bluuebunny/crossref_metadata_2025_split huggingface dataset:
Prevalence of cognitive impairment following chemotherapy treatment for breast cancer: a systematic review and meta-analysis
Authors: Whittaker Alexandra L. , George Rebecca P. , O’Malley Lucy 
Published: February 2022

AbstractBreast cancer survival rates have markedly improved. Consequently, survivorship issues have received increased attention. One common sequel of treatment is chemotherapy-induced cognitive impairment (CICI). CICI causes a range of impairments that can have a significant negative impact on quality of life. Knowledge of the prevalence of this condition is required to inform survivorship plans, and ensure adequate resource allocation and support is available for sufferers, hence a systematic review of prevalence data was performed. Medline, Scopus, CINAHL and PSYCHInfo were searched for eligible studies which included prevalence data on CICI, as ascertained though the use of self-report, or neuropsychological tests. Methodological quality of included studies was assessed. Findings were synthesised narratively, with meta-analyses being used to calculate pooled prevalence when impairment was assessed by neuropsychological tests. The review included 52 studies. Time-points considered ranged from the chemotherapy treatment period to greater than 10 years after treatment cessation. Summary prevalence figures (across time-points) using self-report, short cognitive screening tools and neuropsychological test batteries were 44%, 16% and 21–34% respectively (very low GRADE evidence). Synthesised findings demonstrate that 1 in 3 breast cancer survivors may have clinically significant cognitive impairment. Prevalence is higher when self-report based on patient experience is considered. This review highlights a number of study design issues that may have contributed to the low certainty rating of the evidence. Future studies should take a more consistent approach to the criteria used to assess impairment. Larger studies are urgently needed.
Class imbalance learning with Bayesian optimization applied in drug discovery
Authors: Guan Shenmin , Fu Ning 
Published: February 2022

AbstractMachine intelligence (MI), including machine learning and deep learning, have been regarded as promising methods to reduce the prohibitively high cost of drug development. However, a dilemma within MI has limited its wide application: machine learning models are easier to interpret but yield worse predictive performance than deep learning models. Therefore, we propose a pipeline called Class Imbalance Learning with Bayesian Optimization (CILBO) to improve the performance of machine learning models in drug discovery. To demonstrate the efficacy of the CILBO pipeline, we developed an example model to predict antibacterial candidates. Comparison of the antibacterial prediction performance between our model and a well-known deep learning model published by Stokes et al. suggests that our model can perform as well as the deep learning model in drug activity prediction. The CILBO pipeline we propose provides a simple, alternative approach to accelerate preliminary screenings and decrease the cost of drug discovery.
A joint convolutional-recurrent neural network with an attention mechanism for detecting intracranial hemorrhage on noncontrast head CT
Authors: Alis Deniz , Alis Ceren , Yergin Mert , Topel Cagdas , Asmakutlu Ozan , Bagcilar Omer , Senli Yeseren Deniz , Ustundag Ahmet , Salt Vefa , Dogan Sebahat Nacar , Velioglu Murat , Selcuk Hakan Hatem , Kara Batuhan , Ozer Caner , Oksuz Ilkay , Kizilkilic Osman , Karaarslan Ercan 
Published: February 2022

AbstractTo investigate the performance of a joint convolutional neural networks-recurrent neural networks (CNN-RNN) using an attention mechanism in identifying and classifying intracranial hemorrhage (ICH) on a large multi-center dataset; to test its performance in a prospective independent sample consisting of consecutive real-world patients. All consecutive patients who underwent emergency non-contrast-enhanced head CT in five different centers were retrospectively gathered. Five neuroradiologists created the ground-truth labels. The development dataset was divided into the training and validation set. After the development phase, we integrated the deep learning model into an independent center’s PACS environment for over six months for assessing the performance in a real clinical setting. Three radiologists created the ground-truth labels of the testing set with a majority voting. A total of 55,179 head CT scans of 48,070 patients, 28,253 men (58.77%), with a mean age of 53.84 ± 17.64 years (range 18–89) were enrolled in the study. The validation sample comprised 5211 head CT scans, with 991 being annotated as ICH-positive. The model's binary accuracy, sensitivity, and specificity on the validation set were 99.41%, 99.70%, and 98.91, respectively. During the prospective implementation, the model yielded an accuracy of 96.02% on 452 head CT scans with an average prediction time of 45 ± 8 s. The joint CNN-RNN model with an attention mechanism yielded excellent diagnostic accuracy in assessing ICH and its subtypes on a large-scale sample. The model was seamlessly integrated into the radiology workflow. Though slightly decreased performance, it provided decisions on the sample of consecutive real-world patients within a minute.
Taking a machine learning approach to optimize prediction of vaccine hesitancy in high income countries
Authors: Lincoln Tania M. , Schlier Björn , Strakeljahn Felix , Gaudiano Brandon A. , So Suzanne H. , Kingston Jessica , Morris Eric M.J. , Ellett Lyn 
Published: February 2022

AbstractUnderstanding factors driving vaccine hesitancy is crucial to vaccination success. We surveyed adults (N = 2510) from February to March 2021 across five sites (Australia = 502, Germany = 516, Hong Kong = 445, UK = 512, USA = 535) using a cross-sectional design and stratified quota sampling for age, sex, and education. We assessed willingness to take a vaccine and a comprehensive set of putative predictors. Predictive power was analysed with a machine learning algorithm. Only 57.4% of the participants indicated that they would definitely or probably get vaccinated. A parsimonious machine learning model could identify vaccine hesitancy with high accuracy (i.e. 82% sensitivity and 79–82% specificity) using 12 variables only. The most relevant predictors were vaccination conspiracy beliefs, various paranoid concerns related to the pandemic, a general conspiracy mentality, COVID anxiety, high perceived risk of infection, low perceived social rank, lower age, lower income, and higher population density. Campaigns seeking to increase vaccine uptake need to take mistrust as the main driver of vaccine hesitancy into account.
Super-efficient drilling of metals with ultrafast non diffractive laser beams
Authors: Nguyen Huu Dat , Moreno Enrique , Rudenko Anton , Faure Nicolas , Sedao Xxx , Mauclair Cyril , Colombier Jean-Philippe , Stoian Razvan 
Published: February 2022

AbstractA highly efficient drilling process is found in non-transparent metallic materials enabled by the use of non-diffractive ultrafast Bessel beams. Applied for deep drilling through a 200 μm-thick steel plate, the Bessel beam demonstrates twofold higher drilling efficiency compared to a Gaussian beam of similar fluence and spot size. Notwithstanding that surface ablation occurs with the same efficiency for both beams, the drilling booster results from a self-replication and reconstruction of the beam along the axis, driven by internal reflections within the crater at quasi-grazing incidence, bypassing potential obstacles. The mechanism is the consequence of an oblique wavevectors geometry with low angular dispersion and generates a propagation length beyond the projection range allowed by the geometry of the channel. With only the main lobe being selected by the channel entrance, side-wall reflection determines the refolding of the lobe on the axis, enhancing and replicating the beam multiple times inside the channel. The process is critically assisted by the reduction of particle shielding enabled by the intrinsic self-healing of the Bessel beam. Thus the drilling process is sustained in a way which is uniquely different from that of the conventional Gaussian beam, the latter being damped within its Rayleigh range. These mechanisms are supported and quantified by Finite Difference Time Domain calculations of the beam propagation. The results show key advantages for the quest towards efficient laser drilling and fabrication processes.
Langevin based turbulence model and its relationship with Kappa distributions
Authors: Gallo-Méndez Iván , Moya Pablo S. 
Published: February 2022

AbstractKappa distributions (or $$\kappa $$
κ
-like distributions) represent a robust framework to characterize and understand complex phenomena with high degrees of freedom, as turbulent systems, using non-extensive statistical mechanics. Here we consider a coupled map lattice Langevin based model to analyze the relation of a turbulent flow, with its spatial scale dynamic, and $$\kappa $$
κ
-like distributions. We generate the steady-state velocity distribution of the fluid at each scale, and show that the generated distributions are well fitted by $$\kappa $$
κ
-like distributions. We observe a robust relation between the $$\kappa $$
κ
parameter, the scale, and the Reynolds number of the system, Re. In particular, our results show that there is a closed scaling relation between the level of turbulence and the $$\kappa $$
κ
parameter; namely $$\kappa \sim \text {Re}\,k^{-5/3}$$

κ
∼
Re

k

-
5
/
3

. We expect these results to be useful to characterize turbulence in different contexts, and our numerical predictions to be tested by observations and experimental setups.
Development of SARS-CoV2 humoral response including neutralizing antibodies is not sufficient to protect patients against fatal infection
Authors: Choteau Mathilde , Scohy Anaïs , Messe Stéphane , Luyckx Mathieu , Dechamps Mélanie , Montiel Virginie , Yombi Jean Cyr , Gruson Damien , Limaye Nisha , Michiels Thomas , Dumoutier Laure 
Published: February 2022

AbstractMore than a year after the start of the pandemic, COVID-19 remains a global health emergency. Although the immune response against SARS-CoV-2 has been extensively studied, some points remain controversial. One is the role of antibodies in viral clearance and modulation of disease severity. While passive transfer of neutralizing antibodies protects against SARS-CoV-2 infection in animal models, titers of anti-SARS-CoV-2 antibodies have been reported to be higher in patients suffering from more severe forms of the disease. A second key question for pandemic management and vaccine design is the persistence of the humoral response. Here, we characterized the antibody response in 187 COVID-19 patients, ranging from asymptomatic individuals to patients who died from COVID-19, and including patients who recovered. We developed in-house ELISAs to measure titers of IgG, IgM and IgA directed against the RBD or N regions in patient serum or plasma, and a spike-pseudotyped neutralization assay to analyse seroneutralization. Higher titers of virus-specific antibodies were detected in patients with severe COVID-19, including deceased patients, compared to asymptomatic patients. This demonstrates that fatal infection is not associated with defective humoral response. Finally, most of recovered patients still had anti-SARS-CoV-2 IgG more than 3 months after infection.
Application of consecutive polyethylene glycol treatments for modeling the seminal root growth of rice under water stress
Authors: Susilawati Pepi Nur , Tajima Ryosuke , Giamerti Yuti , Yang Yi , Yufdy Muhammad Prama , Lubis Iskandar , Homma Koki 
Published: February 2022

AbstractThe response of root growth to various osmotic potentials is quite important in assessing the drought resistance of rice. This study modeled seminal root growth by applying consecutive polyethylene glycol (PEG) treatments (from 0 to 25%, 1% step), mathematical equations and noncontact image analysis to quantitatively evaluate the root response. Treatment began after seeds were germinated, and root growth was recorded by a digital camera every day from 7 to 20 days after seeding (DAS). Although the seminal root length (SRL) measured by image analysis slightly varied with DAS, the equations explained the differences in SRL increases under each PEG concentration relatively well (R2 = 0.774). The equations also suggested that the maximum seminal root length was observed at 5.9% PEG. This numerical characterization of root growth is an effective means of evaluating drought resistance.
Synthetic flow-based cryptomining attack generation through Generative Adversarial Networks
Authors: Mozo Alberto , González-Prieto Ángel , Pastor Antonio , Gómez-Canaval Sandra , Talavera Edgar 
Published: February 2022

AbstractDue to the growing rise of cyber attacks in the Internet, the demand of accurate intrusion detection systems (IDS) to prevent these vulnerabilities is increasing. To this aim, Machine Learning (ML) components have been proposed as an efficient and effective solution. However, its applicability scope is limited by two important issues: (i) the shortage of network traffic data datasets for attack analysis, and (ii) the data privacy constraints of the data to be used. To overcome these problems, Generative Adversarial Networks (GANs) have been proposed for synthetic flow-based network traffic generation. However, due to the ill-convergence of the GAN training, none of the existing solutions can generate high-quality fully synthetic data that can totally substitute real data in the training of ML components. In contrast, they mix real with synthetic data, which acts only as data augmentation components, leading to privacy breaches as real data is used. In sharp contrast, in this work we propose a novel and deterministic way to measure the quality of the synthetic data produced by a GAN both with respect to the real data and to its performance when used for ML tasks. As a by-product, we present a heuristic that uses these metrics for selecting the best performing generator during GAN training, leading to a novel stopping criterion, which can be applied even when different types of synthetic data are to be used in the same ML task. We demonstrate the adequacy of our proposal by generating synthetic cryptomining attacks and normal traffic flow-based data using an enhanced version of a Wasserstein GAN. The results evidence that the generated synthetic network traffic can completely replace real data when training a ML-based cryptomining detector, obtaining similar performance and avoiding privacy violations, since real data is not used in the training of the ML-based detector.
Evolution of an increased performance under acute challenge does not exacerbate vulnerability to chronic stress
Authors: Lipowska Małgorzata M. , Sadowska Edyta T. , Palme Rupert , Koteja Paweł 
Published: February 2022

AbstractAn adequate stress response plays a vital role in coping with challenges. However, if selection for improved coping with an acute challenge affects the entire stress response system, susceptibility to adverse effects of chronic stressors can be deepened. Here, we used bank voles from lines selected for high swim-induced aerobic metabolism (A) and unselected control (C), and asked if the selection affected sensitivity to chronic mild stress (CMS). The voles were first habituated to daily weighing and feces collection for three weeks, and then for two weeks were exposed to CMS or remained undisturbed. The habituation itself resulted in an increased swim-induced oxygen consumption in both line types, and a decreased body mass. The CMS treatment caused reduction of food consumption in the second week of the experiment, and, in males, a decline in the metabolic rate. Paradoxically, fecal corticosterone metabolites decreased in the CMS-treated group. The response to CMS did not differ between the line types. Thus, the selection for increased performance was not traded off by increased vulnerability to chronic stress. The counter-intuitive results may even lead to a speculation that bank voles—and perhaps also other animals—prefer experiencing unpredictable, unpleasant stressors over the monotony of standard laboratory housing.
Self-aligning roly-poly RFID tag
Authors: Dobrykh Dmitry , Yusupov Ildar , Ginzburg Pavel , Slobozhanyuk Alexey , Filonov Dmitry 
Published: February 2022

AbstractRadio frequency identification (RFID) is a mature technology that allows contactless data readout via a wireless communication link. While numerous passive RFID tags are available on the market, accurate alignment between tags and readers is required in a vast majority of cases to mitigate polarization mismatches. We show that enhancing electromagnetic designs with additional mechanical degrees of freedom allows bypassing fundamental limitations and approach ideal performances. Here, we demonstrate a new miniature tag, accessible from any direction and immune to rotations in space. Our tag is made of a high permittivity ceramic resonator, inductively coupled to a metal ring, which contains an RFID chip. The structure is placed inside a spherical plastic holder. In this architecture, the ceramic resonator serves several functions. First, it allows reducing the device footprint without significant bandwidth degradation. Second, it acts as a bob, aligning the electromagnetic structure parallel to the ground, regardless of its initial orientation in space. The bob is designed to slide inside the plastic holder. This roly-poly effect relaxes the constraint on a mutual tag-reader orientation, including the polarization mismatch, and provides next to perfect long-range operation. Being only 55 mm in diameter, our device can be interrogated from a 12 m distance, regardless of the tag’s orientation in space. Introducing mechanical degrees of freedom into electromagnetic designs allows obtaining new functionalities, contributing to applications where a mutual orientation between transvers is required.
Age-related long-term response in rat thyroid tissue and plasma after internal low dose exposure to 131I
Authors: Larsson Malin , Rudqvist Nils-Petter , Spetz Johan , Parris Toshima Z. , Langen Britta , Helou Khalil , Forssell-Aronsson Eva 
Published: February 2022

Abstract131I is used clinically for therapy, and may be released during nuclear accidents. After the Chernobyl accident papillary thyroid carcinoma incidence increased in children, but not adults. The aims of this study were to compare 131I irradiation-dependent differences in RNA and protein expression in the thyroid and plasma of young and adult rats, and identify potential age-dependent biomarkers for 131I exposure. Twelve young (5 weeks) and twelve adult Sprague Dawley rats (17 weeks) were i.v. injected with 50 kBq 131I (absorbed dose to thyroid = 0.1 Gy), and sixteen unexposed age-matched rats were used as controls. The rats were killed 3–9 months after administration. Microarray analysis was performed using RNA from thyroid samples, while LC–MS/MS analysis was performed on proteins extracted from thyroid tissue and plasma. Canonical pathways, biological functions and upstream regulators were analysed for the identified transcripts and proteins. Distinct age-dependent differences in gene and protein expression were observed. Novel biomarkers for thyroid 131I exposure were identified: (PTH), age-dependent dose response (CA1, FTL1, PVALB (youngsters) and HSPB6 (adults)), thyroid function (Vegfb (adults)). Further validation using clinical samples are needed to explore the role of the identified biomarkers.
Structure of Escherichia coli O157:H7 bacteriophage CBA120 tailspike protein 4 baseplate anchor and tailspike assembly domains (TSP4-N)
Authors: Chao Kinlin L. , Shang Xiaoran , Greenfield Julia , Linden Sara B. , Alreja Adit B. , Nelson Daniel C. , Herzberg Osnat 
Published: February 2022

AbstractFour tailspike proteins (TSP1-4) of Escherichia coli O157:H7 bacteriophage CBA120 enable infection of multiple hosts. They form a branched complex that attaches to the tail baseplate. Each TSP recognizes a different lipopolysaccharide on the membrane of a different bacterial host. The 335 N-terminal residues of TSP4 promote the assembly of the TSP complex and anchor it to the tail baseplate. The crystal structure of TSP4-N335 reveals a trimeric protein comprising four domains. The baseplate anchor domain (AD) contains an intertwined triple-stranded β-helix. The ensuing XD1, XD2 and XD3 β-sheet containing domains mediate the binding of TSP1-3 to TSP4. Each of the XD domains adopts the same fold as the respective XD domains of bacteriophage T4 gp10 baseplate protein, known to engage in protein–protein interactions via its XD2 and XD3 domains. The structural similarity suggests that XD2 and XD3 of TSP4 also function in protein–protein interactions. Analytical ultracentrifugation analyses of TSP4-N335 and of domain deletion proteins showed how TSP4-N335 promotes the formation of the TSP quaternary complex. TSP1 and TSP2 bind directly to TSP4 whereas TSP3 binding requires a pre-formed TSP4-N335:TSP2 complex. A 3-dimensional model of the bacteriophage CBA120 TSP complex has been developed based on the structural and ultracentrifuge information.
Irreversible alteration of extracellular vesicle and cell-free messenger RNA profiles in human plasma associated with blood processing and storage
Authors: Kim Hyun Ji , Rames Matthew J. , Tassi Yunga Samuel , Armstrong Randall , Morita Mayu , Ngo Anh T. P. , McCarty Owen J. T. , Civitci Fehmi , Morgan Terry K. , Ngo Thuy T. M. 
Published: February 2022

AbstractThe discovery and utility of clinically relevant circulating biomarkers depend on standardized methods that minimize preanalytical errors. Despite growing interest in studying extracellular vesicles (EVs) and cell-free messenger RNA (cf-mRNA) as potential biomarkers, how blood processing and freeze/thaw impacts the profiles of these analytes in plasma was not thoroughly understood. We utilized flow cytometric analysis to examine the effect of differential centrifugation and a freeze/thaw cycle on EV profiles. Utilizing flow cytometry postacquisition analysis software (FCMpass) to calibrate light scattering and fluorescence, we revealed how differential centrifugation and post-freeze/thaw processing removes and retains EV subpopulations. Additionally, cf-mRNA levels measured by RT-qPCR profiles from a panel of housekeeping, platelet, and tissue-specific genes were preferentially affected by differential centrifugation and post-freeze/thaw processing. Critically, freezing plasma containing residual platelets yielded irreversible ex vivo generation of EV subpopulations and cf-mRNA transcripts, which were not removable by additional processing after freeze/thaw. Our findings suggest the importance of minimizing confounding variation attributed to plasma processing and platelet contamination.