bluuebunny/crossref_metadata_2025 huggingface dataset:
Reconstruction of single cell lineage trajectories and identification of diversity in fates during the epithelial-to-mesenchymal transition
Authors: Cheng Yu-Chen , Zhang Yun , Tripathi Shubham , Harshavardhan BV , Jolly Mohit Kumar , Schiebinger Geoffrey , Levine Herbert , McDonald Thomas O. , Michor Franziska
Published: September 2023

Exploring the complexity of the epithelial-to-mesenchymal transition (EMT) unveils a diversity of potential cell fates; however, the exact timing and intricate mechanisms by which early cell states diverge into distinct EMT trajectories remain unclear. Studying these EMT trajectories through single cell RNA sequencing is challenging due to the necessity of sacrificing cells for each measurement. In this study, we employed optimal-transport (OT) analysis to reconstruct the past trajectories of different cell fates during TGF-beta-induced EMT in the MCF10A cell line. Our analysis revealed three distinct trajectories leading to low EMT, partial EMT, and high EMT states. Cells along partial EMT trajectory showed substantial variations in the EMT signature and exhibited pronounced stemness. Throughout this EMT trajectory, we observed a consistent downregulation of theEEDandEZH2genes. This finding was validated by recent inhibitor screens of EMT regulators and CRISPR screen studies. Moreover, we applied our analysis of early-phase differential gene expression to gene sets associated with stemness and proliferation, pinpointingITGB4,LAMA3, andLAMB3as genes differentially expressed in the initial stages of the partial versus high EMT trajectories. We also found thatCENPF,CKS1B, andMKI67showed significant upregulation in the high EMT trajectory. While the first group of genes aligns with findings from previous studies, our work uniquely pinpoints the precise timing of these upregulations. Finally, the latter group of genes represents newly identified regulators, shedding light on potential targets for modulating EMT trajectories.

Significance Statement

In our study, we investigated cellular trajectories during EMT using a time-series scRNAseq dataset. OT analysis was used to infer cell-to-cell connections from scRNAseq data, allowing us to predict cell linkages and overcome limitations of sequencing such as the need to sacrifice cells for each measurement. This approach allowed us to identify diverse EMT responses under uniform treatment, a significant advancement over previous studies limited by the static nature of scRNAseq data. Our analysis identified a broad set of genes involved in the EMT process, uncovering novel insights such as the upregulation of cell cycle genes in cells predisposed to a high EMT state and the enhancement of cell adhesion marker genes in cells veering towards a partial EMT state. This work enriches our understanding of the dynamic processes of EMT, showcasing the varied cellular fates within the same experimental setup.
Motif selection enables efficient sequence-based classification of non-coding RNA
Authors: Chegrane Ibrahim , Benjaa Nabil , Ouangraoua Aida
Published: September 2023

Motivation: Non-coding RNAs (ncRNAs) classification is important for genome annotation and to perform functional analyses of these biological molecules. Efficient methods for large-scale RNA classification remain challenging. Existing methods often rely on structure similarity, and they require huge computing times because they use secondary structure information, which impedes any large scale use. Results: We present a sequence-based method that relies on the computation and the selection of common sequence motifs to provide a set of features for effectively classifying ncRNAs families by a supervised learning approach. The results show that our method achieves an equal or higher accuracy than existing structure-based methods and drastically reduces required computing times. Results also demonstrate that, thanks to an appropriate selection of local sequence motifs, an efficient sequencebased ncRNA classification can be achieved through supervised learning.
Identifying Temporal Correlations Between Natural Single-shot Videos and EEG Signals
Authors: Yao Yuanyuan , Stebner Axel , Tuytelaars Tinne , Geirnaert Simon , Bertrand Alexander
Published: September 2023

Abstract

Objective

Electroencephalography (EEG) is a widely used technology for recording brain activity in brain-computer interface (BCI) research, where understanding the encoding-decoding relationship between stimuli and neural responses is a fundamental challenge. Recently, there is a growing interest in encoding-decoding natural stimuli in a single-trial setting, as opposed to traditional BCI literature where multi-trial presentations of synthetic stimuli are commonplace. While EEG responses to natural speech have been extensively studied, such stimulus-following EEG responses to natural video footage remain underexplored.

Approach

We collect a new EEG dataset with subjects passively viewing a film clip and extract a few video features that have been found to be temporally correlated with EEG signals. However, our analysis reveals that these correlations are mainly driven by shot cuts in the video. To avoid the confounds related to shot cuts, we construct another EEG dataset with natural single-shot videos as stimuli and propose a new set of object-based features.

Main Results

We demonstrate that previous video features lack robustness in capturing the coupling with EEG signals in the absence of shot cuts, and that the proposed object-based features exhibit significantly higher correlations. Furthermore, we show that the correlations obtained with these proposed features are not dominantly driven by eye movements. Additionally, we quantitatively verify the superiority of the proposed features in a match-mismatch (MM) task. Finally, we evaluate to what extent these proposed features explain the variance in coherent stimulus responses across subjects.

Significance

This work provides valuable insights into feature design for video-EEG analysis and paves the way for applications such as visual attention decoding.
pyHiM, a new open-source, multi-platform software package for spatial genomics based on multiplexed DNA-FISH imaging
Authors: Xavier Devos , Jean-Bernard Fiche , Marion Bardou , Olivier Messina , Christophe Houbron , Julian Gurgo , Marie Schaeffer , Markus Götz , Thomas Walter , Florian Mueller , Marcelo Nollmann
Published: September 2023

The three-dimensional (3D) nuclear organization of chromatin in eukaryotes plays a crucial role in gene regulation, DNA replication, and DNA damage repair. While genome-wide ensemble methods have enhanced our understanding of chromatin organization, they lack the ability to capture single-cell heterogeneity and preserve spatial information. To overcome these limitations, a new family of imaging-based methods has emerged, giving rise to the field of spatial genomics. In this study, we present pyHiM, an open-source and modular software toolbox specifically designed for the robust, automatic analysis of sequential spatial genomics data. pyHiM enables the reconstruction of chromatin traces in individual cells from raw, multicolor images, offering novel, robust and validated algorithms, extensive documentation, and tutorials. Its user-friendly graphical interface and command-line interface allow for easy installation and execution on various hardware platforms. The software employs a modular architecture, allowing independent execution of analysis steps and customization according to sample specificity and computing resources. pyHiM supports preprocessing, spot detection, mask detection, and trace generation, generating human-readable reports and intermediate results for data validation and further analysis. Moreover, it offers additional features for data formatting, result display, and post-processing. pyHiM’s scalability and parallelization capabilities enable the analysis of large, complex datasets in a reasonable time frame. Overall, pyHiM aims to facilitate the democratization and standardization of spatial genomics analysis, foster collaborative developments, and promote the growth of a user community to drive discoveries in the field of chromatin organization.
SENSE-PPI reconstructs protein-protein interactions of various complexities, within, across, and between species, with sequence-based evolutionary scale modeling and deep learning
Authors: Volzhenin Konstantin , Bittner Lucie , Carbone Alessandra
Published: September 2023

Ab initiocomputational reconstructions of protein-protein interaction (PPI) networks will provide invaluable insights on cellular systems, enabling the discovery of novel molecular interactions and elucidating biological mechanisms within and between organisms. Leveraging latest generation protein language models and recurrent neural networks, we presentSENSE-PPI, a sequence-based deep learning model that efficiently reconstructsab initioPPIs, distinguishing partners among tens of thousands of proteins and identifying specific interactions within functionally similar proteins.SENSE-PPIdemonstrates high accuracy, limited training requirements, and versatility in cross-species predictions, even with non-model organisms and human-virus interactions. Its performance decreases for phylogenetically more distant model and non-model organisms, but signal alteration is very slow.SENSE-PPIis state-of-the-art, outperforming all existing methods. In this regard, it demonstrates the important role of parameters in protein language models.SENSE-PPIis very fast and can test 10,000 proteins against themselves in a matter of hours, enabling the reconstruction of genome-wide proteomes.

Graphical abstract

SENSE-PPIis a general deep learning architecture predicting protein-protein interactions of different complexities, between stable proteins, between stable and intrinsically disordered proteins, within a species, and between species. Trained on one species, it accurately predicts interactions and reconstructs complete specialized subnetworks for model and non-model organisms, and trained on human-virus interactions, it predicts human-virus interactions for new viruses.
Sex Chromosomes and Gonads Shape the Sex-Biased Transcriptomic Landscape in Tlr7-Mediated Demyelination During Aging
Authors: Lopez-Lee Chloe , Kodama Lay , Fan Li , Wong Man Ying , Foxe Nessa R. , Jiaz Laraib , Yu Fangmin , Ye Pearly , Zhu Jingjie , Norman Kendra , Torres Eileen Ruth , Kim Rachel D. , Mousa Gergey Alzaem , Dubal Dena , Liddelow Shane , Luo Wenjie , Gan Li
Published: September 2023

Demyelination occurs in aging and associated diseases, including Alzheimer’s disease. Several of these diseases exhibit sex differences in prevalence and severity. Biological sex primarily stems from sex chromosomes and gonads releasing sex hormones. To dissect mechanisms underlying sex differences in demyelination of aging brains, we constructed a transcriptomic atlas of cell type-specific responses to illustrate how sex chromosomes, gonads, and their interaction shape responses to demyelination. We found that sex-biased oligodendrocyte and microglial responses are driven by interaction of sex chromosomes and gonads prior to myelin loss. Post demyelination, sex chromosomes mainly guide microglial responses, while gonadal composition influences oligodendrocyte signaling. Significantly, ablation of the X-linked gene Toll-like receptor 7 (Tlr7), which exhibited sex-biased expression during demyelination, abolished the sex-biased responses and protected against demyelination.

One-sentence summary

Cell type-specific processes underlying aged demyelination are sex-biased and mediated byTlr7.
Mineralocorticoid receptor-independent activation of ENaC in bile duct ligated mice
Authors: Wang Xue-Ping , Mutchler Stephanie M. , Carrisoza-Gáytan Rolando , Al-Bataineh Mohammad , Baty Catherine J. , Vandevender Amber , Srinivasan Priyanka , Tan Roderick J. , Jurczak Michael J. , Satlin Lisa M. , Kashlan Ossama B.
Published: September 2023

Sodium and fluid retention in liver disease is classically thought to result from reduced effective circulating volume and stimulation of the renin-angiotensin-aldosterone system (RAAS). Aldosterone dives Na+retention by activating the mineralocorticoid receptor and promoting the maturation and apical surface expression of the epithelial Na+channel (ENaC), found in the aldosterone-sensitive distal nephron. However, evidence of fluid retention without RAAS activation suggests the involvement of additional mechanisms. Liver disease can greatly increase plasma and urinary bile acid concentrations and have been shown to activate ENaCin vitro. We hypothesize that elevated bile acids in liver disease activate ENaC and drive fluid retention independent of RAAS. We therefore increased circulating bile acids in mice through bile duct ligation (BDL) and measured effects on urine and body composition, while using spironolactone to antagonize the mineralocorticoid receptor. We found BDL lowered blood [K+] and hematocrit, and increased benzamil-sensitive natriuresis compared to sham, consistent with ENaC activation. BDL mice also gained significantly more body water. Blocking ENaC reversed fluid gains in BDL mice but had no effect in shams. In isolated collecting ducts from rabbits, taurocholic acid stimulated net Na+absorption but had no effect on K+secretion or flow-dependent ion fluxes. Our results provide experimental evidence for a novel aldosterone-independent mechanism for sodium and fluid retention in liver disease which may provide additional therapeutic options for liver disease patients.

Significance

Advanced liver disease is often complicated by renal sodium retention, leading to fluid retention, poor outcomes, and increased mortality. This is currently thought to be driven by increased levels of the hormone aldosterone, although numerous published reports demonstrate normal aldosterone levels in many liver patients with volume overload. Management of these patients relies on diuretics and Na+restriction, or more invasive procedures with increased risks for diuretic-resistant patients. Here, we report a novel mechanism for fluid retention in liver disease, which provides a basis to develop new strategies to treat fluid retention in these patients.
Targeted MRM-analysis of plasma proteins in frozen whole blood samples from patients with COVID-19
Authors: Bugrova Anna E. , Strelnikova Polina A. , Kononikhin Alexey S. , Zakharova Natalia V. , Diyachkova Elizaveta O. , Brzhozovskiy Alexander G. , Indeykina Maria I. , Kurochkin Ilya N. , Averyanov Alexander V. , Nikolaev Evgeny N.
Published: September 2023

The COVID-19 pandemic has exposed a number of key challenges that need to be urgently addressed. In particular, rapid identification and validation of prognostic markers is required. Mass spectrometric studies of blood plasma proteomics provide a deep understanding of the relationship between the severe course of infection and activation of specific pathophysiological pathways. Analysis of plasma proteins in whole blood may also be relevant for the pandemic as it requires minimal sample preparation. Here, for the first time, frozen whole blood samples were used to analyze 189 plasma proteins using multiple reaction monitoring (MRM) mass spectrometry and stable isotope-labeled peptide standards (SIS). A total of 128 samples (FRCC, Russia) from patients with mild (n=40), moderate (n=36) and severe (n=19) COVID-19 infection and healthy controls (n=33) were analyzed. Levels of 114 proteins were quantified and compared. Significant differences between all of the groups were revealed for 61 proteins. Changes in the levels of 30 reproducible COVID-19 markers (SERPING1, CRP, C9, ORM1, APOA1, SAA1/SAA2, LBP, AFM, IGFALS, etc.) were consistent with studies performed with serum/plasma samples. Levels of 70 proteins correlated between whole blood and plasma samples. The best-performing classifier built with 13 significantly different proteins achieved the best combination of ROC-AUC (0.93-0.95) and accuracy (0.87-0.93) metrics and distinguished patients from controls, as well as patients by severity and risk of mortality. Overall, the results support the use of frozen whole blood for MRM analysis of plasma proteins and assessment of the status of patients with COVID-19.
ANS: Adjusted Neighborhood Scoring to improve gene signature-based cell annotation in single-cell RNA-seq data
Authors: Ciernik Laure , Kraft Agnieszka , Barkmann Florian , Yates Josephine , Boeva Valentina
Published: September 2023

In the field of single-cell RNA sequencing (scRNA-seq), gene signature scoring is integral for pinpointing and characterizing distinct cell populations. However, challenges arise in ensuring the robustness and comparability of scores across various gene signatures and across different batches and conditions. Here, we evaluated the stability of established methods such as Scanpy, UCell, and JASMINE in the context of scoring cells of different types and states. On eight cancer and healthy scRNA-seq datasets, we reported that none of the existing methods provide fair gene signature scores that can be used in unsupervised cell state annotation based on the highest signature values. Addressing this challenge, we introduced a new scoring method, the Adjusted Neighbourhood Scoring (ANS), that builds on the traditional Scanpy method and improves the handling of the control gene sets. We further exemplified the usability of ANS scoring in differentiating between cancer-associated fibroblasts and malignant cells undergoing epithelial-mesenchymal transition (EMT) in four cancer types, and evidenced excellent classification performance (AUCPR train: 0.95-0.99, AUCPR test: 0.91-0.99). In summary, our research introduces ANS as a robust and deterministic scoring approach that enables the comparison of diverse gene signatures and score-based annotation of cell types and states. The results of our study contribute to the development of more accurate and reliable methods for analyzing scRNA-seq data.
Intraspecific trait variation modulates the temperature effect on elemental quotas and stoichiometry in marineSynechococcus
Authors: Davis Austin , Garcia Nathan S , Martiny Adam C
Published: September 2023

Diverse phytoplankton modulate the coupling between the ocean carbon and nutrient cycles through life-history traits such as cell size, elemental quotas, and ratios. Biodiversity is mostly considered at broad functional levels, but major phytoplankton lineages are themselves highly diverse. As an example,Synechococcusis found in nearly all ocean regions and contain extensive intraspecific variation. Here, we grew four closely relatedSynechococcusisolates in semi-continuous cultures across a range of temperatures (16-25°C) to quantify for the relative role of intraspecific trait variation vs. environmental change. We report differences in cell size (p<0.01) as a function of strain and clade (p<0.01). The carbon (QC), nitrogen (QN), and phosphorus (QP) cell quotas all increased with cell size. Furthermore, cell size has an inverse relationship to growth rate. Within our experimental design, temperature alone had a weak physiological effect on cell quota and elemental ratios. Instead, we find systemic intraspecific variance of C:N:P, with cell size and N:P having an inverse relationship. Our results suggest a key role for intraspecific life history traits in determining elemental quotas and stoichiometry. Thus, the extensive biodiversity harbored within many lineages may modulate the impact of environmental change on ocean biogeochemical cycles.
Olfactory learning modulates a neural circuit mediating innate odor-guided behavior inDrosophila
Authors: Campetella Florencia , Huber Roman , Klappenbach Martin , Warnecke Carolin , Locatelli Fernando , Felsenberg Johannes , Hansson Bill S. , Knaden Markus , Sachse Silke
Published: September 2023

Behavior is often categorized as being innate or learned, with the specific circuits being assigned to one of these categories. InDrosophila, neural circuits mediating an innate behavioral response are considered as being “hard-wired”, as activation of these neuronal pathways leads to stereotyped behaviors. However, only a limited number of studies assessed whether innate behaviors and their underlying neural circuits are plastic or show experience-dependent modulation. Here, we show that experience modulates second-order olfactory neurons involved in innate behavioral responses. We focus on the neural circuit defined by multiglomerular projection neurons (mPNs) that target the lateral horn, a structure relevant in the genesis of innate behavior. We show that mPNs, coding for odor attraction, are bidirectionally modulated after olfactory associative learning: when an olfactory stimulus is paired with an aversive electric shock, the activity of these neurons is decreased, while when the odor is paired with a sucrose-reward they are potentiated. We further show that this modulation requires glutamate and serotonin signaling, and that downstream third-order neurons are consequently affected. The bidirectional nature of the plasticity in these neurons is reflected in behavior: silencing mPN activity leads to odor avoidance, while artificial activation induces approach. While output from the mPNs is not required in aversive olfactory conditioning, silencing these neurons during retrieval of appetitive memories leads to a significant memory impairment. Artificially activating these neurons during odor presentation is sufficient to generate a 3 h appetitive memory. Our study in flies shows that a neural circuit coding for innate odor attraction can contribute to learned behavior, is modulated by olfactory learning and can provide reward-like reinforcement.
Dissecting Factors Underlying Phylogenetic Uncertainty Using Machine Learning Models
Authors: Rosas-Puchuri Ulises , Duarte-Ribeiro Emanuell , Khanmohammadi Sina , Arcila Dahiana , Ortí Guillermo , Betancur-R Ricardo
Published: September 2023

Phylogenetic inference can be influenced by both underlying biological processes and methodological factors. While biological processes can be modeled, these models frequently make the assumption that methodological factors do not significantly influence the outcome of phylogenomic analyses. Depending on their severity, methodological factors can introduce inconsistency and uncertainty into the inference process. Although search protocols have been proposed to mitigate these issues, many solutions tend to treat factors independently or assume a linear relationship among them. In this study, we capitalize on the increasing size of phylogenetic datasets, using them to train machine learning models. This approach transcends the linearity assumption, accommodating complex non-linear relationships among features. We examined two phylogenomic datasets for teleost fishes: a newly generated dataset for protacanthopterygians (salmonids, galaxiids, marine smelts, and allies), and a reanalysis of a dataset for carangarians (flatfishes and allies). Upon testing five supervised machine learning models, we found that all outperformed the linear model (p < 0.05), with the deep neural network showing the best fit for both empirical datasets tested. Feature importance analyses indicated that influential factors were specific to individual datasets. The insights obtained have the potential to significantly enhance decision-making in phylogenetic analyses, assisting, for example, in the choice of suitable DNA sequence models and data transformation methods. This study can serve as a baseline for future endeavors aiming to capture non-linear interactions of features in phylogenomic datasets using machine learning and complement existing tools for phylogenetic analyses.
Fabrication of size-coded amphiphilic particles with a configurable 3D-printed microfluidic device for the formation of particle-templated droplets
Authors: Akhtar Muhammad Usman , Sahin Mehmet Akif , Werner Helen , Destgeer Ghulam
Published: September 2023

Compartmentalizing an aqueous media into numerous nanoliter-scale droplets has substantially improved the performance of amplification assays. Particle-templated droplets ordropiclesoffer a user-friendly workflow for creating uniform volume compartments upon simple mixing of reagents and particles by using common laboratory apparatus. Amphiphilic shape-coded particles have been demonstrated to spontaneously hold aqueous droplets within hydrophilic cavities for multiplexed diagnostic assays. Here, we have proposed a configurable 3D-printed microfluidic device for the tunable fabrication of amphiphilic size-coded particles. The device was configured with multiple outlet tubings of different diameters and photomasks of variable slit lengths to fabricate a wide range of size-coded particles. We have fabricated >10 unique particle codes using a single reconfigurable device. The cross-sectional profile of the particles was further engineered by tuning the flow rate ratios of precursor streams to vary the inner and outer diameters of the particles and the thicknesses of the inner hydrophilic and outer hydrophobic layers. A range of cavity diameters and particle lengths enableddropiclevolumes of ∼1nL to ∼30nL. The fabricated particles were characterized by their ability to hold uniform droplet volumes and to orient themselves facing upwards or sideways in a well plate based on their aspect ratios.
The vaccinia virus E3L dsRNA binding protein detects distinct production patterns of exogenous and endogenous dsRNA
Authors: Zhang Wenjing , Tanneti Nikhila S. , Fausto Alejandra , Nouel Joangela , Reyes Hanako , Weiss Susan R. , Li Yize
Published: September 2023

Double-stranded RNA (dsRNA) is a pathogen associated molecular pattern recognized by multiple pattern recognition receptors and induces innate immune responses. Viral infections can generate dsRNA during virus replication. Genetic mutations can also lead to endogenous dsRNA accumulation. DsRNA is present in multiple conformations such as the A form (A-dsRNA) or Z form (Z-dsRNA). A-dsRNA has been detected from multiple viruses with positive-stranded RNA genomes (+ssRNA) but rarely from viruses with negative RNA genomes (-RNA); Z-dsRNA can be detected from influenza virus and poxvirus infections. Viruses have evolved mechanisms to antagonize cellular antiviral responses triggered by dsRNAs. For example, the vaccinia-virus E3L protein can bind and sequester dsRNA to evade host immune responses. The E3L protein encodes a Z-DNA and a dsRNA binding domains that bind to Z-form nucleic acids or dsRNA, respectively. Here we developed recombinant E3L proteins to detect dsRNA and Z-dsRNA generated from viral infections or endogenous cellular mutations. We demonstrate that the E3L recombinant protein specifically detects A-dsRNA generated from +ssRNA viruses but not-RNA viruses. We observe that among various virus infections assayed, only the influenza A virus generates Z-RNA that can be detected by anti-Z-NA antibody but not by the E3L recombinant protein containing the Z-DNA domain. The E3L recombinant protein can also detect endogenous dsRNA in PNPT1 or SUV3L1 knockout cells. Together we concluded that A-dsRNA can be produced and detected from viruses with +ssRNA genomes but not-RNA genomes, and Z-dsRNA can be produced and detected from influenza A virus.

Importance

The detection of dsRNAs, which exist in the A-dsRNA or Z-RNA conformation, is important for the induction of innate immune responses. dsRNA are generated during a virus infection due to virus replication, or can accumulate to genetic mutations. We engineered recombinant vaccinia virus E3L protein that can detect A-dsRNA generated during infection with a positive-sense RNA genome virus but not a negative-sense RNA genome virus. Infection with influenza A virus generates Z-RNA that can be detected with an anti-z-antibody but not the E3L recombinant protein. The E3L recombinant protein also detects endogenous dsRNA in PNPT1 or SUV3L knockout cells. These findings highlight important characteristics of dsRNA structure and detection.
Systematic Profiling of Ale Yeast Protein Dynamics across Fermentation and Repitching
Authors: Garge Riddhiman K. , Geck Renee C. , Armstrong Joseph O. , Dunn Barbara , Boutz Daniel R. , Battenhouse Anna , Leutert Mario , Dang Vy , Jiang Pengyao , Kwiatkowski Dusan , Peiser Thorin , McElroy Hoyt , Marcotte Edward M. , Dunham Maitreya J.
Published: September 2023

Studying the genetic and molecular characteristics of brewing yeast strains is crucial for understanding their domestication history and adaptations accumulated over time in fermentation environments, and for guiding optimizations to the brewing process itself.Saccharomyces cerevisiae(brewing yeast) is amongst the most profiled organisms on the planet, yet the temporal molecular changes that underlie industrial fermentation and beer brewing remain understudied. Here, we characterized the genomic makeup of aSaccharomyces cerevisiaeale yeast widely used in the production of Hefeweizen beers, and applied shotgun mass spectrometry to systematically measure the proteomic changes throughout two fermentation cycles which were separated by 14 rounds of serial repitching. The resulting brewing yeast proteomics resource includes 64,740 protein abundance measurements. We found that this strain possesses typical genetic characteristics ofSaccharomyces cerevisiaeale strains and displayed progressive shifts in molecular processes during fermentation based on protein abundance changes. We observed protein abundance differences between early fermentation batches compared to those separated by 14 rounds of serial repitching. The observed abundance differences occurred mainly in proteins involved in the metabolism of ergosterol and isobutyraldehyde. Our systematic profiling serves as a starting point for deeper characterization of how the yeast proteome changes during commercial fermentations and additionally serves as a resource to guide fermentation protocols, strain handling, and engineering practices in commercial brewing and fermentation environments. Finally, we created a web interface (https://brewing-yeast-proteomics.ccbb.utexas.edu/) to serve as a valuable resource for yeast geneticists, brewers, and biochemists to provide insights into the global trends underlying commercial beer production.
Data-driven image mechanics (D2IM): a deep learning approach to predict displacement and strain fields from undeformed X-ray tomography images - Evaluation of bone mechanics
Authors: Soar Peter , Tozzi Gianluca
Published: September 2023

Experimental measurement of displacement and strain fields using techniques such as digital volume correlation (DVC) from in situ X-ray computed tomography (XCT) has notably advanced the understanding of bone mechanics from organ to tissue level. Being experimental in nature, DVC output has been often employed to validate finite element (FE) models of bone improving their predictive ability. Despite the excellent results achieved, these techniques are complex, time consuming, potentially affecting tissue mechanical properties, and their predictive ability requiring prior knowledge of material properties. The recent advent of deep learning (DL) has enabled data-driven models, paving the way for the full exploitation of rich image datasets from which physics can be learnt and retained. Here we propose a novel data-driven image mechanics (D2IM) approach based on feed forward convolutional neural network (CNN) that learns from DVC displacement fields of vertebrae, predicting displacement and strain fields for undeformed XCT images. D2IM successfully predicted all displacement fields, particularly the one for the z loading axis (w), where high correlation (R2=0.93) and minimal error (as low as less than 1μm) were found when comparing measured against predicted displacements. The predicted axial strain field in z (εzz) was also consistent in distribution with the measured one, displaying generally reduced errors (as low as few tens of με) in the regions within the vertebral body where the effect of border outliers was minimal. This is the first study using experimental full-field measurements on bone structures from DVC to inform DL-based model such as D2IM, which represents a major contribution in the prediction of displacement and strain fields only based on the greyscale content of undeformed XCT images. The future development of D2IM will incorporate a wider range of structures and loading scenarios for accurate prediction of physical fields in both hard and soft tissues, aiming at clinical translation for improved diagnostics.
Exploring logistics service providers for small and medium-sized enterprises and large corporations: benefits and challenges
Authors: Alkaabi Khaula
Published: September 2023

Purpose

The purpose of this study is to examine the benefits and challenges of using logistics service providers (LSPs) for small and medium-sized enterprises (SMEs) and large corporations.

Design/methodology/approach

This study uses the focus group interview method with eight LSP companies of varying sizes in the UAE. The aim is to obtain detailed insights into the advantages and obstacles associated with using LSPs.

Findings

The interview results reveal differences in the services and payment options offered by large and small-medium-sized LSPs, leading to varying impacts on SMEs. Additionally, both large and small-medium-sized LSPs encounter challenges in delivering services to SMEs, including sustaining logistic excellence in a competitive market, meeting the high customer expectations from SMEs and large enterprises, difficulty in recruiting skilled and competent employees and high costs of business technology.

Research limitations/implications

This study is limited to the UAE context and the perspectives of eight LSP companies. The findings may not be generalizable to other regions or industries.

Practical implications

The findings of this research provide valuable insights for SMEs and large corporations considering the use of LSPs. Understanding the benefits and challenges associated with outsourcing logistics services can assist in making informed decisions and developing effective collaborations with LSPs.

Originality/value

This research contributes to the existing literature by specifically examining the benefits and challenges of using LSPs for SMEs and large corporations. This study provides a comprehensive analysis of the factors that influence logistics outsourcing decisions and highlights the unique challenges faced by LSPs and SMEs in the context of the UAE.
Privacy in hospitality: managing biometric and biographic data with immersive technology
Authors: Liyanaarachchi Gajendra , Viglia Giampaolo , Kurtaliqi Fidan
Published: September 2023

Purpose

This study aims to investigate the implications, risks and challenges of data privacy due to the use of immersive technology in the hospitality industry.

Design/methodology/approach

The authors adopt a mixed-method approach. Study 1 is a focus group. The authors then provide external and ecological validity with a field experiment conducted with 139 hotel clients at a three-star continental European hotel.

Findings

Collecting biometric data results in unbalanced privacy compared to biographic data, as it diminishes individuals’ control over their data and grants organizations absolute power. This unbalanced privacy directly influences consumers’ willingness to disclose information, affecting their choice of hotels and access to services.

Practical implications

Hotels should redesign their strategies to accommodate heightened privacy risks with biometric data. This can be obtained by introducing systems that foster customer confidence in data usage and facilitate customers’ willingness to disclose biometrics through immersive technology or biographic data.

Originality/value

This study introduces unbalanced privacy as a unique state due to sharing biometric data. The authors propose a novel doctrine, the uncontrollable privacy paradox, which is a shift from the privacy paradox. The uncontrollable privacy paradox addresses the unbalanced privacy envisaged through consumer powerlessness in data management. This research addresses the literature gap on the privacy paradox by offering a broader perspective, including business, industry and mixed reality considerations.
A study to forecast healthcare capacity dynamics in the wake of the COVID-19 pandemic
Authors: Patil Anchal , Shardeo Vipulesh , Madaan Jitender , Dwivedi Ashish , Paul Sanjoy Kumar
Published: September 2023

Purpose

This study aims to evaluate the dynamics between healthcare resource capacity expansion and disease spread. Further, the study estimates the resources required to respond to a pandemic appropriately.

Design/methodology/approach

This study adopts a system dynamics simulation and scenario analysis to experiment with the modification of the susceptible exposed infected and recovered (SEIR) model. The experiments evaluate diagnostic capacity expansion to identify suitable expansion plans and timelines. Afterwards, two popularly used forecasting tools, artificial neural network (ANN) and auto-regressive integrated moving average (ARIMA), are used to estimate the requirement of beds for a period when infection data became available.

Findings

The results from the study reflect that aggressive testing with isolation and integration of quarantine can be effective strategies to prevent disease outbreaks. The findings demonstrate that decision-makers must rapidly expand the diagnostic capacity during the first two weeks of the outbreak to support aggressive testing and isolation. Further, results confirm a healthcare resource deficit of at least two months for Delhi in the absence of these strategies. Also, the study findings highlight the importance of capacity expansion timelines by simulating a range of contact rates and disease infectivity in the early phase of the outbreak when various parameters are unknown. Further, it has been reflected that forecasting tools can effectively estimate healthcare resource requirements when pandemic data is available.

Practical implications

The models developed in the present study can be utilised by policymakers to suitably design the response plan. The decisions regarding how much diagnostics capacity is needed and when to expand capacity to minimise infection spread have been demonstrated for Delhi city. Also, the study proposed a decision support system (DSS) to assist the decision-maker in short- and long-term planning during the disease outbreak.

Originality/value

The study estimated the resources required for adopting an aggressive testing strategy. Several experiments were performed to successfully validate the robustness of the simulation model. The modification of SEIR model with diagnostic capacity increment, quarantine and testing block has been attempted to provide a distinct perspective on the testing strategy. The prevention of outbreaks has been addressed systematically.
Conflict management strategies and their relationships with exchange performance in supply chains
Authors: Huo Baofeng , He Huan , Tian Min
Published: September 2023

Purpose

Developing appropriate conflict management strategies (CMSs) is important for a firm to achieve better relationships with its supply chain partners. However, the literature has rarely considered how firms may simultaneously adopt various CMSs to address interfirm conflicts. Accordingly, this study aims to identify manufacturers' CMS archetypes with their main suppliers based on interfirm interdependence structure, further examining the relationship between various CMS archetypes and exchange performance.

Design/methodology/approach

First, the authors theoretically propose a manufacturer's CMS archetypes configured with Rahim's five CMSs based on interfirm interdependence structure. Second, cluster analysis with data from 200 Chinese manufacturers is used to generate a manufacturer's actual CMS archetypes. Third, the authors analyze the relationship between interfirm interdependence structure and Rahim's five CMSs as well as that with the new configured CMS archetypes. Finally, the authors use Analysis of Variance (ANOVA) to compare exchange performance disparities among manufacturers using different CMS archetypes.

Findings

First, cluster analysis results show that three archetypes – cooperative, competitive and cooperative-competitive CMS – emerge during interfirm conflict management. Second, regression analysis shows how interfirm interdependence structure can affect manufacturers' choice of different CMSs and CMS archetypes. Third, ANOVA results indicate that when addressing interfirm conflicts, the strong cooperative, strong competitive and strong cooperative-competitive CMS archetypes can help manufacturers get good relationship satisfaction with main suppliers. Regarding supplier opportunism, while both strong cooperative and strong competitive CMS archetypes are effective at restraining opportunism, the cooperative-competitive CMS archetype may trigger higher levels of supplier opportunism.

Originality/value

This study enriches the interfirm relationship management literature and provides insights for manufacturers to better address interfirm conflicts.
Assessment of critical success factors, barriers and initiatives of total productive maintenance (TPM) in selected Ethiopian manufacturing industries
Authors: Gelaw Mulatu Tilahun , Azene Daniel Kitaw , Berhan Eshetie
Published: September 2023

Purpose

This research aims to investigate critical success factors, barriers and initiatives of total productive maintenance (TPM) implementation in selected manufacturing industries in Addis Ababa, Ethiopia.

Design/methodology/approach

This study built and looked into a conceptual research framework. The potential barriers and success factors to TPM implementation have been highlighted. The primary study techniques used to collect relevant data were a closed-ended questionnaire and semi-structured interview questions. With the use of SPSS version 23 and SmartPLS 3.0 software, the data were examined using descriptive statistics and the inferential Partial Least Square Structural Equation Modeling (PLS-SEM) techniques.

Findings

According to the results of descriptive statistics and multivariate analysis using PLS-SEM, the case manufacturing industries' TPM implementation initiative is in its infancy; break down maintenance is the most widely used maintenance policy; top managers are not dedicated to the implementation of TPM; and there are TPM pillars that have been weakly and strongly addressed by the case manufacturing companies.

Research limitations/implications

The small sample size is a limitation to this study. It is therefore challenging to extrapolate the research findings to other industries. The only manufacturing KPI utilized in this study is overall equipment effectiveness (OEE). It is possible to add more parameters to the manufacturing performance measurement KPI. The relationships between TPM and other lean production methods may differ from those observed in this cross-sectional study. Longitudinal experimental studies and in-depth analyses of TPM implementations may shed further light on this.

Practical implications

Defining crucial success factors and barriers to TPM adoption, as well as identifying the weak and strong TPM pillars, will help companies in allocating their scarce resources exclusively to the most important areas. TPM is not a quick solution. It necessitates a change in both the company's and employees' attitude and their values, which takes time to bring about. Hence, it entails a long-term planning. The commitment of top managers is very important in the initiatives of TPM implementation.

Originality/value

This study is unique in that, it uses a new conceptual research model and the PLS-SEM technique to analyze relationships between TPM pillars and OEE in depth.
The impact of inclusive leadership on innovative work behavior: a mediated moderation model
Authors: AlMulhim Abdullah Fahad , Mohammed Sanaa Mostafa
Published: September 2023

Purpose

Applicable to telecom companies operating in Saudi Arabia, this study aims to investigate the mediating role of workplace dignity (WD) in the relationship between inclusive leadership and innovative work behavior (IWB) as well as the moderate role of workplace inclusion (WI) in said relationship.

Design/methodology/approach

A total of 364 telecom companies in Saudi Arabia participated in the survey study. SmartPLS was employed to evaluate the data and test the research variables.

Findings

According to this study, inclusive leadership has a favorable impact on IWB. Additionally, the study concluded that inclusive leadership enhances WD. Furthermore, the authors discovered that WD has a favorable impact on IWB. The findings showed that the association between inclusive leadership and IWB is mediated by WD. Finally, WI has a statistically positive moderating effect between WD and IWB on the moderation side of the analysis.

Originality/value

This study is the first to consider WD as a mediator in the relationship between inclusive leadership and IWB. Examining WI's role as a moderator also deepens the authors' comprehension of the connection between WD and IWB.
The Politics of England: National Identities and Political Englishness
Authors: Denham John , Mckay Lawrence
Published: September 2023

Over two decades, voters who emphasised their English identity played an influential role in the rise of UKIP and the Brexit Party, the Brexit referendum and the election of Conservative governments—a trend overlooked in most electoral analyses. Using twenty years of data from the British Election Study and British Social Attitudes Survey, as well as recent original surveys, the article explores the evolving political behaviour of national identity groups. It finds that ‘more English’ and ‘more British’ identifiers increasingly voted for different parties. The analysis also identifies growing differences in the demographics, social values and immigration attitudes of these groups, which descriptive and regression analysis suggests may underpin these divergent political behaviours. However, a fuller understanding of electoral behaviour must take account of ideas of national democracy and sovereignty. The electoral impact of both the characteristics of English identifying voters and ideas associated with English identity constitute ‘political Englishness’.
Rare primary angiitis of the central nervous system affecting the clivus: A case report
Authors: Sun Shujuan , Wan Yuzhu , Ji Hongzhi , Shi Li
Published: September 2023

Diseases involving the clivus are highly variable, and the incidence of each disease is rare. Primary central nervous system vasculitis (PACNS) is a rare disease with very heterogeneous clinical manifestations, its diagnosis is often challenging, and histopathology is the gold standard. We report a patient with PACNS of the clivus, with a 1‐month history of headache and diplopia, who was misdiagnosed as having a tumor of the clivus during prior treatment, due to computed tomography findings of clivus occupation and bone destruction. Endoscopic resection of the nasal clivus lesions was performed. Pathological examination revealed a small abscess with hemorrhage, necrosis, extensive infiltration of lymphocytes and plasma cells, and granulation tissue scar formation. After histopathological examination, the diagnosis was confirmed, and oral glucocorticoid and cyclophosphamide were commenced. This study is the first to report a tumor‐like PACNS, that occurs in the clivus, thereby enriching our understanding of PACNS.