. However, in many applications, the event of interest is represented in multimodal data consisting of both Gaussian and point-process time series. Neuroscience experiments, for example, can simultaneously record multimodal neural signals such as local field potentials (LFPs), which can be modeled as Gaussian, and neuronal spikes, which can be modeled as point processes. Currently, no method exists for event detection from such multimodal data, and as such our objective in this work is to develop a method to meet this need. Here we address this challenge by developing the multimodal event detector (MED) algorithm which simultaneously estimates event times and classes. To do this, we write a multimodal likelihood function for Gaussian and point-process observations and derive the associated maximum likelihood estimator of simultaneous event times and classes. We additionally introduce a cross-modal scaling parameter to account for model mismatch in real datasets. We validate this method in extensive simulations as well as in a neural spike-LFP dataset recorded during an eye-movement task, where the events of interest are eye movements with unknown times and directions. We show that the MED can successfully detect eye movement onset and classify eye movement direction. Further, the MED successfully combines information across data modalities, with multimodal performance exceeding unimodal performance. This method can facilitate applications such as the discovery of latent events in multimodal neural population activity and the development of brain-computer interfaces for naturalistic settings without constrained tasks or prior knowledge of event times.
From data to data asset: conceptual evolution and strategic imperatives in the digital economy era
Authors: Xu Tao , Shi Hanning , Shi Yongjiang , You Jianxin 
Published: December 2023

Purpose
The purpose of this paper is to explore the concept of data assets and how companies can assetize their data. Using the literature review methodology, the paper first summarizes the conceptual controversies over data assets in the existing literature. Subsequently, the paper defines the concept of data assets. Finally, keywords from the existing research literature are presented visually and a foundational framework for achieving data assetization is proposed.

Design/methodology/approach
This paper uses a systematic literature review approach to discuss the conceptual evolution and strategic imperatives of data assets. To establish a robust research methodology, this paper takes into account two main aspects. First, it conducts a comprehensive review of the existing literature on digital technology and data assets, which enables the derivation of an evolutionary path of data assets and the development of a clear and concise definition of the concept. Second, the paper uses Citespace, a widely used software for literature review, to examine the research framework of enterprise data assetization.

Findings
The paper offers pivotal insights into the realm of data assets. It highlights the changing perceptions of data assets with digital progression and addresses debates on data asset categorization, value attributes and ownership. The study introduces a definitive concept of data assets as electronically recorded data resources with real or potential value under legal parameters. Moreover, it delineates strategic imperatives for harnessing data assets, presenting a practical framework that charts the stages of “resource readiness, capacity building, and data application”, guiding businesses in optimizing their data throughout its lifecycle.

Originality/value
This paper comprehensively explores the issue of data assets, clarifying controversial concepts and categorizations and bridging gaps in the existing literature. The paper introduces a clear conceptualization of data assets, bridging the gap between academia and practice. In addition, the study proposes a strategic framework for data assetization. This study not only helps to promote a unified understanding among academics and professionals but also helps businesses to understand the process of data assetization.
Why employees engage in proactive career behavior: examining the role of family motivation
Authors: Wang Yating , Hou Qinghui , Xue Zewei , Li Huan 
Published: December 2023

PurposeThis study established and examined a moderated mediation model connecting family motivation with proactive career behavior (PCB). Drawing on social cognitive career theory model of career self-management and person-environment fit theory, the authors posit that the influence of family motivation on PCB is mediated by career goal setting (CGS) and moderated by career support climate (CSC).Design/methodology/approachUsing multilevel modeling techniques, data were collected from 98 leaders and 416 employees at two-time points.FindingsResults show that family motivation has a positive direct and indirect effect on PCB through CGS. Additionally, CSC strengthens the positive effect of family motivation on PCB through CGS.Practical implicationsFor employees, proactive career management and the establishment of clear career goals are vital for fulfilling family responsibilities. Simultaneously, for managers and organizations, creating a supportive work environment is crucial to encourage employees in career management.Originality/valueWhile existing research highlights the detrimental effect of fulfilling family caregiving responsibilities on employees' career development, the comprehension of this relationship remains limited. Taking a family motivation perspective, this study explores the mechanisms through which family motivation stimulates employees to engage in PCB, with a specific focus on the moderating influence of CSC. The findings offer fresh insights into the role of family motivation in career development, contributing significantly to the broader literature on the intersection of family and careers.
Modeling and simulation analysis of current transformer based on magnetizing current as the solution variable
Authors: You Hongsen , Gan Mengying , Duan Dapeng , Zhao Cheng , Chi Yuan , Gao Shuai , Yuan Jiansheng 
Published: December 2023

Purpose
This paper aims to develop a model that reflects the current transformer (CT) core materials nonlinearity. The model enables simulation and analysis of the CT excitation current that includes the inductive magnetizing current and the resistive excitation current.

Design/methodology/approach
A nonlinear CT model is established with the magnetizing current as the solution variable. This model presents the form of a nonlinear differential equation and can be solved discretely using the Runge–Kutta method.

Findings
By simulating variations in the excitation current for different primary currents, loads and core materials, the results demonstrate that enhancing the permeability of the B–H curve leads to a more significant improvement in the CT ratio error at low primary currents.

Originality/value
The proposed model has three obvious advantages over the previous models with the secondary current as the solution variable: (1) The differential equation is simpler and easier to solve. Previous models contain the time differential terms of the secondary current and excitation flux or the integral term of the flux, making the iterative solution complicated. The proposed model only contains the time differential of the magnetizing current. (2) The accuracy of the excitation current obtained by the proposed model is higher. Previous models calculate the excitation current by subtracting the secondary current from the converted primary current. Because these two currents are much greater than the excitation current, the error of calculating the small excitation current by subtracting two large numbers is greatly enlarged. (3) The proposed model can calculate the distorted waveform of the excitation current and error for any form of time-domain primary current, while previous models can only obtain the effective value.
A systematic review of lean implementation frameworks and roadmaps: lessons learned and the way forward
Authors: Mamoojee-Khatib Hafeeza , Antony Jiju , Teeroovengadum Viraiyan , Garza-Reyes Jose Arturo , Tortorella Guilherme Luz , Foster Monika , Cudney Elizabeth A. 
Published: December 2023

PurposeThe purpose of this study is to carry out a comprehensive systematic review of lean implementation frameworks and roadmaps developed over the past decade and report the key findings along with the limitations and the way forward.Design/methodology/approachA systematic review methodology proposed by Tranfield (2003), was followed to identify the relevant works on the research topic. Articles were searched using a set of inclusion criteria in various databases including Google Scholar, Web of Science and Science Direct over a period of 30 years.FindingsThe high failure rate of lean system implementation, reaching a range between 70 and 90% in almost all industries, is a matter of concern. This failure rate is still high even though numerous frameworks and roadmap models exist to streamline lean implementation. There is no standard framework or roadmap identified in the literature and many organisations are implementing lean in their unique ways. However, it would be desirable to develop a practical and systematic roadmap on lean-looking into the cultural and leadership dimensions rather than focusing on a set of tools. Moreover, most frameworks and roadmaps lack the sustenance aspects of lean implementation.Research limitations/implicationsThis research only identifies the fundamental gaps with the existing frameworks and roadmaps on lean implementation. The next phase of the research is to develop a roadmap and validate it with a number of organisations in different cultural contexts and leadership styles.Originality/valueThe authors argue that this is one of the most comprehensive systematic review on lean frameworks and roadmaps, ever produced in the literature to date.
Is Hong Kong still an entrepôt under the Sino‐U.S. trade war?
Authors: Chong Terence Tai Leung , Lo Vincent Pok Ho 
Published: December 2023

AbstractThis paper studies the Hong Kong economy from a trade intermediary's perspective. Using a structural vector autoregression (SVAR) framework, we discover that trade‐related external shocks mainly affect the economy through re‐exports channel over the offshore trade channel. We observe that re‐exports shock does not transmit to the real economy through the employment channel. A puzzling phenomenon is that trade‐related employment has been in decline since 2008. Moreover, we notice that although the Sino‐U.S. trade war exerted much downward pressure on economic growth between 2018 and 2019, the trade intermediary sector swiftly recovered in 2020, reflecting the versatility of the external trade sector in Hong Kong.