bluuebunny/crossref_metadata_2025_split huggingface dataset:
Adenovirus type 34 and HVR1-deleted Adenovirus type 5 do not bind to PF4: clearing the path towards vectors without thrombosis risk
Authors: Sallard Erwan , Pembaur Daniel , Schröer Katrin , Schellhorn Sebastian , Koukou Georgia , Schmidt Natascha , Zhang Wenli , Kreppel Florian , Ehrhardt Anja 
Published: November 2022

AbstractThe adenoviral vector based AstraZeneca and Janssen COVID vaccines have been associated with rare cases of thrombosis, a condition which depends on adenovirus binding to the blood protein Platelet Factor 4 (PF4).In order to identify adenoviruses with low or absent affinity for PF4, we screened dozens of types from various adenovirus species, and Adenovirus type 5 (Ad5) derived vectors carrying genetic or chemical modifications of different hexon hyper-variable regions (HVR). For this purpose, we established an armamentarium of techniques including ELISA-qPCR and Aggregate Pull-Down (APD), which enabled fast and sensitive assessments of virus-protein interactions.Unlike most tested serotypes, Ad34 did not bind to PF4. Likewise, the deletion or shielding of the HVR1 loop of Ad5 seemingly ablated its PF4 binding. Therefore, we showed that PF4 binds to adenovirus hexon through interactions dependent on HVR1, and identified vectors that may avoid or decrease the risk of thrombosis and represent safer candidates for vaccine or gene therapy vector development.
NOX2 Inhibition Enables Retention of the Circadian Clock in BV2 Microglia and Primary Macrophages
Authors: Muthukumarasamy Iswarya , Buel Sharleen M. , Hurley Jennifer M. , Dordick Jonathan S. 
Published: November 2022

ABSTRACTSustained neuroinflammation is a major contributor to the progression of neurodegenerative diseases such as Alzheimer’s (AD) and Parkinson’s (PD) diseases. Neuroinflammation, like other cellular processes, is affected by the circadian clock. Microglia, the resident immune cells in the brain, act as major contributors to neuroinflammation and are under the influence of the circadian clock. Microglial responses such as activation, recruitment, and cytokine expression are rhythmic in their response to various stimuli. While the link between circadian rhythms and neuroinflammation is clear, significant gaps remain in our understanding of this complex relationship. To further our understanding of this relationship, we studied the interaction between the microglial circadian clock and the enzyme NADPH Oxidase Isoform 2 (NOX2), an enzyme essential for the production of reactive oxygen species (ROS) in oxidative stress, an integral characteristic of neuroinflammation. We examined BV2 microglia over circadian time, demonstrating oscillations of the clock genesPer2andBmal1and the NOX2 subunitsgp91phoxandp47phox. We discovered the BV2 microglial clock exerted significant control over NOX2 expression and that the inhibition of NOX2 enabled the microglia to retain a functional circadian clock while reducing levels of ROS and inflammatory cytokines. These trends were mirrored in mouse bone marrow-derived primary macrophages. Our findings indicate NOX2 plays a crucial role in the interaction between the circadian clock and the activation of microglia/macrophages into their pro-inflammatory state.
Genetic association models are robust to common population kinship estimation biases
Authors: Hou Zhuoran , Ochoa Alejandro 
Published: November 2022

AbstractCommon genetic association models for structured populations, including Principal Component Analysis (PCA) and Linear Mixed-effects Models (LMM), model the correlation structure between individuals using population kinship matrices, also known as Genetic Relatedness Matrices or “GRMs”. However, the most common kinship estimators can have severe biases that were only recently determined. Here we characterize the effect of these kinship biases on genetic association. We employ a large simulated admixed family and genotypes from the 1000 Genomes Project, both with simulated traits, to evaluate key kinship estimators. Remarkably, we find practically invariant association statistics for kinship matrices of different bias types (matching all other features). We then prove using statistical theory and linear algebra that LMM association tests are invariant to these kinship biases, and PCA approximately so. Our proof shows that the intercept and relatedness effect coefficients compensate for the kinship bias, an argument that extends to generalized linear models. As a corollary, association testing is also invariant to changing the reference ancestral population of the kinship matrix. Lastly, we observed that all kinship estimators, except for popkin ROM, can give improper non-positive semidefinite matrices, which can be problematic although some LMMs handle them surprisingly well, and condition numbers can be used to choose kinship estimators. Overall, we find that existing association studies are robust to kinship estimation bias, and our calculations may help improve association methods by taking advantage of this unexpected robustness, as well as help determine the effects of kinship bias in related problems.
The grape powdery mildew resistance lociRen2, Ren3, Ren4D, Ren4U, Run1, Run1.2b, Run2.1, andRun2.2activate different transcriptional responses toErysiphe necator
Authors: Massonnet Mélanie , Riaz Summaira , Pap Dániel , Figueroa-Balderas Rosa , Walker M. Andrew , Cantu Dario 
Published: November 2022

AbstractMultiple grape powdery mildew (PM) genetic resistance (R) loci have been found in wild grape species. Little is known about the defense responses associated with eachRlocus. In this study, we compare the defense mechanisms associated with PM resistance in interspecific crosses segregating for a singleRlocus fromMuscadinia rotundifolia(Run1, Run1.2b, Run2.1, Run2.2),Vitis cinerea(Ren2),V. romanetii(Ren4DandRen4U), and the interspecific hybrid Villard blanc (Ren3). By combining optical microscopy, visual scoring, and biomass estimation, we show that the eightRloci confer resistance by limiting infection at different stages. We assessed the defense mechanisms triggered in response to PM at 1 and 5 days post inoculation (dpi) via RNA sequencing. To account for the genetic differences between species, we developed for each accession a diploid synthetic reference transcriptome by incorporating into thePN40024reference homozygous and heterozygous sequence variants andde novoassembled transcripts. Most of theRloci exhibited a higher number of differentially expressed genes (DEGs) associated with PM resistance at 1 dpi compared to 5 dpi, suggesting that PM resistance is mostly associated with an early transcriptional reprogramming. Comparison of the PM resistance-associated DEGs showed a limited overlap between pairs ofRloci, and nearly half of the DEGs were specific to a singleRlocus. The largest overlap of PM resistance-associated DEGs was found betweenRen3+,Ren4D+, andRen4U+genotypes at 1 dpi, and betweenRen4U+andRun1+accessions at 5 dpi. TheRen3+,Ren4D+, andRen4U+were also found to have the highest number ofRlocus-specific DEGs in response to PM. Both shared andRlocus-specific DEGs included genes from different defense-related categories, indicating that the presence ofE. necatortriggered distinct transcriptional responses in the eightRloci.
Optimizing the Martini 3 force field reveals the effects of the intricate balance between protein-water interaction strength and salt concentration on biomolecular condensate formation
Authors: Zerze Gül H. 
Published: November 2022

AbstractCondensation/dissolution has become a widely acknowledged biological macromolecular assembly phenomenon in subcellular compartmentalization. MARTINI force field offers a coarse-grained protein model with a resolution that preserves molecular details with an explicit (CG) solvent. Despite its relatively higher resolution, it can still achieve condensate formation in reasonable computing time with explicit solvent and ionic species. Therefore, it is highly desirable to tune this force field to be able to reproduce the experimentally observed properties of the condensate formation. In this work, we studied the condensate formation of the low-sequence complexity (LC) domain of FUsed in Sarcoma (FUS) protein using a MARTINI 3 force field by systematically modifying (increasing) the protein-water interaction strength and varying salt concentration. We found that the condensate formation is sensitive both to the protein-water interaction strength and the presence of salt. While the unmodified MARTINI force field yields a complete collapse of proteins into one dense phase (i.e., no dilute phase), we reported a range of modified protein-water interaction strength that is capable of capturing the experimentally found transfer free energy between dense and dilute phases. We also found that the condensates lose their spherical shape upon the addition of salt, especially when the protein-water interactions are weak. Inter-chain amino acid contact map analysis showed one explanation for this observation: the protein-protein contact fraction reduces as salt is added to systems (when the protein-water interactions are weak), consistent with electrostatic screening effects. This reduction might be responsible for the condensates becoming nonspherical upon the addition of salt by reducing the need for minimizing interfacial area. However, as the protein-water interactions become stronger to the extent that makes the transfer free energy agree well with experimentally observed transfer free energy, we found an increase in protein-protein contact fraction upon the addition of salt, consistent with the salting-out effects. Therefore, we concluded that there is an intricate balance between screening effects and salting-out effects upon the addition of salt and this balance is highly sensitive to the strength of protein-water interactions.
Configurational entropy-based screening and selection of hydrophilic polymers using the tripartite split green fluorescent protein
Authors: Banerjee Shounak , Minko Yury , Anaya Elizabeth S , Sasiene Zachary J , Schmidt Jurgen G , Strauss Charlie E M , Waldo Geoffrey S 
Published: November 2022

AbstractMeasuring the entropic properties of polymers such as proteins is critical to accurate prediction of their functional properties. However, the measurement of configurational entropy is possible only by low throughput techniques such as calorimetry, NMR and CD spectroscopy. Moreover, to our knowledge no system exists that allows molecular selection/enrichment based on the molecules configurational entropy. We tested the ability of the scalable tripartite GFP system to offer fine resolution of differences in configurational entropy in molecules and to isolate molecules based on their configurational entropy. The system was able to both finely resolve molecules with different configurational entropies, as well as capture them for isolation. We were able to tune the sensitivity of the system by using different mutations of the protein components. Lastly, we were able to apply the system to polypeptoid molecules and posit that the system may be applied to any other hydrophilic polymer of up to 10^3 repeating units.
Bursting Translation on Single mRNAs in Live Cells
Authors: Livingston Nathan M. , Kwon Jiwoong , Valera Oliver , Saba James A. , Sinha Niladri K. , Reddy Pranav , Nelson Blake , Wolfe Clara , Ha Taekjip , Green Rachel , Liu Jian , Wu Bin 
Published: November 2022

SummaryStochasticity has emerged as a mechanism to control gene expression. Much of this so-called “noise” has been attributed to bursting transcription. However, the stochasticity of translation has not similarly been investigated due to a lack of enabling imaging technologies. We developed techniques to track single mRNAs and their translation in live cells for hours, allowing measurement of previously uncharacterized translation dynamics. We applied genetic and pharmacological perturbations to control translation kinetics. Like transcription, translation is not a constitutive process but instead cycles between inactive and active states or “bursts”. But unlike transcription, which is largely frequency modulated, complex structure in the 5’-untranslated region alters burst amplitude. Bursting frequency can be controlled through cap-proximal sequences andtrans-acting factors such as eIF4F. We coupled single molecule imaging with stochastic modeling to deduce the fundamental kinetic parameters of translational bursting, a new dimension of translational control.HighlightsLong-term tracking of single mRNAs reveals multi-state, bursting translationStructure in the 5’-untranslated region modulates translational burst amplitude5’-cap proximal sequences modulate translational burst frequencymTOR signaling adjusts translation bursting to respond to environmental cues
Localization ofKif1cmRNA to cell protrusions dictates binding partner specificity of the encoded protein
Authors: Norris Megan L. , Mendell Joshua T. 
Published: November 2022

ABSTRACTSubcellular localization of messenger RNA (mRNA) is a widespread phenomenon that can impact the regulation and function of the encoded protein. In non-neuronal cells, a subset of mRNAs localize to cell protrusions and proper mRNA localization is required for cell migration. However, the mechanisms by which mRNA localization regulates protein function in this setting remain unclear. Here, we examined the functional consequences of localization of the mRNA encoding KIF1C. KIF1C is a kinesin motor protein required for cell migration and mRNA trafficking, including trafficking of its own mRNA. We show thatKif1cmRNA localization does not regulate KIF1C protein abundance, distribution, or ability to traffic other mRNAs. Conversely, robustKif1cmRNA localization is required for directed cell migration. We used mass spectrometry to identify binding partners of endogenous KIF1C, which revealed dramatic dysregulation of the number and identity of KIF1C interactors in response toKif1cmRNA mis-localization. These results therefore uncovered a mechanistic connection between mRNA localization to cell protrusions and the specificity of protein-protein interactions. We anticipate that this mechanism is not limited toKif1cand is likely to be a general principle used by protrusion-enriched mRNAs in non-neuronal cells.
STAMarker: Determining spatial domain-specific variable genes with saliency maps in deep learning
Authors: Zhang Chihao , Dong Kangning , Aihara Kazuyuki , Chen Luonan , Zhang Shihua 
Published: November 2022

AbstractSpatial transcriptomics characterizes gene expression profiles while retaining the information of the spatial context, providing an unprecedented opportunity to understand cellular systems. One of the essential tasks in such data analysis is to determine spatially variable genes (SVGs), which demonstrate spatial expression patterns. Existing methods only consider genes individually and fail to model the inter-dependence of genes. To this end, we present an analytic tool STAMarker for robustly determining spatial domain-specific SVGs with saliency maps in deep learning. STAMarker is a three-stage ensemble framework consisting of graphattention autoencoders, multilayer perceptron (MLP) classifiers, and saliency map computation by the backpropagated gradient. We illustrate the effectiveness of STAMarker and compare it with three competing methods on four spatial transcriptomic data generated by various platforms. STAMarker considers all genes at once and is more robust when the dataset is very sparse. STAMarker could identify spatial domain-specific SVGs for characterizing spatial domains and enable in-depth analysis of the region of interest in the tissue section.
Five-dimensional memristive Hopfield neural network dynamics analysis and its application in secure communication
Authors: Yin Xinxing , Chen Juan , Yu Wenxin , Huang Yuan , Wei Wenxiang , Xiang Xinjie , Yan Hao 
Published: November 2022

Purpose
This study aims to improve the complexity of chaotic systems and the security accuracy of information encrypted transmission. Applying five-dimensional memristive Hopfield neural network (5D-HNN) to secure communication will greatly improve the confidentiality of signal transmission and greatly enhance the anticracking ability of the system.

Design/methodology/approach
Chaos masking: Chaos masking is the process of superimposing a message signal directly into a chaotic signal and masking the signal using the randomness of the chaotic output. Synchronous coupling: The coupled synchronization method first replicates the drive system to get the response system, and then adds the appropriate coupling term between the drive The synchronization error and the coupling term of the system will eventually converge to zero with time. The synchronization error and coupling term of the system will eventually converge to zero over time.

Findings
A 5D memristive neural network is obtained based on the original four-dimensional memristive neural network through the feedback control method. The system has five equations and contains infinite balance points. Compared with other systems, the 5D-HNN has rich dynamic behaviors, and the most unique feature is that it has multistable characteristics. First, its dissipation property, equilibrium point stability, bifurcation graph and Lyapunov exponent spectrum are analyzed to verify its chaotic state, and the system characteristics are more complex. Different dynamic characteristics can be obtained by adjusting the parameter k.

Originality/value
A new 5D memristive HNN is proposed and used in the secure communication
CEO demographics and gender diversity in senior management in large Scandinavian firms
Authors: Garcia-Blandon Josep , Argilés-Bosch Josep , Ravenda Diego 
Published: November 2022

Purpose
This study aims to investigate whether chief executive officer (CEO) demographics are associated with gender diversity in senior management in the Scandinavia region.

Design/methodology/approach
The research design draws on multivariate cross-sectional analysis. The demographic characteristics examined are gender, age and education. A total of six hypotheses are developed and tested. The sample includes the largest 106 public firms from Denmark, Finland, Norway and Sweden.

Findings
Results show that firms with female CEOs have more women in senior management than other firms. However, neither age nor level of formal education of CEOs shows significant results, with the exception of CEOs holding MBA degrees, who are associated with fewer women in these positions. Interestingly, the association between educational background and gender diversity is principally driven by study-abroad experiences. Finally, results show that gender diversity in senior management has an important country component, whereas the industry component is negligible.

Originality/value
The relationship between managers’ demographics and gender diversity among subordinates is a relatively unexplored research issue, as previous works have focused on general comparisons between male and female managers. Furthermore, the Scandinavian context is particularly interesting as this region leads gender equality rankings.
The spatial-temporal evolution analysis of carbon emission of China's thermal power industry based on the three-stage SBM—DEA model
Authors: Xu Guoquan , Feng Shiwei , Guo Shucen , Ye Xiaolan 
Published: November 2022

Purpose
China has proposed two-stage goals of carbon peaking by 2030 and carbon neutralization by 2060. The carbon emission reduction effect of the power industry, especially the thermal power industry, will directly affect the progress of the goal. This paper aims to reveal the spatial-temporal characteristics and influencing factors of carbon emission efficiency of the thermal power industry and proposes policy suggestions for realizing China’s carbon peak and carbon neutralization goals.

Design/methodology/approach
This paper evaluates and compares the carbon emission efficiency of the thermal power industry in 29 provinces and regions in China from 2014 to 2019 based on the three-stage slacks-based measure (SBM) of efficiency in data envelopment analysis (DEA) model of undesired output, excluding the influence of environmental factors and random errors.

Findings
Empirical results show that during the sample period, the carbon emission efficiency of China’s thermal power industry shows a fluctuating upward trend, and the carbon emission efficiency varies greatly among the provincial regions. The carbon emission efficiency of the interregional thermal power industry presents a pattern of “eastern > central > western,” which is consistent with the level of regional economic development. Environmental factors such as economic level and environmental regulation level are conducive to the improvement of carbon emission efficiency of the thermal power industry, but the proportion of thermal power generation and industrial structure is the opposite.

Originality/value
This paper adopts the three-stage SBM–DEA model of undesired output and takes CO2 as the undesired output to reveal the spatial-temporal characteristics and influencing factors of carbon emission efficiency in China’s thermal power industry. The results provide a more comprehensive perspective for regional comparative evaluation and influencing factors of carbon emission efficiency in China’s thermal power industry.
Predictive Six Sigma for Turkish manufacturers: utilization of machine learning tools in DMAIC
Authors: Uluskan Meryem , Karşı Merve Gizem 
Published: November 2022

Purpose
This study aims to emphasize utilization of Predictive Six Sigma to achieve process improvements based on machine learning (ML) techniques embedded in define, measure, analyze, improve, control (DMAIC). With this aim, this study presents selection and utilization of ML techniques, including multiple linear regression (MLR), artificial neural network (ANN), random forests (RF), gradient boosting machines (GBM) and k-nearest neighbors (k-NN) in the analyze and improve phases of Six Sigma DMAIC.

Design/methodology/approach
A data set containing 320 observations with nine input and one output variables is used. To achieve the objective which was to decrease the number of fabric defects, five ML techniques were compared in terms of prediction performance and best tools were selected. Next, most important causes of defects were determined via these tools. Finally, parameter optimization was conducted for minimum number of defects.

Findings
Among five ML tools, ANN, GBM and RF are found to be the best predictors. Out of nine potential causes, “machine speed” and “fabric width” are determined as the most important variables by using these tools. Then, optimum values for “machine speed” and “fabric width” for fabric defect minimization are determined both via regression response optimizer and ANN surface optimization. Ultimately, average defect number was decreased from 13/roll to 3/roll, which is a considerable decrease attained through utilization of ML techniques in Six Sigma.

Originality/value
Addressing an important gap in Six Sigma literature, in this study, certain ML techniques (i.e. MLR, ANN, RF, GBM and k-NN) are compared and the ones possessing best performances are used in the analyze and improve phases of Six Sigma DMAIC.