bluuebunny/crossref_metadata_2025_split huggingface dataset:
Reprogramming of myeloid cells and their progenitors in patients with non-medullary thyroid carcinoma
Authors: Rabold Katrin , Zoodsma Martijn , Grondman Inge , Kuijpers Yunus , Bremmers Manita , Jaeger Martin , Zhang Bowen , Hobo Willemijn , Bonenkamp Han J. , de Wilt Johannes H. W. , Janssen Marcel J. R. , Cornelissen Lenneke A. M. , van Engen-van Grunsven Ilse C. H. , Mulder Willem J. M. , Smit Jan W. A. , Adema Gosse J. , Netea Mihai G. , Li Yang , Xu Cheng-Jian , Netea-Maier Romana T. 
Published: October 2022

AbstractMyeloid cells, crucial players in antitumoral defense, are affected by tumor-derived factors and treatment. The role of myeloid cells and their progenitors prior to tumor infiltration is poorly understood. Here we show single-cell transcriptomics and functional analyses of the myeloid cell lineage in patients with non-medullary thyroid carcinoma (TC) and multinodular goiter, before and after treatment with radioactive iodine compared to healthy controls. Integrative data analysis indicates that monocytes of TC patients have transcriptional upregulation of antigen presentation, reduced cytokine production capacity, and overproduction of reactive oxygen species. Interestingly, these cancer-related pathological changes are partially removed upon treatment. In bone marrow, TC patients tend to shift from myelopoiesis towards lymphopoiesis, reflected in transcriptional differences. Taken together, distinct transcriptional and functional changes in myeloid cells arise before their infiltration of the tumor and are already initiated in bone marrow, which suggests an active role in forming the tumor immune microenvironment.
Intercomparison of regional loss estimates from global synthetic tropical cyclone models
Authors: Meiler Simona , Vogt Thomas , Bloemendaal Nadia , Ciullo Alessio , Lee Chia-Ying , Camargo Suzana J. , Emanuel Kerry , Bresch David N. 
Published: October 2022

AbstractTropical cyclones (TCs) cause devastating damage to life and property. Historical TC data is scarce, complicating adequate TC risk assessments. Synthetic TC models are specifically designed to overcome this scarcity. While these models have been evaluated on their ability to simulate TC activity, no study to date has focused on model performance and applicability in TC risk assessments. This study performs the intercomparison of four different global-scale synthetic TC datasets in the impact space, comparing impact return period curves, probability of rare events, and hazard intensity distribution over land. We find that the model choice influences the costliest events, particularly in basins with limited TC activity. Modelled direct economic damages in the North Indian Ocean, for instance, range from 40 to 246 billion USD for the 100-yr event over the four hazard sets. We furthermore provide guidelines for the suitability of the different synthetic models for various research purposes.
Biofilm antimicrobial susceptibility through an experimental evolutionary lens
Authors: Coenye Tom , Bové Mona , Bjarnsholt Thomas 
Published: October 2022

AbstractExperimental evolution experiments in which bacterial populations are repeatedly exposed to an antimicrobial treatment, and examination of the genotype and phenotype of the resulting evolved bacteria, can help shed light on mechanisms behind reduced susceptibility. In this review we present an overview of why it is important to include biofilms in experimental evolution, which approaches are available to study experimental evolution in biofilms and what experimental evolution has taught us about tolerance and resistance in biofilms. Finally, we present an emerging consensus view on biofilm antimicrobial susceptibility supported by data obtained during experimental evolution studies.
Low levels of monkeypox virus-neutralizing antibodies after MVA-BN vaccination in healthy individuals
Authors: Zaeck Luca M. , Lamers Mart M. , Verstrepen Babs E. , Bestebroer Theo M. , van Royen Martin E. , Götz Hannelore , Shamier Marc C. , van Leeuwen Leanne P. M. , Schmitz Katharina S. , Alblas Kimberley , van Efferen Suzanne , Bogers Susanne , Scherbeijn Sandra , Rimmelzwaan Guus F. , van Gorp Eric C. M. , Koopmans Marion P. G. , Haagmans Bart L. , GeurtsvanKessel Corine H. , de Vries Rory D. 
Published: October 2022

AbstractIn July 2022, the ongoing monkeypox (MPX) outbreak was declared a public health emergency of international concern. Modified vaccinia Ankara—Bavarian Nordic (MVA-BN, also known as Imvamune, JYNNEOS or Imvanex) is a third-generation smallpox vaccine that is authorized and in use as a vaccine against MPX. To date, there are no data showing MPX virus (MPXV)-neutralizing antibodies in vaccinated individuals nor vaccine efficacy against MPX. Here we show that MPXV-neutralizing antibodies can be detected after MPXV infection and after historic smallpox vaccination. However, a two-shot MVA-BN immunization series in non-primed individuals yields relatively low levels of MPXV-neutralizing antibodies. Dose-sparing of an MVA-based influenza vaccine leads to lower MPXV-neutralizing antibody levels, whereas a third vaccination with the same MVA-based vaccine significantly boosts the antibody response. As the role of MPXV-neutralizing antibodies as a correlate of protection against disease and transmissibility is currently unclear, we conclude that cohort studies following vaccinated individuals are necessary to assess vaccine efficacy in at-risk populations.
Automated system for classification of COVID-19 infection from lung CT images based on machine learning and deep learning techniques
Authors: Guhan Bhargavee , Almutairi Laila , Sowmiya S. , Snekhalatha U. , Rajalakshmi T. , Aslam Shabnam Mohamed 
Published: October 2022

AbstractThe objectives of our proposed study were as follows: First objective is to segment the CT images using a k-means clustering algorithm for extracting the region of interest and to extract textural features using gray level co-occurrence matrix (GLCM). Second objective is to implement machine learning classifiers such as Naïve bayes, bagging and Reptree to classify the images into two image classes namely COVID and non-COVID and to compare the performance of the three pre-trained CNN models such as AlexNet, ResNet50 and SqueezeNet with that of the proposed machine learning classifiers. Our dataset consists of 100 COVID and non-COVID images which are pre-processed and segmented with our proposed algorithm. Following the feature extraction process, three machine learning classifiers (Naive Bayes, Bagging, and REPTree) were used to classify the normal and covid patients. We had implemented the three pre-trained CNN models such as AlexNet, ResNet50 and SqueezeNet for comparing their performance with machine learning classifiers. In machine learning, the Naive Bayes classifier achieved the highest accuracy of 97%, whereas the ResNet50 CNN model attained the highest accuracy of 99%. Hence the deep learning networks outperformed well compared to the machine learning techniques in the classification of Covid-19 images.
Sustained antibacterial coating with graphene oxide ultrathin film combined with cationic surface-active agents in a wet environment
Authors: Miyaji Hirofumi , Kanemoto Yukimi , Hamamoto Asako , Shitomi Kanako , Nishida Erika , Kato Akihito , Sugaya Tsutomu , Tanaka Saori , Aikawa Natsuha , Kawasaki Hideya , Gohda Syun , Ono Hironobu 
Published: October 2022

AbstractAntimicrobial surfactants contained in mouthrinse have excellent efficacy, but are not retained on the tooth surface (are rinsed away) due to their low water resistance and thus do not exhibit sustained antibacterial activity. We have developed a new coating method using graphene oxide (GO) that retains the surfactant on the tooth surface even after rinsing with water, thus providing a sustained antibacterial effect. Ultra-thin films of GO and an antimicrobial agent were prepared by (1) applying GO to the substrate surface, drying, and thoroughly rinsing with water to remove excess GO to form an ultrathin film (almost a monolayer, transparent) on the substrate surface, then (2) applying antimicrobial cationic surface active agents (CSAAs) on the GO film to form a composite coating film (GO/CSAA). GO/CSAA formation was verified by scanning electron microscopy, Raman spectroscopy, X-ray photoelectron spectroscopy, and ζ-potential and contact angle measurements. GO/CSAA was effective at inhibiting the growth of oral pathogens for up to 7 days of storage in water, and antibacterial activity was recovered by reapplication of the CSAA. Antibacterial GO/CSAA films were also formed on a tooth substrate. The results suggest that GO/CSAA coatings are effective in preventing oral infections.
Using 157Gd doped carbon and 157GdF4 nanoparticles in proton-targeted therapy for effectiveness enhancement and thermal neutron reduction: a simulation study
Authors: Tabbakh Farshid , Hosmane Narayan S. , Tajudin Suffian M. , Ghorashi Amir-Hossein , Morshedian Nader 
Published: October 2022

AbstractThere are two major problems in proton therapy. (1) In comparison with the gamma-ray therapy, proton therapy has only ~ 10% greater biological effectiveness, and (2) the risk of the secondary neutrons in proton therapy is another unsolved problem. In this report, the increase of biological effectiveness in proton therapy has been evaluated with better performance than 11B in the presence of two proposed nanomaterials of 157GdF4 and 157Gd doped carbon with the thermal neutron reduction due to the presence of 157Gd isotope. The present study is based on the microanalysis calculations using GEANT4 Monte Carlo tool and GEANT4-DNA package for the strand breaks measurement. It was found that the proposed method will increase the effectiveness corresponding to the alpha particles by more than 100% and also, potentially will decrease the thermal neutrons fluence, significantly. Also, in this work, a discussion is presented on a significant contribution of the secondary alpha particles in total effectiveness in proton therapy.
Head versus heart: social media reveals differential language of loneliness from depression
Authors: Liu Tingting , Ungar Lyle H. , Curtis Brenda , Sherman Garrick , Yadeta Kenna , Tay Louis , Eichstaedt Johannes C. , Guntuku Sharath Chandra 
Published: October 2022

AbstractWe study the language differentially associated with loneliness and depression using 3.4-million Facebook posts from 2986 individuals, and uncover the statistical associations of survey-based depression and loneliness with both dictionary-based (Linguistic Inquiry Word Count 2015) and open-vocabulary linguistic features (words, phrases, and topics). Loneliness and depression were found to have highly overlapping language profiles, including sickness, pain, and negative emotions as (cross-sectional) risk factors, and social relationships and activities as protective factors. Compared to depression, the language associated with loneliness reflects a stronger cognitive focus, including more references to cognitive processes (i.e., differentiation and tentative language, thoughts, and the observation of irregularities), and cognitive activities like reading and writing. As might be expected, less lonely users were more likely to reference social relationships (e.g., friends and family, romantic relationships), and use first-person plural pronouns. Our findings suggest that the mechanisms of loneliness include self-oriented cognitive activities (i.e., reading) and an overattention to the interpretation of information in the environment. These data-driven ecological findings suggest interventions for loneliness that target maladaptive social cognitions (e.g., through reframing the perception of social environments), strengthen social relationships, and treat other affective distress (i.e., depression).
Mitochondrial DNA maintenance in Drosophila melanogaster
Authors: Rodrigues Ana P.C. , Novaes Audrey C. , Ciesielski Grzegorz L. , Oliveira Marcos T. 
Published: October 2022

Abstract
All 37 mitochondrial DNA (mtDNA)-encoded genes involved with oxidative phosphorylation and intramitochondrial protein synthesis, and several nuclear-encoded genes involved with mtDNA replication, transcription, repair and recombination are conserved between the fruit fly Drosophila melanogaster and mammals. This, in addition to its easy genetic tractability, has made Drosophila a useful model for our understanding of animal mtDNA maintenance and human mtDNA diseases. However, there are key differences between the Drosophila and mammalian systems that feature the diversity of mtDNA maintenance processes inside animal cells. Here, we review what is known about mtDNA maintenance in Drosophila, highlighting areas for which more research is warranted and providing a perspective preliminary in silico and in vivo analyses of the tissue specificity of mtDNA maintenance processes in this model organism. Our results suggest new roles (or the lack thereof) for well-known maintenance proteins, such as the helicase Twinkle and the accessory subunit of DNA polymerase γ, and for other Drosophila gene products that may even aid in shedding light on mtDNA maintenance in other animals. We hope to provide the reader some interesting paths that can be taken to help our community show how Drosophila may impact future mtDNA maintenance research.
A data‐driven fault detection and fault‐tolerant control scheme for large‐scale systems and its application on multi‐area interconnected power systems
Authors: Gao Jingjing , Yang Xu , Huang Jian , Peng Kaixiang 
Published: October 2022

AbstractStimulated by the enhancing requirements for system safety as well as process reliability in complex industrial processes, this paper investigates a data‐driven fault detection and fault‐tolerant control scheme for large‐scale systems. To this end, a pair‐wise decomposition approach is first presented based on the inclusion principle, which expands the original large‐scale system into a set of disjointed pair‐wise subsystems. Then, for the pair‐wise subsystems, the observer‐based residual generators and observer‐based state feedback controllers for fault detection and fault‐tolerant control purpose are further developed in a decentralized way. On this basis, the fault‐tolerant controller for the original large‐scale system is obtained by the coordination and contraction of the decentralized observer‐based state feedback controllers. Finally, the feasibility and effectiveness of the proposed scheme are demonstrated through the case study on a multi‐area interconnected power system.
Data‐driven sampling pattern design for sparse spotlight SAR imaging
Authors: Zhao Yao , Huang Wenkun , Quan Xiangyin , Ling Wing‐Kuen , Zhang Zhe 
Published: October 2022

AbstractThis paper proposes a joint optimization method for the imaging algorithm and sampling scheme of sparse spotlight synthetic aperture radar (SAR) imaging based on deep convolutional neural networks. Traditional compressed sensing‐based sparse SAR imaging has been widely studied. Deep learning and sparse unfolding networks have been introduced into sparse SAR imaging, but most current works focus only on the imaging stage and simply adopt the conventional uniform or random downsampling scheme. Considering that the imaging quality also depends on the sampling pattern besides the imaging algorithm, this paper introduces a learning‐based strategy to jointly optimize the sampling scheme and the imaging network parameters of the reconstruction module. In a deep learning‐based image reconstruction scheme, joint and continuous optimization of the sampling patterns and convolutional neural network parameters is achieved to improve the image quality. Simulation results based on real SAR image data set illustrate the effectiveness and superiority of the proposed framework.
Small‐signal modelling considering internal perturbations for modular multilevel converters
Authors: Song Shuguang , Liu Jinjun , Chen Xingxing , Sheng Junyi , Liu Hanjun 
Published: October 2022

AbstractSmall‐signal modelling of modular multilevel converters (MMC) has drawn lots of attention recently. Existing research mainly studies MMC terminal impedances by injecting corresponding AC‐side and DC‐side external small‐signal perturbations. This paper focuses on the MMC internal small‐signal perturbations, that is, perturbations in control signal and their effect. There are two kinds, that is, differential‐mode and common‐mode, of internal small‐signal perturbations, which influence MMC external and internal state variables, respectively. Then, the existing MMC small‐signal model is improved. With its help, accurate MMC control‐signal to state‐variables transfer functions are established, which can aid precise MMC controller analysis and performance optimization. In this paper, an analysis of MMC phase capacitor voltage regulation control is demonstrated as an example. The stability can be precisely ascertained based on the obtained transfer function. The effect of parameter variation of the proportional‐integral controller is also studied. Moreover, an additional compensator is introduced in the existing control loop to attenuate resonance peaks in the transfer function, which mitigates the large voltage overshoot and makes the transient state much smoother. Simulation and experimental results support and well confirm the analysis of this paper.
Optimal coordination of electric vehicle charging and photovoltaic power curtailment in unbalanced low voltage networks: An experimental case
Authors: Cortés Borray Andrés Felipe , Rauma Kalle , Torres Esther 
Published: October 2022

AbstractThis study introduces a quadratic programming‐based optimisation method to coordinate electric vehicle (EV) charging and photovoltaic (PV) curtailment in unbalanced low voltage (LV) networks. The proposed model is defined as a convex model that guarantees the optimal global solution of the problem avoiding the complexity of non‐linear models and surpassing the limitations of local solutions derived from meta‐heuristics algorithms reported in the literature. The coordination is carried out through a centralised controller installed at the header of the LV feeder. The objective of the proposed strategy is to minimise the power curtailment of all PV systems and maximise the power delivered to all EVs by optimising at every time step a suitable setpoint for the PV units and the charging rate of each EV connected without surpassing network constraints. A new energy‐boundary model is also proposed to meet the energy requirements of all EVs, which is based on a recurrent function that depends on the arrival‐and‐desired energy states of the vehicle to compute its charging trajectory optimally. The effectiveness of the proposed coordination strategy was successfully proven through three scenarios in a laboratory environment, making use of two commercial EVs and a PV inverter in a Power Hardware‐in‐the‐Loop setup.
Improved asteroseismic inversions for red-giant surface rotation rates
Authors: Ahlborn F. , Bellinger E. P. , Hekker S. , Basu S. , Mokrytska D. 
Published: October 2022

Context. Asteroseismic observations of internal stellar rotation have indicated a substantial lack of angular momentum transport in theoretical models of subgiant and red-giant stars. Accurate core and surface rotation rate measurements are therefore needed to constrain the internal transport processes included in the models.
Aims. We eliminate substantial systematic errors of asteroseismic surface rotation rates found in previous studies.
Methods. We propose a new objective function for the optimally localised averages method of rotational inversions for red-giant stars, which results in more accurate envelope rotation rate estimates obtained from the same data. We use synthetic observations from stellar models across a range of evolutionary stages and masses to demonstrate the improvement.
Results. We find that our new inversion technique allows us to obtain estimates of the surface rotation rate that are independent of the core rotation. For a star at the base of the red-giant branch, we reduce the systematic error from about 20% to a value close to 0, assuming constant envelope rotation. We also show the equivalence between this method and the method of linearised rotational splittings.
Conclusions. Our new rotational inversion method substantially reduces the systematic errors of red-giant surface rotation rates. In combination with independent measures of the surface rotation rate, this will allow better constraints to be set on the internal rotation profile. This will be a very important probe for further constraining the internal angular momentum transport along the lower part of the red-giant branch.
Ammonia, carbon dioxide, and the non-detection of the 2152 cm−1 CO band
Authors: He Jiao , Perotti Giulia , Emtiaz Shahnewaz M. , Toriello Francis E. , Boogert Adwin , Henning Thomas , Vidali Gianfranco 
Published: October 2022

Context. COa title should be crisp and newspaper-style, and direct questions are to be avoided in scientific writing. Please rephrase your title and take this into account. Your running title may be a good place to start is one of the most abundant ice components on interstellar dust grains. When it is mixed with amorphous solid water (ASW) or located on its surface, an absorption band of CO at 2152 cm−1 is always present in laboratory measurements. This spectral feature is attributed to the interaction of CO with dangling-OH bonds (dOH) in ASW. However, this band is absent in observational spectra of interstellar ices. This raises the question whether CO forms a relatively pure layer on top of ASW or is in close contact with ASW, but not via dangling bonds.
Aims. We aim to determine whether the incorporation of NH3 or CO2 into ASW blocks the dOH and therefore reduces the 2152 cm−1 band.
Methods. We performed laboratory experiments to simulate the layered structure of the ice mantle, that is, we grew CO ice on top of 1) pure ASW, 2) NH3:H2O=10:100 mixed ice, and 3) CO2:H2O=20:100 mixed ice. Infrared spectra were measured to quantify the strength of the 2152 cm−1 band. In addition, a second set of experiments were performed to determine how the incorporation of NH3 into ASW affects the dOH band.
Results. We found that annealing the ice reduces the 2152 cm−1 band and that NH3 blocks the dOH on ASW surface and therefore reduces the 2152 cm−1 band more effectively than CO2. We suggest that this difference between NH3 and CO2 can be ascribed to the polarity of the guest molecule (NH3 is a polar species, whereas CO2 is apolar). The polarity implies that the formation of an H-bond between the N atom of ammonia and the dOH is a barrier-less reaction. We also determined the pore surface area of the ice mixtures as a function of the annealing temperature, and found that the nondetection of 2152 cm−1 band does not necessarily exclude the possibility of a porous ice mantle.
Ligandrol-induzierte toxische Hepatopathie – ein Fallbericht
Authors: Wallstab Florian , Jechorek Doerthe , Keitel-Anselmino Verena , Arnim Ulrike von 
Published: October 2022

ZusammenfassungEin 37-jähriger Patient stellte sich mit schmerzlosem Ikterus und Juckreiz vor. Laborchemisch wurden ein Bilirubin 30-fach über dem oberen Normwert sowie eine leicht erhöhte Alanin-Aminotransferase (ALT) mit ansonsten geringfügig erhöhten Cholestaseparametern gesehen. Bei unauffälliger Umfelddiagnostik mit Ausschluss häufiger Ursachen einer Hepatitis wurde eine Leberbiopsie durchgeführt. Diese ergab eine kanalikuläre Cholestase mit Duktopenie und periportaler Fibrose. Erst nach wiederholter, eindringlicher Anamnese konnte ein Ligandrol-Abusus als Ursache für die Symptomatik ermittelt werden. Ligandrol wird als Selektiver-Androgen-Rezeptor-Modulator zur Förderung des Muskelaufbaus eingesetzt. Dieser Fall stellt keinen Einzelfall für den Missbrauch anaboler Substanzen im Amateursport dar.